Customizable BTC Seasonality StrategyThis strategy leverages intraday seasonality effects in Bitcoin, specifically targeting hours of statistically significant returns during periods when traditional financial markets are closed. Padysak and Vojtko (2022) demonstrate that Bitcoin exhibits higher-than-average returns from 21:00 UTC to 23:00 UTC, a period in which all major global exchanges, such as the New York Stock Exchange (NYSE), Tokyo Stock Exchange, and London Stock Exchange, are closed. The absence of competing trading activity from traditional markets during these hours appears to contribute to these statistically significant returns.
The strategy proceeds as follows:
Entry Time: A long position in Bitcoin is opened at a user-specified time, which defaults to 21:00 UTC, aligning with the beginning of the identified high-return window.
Holding Period: The position is held for two hours, capturing the positive returns typically observed during this period.
Exit Time: The position is closed at a user-defined time, defaulting to 23:00 UTC, allowing the strategy to exit as the favorable period concludes.
This simple seasonality strategy aims to achieve a 33% annualized return with a notably reduced volatility of 20.93% and maximum drawdown of -22.45%. The results suggest that investing only during these high-return hours is more stable and less risky than a passive holding strategy (Padysak & Vojtko, 2022).
References
Padysak, M., & Vojtko, R. (2022). Seasonality, Trend-following, and Mean reversion in Bitcoin.
المؤشرات والاستراتيجيات
SuperATR 7-Step Profit - Strategy [presentTrading] Long time no see!
█ Introduction and How It Is Different
The SuperATR 7-Step Profit Strategy is a multi-layered trading approach that integrates adaptive Average True Range (ATR) calculations with momentum-based trend detection. What sets this strategy apart is its sophisticated 7-step take-profit mechanism, which combines four ATR-based exit levels and three fixed percentage levels. This hybrid approach allows traders to dynamically adjust to market volatility while systematically capturing profits in both long and short market positions.
Traditional trading strategies often rely on static indicators or single-layered exit strategies, which may not adapt well to changing market conditions. The SuperATR 7-Step Profit Strategy addresses this limitation by:
- Using Adaptive ATR: Enhances the standard ATR by making it responsive to current market momentum.
- Incorporating Momentum-Based Trend Detection: Identifies stronger trends with higher probability of continuation.
- Employing a Multi-Step Take-Profit System: Allows for gradual profit-taking at predetermined levels, optimizing returns while minimizing risk.
BTCUSD 6hr Performance
█ Strategy, How It Works: Detailed Explanation
The strategy revolves around detecting strong market trends and capitalizing on them using an adaptive ATR and momentum indicators. Below is a detailed breakdown of each component of the strategy.
🔶 1. True Range Calculation with Enhanced Volatility Detection
The True Range (TR) measures market volatility by considering the most significant price movements. The enhanced TR is calculated as:
TR = Max
Where:
High and Low are the current bar's high and low prices.
Previous Close is the closing price of the previous bar.
Abs denotes the absolute value.
Max selects the maximum value among the three calculations.
🔶 2. Momentum Factor Calculation
To make the ATR adaptive, the strategy incorporates a Momentum Factor (MF), which adjusts the ATR based on recent price movements.
Momentum = Close - Close
Stdev_Close = Standard Deviation of Close over n periods
Normalized_Momentum = Momentum / Stdev_Close (if Stdev_Close ≠ 0)
Momentum_Factor = Abs(Normalized_Momentum)
Where:
Close is the current closing price.
n is the momentum_period, a user-defined input (default is 7).
Standard Deviation measures the dispersion of closing prices over n periods.
Abs ensures the momentum factor is always positive.
🔶 3. Adaptive ATR Calculation
The Adaptive ATR (AATR) adjusts the traditional ATR based on the Momentum Factor, making it more responsive during volatile periods and smoother during consolidation.
Short_ATR = SMA(True Range, short_period)
Long_ATR = SMA(True Range, long_period)
Adaptive_ATR = /
Where:
SMA is the Simple Moving Average.
short_period and long_period are user-defined inputs (defaults are 3 and 7, respectively).
🔶 4. Trend Strength Calculation
The strategy quantifies the strength of the trend to filter out weak signals.
Price_Change = Close - Close
ATR_Multiple = Price_Change / Adaptive_ATR (if Adaptive_ATR ≠ 0)
Trend_Strength = SMA(ATR_Multiple, n)
🔶 5. Trend Signal Determination
If (Short_MA > Long_MA) AND (Trend_Strength > Trend_Strength_Threshold):
Trend_Signal = 1 (Strong Uptrend)
Elif (Short_MA < Long_MA) AND (Trend_Strength < -Trend_Strength_Threshold):
Trend_Signal = -1 (Strong Downtrend)
Else:
Trend_Signal = 0 (No Clear Trend)
🔶 6. Trend Confirmation with Price Action
Adaptive_ATR_SMA = SMA(Adaptive_ATR, atr_sma_period)
If (Trend_Signal == 1) AND (Close > Short_MA) AND (Adaptive_ATR > Adaptive_ATR_SMA):
Trend_Confirmed = True
Elif (Trend_Signal == -1) AND (Close < Short_MA) AND (Adaptive_ATR > Adaptive_ATR_SMA):
Trend_Confirmed = True
Else:
Trend_Confirmed = False
Local Performance
🔶 7. Multi-Step Take-Profit Mechanism
The strategy employs a 7-step take-profit system
█ Trade Direction
The SuperATR 7-Step Profit Strategy is designed to work in both long and short market conditions. By identifying strong uptrends and downtrends, it allows traders to capitalize on price movements in either direction.
