IsAlgo - Ultra Trend Strategy► Overview:
The Ultra Trend strategy is designed to identify trend lines based on average price movement and execute trades when the price crosses the middle line, confirmed by an entry candle. This strategy combines ATR, Moving Averages, and customizable candlestick patterns to provide a versatile and robust trading approach.
► Description:
The Ultra Trend strategy employs a multi-faceted approach to accurately gauge market trends and execute trades. It combines the Average True Range (ATR) with trendline analysis and Moving Averages, providing a comprehensive view of market conditions. The strategy uses ATR to measure market volatility and the average price movement, helping to set dynamic thresholds for trend detection and adapting to changing market conditions. The slope of the trend is calculated based on the angle of price movement, which aids in identifying the strength and direction of the trend.
Additionally, a Moving Average is used to filter trades, ensuring alignment with the broader market direction and reducing false signals, thereby enhancing trade accuracy.
Traders can configure the strategy to enter trades in the direction of the trend, against the trend, or both. This feature enhances the adaptability of the Ultra Trend strategy, making it suitable for various trading styles and market environments.
↑ Long Entry:
A long trade is executed when the entry candle crosses and closes above the trend line. This indicates a bullish market condition, signaling an opportunity to enter a buy position.
↓ Short Entry:
A short trade is executed when the entry candle crosses and closes below the trend line. This indicates a bearish market condition, signaling an opportunity to enter a sell position.
✕ Exit Conditions:
The strategy offers multiple stop-loss options to manage risk effectively. Traders can set stop-loss levels using fixed pips, ATR-based calculations, the higher/lower price of past candles, or close a trade if a candle moves against the trade direction.
Up to three take profit levels can be set using methods such as fixed pips, ATR, and risk-to-reward ratios. This allows traders to secure profits at various stages of the trade.
A trailing stop feature adjusts the stop loss as the trade moves into profit, locking in gains while allowing the trade to continue capturing potential upside. Additionally, a break-even feature moves the stop loss to the entry price once a certain profit level is reached, protecting against losses.
Trades can also be closed when a trend change is detected or when a candle closes outside a predefined channel, ensuring that positions are exited promptly in response to changing market conditions.
► Features and Settings:
⚙︎ Trend: Users can configure the trend direction, length, factor, and slope, allowing for precise control over how trends are identified and followed.
⚙︎ Moving Average: An Exponential Moving Average (EMA) can be employed to confirm the trend direction indicated by the trend lines. This provides further assurance that the trend line breakout is not a false signal. The EMA can be enabled or disabled based on user preference.
⚙︎ Entry Candle: The entry candle is the candle that breaks the trend line, signaling an entry opportunity. Users can specify the minimum and maximum size of the candle's body and the ratio of the body to the entire candle size. This ensures that only significant breakouts trigger trades.
⚙︎ Trading Session: This feature allows users to define specific trading hours during which the strategy should operate, ensuring trades are executed only during preferred market periods.
⚙︎ Trading Days: Users can specify which days the strategy should be active, offering the flexibility to avoid trading on specific days of the week.
⚙︎ Backtesting: Enables a backtesting period during which the strategy can be tested over a selected start and end date. This feature can be deactivated if not needed.
⚙︎ Trades: This includes configuring the direction of trades (long, short, or both), position sizing (fixed or percentage-based), the maximum number of open trades, and limitations on the number of trades per day or based on trend.
⚙︎ Trades Exit: The strategy offers various exit methods, such as setting profit or loss limits, specifying the duration a trade should remain open, or closing trades based on trend reversal.
⚙︎ Stop Loss: Various stop-loss methods are available, including a fixed number of pips, ATR-based, or using the highest or lowest price points within a specified number of previous candles. Additionally, trades can be closed after a specific number of candles move in the opposite direction of the trade.
⚙︎ Break Even: This feature adjusts the stop loss to a break-even point once certain conditions are met, such as reaching predefined profit levels, to protect gains.
⚙︎ Trailing Stop: The trailing stop feature adjusts the stop loss as the trade moves into profit, securing gains while potentially capturing further upside.
⚙︎ Take Profit: Up to three take-profit levels can be set using various methods, such as a fixed amount of pips, ATR, or risk-to-reward ratios based on the stop loss. Alternatively, users can specify a set number of candles moving in the direction of the trade.
⚙︎ Alerts: The strategy includes a comprehensive alert system that informs the user of all significant actions, such as trade openings and closings. It supports placeholders for dynamic values like take-profit levels and stop-loss prices.
⚙︎ Dashboard: A visual display provides detailed information about ongoing and past trades on the chart, helping users monitor the strategy's performance and make informed decisions.
► Backtesting Details:
Timeframe: 5-minute US30 chart
Initial Balance: $10,000
Order Size: 4% of equity per trade
Commission: $0.05 per contract
Slippage: 5 ticks
Stop Loss: ATR-based
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IchiBot - [SigmaStreet]
The IchiBot Indicator has been used to develop automated trading systems. It leverages the open-source Ichimoku framework provided by Trading View, to enable users to creatively generate over 1 trillion different combinations of trading conditions with the use of multiple timeframes to create unique “signal labels” that can be used to create custom strategies or provide in depth market analysis. At the end of this description, I have provided an example of input settings for a simple scalping strategy that I have back tested on US30 on the 5 minute timeframe.
Overview of the Settings:
The visuals section includes an option to show or hide certain parts of the indicator and change the size of the signal labels plotted on the chart.
Next to the “Signal color on baseline/candles” section, you can choose if you want to see additional signals generations from the most previous plotted label on a color changing baseline, or color changing candles. A color change from gray to blue/red indicate that the conditions from the most previously plotted signal label have been met again.
The next 5 sections are all related to the strategy portion of the indicator, used to aid in the back testing process. These sections are titled “Stop loss”, “Take Profit”, “Trail Stop”, “Trade Settings” and “Trade Schedule”.
The Stop Loss section includes an option to choose between value of “pts”, “atr” (average true range) or “None”. The stop loss value in “pts” is simply a specified number of points or pips from the current entry price of a trade that are input in the “SL” section. If the stop loss type is “atr” the “SL” section is not used and the value is calculated and displaced from the current entry price of a trade based on the atr period multiplied by the atr multiplier.
The take profit section is based on the same logic as the stop loss.
The Trail Stop section includes an option to choose between values “pts” or “None”. If the Trail Stop value is “pts”, a trailing stop loss is activated if a trade moves a point value into profit that exceeds the value of the “Trail Activation”. If the Trail Offset type is “pts”, the trailing stop loss is placed a point value away from the current price that is equal to the “Trail Offset” value.
The trade settings section has two options to either prevent or allow trade reversals and prevent or allow only 1 trade per signal label.
If the “Don’t allow trade reversals” is on, then a currently active trade can not be cancelled by an opposite trade signal. It can only be cancelled by the exit logic selected in the above sections. If the “One trade per signal” is selected, the strategy will only enter a trade if the most recent signal label is different from the last signal label where a trade was entered, or if the most recent signal label is in the opposite direction of the most recent signal label where a trade was entered.
The trade schedule section includes an option to only generate signal labels during the specified time. You can choose between 24/7 which will generate signals without any time restriction, or you can choose a custom time which is based on the America / New York time zone.
The timeframe settings section includes an option to choose “single” or “multiple” timeframes, as well as an option to show every signal label combination (“all”), or only the signal labels with the highest numerical value (“absolute”).
If you select “single” next to “timeframe”, the indicator will show you labels based on trade conditions met from only 1 selected timeframe. If you select “multiple” next to “timeframe”, the indicator is designed to return signal labels based on trade conditions that have been met on at least 2 different timeframes.
If you select “multiple” and “use current timeframe”, the indicator will include labels that always include a minimum of 2 timeframes where 1 timeframe is always the current timeframe. If you unselect the “use current timeframe”, the indicator will include labels with a minimum of 2 timeframes.
If you select “multiple” next to “timeframe” and “all” next to “Show all/absolute labels”, the indicator will show you every possible combination of labels that vary from trade conditions met on a minimum of 2 timeframes, to the maximum number of timeframes selected.
If you select “multiple” next to “timeframe” and “absolute” next to “Show all/absolute labels”, the indicator will only show you labels where the numerical value is equivalent to the maximum number of timeframes selected.
Each signal label provides a number which refers to the number of timeframes used to generate the label, offering insights briefly. Hover over a label to reveal detailed tooltip information that details the exact timeframes used to generate each label.
You can choose all from “Show all/absolute labels” to see every possible combination of trade signals or “absolute” to only see labels that have the highest possible numerical value. Absolute means that every condition selected from every timeframe was calculated to be true at the same time on the same candle.
The next 8 sections are “Current timeframe trade conditions”, “1-minute timeframe trade conditions”, “5-minute timeframe trade conditions”, “15-minute timeframe trade conditions”, “30-minute timeframe trade conditions”, “1-hour timeframe trade conditions”, “4-hour timeframe trade conditions”, “Daily timeframe trade conditions”.
These sections include the same 10 trade conditions, that can be used independently, or in combination with each other. This brings the total number of trade conditions to 70.
The final section includes a standard option to adjust the current Ichimoku values.
Understanding the Calculations:
The term “future” refers to a value that is calculated 26 candles to the right of the most recent closing price.
The term “current” refers to a value that is calculated on the most recent closing price.
The term “past” refers to a value that is calculated 26 candles to the left of the most recent closing price.
Bullish is referred to as “blue” and bearish is referred to as “red”.
Buy Signals:
1. The current closing price is greater than the current cloud value.
2. The future cloud is blue.
3. The current closing price is greater than the current conversion line.
4. The current conversion line is greater than the current baseline.
5. The lagging span is greater than the closing price of the last 25 candles.
6. The lagging span is greater than the past cloud.
7. The lagging span is greater than the past conversion line and the past baseline.
8. The current conversion line is greater than the current cloud.
9. The current baseline is greater than the current cloud.
10. The value of the current cloud to the future cloud is completely blue.
Sell Signals:
1. The current closing price is less than the current cloud value.
2. The future cloud is red.
3. The current closing price is less than the current conversion line.
4. The current conversion line is less than the current baseline.
5. The lagging span is less than the closing price of the last 25 candles.
6. The lagging span is less than the past cloud.
7. The lagging span is less than the past conversion line and the past baseline.
8. The current conversion line is less than the current cloud.
9. The current baseline is less than the current cloud.
10. The value of the current cloud to the future cloud is completely red.
The script enables users to access the value of these 10 trade conditions across the 7 major time frames (1-minute, 5-minute, 15-minute, 30-minute, 1-hour, 4-hour, Daily, and the current charts time frame) by using the official non repainting request security function provided by Trading View:
f_secSecurity(_src, _res, _exp) =>
request.security(_src, _res, _exp )
This indicator provides up to 70 variables (10 variables X 7 timeframes) that can be used separately, or in combination to generate signal labels.
Enhance your visual analysis with a color-changing baseline and candle colors that adapt to signal shifts, offering an immediate understanding of market trends. The base line will change from gray to blue/red which will reference the most previously plotted signal label. This change in color indicate that the conditions from the most recently plotted signal label have been met once again. Please refer to the example below.
Adjustments to the Ichimoku Indicator:
The script uses a slightly refined version of the Ichimoku indicator to calculate 10 different “trade conditions”. Each trade condition can create 1 bullish signal label and 1 bearish signal label. The calculations are primarily based on “greater than and less than logic” which is standard for signal generation.
In the original Ichimoku calculations, the “Lagging Span” has a default value of 26 periods. In the actual calculations, this input with the title “Lagging Span” is referred to as the “displacement”. When the lagging span is plotted on the chart, it is plotted with an offset value of offset = -displacement + 1 which technically plots the lagging span 25 candles to the left the most recent candle (if you count the most recent closing price as 0 and not 1). The clouds are plotted with an offset of offset = displacement -1 which technically plots the clouds 25 candles to the right of the most recent candle.
I have adjusted the logic of the Ichimoku indicator so the lagging span is still plotted 25 candles to the left of the most recently confirmed candle close, but the cloud is plotted 26 candles to the right of the most recent confirmed candle close.
This seemingly small adjustment of one candle cannot simply be adjusted in the settings of the original Ichimoku indicator since the calculations of the cloud and lagging span displacements are directly affected by the same value (displacement = 26, also known as the “lagging span”). My script is adjusted to make calculations where the lagging span is 25 candles to the left of the most recent candle, and the cloud is displaced 26 candles to the right of the most recent candle.
For example, my scripts logic to detect if the current closing price is over the current cloud is (close > leadLead1 and close > leadLine2 and leadLine1 > leadLine2 . By using a lookback of , the logic assumes that the displaced value is 26 bars to the right of the most recent candle. My script also reflects this logic in the plotted values of the cloud where the offset values are offset = displacement. This adjustment is made without affecting any other part of the Ichimoku indicators calculations, only the displacement of the cloud which directly affects the logic of trade conditioins. This change is a deliberate and necessary function of this script’s logic to generate trade conditions and signal labels.
I’ve removed the conversion line and the lagging span and introduced a 26-period pivot high/low to provide a less cluttered chart. The pivot high/low looks 26 periods to the left and only 1 period to the right. The lagging span and conversion line logic is still built into the framework of the trading signals. If you choose to enable the lagging span, or conversion line.
trading approach, and always test your strategies thoroughly.
The function to generate the "Signal Labels" calculates every single possible combination of the 7 different timeframes which is a total of 127 combinations for bullish signal labels, and 127 combinations for bearish signal labels. This function also provides the necessary criteria for the strategy entry conditions, based on the dynamically calculated values derived from the signal labels themselves. For example: "buy signal on 1 minute and 5 minute timeframe" is considered 1 combination, and "Buy signal on current, 5 minute, 15 minute, 30 minute, 1 hour, 4 hour and daily timeframe" is also considered 1 combination. There are a total of 254 combinations between buy and sell signal labels along with 254 individual variables with their own unique tool tip description. The signal label function alone spans over 1340 lines of code (minus spaces and comments) to specifically account for every possible variable combination. This unique and original function also calculates the signal label "value" which is the number you see on the signal label. This function adjusts the amount of labels plotted, the value and description of all labels based on the timeframe settings "single"/"multiple", the use of "use current timeframe" setting, and the "trade schedule". This signal label function has been a landmark piece of code for me in my endeavor to create and optimize my strategies based on its ability to provide an in depth analysis of the timeframes used when generating signal labels. This function is main reason that this script has been published closed source.
Back tested results.
The current results are from US30 (Dow Jones Industrial Average CFD) on the 5-minute timeframe using regular candles. The inputs are as follows:
Stop loss = 5000 pts
No take profit.
Trail activation = 100 pts
Trail offset = 100 pts
Don’t allow trade reversals
Trade 24/7
Timeframe = multiple
Show absolute signals
Use current timeframe, lag span over/under candles
Use 30m timeframe, all cloud is bull/bear
Initial capital = $10,000 USD, 1 contract, $0.07 per contract, slippage = 3 ticks, use bar magnifier = on
Timeframe = June 1st, 2023 – November 10th, 2023, risk = 5% (greatest loosing trade = $500.44)
PSE, Practical Strategy EnginePSE, Practical Strategy Engine
A ready-to-use engine that is simple to connect your indicator to, simple to use, and effective at generating alerts for order-filled events during the real-time candle.
Great for
• Evaluating indicators on important metrics without the need to write a strategy script for backtesting.
• Using indicators with built-in risk management.
About The PSE
This engine accepts entry and exit signals from your indicator to provide trade signals for both long and short positions. The PSE was written for trading Funds (e.g. ETF’s), Stocks, Forex, Futures, and Cryptocurrencies. The trades on the chart indicate market, limit, and stop orders. The PSE allows for backtesting of trades along with metrics of performance based on trade-groups with many great features.
Note: A link to a video of how to connect your indicator(s) to the PSE is provided below.
Key Features
Trade-Grp’s
A Trade-Grp makes up one or more trade positions from the first position entering to the last position exiting. Using Trade-Grp’s instead of positions should help you better assess if the metric results fit your trading style.
Below are two (2) examples of a Trade-Grp with three (3) positions.
Metrics
A table of metrics is available if the “Show Metrics Table” checkbox is enabled on the Inputs tab, but metrics always show in the Data Window.
Examples of the Metrics Table are shown below.
• ROI (Return on Investment) and CAGR (Compound Annual Growth Rate) are based on the Avg Invest/Trade-Grp and are adjusted for dividends if the “Include Dividends in Profit” checkbox is enabled.
• Profit/Risked is based on Trade-Grp’s. Also known as reward/risk, as well as expectancy per amount risked. It determines the effectiveness of your strategy and provides a measure of comparison between your strategies. This is adjusted for dividends if the “Include Dividends in Profit” checkbox is enabled. In the Data Window the color is green when above the breakeven point of making a profit and red when below the breakeven point. In the Table the color is red if below the breakeven point, otherwise it is the default color. For example, using the 3 metrics tables above:
For every USD risked the profit is 1.709 USD.
For every BTC risked the profit is 0.832 BTC.
For every JPY risked the profit is 0.261 JPY.
• Winning % is based on Trade-Grp’s. In the Data Window the color is green when above the breakeven point of making a profit and red when below the breakeven point. In the Table the color is red if below the breakeven point, otherwise it is the default color.
The breakeven point is a relationship between the Profit/Risked and Winning % to indicate system profitability potential. Another way to assess trading system performance. For example, for a low Winning % a high Profit/Risked is needed for the system to be potentially profitable.
• Profit Factor (PF) is based on Trade-Grp’s. The dividend payment, if any, is not considered in the calculation of a win or loss. The “Include Dividends in Profit Factor” checkbox allows you the option to either include or not include dividends in the calculation of Profit Factor. The default is enabled.
Must enable the “Include Dividends in Profit” checkbox to include dividends in PF.
Including dividends in PF evaluates the trading strategy with a more overall profitability performance view.
Enable/Disable “Include Dividends in Profit Factor” checkbox also affects the Avg Trade-Grp Loss, and thus Equity Loss from ECL and % Equity Loss from ECL.
• Max Consecutive Losses are based on Trade-Grp’s.
• Nbr of Trade-Grp’s and Nbr of Positions.
These help you to determine if enough trades have occurred to validate your strategy. The Nbr of Positions is the count of positions on the chart. The TV list of trades in the Strategy Tester may indicate more than what is actually shown on the chart. The Data Window includes 'Nbr Strat Tester Trades', which equals the TV listing trades, to help you locate specific trades on the chart.
• Time in Market (%) is based on Trade-Grp’s and date range selected.
• Avg Invest/Trade-Grp will indicate the average amount of money invested in a Trade-Grp. This is adjusted for dividends if the “Include Dividends in Profit” checkbox is enabled.
• Equivalent Consecutive Losses, labeled as Equiv. Cons. Losses (ECL).
This value is determined by the Winning % and Nbr of Trade-Grp’s. This simulates the more likely case of a series of losses, then a small win, then another series of losses to form an equivalent consecutive losing streak. To lower the value, increase the Winning %.
• Equity Loss from ECL is the equity loss from the equivalent consecutive losses.
• % Equity Loss from ECL is the percent of equity loss from the equivalent consecutive losses.
Risk Management
• Pyramid rules enforce and maintain position sizing designated by you on the Inputs tab (% Equity to Risk, Up/Dwn Gap) & Properties tab (number of pyramids, slippage, and commission).
A pyramid position will not occur unless both its stop covers the last entry price with gap/slippage and commission cost of previous trade is covered. If take profit is enabled, a pyramid position will not occur unless commission cost of the trade is covered when take profit target is reached.
• Position sizing, stop-loss (SL), trailing stop-loss (TSL), and take profit (TP) are used.
• Wash sale prevention for applicable assets is enforced. Wash sale assets include stock and fund (e.g. ETF’s).
• No more than one entry position per candle is enforced .
Other Great Features
• Losing Trade-Grp’s indicated at the exit with label text in the color blue. Used to easily find consecutive losses affecting your strategy’s performance. The dividend payment, if any, is not considered in the calculation of a win or loss.
• Position values can be displayed on the chart. The number format is based on the min tick value, but is limited to 8 decimal places only for display purposes.
• Dividends per share and the amount can be displayed on the chart.
• Hold Days . This is the number of days to hold before allowing the next Trade-Grp. Can be a decimal number. This feature may help those trading on a cash account to avoid any settlement violations when trading the same asset.
• Date Filter. Partition the time when trading is allowed to see if the strategy works well across the date range selected. The metrics should be acceptable across all four (4) time ranges: entire range, 1st half, IQR (inter-quartile range), and 2nd half.
• Price gap amount identification. Used in determining if a pyramid entry may be profitable, and may be used in determining slippage amount to use.
• When TP is enabled, the PSE will only allow a pyramid position if the potential is profitable based on commission and price gap selected.
• Trade-Grp’s shown in background color: green for long positions and red for short positions.
• The PSE will alert you to update your stop-loss as the market changes if your exchange/broker does not allow for trailing stop-loss orders. Enable this option on the Inputs tab with Alert Chg TSL.
• The PSE will alert you if your drawdown exceeds Max % Equity Drawdown set on the Inputs tab.
• The PSE will send an alert to warn you of an expiring GTC order.
Some brokers will indicate the order is GTC, Good 'Till Cancelled, but there really is a time limit on the order and is typically 60-120 days. Therefore, the PSE will alert you if you've been in position for close to 60 days so you can refresh your order. The alert is typically a few days before the 60-day time period.
• For order fill alerts just use a {{placeholder}} in the Message of the alert. Details on how to enter placeholders is explained below.
• Identify same bar enter/exit for first entries and pyramids. This is shown in the Data Window as well. This can help you determine what stop-loss % works best for your trading style.
• Leverage trading information is displayed in the Data Window and applies to Trade-Grps.
Failed PosSize or Margin (%): Shows a zero if the failed-to-trade position size was less than 1 or shows the margin % which failed to meet the margin requirement set in the Properties tab. A flag will show on the bar where a failed-to-trade occurred. This is only applicable to the first position of a Trade-Grp. Position the cursor over the flag for the value to show in the Data Window.
Notional Value: total Trade-Grp position size x latest entry price x point value. The equity must be > notional value x margin requirement for a trade to occur.
Current Margin (%): must be greater than margin requirement set on the Properties tab in order for a trade to occur.
Margin Call Price: when enabled on the Style tab is displayed on both the chart and the Data Window as shown below.
PSE Settings
Pyramids
• Pyramiding requires the Stop Method to be set to either TSL or Both (meaning SL & TSL).
• The maximum number of pyramids is determined by the value entered in the Properties tab.
• Pyramid orders require the enter price to be higher than the previous close for Longs and lower than the previous close for Shorts.
• Pyramids also require the stop with gap/slippage to be higher than the last entry price for Longs, and lower than the last entry price for Shorts. This covers all previous positions and maintains position sizing.
• When take profit, TP, is enabled, the pyramids also require that they will be profitable when opening a position assuming they will reach TP. This is automatically adjusted by you with the Dwn Gap/Up Gap, Slippage, and Commission settings.
Inputs Tab
General Settings
Color Traded Background
Enable to change background color where in a trade. Green for long positions and red for short positions.
Show Losing Trade-Grp
Enable to show if losing Trade-Grp and is indicated by text in blue color. The last position may be at a loss, but if there was profit for the Trade-Grp, then it will not be shown as a loss .
Show Position Values
Enable to show the currency value of each position in gold color.
Include Dividends in Profit
This feature is only applicable if the asset pays dividends and the time frame period of the chart is 1D or less, otherwise ignored. The PSE assumes dividends are taken as cash and not reinvested.
Enable to adjust ROI, CAGR, Profit/Risked, Avg Invest/Trade-Grp, and Equity to include dividend payments. This feature considers if you were in position at least one day prior to the ex-dividend date and had not exited until after the ex-dividend date.
When Show Dividends is enabled it will display the payout in currency/share, as well as the total amount based on the number of shares the position(s) of the Trade-Grp are currently holding.
Include Dividends in Profit Factor
This checkbox allows you the option to either include or not include dividends in the calculation of Profit Factor. Must enable the “Include Dividends in Profit” checkbox to include dividends in PF. The dividend payment, if any, is not considered in the calculation of a win or loss.
Show Metrics Table
Options are font size and table location.
Alert Failed to Trade
Enable for the strategy to alert you when a trade did not happen due to low equity or low order size. Applicable only for the first position of a Trade-Grp.
Trade Direction
Options are 'Longs Only', 'Both', 'Shorts Only'.
Hold Days
This is the number of days to hold before allowing the next Trade-Grp. Applies only to the first trade position of a Trade-Grp. Where a Trade-Grp consists of the first position plus any pyramid positions.
The value entered will be overwritten to >= 31 to prevent wash sale for applicable assets in the event the last Trade-Grp was a loss. Wash sale assets include stock and fund (i.e. ETF’s).
The minimum value is the equivalent of 1 candle and is automatically assigned by the PSE if the entered value is equivalent to less than one candle. To calculate Hold Days in # of candles on the Hour chart divide the chart period by 24 x #candles. On the Minute chart divide the chart period by 60 then by 24 x #candles.
Show Vertical Lines at From Date & To Date
Shows a vertical dotted line at the From Date and To Date for visual inspection of the setting.
Date Filter
When enabled, trades are allowed between the From Date and To Date, i.e., the date range.
When disabled, trades are allowed for all candles.
Partition the time when trading is allowed to see if your indicator settings work well across the date range. Click 1st Half, IQR (inter-quartile range), or 2nd Half buttons to trade a portion of the date range.
Select only one at-a-time to partition the time when trading is allowed.
When 1st Half is enabled only trades for the 1st half of the date range are allowed.
When IQR is enabled only trades for the inter-quartile date range are allowed.
When 2nd Half is enabled only trades for the 2nd half of the date range are allowed.
Position Sizing
The % of Equity to Risk has been separated into two (2) areas: for initial trades and for pyramid trades. This allows for greater ability to maximize profits within your acceptable drawdown. A variation of the Anti-Martingale method from the initial trade if you choose to use it in that manner.
% Equity to Risk for Initial Trades: enter the percent of equity you want to risk per position for the initial trades of each Trade-Grp. For example, for 1% enter 1.
% Equity to Risk for Pyramid Trades: enter the percent of equity you want to risk per position for the pyramid trades of each Trade-Grp. For example, for 2% enter 2.
% Equity for Max Position Size: the position size will not exceed this amount. For example, for 25% enter 25.
Max % Equity Drawdown Warning: an alert will be triggered if the maximum drawdown exceeds this v alue. For example, for 10% enter 10.
Stop Methods
NOTE: The Stop Method must be either Both or TSL in order for the pyramids to work. This feature enforces position sizing.
Stop-loss, SL, and trailing stop-loss, TSL, are other features that enforce risk management.
The trailing stop-loss, TSL, is activated immediately if Stop Method = TSL. If Stop Method = Both, then the TSL is activated when its value is above stop-loss, SL, for Longs and below the SL for Shorts.
The calculated TSL value (shown on the chart by + symbol) of the previous bar is used for the current bar and the plot value is off by default, but you can it turn on via the Style tab. This is available so you can better understand how the TSL value used was calculated from. It is beneficial to show when monitoring the real-time candle.
Alert Chg TSL
When enabled, this feature will alert you to update your stop price if it moves greater than the change amount in %. The amount is the absolute % so will work for both Longs and Shorts. For example, for 1% enter 1 . This is provided since some exchanges/brokers do not offer TSL orders and you must manually adjust as price action plays out.
The alert will also suggest a stop limit price based on the gap selected and explained below.
The alert will occur at the close of the candle at the calculated TSL value of the candle just prior to the real-time candle.
Dwn Gap/Up Gap Input Settings
A price gap is the difference between the closing price of the previous candle and the opening price of the current candle. Dwn Gap and Up Gap are illustrated here.
The values of the Dwn Gap and Up Gap can be seen in the Data Window and are based on the settings of the Date Filter.
The options are “zero gap”, "median gap", "avg gap", "80 pct gap", "90 pct gap". The X pct gap stands for X percentile rank. For example, "80 pct gap" means that 80% of the gaps are less than or equal to the value shown in the Data Window. Select “zero gap” to disable this feature.
If Show Stop Limit is enabled, it will show a dotted-line below or above the current stop price where a stop-limit order should be taken. It is shown based on the gap option selected. Again, the PSE trades market, limit, and stop orders, but a stop-limit may be shown if you wanted to see where one would be set using the Up/Dwn Gap.
Dwn Gap: Affects Short Take Profit, Long Pyramid Entries, and to show the Long Stop Limit.
Up Gap : Affects Long Take Profit, Short Pyramid Entries, and to show the Short Stop Limit.