Long Trades: Initiated when the market shows strong upward momentum and the trend is confirmed.
Short Trades: Initiated when the market exhibits strong downward momentum and the trend is confirmed.
█ Usage
To implement the SuperATR 7-Step Profit Strategy:
1. Configure the Strategy Parameters:
- Adjust the short_period, long_period, and momentum_period to match the desired sensitivity.
- Set the trend_strength_threshold to control how strong a trend must be before acting.
2. Set Up the Multi-Step Take-Profit Levels:
- Define ATR multipliers and fixed percentage levels according to risk tolerance and profit goals.
- Specify the percentage of the position to close at each level.
3. Apply the Strategy to a Chart:
- Use the strategy on instruments and timeframes where it has been tested and optimized.
- Monitor the positions and adjust parameters as needed based on performance.
4. Backtest and Optimize:
- Utilize TradingView's backtesting features to evaluate historical performance.
- Adjust the default settings to optimize for different market conditions.
█ Default Settings
Understanding default settings is crucial for optimal performance.
Short Period (3): Affects the responsiveness of the short-term MA.
Effect: Lower values increase sensitivity but may produce more false signals.
Long Period (7): Determines the trend baseline.
Effect: Higher values reduce noise but may delay signals.
Momentum Period (7): Influences adaptive ATR and trend strength.
Effect: Shorter periods react quicker to price changes.
Trend Strength Threshold (0.5): Filters out weaker trends.
Effect: Higher thresholds yield fewer but stronger signals.
ATR Multipliers: Set distances for ATR-based exits.
Effect: Larger multipliers aim for bigger moves but may reduce hit rate.
Fixed TP Levels (%): Control profit-taking on smaller moves.
Effect: Adjusting these levels affects how quickly profits are realized.
Exit Percentages: Determine how much of the position is closed at each TP level.
Effect: Higher percentages reduce exposure faster, affecting risk and reward.
Adjusting these variables allows you to tailor the strategy to different market conditions and personal risk preferences.
By integrating adaptive indicators and a multi-tiered exit strategy, the SuperATR 7-Step Profit Strategy offers a versatile tool for traders seeking to navigate varying market conditions effectively. Understanding and adjusting the key parameters enables traders to harness the full potential of this strategy.
DSL Strategy [DailyPanda]
Overview
The DSL Strategy by DailyPanda is a trading strategy that synergistically combines the idea from indicators to create a more robust and reliable trading tool. By integrating these indicators, the strategy enhances signal accuracy and provides traders with a comprehensive view of market trends and momentum shifts. This combination allows for better entry and exit points, improved risk management, and adaptability to various market conditions.
Combining ideas from indicators adds value by:
Enhancing Signal Confirmation : The strategy requires alignment between trend and momentum before generating trade signals, reducing false entries.
Improving Accuracy : By integrating price action with momentum analysis, the strategy captures more reliable trading opportunities.
Providing Comprehensive Market Insight : The combination offers a better perspective on the market, considering both the direction (trend) and the strength (momentum) of price movements.
How the Components Work Together
1. Trend Identification with DSL Indicator
Dynamic Signal Lines : Calculates upper and lower DSL lines based on a moving average (SMA) and dynamic thresholds derived from recent highs and lows with a specified offset. These lines adapt to market conditions, providing real-time trend insights.
ATR-Based Bands : Adds bands around the DSL lines using the Average True Range (ATR) multiplied by a width factor. These bands account for market volatility and help identify potential stop-loss levels.
Trend Confirmation : The relationship between the price, DSL lines, and bands determines the current trend. For example, if the price consistently stays above the upper DSL line, it indicates a bullish trend.
2. Momentum Analysis
RSI Calculation : Computes the RSI over a specified period to measure the speed and change of price movements.
Zero-Lag EMA (ZLEMA) : Applies a ZLEMA to the RSI to minimize lag and produce a more responsive oscillator.
DSL Application on Oscillator : Implements the DSL concept on the oscillator by calculating dynamic upper and lower levels. This helps identify overbought or oversold conditions more accurately.
Signal Generation : Detects crossovers between the oscillator and its DSL lines. A crossover above the lower DSL line signals potential bullish momentum, while a crossover below the upper DSL line signals potential bearish momentum.
3. Integrated Signal Filtering
Confluence Requirement : A trade signal is generated only when both the DSL indicator and oscillator agree. For instance, a long entry requires both an uptrend confirmation from the DSL indicator and a bullish momentum signal from the oscillator.
Risk Management Integration : The strategy uses the DSL indicator's bands for setting stop-loss levels and calculates take-profit levels based on a user-defined risk-reward ratio. This ensures that every trade has a predefined risk management plan.
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Originality and Value Added to the Community
Unique Synergy : While both indicators are available individually, this strategy is original in how it combines them to enhance their strengths and mitigate their weaknesses, offering a novel approach not present in existing scripts.
Enhanced Reliability : By requiring confirmation from both trend and momentum indicators, the strategy reduces false signals and increases the likelihood of successful trades.
Versatility : The customizable parameters allow traders to adapt the strategy to different instruments, timeframes, and trading styles, making it a valuable tool for a wide range of trading scenarios.
Educational Contribution : The script demonstrates an effective method of combining indicators for improved trading performance, providing insights that other traders can learn from and apply to their own strategies.
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How to Use the Strategy
Adding the Strategy to Your Chart
Apply the DSL Strategy to your desired trading instrument and timeframe on TradingView.
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Configuring Parameters
DSL Indicator Settings :
Length (len) : Adjusts the sensitivity of the DSL lines (default is 34).
Offset : Determines the look-back period for threshold calculations (default is 30).
Bands Width (width) : Changes the distance of the ATR-based bands from the DSL lines (default is 1).
DSL-BELUGA Oscillator Settings :
Beluga Length (len_beluga) : Sets the period for the RSI calculation in the oscillator (default is 10).
DSL Lines Mode (dsl_mode) : Chooses between "Fast" (more responsive) and "Slow" (smoother) modes for the oscillator's DSL lines.
Risk Management :
Risk Reward (risk_reward) : Defines your desired risk-reward ratio for calculating take-profit levels (default is 1.5).
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Interpreting Signals
Long Entry Conditions :
Trend Confirmation : Price is above the upper DSL line and the upper DSL band (dsl_up1 > dsl_dn).
Price Behavior : The last three candles have both their opens and closes above the upper DSL line.
Momentum Signal : The DSL-BELUGA oscillator crosses above its lower DSL line (up_signal), indicating bullish momentum.
Short Entry Conditions :
Trend Confirmation : Price is below the lower DSL line and the lower DSL band (dsl_dn < dsl_up1).
Price Behavior : The last three candles have both their opens and closes below the lower DSL band.
Momentum Signal : The DSL-BELUGA oscillator crosses below its upper DSL line (dn_signal), indicating bearish momentum.
Exit Conditions :
Stop-Loss : Automatically set at the DSL indicator's band level (upper band for longs, lower band for shorts).
Take-Profit : Calculated based on the risk-reward ratio and the initial risk determined by the stop-loss distance.
Visual Aids
Signal Arrows : Upward green arrows for long entries and downward blue arrows for short entries appear on the chart when conditions are met.
Stop-Loss and Take-Profit Lines : Red and green lines display the calculated stop-loss and take-profit levels for active trades.
Background Highlighting : The chart background subtly changes color to indicate when a signal has been generated.
Backtesting and Optimization
Use TradingView's strategy tester to backtest the strategy over historical data.
Adjust parameters to optimize performance for different instruments or market conditions.
Regularly review backtesting results to ensure the strategy remains effective.
RVI Crossover Strategy[Kopottaja]Overview of the RVI Crossover Strategy
Strategy Name: RVI Crossover Strategy
Purpose: The RVI Crossover Strategy is based on the crossover signals between the Relative Vigor Index (RVI) and its moving average signal line. This strategy aims to identify potential buy and sell signals by evaluating the market’s directional trend.
Key Indicator Features
Relative Vigor Index (RVI): This indicator measures the momentum of price changes over a specified period and helps identify the market’s current trend. The RVI is based on the idea that prices generally close higher than they open in an uptrend (and lower in a downtrend). The RVI helps provide an indication of the strength and direction of a trend.
Signal Line: A moving average (e.g., SMA) is applied to the RVI values, creating a "signal line." When the RVI crosses above or below this line, it signals a potential trading opportunity.
Calculations and Settings
Calculating the RVI: The RVI is calculated by comparing the difference between the close and open prices to the difference between high and low prices. This provides information about the direction and momentum of price movement:
RVI= Sum(SWMA(high−low))Sum(SWMA(close−open))
where SWMA is a smoothed weighted moving average over a specified period.
Signal Line Calculation: The RVI value is smoothed by applying a simple moving average (SMA) to create the signal line. This signal line helps filter crossover signals for improved accuracy.
Buy and Sell Conditions: Buy and sell conditions are identified based on crossovers between the RVI and its signal line.
Buy Signal: A buy condition is triggered when the RVI crosses above the signal line, provided that the "Bearish" condition (trend confirmation) is met.
Sell Signal: A sell condition occurs when the RVI crosses below the signal line, alongside the "Bullish" trend confirmation.
Volume-Weighted Moving Averages (VWMA): VWMA indicators are used to assess price-volume relationships over different timeframes:
Fast VWMA: A short-period volume-weighted moving average.