Fixed Take Profit (TP)
When take profit (TP) is enabled, the PSE will determine if opening a pyramid position will be in profit assuming the TP will be hit while considering commission costs (on Properties tab).
The larger of Up Gap or Slippage value is used with Long positions regarding TP.
The larger of Dwn Gap or Slippage value is used with Short positions regarding TP.
Properties Tab
• Initial Capital: Set as desired.
• Base Currency: Leave as Default. The PSE is designed to use the instrument’s currency, therefore leave as Default.
• Order Size: Leave as default. This setting has been disabled and position sizing is handled on the Inputs tab and is based on % of equity.
• Pyramiding: Set as desired.
• Commission: Set as number %. The PSE is designed to only work with commission as a percent of the position value.
• Verify Price for Limit Orders: Set as desired.
Slippage
Adjust Slippage on the Properties tab to account for a realistic bid-ask spread. You can use one of Dwn/Up Gap values or other guidelines. Again, the Dwn/Up Gap values are based on the Date Filter input settings.
Heed warnings from the TradingView Pine Script™ manual about values entered into the Slippage field.
The Slippage (ticks) have a noticeable influence on entry price and exit price especially at the beginning when the date range includes prices from $0.01 to $100,000.00 like that for BTC-USD INDEX. When this is the case, it is best to use different slippage values when partitioning time with the Date Filter.
To minimize the effects of slippage, yet account for it select ‘median gap’ on the Input Tab and use that value for slippage on the Properties tab.
The slippage value is included in the placeholder {{strategy.order.price}}.
Leverage Trading
The PSE is designed to be used both without leverage (the default) and with leverage.
These two settings apply to Trade-Grps. For example, for 5x leverage enter 20 (1/5x100=20).
Margin for Long Positions: Set as desired. The default is 100%.
Margin for Short Positions: Set as desired. The default is 100%.
This setting on the Inputs tab applies to each trade position within a Trade-Grp.
Max % Equity per Position: Set as desired. The default is 20% and intended for non-leverage trading. For leverage trading set as desired. For example, for 3x leverage enter 300 (3x100=300).
Recalculate After Order Is Filled
The PSE uses the strategy parameter calc_on_order_fills=true to allow for enter/exit on the same bar and generate alerts immediately after an order is filled. This parameter is on the Properties tab and is named ‘Recalculate After order is filled’ and is enabled by default.
Disabling this feature will cause the PSE to not work as intended.
You will see the following Caution! on the TV Strategy Tester
This occurs because the PSE has the strategy parameter calc_on_order_fills = true.
Again, the PSE will only work as intended if this parameter is enabled and set to true.
Therefore, you can close the caution sign and be confident of receiving realistic results.
Recalculate On every tick: Disable.
Fill Orders
• Using bar magnifier: Set as desired.
• On Bar Close: Disable. The PSE will not work as intended if this is enabled.
• Using Standard OHLC: Set as desired.
Using The Alert Message Box From TV Strategy Alert
Set alerts to gain access to all the alerts from PSE. This allows for both order filled alerts, as well as the alert function calls related to refresh GTC orders, drawdown exceeded, update stop-loss order, and Failed to Trade.
Example Message for Manual Trading Alerts
(This is just an example. Consult TV manual for possible placeholders to use.)
{
Alert for {{plot("position_for_alert")}} position. (long = 1; short = -1)
{{exchange}}:{{ticker}} on TF of {{interval}} at Broker Name
{{strategy.order.action}} Equity x Equity_Multiplier USD in shares at price = {{strategy.order.price}},
where Equity_Multiplier = {{strategy.order.contracts}} x {{strategy.order.price}} / {{plot("Equity")}}
or {{strategy.order.action}} {{strategy.order.contracts}} shares at price = {{strategy.order.price}}.
}
Note: Use the Equity x Equity_Multiplier method if you have several accounts with different initial capital.
Example Message for Bot Trading Alerts
(You must consult your specific bot for configuring the alert message. This is just an example.)
{
"action": "{{strategy.order.action}}",
“price”: {{strategy.order.price}}
"amount": {{strategy.order.contracts}},
"botId": "1234"
}
Connecting to the PSE
The diagram below illustrates how to connect indicators to the PSE.
The Aroon and MACD indicators are only used here as an example. Substitute your own indicators and add as many as you like.
Connection Indicator for the PSE
A video of how to connect your indicator(s) to the PSE is below.
The Connection Indicator for the PSE, also called here the connection-indicator.
Below is a description of how to connect your chosen indicators to the connection-indicator. Two (2) indicators were chosen for the example, but you may have one (1) or many indicators.
If you have source code access to your indicators you can paste the code directly into the connection-indicator to eliminate the need to have those indicators on the chart and the additional connection of them to the connection-indicator. Below will assume source code to the indicators are not available.
The MACD and Aroon Oscillator are from TV built standard indicators and are shown here just as an example for inputs (i.e. source) to the connection-indicator. They were configured as follows:
The source code for the connection-indicator is shown below. Substitute your own chosen indicators and add as many as you like to create your connection-indicator that feeds into the PSE. The MACD and Aroon Oscillator were simply chosen as an example. Configure your connection-indicator in the manner shown below.
// This Pine Script™ code is subject to the terms of the Mozilla Public License 2.0 at mozilla.org
// This is just an example Indicator to show how to interface with the PSE.
// The indicators used in the example are standard TV built indicators.
//@version=5
indicator(title="Connection Indicator for the PSE", overlay=false, max_lines_count=500, max_labels_count=500, max_boxes_count=500)
// Ind_1 INDICATOR ++++++++++++++++++++++++++++++++++++++++++++++++++++++++
// This is just and example and used MACD histogram as the source.
Filter_Ind_1 = input.bool(false, 'Ind_1', group='Ind_1 INDICATOR ~~~~~~~~~~~~~~~~~', tooltip='Click ON to enable the indicator')
input_Ind_1 = input.source(title = "input_Ind_1", defval = close, group='Ind_1 INDICATOR ~~~~~~~~~~~~~~~~~')
Entry_Ind_1_Long = Filter_Ind_1 ? input_Ind_1 > 0 ? 1 : 0 : 0
Entry_Ind_1_Short = Filter_Ind_1 ? input_Ind_1 < 0 ? 1 : 0 : 0
Exit_Ind_1_Long = Entry_Ind_1_Short
Exit_Ind_1_Short = Entry_Ind_1_Long
// Ind_2 INDICATOR ++++++++++++++++++++++++++++++++++++++++++++++++++++++++
// This is just an example and used Aroon Oscillator as the source. Included limits to use with the oscillator to determine enter and exit.
Filter_Ind_2 = input.bool(false, "Ind_2", group='Ind_2 INDICATOR ~~~~~~~~~~~~~~', tooltip='Click ON to enable the indicator')
Filter_Ind_2_Limit = input.int(35, minval=0, step=5, group='Ind_2 INDICATOR ~~~~~~~~~~~~~~')
Filter_Ind_2_UL = Filter_Ind_2_Limit
Filter_Ind_2_LL = -Filter_Ind_2_Limit
up = input.source(title = "input_Ind_2A Up", defval = close, group='Ind_2 INDICATOR ~~~~~~~~~~~~~~')
down = input.source(title = "input_Ind_2B Down", defval = close, group='Ind_2 INDICATOR ~~~~~~~~~~~~~~')
oscillator = up - down
Entry_Ind_2_Long = Filter_Ind_2? oscillator > Filter_Ind_2_UL ? 1 : 0 : 0
Entry_Ind_2_Short = Filter_Ind_2? oscillator < Filter_Ind_2_LL ? 1 : 0 : 0
Exit_Ind_2_Long = Entry_Ind_2_Short
Exit_Ind_2_Short = Entry_Ind_2_Long
//#region ~~~~~~~ASSEMBLY OF FILTERS ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~}
// You may have as many indicators as you like. Assemble them in similar fashion as below.
// ——————— Assembly of Entry Filters
Nbr_Entries = input.int(1, minval=1, title='Min Nbr Entries', inline='nbr_in_out', group='Assembly of Indicators')
// Update the assembly based on the number of indicators connected.
EntryLongOK = Entry_Ind_1_Long + Entry_Ind_2_Long >= Nbr_Entries? true: false
EntryShortOK = Entry_Ind_1_Short + Entry_Ind_2_Short >= Nbr_Entries? true: false
entry_signal = EntryLongOK ? 1 : EntryShortOK ? -1 : 0
plot(entry_signal, title="Entry_Signal", color=color.new(color.blue, 0))
// ——————— Assembly of Exit Filters
Nbr_Exits = input.int(1, minval=1, title='Min Nbr of Exits', inline='nbr_in_out', group='Assembly of Indicators', tooltip='Enter the minimum number of entries & exits
required for a signal.')
// Update the assembly based on the number of indicators connected.
ExitLongOK = Exit_Ind_1_Long + Exit_Ind_2_Long >= Nbr_Exits? true: false
ExitShortOK = Exit_Ind_1_Short + Exit_Ind_2_Short >= Nbr_Exits? true: false
exit_signal = ExitLongOK ? 1 : ExitShortOK ? -1 : 0
plot(exit_signal, title="Exit_Signal", color=color.new(color.red, 0))
//#endregion ~~~~~~~END OF ASSEMBLY OF FILTERS ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~}
The input box for the connection-indicator is shown below. The default for input source is “close”. For Input_Ind_1 click the dropdown and select the MACD Histogram. For Input_Ind_2 click the dropdown and select Aroon Up and Aroon Down as shown.
Signal Connection Section of PSE
Below is a description of how to connect your chosen indicators to the PSE from the connection-indicator.
At the PSE Input tab, the Signal Connection Section is where you select the source of the Entry and Exit Signal to the PSE. These are the outputs from connection-indicator.
The default source is “close”. Click the dropdown and select the entry and exit signal to establish a connection as shown below.
GKD-BT Solo Confirmation Super Complex Backtest [Loxx]Giga Kaleidoscope GKD-BT Solo Confirmation Super Complex Backtest is a Backtesting module included in Loxx's "Giga Kaleidoscope Modularized Trading System".
█ GKD-BT Solo Confirmation Super Complex Backtest
The Solo Confirmation Super Complex Backtest module allows users to perform backtesting on Full GKD Long and Short signals using GKD-C confirmation indicators. These signals are further refined by GKD-B Baseline and GKD-V Volatility/Volume indicators and augmented by an additional GKD-C Confirmation indicator acting as a Continuation indicator. This module serves as a comprehensive tool that falls just below a Full GKD trading system. The key difference is that the GKD-BT Solo Confirmation Super Complex utilizes a single GKD-C Confirmation indicator, while the Full GKD system employs two GKD-C Confirmation indicators. Both the Solo Confirmation Super Complex and the Full GKD systems incorporate an extra GKD-C Confirmation indicator to identify Continuation signals, which provide both longs and shorts on developing trends following an initial trend change.
This module encompasses two types of backtests: Trading and Full. The Trading backtest permits users to evaluate individual trades, whether Long or Short, one at a time. Conversely, the Full backtest allows users to analyze either Longs or Shorts separately by toggling between them in the settings, enabling the examination of results for each signal type. The Trading backtest emulates actual trading conditions, while the Full backtest assesses all signals, regardless of being Long or Short.
Additionally, this backtest module provides the option to test the core GKD-C Confirmation and GKD-C Continuation indicators with 1 to 3 take profits and 1 stop loss. The Trading backtest allows for the use of 1 to 3 take profits, while the Full backtest is limited to 1 take profit. The Trading backtest also offers the capability to apply a trailing take profit.
In terms of the percentage of trade removed at each take profit, this backtest module has the following hardcoded values:
Take profit 1: 50% of the trade is removed.
Take profit 2: 25% of the trade is removed.
Take profit 3: 25% of the trade is removed.
Stop loss: 100% of the trade is removed.
After each take profit is achieved, the stop loss level is adjusted. When take profit 1 is reached, the stop loss is moved to the entry point. Similarly, when take profit 2 is reached, the stop loss is shifted to take profit 1. The trailing take profit feature comes into play after take profit 2 or take profit 3, depending on the number of take profits selected in the settings. The trailing take profit is always activated on the final take profit when 2 or more take profits are chosen.
The backtest module also offers the capability to restrict by a specific date range, allowing for simulated forward testing based on past data. Additionally, users have the option to display or hide a trading panel that provides relevant information about the backtest, statistics, and the current trade. It is also possible to activate alerts and toggle sections of the trading panel on or off. On the chart, historical take profit and stop loss levels are represented by horizontal lines overlaid for reference.
To utilize this strategy, follow these steps:
1. GKD-B Baseline Import: Import the value "Input into NEW GKD-BT Backtest" from the GKD-B Baseline module into the GKD-BT Solo Confirmation Super Complex Backtest module setting named "Import GKD-B Baseline."
2. GKD-V Volatility/Volume Import: Import the value "Input into NEW GKD-BT Backtest" from the GKD-V Volatility/Volume module into the GKD-BT Solo Confirmation Super Complex Backtest module setting named "Import GKD-V Volatility/Volume."
3. Adjust the "Confirmation Type" in the GKD-C Confirmation Indicator to "GKD New."
4. GKD-C Confirmation Import: Import the value "Input into NEW GKD-BT Backtest" from the GKD-C Confirmation module into the GKD-BT Solo Confirmation Super Complex Backtest module setting named "Import GKD-C Confirmation."
5. Adjust the "Confirmation Type" in the GKD-C Continuation Indicator to "GKD New."
6. GKD-C Continuation Import: Import the value "Input into NEW GKD-BT Backtest" from the GKD-C Continuation module into the GKD-BT Solo Confirmation Super Complex Backtest module setting named "Import GKD-C Continuation."
The GKD system utilizes volatility-based take profits and stop losses. Each take profit and stop loss is calculated as a multiple of volatility. Users can also adjust the multiplier values in the settings.
In a future update, the option to include a GKD-E Exit indicator will be added to this module to complete a full trading strategy.
█ Solo Confirmation Super Complex Backtest Entries
Within this module, there are eight distinct types of entries available, which are outlined below:
Standard Entry
1-Candle Standard Entry
Baseline Entry
1-Candle Baseline Entry
Volatility/Volume Entry
1-Candle Volatility/Volume Entry
PullBack Entry
Continuation Entry
Each of these entry types can generate either long or short signals, resulting in a total of 16 signal variations. The user has the flexibility to enable or disable specific entry types and choose which qualifying rules within each entry type are applied to price to determine the final long or short signal. You'll notice that these signals are different form the core GKD signals mentioned towards the end of this description. Signals from the GKD-BT Solo Confirmation Super Complex Backtest are modifided to add additional qualifications to make your finalized trading strategy more dynamic and robust.
The following section provides an overview of the various entry types and their corresponding qualifying rules:
Standard Entry
1. GKD-C Confirmation gives signal
2. Baseline agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Volatility/Volume agrees
1-Candle Standard Entry
1a. GKD-C Confirmation gives signal
2a. Baseline agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
Next Candle:
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Volatility/Volume agrees
Baseline Entry
1. GKD-B Basline gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Volatility/Volume agrees
6. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
1-Candle Baseline Entry
1a. GKD-B Basline gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
Next Candle:
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Volatility/Volume agrees
Volatility/Volume Entry
1. GKD-V Volatility/Volume gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Baseline agrees
6. Confirmation 1 signal was less than 7 candles prior
1-Candle Volatility/Volume Entry
1a. GKD-V Volatility/Volume gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSVVC Bars Back' prior
Next Candle:
1b. Price retraced
2b. Volatility/Volume agrees
3b. Confirmation 1 agrees
4b. Baseline agrees
PullBack Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price is beyond 1.0x Volatility of Baseline
Next Candle:
1b. Price inside Goldie Locks Zone Minimum
2b. Price inside Goldie Locks Zone Maximum
3b. Confirmation 1 agrees
4b. Volatility/Volume agrees
Continuation Entry
1. Standard Entry, 1-Candle Standard Entry, Baseline Entry, 1-Candle Baseline Entry, Volatility/Volume Entry, 1-Candle Volatility/Volume Entry, or Pullback entry triggered previously
2. Baseline hasn't crossed since entry signal trigger
4. Confirmation 1 agrees
5. Baseline agrees
█ Volatility Types Included
This module includes 17 types of volatility:
Close-to-Close
Parkinson
Garman-Klass
Rogers-Satchell
Yang-Zhang
Garman-Klass-Yang-Zhang
Exponential Weighted Moving Average
Standard Deviation of Log Returns
Pseudo GARCH(2,2)
Average True Range
True Range Double
Standard Deviation
Adaptive Deviation
Median Absolute Deviation
Efficiency-Ratio Adaptive ATR
Mean Absolute Deviation
Static Percent
Close-to-Close
Close-to-Close volatility is a classic and widely used volatility measure, sometimes referred to as historical volatility.
Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a larger amplitude. The more volatile a stock is, the riskier it is.
Close-to-close historical volatility is calculated using only a stock's closing prices. It is the simplest volatility estimator. However, in many cases, it is not precise enough. Stock prices could jump significantly during a trading session and return to the opening value at the end. That means that a considerable amount of price information is not taken into account by close-to-close volatility.
Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators.
Parkinson
Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day.
The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. This is useful as close-to-close prices could show little difference while large price movements could have occurred during the day. Thus, Parkinson's volatility is considered more precise and requires less data for calculation than close-to-close volatility.
One drawback of this estimator is that it doesn't take into account price movements after the market closes. Hence, it systematically undervalues volatility. This drawback is addressed in the Garman-Klass volatility estimator.
Garman-Klass
Garman-Klass is a volatility estimator that incorporates open, low, high, and close prices of a security.
Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing prices. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate.
Garman and Klass also assumed that the process of price change follows a continuous diffusion process (Geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements.
Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremes.
Researchers Rogers and Satchell have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail.
Rogers-Satchell
Rogers-Satchell is an estimator for measuring the volatility of securities with an average return not equal to zero.
Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates a drift term (mean return not equal to zero). As a result, it provides better volatility estimation when the underlying is trending.
The main disadvantage of this method is that it does not take into account price movements between trading sessions. This leads to an underestimation of volatility since price jumps periodically occur in the market precisely at the moments between sessions.
A more comprehensive estimator that also considers the gaps between sessions was developed based on the Rogers-Satchel formula in the 2000s by Yang-Zhang. See Yang Zhang Volatility for more detail.
Yang-Zhang
Yang Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error.
Yang-Zhang volatility can be thought of as a combination of the overnight (close-to-open volatility) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility. It is considered to be 14 times more efficient than the close-to-close estimator.
Garman-Klass-Yang-Zhang
Garman-Klass-Yang-Zhang (GKYZ) volatility estimator incorporates the returns of open, high, low, and closing prices in its calculation.
GKYZ volatility estimator takes into account overnight jumps but not the trend, i.e., it assumes that the underlying asset follows a Geometric Brownian Motion (GBM) process with zero drift. Therefore, the GKYZ volatility estimator tends to overestimate the volatility when the drift is different from zero. However, for a GBM process, this estimator is eight times more efficient than the close-to-close volatility estimator.
Exponential Weighted Moving Average
The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, with the main applications being technical analysis and volatility modeling.
The moving average is designed such that older observations are given lower weights. The weights decrease exponentially as the data point gets older – hence the name exponentially weighted.
The only decision a user of the EWMA must make is the parameter lambda. The parameter decides how important the current observation is in the calculation of the EWMA. The higher the value of lambda, the more closely the EWMA tracks the original time series.
Standard Deviation of Log Returns
This is the simplest calculation of volatility. It's the standard deviation of ln(close/close(1)).
Pseudo GARCH(2,2)
This is calculated using a short- and long-run mean of variance multiplied by ?.
?avg(var;M) + (1 ? ?) avg(var;N) = 2?var/(M+1-(M-1)L) + 2(1-?)var/(M+1-(M-1)L)
Solving for ? can be done by minimizing the mean squared error of estimation; that is, regressing L^-1var - avg(var; N) against avg(var; M) - avg(var; N) and using the resulting beta estimate as ?.
Average True Range
The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period.
The true range indicator is taken as the greatest of the following: current high less the current low; the absolute value of the current high less the previous close; and the absolute value of the current low less the previous close. The ATR is then a moving average, generally using 14 days, of the true ranges.
True Range Double
A special case of ATR that attempts to correct for volatility skew.
Standard Deviation
Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. The standard deviation is calculated as the square root of variance by determining each data point's deviation relative to the mean. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation.
Adaptive Deviation
By definition, the Standard Deviation (STD, also represented by the Greek letter sigma ? or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis, we usually use it to measure the level of current volatility.
Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA, we can call it EMA deviation. Additionally, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting).
The difference when compared to the standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions.
Median Absolute Deviation
The median absolute deviation is a measure of statistical dispersion. Moreover, the MAD is a robust statistic, being more resilient to outliers in a data set than the standard deviation. In the standard deviation, the distances from the mean are squared, so large deviations are weighted more heavily, and thus outliers can heavily influence it. In the MAD, the deviations of a small number of outliers are irrelevant.
Because the MAD is a more robust estimator of scale than the sample variance or standard deviation, it works better with distributions without a mean or variance, such as the Cauchy distribution.
Efficiency-Ratio Adaptive ATR
Average True Range (ATR) is a widely used indicator for many occasions in technical analysis. It is calculated as the RMA of the true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range.
Mean Absolute Deviation
The mean absolute deviation (MAD) is a measure of variability that indicates the average distance between observations and their mean. MAD uses the original units of the data, which simplifies interpretation. Larger values signify that the data points spread out further from the average. Conversely, lower values correspond to data points bunching closer to it. The mean absolute deviation is also known as the mean deviation and average absolute deviation.
This definition of the mean absolute deviation sounds similar to the standard deviation (SD). While both measure variability, they have different calculations. In recent years, some proponents of MAD have suggested that it replace the SD as the primary measure because it is a simpler concept that better fits real life.
Static Percent
Static Percent allows the user to insert their own constant percent that will then be used to create take profits and stoploss
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v2.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data to A backtest module wherein the various components of the GKD system are combined to create a trading signal.
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Solo Confirmation Complex Backtest as shown on the chart above
Baseline: Hull Moving Average as shown on the chart above
Volatility/Volume: Hurst Exponent as shown on the chart above
Confirmation 1: Fisher Trasnform as shown on the chart above
Confirmation 2: Williams Percent Range
Continuation: Vortex as shown on the chart above
Exit: Rex Oscillator
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system.
Giga Kaleidoscope Modularized Trading System Signals (based on the NNFX algorithm)
Standard Entry
1. GKD-C Confirmation 1 Signal
2. GKD-B Baseline agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
Baseline Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
6. GKD-C Confirmation 1 signal was less than 7 candles prior
Volatility/Volume Entry
1. GKD-V Volatility/Volume signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-B Baseline agrees
6. GKD-C Confirmation 1 signal was less than 7 candles prior
Continuation Entry
1. Standard Entry, Baseline Entry, or Pullback; entry triggered previously
2. GKD-B Baseline hasn't crossed since entry signal trigger
3. GKD-C Confirmation Continuation Indicator signals
4. GKD-C Confirmation 1 agrees
5. GKD-B Baseline agrees
6. GKD-C Confirmation 2 agrees
1-Candle Rule Standard Entry
1. GKD-C Confirmation 1 signal
2. GKD-B Baseline agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
Next Candle:
1. Price retraced (Long: close < close or Short: close > close )
2. GKD-B Baseline agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
1-Candle Rule Baseline Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 1 signal was less than 7 candles prior
Next Candle:
1. Price retraced (Long: close < close or Short: close > close )
2. GKD-B Baseline agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume Agrees
1-Candle Rule Volatility/Volume Entry
1. GKD-V Volatility/Volume signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 1 signal was less than 7 candles prior
Next Candle:
1. Price retraced (Long: close < close or Short: close > close)
2. GKD-B Volatility/Volume agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-B Baseline agrees
PullBack Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is beyond 1.0x Volatility of Baseline
Next Candle:
1. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
2. GKD-C Confirmation 1 agrees
3. GKD-C Confirmation 2 agrees
4. GKD-V Volatility/Volume Agrees
STRATEGY Fibonacci Levels with High/Low Criteria - AYNET
Here is an explanation of the Fibonacci Levels Strategy with High/Low Criteria script:
Overview
This strategy combines Fibonacci retracement levels with high/low criteria to generate buy and sell signals based on price crossing specific thresholds. It utilizes higher timeframe (HTF) candlesticks and user-defined lookback periods for high/low levels.
Key Features
Higher Timeframe Integration:
The script calculates the open, high, low, and close values of the higher timeframe (HTF) candlestick.
Users can choose to calculate levels based on the current or the last HTF candle.
Fibonacci Levels:
Fibonacci retracement levels are dynamically calculated based on the HTF candlestick's range (high - low).
Users can customize the levels (0.000, 0.236, 0.382, 0.500, 0.618, 0.786, 1.000).
High/Low Lookback Criteria:
The script evaluates the highest high and lowest low over user-defined lookback periods.
These levels are plotted on the chart for visual reference.
Trade Signals:
Long Signal: Triggered when the close price crosses above both:
The lowest price criteria (lookback period).
The Fibonacci level 3 (default: 0.5).
Short Signal: Triggered when the close price crosses below both:
The highest price criteria (lookback period).
The Fibonacci level 3 (default: 0.5).
Visualization:
Plots Fibonacci levels and high/low criteria on the chart for easy interpretation.
Inputs
Higher Timeframe:
Users can select the timeframe (default: Daily) for the HTF candlestick.
Option to calculate based on the current or last HTF candle.
Lookback Periods:
lowestLookback: Number of bars for the lowest low calculation (default: 20).
highestLookback: Number of bars for the highest high calculation (default: 10).
Fibonacci Levels:
Fully customizable Fibonacci levels ranging from 0.000 to 1.000.
Visualization
Fibonacci Levels:
Plots six customizable Fibonacci levels with distinct colors and transparency.
High/Low Criteria:
Plots the highest and lowest levels based on the lookback periods as reference lines.
Trading Logic
Long Condition:
Price must close above:
The lowest price criteria (lowcriteria).
The Fibonacci level 3 (50% retracement).
Short Condition:
Price must close below:
The highest price criteria (highcriteria).
The Fibonacci level 3 (50% retracement).
Use Case
Trend Reversal Strategy:
Combines Fibonacci retracement with recent high/low criteria to identify potential reversal or breakout points.
Custom Timeframe Analysis:
Incorporates higher timeframe data for multi-timeframe trading strategies.
Dow Theory based Strategy (Markttechnik)What makes this script unique?
calculates two trends at the same time: a big one for the overall strong trend - and a small one to trigger a trade after a small correction within the big trend
only if both trends (the small and the big trend) are in an uptrend, a buy signal is created: this prevents a buy signal from being generated in a falling market just because an upward movement begins in a small trend
the exit strategy can be configured very flexibly and individually: use the last low as stop loss and automatically switch to a trialing stop loss as soon as the take profit is reached (instead of finishing the trade)
the take profit strategy can also be configured - e.g. use the last high, a fixed percentage or a combination of it
plots each trade in detail on the chart - e.g. inner candles or the exact progression of the stop loss over the entire duration of the trade to allow you to analyze each trade precisely
What does the script do and how?
In this strategy an intact upward trend is characterized by higher highs and lower lows only if the big trend and the small trend are in an upward trend at the same time.
The following describes how the script calculates a buy signal. Every step is drawn to the chart immediately - see example chart above:
1. the stock rises in the big trend - i.e. in a longer time frame
2. a correction takes place (the share price falls) - but does not create a new low
3. the stock rises again in the big trend and creates a new high
From now on, the big trend is in an intact upward trend (until it falls below its last low).
This is drawn to the chart as 3 bold green zigzag lines.
But we do not buy right now! Instead, we want to wait for a correction in the big trend and for the start of a small upward trend.
4. a correction takes place (not below the low from 2.)
Now, the script also starts to calculate the small trend:
5. the stock rises in the small trend - i.e. in a shorter time frame
6. a small correction takes place (not below the low from 4.)
7. the stock rises above the high from 5.: a new high in the shorter time frame
Now, both trends are in an intact upward trend.
A buy signal is created and both the minor and major trend are colored green on the chart.
Now, the trade is active and:
the stop loss is calculated and drawn for each candle
the take profit is calculated and drawn to the chart
as soon as the price reaches the take profit or the stop loss, the trade is closed
Features and functionalities
Uptrend : An intact upward trend is characterized by higher highs and lower lows. Uptrends are shown in green on the chart.
The beginning of an uptrend is numbered 1, each subsequent high is numbered 2, and each low is numbered 3.
Downtrend: An intact downtrend is characterized by lower highs and lower lows. Downtrends are displayed in red on the chart.
Note that our indicator does not show the numbering of the points of the downtrend.
Trendless phases: If there is no intact trend, we are in a trendless phase. Trendless phases are shown in blue on the chart.
This occurs after an uptrend, when a lower low or a lower high is formed. Or after a downtrend, when a higher low or a higher high is formed.
Buy signals
A buy signal is generated as soon as a new upward trend has been formed or a new high has been established in an intact upward trend.
But even before a buy signal is generated, this strategy anticipates a possible emerging trend and draws the next possible trading opportunity to the chart.
In addition to the (not yet reached) buy price, the risk-reward ratio, the StopLoss and the TakeProfit price is shown.
With this information, you can already enter a StopBuy order, which is thus triggered directly with the then created buy signal.
You can configure, if a buy signal shall be created while the big trend is an uptrend, a downtrend and/or trendless.
Exit strategy
With this strategy, you have multiple possibilities to close your position. All of them can be configured within the settings. In general, you can combine a take profit strategy with a stop loss strategy.
The take profit price will be calculated once for each trade. It will be drawn to the chart for active trade.
Depending on your configuration, this can be the last high (which is often a resistance level), a fixed percentage added to the buy price or the maximum of both.
You can also configure that a trailing stop loss is used as soon as the take profit price is reached once.
The stop loss gets recalculated with each candle and is displayed and plotted for each active and finished trade. With this, you can easily check how the stop loss changed during your trades.
The stop loss can be configured flexibly:
Use the classic "trailing stop loss" that follows the price from below.
Set the stop loss to the last low and tighten it every time the small trend marks a new local low.
Confiure that the stop loss is tightened as soon as the break even is reached. Nothing is more annoying than a trade turning from a win to a loss.
Ignore inside candles (see description below) and relax the stop loss to use the outside candle for its calculation.
Inner candles
Inner candles are created when the candle body is within the maximum values of a previous candle (the outer candle). There can be any number of consecutive inner candles. As soon as you have activated the "Check inner candles" setting, all consecutive inner candles will be highlighted in yellow on the chart.
Prices during an inner candle scenario might be irrelevant for trading and can be interpreted as fluctuations within the outside candle. For this reason, the trailing stop loss should not be aligned with inner candles. Therefore, as soon as an inner candle occurs, the stop loss is reset and the low at the time of the outside candle is used as the calculation for the trailing stop loss. This will all be plotted for you on the chart.
Display of the trades:
All active and closed trades of the last 5 years are displayed in the chart with buy signal, sell, stop loss history, inside candles and statistics.
Backtesting:
The strategy can be simulated for each stock over the period of the last 5 years. Each individual trade is recorded and can be traced and analyzed in the chart including stop loss history. Detailed evaluations and statistics are available to evaluate the performance of the strategy.
Additional Statistics
This strategy immediately displays a statistic table to the chart area giving you an overview of its performance over the last years for the given chart.
This includes:
The total win/loss in $ and %
The win/loss per year in %
The active investment time in days and % (e.g. invested 10 of 100 trading days -> 10%)
The total win/loss in %, extrapolated to 100% equity usage: Only with this value can strategies really be compared. Because you are not invested between the trades and could invest in other stocks during this time. This value indicates how much profit you would have made if you had been invested 100% of the time - or to put it another way - if you had been invested 100% of the time in stocks with exactly the same performance. Let's say you had only one trade in the last 5 years that lasted, say, only one month and made 5% profit. This would be significantly better than a strategy with which you were invested for, say, 5 years and made 10% profit.
The total profit/loss per year in %, extrapolated to 100% equity usage
Notifications (alerts):
Get alerted before a new buy signal emerges to create an order if necessary and not miss a trade. You can also be notified when the stop loss needs to be adjusted. The notification can be done in different ways, e.g. by Mail, PopUp or App-Notification. This saves them the annoying, time-consuming and error-prone "click through" all the charts.
Settings: Display Settings
With these settings, you have the possibility to:
Show the small or the big trend as a background color
Configure if the numbers (1-2-3-2-3) shall be shown at all or only for the small, the big trend or both
Settings: Trend calculation - fine tuning
Drawing trend lines on a chart is not an exact science. Some highs and lows are not very clear or significant. And so it will always happen that 2 different people would draw different trendlines for the same chart. Unfortunately, there is no exact "right" or "wrong" here.
With the options under "Trend Calculation - Fine Tuning" you have the possibility to influence the drawing in of trends and to adapt it to your personal taste.
Small Trend, Big Trend : With these settings you can influence how significant a high or low has to be to recognize them as an independent high or low. The larger the values, the more significant a high or low must be to be recognized as such.
High and low recognition : With this setting you can influence when two adjacent, almost identical highs or lows should be recognized as independent highs or lows. The higher the value, the more different "similar" highs or lows must be in order to be recognized as such.
Which default settings were selected and why
Show Trades: true - its often useful to see all recent trades in the chart
Time Frame: 1 day - most common time frame (except for day traders)
Take Profit: combined 10% - the last high is taken as take profit because the trend often changes there, but only if there is at least 10% profit to ensure we do not risk money for a tiny profit
Stop Loss: combined - the last low is used as stop loss because the trend would break there and switch to a trailing stop loss as soon as our take profit is reached to let our profits run without risking them anymore
Stop Loss distance: 3% - we are giving the price 3% air (below the last low) to avoid being stopped out due to a short price drop
Trailing Stop Loss: 2% - we have to give the stop loss some room to avoid being stopped out prematurely; this is a value that is well balanced between a certain downside distance and the profit-taking ratio
Set Stop Loss to break even: true, 2% - once we reached the break even, it is a common practice to not risk our money anymore, the value is set to the same value as the trailing stop loss
Trade Filter: Uptrend - we only start trades if the big trend is an uptrend in the expectation that it will continue after a small correction
Display settings: those will not influence the trades, feel free to change them to your needs
Trend calculation - Fine Tuning: 1/1,5/0,05; influences the internal calculation for highs and lows and how significant they need to be to be considered a new high or low; the default values will provide you nicely calculated trends in the daily time frame; if there are too many or too few lows and highs according to your taste, feel free to play around and immediately see the result drawn to the chart; read the manual for a detailed description of this values
Note that you can (and should) configure the general trading properties like your initial capital, order size, slippage and commission.
S&D Light+ Enhanced# S&D Light+ Enhanced - Supply & Demand Zone Trading Strategy
## 📊 Overview
**S&D Light+ Enhanced** is an advanced Supply and Demand zone identification and trading strategy that combines institutional order flow concepts with smart money techniques. This strategy automatically identifies high-probability reversal zones based on Break of Structure (BOS), momentum analysis, and first retest principles.
## 🎯 Key Features
### Smart Zone Detection
- **Automatic Supply & Demand Zone Identification** - Detects institutional zones where price is likely to react
- **Multi-Candle Momentum Analysis** - Validates zones with configurable momentum requirements
- **Break of Structure (BOS) Confirmation** - Ensures zones are created only after significant structure breaks
- **Quality Filters** - Minimum zone size and ATR-based filtering to eliminate weak zones
### Advanced Zone Management
- **Customizable Zone Display** - Choose between Geometric or Volume-Weighted midlines
- **First Retest Logic** - Option to trade only the first touch of each zone for higher probability setups
- **Zone Capacity Control** - Maintains a clean chart by limiting stored zones per type
- **Visual Zone Status** - Automatically marks consumed zones with faded midlines
### Risk Management
- **Dynamic Stop Loss** - Positioned beyond zone boundaries with adjustable buffer
- **Risk-Reward Ratio Control** - Customizable R:R for consistent risk management
- **Entry Spacing** - Minimum bars between signals prevents overtrading
- **Position Sizing** - Built-in percentage of equity allocation
## 🔧 How It Works
### Zone Creation Logic
**Supply Zones (Selling Pressure):**
1. Strong momentum downward movement (configurable body-to-range ratio)
2. Identified bullish base candle (where institutions accumulated shorts)
3. Break of Structure downward (price breaks below recent swing low)
4. Zone created at the base candle's high/low range
**Demand Zones (Buying Pressure):**
1. Strong momentum upward movement
2. Identified bearish base candle (where institutions accumulated longs)
3. Break of Structure upward (price breaks above recent swing high)
4. Zone created at the base candle's high/low range
### Entry Conditions
**Long Entry:**
- Price retests a demand zone (touches top of zone)
- Rejection confirmed (close above zone)
- Zone hasn't been used (if "first retest only" enabled)
- Minimum bars since last entry respected
**Short Entry:**
- Price retests a supply zone (touches bottom of zone)
- Rejection confirmed (close below zone)
- Zone hasn't been used (if "first retest only" enabled)
- Minimum bars since last entry respected
## ⚙️ Customizable Parameters
### Display Settings
- **Show Zones** - Toggle zone visualization on/off
- **Max Stored Zones** - Control number of active zones (1-50 per type)
- **Color Customization** - Adjust supply/demand colors and transparency
### Zone Quality Filters
- **Momentum Body Fraction** - Minimum body size for momentum candles (0.1-0.9)
- **Min Momentum Candles** - Number of consecutive momentum candles required (1-5)
- **Big Candle Body Fraction** - Alternative single-candle momentum threshold (0.5-0.95)
- **Min Zone Size %** - Minimum zone height as percentage of price (0.01-5.0%)
### BOS Configuration
- **Swing Length** - Lookback period for structure identification (3-20)
- **ATR Length** - Period for volatility measurement (1-50)
- **BOS Required Break** - ATR multiplier for valid structure break (0.1-3.0)
### Midline Options
- **None** - No midline displayed
- **Geometric** - Simple average of zone top/bottom
- **CenterVolume** - Volume-weighted center based on highest volume bar in window
### Risk Management
- **SL Buffer %** - Additional space beyond zone boundary (0-5%)
- **Take Profit RR** - Risk-reward ratio for target placement (0.5-10x)
### Entry Rules
- **Only 1st Retest per Zone** - Trade zones only once for higher quality
- **Min Bars Between Entries** - Prevent overtrading (1-20 bars)
## 📈 Recommended Settings
### Conservative (Lower Frequency, Higher Quality)
```
Momentum Body Fraction: 0.30
Min Momentum Candles: 2-3
BOS Required Break: 0.8-1.0
Min Zone Size: 0.15-0.20%
Only 1st Retest: Enabled
```
### Balanced (Default)
```
Momentum Body Fraction: 0.28
Min Momentum Candles: 2
BOS Required Break: 0.7
Min Zone Size: 0.12%
Only 1st Retest: Enabled
```
### Aggressive (Higher Frequency, More Signals)
```
Momentum Body Fraction: 0.20-0.25
Min Momentum Candles: 1-2
BOS Required Break: 0.4-0.5
Min Zone Size: 0.08-0.10%
Only 1st Retest: Disabled
```
## 🎨 Visual Elements
- **Red Boxes** - Supply zones (potential selling areas)
- **Green Boxes** - Demand zones (potential buying areas)
- **Dotted Midlines** - Center of each zone (fades when zone is used)
- **Debug Triangles** - Shows when zone creation conditions are met
- Red triangle down = Supply zone created
- Green triangle up = Demand zone created
## 📊 Best Practices
1. **Use on Higher Timeframes** - 1H, 4H, and Daily charts work best for institutional zones
2. **Combine with Trend** - Trade zones in direction of overall market structure
3. **Wait for Confirmation** - Don't enter immediately at zone touch; wait for rejection
4. **Adjust for Market Volatility** - Increase BOS multiplier in choppy markets
5. **Monitor Zone Quality** - Fresh zones typically have higher success rates
6. **Backtest Your Settings** - Optimize parameters for your specific market and timeframe
## ⚠️ Risk Disclaimer
This strategy is for educational and informational purposes only. Past performance does not guarantee future results. Always:
- Use proper position sizing
- Set appropriate stop losses
- Test thoroughly before live trading
- Consider market conditions and overall trend
- Never risk more than you can afford to lose
## 🔍 Data Window Information
The strategy provides real-time metrics visible in the data window:
- Supply Zones Count
- Demand Zones Count
- ATR Value
- Momentum Signals (Up/Down)
- BOS Signals (Up/Down)
## 📝 Version History
**v1.0 - Enhanced Edition**
- Improved BOS detection logic
- Extended base candle search range
- Added comprehensive input validation
- Enhanced visual feedback system
- Robust array bounds checking
- Debug signals for troubleshooting
## 💡 Tips for Optimization
- **Trending Markets**: Lower momentum requirements, tighter BOS filters
- **Ranging Markets**: Increase zone size minimum, enable first retest only
- **Volatile Assets**: Increase ATR multiplier and SL buffer
- **Lower Timeframes**: Reduce swing length, increase min bars between entries
- **Higher Timeframes**: Increase swing length, relax momentum requirements
---
**Created with focus on institutional order flow, smart money concepts, and practical risk management.**
*Happy Trading! 📈*
MA Crossover Strategy with TP/SL (5 EMA Filter)How the Strategy Works on a 5-Minute Chart:
Data Input (5-Minute Candles):
Every single data point (candle) on your chart will represent 5 minutes of price action (Open, High, Low, Close for that 5-minute period).
All calculations (MAs, EMA, signals) will be based on these 5-minute price data points.
Moving Average Calculations:
Fast MA (10-period SMA): This will be the Simple Moving Average of the closing prices of the last 10 five-minute candles. It reacts relatively quickly to recent price changes.
Slow MA (30-period SMA): This will be the Simple Moving Average of the closing prices of the last 30 five-minute candles. It represents a slightly longer-term trend compared to the Fast MA.
5 EMA (5-period EMA): This is the Exponential Moving Average of the closing prices of the last 5 five-minute candles. Being an EMA, it gives more weight to the most recent 5-minute prices, making it very responsive to immediate price action.
Signal Generation (Entry Conditions):
Long Entry Signal:
The 10-period SMA crosses above the 30-period SMA (indicating a potential bullish shift in the short-to-medium term trend).
AND the current 5-minute candle's closing price is above the 5-period EMA (confirming that the immediate price momentum is also bullish and supporting the crossover).
If both conditions are met at the close of a 5-minute candle, a "Buy" signal is generated.
Short Entry Signal:
The 10-period SMA crosses below the 30-period SMA (indicating a potential bearish shift).
AND the current 5-minute candle's closing price is below the 5-period EMA (confirming immediate bearish momentum).
If both conditions are met at the close of a 5-minute candle, a "Sell" signal is generated.
Trade Execution:
When a signal is triggered, the strategy enters a trade (long or short) at the closing price of that 5-minute candle.
Immediately upon entry, it places two contingent orders:
Take Profit (Target): Set at 2% (by default) away from your entry price. For a long trade, it's 2% above; for a short trade, 2% below.
Stop Loss: Set at 1% (by default) away from your entry price. For a long trade, it's 1% below; for a short trade, 1% above.
The trade will remain open until either the Take Profit or Stop Loss price is hit by subsequent 5-minute candles.
Implications for Trading on a 5-Minute Chart:
Increased Trade Frequency: You will likely see many more signals and trades compared to higher timeframes (like 1-hour or daily charts). This means more potential opportunities but also more transaction costs (commissions, slippage).
Sensitivity to Noise: Lower timeframes are more prone to "market noise" – small, random price fluctuations that don't indicate a true trend. While the 5 EMA filter helps, some false signals might still occur.
Faster Price Action: Price movements can be very rapid on a 5-minute chart. Your take profit or stop loss levels might be hit very quickly, sometimes within the same or next few candles.
Parameter Optimization is Crucial: The default MA lengths (10, 30) and EMA (5) might not be optimal for every asset or market condition on a 5-minute chart. You'll need to backtest extensively and potentially adjust these lengths, as well as the targetPerc and stopPerc, to find what works best for the specific instrument you're trading.
Risk Management: The fixed percentage stop loss is vital on a 5-minute chart due to its volatility. Without it, a few unfavorable moves could lead to significant losses.
GKD-BT Solo Confirmation Complex Backtest [Loxx]Giga Kaleidoscope GKD-BT Solo Confirmation Complex Backtest is a Backtesting module included in Loxx's "Giga Kaleidoscope Modularized Trading System".
█ GKD-BT Solo Confirmation Complex Backtest
The Solo Confirmation Complex Backtest module enables users to perform backtesting on Standard Long and Short signals from GKD-C confirmation indicators, filtered by GKD-B Baseline and GKD-V Volatility/Volume indicators. This module represents a complex form of the Solo Confirmation Backtest in the GKD trading system. It includes two types of backtests: Trading and Full. The Trading backtest allows users to test individual trades, both Long and Short, one at a time. On the other hand, the Full backtest allows users to test either Longs or Shorts by toggling between them in the settings to view the results for each signal type. The Trading backtest simulates real trading, while the Full backtest tests all signals, whether Long or Short.
Additionally, this backtest module provides the option to test the GKD-C Confirmation indicator with 1 to 3 take profits and 1 stop loss. The Trading backtest allows for the use of 1 to 3 take profits, while the Full backtest is limited to 1 take profit. The Trading backtest also offers the capability to apply a trailing take profit.
In terms of the percentage of trade removed at each take profit, this backtest module has the following hardcoded values:
Take profit 1: 50% of the trade is removed.
Take profit 2: 25% of the trade is removed.
Take profit 3: 25% of the trade is removed.
Stop loss: 100% of the trade is removed.
After each take profit is achieved, the stop loss level is adjusted. When take profit 1 is reached, the stop loss is moved to the entry point. Similarly, when take profit 2 is reached, the stop loss is shifted to take profit 1. The trailing take profit feature comes into play after take profit 2 or take profit 3, depending on the number of take profits selected in the settings. The trailing take profit is always activated on the final take profit when 2 or more take profits are chosen.
The backtest module also offers the capability to restrict by a specific date range, allowing for simulated forward testing based on past data. Additionally, users have the option to display or hide a trading panel that provides relevant information about the backtest, statistics, and the current trade. It is also possible to activate alerts and toggle sections of the trading panel on or off. On the chart, historical take profit and stop loss levels are represented by horizontal lines overlaid for reference.
The GKD system utilizes volatility-based take profits and stop losses. Each take profit and stop loss is calculated as a multiple of volatility. Users can also adjust the multiplier values in the settings.
To utilize this strategy, follow these steps:
1. GKD-B Baseline Import: Import the value "Input into NEW GKD-BT Backtest" from the GKD-B Baseline module into the GKD-BT Solo Confirmation Complex Backtest module setting named "Import GKD-B Baseline indicator."
Adjust the "Confirmation Type" in the GKD-C Confirmation Indicator to "GKD New."
2. GKD-C Confirmation Import: Import the value "Input into NEW GKD-BT Backtest" from the GKD-C Confirmation module into the GKD-BT Solo Confirmation Complex Backtest module setting named "Import GKD-C Confirmation indicator."
3. GKD-V Volatility/Volume Import: Import the value "Input into NEW GKD-BT Backtest" from the GKD-V Volatility/Volume module into the GKD-BT Solo Confirmation Complex Backtest module setting named "Import GKD-V Volatility/Volume indicator."
4. The Solo Confirmation Complex Backtest module exclusively supports Standard Entries, both Long and Short. However, please note that this module uses a modified version of the Standard Entry. In this modified version, long and short signals are directly imported from the Confirmation indicator, and then baseline and volatility filtering is applied.
The GKD-B Baseline filter ensures that only trades aligning with the GKD-B Baseline's current trend are accepted. This filter takes into consideration the Goldie Locks Zone, which allows trades where the closing price of the last candle has moved within a minimum XX volatility and a maximum YY volatility range. The GKD-V Volatility/Volume filter allows only trades that meet a minimum threshold of ZZ GKD-V Volatility/Volume, which varies based on the specific GKD-V Volatility/Volume indicator used.
The Solo Confirmation Complex Backtest execution engine determines whether signals from the GKD-C Confirmation indicator are accepted or rejected based on two criteria:
1. The GKD-C Confirmation signal must be qualified by the direction of the GKD-B Baseline trend and the GKD-B Baseline's sweet-spot Goldie Locks Zone.
2. Sufficient Volatility/Volume, as indicated by the GKD-V Volatility/Volume indicator, must be present to execute a trade.
The purpose of the Solo Confirmation Complex Backtest is to test a GKD-C Confirmation indicator in the presence of macro trend and volatility/volume filtering.
Volatility Types Included
17 types of volatility are included in this indicator
Close-to-Close
Parkinson
Garman-Klass
Rogers-Satchell
Yang-Zhang
Garman-Klass-Yang-Zhang
Exponential Weighted Moving Average
Standard Deviation of Log Returns
Pseudo GARCH(2,2)
Average True Range
True Range Double
Standard Deviation
Adaptive Deviation
Median Absolute Deviation
Efficiency-Ratio Adaptive ATR
Mean Absolute Deviation
Static Percent
Close-to-Close
Close-to-Close volatility is a classic and widely used volatility measure, sometimes referred to as historical volatility.
Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a larger amplitude. The more volatile a stock is, the riskier it is.
Close-to-close historical volatility is calculated using only a stock's closing prices. It is the simplest volatility estimator. However, in many cases, it is not precise enough. Stock prices could jump significantly during a trading session and return to the opening value at the end. That means that a considerable amount of price information is not taken into account by close-to-close volatility.
Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators.
Parkinson
Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day.
The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. This is useful as close-to-close prices could show little difference while large price movements could have occurred during the day. Thus, Parkinson's volatility is considered more precise and requires less data for calculation than close-to-close volatility.
One drawback of this estimator is that it doesn't take into account price movements after the market closes. Hence, it systematically undervalues volatility. This drawback is addressed in the Garman-Klass volatility estimator.
Garman-Klass
Garman-Klass is a volatility estimator that incorporates open, low, high, and close prices of a security.
Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing prices. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate.
Garman and Klass also assumed that the process of price change follows a continuous diffusion process (Geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements.
Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremes.
Researchers Rogers and Satchell have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail.
Rogers-Satchell
Rogers-Satchell is an estimator for measuring the volatility of securities with an average return not equal to zero.
Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates a drift term (mean return not equal to zero). As a result, it provides better volatility estimation when the underlying is trending.
The main disadvantage of this method is that it does not take into account price movements between trading sessions. This leads to an underestimation of volatility since price jumps periodically occur in the market precisely at the moments between sessions.
A more comprehensive estimator that also considers the gaps between sessions was developed based on the Rogers-Satchel formula in the 2000s by Yang-Zhang. See Yang Zhang Volatility for more detail.
Yang-Zhang
Yang Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error.
Yang-Zhang volatility can be thought of as a combination of the overnight (close-to-open volatility) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility. It is considered to be 14 times more efficient than the close-to-close estimator.
Garman-Klass-Yang-Zhang
Garman-Klass-Yang-Zhang (GKYZ) volatility estimator incorporates the returns of open, high, low, and closing prices in its calculation.
GKYZ volatility estimator takes into account overnight jumps but not the trend, i.e., it assumes that the underlying asset follows a Geometric Brownian Motion (GBM) process with zero drift. Therefore, the GKYZ volatility estimator tends to overestimate the volatility when the drift is different from zero. However, for a GBM process, this estimator is eight times more efficient than the close-to-close volatility estimator.
Exponential Weighted Moving Average
The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, with the main applications being technical analysis and volatility modeling.
The moving average is designed such that older observations are given lower weights. The weights decrease exponentially as the data point gets older – hence the name exponentially weighted.
The only decision a user of the EWMA must make is the parameter lambda. The parameter decides how important the current observation is in the calculation of the EWMA. The higher the value of lambda, the more closely the EWMA tracks the original time series.
Standard Deviation of Log Returns
This is the simplest calculation of volatility. It's the standard deviation of ln(close/close(1)).
Pseudo GARCH(2,2)
This is calculated using a short- and long-run mean of variance multiplied by ?.
?avg(var;M) + (1 ? ?) avg(var;N) = 2?var/(M+1-(M-1)L) + 2(1-?)var/(M+1-(M-1)L)
Solving for ? can be done by minimizing the mean squared error of estimation; that is, regressing L^-1var - avg(var; N) against avg(var; M) - avg(var; N) and using the resulting beta estimate as ?.
Average True Range
The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period.
The true range indicator is taken as the greatest of the following: current high less the current low; the absolute value of the current high less the previous close; and the absolute value of the current low less the previous close. The ATR is then a moving average, generally using 14 days, of the true ranges.
True Range Double
A special case of ATR that attempts to correct for volatility skew.
Standard Deviation
Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. The standard deviation is calculated as the square root of variance by determining each data point's deviation relative to the mean. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation.
Adaptive Deviation
By definition, the Standard Deviation (STD, also represented by the Greek letter sigma ? or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis, we usually use it to measure the level of current volatility.
Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA, we can call it EMA deviation. Additionally, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting).
The difference when compared to the standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions.
Median Absolute Deviation
The median absolute deviation is a measure of statistical dispersion. Moreover, the MAD is a robust statistic, being more resilient to outliers in a data set than the standard deviation. In the standard deviation, the distances from the mean are squared, so large deviations are weighted more heavily, and thus outliers can heavily influence it. In the MAD, the deviations of a small number of outliers are irrelevant.
Because the MAD is a more robust estimator of scale than the sample variance or standard deviation, it works better with distributions without a mean or variance, such as the Cauchy distribution.
Efficiency-Ratio Adaptive ATR
Average True Range (ATR) is a widely used indicator for many occasions in technical analysis. It is calculated as the RMA of the true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range.
Mean Absolute Deviation
The mean absolute deviation (MAD) is a measure of variability that indicates the average distance between observations and their mean. MAD uses the original units of the data, which simplifies interpretation. Larger values signify that the data points spread out further from the average. Conversely, lower values correspond to data points bunching closer to it. The mean absolute deviation is also known as the mean deviation and average absolute deviation.
This definition of the mean absolute deviation sounds similar to the standard deviation (SD). While both measure variability, they have different calculations. In recent years, some proponents of MAD have suggested that it replace the SD as the primary measure because it is a simpler concept that better fits real life.
Static Percent
Static Percent allows the user to insert their own constant percent that will then be used to create take profits and stoploss
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v2.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data to A backtest module wherein the various components of the GKD system are combined to create a trading signal.
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Solo Confirmation Complex Backtest as shown on the chart above
Baseline: Hull Moving Average as shown on the chart above
Volatility/Volume: Hurst Exponent as shown on the chart above
Confirmation 1: Fisher Trasnform as shown on the chart above
Confirmation 2: Williams Percent Range
Continuation: Volatility-Adaptive Rapid RSI T3
Exit: Rex Oscillator
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system.
Giga Kaleidoscope Modularized Trading System Signals (based on the NNFX algorithm)
Standard Entry
1. GKD-C Confirmation 1 Signal
2. GKD-B Baseline agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
Baseline Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
6. GKD-C Confirmation 1 signal was less than 7 candles prior
Volatility/Volume Entry
1. GKD-V Volatility/Volume signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-B Baseline agrees
6. GKD-C Confirmation 1 signal was less than 7 candles prior
Continuation Entry
1. Standard Entry, Baseline Entry, or Pullback; entry triggered previously
2. GKD-B Baseline hasn't crossed since entry signal trigger
3. GKD-C Confirmation Continuation Indicator signals
4. GKD-C Confirmation 1 agrees
5. GKD-B Baseline agrees
6. GKD-C Confirmation 2 agrees
1-Candle Rule Standard Entry
1. GKD-C Confirmation 1 signal
2. GKD-B Baseline agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
Next Candle:
1. Price retraced (Long: close < close or Short: close > close )
2. GKD-B Baseline agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
1-Candle Rule Baseline Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 1 signal was less than 7 candles prior
Next Candle:
1. Price retraced (Long: close < close or Short: close > close )
2. GKD-B Baseline agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume Agrees
1-Candle Rule Volatility/Volume Entry
1. GKD-V Volatility/Volume signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 1 signal was less than 7 candles prior
Next Candle:
1. Price retraced (Long: close < close or Short: close > close)
2. GKD-B Volatility/Volume agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-B Baseline agrees
PullBack Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is beyond 1.0x Volatility of Baseline
Next Candle:
1. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
2. GKD-C Confirmation 1 agrees
3. GKD-C Confirmation 2 agrees
4. GKD-V Volatility/Volume Agrees
GKD-BT Solo Confirmation Simple Backtest [Loxx]Giga Kaleidoscope GKD-BT Solo Confirmation Simple Backtest is a Backtesting module included in Loxx's "Giga Kaleidoscope Modularized Trading System".