Slow VWMA: A longer-period volume-weighted moving average. These values are used to strengthen the buy and sell conditions by confirming trend directions (Bullish or Bearish).
Disclaimer: This is an educational and informational tool. Past performance is not indicative of future results. Always backtest before using in live markets
Harmony Signal Flow By ArunThis Pine Script strategy, titled "Harmony Signal Flow By Arun," uses the Relative Strength Index (RSI) indicator to generate buy and sell signals based on custom thresholds. The script incorporates stop-loss and target management and restricts new trades until the previous position closes. Here's a detailed description:
Custom RSI Metric:
The strategy calculates a 5-period RSI based on the closing price, aiming for a more responsive measure of price momentum.
RSI thresholds are defined:
Lower threshold (30): Indicates oversold conditions, triggering a potential buy.
Upper threshold (70): Indicates overbought conditions, prompting a possible sell.
Entry Conditions:
Buy Signal: The strategy initiates a buy order when the RSI crosses above the lower threshold (30), indicating a shift from oversold conditions.
Sell Signal: A sell order is triggered when the RSI crosses below the upper threshold (70), suggesting an overbought reversal.
Only one order (buy or sell) can be active at a time, ensuring that a new trade begins only when there’s no existing position.
Stop-Loss and Target Management:
For each trade, stop-loss and target conditions are applied to manage risk and secure profits.
For Buy Positions:
Stop-loss is set 100 points below the entry price.
Target is set 150 points above the entry price.
For Sell Positions:
Stop-loss is set 100 points above the entry price.
Target is 150 points below the entry price.
The strategy closes the trade when either the stop-loss or target is met, marking the trade as "closed" and allowing a new trade entry.
Trade Sequencing:
A new trade (buy or sell) is only permitted after the previous position hits either its stop-loss or target, preventing overlapping trades and ensuring clear trade sequences.
This sequential approach enhances risk management by ensuring only one active position at any time.
End-of-Day Closure:
All open positions are closed automatically at 3:25 PM (Indian market time) to avoid overnight exposure, ensuring the strategy remains strictly intraday.
The flag for trade entry is reset at the end of each day, enabling fresh trades the next day.
Chart Indicators:
The script plots buy and sell signals directly on the chart with visible labels.
It also displays the custom RSI metric with horizontal lines for the lower and upper thresholds, providing visual cues for entry and exit points.
Summary
This strategy is a momentum-based intraday trading approach that uses the RSI for identifying potential reversals and manages trades through predefined stop-loss and target levels. By enforcing trade sequencing and closing positions at the end of the trading day, it prioritizes risk management and seeks to capitalize on short-term trends while avoiding overnight market risks.
[ETH] Optimized Trend Strategy - Lorenzo SuperScalpStrategy Title: Optimized Trend Strategy - Lorenzo SuperScalp
Description:
The Optimized Trend Strategy is a comprehensive trading system tailored for Ethereum (ETH) and optimized for the 15-minute timeframe but adaptable to various timeframes. This strategy utilizes a combination of technical indicators—RSI, Bollinger Bands, and MACD—to identify and act on price trends efficiently, providing traders with actionable buy and sell signals based on market conditions.
Key Features:
Multi-Indicator Approach:
RSI (Relative Strength Index): Identifies overbought and oversold conditions to time market entries and exits.
Bollinger Bands: Acts as a dynamic support and resistance level, helping to pinpoint precise entry and exit zones.
MACD (Moving Average Convergence Divergence): Detects momentum changes through bullish and bearish crossovers.
Signal Conditions:
Buy Signal:
RSI is below 45 (indicating an oversold condition).
Price is near or below the lower Bollinger Band.
MACD bullish crossover occurs.
Sell Signal:
RSI is above 55 (indicating an overbought condition).
Price is near or above the upper Bollinger Band.
MACD bearish crossunder occurs.
Trade Execution Logic:
Long Trades: Opened when a buy signal flashes. If there’s an open short position, it is closed before opening a long.
Short Trades: Opened when a sell signal flashes. If there’s an open long position, it is closed before opening a short.
The strategy also ensures a minimum number of bars between consecutive trades to avoid rapid trading in choppy conditions.
Pyramiding Support:
Up to 3 consecutive trades in the same direction are allowed, enabling traders to scale into positions based on strong signals.
Visual Indicators:
RSI Levels: Dotted lines at 45 and 55 for quick reference to oversold and overbought levels.
Buy and Sell Signals: Visual markers on the chart indicate where trades are executed, ensuring clarity on entry and exit points.
Best Used For:
Swing Trading & Scalping: While optimized for the 15-minute timeframe, this strategy works across various timeframes, making it suitable for both short-term scalping and swing trading.
Crypto Trading: Tailored for Ethereum but effective for other cryptocurrencies due to its dynamic indicator setup.
Triple EMA Crossover StrategyTriple EMA Crossover Strategy
Overview
The Triple EMA Crossover Strategy is a trend-following trading system that utilizes three Exponential Moving Averages (EMAs) to identify potential entry and exit points in the market. This strategy is based on the principle that when shorter-term prices cross above longer-term prices, it can indicate a bullish trend, and conversely when they cross below, it can signal a bearish trend.