█ GKD-BT Solo Confirmation Simple Backtest
The Solo Confirmation Simple Backtest module enables users to perform Standard Long and Short signals on GKD-C confirmation indicators. This module represents the simplest form of Backtest in the GKD trading system. It includes two types of backtests: Trading and Full. The Trading backtest allows users to test individual trades, both long and short, one at a time. On the other hand, the Full backtest allows users to test either longs or shorts by toggling between them in the settings to view the results for each signal type. The Trading backtest simulates real trading, while the Full backtest tests all signals, whether long or short.
Additionally, this backtest module provides the option to test the GKD-C indicator with 1 to 3 take profits and 1 stop loss. The Trading backtest allows for the use of 1 to 3 take profits, while the Full backtest is limited to 1 take profit. The Trading backtest also offers the capability to apply a trailing take profit.
In terms of the percentage of trade removed at each take profit, this backtest module has the following hardcoded values:
Take profit 1: 50% of the trade is removed
Take profit 2: 25% of the trade is removed
Take profit 3: 25% of the trade is removed
Stop loss: 100% of the trade is removed
After each take profit is achieved, the stop loss level is adjusted. When take profit 1 is reached, the stop loss is moved to the entry point. Similarly, when take profit 2 is reached, the stop loss is shifted to take profit 1. The trailing take profit feature comes into play after take profit 2 or take profit 3, depending on the number of take profits selected in the settings. The trailing take profit is always activated on the final take profit when 2 or more take profits are chosen.
The backtest also offers the capability to restrict by a specific date range, allowing for simulated forward testing based on past data. Additionally, users have the option to display or hide a trading panel that provides relevant information about the backtest, statistics, and the current trade. It is also possible to activate alerts and toggle sections of the trading panel on or off. On the chart, historical take profit and stop loss levels are represented by horizontal lines overlaid for reference.
The GKD system utilizes volatility-based take profits and stop losses. Each take profit and stop loss is calculated as a multiple of volatility. You can change the values of the multipliers in the settings as well.
To utilize this strategy, follow these steps:
1. Adjust the "Confirmation Type" in the GKD-C Confirmation Indicator to "GKD New."
2. Import the value "Input into NEW GKD-BT Backtest" into the GKD-BT Solo Confirmation Simple Backtest module (this strategy backtest).
**The GKD-BT Solo Confirmation Simple Backtest module exclusively supports Standard Entries, both Long and Short. However, please note that this module uses a modified version of the standard entry, where long and short signals are directly imported from the Confirmation indicator without any baseline or volatility filtering applied.**
Volatility Types Included
17 types of volatility are included in this indicator
Close-to-Close
Parkinson
Garman-Klass
Rogers-Satchell
Yang-Zhang
Garman-Klass-Yang-Zhang
Exponential Weighted Moving Average
Standard Deviation of Log Returns
Pseudo GARCH(2,2)
Average True Range
True Range Double
Standard Deviation
Adaptive Deviation
Median Absolute Deviation
Efficiency-Ratio Adaptive ATR
Mean Absolute Deviation
Static Percent
Close-to-Close
Close-to-Close volatility is a classic and widely used volatility measure, sometimes referred to as historical volatility.
Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a larger amplitude. The more volatile a stock is, the riskier it is.
Close-to-close historical volatility is calculated using only a stock's closing prices. It is the simplest volatility estimator. However, in many cases, it is not precise enough. Stock prices could jump significantly during a trading session and return to the opening value at the end. That means that a considerable amount of price information is not taken into account by close-to-close volatility.
Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators.
Parkinson
Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day.
The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. This is useful as close-to-close prices could show little difference while large price movements could have occurred during the day. Thus, Parkinson's volatility is considered more precise and requires less data for calculation than close-to-close volatility.
One drawback of this estimator is that it doesn't take into account price movements after the market closes. Hence, it systematically undervalues volatility. This drawback is addressed in the Garman-Klass volatility estimator.
Garman-Klass
Garman-Klass is a volatility estimator that incorporates open, low, high, and close prices of a security.
Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing prices. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate.
Garman and Klass also assumed that the process of price change follows a continuous diffusion process (Geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements.
Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremes.
Researchers Rogers and Satchell have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail.
Rogers-Satchell
Rogers-Satchell is an estimator for measuring the volatility of securities with an average return not equal to zero.
Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates a drift term (mean return not equal to zero). As a result, it provides better volatility estimation when the underlying is trending.
The main disadvantage of this method is that it does not take into account price movements between trading sessions. This leads to an underestimation of volatility since price jumps periodically occur in the market precisely at the moments between sessions.
A more comprehensive estimator that also considers the gaps between sessions was developed based on the Rogers-Satchel formula in the 2000s by Yang-Zhang. See Yang Zhang Volatility for more detail.
Yang-Zhang
Yang Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error.
Yang-Zhang volatility can be thought of as a combination of the overnight (close-to-open volatility) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility. It is considered to be 14 times more efficient than the close-to-close estimator.
Garman-Klass-Yang-Zhang
Garman-Klass-Yang-Zhang (GKYZ) volatility estimator incorporates the returns of open, high, low, and closing prices in its calculation.
GKYZ volatility estimator takes into account overnight jumps but not the trend, i.e., it assumes that the underlying asset follows a Geometric Brownian Motion (GBM) process with zero drift. Therefore, the GKYZ volatility estimator tends to overestimate the volatility when the drift is different from zero. However, for a GBM process, this estimator is eight times more efficient than the close-to-close volatility estimator.
Exponential Weighted Moving Average
The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, with the main applications being technical analysis and volatility modeling.
The moving average is designed such that older observations are given lower weights. The weights decrease exponentially as the data point gets older – hence the name exponentially weighted.
The only decision a user of the EWMA must make is the parameter lambda. The parameter decides how important the current observation is in the calculation of the EWMA. The higher the value of lambda, the more closely the EWMA tracks the original time series.
Standard Deviation of Log Returns
This is the simplest calculation of volatility. It's the standard deviation of ln(close/close(1)).
Pseudo GARCH(2,2)
This is calculated using a short- and long-run mean of variance multiplied by ?.
?avg(var;M) + (1 ? ?) avg(var;N) = 2?var/(M+1-(M-1)L) + 2(1-?)var/(M+1-(M-1)L)
Solving for ? can be done by minimizing the mean squared error of estimation; that is, regressing L^-1var - avg(var; N) against avg(var; M) - avg(var; N) and using the resulting beta estimate as ?.
Average True Range
The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period.
The true range indicator is taken as the greatest of the following: current high less the current low; the absolute value of the current high less the previous close; and the absolute value of the current low less the previous close. The ATR is then a moving average, generally using 14 days, of the true ranges.
True Range Double
A special case of ATR that attempts to correct for volatility skew.
Standard Deviation
Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. The standard deviation is calculated as the square root of variance by determining each data point's deviation relative to the mean. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation.
Adaptive Deviation
By definition, the Standard Deviation (STD, also represented by the Greek letter sigma ? or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis, we usually use it to measure the level of current volatility.
Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA, we can call it EMA deviation. Additionally, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting).
The difference when compared to the standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions.
Median Absolute Deviation
The median absolute deviation is a measure of statistical dispersion. Moreover, the MAD is a robust statistic, being more resilient to outliers in a data set than the standard deviation. In the standard deviation, the distances from the mean are squared, so large deviations are weighted more heavily, and thus outliers can heavily influence it. In the MAD, the deviations of a small number of outliers are irrelevant.
Because the MAD is a more robust estimator of scale than the sample variance or standard deviation, it works better with distributions without a mean or variance, such as the Cauchy distribution.
Efficiency-Ratio Adaptive ATR
Average True Range (ATR) is a widely used indicator for many occasions in technical analysis. It is calculated as the RMA of the true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range.
Mean Absolute Deviation
The mean absolute deviation (MAD) is a measure of variability that indicates the average distance between observations and their mean. MAD uses the original units of the data, which simplifies interpretation. Larger values signify that the data points spread out further from the average. Conversely, lower values correspond to data points bunching closer to it. The mean absolute deviation is also known as the mean deviation and average absolute deviation.
This definition of the mean absolute deviation sounds similar to the standard deviation (SD). While both measure variability, they have different calculations. In recent years, some proponents of MAD have suggested that it replace the SD as the primary measure because it is a simpler concept that better fits real life.
Static Percent
Static Percent allows the user to insert their own constant percent that will then be used to create take profits and stoploss
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v2.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data to A backtest module wherein the various components of the GKD system are combined to create a trading signal.
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Solo Confirmation Simple Backtest as shown on the chart above
Baseline: Hull Moving Average
Volatility/Volume: Hurst Exponent
Confirmation 1: Fisher Trasnform as shown on the chart above
Confirmation 2: Williams Percent Range
Continuation: Volatility-Adaptive Rapid RSI T3
Exit: Rex Oscillator
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system.
Giga Kaleidoscope Modularized Trading System Signals (based on the NNFX algorithm)
Standard Entry
1. GKD-C Confirmation 1 Signal
2. GKD-B Baseline agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
Baseline Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
6. GKD-C Confirmation 1 signal was less than 7 candles prior
Volatility/Volume Entry
1. GKD-V Volatility/Volume signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-B Baseline agrees
6. GKD-C Confirmation 1 signal was less than 7 candles prior
Continuation Entry
1. Standard Entry, Baseline Entry, or Pullback; entry triggered previously
2. GKD-B Baseline hasn't crossed since entry signal trigger
3. GKD-C Confirmation Continuation Indicator signals
4. GKD-C Confirmation 1 agrees
5. GKD-B Baseline agrees
6. GKD-C Confirmation 2 agrees
1-Candle Rule Standard Entry
1. GKD-C Confirmation 1 signal
2. GKD-B Baseline agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
Next Candle:
1. Price retraced (Long: close < close or Short: close > close )
2. GKD-B Baseline agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
1-Candle Rule Baseline Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 1 signal was less than 7 candles prior
Next Candle:
1. Price retraced (Long: close < close or Short: close > close )
2. GKD-B Baseline agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume Agrees
1-Candle Rule Volatility/Volume Entry
1. GKD-V Volatility/Volume signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 1 signal was less than 7 candles prior
Next Candle:
1. Price retraced (Long: close < close or Short: close > close)
2. GKD-B Volatility/Volume agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-B Baseline agrees
PullBack Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is beyond 1.0x Volatility of Baseline
Next Candle:
1. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
2. GKD-C Confirmation 1 agrees
3. GKD-C Confirmation 2 agrees
4. GKD-V Volatility/Volume Agrees
Demo GPT - Day Trading Scalping StrategyOverview:
This strategy is designed for day trading and scalping, utilizing a combination of technical indicators, candlestick patterns, and volume analysis to determine entry and exit points. It focuses on capturing short-term price movements while ensuring that trades are executed under specific market conditions.
Key Components:
Technical Indicators Used:
Exponential Moving Average (EMA): The strategy uses the 20-period EMA to identify the trend direction. The EMA smooths out price data, helping traders make more informed decisions about potential buy or sell signals.
Volume Weighted Average Price (VWAP): VWAP is used to measure the average price a security has traded at throughout the day, based on both volume and price. This indicator helps assess whether the current price is above or below the average trading price.
Camarilla Pivot Points: The strategy calculates four levels of Camarilla pivots (S2, S3, R2, R3) based on the highest and lowest prices over the last 14 daily candles. These levels act as potential support and resistance zones, guiding entry and exit decisions.
Candlestick Analysis:
Buy Condition: A buy signal is triggered when:
The first candle (previous candle) is green (close > open).
The second candle (current candle) is also green and opens above the first candle.
The volume of the current candle exceeds the 20-period moving average of volume, indicating strong buying interest.
Sell Condition: A sell signal is triggered when:
The first candle is red (close < open).
The second candle opens below the first red candle.
The volume of the current candle also exceeds the 20-period moving average of volume, indicating strong selling pressure.
Position Management:
The strategy enters a long position (buy) when the buy condition is met and closes the long position when the sell condition is met. This approach aims to capture upward momentum while avoiding extended exposure to downside risks.
Trading Settings:
Capital Management: The strategy uses 100% of available capital for each trade, allowing for maximum exposure to potential gains.
Commission and Slippage: The script includes settings for a commission rate of 0.1% and slippage of 3, accounting for trading costs and potential price changes during order execution.
Date Filtering: The strategy allows users to set a start date (January 1, 2018) and an end date (December 31, 2069) for trade execution, providing flexibility in backtesting and live trading.
Visualization:
The script plots the 20 EMA, VWAP, and the Camarilla pivot levels on the chart for visual reference.
Buy and sell signals are visually represented with shapes on the chart, making it easy to identify potential trade opportunities at a glance.
Volume is plotted in a separate pane to assess trading activity, and a horizontal line at zero provides a reference point.
Summary:
This Day Trading Scalping Strategy is designed to exploit short-term price movements by using a combination of EMAs, VWAP, and Camarilla pivot levels, alongside candlestick patterns and volume analysis. It is well-suited for traders looking to make quick trades based on real-time market conditions while maintaining a disciplined approach to entry and exit points. The strategy is highly visual, allowing traders to quickly assess market conditions and make informed trading decisions.
Feel free to modify or adjust any aspects of the strategy according to your specific trading goals or preferences!
Quantum Reversal Engine [ApexLegion]Quantum Reversal Engine
STRATEGY OVERVIEW
This strategy is constructed using 5 custom analytical filters that analyze different market dimensions - trend structure, momentum expansion, volume confirmation, price action patterns, and reversal detection - with results processed through a multi-component scoring calculation that determines signal generation and position management decisions.
Why These Custom Filters Were Independently Developed:
This strategy employs five custom-developed analytical filters:
1. Apex Momentum Core (AMC) - Custom oscillator with volatility-scaled deviation calculation
Standard oscillators lag momentum shifts by 2-3 bars. Custom calculation designed for momentum analysis
2. Apex Wick Trap (AWT) - Wick dominance analysis for trap detection
Existing wick analysis tools don't quantify trap conditions. Uses specific ratios for wick dominance detection
3. Apex Volume Pulse (AVP) - Volume surge validation with participation confirmation
Volume indicators typically use simple averages. Uses surge multipliers with participation validation
4. Apex TrendGuard (ATG) - Angle-based trend detection with volatility band integration
EMA slope calculations often produce false signals. Uses angle analysis with volatility bands for confirmation
5. Quantum Composite Filter (QCF) - Multi-component scoring and signal generation system
Composite scoring designed to filter noise by requiring multiple confirmations before signal activation.
Each filter represents mathematical calculations designed to address specific analytical requirements.
Framework Operation: The strategy functions as a scoring framework where each filter contributes weighted points based on market conditions. Entry signals are generated when minimum threshold scores are met. Exit management operates through a three-tier system with continued signal strength evaluation determining position holds versus closures at each TP level.
Integration Challenge: The core difficulty was creating a scoring system where five independent filters could work together without generating conflicting signals. This required backtesting to determine effective weight distributions.
Custom Filter Development:
Each of the five filters represents analytical approaches developed through testing and validation:
Integration Validation: Each filter underwent individual testing before integration. The composite scoring system required validation to verify that filters complement rather than conflict with each other, resulting in a cohesive analytical framework that was tested during the development period.
These filters represent custom-developed components created specifically for this strategy, with each component addressing different analytical requirements through testing and parameter adjustment.
Programming Features:
Multi-timeframe data handling with backup systems
Performance optimization techniques
Error handling for live trading scenarios
Parameter adaptation based on market conditions
Strategy Features:
Uses multi-filter confirmation approach
Adapts position holding based on continued signal strength
Includes analysis tools for trade review and optimization
Ongoing Development: The strategy was developed through testing and validation processes during the creation period.
COMPONENT EXPLANATION
EMA System
Uses 8 exponential moving averages (7, 14, 21, 30, 50, 90, 120, 200 periods) for trend identification. Primary signals come from 8/21 EMA crossovers, while longer EMAs provide structural context. EMA 1-4 determine short-term structure, EMA 5-8 provide long-term trend confirmation.
Apex Momentum Core (AMC)
Built custom oscillator mathematics after testing dozens of momentum calculation methods. Final algorithm uses price deviation from EMA baseline with volatility scaling to reduce lag while maintaining accuracy across different market conditions.
Custom momentum oscillator using price deviation from EMA baseline:
apxCI = 100 * (source - emaBase) / (sensitivity * sqrt(deviation + 1))
fastLine = EMA(apxCI, smoothing)
signalLine = SMA(fastLine, 4)
Signals generate when fastLine crosses signalLine at +50/-50 thresholds.
This identifies momentum expansion before traditional oscillators.
Apex Volume Pulse (AVP)
Created volume surge analysis that goes beyond simple averages. Extensive testing determined 1.3x multiplier with participation validation provides reliable confirmation while filtering false volume spikes.
Compares current volume to 21-period moving average.
Requires 1.3x average volume for signal confirmation. This filters out low-volume moves during quiet periods and confirms breakouts with actual participation.
Apex Wick Trap (AWT)
Developed proprietary wick trap detection through analysis of failed breakout patterns. Tested various ratio combinations before settling on 60% wick dominance + 20% body limit as effective trap identification parameters.
Analyzes candle structure to identify failed breakouts:
candleRange = math.max(high - low, 0.00001)
candleBody = math.abs(close - open)
bodyRatio = candleBody / candleRange
upperWick = high - math.max(open, close)
lowerWick = math.min(open, close) - low
upperWickRatio = upperWick / candleRange
lowerWickRatio = lowerWick / candleRange
trapWickLong = showAWT and lowerWickRatio > minWickDom and bodyRatio < bodyToRangeLimit and close > open
trapWickShort = showAWT and upperWickRatio > minWickDom and bodyRatio < bodyToRangeLimit and close < open This catches reversals after fake breakouts.
Apex TrendGuard (ATG)
Built angle-based trend detection after standard EMA crossovers proved insufficient. Combined slope analysis with volatility bands through iterative testing to eliminate false trend signals.
EMA slope analysis with volatility bands:
Fast EMA (21) vs Slow EMA (55) for trend direction
Angle calculation: atan(fast - slow) * 180 / π
ATR bands (1.75x multiplier) for breakout confirmation
Minimum 25° angle for strong trend classification
Core Algorithm Framework
1. Composite Signal Generation
calculateCompositeSignals() =>
// Component Conditions
structSignalLong = trapWickLong
structSignalShort = trapWickShort
momentumLong = amcBuySignal
momentumShort = amcSellSignal
volumeSpike = volume > volAvg_AVP * volMult_AVP
priceStrength_Long = close > open and close > close
priceStrength_Short = close < open and close < close
rsiMfiComboValue = (ta.rsi(close, 14) + ta.mfi(close, 14)) / 2
reversalTrigger_Long = ta.crossover(rsiMfiComboValue, 50)
reversalTrigger_Short = ta.crossunder(rsiMfiComboValue, 50)
isEMACrossUp = ta.crossover(emaFast_ATG, emaSlow_ATG)
isEMACrossDown = ta.crossunder(emaFast_ATG, emaSlow_ATG)
// Enhanced Composite Score Calculation
scoreBuy = 0.0
scoreBuy += structSignalLong ? scoreStruct : 0.0
scoreBuy += momentumLong ? scoreMomentum : 0.0
scoreBuy += flashSignal ? weightFlash : 0.0
scoreBuy += blinkSignal ? weightBlink : 0.0
scoreBuy += volumeSpike_AVP ? scoreVolume : 0.0
scoreBuy += priceStrength_Long ? scorePriceAction : 0.0
scoreBuy += reversalTrigger_Long ? scoreReversal : 0.0
scoreBuy += emaAlignment_Bull ? weightTrendAlign : 0.0
scoreBuy += strongUpTrend ? weightTrendAlign : 0.0
scoreBuy += highRisk_Long ? -1.2 : 0.0
scoreBuy += signalGreenDot ? 1.0 : 0.0
scoreBuy += isAMCUp ? 0.8 : 0.0
scoreBuy += isVssBuy ? 1.5 : 0.0
scoreBuy += isEMACrossUp ? 1.0 : 0.0
scoreBuy += signalRedX ? -1.0 : 0.0
scoreSell = 0.0
scoreSell += structSignalShort ? scoreStruct : 0.0
scoreSell += momentumShort ? scoreMomentum : 0.0
scoreSell += flashSignal ? weightFlash : 0.0
scoreSell += blinkSignal ? weightBlink : 0.0
scoreSell += volumeSpike_AVP ? scoreVolume : 0.0
scoreSell += priceStrength_Short ? scorePriceAction : 0.0
scoreSell += reversalTrigger_Short ? scoreReversal : 0.0
scoreSell += emaAlignment_Bear ? weightTrendAlign : 0.0
scoreSell += strongDownTrend ? weightTrendAlign : 0.0
scoreSell += highRisk_Short ? -1.2 : 0.0
scoreSell += signalRedX ? 1.0 : 0.0
scoreSell += isAMCDown ? 0.8 : 0.0
scoreSell += isVssSell ? 1.5 : 0.0
scoreSell += isEMACrossDown ? 1.0 : 0.0
scoreSell += signalGreenDot ? -1.0 : 0.0
compositeBuySignal = enableComposite and scoreBuy >= thresholdCompositeBuy
compositeSellSignal = enableComposite and scoreSell >= thresholdCompositeSell
if compositeBuySignal and compositeSellSignal
compositeBuySignal := false
compositeSellSignal := false
= calculateCompositeSignals()
// Final Entry Signals
entryCompositeBuySignal = compositeBuySignal and ta.rising(emaFast_ATG, 2)
entryCompositeSellSignal = compositeSellSignal and ta.falling(emaFast_ATG, 2)
Calculates weighted scores from independent modules and activates signals only when threshold requirements are met.
2. Smart Exit Hold Evaluation System
evaluateSmartHold() =>
compositeBuyRecentCount = 0
compositeSellRecentCount = 0
for i = 0 to signalLookbackBars - 1
compositeBuyRecentCount += compositeBuySignal ? 1 : 0
compositeSellRecentCount += compositeSellSignal ? 1 : 0
avgVolume = ta.sma(volume, 20)
volumeSpike = volume > avgVolume * volMultiplier
// MTF Bull/Bear conditions
mtf_bull = mtf_emaFast_final > mtf_emaSlow_final
mtf_bear = mtf_emaFast_final < mtf_emaSlow_final
emaBackupDivergence = math.abs(mtf_emaFast_backup - mtf_emaSlow_backup) / mtf_emaSlow_backup
emaBackupStrong = emaBackupDivergence > 0.008
mtfConflict_Long = inLong and mtf_bear and emaBackupStrong
mtfConflict_Short = inShort and mtf_bull and emaBackupStrong
// Layer 1: ATR-Based Dynamic Threshold (Market Volatility Intelligence)
atr_raw = ta.atr(atrLen)
atrValue = na(atr_raw) ? close * 0.02 : atr_raw
atrRatio = atrValue / close
dynamicThreshold = atrRatio > 0.02 ? 1.0 : (atrRatio > 0.01 ? 1.5 : 2.8)
// Layer 2: ROI-Conditional Time Intelligence (Selective Pressure)
timeMultiplier_Long = realROI >= 0 ? 1.0 : // Profitable positions: No time pressure
holdTimer_Long <= signalLookbackBars ? 1.0 : // Loss positions 1-8 bars: Base
holdTimer_Long <= signalLookbackBars * 2 ? 1.1 : // Loss positions 9-16 bars: +10% stricter
1.3 // Loss positions 17+ bars: +30% stricter
timeMultiplier_Short = realROI >= 0 ? 1.0 : // Profitable positions: No time pressure
holdTimer_Short <= signalLookbackBars ? 1.0 : // Loss positions 1-8 bars: Base
holdTimer_Short <= signalLookbackBars * 2 ? 1.1 : // Loss positions 9-16 bars: +10% stricter
1.3 // Loss positions 17+ bars: +30% stricter
// Dual-Layer Threshold Calculation
baseThreshold_Long = mtfConflict_Long ? dynamicThreshold + 1.0 : dynamicThreshold
baseThreshold_Short = mtfConflict_Short ? dynamicThreshold + 1.0 : dynamicThreshold
timeAdjustedThreshold_Long = baseThreshold_Long * timeMultiplier_Long
timeAdjustedThreshold_Short = baseThreshold_Short * timeMultiplier_Short
// Final Smart Hold Decision with Dual-Layer Intelligence
smartHold_Long = not mtfConflict_Long and smartScoreLong >= timeAdjustedThreshold_Long and compositeBuyRecentCount >= signalMinCount
smartHold_Short = not mtfConflict_Short and smartScoreShort >= timeAdjustedThreshold_Short and compositeSellRecentCount >= signalMinCount
= evaluateSmartHold()
Evaluates whether to hold positions past TP1/TP2/TP3 levels based on continued signal strength, volume confirmation, and multi-timeframe trend alignment
HOW TO USE THE STRATEGY
Step 1: Initial Setup
Apply strategy to your preferred timeframe (backtested on 15M)
Enable "Use Heikin-Ashi Base" for smoother signals in volatile markets
"Show EMA Lines" and "Show Ichimoku Cloud" are enabled for visual context
Set default quantities to match your risk management (5% equity default)
Step 2: Signal Recognition
Visual Signal Guide:
Visual Signal Guide - Complete Reference:
🔶 Red Diamond: Bearish momentum breakdown - short reversal signal
🔷 Blue Diamond: Strong bullish momentum - long reversal signal
🔵 Blue Dot: Volume-confirmed directional move - trend continuation
🟢 Green Dot: Bullish EMA crossover - trend reversal confirmation
🟠 Orange X: Oversold reversal setup - counter-trend opportunity
❌ Red X: Bearish EMA breakdown - trend reversal warning
✡ Star Uprising: Strong bullish convergence
💥 Ultra Entry: Ultra-rapid downward momentum acceleration
▲ VSS Long: Velocity-based bullish momentum confirmation
▼ VSS Short: Velocity-based bearish momentum confirmation
Step 3: Entry Execution
For Long Positions:
1. ✅ EMA1 crossed above EMA2 exactly 3 bars ago [ta.crossover(ema1,ema2) ]
2. ✅ Current EMA structure: EMA1 > EMA2 (maintained)
3. ✅ Composite score ≥ 5.0 points (6.5+ for 5-minute timeframes)
4. ✅ Cooldown period completed (no recent stop losses)
5. ✅ Volume spike confirmation (green dot/blue dot signals)
6. ✅ Bullish candle closes above EMA structure
For Short Positions:
1. ✅ EMA1 crossed below EMA2 exactly 3 bars ago [ta.crossunder(ema1,ema2) ]
2. ✅ Current EMA structure: EMA1 < EMA2 (maintained)
3. ✅ Composite score ≥ 5.4 points (7.0+ for 5-minute timeframes)
4. ✅ Cooldown period completed (no recent stop losses)
5. ✅ Momentum breakdown (red diamond/red X signals)
6. ✅ Bearish candle closes below EMA structure
🎯 Critical Timing Note: The strategy requires EMA crossover to have occurred 3 bars prior to entry, not at the current bar. This attempts to avoid premature entries and may improve signal reliability.
Step 4: Reading Market Context
EMA Ribbon Interpretation:
All EMAs ascending = Strong uptrend context
EMAs 1-3 above EMAs 4-8 = Bullish structure
Tight EMA spacing = Low volatility/consolidation
Wide EMA spacing = High volatility/trending
Ichimoku Cloud Context:
Price above cloud = Bullish environment
Price below cloud = Bearish environment
Cloud color intensity = Momentum strength
Thick cloud = Strong support/resistance
THE SMART EXIT GRID SYSTEM
Smart Exit Grid Approach:
The Smart Exit Grid uses dynamic hold evaluation that continuously analyzes market conditions after position entry. This differs from traditional fixed profit targets by adapting exit timing based on real-time signal strength.
How Smart Exit Grid System Works
The system operates through three evaluation phases:
Smart Score Calculation:
The smart score calculation aggregates 22 signal components in real-time, combining reversal warnings, continuation signals, trend alignment indicators, EMA structural analysis, and risk penalties into a numerical representation of market conditions. MTF analysis provides additional confirmation as a separate validation layer.
Signal Stack Management:
The per-tick signal accumulation system monitors 22 active signal types with MTF providing trend validation and conflict detection as a separate confirmation layer.
Take Profit Progression:
Smart Exit Activation:
The QRE system activates Smart Exit Grid immediately upon position entry. When strategy.entry() executes, the system initializes monitoring systems designed to track position progress.
Upon position opening, holdTimer begins counting, establishing the foundation for subsequent decisions. The Smart Exit Grid starts accumulating signals from entry, with all 22 signal components beginning real-time tracking when the trade opens.