Components
Exponential Moving Averages (EMAs):
Short EMA: A fast-moving average that reacts quickly to price changes (commonly set to 9 periods).
Medium EMA: A medium-term average that smooths out price data and helps confirm trends (commonly set to 21 periods).
Long EMA: A slow-moving average that helps identify the overall trend direction (commonly set to 55 periods).
Trading Signals:
Buy Signal: A long entry is triggered when:
The Short EMA (9) crosses above the Medium EMA (21).
The Medium EMA (21) is above the Long EMA (55).
Sell Signal: A short entry is signaled when:
The Short EMA (9) crosses below the Medium EMA (21).
The Medium EMA (21) is below the Long EMA (55).
Stop Loss and Take Profit:
Stop Loss: Implement a predefined percentage or ATR-based stop loss to limit potential losses.
Take Profit: Set a target based on a risk-to-reward ratio that reflects your trading strategy's goals.
Advantages
Trend Identification: The EMA crossover system allows traders to identify the current trend dynamically, focusing on upward or downward price movements.
Simplicity: The strategy is straightforward, making it accessible for both new and experienced traders.
Flexibility: This method can be applied across multiple timeframes and asset classes, making it versatile for various trading styles.
Disadvantages
Lagging Indicator: Moving averages are lagging indicators, meaning signals may come later than the actual price movement, which can lead to missed opportunities.
Whipsaw Effect: In ranging markets, the strategy may produce false signals leading to potential losses.
MT Enhanced Trend Reversal StrategyThis strategy, called **"Enhanced Trend Reversal Strategy with Take Profit,"** is designed to identify trend reversal points based on several indicators: **Exponential Moving Averages (EMA), MACD**, and **RSI**. The strategy also includes **take-profit levels** to provide traders with suggested profit-taking points.
Key Components of the Strategy
1. **Exponential Moving Averages (EMA)**:
- The strategy uses **20 and 50-period EMAs** to determine trend direction. The shorter period (EMA 20) reacts more quickly to price changes, while the longer period (EMA 50) smooths out fluctuations.
- An **uptrend** (bullish market) is indicated when the EMA 20 is above the EMA 50. In this case, the main trend line is colored green.
- A **downtrend** (bearish market) is indicated when the EMA 20 is below the EMA 50, in which case the trend line is colored red.
- This visual indication simplifies analysis and allows traders to quickly assess the market condition.
2. **MACD (Moving Average Convergence Divergence)**:
- MACD is an oscillator that shows the difference between two EMAs (with periods 6 and 13) and a **signal line** with a period of 5.
- A **buy signal** is generated when the MACD line crosses above the signal line, indicating a potential bullish trend.
- A **sell signal** is generated when the MACD line crosses below the signal line, indicating a possible bearish trend.
- Shorter MACD periods make the strategy more sensitive to price changes, allowing for more frequent trading signals.
3. **RSI (Relative Strength Index)**:
- RSI measures the speed and magnitude of directional price movements to determine if an asset is overbought or oversold.
- The strategy uses a standard RSI period of 14, but with relaxed levels for more signals.
- **For buy entries**, RSI should be above 40, signaling the start of a bullish impulse without indicating overbought conditions.
- **For sell entries**, RSI should be below 60, signaling potential bearish movement without being oversold.
Entry Conditions
- **Buy Signal**:
- The MACD line crosses above the signal line.
- EMA 20 is above EMA 50 (uptrend).
- RSI is above 40, indicating a potential rise without overbought conditions.
- When these conditions are met, the strategy enters a **long position**.
- **Sell Signal**:
- The MACD line crosses below the signal line.
- EMA 20 is below EMA 50 (downtrend).
- RSI is below 60, indicating a possible decline without being oversold.
- When these conditions are met, the strategy enters a **short position**.
Take-Profit Levels
- **Take Profit** is calculated at 1.5% of the entry price:
- **For long positions**, take profit is set at a level 1.5% above the entry price.
- **For short positions**, take profit is set at a level 1.5% below the entry price.
- This take-profit level is displayed as a blue line on the chart, giving traders a clear idea of the target profit point for each trade.
Visualization and Colors
- The main trend line (EMA 20) changes to green in an uptrend and red in a downtrend. This provides a clear visual indicator of the current trend direction.
- Take-profit levels are displayed as blue lines, helping traders follow targets and lock in profits at recommended levels.
Usage Recommendations
- **Timeframe**: The strategy is optimized for a 30-minute timeframe. At this interval, signals are frequent enough without being overly sensitive to noise.
- **Applicability**: The strategy works well for assets with moderate to high volatility, such as stocks, cryptocurrencies, and currency pairs.
- **Risk Management**: In addition to take profit, a stop loss at around 1-2% is recommended to minimize losses in case of sudden trend reversals.