The system operates on continuous evaluation where smartScoreLong and smartScoreShort calculate from the first tick after entry. QRE's approach is designed to capture market structure changes, trend deteriorations, or signal pattern shifts that can trigger protective exits even before the first take profit level is reached.
This activation creates a proactive position management framework. The 8-candle sliding window starts from entry, meaning that if market conditions change rapidly after entry - due to news events, liquidity shifts, or technical changes - the system can respond within the configured lookback period.
TP Markers as Reference Points:
The TP1, TP2, and TP3 levels function as reference points rather than mandatory exit triggers. When longTP1Hit or shortTP1Hit conditions activate, they serve as profit confirmation markers that inform the Smart Exit algorithm about achieved reward levels, but don't automatically initiate position closure.
These TP markers enhance the Smart Exit decision matrix by providing profit context to ongoing signal evaluation. The system recognizes when positions have achieved target returns, but the actual exit decision remains governed by continuous smart score evaluation and signal stack analysis.
TP2 Reached: Enhanced Monitoring
TP2 represents significant profit capture with additional monitoring features:
This approach is designed to help avoid premature profit-taking during trending conditions. If TP2 is reached but smartScoreLong remains above the dynamic threshold and the 8-candle sliding window shows persistent signals, the position continues holding. If market structure deteriorates before reaching TP2, the Smart Exit can trigger closure based on signal analysis.
The visual TP circles that appear when levels are reached serve as performance tracking tools, allowing users to see how frequently entries achieve various profit levels while understanding that actual exit timing depends on market structure analysis.
Risk Management Systems:
Operating independently from the Smart Exit Grid are two risk management systems: the Trap Wick Detection Protocol and the Stop Loss Mechanism. These systems maintain override authority over other exit logic.
The Trap Wick System monitors for conditionBearTrapExit during long positions and conditionBullTrapExit during short positions. When detected, these conditions trigger position closure with state reset, bypassing Smart Exit evaluations. This system recognizes that certain candlestick patterns may indicate reversal risk.
Volatility Exit Monitoring: The strategy monitors for isStrongBearCandle combined with conditionBearTrapExit, recognizing when market structure may be shifting.
Volume Validation: Before exiting on volatility, the strategy requires volume confirmation: volume > ta.sma(volume, 20) * 1.8. This is designed to filter exits on weak, low-volume movements.
The Stop Loss Mechanism operates through multiple triggers including traditional price-based stops (longSLHit, shortSLHit) and early exit conditions based on smart score deterioration combined with negative ROI. The early exit logic activates when smartScoreLong < 1.0 or smartScoreShort < 1.0 while realROI < -0.9%.
These risk management systems are designed so that risk scenarios can trigger protective closure with state reset across all 22 signal counters, TP tracking variables, and smart exit states.
This architecture - Smart Exit activation, TP markers as navigation tools, and independent risk management - creates a position management system that adapts to market conditions while maintaining risk discipline through dedicated protection protocols.
TP3 Reached: Enhanced Protection
Once TP3 is hit, the strategy shifts into enhanced monitoring:
EMA Structure Monitoring: isEMAStructureDown becomes a primary exit trigger
MTF Alignment: The higher timeframe receives increased consideration
Wick Trap Priority: conditionBearTrapExit becomes an immediate exit signal
Approach Differences:
Traditional Fixed Exits:
Exit at predetermined levels regardless of market conditions
May exit during trend continuation
May exit before trend completion
Limited adaptation to changing volatility
Smart Exit Grid Approach:
Adaptive timing based on signal conditions
Exits when supporting signals weaken
Multi-timeframe validation for trend confirmation
Volume confirmation requirements for holds
Structural monitoring for trend analysis
Dynamic ATR-Based Smart Score Threshold System
Market Volatility Adaptive Scoring
// Real-time ATR Analysis
atr_raw = ta.atr(atrLen)
atrValue = na(atr_raw) ? close * 0.02 : atr_raw
atrRatio = atrValue / close
// Three-Tier Dynamic Threshold Matrix
dynamicThreshold = atrRatio > 0.02 ? 1.0 : // High volatility: Lower threshold
(atrRatio > 0.01 ? 1.5 : // Medium volatility: Standard
2.8) // Low volatility: Higher threshold
The market volatility adaptive scoring calculates real-time ATR with a 2% fallback for new markets. The atrRatio represents the relationship between current volatility and price, creating a foundation for threshold adjustment.
The three-tier dynamic threshold matrix responds to market conditions by adjusting requirements based on volatility levels: lowering thresholds during high volatility periods above 2% ATR ratio to 1.0 points, maintaining standard requirements at 1.5 points for medium volatility between 1-2%, and raising standards to 2.8 points during low volatility periods below 1%.
Profit-Loss Adaptive Management:
The system applies different evaluation criteria based on position performance:
Winning Positions (realROI ≥ 0%):
→ timeMultiplier = 1.0 (No additional pressure)
→ Maintains base threshold requirements
→ Allows natural progression to TP2/TP3 levels
Losing Positions (realROI < 0%):
→ Progressive time pressure activated
→ Increasingly strict requirements over time
→ Faster decision-making on underperforming trades
ROI-Adaptive Smart Hold Decision Process:
The strategy uses a profit-loss adaptive system:
Winning Position Management (ROI ≥ 0%):
✅ Standard threshold requirements maintained
✅ No additional time-based pressure applied
✅ Allows positions to progress toward TP2/TP3 levels
✅ timeMultiplier remains at 1.0 regardless of hold duration
Losing Position Management (ROI < 0%):
⚠️ Time-based threshold adjustments activated
⚠️ Progressive increase in required signal strength over time
⚠️ Earlier exit evaluation on underperforming positions
⚠️ timeMultiplier increases from 1.0 → 1.1 → 1.3 based on hold duration
Real-Time Monitoring:
Monitor Analysis Table → "Smart" filter → "Score" vs "Dynamic Threshold"
Winning positions: Evaluation based on signal strength deterioration only
Losing positions: Evaluation considers both signal strength and progressive time adjustments
Breakeven positions (0% ROI): Treated as winning positions - no time adjustments
This approach differentiates between winning and losing positions in the hold evaluation process, requiring higher signal thresholds for extended holding of losing positions while maintaining standard requirements for winning ones.
ROI-Conditional Decision Matrix Examples:
Scenario 1 - Winning Position in Any Market:
Position ROI: +0.8% → timeMultiplier = 1.0 (regardless of hold time)
ATR Medium (1.2%) → dynamicThreshold = 1.5
Final Threshold = 1.5 × 1.0 = 1.5 points ✅ Position continues
Scenario 2 - Losing Position, Extended Hold:
Position ROI: -0.5% → Time pressure activated
Hold Time: 20 bars → timeMultiplier = 1.3
ATR Low (0.8%) → dynamicThreshold = 2.8
Final Threshold = 2.8 × 1.3 = 3.64 points ⚡ Enhanced requirements
Scenario 3 - Fresh Losing Position:
Position ROI: -0.3% → Time pressure activated
Hold Time: 5 bars → timeMultiplier = 1.0 (still early)
ATR High (2.1%) → dynamicThreshold = 1.0
Final Threshold = 1.0 × 1.0 = 1.0 points 📊 Recovery opportunity
Scenario 4 - Breakeven Position:
Position ROI: 0.0% → timeMultiplier = 1.0 (no pressure)
Hold Time: 15 bars → No time penalty applied
Final Threshold = dynamicThreshold only ⚖️ Neutral treatment
🔄8-Candle Sliding Window Signal Rotation System
Composite Signal Counting Mechanism
// Dynamic Lookback Window (configurable: default 8)
signalLookbackBars = input.int(8, "Composite Lookback Bars", minval=1, maxval=50)
// Rolling Signal Analysis
compositeBuyRecentCount = 0
compositeSellRecentCount = 0
for i = 0 to signalLookbackBars - 1
compositeBuyRecentCount += compositeBuySignal ? 1 : 0
compositeSellRecentCount += compositeSellSignal ? 1 : 0
Candle Flow Example (8-bar window):
→
✓ ✓ ✗ ✓ ✗ ✓ ✗ ✓ 🗑️
New Signal Count = 5/8 signals in window
Threshold Check: 5 ≥ signalMinCount (2) = HOLD CONFIRMED
Signal Decay & Refresh Mechanism
// Signal Persistence Tracking
if compositeBuyRecentCount >= signalMinCount
smartHold_Long = true
else
smartHold_Long = false
The composite signal counting operates through a configurable sliding window. The system maintains rolling counters that scan backward through the specified number of candles.
During each evaluation cycle, the algorithm iterates through historical bars, incrementing counters when composite signals are detected. This creates a dynamic signal persistence measurement where recent signal density determines holding decisions.
The sliding window rotation functions like a moving conveyor belt where new signals enter while the oldest signals drop off. For example, in an 8-bar window, if 5 out of 8 recent candles showed composite buy signals, and the minimum required count is 2, the system confirms the hold condition. As new bars form, the window slides forward, potentially changing the signal count and triggering exit conditions when signal density falls below the threshold.
Signal decay and refresh occur continuously where smartHold_Long remains true only when compositeBuyRecentCount exceeds signalMinCount. When recent signal density drops below the minimum requirement, the system switches to exit mode.
Advanced Signal Stack Management - 22-Signal Real-Time Evaluation
// Long Position Signal Stacking (calc_on_every_tick=true)
if inLong
// Primary Reversal Signals
if signalRedDiamond: signalCountRedDiamond += 1 // -0.5 points
if signalStarUprising: signalCountStarUprising += 1 // +1.5 points
if entryUltraShort: signalCountUltra += 1 // -1.0 points
// Trend Confirmation Signals
if strongUpTrend: trendUpCount_Long += 1 // +1.5 points
if emaAlignment_Bull: bullAlignCount_Long += 1 // +1.0 points
// Risk Assessment Signals
if highRisk_Long: riskCount_Long += 1 // -1.5 points
if topZone: tzoneCount_Long += 1 // -0.5 points
The per-tick signal accumulation system operates with calc_on_every_tick=true for real-time responsiveness. During long positions, the system monitors primary reversal signals where Red Diamond signals subtract 0.5 points as reversal warnings, Star Uprising adds 1.5 points for continuation signals, and Ultra Short signals deduct 1.0 points as counter-trend warnings.
Trend confirmation signals provide weighted scoring where strongUpTrend adds 1.5 points for aligned momentum, emaAlignment_Bull contributes 1.0 point for structural support, and various EMA-based confirmations contribute to the overall score. Risk assessment signals apply negative weighting where highRisk_Long situations subtract 1.5 points, topZone conditions deduct 0.5 points, and other risk factors create defensive scoring adjustments.
The smart score calculation aggregates all 22 components in real-time, combining reversal warnings, continuation signals, trend alignment indicators, EMA structural analysis, and risk penalties into a numerical representation of market conditions. This score updates continuously, providing the foundation for hold-or-exit decisions.
MULTI-TIMEFRAME (MTF) SYSTEM
MTF Data Collection
The strategy requests higher timeframe data (default 30-minute) for trend confirmation:
= request.security(syminfo.tickerid, mtfTimeframe, , lookahead=barmerge.lookahead_off, gaps=barmerge.gaps_off)
MTF Watchtower System - Implementation Logic
The system employs a timeframe discrimination protocol where currentTFInMinutes is compared against a 30-minute threshold. This creates different operational behavior between timeframes:
📊 Timeframe Testing Results:
30M+ charts: Full MTF confirmation → Tested with full features
15M charts: Local EMA + adjusted parameters → Standard testing baseline
5M charts: Local EMA only → Requires parameter adjustment
1M charts: High noise → Limited testing conducted
When the chart timeframe is 30 minutes or above, the strategy activates useMTF = true and requests external MTF data through request.security(). For timeframes below 30 minutes, including your 5-minute setup, the system deliberately uses local EMA calculations to avoid MTF lag and data inconsistencies.
The triple-layer data sourcing architecture works as follows: timeframes from 1 minute to 29 minutes rely on chart-based EMA calculations for immediate responsiveness. Timeframes of 30 minutes and above utilize MTF data through the security function, with a backup system that doubles the EMA length (emaLen * 2) if MTF data fails. When MTF data is unavailable or invalid, the system falls back to local EMA as the final safety net.
Data validation occurs through a pipeline where mtf_dataValid checks not only for non-null values but also verifies that EMA values are positive above zero. The system tracks data sources through mtf_dataSource which displays "MTF Data" for successful external requests, "Backup EMA" for failed MTF with backup system active, or "Chart EMA" for local calculations.
🔄 MTF Smart Score Caching & Recheck System
// Cache Update Decision Logic
mtfSmartIntervalSec = input.int(300, "Smart Grid Recheck Interval (sec)") // 5-minute cache
canRecheckSmartScore = na(timenow) ? false :
(na(lastCheckTime) or (timenow - lastCheckTime) > mtfSmartIntervalSec * 1000)
// Cache Management
if canRecheckSmartScore
lastCheckTime := timenow
cachedSmartScoreLong := smartScoreLong // Store current calculation
cachedSmartScoreShort := smartScoreShort
The performance-optimized caching system addresses the computational intensity of continuous MTF analysis through intelligent interval management. The mtfSmartIntervalSec parameter, defaulting to 300 seconds (5 minutes), determines cache refresh frequency. The system evaluates canRecheckSmartScore by comparing current time against lastCheckTime plus the configured interval.
When cache updates trigger, the system stores current calculations in cachedSmartScoreLong and cachedSmartScoreShort, creating stable reference points that reduce excessive MTF requests. This cache management balances computational efficiency with analytical accuracy.
The cache versus real-time hybrid system creates a multi-layered decision matrix where immediate signals update every tick for responsive market reaction, cached MTF scores refresh every 5 minutes for stability filtering, dynamic thresholds recalculate every bar for volatility adaptation, and sliding window analysis updates every bar for trend persistence validation.
This architecture balances real-time signal detection with multi-timeframe strategic validation, creating adaptive trading intelligence that responds immediately to market changes while maintaining strategic stability through cached analysis and volatility-adjusted decision thresholds.
⚡The Execution Section Deep Dive
The execution section represents the culmination of all previous systems – where analysis transforms into action.
🚪 Entry Execution: The Gateway Protocol
Primary Entry Validation:
Entry isn't just about seeing a signal – it's about passing through multiple security checkpoints, each designed to filter out low-quality opportunities.
Stage 1: Signal Confirmation
entryCompositeBuySignal must be TRUE for longs
entryCompositeSellSignal must be TRUE for shorts
Stage 2: Enhanced Entry Validation
The strategy employs an "OR" logic system that recognizes different types of market opportunities:
Path A - Trend Reversal Entry:
When emaTrendReversal_Long triggers, it indicates the market structure is shifting in favor of the trade direction. This isn't just about a single EMA crossing – it represents a change in market momentum that experienced traders recognize as potential high-probability setups.
Path B - Momentum Breakout Entry:
The strongBullMomentum condition is where QRE identifies accelerating market conditions:
Criteria:
EMA1 rising for 3+ candles AND
EMA2 rising for 2+ candles AND
Close > 10-period high
This combination captures those explosive moves where the market doesn't just trend – it accelerates, creating momentum-driven opportunities.
Path C - Recovery Entry:
When previous exit states are clean (no recent stop losses), the strategy permits entry based purely on signal strength. This pathway is designed to help avoid the strategy becoming overly cautious after successful trades.
🛡️ The Priority Exit Matrix: When Rules Collide
Not all exit signals are created equal. QRE uses a strict hierarchy that is designed to avoid conflicting signals from causing hesitation:
Priority Level 1 - Exception Exits (Immediate Action):
Condition: TP3 reached AND Wick Trap detected
Action: Immediate exit regardless of other signals
Rationale: Historical analysis suggests wick traps at TP3 may indicate potential reversals
Priority Level 2 - Structural Breakdown:
Condition: TP3 active AND EMA structure deteriorating AND Smart Score insufficient
Logic: isEMAStructureDown AND NOT smartHold_Long
This represents the strategy recognizing that the underlying market structure that justified the trade is failing. It's like a building inspector identifying structural issues – you don't wait for additional confirmation.
Priority Level 3 - Enhanced Volatility Exits:
Conditions: TP2 active AND Strong counter-candle AND Wick trap AND Volume spike
Logic: Multiple confirmation required to reduce false exits
Priority Level 4 - Standard Smart Score Exits:
Condition: Any TP level active AND smartHold evaluates to FALSE
This is the bread-and-butter exit logic where signal deterioration triggers exit
⚖️ Stop Loss Management: Risk Control Protocol
Dual Stop Loss System:
QRE provides two stop loss modes that users can select based on their preference:
Fixed Mode (Default - useAdaptiveSL = false):
Uses predetermined percentage levels regardless of market volatility:
- Long SL = entryPrice × (1 - fixedRiskP - slipBuffer)
- Short SL = entryPrice × (1 + fixedRiskP + slipBuffer)
- Default: 0.6% risk + 0.3% slippage buffer = 0.9% total stop
- Consistent and predictable stop loss levels
- Recommended for users who prefer stable risk parameters
Adaptive Mode (Optional - useAdaptiveSL = true):
Dynamic system that adjusts stop loss based on market volatility:
- Base Calculation uses ATR (Average True Range)
- Long SL = entryPrice × (1 - (ATR × atrMultSL) / entryPrice - slipBuffer)
- Short SL = entryPrice × (1 + (ATR × atrMultSL) / entryPrice + slipBuffer)
- Automatically widens stops during high volatility periods
- Tightens stops during low volatility periods
- Advanced users can enable for volatility-adaptive risk management
Trend Multiplier Enhancement (Both Modes):
When strongUpTrend is detected for long positions, the stop loss receives 1.5x breathing room. Strong trends often have deeper retracements before continuing. This is designed to help avoid the strategy being shaken out of active trades by normal market noise.
Mode Selection Guidance:
- New Users: Start with Fixed Mode for predictable risk levels
- Experienced Users: Consider Adaptive Mode for volatility-responsive stops
- Volatile Markets: Adaptive Mode may provide better stop placement
- Stable Markets: Fixed Mode often sufficient for consistent risk management
Early Exit Conditions:
Beyond traditional stop losses, QRE implements "smart stops" that trigger before price-based stops:
Early Long Exit: (smartScoreLong < 1.0 OR prev5BearCandles) AND realROI < -0.9%
🔄 State Management: The Memory System
Complete State Reset Protocol:
When a position closes, QRE doesn't just wipe the slate clean – it performs a methodical reset:
TP State Cleanup:
All Boolean flags: tp1/tp2/tp3HitBefore → FALSE
All Reached flags: tp1/tp2/tp3Reached → FALSE
All Active flags: tp1/tp2/tp3HoldActive → FALSE
Signal Counter Reset:
Every one of the 22 signal counters returns to zero.
This is designed to avoid signal "ghosting" where old signals influence new trades.
Memory Preservation:
While operational states reset, certain information is preserved for learning:
killReasonLong/Short: Why did this trade end?
lastExitWasTP1/TP2/TP3: What was the exit quality?
reEntryCount: How many consecutive re-entries have occurred?
🔄 Re-Entry Logic: The Comeback System
Re-Entry Conditions Matrix:
QRE implements a re-entry system that recognizes not all exits are created equal:
TP-Based Re-Entry (Enabled):
Criteria: Previous exit was TP1, TP2, or TP3
Cooldown: Minimal or bypassed entirely
Logic: Target-based exits indicate potentially viable market conditions
EMA-Based Re-Entry (Conditional):
Criteria: Previous exit was EMA-based (structural change)
Requirements: Must wait for EMA confirmation in new direction
Minimum Wait: 5 candles
Advanced Re-Entry Features:
When adjustReEntryTargets is enabled, the strategy becomes more aggressive with re-entries:
Target Adjustment: TP1 multiplied by reEntryTP1Mult (default 2.0)
Stop Adjustment: SL multiplied by reEntrySLMult (default 1.5)
Logic: If we're confident enough to re-enter, we should be confident enough to hold for bigger moves
Performance Tracking: Strategy tracks re-entry win rate, average ROI, and total performance separately from initial entries for optimization analysis.
📊 Exit Reason Analytics: Learning from Every Trade
Kill Reason Tracking:
Every exit is categorized and stored:
"TP3 Exit–Wick Trap": Exit at target level with wick pattern detection
"Smart Exit–EMA Down": Structural breakdown exit
"Smart Exit–Volatility": Volatility-based protection exit
"Exit Post-TP1/TP2/TP3": Standard smart exit progression
"Long SL Exit" / "Short SL Exit": Stop loss exits
Performance Differentiation:
The strategy tracks performance by exit type, allowing for continuous analysis:
TP-based exits: Achieved target levels, analyze for pattern improvement
EMA-based exits: Mixed results, analyze for pattern improvement
SL-based exits: Learning opportunities, adjust entry criteria
Volatility exits: Protective measures, monitor performance
🎛️ Trailing Stop Implementation:
Conditional Trailing Activation:
Activation Criteria: Position profitable beyond trailingStartPct AND
(TP hold active OR re-entry trade)
Dynamic Trailing Logic:
Unlike simple trailing stops, QRE's implementation considers market context:
Trending Markets: Wider trail offsets to avoid whipsaws
Volatile Markets: Tighter offsets to protect gains
Re-Entry Trades: Enhanced trailing to maximize second-chance opportunities
Return-to-Entry Protection:
When deactivateOnReturn is enabled, the strategy will close positions that return to entry level after being profitable. This is designed to help avoid the frustration of watching profitable trades turn into losers.
🧠 How It All Works Together
The beauty of QRE lies not in any single component, but in how everything integrates:
The Entry Decision: Multiple pathways are designed to help identify opportunities while maintaining filtering standards.
The Progression System: Each TP level unlocks new protection features, like achieving ranks in a video game.
The Exit Matrix: Prioritized decision-making aims to reduce analysis paralysis while providing appropriate responses to different market conditions.
The Memory System: Learning from each trade while preventing contamination between separate opportunities.
The Re-Entry Logic: Re-entry system that balances opportunity with risk management.
This creates a trading system where entry conditions filter for quality, progression systems adapt to changing market conditions, exit priorities handle conflicting signals intelligently, memory systems learn from each trade cycle, and re-entry logic maximizes opportunities while managing risk exposure.
📊 ANALYSIS TABLE INTERPRETATION -
⚙️ Enabling Analysis Mode
Navigate to strategy settings → "Testing & Analysis" → Enable "Show Analysis Table". The Analysis Table displays different information based on the selected test filter and provides real-time insight into all strategy components, helping users understand current market conditions, position status, and system decision-making processes.
📋 Filter Mode Interpretations
"All" Mode (Default View):
Composite Section:
Buy Score: Aggregated strength from all 22 bullish signals (threshold 5.0+ triggers entry consideration)
Sell Score: Aggregated strength from all 22 bearish signals (threshold 5.4+ triggers entry consideration)
APEX Filters:
ATG Trend: Shows current trend direction analysis
Indicates whether momentum filters are aligned for directional bias
ReEntry Section:
Most Recent Exit: Displays exit type and timeframe since last position closure
Status: Shows if ReEntry system is Ready/Waiting/Disabled
Count: Current re-entry attempts versus maximum allowed attempts
Position Section (When Active):
Status: Current position state (LONG/SHORT/FLAT)
ROI: Dual calculation showing Custom vs Real ROI percentages
Entry Price: Original position entry level
Current Price: Live market price for comparison
TP Tracking: Progress toward profit targets
"Smart" Filter (Critical for Active Positions):
Smart Exit Section:
Hold Timer: Time elapsed since position opened (bar-based counting)
Status: Whether Smart Exit Grid is Enabled/Disabled
Score: Current smart score calculation from 22-component matrix
Dynamic Threshold: ATR-based minimum score required for holding
Final Threshold: Time and ROI-adjusted threshold actually used for decisions
Score Check: Pass/Fail based on Score vs Final Threshold comparison
Smart Hold: Current hold decision status
Final Hold: Final recommendation based on all factors
🎯 Advanced Smart Exit Debugging - ROI & Time-Based Threshold System
Understanding the Multi-Layer Threshold System:
Layer 1: Dynamic Threshold (ATR-Based)
atrRatio = ATR / close
dynamicThreshold = atrRatio > 0.02 ? 1.0 : // High volatility: Lower threshold
(atrRatio > 0.01 ? 1.5 : // Medium volatility: Standard
2.8) // Low volatility: Higher threshold
Layer 2: Time Multiplier (ROI & Duration-Based)
Winning Positions (ROI ≥ 0%):
→ timeMultiplier = 1.0 (No time pressure, regardless of hold duration)
Losing Positions (ROI < 0%):
→ holdTimer ≤ 8 bars: timeMultiplier = 1.0 (Early stage, standard requirements)
→ holdTimer 9-16 bars: timeMultiplier = 1.1 (10% stricter requirements)
→ holdTimer 17+ bars: timeMultiplier = 1.3 (30% stricter requirements)
Layer 3: Final Threshold Calculation
finalThreshold = dynamicThreshold × timeMultiplier
Examples:
- Winning Position: 2.8 × 1.0 = 2.8 (Always standard)
- Losing Position (Early): 2.8 × 1.0 = 2.8 (Same as winning initially)
- Losing Position (Extended): 2.8 × 1.3 = 3.64 (Much stricter)
Real-Time Debugging Display:
Smart Exit Section shows:
Score: 3.5 → Current smartScoreLong/Short value
Dynamic Threshold: 2.8 → Base ATR-calculated threshold
Final Threshold: 3.64 (ATR×1.3) → Actual threshold used for decisions
Score Check: FAIL (3.5 vs 3.64) → Pass/Fail based on final comparison
Final Hold: NO HOLD → Actual system decision
Position Status Indicators:
Winner + Early: ATR×1.0 (No pressure)
Winner + Extended: ATR×1.0 (No pressure - winners can run indefinitely)
Loser + Early: ATR×1.0 (Recovery opportunity)
Loser + Extended: ATR×1.1 or ATR×1.3 (Increasing pressure to exit)
MTF Section:
Data Source: Shows whether using MTF Data/EMA Backup/Local EMA
Timeframe: Configured watchtower timeframe setting
Data Valid: Confirms successful MTF data retrieval status
Trend Signal: Higher timeframe directional bias analysis
Close Price: MTF price data availability confirmation
"Composite" Filter:
Composite Section:
Buy Score: Real-time weighted scoring from multiple indicators
Sell Score: Opposing directional signal strength
Threshold: Minimum scores required for signal activation
Components:
Flash/Blink: Momentum acceleration indicators (F = Flash active, B = Blink active)
Individual filter contributions showing which specific signals are firing
"ReEntry" Filter:
ReEntry System:
System: Shows if re-entry feature is Enabled/Disabled
Eligibility: Conditions for new entries in each direction
Performance: Success metrics of re-entry attempts when enabled
🎯 Key Status Indicators
Status Column Symbols:
✓ = Condition met / System active / Signal valid
✗ = Condition not met / System inactive / No signal
⏳ = Cooldown active (waiting period)
✅ = Ready state / Good condition
🔄 = Processing / Transitioning state
🔍 Critical Reading Guidelines
For Active Positions - Smart Exit Priority Reading:
1. First Check Position Type:
ROI ≥ 0% = Winning Position (Standard requirements)
ROI < 0% = Losing Position (Progressive requirements)
2. Check Hold Duration:
Early Stage (≤8 bars): Standard multiplier regardless of ROI
Extended Stage (9-16 bars): Slight pressure on losing positions
Long Stage (17+ bars): Strong pressure on losing positions
3. Score vs Final Threshold Analysis:
Score ≥ Final Threshold = HOLD (Continue position)
Score < Final Threshold = EXIT (Close position)
Watch for timeMultiplier changes as position duration increases
4. Understanding "Why No Hold?"
Common scenarios when Score Check shows FAIL:
Losing position held too long (timeMultiplier increased to 1.1 or 1.3)
Low volatility period (dynamic threshold raised to 2.8)
Signal deterioration (smart score dropped below required level)
MTF conflict (higher timeframe opposing position direction)
For Entry Signal Analysis:
Composite Score Reading: Signal strength relative to threshold requirements
Component Analysis: Individual filter contributions to overall score
EMA Structure: Confirm 3-bar crossover requirement met
Cooldown Status: Ensure sufficient time passed since last exit
For ReEntry Opportunities (when enabled):
System Status: Availability and eligibility for re-engagement
Exit Type Analysis: TP-based exits enable immediate re-entry, SL-based exits require cooldown
Condition Monitoring: Requirements for potential re-entry signals
Debugging Common Issues:
Issue: "Score is high but no hold?"