Conclusion
This strategy is designed for more frequent signals by using faster indicators and relaxed RSI conditions. It is suitable for traders seeking quick trade opportunities and clearly defined take-profit levels.
Price Action StrategyThe **Price Action Strategy** is a tool designed to capture potential market reversals by utilizing classic reversal candlestick patterns such as Hammer, Shooting Star, Doji, and Pin Bar near dinamic support and resistance levels.
***Note to moderators
- The moving average was removed from the strategy because it was not suitable for the strategy and not participating in the entry or exit criteria.
- The moving average length has been replaced/renamed by the support/resistance lenght.
- The bullish engulfing and bearish engulfing patterns were also removed because in practice they were not working as entry criteria, since the candle price invariably closes far from the support/resistance level even considering the sensitivity range. There was no change in the backtest results after removing these patterns.
### Key Elements of the Strategy
1. Support and Resistance Levels
- Support and resistance are pivotal price levels where the asset has previously struggled to move lower (support) or higher (resistance). These levels act as psychological barriers where buying interest (at support) or selling interest (at resistance) often increases, potentially causing price reversals.
- In this strategy, support is calculated as the lowest low and resistance as the highest high over a 16-period length. When the price nears these levels, it indicates possible zones for a reversal, and the strategy looks for specific candlestick patterns to confirm an entry.
2. Candlestick Patterns
- This strategy uses classic reversal patterns, including:
- **Hammer**: Indicates a buy signal, suggesting rejection of lower prices.
- **Shooting Star**: Suggests a sell signal, showing rejection of higher prices.
- **Doji**: Reflects indecision and potential reversal.
- **Pin Bar**: Represents price rejection with a long shadow, often signaling a reversal.
By combining these reversal patterns with the proximity to dinamic support or resistance levels, the strategy aims to capture potential reversal movements.
3. Sensitivity Level
- The sensitivity parameter adjusts the acceptable range (Default 0.018 = 1.8%) around support and resistance levels within which reversal patterns can trigger trades (i.e. the closing price of the candle must occur within the specified range defined by the sensitivity parameter). A higher sensitivity value expands this range, potentially leading to less accurate signals, as it may allow for more false positives.
4. Entry Criteria
- **Buy (Long)**: A Hammer, Doji, or Pin Bar pattern near support.
- **Sell (Short)**: A Shooting Star, Doji, or Pin Bar near resistance.
5. Exit criteria
- Take profit = 9.5%
- Stop loss = 16%
6. No Repainting
- The Price Action Strategy is not subject to repainting.
7. Position Sizing by Equity and risk management
- This strategy has a default configuration to operate with 35% of the equity. The stop loss is set to 16% from the entry price. This way, the strategy is putting at risk about 16% of 35% of equity, that is, around 5.6% of equity for each trade. The percentage of equity and stop loss can be adjusted by the user according to their risk management.
8. Backtest results
- This strategy was subjected to deep backtest and operations in replay mode on **1000000MOGUSDT.P**, with the inclusion of transaction fees at 0.12% and slipagge of 5 ticks, and the past results have shown consistent profitability. Past results are no guarantee of future results. The strategy's backtest results may even be due to overfitting with past data.
9. Chart Visualization
- Support and resistance levels are displayed as green (support) and red (resistance) lines.
- Only the candlestick pattern that generated the entry signal to triger the trade is identified and labeled on the chart. During the operation, the occurrence of new Doji, Pin Bar, Hammer and Shooting Star patterns will not be demonstrated on the chart, since the exit criteria are based on percentage take profit and stop loss.
Doji:
Pin Bar and Doji
Shooting Star and Doji
Hammer
10. Default settings
Chart timeframe: 20 min
Moving average lenght: 16
Sensitivity: 0.018
Stop loss (%): 16
Take Profit (%): 9.5
BYBIT:1000000MOGUSDT.P
Supertrend StrategyThe Supertrend Strategy was created based on the Supertrend and Relative Strength Index (RSI) indicators, widely respected tools in technical analysis. This strategy combines these two indicators to capture market trends with precision and reliability, looking for optimizing exit levels at oversold or overbought price levels.
The Supertrend indicator identifies trend direction based on price and volatility by using the Average True Range (ATR). The ATR measures market volatility by calculating the average range between an asset’s high and low prices over a set period. It provides insight into price fluctuations, with higher ATR values indicating increased volatility and lower values suggesting stability. The Supertrend Indicator plots a line above or below the price, signaling potential buy or sell opportunities: when the price closes above the Supertrend line, an uptrend is indicated, while a close below the line suggests a downtrend. This line shifts as price movements and volatility levels change, acting as both a trailing stop loss and trend confirmation.
To enhance the Supertrend strategy, the Relative Strength Index (RSI) has been added as an exit criterion. As a momentum oscillator, the RSI indicates overbought (usually above 70) or oversold (usually below 30) conditions. This integration allows trades to close when the asset is overbought or oversold, capturing gains before a possible reversal, even if the percentage take profit level has not been reached. This mechanism aims to prevent losses due to market reversals before the Supertrend signal changes.