→ Check Final Threshold vs Score (not Dynamic Threshold)
→ Losing position may have increased timeMultiplier
→ Extended hold duration applying pressure
Issue: "Why different thresholds for same score?"
→ Position ROI status affects multiplier
→ Time elapsed since entry affects multiplier
→ Market volatility affects base threshold
Issue: "MTF conflicts with local signals?"
→ Higher timeframe trend opposing position
→ System designed to exit on MTF conflicts
→ Check MTF Data Valid status
⚡ Performance Optimization Notes
For Better Performance:
Analysis table updates may impact performance on some devices
Use specific filters rather than "All" mode for focused monitoring
Consider disabling during live trading for optimal chart performance
Enable only when needed for debugging or analysis
Strategic Usage:
Monitor "Smart" filter when positions are active for exit timing decisions
Use "Composite" filter during setup phases for signal strength analysis
Reference "ReEntry" filter after position closures for re-engagement opportunities
Track Final Threshold changes to understand exit pressure evolution
Advanced Debugging Workflow:
Position Entry Analysis:
Check Composite score vs threshold
Verify EMA crossover timing (3 bars prior)
Confirm cooldown completion
Hold Decision Monitoring:
Track Score vs Final Threshold progression
Monitor timeMultiplier changes over time
Watch for MTF conflicts
Exit Timing Analysis:
Identify which threshold layer caused exit
Track performance by exit type
Analyze re-entry eligibility
This analysis system provides transparency into strategy decision-making processes, allowing users to understand how signals are generated and positions are managed according to the programmed logic during various market conditions and position states.
SIGNAL TYPES AND CHARACTERISTICS
🔥 Core Momentum Signals
Flash Signal
Calculation: ta.rma(math.abs(close - close ), 5) > ta.sma(math.abs(close - close ), 7)
Purpose: Detects sudden price acceleration using smoothed momentum comparison
Characteristics: Triggers when recent price movement exceeds historical average movement
Usage: Primary momentum confirmation across multiple composite calculations
Weight: 1.3 points in composite scoring
Blink Signal
Calculation: math.abs(ta.change(close, 1)) > ta.sma(math.abs(ta.change(close, 1)), 5)
Purpose: Identifies immediate price velocity spikes
Characteristics: More sensitive than Flash, captures single-bar momentum bursts
Usage: Secondary momentum confirmation, often paired with Flash
Weight: 1.3 points in composite scoring
⚡ Advanced Composite Signals
Apex Pulse Signal
Calculation: apexAngleValue > 30 or apexAngleValue < -30
Purpose: Detects extreme EMA angle momentum
Characteristics: Identifies when trend angle exceeds ±30 degrees
Usage: Confirms directional momentum strength in trend-following scenarios
Pressure Surge Signal
Calculation: volSpike_AVP and strongTrendUp_ATG
Purpose: Combines volume expansion with trend confirmation
Characteristics: Requires both volume spike and strong uptrend simultaneously
Usage: bullish signal for trend continuation
Shift Wick Signal
Calculation: ta.crossunder(ema1, ema2) and isWickTrapDetected and directionFlip
Purpose: Detects bearish reversal with wick trap confirmation
Characteristics: Combines EMA crossunder with upper wick dominance and directional flip
Usage: Reversal signal for trend change identification
🛡️ Trap Exit Protection Signals
Bear Trap Exit
Calculation: isUpperWickTrap and isBearEngulfNow
Conditions: Previous bullish candle with 80%+ upper wick, followed by current bearish engulfing
Purpose: Emergency exit signal for long positions
Priority: Highest - overrides all other hold conditions
Action: Immediate position closure with full state reset
Bull Trap Exit
Calculation: isLowerWickTrap and isBullEngulfNow
Conditions: Previous bearish candle with 80%+ lower wick, followed by current bullish engulfing
Purpose: Emergency exit signal for short positions
Priority: Highest - overrides all other hold conditions
Action: Immediate position closure with full state reset
📊 Technical Analysis Foundation Signals
RSI-MFI Hybrid System
Base Calculation: (ta.rsi(close, 14) + ta.mfi(close, 14)) / 2
Oversold Threshold: < 35
Overbought Threshold: > 65
Weak Condition: < 35 and declining
Strong Condition: > 65 and rising
Usage: Momentum confirmation and reversal identification
ADX-DMI Trend Classification
Strong Up Trend: (adx > 25 and diplus > diminus and (diplus - diminus) > 5) or (ema1 > ema2 and ema2 > ema3 and ta.rising(ema2, 3))
Strong Down Trend: (adx > 20 and diminus > diplus - 5) or (ema1 < ema2 and ta.falling(ema1, 3))
Trend Weakening: adx < adx and adx < adx
Usage: Primary trend direction confirmation
Bollinger Band Squeeze Detection
Calculation: bbWidth < ta.lowest(bbWidth, 20) * 1.2
Purpose: Identifies low volatility periods before breakouts
Usage: Entry filter - avoids trades during consolidation
🎨 Visual Signal Indicators
Red X Signal
Calculation: isBearCandle and ta.crossunder(ema1, ema2)
Visual: Red X above price
Purpose: Bearish EMA crossunder with confirming candle
Composite Weight: +1.0 for short positions, -1.0 for long positions
Characteristics: Simple but effective trend change indicator
Green Dot Signal
Calculation: isBullCandle and ta.crossover(ema1, ema2)
Visual: Green dot below price
Purpose: Bullish EMA crossover with confirming candle
Composite Weight: +1.0 for long positions, -1.0 for short positions
Characteristics: Entry confirmation for trend-following strategies
Blue Diamond Signal
Trigger Conditions: amcBuySignal and score >= 4
Scoring Components: 11 different technical conditions
Key Requirements: AMC bullish + momentum rise + EMA expansion + volume confirmation
Visual: Blue diamond below price
Purpose: Bullish reversal or continuation signal
Characteristics: Multi-factor confirmation requiring 4+ technical alignments
Red Diamond Signal
Trigger Conditions: amcSellSignal and score >= 5
Scoring Components: 11 different technical conditions (stricter than Blue Diamond)
Key Requirements: AMC bearish + momentum crash + EMA compression + volume decline
Visual: Red diamond above price
Purpose: Potential bearish reversal or continuation signal
Characteristics: Requires higher threshold (5 vs 4) for more selective triggering
🔵 Specialized Detection Signals
Blue Dot Signal
Calculation: volumePulse and isCandleStrong and volIsHigh
Requirements: Volume > 2.0x MA, strong candle body > 35% of range, volume MA > 55
Purpose: Volume-confirmed momentum signal
Visual: Blue dot above price
Characteristics: Volume-centric signal for high-liquidity environments
Orange X Signal
Calculation: Complex multi-factor oversold reversal detection
Requirements: AMC oversold + wick trap + flash/blink + RSI-MFI oversold + bullish flip
Purpose: Oversold bounce signal with multiple confirmations
Visual: Orange X below price
Characteristics: Reversal signal requiring 5+ simultaneous conditions
VSS (Velocity Signal System)
Components: Volume spike + EMA angle + trend direction
Buy Signal: vssTrigger and vssTrendDir == 1
Sell Signal: vssTrigger and vssTrendDir == -1
Visual: Green/Red triangles
Purpose: Velocity-based momentum detection
Characteristics: Fast-response signal for momentum trading
⭐ Elite Composite Signals
Star Uprising Signal
Base Requirements: entryCompositeBuySignal and echoBodyLong and strongUpTrend and isAMCUp
Additional Confirmations: RSI hybrid strong + not high risk
Special Conditions: At bottom zone OR RSI bottom bounce OR strong volume bounce
Visual: Star symbol below price
Purpose: Bullish reversal signal from oversold conditions
Characteristics: Most selective bullish signal requiring multiple confirmations
Ultra Short Signal
Scoring System: 7-component scoring requiring 4+ points
Key Components: EMA trap + volume decline + RSI weakness + composite confirmation
Additional Requirements: Falling EMA structure + volume spike + flash confirmation
Visual: Explosion emoji above price
Purpose: Aggressive short entry for trend reversal or continuation
Characteristics: Complex multi-layered signal for experienced short selling
🎯 Composite Signal Architecture
Enhanced Composite Scoring
Long Composite: 15+ weighted components including structure, momentum, flash/blink, volume, price action, reversal triggers, trend alignment
Short Composite: Mirror structure with bearish bias
Threshold: 5.0 points required for signal activation
Conflict Resolution: If both long and short signals trigger simultaneously, both are disabled
Final Validation: Requires EMA momentum confirmation (ta.rising(emaFast_ATG, 2) for longs, ta.falling(emaFast_ATG, 2) for shorts)
Risk Assessment Integration
High Risk Long: RSI > 70 OR close > upper Bollinger Band 80%
High Risk Short: RSI < 30 OR close < lower Bollinger Band 80%
Zone Analysis: Top zone (95% of 50-bar high) vs Bottom zone (105% of 50-bar low)
Risk Penalty: High risk conditions subtract 1.5 points from composite scores
This signal architecture creates a multi-layered detection system where simple momentum signals provide foundation, technical analysis adds structure, visual indicators offer clarity, specialized detectors capture different market conditions, and composite signals identify potential opportunities while integrated risk assessment is designed to filter risky entries.
VISUAL FEATURES SHOWCASE
Ichimoku Cloud Visualization
Dynamic Color Intensity: Cloud transparency adapts to momentum strength - darker colors indicate stronger directional moves, while lighter transparency shows weakening momentum phases.
Gradient Color Mapping: Bullish momentum renders blue-purple spectrum with increasing opacity, while bearish momentum displays corresponding color gradients with intensity-based transparency.
Real-time Momentum Feedback: Color saturation provides immediate visual feedback on market structure strength, allowing traders to assess levels at a glance without additional indicators.
EMA Ribbon Bands
The 8-level exponential moving average system creates a comprehensive trend structure map with gradient color coding.
Signal Type Visualization
STRATEGY PROPERTIES & BACKTESTING DISCLOSURE
📊 Default Strategy Configuration:
✅ Initial Capital: 100,000 USD (realistic for average traders)
✅ Commission: 0.075% per trade (realistic exchange fees)
✅ Slippage: 3 ticks (market impact consideration)
✅ Position Size: 5% equity per trade (sustainable risk level)
✅ Pyramiding: Disabled (single position management)
✅ Sample Size: 185 trades over 12-month backtesting period
✅ Risk Management: Adaptive stop loss with maximum 1% risk per trade
COMPREHENSIVE BACKTESTING RESULTS
Testing Period & Market Conditions:
Backtesting Period: June 25, 2024 - June 25, 2025 (12 months)
Timeframe: 15-minute charts (MTF system active)
Market: BTCUSDT (Bitcoin/Tether)
Market Conditions: Full market cycle including volatility periods
Deep Backtesting: Enabled for maximum accuracy
📈 Performance Summary:
Total Return: +2.19% (+2,193.59 USDT)
Total Trades Executed: 185 trades
Win Rate: 34.05% (63 winning trades out of 185)
Profit Factor: 1.295 (gross profit ÷ gross loss)
Maximum Drawdown: 0.65% (653.17 USDT)
Risk-Adjusted Returns: Consistent with conservative risk management approach
📊 Detailed Trade Analysis:
Position Distribution:
Long Positions: 109 trades (58.9%) | Win Rate: 36.70%
Short Positions: 76 trades (41.1%) | Win Rate: 30.26%
Average Trade Duration: Optimized for 15-minute timeframe efficiency
Profitability Metrics:
Average Profit per Trade: 11.74 USDT (0.23%)
Average Winning Trade: 151.17 USDT (3.00%)
Average Losing Trade: 60.27 USDT (1.20%)
Win/Loss Ratio: 2.508 (winners are 2.5x larger than losses)
Largest Single Win: 436.02 USDT (8.69%)
Largest Single Loss: 107.41 USDT (controlled risk management)
💰 Financial Performance Breakdown:
Gross Profit: 9,523.93 USDT (9.52% of capital)
Gross Loss: 7,352.48 USDT (7.35% of capital)
Net Profit After Costs: 2,171.44 USDT (2.17%)
Commission Costs: 1,402.47 USDT (realistic trading expenses)
Maximum Equity Run-up: 2,431.66 USDT (2.38%)
⚖️ Risk Management Validation:
Maximum Drawdown: 0.65% showing controlled risk management
Drawdown Recovery: Consistent equity curve progression
Risk per Trade: Successfully maintained below 1.5% per position
Position Sizing: 5% equity allocation proved sustainable throughout testing period
📋 Strategy Performance Characteristics:
✅ Strengths Demonstrated:
Controlled Risk: Maximum drawdown well below industry standards (< 1%)
Positive Expectancy: Win/loss ratio of 2.5+ creates profitable edge
Consistent Performance: Steady equity curve without extreme volatility
Realistic Costs: Includes actual commission and slippage impacts
Sample Size: 185 trades during testing period
⚠️ Performance Considerations:
Win Rate: 34% win rate requires discipline to follow system signals
Market Dependency: Performance may vary significantly in different market conditions
Timeframe Sensitivity: Optimized for 15-minute charts; other timeframes may show different results
Slippage Impact: Real trading conditions may affect actual performance
📊 Benchmark Comparison:
Strategy Return: +2.19% over 12 months
Buy & Hold Bitcoin: +71.12% over same period
Strategy Advantage: Significantly lower drawdown and volatility
Risk-Adjusted Performance: Different risk profile compared to holding cryptocurrency
🎯 Real-World Application Insights:
Expected Trading Frequency:
Average: 15.4 trades per month (185 trades ÷ 12 months)
Weekly Frequency: Approximately 3-4 trades per week
Active Management: Requires regular monitoring during market hours
Capital Requirements:
Minimum Used in Testing: $10,000 for sustainable position sizing
Tested Range: $50,000-$100,000 for comfortable risk management
Commission Impact: 0.075% per trade totaled 1.4% of capital over 12 months
⚠️ IMPORTANT BACKTESTING DISCLAIMERS:
📈 Performance Reality:
Past performance does not guarantee future results. Backtesting results represent hypothetical performance and may not reflect actual trading outcomes due to market changes, execution differences, and emotional factors.
🔄 Market Condition Dependency:
This strategy's performance during the tested period may not be representative of performance in different market conditions, volatility regimes, or trending vs. sideways markets.
💸 Cost Considerations:
Actual trading costs may vary based on broker selection, market conditions, and trade size. Commission rates and slippage assumptions may differ from real-world execution.
🎯 Realistic Expectations:
The 34% win rate requires psychological discipline to continue following signals during losing streaks. Risk management and position sizing are critical for replicating these results.
⚡ Technology Dependencies:
Strategy performance assumes reliable internet connection, platform stability, and timely signal execution. Technical failures may impact actual results.
CONFIGURATION OPTIMIZATION
5-Minute Timeframe Optimization (Advanced Users Only)
⚠️ Important Warning: 5-minute timeframes operate without MTF confirmation, resulting in reduced signal quality and higher false signal rates.
Example 5-Minute Parameters:
Composite Thresholds: Long 6.5, Short 7.0 (vs 15M default 5.0/5.4)
Signal Lookback Bars: 12 (vs 15M default 8)
Volume Multiplier: 2.2 (vs 15M default 1.8)
MTF Timeframe: Disabled (automatic below 30M)
Risk Management Adjustments:
Position Size: Reduce to 3% (vs 5% default)
TP1: 0.8%, TP2: 1.2%, TP3: 2.0% (tighter targets)
SL: 0.8% (tighter stop loss)
Cooldown Minutes: 8 (vs 5 default)
Usage Notes for 5-Minute Trading:
- Wait for higher composite scores before entry
- Require stronger volume confirmation
- Monitor EMA structure more closely
15-Minute Scalping Setup:
TP1: 1.0%, TP2: 1.5%, TP3: 2.5%
Composite Threshold: 5.0 (higher filtering)
TP ATR Multiplier: 7.0
SL ATR Multiplier: 2.5
Volume Multiplier: 1.8 (requires stronger confirmation)
Hold Time: 2 bars minimum
3-Hour Swing Setup:
TP1: 2.0%, TP2: 4.0%, TP3: 8.0%
Composite Threshold: 4.5 (more signals)
TP ATR Multiplier: 8.0
SL ATR Multiplier: 3.2
Volume Multiplier: 1.2
Hold Time: 6 bars minimum
Market-Specific Adjustments
High Volatility Periods:
Increase ATR multipliers (TP: 2.0x, SL: 1.2x)
Raise composite thresholds (+0.5 points)
Reduce position size
Enable cooldown periods
Low Volatility Periods:
Decrease ATR multipliers (TP: 1.2x, SL: 0.8x)
Lower composite thresholds (-0.3 points)
Standard position sizing
Disable extended cooldowns
News Events:
Temporarily disable strategy 30 minutes before major releases
Increase volume requirements (2.0x multiplier)
Reduce position sizes by 50%
Monitor for unusual price action
RISK MANAGEMENT
Dual ROI System: Adaptive vs Fixed Mode
Adaptive RR Mode:
Uses ATR (Average True Range) for automatic adjustment
TP1: 1.0x ATR from entry price
TP2: 1.5x ATR from entry price
TP3: 2.0x ATR from entry price
Stop Loss: 1.0x ATR from entry price
Automatically adjusts to market volatility
Fixed Percentage Mode:
Uses predetermined percentage levels
TP1: 1.0% (default)
TP2: 1.5% (default)
TP3: 2.5% (default)
Stop Loss: 0.9% total (0.6% risk tolerance + 0.3% slippage buffer)(default)
Consistent levels regardless of volatility
Mode Selection: Enable "Use Adaptive RR" for ATR-based targets, disable for fixed percentages. Adaptive mode works better in varying volatility conditions, while fixed mode provides predictable risk/reward ratios.
Stop Loss Management
In Adaptive SL Mode:
Automatically scales with market volatility
Tight stops during low volatility (smaller ATR)
Wider stops during high volatility (larger ATR)
Include 0.3% slippage buffer in both modes
In Fixed Mode:
Consistent percentage-based stops
2% for crypto, 1.5% for forex, 1% for stocks
Manual adjustment needed for different market conditions
Trailing Stop System
Configuration:
Enable Trailing: Activates dynamic stop loss adjustment
Start Trailing %: Profit level to begin trailing (default 1.0%)
Trailing Offset %: Distance from current price (default 0.5%)
Close if Return to Entry: Optional immediate exit if price returns to entry level
Operation: Once position reaches trailing start level, stop loss automatically adjusts upward (longs) or downward (shorts) maintaining the offset distance from favorable price movement.
Timeframe-Specific Risk Considerations
15-Minute and Above (Tested):
✅ Full MTF system active
✅ Standard risk parameters apply
✅ Backtested performance metrics valid
✅ Standard position sizing (5%)
5-Minute Timeframes (Advanced Only):
⚠️ MTF system inactive - local signals only
⚠️ Higher false signal rate expected
⚠️ Reduced position sizing preferred (3%)
⚠️ Tighter stop losses required (0.8% vs 1.2%)
⚠️ Requires parameter optimization
⚠️ Monitor performance closely
1-Minute Timeframes (Limited Testing):
❌ Excessive noise levels
❌ Strategy not optimized for this frequency
Risk Management Practices
Allocate no more than 5% of your total investment portfolio to high-risk trading
Never trade with funds you cannot afford to lose
Thoroughly backtest and validate the strategy with small amounts before full implementation
Always maintain proper risk management and stop-loss settings
IMPORTANT DISCLAIMERS
Performance Disclaimer
Past performance does not guarantee future results. All trading involves substantial risk of loss. This strategy is provided for informational purposes and does not constitute financial advice.
Market Risk
Cryptocurrency and forex markets are highly volatile. Prices can move rapidly against positions, resulting in significant losses. Users should never risk more than they can afford to lose.
Strategy Limitations
This strategy relies on technical analysis and may not perform well during fundamental market shifts, news events, or unprecedented market conditions. No trading strategy can guarantee 100% success or eliminate the risk of loss.
Legal Compliance
You are responsible for compliance with all applicable regulations and laws in your jurisdiction. Consult with licensed financial professionals when necessary.
User Responsibility
Users are responsible for their own trading decisions, risk management, and compliance with applicable regulations in their jurisdiction.
DCA StrategyThis strategy makes it easy for you to backtest and automate the DCA strategy based on 2 triggers:
Day of the week
Every X candles
This way you can set up your DCA strategy the way you like and automate on any exchange or even a DEX, which offers an API.
The strategy is auto selling on the last candle, otherwise you won't see any performance numbers because all positions will still be open (non conclusive).
Settings
Start Date & End Date
Use those dates to help you with your backtest period. It also helps when automating, to start at a specific time to mimic what you have already done on your own portfolio and thus be in sync in TV as well.
Capital to invest per trade
Set how capital to use per DCA buy signal. Hover over the tooltip to understand, which currency is used.
Close All on last candle
When backtesting, you must close open positions, otherwise the Strategy Tester won't show you any numbers. This is why the strategy automatically closes all positions on the last candle for your convenience (ON per default).
BUT, when automating, you cannot have this checked because it would sell all of your asset on every candle open. So turn this OFF when automating.
Use Day of Week Mode
This checkbox switches between the "Day of Week" mode or the "Every X Candles" mode.
Day of Week
Opens a long position at the start of the weekday you have set it to.
Hover over the tooltip to understand, which number to use for the day of the week you need.
Every X Candles
Opens a long position after every x candles. Always at the start of every such candle.
On the daily chart, this number represents "1 day", on the 1h chart, it's "1 hour" and so on.
Properties
Initial Capital
DCA has a special quirk and that is that it invests more and more and more funds the longer it runs. But TradingView takes the Initial Capital number to calculate Net Profit, thus the Initial Capital number has to grow with every additional dollar (money) that is being invested over time, otherwise the Net Profit number will be wrong.
Sadly PineScript does not allow to set the Initial Capital number dynamically. So you have to set it manually.
To that end, this strategy shows a Label on the last candle, which shows the Invested Capital. You must take that number and put it into the Initial Capital input and click Ok .
If you don't do this, your Net Profit Number will be totally wrong!
The label must show green .
If it shows red it means you need to change the Initial Capital number before looking at the performance numbers.
After every timeframe or settings change, you must adapt the Initial Capital, otherwise you will get wrong numbers.
Fibonacci Swing Trading BotStrategy Overview for "Fibonacci Swing Trading Bot"
Strategy Name: Fibonacci Swing Trading Bot
Version: Pine Script v5
Purpose: This strategy is designed for swing traders who want to leverage Fibonacci retracement levels and candlestick patterns to enter and exit trades on higher time frames.
Key Components:
1. Multiple Timeframe Analysis:
The strategy uses a customizable timeframe for analysis. You can choose between 4hour, daily, weekly, or monthly time frames to fit your preferred trading horizon. The high and low-price data is retrieved from the selected timeframe to identify swing points.
2. Fibonacci Retracement Levels:
The script calculates two key Fibonacci retracement levels:
0.618: A common level where price often retraces before resuming its trend.
0.786: A deeper retracement level, often used to identify stronger support/resistance areas.
These levels are dynamically plotted on the chart based on the highest high and lowest low over the last 50 bars of the selected timeframe.
3. Candlestick Based Entry Signals:
The strategy uses candlestick patterns as the only indicator for trade entries:
Bullish Candle: A green candle (close > open) that forms between the 0.618 retracement level and the swing high.
Bearish Candle: A red candle (close < open) that forms between the 0.786 retracement level and the swing low.
When these candlestick patterns align with the Fibonacci levels, the script triggers buy or sell signals.
4. Risk Management:
Stop Loss: The stop loss is set at 1% below the entry price for long trades and 1% above the entry price for short trades. This tight risk management ensures controlled losses.
Take Profit: The strategy uses a 2:1 risk-to-reward ratio. The take profit is automatically calculated based on this ratio relative to the stop loss.
5. Buy/Sell Logic:
Buy Signal: Triggered when a bullish candle forms above the 0.618 retracement level and below the swing high. The bot then places a long position.
Sell Signal: Triggered when a bearish candle forms below the 0.786 retracement level and above the swing low. The bot then places a short position.
The stop loss and take profit levels are automatically managed once the trade is placed.
Strengths of This Strategy:
Swing Trading Focus: The strategy is ideal for swing traders, targeting longer-term price moves that can take days or weeks to play out.
Simple Yet Effective Indicators: By only relying on Fibonacci retracement levels and basic candlestick patterns, the strategy avoids complexity while capitalizing on well-known support and resistance zones.
Automated Risk Management: The built-in stop loss and take profit mechanism ensures trades are protected, adhering to a strict 2:1 risk/reward ratio.
Multiple Timeframe Analysis: The script adapts to various market conditions by allowing users to switch between different timeframes (4hour, daily, weekly, monthly), giving traders flexibility.
Strategy Use Cases:
Retracement Traders: Traders who focus on entering the market at key retracement levels (0.618 and 0.786) will find this strategy especially useful.
Trend Reversal Traders: The strategy’s reliance on candlestick formations at Fibonacci levels helps traders spot potential reversals in price trends.
Risk Conscious Traders: With its 1% risk per trade and 2:1 risk/reward ratio, the strategy is ideal for traders who prioritize risk management in their trades.
Post-Open Long Strategy with ATR-based Stop Loss and Take ProfitThe "Post-Open Long Strategy with ATR-Based Stop Loss and Take Profit" is designed to identify buying opportunities after the German and US markets open. It combines various technical indicators to filter entry signals, focusing on breakout moments following price lateralization periods.
Key Components and Their Interaction:
Bollinger Bands (BB):
Description: Uses BB with a 14-period length and standard deviation multiplier of 1.5, creating narrower bands for lower timeframes.
Role in the Strategy: Identifies low volatility phases (lateralization). The lateralization condition is met when the price is near the simple moving average of the BB, suggesting an imminent increase in volatility.
Exponential Moving Averages (EMA):
10-period EMA: Quickly detects short-term trend direction.
200-period EMA: Filters long-term trends, ensuring entries occur in a bullish market.
Interaction: Positions are entered only if the price is above both EMAs, indicating a consolidated positive trend.
Relative Strength Index (RSI):
Description: 7-period RSI with a threshold above 30.
Role in the Strategy: Confirms the market is not oversold, supporting the validity of the buy signal.
Average Directional Index (ADX):
Description: 7-period ADX with 7-period smoothing and a threshold above 10.
Role in the Strategy: Assesses trend strength. An ADX above 10 indicates sufficient momentum to justify entry.
Average True Range (ATR) for Dynamic Stop Loss and Take Profit:
Description: 14-period ATR with multipliers of 2.0 for Stop Loss and 4.0 for Take Profit.
Role in the Strategy: Adjusts exit levels based on current volatility, enhancing risk management.
Resistance Identification and Breakout:
Description: Analyzes the highs of the last 20 candles to identify resistance levels with at least two touches.
Role in the Strategy: A breakout above this level signals a potential continuation of the bullish trend.
Time Filters and Market Conditions:
Trading Hours: Operates only during the opening of the German market (8:00 - 12:00) and US market (15:30 - 19:00).
Panic Candle: The current candle must close negative, leveraging potential emotional reactions in the market.
Avoiding Entry During Pullbacks:
Description: Checks that the two previous candles are not both bearish.
Role in the Strategy: Avoids entering during a potential pullback, improving trade success probability.
Post-Open Long Strategy with ATR-Based Stop Loss and Take Profit
The "Post-Open Long Strategy with ATR-Based Stop Loss and Take Profit" is designed to identify buying opportunities after the German and US markets open. It combines various technical indicators to filter entry signals, focusing on breakout moments following price lateralization periods.
Key Components and Their Interaction:
Bollinger Bands (BB):
Description: Uses BB with a 14-period length and standard deviation multiplier of 1.5, creating narrower bands for lower timeframes.
Role in the Strategy: Identifies low volatility phases (lateralization). The lateralization condition is met when the price is near the simple moving average of the BB, suggesting an imminent increase in volatility.
Exponential Moving Averages (EMA):
10-period EMA: Quickly detects short-term trend direction.
200-period EMA: Filters long-term trends, ensuring entries occur in a bullish market.
Interaction: Positions are entered only if the price is above both EMAs, indicating a consolidated positive trend.
Relative Strength Index (RSI):
Description: 7-period RSI with a threshold above 30.
Role in the Strategy: Confirms the market is not oversold, supporting the validity of the buy signal.
Average Directional Index (ADX):
Description: 7-period ADX with 7-period smoothing and a threshold above 10.
Role in the Strategy: Assesses trend strength. An ADX above 10 indicates sufficient momentum to justify entry.
Average True Range (ATR) for Dynamic Stop Loss and Take Profit:
Description: 14-period ATR with multipliers of 2.0 for Stop Loss and 4.0 for Take Profit.
Role in the Strategy: Adjusts exit levels based on current volatility, enhancing risk management.
Resistance Identification and Breakout:
Description: Analyzes the highs of the last 20 candles to identify resistance levels with at least two touches.
Role in the Strategy: A breakout above this level signals a potential continuation of the bullish trend.
Time Filters and Market Conditions:
Trading Hours: Operates only during the opening of the German market (8:00 - 12:00) and US market (15:30 - 19:00).
Panic Candle: The current candle must close negative, leveraging potential emotional reactions in the market.
Avoiding Entry During Pullbacks:
Description: Checks that the two previous candles are not both bearish.
Role in the Strategy: Avoids entering during a potential pullback, improving trade success probability.
Entry and Exit Conditions:
Long Entry:
The price breaks above the identified resistance.
The market is in a lateralization phase with low volatility.
The price is above the 10 and 200-period EMAs.
RSI is above 30, and ADX is above 10.
No short-term downtrend is detected.
The last two candles are not both bearish.
The current candle is a "panic candle" (negative close).
Order Execution: The order is executed at the close of the candle that meets all conditions.
Exit from Position:
Dynamic Stop Loss: Set at 2 times the ATR below the entry price.
Dynamic Take Profit: Set at 4 times the ATR above the entry price.
The position is automatically closed upon reaching the Stop Loss or Take Profit.
How to Use the Strategy:
Application on Volatile Instruments:
Ideal for financial instruments that show significant volatility during the target market opening hours, such as indices or major forex pairs.
Recommended Timeframes:
Intraday timeframes, such as 5 or 15 minutes, to capture significant post-open moves.
Parameter Customization:
The default parameters are optimized but can be adjusted based on individual preferences and the instrument analyzed.
Backtesting and Optimization:
Backtesting is recommended to evaluate performance and make adjustments if necessary.
Risk Management:
Ensure position sizing respects risk management rules, avoiding risking more than 1-2% of capital per trade.
Originality and Benefits of the Strategy:
Unique Combination of Indicators: Integrates various technical metrics to filter signals, reducing false positives.
Volatility Adaptability: The use of ATR for Stop Loss and Take Profit allows the strategy to adapt to real-time market conditions.
Focus on Post-Lateralization Breakout: Aims to capitalize on significant moves following consolidation periods, often associated with strong directional trends.
Important Notes:
Commissions and Slippage: Include commissions and slippage in settings for more realistic simulations.
Capital Size: Use a realistic trading capital for the average user.
Number of Trades: Ensure backtesting covers a sufficient number of trades to validate the strategy (ideally more than 100 trades).
Warning: Past results do not guarantee future performance. The strategy should be used as part of a comprehensive trading approach.
With this strategy, traders can identify and exploit specific market opportunities supported by a robust set of technical indicators and filters, potentially enhancing their trading decisions during key times of the day.
Premium PRISM Algorithm [ByteBoost]The ByteBoost PRISM strategy is designed to operate in various market conditions by leveraging the concept of brownian motion theory, which refers to the unpredictable movement of particles suspended in a fluid. This characteristic of random motion can be effectively utilized for analyzing time series data, such as market candles. Based on this notion, we are making the following assumptions regarding the market.
The stock price exhibits characteristics of Brownian motion.
The stock price is distributed in a log-normal pattern.
Volatility remains constant over time.
Options can only be exercised upon expiration.
Risk-free interest does not fluctuate over time.
There are no random or arbitrary opportunities present in the market.
Development Notes
This Strategy was developed with the PineScript language, version 5. This indicator, and most of the descriptions below, were derived largely from the TradingView reference manual. Feedback and suggestions for improvement are more than welcome, as well as recommended input settings and best practices to assist and guide new users effectively.
Features
The ByteBoost PRISM indicator is capable of analyzing multiple aspects of market behavior simultaneously such as:
Detection of potential trend reversals.
Assessment of trend strength and market sentiment.
Identification of stop loss levels.
Discovery of potential entry and exit points.
Ensuring compatibility and effectiveness with other indicators.
Visualization of strategy using historical data.
Customization options available.
Strategy Description
PRISM is an all in one strategy that allows the visualization of entry and exit points as well as the historical performance for every set of parameters. PRISM is a slow paced indicator recommended for the 1h timeframe, because it operates on the belief that markets move in a Brownian motion, for which it leaves enough space and time for the market to decide a trend and catch it at the right time as well as finding appropriate exits given the trend.
PRISM can exist in either an uptrend or downtrend state, but it does not necessarily imply that it reflects the true trend being observed. Instead, it emphasizes capturing significant movements and capitalizing on them by utilizing oscillator levels and exit points calculated based on oversold or overbought values, along with the volatility associated with these movements.
Usage
To use this strategy it is first needed to select a correct set of inputs that correspond to the market you are using, the extra, win difference and oscillator length are dependent on the current market and the average price it manages, so these inputs need to be modified for every pair of assets used.
The long and short tags signify the opportune moment to initiate a new position in the market, whether it's a long or short position, respectively. The exit tags indicate when these positions should be closed. If no exits occur before a new long or short position emerges, it is essential to conclude the existing position and commence a new one in the opposite direction.
Regarding exits, up to two exits can be executed for each movement. The user has the flexibility to determine how these exits are utilized. In the input section, a specific percentage of equity can be selected to be sold during the first exit. If set to 100%, only a single exit will be presented. Otherwise, the remaining equity will be sold during the second exit or at the next trend reversal depending on which action occurs first.
In case the user requires additional exits beyond the initial two, the alternative exits option can be activated in the inputs. This will provide access to supplementary exits, although they may be less advisable compared to the primary exits.
Inputs / Settings
Capital - If using any leverage multiply the amount of money to invest by the leverage, else input the amount to be invested in every trade.
Start date - The date from which the strategy should begin its analysis. Leave unchanged to start from the earliest available date based on your account's plan.
End date - The date until which the strategy should conduct its analysis. Leave unchanged to continue until the current date.
Extra - The minimum gain required in the market to trigger an exit opportunity. It can be a negative number to allow exits at a loss, potentially minimizing losses.
First exit % - If an exit is decided to be partial, it is very likely that there will be a second exit, this parameter determines the percentage of equity to be sold at the first exit. Note that a second exit may not always be applicable.
Win difference - The minimum difference between the entry point and the first exit to determine whether it should be a full exit or a partial exit, as the exit price approaches the entry price, the probability of a trend reversal increases, a full exit is beneficial.
Limit length - Specifies the number of candles to consider for the overbought and oversold market calculation.
Low limit - Sets the minimum value of the limit to decide a short exit.
High limit - Sets the maximum value of the limit to decide a long exit.
Band length - Determines the number of candles to consider for the volatility analysis.
Band height - Sets the multiplication factor of the band to set the maximum and minimum height.
Increment - Determines the rate at which trend reversals occur. A higher value brings the line closer to the current price faster.
Candles exit - Specifies the minimum number of candles required to pass for an exit to become available after initiating a new position.
Oscillator - Enables or disables the main oscillator, which helps determine entry points. Not all assets may benefit from this parameter.
Oscillator length - Specifies the number of candles considered for the entry points oscillator.
Highlighter - Applies a light color between the trend and average price of each bar.
Trend Labels - Displays labels indicating an uptrend or downtrend.
Signal Labels - View the labels indicating a new long or short position.
Exit Labels - Displays the labels indicating exit points.
Candle color - Color codes the inside of the candles with the current signal.
Cloud - Visualize the average price cloud to determine trend direction.
Oscillator points - Adds visual dots to indicate when the oscillator has changed its trend.
Oscillator line - Displays the values of the oscillator to indicate upcoming trend changes.
Alternative exits - Shows additional exits to the ones we recommend, useful if the user missed an exit or needs to have more than two.
Color uptrend - Determines the color scheme for identifying uptrend movements.
Color downtrend - Determines the color scheme for identifying downtrend movements.
Color long - Sets the color scheme for a new long position.
Color short - Sets the color scheme for a new short position.
Color exit - Decides the color scheme for the exit tag and cross shown.
Color alternative exit - Changes the color scheme for the alternative exit cross.
Color oscillator line - Determines the color scheme used for the oscillator line.
Indicator Visuals
The strategy plots the direction of the trend on the chart and changes its color based on this. It also plots shapes on the chart to denote potential buy (Long) and sell (Short) points, where the signals of short and long will appear as well as exit points which can be found as three different,
Exit 1 - A partial exit which sells the previously selected percentage of equity.
Exit 2 - A second exit that can only happen after an Exit 1 has happened, and sell the remaining amount of equity.
Exit Full - A full exit is executed when the price at the exit point is lower than the entry price plus the win difference value. This condition indicates that it is more advantageous to take a single exit rather than waiting for a second exit.
Strategy Alerts
The strategy does not include built-in alerts. However, alerts can be added using the TradingView interface based on the strategy's buy and sell conditions. This way you will be able to receive notifications on your computer or phone when a new signal goes out.
Details
Repainting: It is important to mention that the strategy can mark false long or short signals, as the oscillator is allowed to repaint on the same candle. So users must make sure the candle has closed on buy/sell conditions.
Excessive capital issue: If you configure the strategy with a big amount of capital (+$1,000,000 for example) it is possible that it will completely stop calculating exit signals, as they will be too big for TradingView’s engine to process.
Conclusion
The ByteBoost PRISM strategy empowers traders by providing comprehensive market analysis, clear entry and exit signals, and the ability to visualize strategy performance using historical data. It is a superior algorithm that maximizes profit potential and minimizes risks, making it the preferred choice for traders seeking a competitive edge in the financial markets.
Disclaimer
This strategy is provided as-is, with no guarantee of profits or responsibility for losses. Trading involves risk, and you should only trade with money you can afford to lose. Always conduct your own research and consider your financial situation before engaging in trading.
v2.0—Tristan's Multi-Indicator Reversal Strategy🎯 Multi-Indicator Reversal Strategy - Optimized for High Win Rates
A powerful confluence-based strategy that combines RSI, MACD, Williams %R, Bollinger Bands, and Volume analysis to identify high-probability reversal points . Designed to let winners run with no stop loss or take profit - positions close only when opposite signals occur.
Also, the 3 hour timeframe works VERY well—just a lot less trades.
📈 Proven Performance
This strategy has been backtested and optimized on multiple blue-chip stocks with 80-90%+ win rates on 1-hour timeframes from Aug 2025 through Oct 2025:
✅ V (Visa) - Payment processor
✅ MSFT (Microsoft) - Large-cap tech
✅ WMT (Walmart) - Retail leader
✅ IWM (Russell 2000 ETF) - Small-cap index
✅ NOW (ServiceNow) - Enterprise software
✅ WM (Waste Management) - Industrial services
These stocks tend to mean-revert at extremes, making them ideal candidates for this reversal-based approach. I only list these as a way to show you the performance of the script. These values and stock choices may change over time as the market shifts. Keep testing!
🔑 How to Use This Strategy Successfully
Step 1: Apply to Chart
Open your desired stock (V, MSFT, WMT, IWM, NOW, WM recommended)
Set timeframe to 1 Hour
Apply this strategy
Check that the Williams %R is set to -20 and -80, and "Flip All Signals" is OFF (can flip this for some stocks to perform better.)
Step 2: Understand the Signals
🟢 Green Triangle (BUY) Below Candle:
Multiple indicators (RSI, Williams %R, MACD, Bollinger Bands) show oversold conditions
Enter LONG position
Strategy will pyramid up to 10 entries if more buy signals occur
Hold until red triangle appears
🔴 Red Triangle (SELL) Above Candle:
Multiple indicators show overbought conditions
Enter SHORT position (or close existing long)
Strategy will pyramid up to 10 entries if more sell signals occur
Hold until green triangle appears
🟣 Purple Labels (EXIT):
Shows when positions close
Displays count if multiple entries were pyramided (e.g., "Exit Long x5")
Step 3: Let the Strategy Work
Key Success Principles:
✅ Be Patient - Signals don't occur every day, wait for quality setups
✅ Trust the Process - Don't manually close positions, let opposite signals exit
✅ Watch Pyramiding - The strategy can add up to 10 positions in the same direction
✅ No Stop Loss - Positions ride through drawdowns until reversal confirmed
✅ Session Filter - Only trades during NY session (9:30 AM - 4:00 PM ET)
⚙️ Winning Settings (Already Set as Defaults)
INDICATOR SETTINGS:
- RSI Length: 14
- RSI Overbought: 70
- RSI Oversold: 30
- MACD: 12, 26, 9 (standard)
- Williams %R Length: 14
- Williams %R Overbought: -20 ⭐ (check this! And adjust to your liking)
- Williams %R Oversold: -80 ⭐ (check this! And adjust to your liking)
- Bollinger Bands: 20, 2.0
- Volume MA: 20 periods
- Volume Multiplier: 1.5x
SIGNAL REQUIREMENTS:
- Min Indicators Aligned: 2
- Require Divergence: OFF
- Require Volume Spike: OFF
- Require Reversal Candle: OFF
- Flip All Signals: OFF ⭐
RISK MANAGEMENT:
- Use Stop Loss: OFF ⭐⭐⭐
- Use Take Profit: OFF ⭐⭐⭐
- Allow Pyramiding: ON ⭐⭐⭐
- Max Pyramid Entries: 10 ⭐⭐⭐
SESSION FILTER:
- Trade Only NY Session: ON
- NY Session: 9:30 AM - 4:00 PM ET
**⭐ = Critical settings for success**
## 🎓 Strategy Logic Explained
### **How It Works:**
1. **Multi-Indicator Confluence**: Waits for at least 2 out of 4 technical indicators to align before generating signals
2. **Oversold = Buy**: When RSI < 30, Williams %R < -80, price below lower Bollinger Band, and/or MACD turning bullish → BUY signal
3. **Overbought = Sell**: When RSI > 70, Williams %R > -20, price above upper Bollinger Band, and/or MACD turning bearish → SELL signal
4. **Pyramiding Power**: As trend continues and more signals fire in the same direction, adds up to 10 positions to maximize gains
5. **Exit Only on Reversal**: No arbitrary stops or targets - only exits when opposite signal confirms trend change
6. **Session Filter**: Only trades during liquid NY session hours to avoid overnight gaps and low-volume periods
### **Why No Stop Loss Works:**
Traditional reversal strategies fail because they:
- Get stopped out too early during normal volatility
- Miss the actual reversal that happens later
- Cut winners short with tight take profits
This strategy succeeds because it:
- ✅ Rides through temporary noise
- ✅ Captures full reversal moves
- ✅ Uses multiple indicators for confirmation
- ✅ Pyramids into winning positions
- ✅ Only exits when technical picture completely reverses
---
## 📊 Understanding the Display
**Live Indicator Counter (Top Corner / end of current candles):**
Bull: 2/4
Bear: 0/4
(STANDARD)
Shows how many indicators currently align bullish/bearish
"STANDARD" = normal reversal mode (buy oversold, sell overbought)
"FLIPPED" = momentum mode if you toggle that setting
Visual Indicators:
🔵 Blue background = NY session active (trading window)
🟡 Yellow candle tint = Volume spike detected
💎 Aqua diamond = Bullish divergence (price vs RSI)
💎 Fuchsia diamond = Bearish divergence
⚡ Advanced Tips
Optimizing for Different Stocks:
If Win Rate is Low (<50%):
Try toggling "Flip All Signals" to ON (switches to momentum mode)
Increase "Min Indicators Aligned" to 3 or 4
Turn ON "Require Divergence"
Test on different timeframe (4-hour or daily)
If Too Few Signals:
Decrease "Min Indicators Aligned" to 2
Turn OFF all requirement filters
Widen Williams %R bands to -15 and -85
If Too Many False Signals:
Increase "Min Indicators Aligned" to 3 or 4
Turn ON "Require Divergence"
Turn ON "Require Volume Spike"
Reduce Max Pyramid Entries to 5
Stock Selection Guidelines:
Best Suited For:
Large-cap stable stocks (V, MSFT, WMT)
ETFs (IWM, SPY, QQQ)
Stocks with clear support/resistance
Mean-reverting instruments
Avoid:
Ultra low-volume penny stocks
Extremely volatile crypto (try traditional settings first)
Stocks in strong one-directional trends lasting months
🔄 The "Flip All Signals" Feature
If backtesting shows poor results on a particular stock, try toggling "Flip All Signals" to ON:
STANDARD Mode (OFF):
Buy when oversold (reversal strategy)
Sell when overbought
May work best for: V, MSFT, WMT, IWM, NOW, WM
FLIPPED Mode (ON):
Buy when overbought (momentum strategy)
Sell when oversold
May work best for: Strong trending stocks, momentum plays, crypto
Test both modes on your stock to see which performs better!
📱 Alert Setup
Create alerts to notify you of signals:
📊 Performance Expectations
With optimized settings on recommended stocks:
Typical results we are looking for:
Win Rate: 70-90%
Average Winner: 3-5%
Average Loser: 1-3%
Signals Per Week: 1-3 on 1-hour timeframe
Hold Time: Several hours to days
Remember: Past performance doesn't guarantee future results. Always use proper risk management.
Universal Breakout Strategy [KedArc Quant]Description:
A flexible breakout framework where you can test different logics (Prev Day, Bollinger, Volume, ATR, EMA Trend, RSI Confirm, Candle Confirm, Time Filter) under one system.
Choose your breakout mode, and the strategy will handle entries, exits, and optional risk management (ATR stops, take-profits, daily loss guard, cooldowns).
An on-chart info table shows live mode values (like Prev High/Low, Bollinger levels, RSI, etc.) plus P&L stats for quick analysis.
Use it to compare which breakout style works best on your instrument and timeframe, whether intraday, swing, or positional trading
🔑 Why it’s useful
* Flexibility: Switch between breakout strategies without loading different indicators.
* Clarity: On-chart info table displays current mode, relevant indicator levels, and live strategy P&L stats.
* Testing efficiency: Quickly A/B test different breakout styles under the same backtest environment.
* Transparency: Every trade is rule-based and displayed with entry/exit markers.
🚀 How it helps traders
* Lets you experiment with breakout strategies quickly without loading multiple scripts.
* Helps identify which breakout method fits your instrument & timeframe.
* Gives clear on-chart visual + statistical feedback for confident decision-making.
⚙️ Input Configuration
* Breakout Mode → choose which strategy to test:
* *Prev Day* → breakouts of yesterday’s High/Low.
* *Bollinger* → Upper/Lower BB pierce.
* *Volume* → Breakout confirmed with volume above average.
* *ATR Stop* → Wide range breakout using ATR filter.
* *Time Filter* → Breakouts inside defined session hours.
* *EMA Trend* → Breakouts only in EMA fast > slow alignment.
* *RSI Confirm* → Breakouts with RSI confirmation (e.g. >55 for longs).
* *Candle Confirm* → Breakouts validated by bullish/bearish candle.
* Lookback / ATR / Bollinger inputs → adjust sensitivity.
* Intrabar mode → option to evaluate breakouts using bar highs/lows instead of closes.
* Table options → show/hide info table, show/hide P&L stats, choose corner placement.
📈 Entry & Exit Logic
* Entry → occurs when breakout condition of chosen mode is met.
* Exit → default exits via opposite signals or optional stop/target if enabled.
* Session filter → optional auto-flat at session end.
* P&L management → optional daily loss guard, cooldown between trades, and ATR-based stop/take profit.
❓ FAQ — Choosing the best setup
Q: Which strategy should I use for which chart?
* *Prev Day Breakouts*: Best on indices, FX, and liquid futures with strong daily levels.
* *Bollinger*: Works well in range-bound environments, or crypto pairs with volatility compression.
* *Volume*: Good on equities where breakout strength is tied to volume spikes.
* *ATR Stop*: Suits volatile instruments (commodities, crypto).
* *EMA Trend*: Useful in trending markets (stocks, indices).
* *RSI Confirm*: Adds momentum filter, better for swing trades.
* *Candle Confirm*: Ideal for scalpers needing visual confirmation.
* *Time Filter*: For intraday traders who want signals only in high-liquidity sessions.
Q: What timeframe should I use?
* Intraday traders → 5m to 15m (Time Filter, Candle Confirm).
* Swing traders → 1H to 4H (EMA Trend, RSI Confirm, ATR Stop).
* Position traders → Daily (Prev Day, Bollinger).
* Breakout
A trade entry condition triggered when price crosses above a resistance level (for longs) or below a support level (for shorts).
* Prev Day High/Low
Formula:
Prev High = High of (Day )
Prev Low = Low of (Day )
* Bollinger Bands
Formula:
Basis = SMA(Close, Length)
Upper Band = Basis + (Multiplier × StdDev(Close, Length))
Lower Band = Basis – (Multiplier × StdDev(Close, Length))
* Volume Confirmation
A breakout is only valid if:
Volume > SMA(Volume, Length)
* ATR (Average True Range)
Measures volatility.
Formula:
ATR = SMA(True Range, Length)
where True Range = max(High–Low, |High–Close |, |Low–Close |)
* EMA (Exponential Moving Average)
Weighted moving average giving more weight to recent prices.
Formula:
EMA = (Price × α) + (EMA × (1–α))
with α = 2 / (Length + 1)
* RSI (Relative Strength Index)
Momentum oscillator scaled 0–100.
Formula:
RSI = 100 – (100 / (1 + RS))
where RS = Avg(Gain, Length) ÷ Avg(Loss, Length)
* Candle Confirmation
Bullish candle: Close > Open AND Close > Close
Bearish candle: Close < Open AND Close < Close
Win Rate (%)
Formula:
Win Rate = (Winning Trades ÷ Total Trades) × 100
* Average Trade P&L
Formula:
Avg Trade = Net Profit ÷ Total Trades
📊 Performance Notes
The Universal Breakout Strategy is designed as a framework rather than a single-asset optimized system. Results will vary depending on the chart, timeframe, and asset chosen.
On the current defaults (15-minute, INR-denominated example), the backtest produced 132 trades over the selected period. This provides a statistically sufficient sample size.
Win rate (~35%) is relatively low, but this is balanced by a positive reward-to-risk ratio (~1.8). In practice, a lower win rate with larger wins versus smaller losses is sustainable.
The average P&L per trade is close to breakeven under default settings. This is expected, as the strategy is not tuned for a single symbol but offered as a universal breakout framework.
Commissions (0.1%) and slippage (1 tick) are included in the simulation, ensuring realistic conditions.
Risk management is conservative, with order sizing set at 1 unit per trade. This avoids over-leveraging and keeps exposure well under the 5-10% equity risk guideline.
👉 Traders are encouraged to:
Experiment with inputs such as ATR period, breakout length, or Bollinger parameters.
Test across different timeframes and instruments (equities, futures, forex, crypto) to find optimal setups.
Combine with filters (trend direction, volatility regimes, or volume conditions) for further refinement.
⚠️ Disclaimer This script is provided for educational purposes only.
Past performance does not guarantee future results.
Trading involves risk, and users should exercise caution and use proper risk management when applying this strategy.
Sunil BB Blast Heikin Ashi StrategySunil BB Blast Heikin Ashi Strategy
The Sunil BB Blast Heikin Ashi Strategy is a trend-following trading strategy that combines Bollinger Bands with Heikin-Ashi candles for precise market entries and exits. It aims to capitalize on price volatility while ensuring controlled risk through dynamic stop-loss and take-profit levels based on a user-defined Risk-to-Reward Ratio (RRR).
Key Features:
Trading Window:
The strategy operates within a user-defined time window (e.g., from 09:20 to 15:00) to align with market hours or other preferred trading sessions.
Trade Direction:
Users can select between Long Only, Short Only, or Long/Short trade directions, allowing flexibility depending on market conditions.
Bollinger Bands:
Bollinger Bands are used to identify potential breakout or breakdown zones. The strategy enters trades when price breaks through the upper or lower Bollinger Band, indicating a possible trend continuation.
Heikin-Ashi Candles:
Heikin-Ashi candles help smooth price action and filter out market noise. The strategy uses these candles to confirm trend direction and improve entry accuracy.
Risk Management (Risk-to-Reward Ratio):
The strategy automatically adjusts the take-profit (TP) level and stop-loss (SL) based on the selected Risk-to-Reward Ratio (RRR). This ensures that trades are risk-managed effectively.
Automated Alerts and Webhooks:
The strategy includes automated alerts for trade entries and exits. Users can set up JSON webhooks for external execution or trading automation.
Active Position Tracking:
The strategy tracks whether there is an active position (long or short) and only exits when price hits the pre-defined SL or TP levels.
Exit Conditions:
The strategy exits positions when either the take-profit (TP) or stop-loss (SL) levels are hit, ensuring risk management is adhered to.
Default Settings:
Trading Window:
09:20-15:00
This setting confines the strategy to the specified hours, ensuring trading only occurs during active market hours.
Strategy Direction:
Default: Long/Short
This allows for both long and short trades depending on market conditions. You can select "Long Only" or "Short Only" if you prefer to trade in one direction.
Bollinger Band Length (bbLength):
Default: 19
Length of the moving average used to calculate the Bollinger Bands.
Bollinger Band Multiplier (bbMultiplier):
Default: 2.0
Multiplier used to calculate the upper and lower bands. A higher multiplier increases the width of the bands, leading to fewer but more significant trades.
Take Profit Multiplier (tpMultiplier):
Default: 2.0
Multiplier used to determine the take-profit level based on the calculated stop-loss. This ensures that the profit target aligns with the selected Risk-to-Reward Ratio.
Risk-to-Reward Ratio (RRR):
Default: 1.0
The ratio used to calculate the take-profit relative to the stop-loss. A higher RRR means larger profit targets.
Trade Automation (JSON Webhooks):
Allows for integration with external systems for automated execution:
Long Entry JSON: Customizable entry condition for long positions.
Long Exit JSON: Customizable exit condition for long positions.
Short Entry JSON: Customizable entry condition for short positions.
Short Exit JSON: Customizable exit condition for short positions.
Entry Logic:
Long Entry:
The strategy enters a long position when:
The Heikin-Ashi candle shows a bullish trend (green close > open).
The price is above the upper Bollinger Band, signaling a breakout.
The previous candle also closed higher than it opened.
Short Entry:
The strategy enters a short position when:
The Heikin-Ashi candle shows a bearish trend (red close < open).
The price is below the lower Bollinger Band, signaling a breakdown.
The previous candle also closed lower than it opened.
Exit Logic:
Take-Profit (TP):
The take-profit level is calculated as a multiple of the distance between the entry price and the stop-loss level, determined by the selected Risk-to-Reward Ratio (RRR).
Stop-Loss (SL):
The stop-loss is placed at the opposite Bollinger Band level (lower for long positions, upper for short positions).
Exit Trigger:
The strategy exits a trade when either the take-profit or stop-loss level is hit.
Plotting and Visuals:
The Heikin-Ashi candles are displayed on the chart, with green candles for uptrends and red candles for downtrends.
Bollinger Bands (upper, lower, and basis) are plotted for visual reference.
Entry points for long and short trades are marked with green and red labels below and above bars, respectively.
Strategy Alerts:
Alerts are triggered when:
A long entry condition is met.
A short entry condition is met.
A trade exits (either via take-profit or stop-loss).
These alerts can be used to trigger notifications or webhook events for automated trading systems.
Notes:
The strategy is designed for use on intraday charts but can be applied to any timeframe.
It is highly customizable, allowing for tailored risk management and trading windows.
The Sunil BB Blast Heikin Ashi Strategy combines two powerful technical analysis tools (Bollinger Bands and Heikin-Ashi candles) with strong risk management, making it suitable for both beginners and experienced traders.
Feebacks are welcome from the users.
Basic PRISM Algorithm [ByteBoost]The Basic ByteBoost PRISM strategy is designed to operate in various market conditions by leveraging the concept of brownian motion theory, which refers to the unpredictable movement of particles suspended in a fluid. This characteristic of random motion can be effectively utilized for analyzing time series data, such as market candles. Based on this notion, we are making the following assumptions regarding the market.
The stock price exhibits characteristics of Brownian motion.
The stock price is distributed in a log-normal pattern.
Volatility remains constant over time.
Options can only be exercised upon expiration.
Risk-free interest does not fluctuate over time.
There are no random or arbitrary opportunities present in the market.
Development Notes
This Strategy was developed with the PineScript language, version 5. This indicator, and most of the descriptions below, were derived largely from the TradingView reference manual. Feedback and suggestions for improvement are more than welcome, as well as recommended input settings and best practices to assist and guide new users effectively.
Features
The ByteBoost PRISM indicator is capable of analyzing multiple aspects of market behavior simultaneously such as:
Detection of potential trend reversals.
Assessment of trend strength and market sentiment.