### Key Features
1. **Entry criteria**:
- The strategy uses the Supertrend indicator calculated by adding or subtracting a multiple of the ATR from the closing price, depending on the trend direction.
- When the price crosses above the Supertrend line, the strategy signals a long (buy) entry. Conversely, when the price crosses below, it signals a short (sell) entry.
- The strategy performs a reversal if there is an open position and a change in the direction of the supertrend occurs
2. **Exit criteria**:
- Take profit of 30% (default) on the average position price.
- Oversold (≤ 5) or overbought (≥ 95) RSI
- Reversal when there is a change in direction of the Supertrend
3. **No Repainting**:
- This strategy is not subject to repainting, as long as the timeframe configured on your chart is the same as the supertrend timeframe .
4. **Position Sizing by Equity and risk management**:
- This strategy has a default configuration to operate with 35% of the equity. At the time of opening the position, the supertrend line is typically positioned at about 12 to 16% of the entry price. This way, the strategy is putting at risk about 16% of 35% of equity, that is, around 5.6% of equity for each trade. The percentage of equity can be adjusted by the user according to their risk management.
5. **Backtest results**:
- This strategy was subjected to deep backtesting and operations in replay mode, including transaction fees of 0.12%, and slippage of 5 ticks.
- The past results in deep backtest and replay mode were compatible and profitable (Variable results depending on the take profit used, supertrend and RSI parameters). However, it should be noted that few operations were evaluated, since the currency in question has been created for a short time and the frequency of operations is relatively small.
- Past results are no guarantee of future results. The strategy's backtest results may even be due to overfitting with past data.
Default Settings
Chart timeframe: 2h
Supertrend Factor: 3.42
ATR period: 14
Supertrend timeframe: 2 h
RSI timeframe: 15 min
RSI Lenght: 5 min
RSI Upper limit: 95
RSI Lower Limit: 5
Take Profit: 30%
BYBIT:1000000MOGUSDT.P
Keltner Channel Strategy by Kevin DaveyKeltner Channel Strategy Description
The Keltner Channel Strategy is a volatility-based trading approach that uses the Keltner Channel, a technical indicator derived from the Exponential Moving Average (EMA) and Average True Range (ATR). The strategy helps identify potential breakout or mean-reversion opportunities in the market by plotting upper and lower bands around a central EMA, with the channel width determined by a multiplier of the ATR.
Components:
1. Exponential Moving Average (EMA):
The EMA smooths price data by placing greater weight on recent prices, allowing traders to track the market’s underlying trend more effectively than a simple moving average (SMA). In this strategy, a 20-period EMA is used as the midline of the Keltner Channel.
2. Average True Range (ATR):
The ATR measures market volatility over a 14-period lookback. By calculating the average of the true ranges (the greatest of the current high minus the current low, the absolute value of the current high minus the previous close, or the absolute value of the current low minus the previous close), the ATR captures how much an asset typically moves over a given period.
3. Keltner Channel:
The upper and lower boundaries are set by adding or subtracting 1.5 times the ATR from the EMA. These boundaries create a dynamic range that adjusts with market volatility.
Trading Logic:
• Long Entry Condition: The strategy enters a long position when the closing price falls below the lower Keltner Channel, indicating a potential buying opportunity at a support level.
• Short Entry Condition: The strategy enters a short position when the closing price exceeds the upper Keltner Channel, signaling a potential selling opportunity at a resistance level.
The strategy plots the upper and lower Keltner Channels and the EMA on the chart, providing a visual representation of support and resistance levels based on market volatility.
Scientific Support for Volatility-Based Strategies:
The use of volatility-based indicators like the Keltner Channel is supported by numerous studies on price momentum and volatility trading. Research has shown that breakout strategies, particularly those leveraging volatility bands such as the Keltner Channel or Bollinger Bands, can be effective in capturing trends and reversals in both trending and mean-reverting markets  .
Who is Kevin Davey?
Kevin Davey is a highly respected algorithmic trader, author, and educator, known for his systematic approach to building and optimizing trading strategies. With over 25 years of experience in the markets, Davey has earned a reputation as an expert in quantitative and rule-based trading. He is particularly well-known for winning several World Cup Trading Championships, where he consistently demonstrated high returns with low risk.
Bollinger Bands Mean Reversion by Kevin Davey Bollinger Bands Mean Reversion Strategy Description
The Bollinger Bands Mean Reversion Strategy is a popular trading approach based on the concept of volatility and market overreaction. The strategy leverages Bollinger Bands, which consist of an upper and lower band plotted around a central moving average, typically using standard deviations to measure volatility. When the price moves beyond these bands, it signals potential overbought or oversold conditions, and the strategy seeks to exploit a reversion back to the mean (the central band).
Strategy Components:
1. Bollinger Bands:
The bands are calculated using a 20-period Simple Moving Average (SMA) and a multiple (usually 2.0) of the standard deviation of the asset’s price over the same period. The upper band represents the SMA plus two standard deviations, while the lower band is the SMA minus two standard deviations. The distance between the bands increases with higher volatility and decreases with lower volatility.