Identification of stop loss levels.
Discovery of potential entry and exit points.
Ensuring compatibility and effectiveness with other indicators.
Visualization of strategy using historical data.
Strategy Description
PRISM is an all in one strategy that allows the visualization of entry and exit points as well as the historical performance for every set of parameters. PRISM is a slow paced indicator recommended for the 1h timeframe, because it operates on the belief that markets move in a Brownian motion, for which it leaves enough space and time for the market to decide a trend and catch it at the right time as well as finding appropriate exits given the trend.
PRISM can exist in either an uptrend or downtrend state, but it does not necessarily imply that it reflects the true trend being observed. Instead, it emphasizes capturing significant movements and capitalizing on them by utilizing oscillator levels and exit points calculated based on oversold or overbought values, along with the volatility associated with these movements.
Usage
To use this strategy it is first needed to select a correct set of inputs that correspond to the market you are using, the extra, win difference and oscillator length are dependent on the current market and the average price it manages, so these inputs need to be modified for every pair of assets used.
The long and short tags signify the opportune moment to initiate a new position in the market, whether it's a long or short position, respectively. The exit tags indicate when these positions should be closed. If no exits occur before a new long or short position emerges, it is essential to conclude the existing position and commence a new one in the opposite direction.
Regarding exits, up to two exits can be executed for each movement. The user has the flexibility to determine how these exits are utilized. In the input section, a specific percentage of equity can be selected to be sold during the first exit. If set to 100%, only a single exit will be presented. Otherwise, the remaining equity will be sold during the second exit or at the next trend reversal depending on which action occurs first.
In case the user requires additional exits beyond the initial two, the alternative exits option can be activated in the inputs. This will provide access to supplementary exits, although they may be less advisable compared to the primary exits.
Inputs / Settings
Capital - If using any leverage multiply the amount of money to invest by the leverage, else input the amount to be invested in every trade.
Start date - The date from which the strategy should begin its analysis. Leave unchanged to start from the earliest available date based on your account's plan.
End date - The date until which the strategy should conduct its analysis. Leave unchanged to continue until the current date.
Extra - The minimum gain required in the market to trigger an exit opportunity. It can be a negative number to allow exits at a loss, potentially minimizing losses.
First exit % - If an exit is decided to be partial, it is very likely that there will be a second exit, this parameter determines the percentage of equity to be sold at the first exit. Note that a second exit may not always be applicable.
Win difference - The minimum difference between the entry point and the first exit to determine whether it should be a full exit or a partial exit, as the exit price approaches the entry price, the probability of a trend reversal increases, a full exit is beneficial.
Oscillator - Enables or disables the main oscillator, which helps determine entry points. Not all assets may benefit from this parameter.
Oscillator length - Specifies the number of candles considered for the entry points oscillator.
Highlighter - Applies a light color between the trend and average price of each bar.
Labels - Displays all the labels on the chart indicating trends, positions and exits.
Candle color - Color codes the inside of the candles with the current signal.
Oscillator points - Adds visual dots to indicate when the oscillator has changed its trend.
Color uptrend - Determines the color scheme for identifying uptrend movements.
Color downtrend - Determines the color scheme for identifying downtrend movements.
Color long - Sets the color scheme for a new long position.
Color short - Sets the color scheme for a new short position.
Color exit - Decides the color scheme for the exit tag and cross shown.
Indicator Visuals
The strategy plots the direction of the trend on the chart and changes its color based on this. It also plots shapes on the chart to denote potential buy (Long) and sell (Short) points, where the signals of short and long will appear as well as exit points which can be found as three different,
Exit 1 - A partial exit which sells the previously selected percentage of equity.
Exit 2 - A second exit that can only happen after an Exit 1 has happened, and sell the remaining amount of equity.
Exit Full - A full exit is executed when the price at the exit point is lower than the entry price plus the win difference value. This condition indicates that it is more advantageous to take a single exit rather than waiting for a second exit.
Strategy Alerts
The strategy does not include built-in alerts. However, alerts can be added using the TradingView interface based on the strategy's buy and sell conditions. This way you will be able to receive notifications on your computer or phone when a new signal goes out.
Details
Repainting: It is important to mention that the strategy can mark false long or short signals, as the oscillator is allowed to repaint on the same candle. So users must make sure the candle has closed on buy/sell conditions.
Excessive capital issue: If you configure the strategy with a big amount of capital (+$1,000,000 for example) it is possible that it will completely stop calculating exit signals, as they will be too big for TradingView’s engine to process.
Conclusion
The ByteBoost PRISM strategy empowers traders by providing comprehensive market analysis, clear entry and exit signals, and the ability to visualize strategy performance using historical data. It is a superior algorithm that maximizes profit potential and minimizes risks, making it the preferred choice for traders seeking a competitive edge in the financial markets.
Disclaimer
This strategy is provided as-is, with no guarantee of profits or responsibility for losses. Trading involves risk, and you should only trade with money you can afford to lose. Always conduct your own research and consider your financial situation before engaging in trading.
Tick StrategyTick Strategy:
Questions many pine coders/traders have is, How to enter/exit trade as soon as trade condition is met i.e. do not wait till candle completion to enter/exit the trade. This strategy will help you to understand one of the way to achieve it.
This is an educational strategy to demonstrate, how one can trade based on tick data. This being a strategy based on tick data, it can be tested only on real time candles. This strategy will not take any trades on historical candles and cannot be used for back testing. All the strategy trades taken on real time candles will disappear (repainting) once chart is refreshed and new trades will be entered on real time candles.
The strategy will do nothing during off market hours and will not take any trades.
The strategy has been designed based on rules/inputs below:
1. Count the ticks from start of a candle till end of candle
2. Bifurcate ticks as up-ticks and down-ticks. If tick price is above previous tick price the tick is considered as up-tick and vice versa
3. Count the successive up-ticks and successive down-ticks
Strategy rules:
1. Track candle type (green or red) continuously on each tick (green candle is when latest tick price > previous tick price)
2. Take a long trade if work in progress (WIP) candle is green candle and we get successive up-ticks equal to user input ticks for trade
3. Take a short trade if work in progress (WIP) candle is red candle and we get successive down-ticks equal to user input ticks for trade
4. Exit the trade when we get successive ticks equal to user input ticks in opposite direction
5. Optionally for trade entry, user can decide whether to calculate successive up-ticks/down-ticks from beginning of candle or successive up-ticks/down-ticks anytime during the candle formation
6. Optionally for trade exit/square off, user can decide whether to apply exit rules on the entry candle or only from subsequent candle
Strategy setting:
1. '' – This is just to describe when trades are entered. This parameter is not used for any calculation
2. 'No of successive ticks to enter the trade' – User input to decide, number of successive ticks for trade entry
3. 'Count successive ticks for trade only from start of candle' – check this to count successive ticks only from beginning of a candle
4. 'Exit if succussive ticks in opposite direction' - User input to decide, number of successive ticks in opposite direction for exiting the trade
5. 'Apply exit criteria on entry candle' – check to allow exit of trade on the entry candle, if un-checked, trade will not be exited on the entry candle i.e. opposite direction ticks will be counted from subsequent candle
Information below will be displayed continuously on the chart:
1. Candle no – Candles are counted from start of the trading session. This is current candle being formed on the chart
2. Candle now – This shows either ‘Green’ or ‘Red’ based on type of candle being formed
3. Tick count – This is current tick number being processed. Tick number starts from 1 for each new candle
4. Up-tick count – Number of up-ticks during formation of current candle
5. Down-tick count – Number of down-ticks during formation of current candle
6. Successive up-ticks – Current successive up-tick count
7. Successive down-ticks – Current successive down-tick count
8. Up-tick volume – Volume associated with up-ticks
9. Down-tick volume – Volume associated with down-ticks
10. Up-tick volume % - This is % of volume associated with up-ticks
11. Total volume – Candle volume till now. (Some times you might observe small difference between total volume and the volume shown by volume indicator. The difference could be because of refresh rate of your screen)
12. Candle completion % - This shows current candles completion %. This is candle progress from start of candle till close of candle
GT 5.1 Strategy═════════════════════════════════════════════════════════════════════════
█ OVERVIEW
People often look an indicator in their technical analysis to enter a position. We may also need to look at the signals of one or more indicators to verify the signals given by some indicators. In this context, I developed a strategy to test whether it really works by choosing some of the indicators that capture trend changes with the same characteristics. Also, since the subject is to catch the trend change, I thought it would be right to include an indicator using the heikin ashi logic. By averaging and smoothing the market noise, Heiken Ashi makes it easier to detect the direction of the trend helps to see possible reversal points on the chart. However, it should be noted that Heiken Ashi is a lagging indicator.
I picked 5 different indicators (but their purpose are similar) and combined them to produce buy and sell signals based on your choice(not repaint). First of all let's get some information about our indicators. So you will understand me why i picked these indicators and what is the meaning of their signals.
1 — Coral Trend Indicator by LazyBear
Coral Trend Indicator is a linear combination of moving averages, all obtained by a triple or higher order exponential smoothing. The indicator comes with a trend indication which is based on the normalized slope of the plot. the usage of this indicator is simple. When the color of the line is green that means the market is in uptrend. But when the color is red that means the market is in downtrend.
As you see the original indicator it is simple to find is it in uptrend or downtrend.
So i added a code to find when the color of the line change. When it turns green to red my script giving sell signals, when it turns red to green it gives buy signals.
I hide the candles to show you more clearly what is happening when you choose only Coral Strategy. But sometimes it is not enough only using itself. Even if green dots turn to red it continues in uptrend. So we need a to look another indicator to approve our signal.
2 — SSL channel by ErwinBeckers
Known as the SSL , the Semaphore Signal Level channel is an indicator that combines moving averages to provide you with a clear visual signal of price movement dynamics. In short, it's designed to show you when a price trend is forming. This indicator creates a band by calculating the high and low values according to the determined period. Simply if you decide 10 as period, it calculates a 10-period moving average on the latest 10 highs. Calculate a 10-period moving average on the latest 10 lows. If the price falls below the low band, the downtrend begins, if the price closes above the high band, the uptrend begins. Lets look the original form of indicator and learn how it using.
If the red line is below and the green band is above, it means that we are in uptrend, and if it is on the opposite side, it means that we are in downtrend. Therefore, it would be logical to enter a position where the trend has changed. So i added a code to find when the crossover has occured.
As you see in my strategy, it gives you signals when the trend has changed. But sometimes it is not enough only using this indicator itself. So lets look 2 indicator together in one chart.
Look circle SSL is saying it is in downtrend but Coral is saying it has entered in uptrend. if we just look to coral signal it can misleads us. So it can be better to look another indicator for validating our signals.
3 — Heikin Ashi RSI Oscillator by JayRogers
The Heikin-Ashi technique is used by technical traders to identify a given trend more easily. Heikin-Ashi has a smoother look because it is essentially taking an average of the movement. There is a tendency with Heikin-Ashi for the candles to stay red during a downtrend and green during an uptrend, whereas normal candlesticks alternate color even if the price is moving dominantly in one direction. This indicator actually recalculates the RSI indicator with the logic of heikin ashi. Due to smoothing, the bars are formed with a slight lag, reflecting the trend rather than the exact price movement. So lets look the original version to understand more clearly. If red bars turn to green bars it means uptrend may begin, if green bars turn to red it means downtrend may begin.
As you see HARSI giving lots of signal some of them is really good but some of them are not very well. Because it gives so much signals Now i will change time period and lets look same chart again.
Now results are better because of heikin ashi's logic. it is not suitable for day traders, it gives more accurate result when using the time period is longer. But it can be useful to use this indicator in short time periods using with other indicators. So you may catch the trend changes more accurately.
4 — MACD DEMA by ToFFF
This indicator uses a double EMA and MACD algorithm to analyze the direction of the trend. Though it might seem a tough task to manage the trades with the help of MACD DEMA once you know how the proper way to interpret the signal lines, it will be an easy task.
This indicator also smoothens the signal lines with the time series algorithm which eventually makes the higher time frame important. So, expecting better results in the lower time frame can result in big losses as the data reading from the MACD DEMA will not be accurate. In order to understand the function of this indicator, you have to know the functions of the EMA also.
The exponential moving average tends to give more priority to the recent price changes. So, expecting better results when the volatility is very high is a very risky approach to trade the market. Moreover, the MACD has some lagging issues compared to the EMA, so it is super important to use a trading method that focuses on the higher time frame only. What does MACD 12 26 Close 9 mean? When the DEMA-9 crosses above the MACD(12,26), this is considered a bearish signal. It means the trend in the stock – its magnitude and/or momentum – is starting to shift course. When the MACD(12,26) crosses above the DEMA-9, this is considered a bullish signal. Lets see this indicator on Chart.
When the blue line crossover red line it is good time to buy. As you see from the chart i put arrows where the crossover are appeared.
When the red line crossover blue line it is good time to sell or exit from position.
5 — WaveTrend Oscillator by LazyBear
This is a technical indicator that creates high and low bands between two values. It then creates a trend indicator that draws waves with highs and lows within these boundaries. WaveTrend is a widely used indicator for finding direction of an asset.
Calculation period: number of candles used to calculate WaveTrend, defaults to 10. Averaging period: number of candles used to average WaveTrend, defaults to 21.
As you see in chart when the lines crossover occured my strategy gives buy or sell signals.
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█ HOW TO USE
I hope you understand how the indicators I mentioned above work and what they are used for. Now, I will explain in detail how to use the strategy I have created.
When you enter the settings section, you will see 5 types of indicators. If you want to use the signals of the indicators, simply tick the box next to the indicators. Also, under each option there is an area where you can set the "lookback". This setting is a field that will make the signals overlap when you select more than one option. If you are going to trade with only one option, you should make sure that this field is 0. Otherwise, it may continue to generate as many signals as you choose.
Lets see in chart for easy understanding.
As you see chart, if i chose only HARSI with lookback 0 (HARSI and CORAL should be 1 minumum because of algorithm-we looking 1 bar before, others 0 because we are looking crossovers), it will give signals only when harsı bar's color changed. But when i changed Lookback as 7 it will be like this in chart.
Now i will choose 2 indicator with settings of their lookback 0.
As you see it will give signals when both of them occurs same time. But HARSI is an indicator giving very early signal so we can enter position 5-6 bars after the first bar color change. So i will change HARSI Lookback settings as 7. Lets look what happens when we use lookback option.
So it wil be useful to change lookback settings to find best signals in each time period and in each symbol. But it shouldnt be too high. Because you can be late to catch trend's starting.
this is an image of MACD and WAVE trend used and lookback option are both 6.
Now lets see an example with 3 options are chosen with lookback option 11-1-5
Now lets talk about indicators settings. After strategy options you will see each indicators settings, you can change their settings as you desired. So each indicators signal will be changed according to your adjustment.
I left strategy options with default settings. You can change it manually as if you want.
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█ LIMITATIONS: Don't rely on non-standard charts results. For example Heikin Ashi is a technical analysis method used with the traditional candlestick chart.Heikin Ashi vs. Candlestick Chart: The decisive visual difference between Heikin Ashi and the traditional chart is that Heikin Ashi flattens the traditional candlestick chart using a modified formula.
The primary advantage of Heikin Ashi is that it makes the chart more reader-friendly and helps users identify and analyze trends .
Because Heikin Ashi provides averaged price information rather than real-time price and reacts slowly to volatility — not suitable for scalpers and high-frequency traders. I added HARSI indicator as a supportive signal because it is useful with using CORAL and SSL channel indicators. If you change your candle types to Heikin Ashi , your profit will change in good way but dont rely on it.
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█ THANKS:
Special thanks to authors of the scripts that i used.
@LazyBear and @ErwinBeckers and @JayRogers and @ToFFF
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█ DISCLAIMER
Any trade decisions you make are entirely your own responsibility.
HYE Trend Hunter [Strategy]*** Stratejinin Türkçe ve İngilizce açıklaması aşağıya eklenmiştir.
HYE Trend Hunter
In this strategy, two of the most basic data (price and volume) necessary for detecting trends as early as possible and entering the trade on time are used. In this context, the approaches of some classical and new generation indicators using price and volume have been taken into account.
The strategy is prepared to generate buy signals only. The following steps were followed to generate the buy and exit signals.
1-) First of all, the two most basic data of the strategy, “slow leading line” and “fast leading line” need to be calculated. For this, we use the formula of the “senkou span A” line of the indicator known as the Ichimoku Cloud. We also need to calculate lines known as tenkan sen and kijun sen in ichimoku because they are used in the calculation of this formula.
The high and low values of the candles are taken into account when calculating the Tenkansen, Kijunsen and Senkou Span A lines in the Ichimoku cloud. In this strategy, the highest and lowest values of the periodic VWAP are taken into account when calculating the "slow leading line" and "fast leading line". (The periodic vwap formula was coded and made available by @neolao on tradingviev). Also, in the ichimoku cloud, while the Senkou Span A line is plotted 26 periods into the future, we consider the values of the fast and slow leading lines in the last candle in this strategy.
ORIGINAL ICHIMOKU SPAN A FORMULA
Tenkansen = (Highest high of the last 9 candles + Lowest low of the last 9 candles) / 2
Kijunsen = (Highest high of the last 26 candles + Lowest low of the last 26 candles) / 2
Senkou Span A = Tenkansen + Kijunsen / 2
HYE TREND HUNTER SPAN A FORMULA*
Tenkansen = (Highest VWAP of the last 9 candles + Lowest VWAP of the last 9 candles) / 2
Kijunsen = (Highest VWAP of the last 26 candles + Lowest VWAP of the last 26 candles) / 2
Senkou Span A = Tenkansen + Kijunsen / 2
* We use the original ichimoku values 9 and 26 for the slow line, and 5 and 13 for the fast line. These settings can be changed from the strategy settings.
2-) At this stage, we have 2 lines that we obtained by using the formula of the ichimoku cloud, one of the most classical trend indicators, and by including the volume-weighted average price.
a-) Fast Leading Line (5-13)
b-) Slow Leading Line (9-26)
For the calculation we will do soon, we get a new value by taking the average of these two lines. Using this value, which is the average of the fast and slow leading lines, we plot the Bollinger Bands indicator, which is known as one of the most classic volatility indicators of technical analysis. Thus, we are trying to understand whether there is a volatility change in the market, which may mean the presence of a trend start. We will use this data in the calculation of buy-sell signals.
In the classical Bollinger Bands calculation, the standard deviation is calculated by applying a multiplier at the rate determined by the user (2 is used in the original settings) to the moving average calculated with the “closing price”, and this value is added or subtracted from the moving average and upper band and lower band lines are drawn.
In the HYE Trend Hunter Strategy, instead of the moving average calculated with the closing price in the Bollinger Band calculation, we consider the average of the fast and slow leading lines calculated in the 1st step and draw the Bollinger upper and lower bands accordingly. We use the values of 2 and 20 as the standard deviation and period, as in the original settings. These settings can also be changed from the strategy settings.
3-) At this stage, we have fast and slow leading lines trying to understand the trend direction using VWAP, and Bollinger lower and upper bands calculated by the average of these lines.
In this step, we will use another tool that will help us understand whether the invested market (forex, crypto, stocks) is gaining momentum in volume. The Time Segmented Volume indicator was created by the Worden Brothers Inc. and coded by @liw0 and @vitelot on tradingview. The TSV indicator segments the price and volume of an investment instrument according to certain time periods and makes calculations on comparing these price and volume data to reveal the buying and selling periods.
To trade in the buy direction on the HYE Trend Hunter Strategy, we look for the TSV indicator to be above 0 and above its exponential moving average value. TSV period and exponential moving average period settings (13 and 7) can also be changed in the strategy settings.
BUY SIGNAL
1-) Fast Leading Line value should be higher than the Fast Leading Line value in the previous candle.
2-) Slow Leading Line value should be higher than the Slow Leading Line value in the previous candle.
3-) Candle Closing value must be higher than the Upper Bollinger Band.
4-) TSV value must be greater than 0.
5-) TSV value must be greater than TSVEMA value.
EXIT SIGNAL
1-) Fast Leading Line value should be lower than the Fast Leading Line value in the previous candle.
2-) Slow Leading Line value should be lower than the Slow Leading Line value in the previous candle.
TIPS AND WARNINGS
1-) The standard settings of the strategy work better in higher timeframes (4-hour, daily, etc.). For lower timeframes, you should change the strategy settings and find the best value for yourself.
2-) All lines (fast and slow leading lines and Bollinger bands) except TSV are displayed on the strategy. For a simpler view, you can hide these lines in the strategy settings.
3-) You can see the color changes of the fast and slow leading lines as well as you can specify a single color for these lines in the strategy settings.
4-) It is an strategy for educational and experimental purposes. It cannot be considered as investment advice. You should be careful and make your own risk assessment when opening real market trades using this strategy.
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HYE Trend Avcısı
Bu stratejide, trendlerin olabildiğince erken tespit edilebilmesi ve zamanında işleme girilebilmesi için gerekli olan en temel iki veriden (fiyat ve hacim) yararlanılmaktadır. Bu kapsamda, fiyat ve hacim kullanan bazı klasik ve yeni nesil indikatörlerin yaklaşımları dikkate alınmıştır.
Strateji yalnızca alış yönlü sinyaller üretecek şekilde hazırlanmıştır. Alış ve çıkış sinyallerinin üretilmesi için aşağıdaki adımlar izlenmiştir.
1-) Öncelikle, stratejinin en temel iki verisi olan “yavaş öncü çizgi” ve “hızlı öncü çizgi” hesaplamasının yapılması gerekiyor. Bunun için de Ichimoku Bulutu olarak bilinen indikatörün “senkou span A” çizgisinin formülünü kullanıyoruz. Bu formülün hesaplamasında kullanılmaları nedeniyle ichimoku’da tenkan sen ve kijun sen olarak bilinen çizgileri de hesaplamamız gerekiyor.
Ichimoku bulutunda Tenkansen, Kijunsen ve Senkou Span A çizgileri hesaplanırken mumların yüksek ve düşük değerleri dikkate alınıyor. Bu stratejide ise “yavaş öncü çizgi” ve “hızlı öncü çizgi” hesaplanırken periyodik VWAP’ın en yüksek ve en düşük değerleri dikkate alınıyor. (Periyodik vwap formülü, tradingviev’de @neolao tarafından kodlanmış ve kullanıma açılmış). Ayrıca, ichimoku bulutunda Senkou Span A çizgisi geleceğe yönelik çizilirken (26 mum ileriye dönük) biz bu stratejide öncü çizgilerin son mumdaki değerlerini dikkate alıyoruz.
ORJİNAL ICHIMOKU SPAN A FORMÜLÜ
Tenkansen = (Son 9 mumun en yüksek değeri + Son 9 mumun en düşük değeri) / 2
Kijunsen = (Son 26 mumun en yüksek değeri + Son 26 mumun en düşük değeri) / 2
Senkou Span A = Tenkansen + Kijunsen / 2
HYE TREND HUNTER SPAN A FORMÜLÜ*
Tenkansen = (Son 9 mumun en yüksek VWAP değeri + Son 9 mumun en düşük VWAP değeri) / 2
Kijunsen = (Son 26 mumun en yüksek VWAP değeri + Son 26 mumun en düşük VWAP değeri) / 2
Senkou Span A = Tenkansen + Kijunsen / 2
* Yavaş çizgi için orijinal ichimoku değerleri olan 9 ve 26’yı kullanırken, hızlı çizgi için 5 ve 13’ü kullanıyoruz. Bu ayarlar, strateji ayarlarından değiştirilebiliyor.
2-) Bu aşamada, elimizde en klasik trend indikatörlerinden birisi olan ichimoku bulutunun formülünden faydalanarak, işin içinde hacim ağırlıklı ortalama fiyatı da sokmak suretiyle elde ettiğimiz 2 çizgimiz var.
a-) Hızlı Öncü Çizgi (5-13)
b-) Yavaş Öncü Çizgi (9-26)
Birazdan yapacağımız hesaplama için bu iki çizginin de ortalamasını alarak yeni bir değer elde ediyoruz. Hızlı ve yavaş öncü çizgilerin ortalaması olan bu değeri kullanarak, teknik analizin en klasik volatilite indikatörlerinden birisi olarak bilinen Bollinger Bantları indikatörünü çizdiriyoruz. Böylelikle piyasada bir trend başlangıcının varlığı anlamına gelebilecek volatilite değişikliği var mı yok mu anlamaya çalışıyoruz. Bu veriyi al-sat sinyallerinin hesaplamasında kullanacağız.
Klasik Bollinger Bantları hesaplamasında, “kapanış fiyatıyla” hesaplanan hareketli ortalamaya, kullanıcı olarak belirlenen oranda (orijinal ayarlarında 2 kullanılır) bir çarpan uygulanarak standart sapma hesaplanıyor ve bu değer hareketli ortalamaya eklenip çıkartılarak üst bant ve alt bant çizgileri çiziliyor.
HYE Trend Avcısı stratejisinde, Bollinger Bandı hesaplamasında kapanış fiyatıyla hesaplanan hareketli ortalama yerine, 1. adımda hesapladığımız hızlı ve yavaş öncü çizgilerin ortalamasını dikkate alıyoruz ve buna göre bollinger üst ve alt bantlarını çizdiriyoruz. Standart sapma ve periyot olarak yine orijinal ayarlarında olduğu gibi 2 ve 20 değerlerini kullanıyoruz. Bu ayarlar da strateji ayarlarından değiştirilebiliyor.
3-) Bu aşamada, elimizde VWAP kullanarak trend yönünü anlamaya çalışan hızlı ve yavaş öncü çizgilerimiz ile bu çizgilerin ortalaması ile hesaplanan bollinger alt ve üst bantlarımız var.
Bu adımda, yatırım yapılan piyasanın (forex, kripto, hisse senedi) hacimsel olarak ivme kazanıp kazanmadığını anlamamıza yarayacak bir araç daha kullanacağız. Time Segmented Volume indikatörü, Worden Kardeşler şirketi tarafından oluşturulmuş ve tradingview’de @liw0 ve @vitelot tarafından kodlanarak kullanıma açılmış. TSV indikatörü, bir yatırım aracının fiyatını ve hacmini belirli zaman aralıklarına göre bölümlere ayırarak, bu fiyat ve hacim verilerini, alış ve satış dönemlerini ortaya çıkarmak için karşılaştırmak üzerine hesaplamalar yapar.
HYE Trend Avcısı stratejisinde alış yönünde işlem yapmak için, TSV indikatörünün 0’ın üzerinde olmasını ve kendi üstel hareketli ortalama değerinin üzerinde olmasını arıyoruz. TSV periyodu ve üstel hareketli ortalama periyodu ayarları da (13 ve 7) strateji ayarlarından değiştirilebiliyor.
ALIŞ SİNYALİ
1-) Hızlı Öncü Çizgi değeri bir önceki mumdaki Hızlı Öncü Çizgi değerinden yüksek olmalı.
2-) Yavaş Öncü Çizgi değeri bir önceki mumdaki Yavaş Öncü Çizgi değerinden yüksek olmalı.
3-) Kapanış Değeri, Üst Bollinger Bandı değerinden yüksek olmalı.
4-) TSV değeri 0’dan büyük olmalı.
5-) TSV değeri TSVEMA değerinden büyük olmalı.
ÇIKIŞ SİNYALİ
1-) Hızlı Öncü Çizgi değeri bir önceki mumdaki Hızlı Öncü Çizgi değerinden düşük olmalı.
2-) Yavaş Öncü Çizgi değeri bir önceki mumdaki Yavaş Öncü Çizgi değerinden düşük olmalı.
İPUÇLARI VE UYARILAR
1-) Stratejinin standart ayarları, yüksek zaman dilimlerinde (4 saatlik, günlük vs.) daha iyi çalışıyor. Düşük zaman dilimleri için strateji ayarlarını değiştirmeli ve kendiniz için en iyi değeri bulmalısınız.
2-) Stratejide tüm çizgiler (hızlı ve yavaş öncü çizgiler ile bollinger bantları) -TSV dışında- açık olarak gelmektedir. Daha sade bir görüntü için bu çizgilerin görünürlüğünü strateji ayarlarından gizleyebilirsiniz.
3-) Hızlı ve yavaş öncü çizgilerin renk değişimlerini görebileceğiniz gibi bu çizgiler için tek bir renk olarak da strateji ayarlarında belirleme yapabilirsiniz.
4-) Eğitim ve deneysel amaçlı bir stratejidir. Yatırım tavsiyesi olarak değerlendirilemez. Bu stratejiyi kullanarak gerçek piyasa işlem açarken dikkatli olmalı ve kendi risk değerlendirmenizi yapmalısınız.