2. Mean Reversion:
Mean reversion theory suggests that, over time, prices tend to move back toward their historical average. In this strategy, a buy signal is triggered when the price falls below the lower Bollinger Band, indicating a potential oversold condition. Conversely, the position is closed when the price rises back above the upper Bollinger Band, signaling an overbought condition.
Entry and Exit Logic:
Buy Condition: The strategy enters a long position when the price closes below the lower Bollinger Band, anticipating a mean reversion to the central band (SMA).
Sell Condition: The long position is exited when the price closes above the upper Bollinger Band, implying that the market is likely overbought and a reversal could occur.
This approach uses mean reversion principles, aiming to capitalize on short-term price extremes and volatility compression, often seen in sideways or non-trending markets. Scientific studies have shown that mean reversion strategies, particularly those based on volatility indicators like Bollinger Bands, can be effective in capturing small but frequent price reversals  .
Scientific Basis for Bollinger Bands:
Bollinger Bands, developed by John Bollinger, are widely regarded in both academic literature and practical trading as an essential tool for volatility analysis and mean reversion strategies. Research has shown that Bollinger Bands effectively identify relative price highs and lows, and can be used to forecast price volatility and detect potential breakouts . Studies in financial markets, such as those by Fernández-Rodríguez et al. (2003), highlight the efficacy of Bollinger Bands in detecting overbought or oversold conditions in various assets .
Who is Kevin Davey?
Kevin Davey is an award-winning algorithmic trader and highly regarded expert in developing and optimizing systematic trading strategies. With over 25 years of experience, Davey gained significant recognition after winning the prestigious World Cup Trading Championships multiple times, where he achieved triple-digit returns with minimal drawdown. His success has made him a key figure in algorithmic trading education, with a focus on disciplined and rule-based trading systems.
Universal Trend Following Strategy | RocheurUniversal All Assets Strategy by Rocheur
The Universal All Assets Strategy is a cutting-edge, trend-following algorithm designed to operate seamlessly across multiple asset classes, including equities, commodities, forex, and cryptocurrencies. This strategy leverages the power of eight unique indicators, offering traders robust, adaptive signals. Its dynamic logic, combined with a comprehensive risk management framework, allows for precision trading in a variety of market conditions.
Core Methodologies and Features
1. Eight Integrated Trend Indicators
At the heart of the Universal All Assets Strategy are eight sophisticated trend-following indicators, each designed to capture different facets of market behavior. These indicators work together to provide a multi-dimensional analysis of price trends, filtering out noise and reacting only to significant movements:
Directional Moving Averages : Tracks the primary market trend, offering a clear indication of long-term price direction, ideal for identifying sustained upward or downward movements.
Smoothed Moving Averages : Reduces short-term volatility and noise to reveal the underlying trend, enhancing signal clarity and helping traders avoid reacting to temporary price spikes.
RSI Loops : Utilizes the Relative Strength Index (RSI) to assess market momentum, using a unique for loop mechanism to smooth out data and enhance precision.
Supertrend Filters : This indicator dynamically adjusts to market volatility, closely following price action to detect significant breakouts or reversals. The Supertrend is a core component for identifying shifts in trend direction with minimal lag.
RVI for Loop : The Relative Volatility Index (RVI) measures the strength of market volatility. It is optimized with a for loop mechanism, which smooths out the data and improves directional cues, especially in choppy or sideways markets.
Hull for Loop : The Hull Moving Average is designed to minimize lag while offering a smooth, responsive trend line. The for loop mechanism further enhances this by making the Hull even more sensitive to trend shifts, ensuring faster reaction to market movements without generating excessive noise.
These indicators evaluate market conditions independently, assigning a score of 1 for bullish trends and -1 for bearish trends. The average score across all eight indicators is calculated for each time frame (or bar), and this score determines whether the strategy should enter, exit, or remain neutral in a trade.
2. Scoring and Signal Confirmation
The strategy’s confirmation system ensures that trades are initiated only when there is strong alignment across multiple indicators:
A Long Position (Buy) is initiated when the majority of indicators generate a bullish signal, i.e., the average score exceeds a predefined upper threshold.
A Short Position or Exit is triggered when the average score falls below a lower threshold, signaling a bearish trend or neutral market.
By using a majority-rule confirmation system, the strategy filters out weak signals, reducing the chances of reacting to market noise or false positives. This ensures that only robust trends—those supported by multiple indicators—trigger trades.
Adaptive Logic for All Asset Classes
The Universal All Assets Strategy stands out for its ability to adapt dynamically across different asset classes. Whether it’s applied to highly volatile assets like cryptocurrencies or more stable instruments like equities, the strategy fine-tunes its behavior to match the asset’s volatility profile and price behavior.
Volatility Filters : The system incorporates volatility-sensitive filters, such as the Average True Range (ATR) and standard deviation metrics, which dynamically adjust its sensitivity based on market conditions. This ensures the strategy remains responsive to significant price movements while filtering out inconsequential fluctuations.
This adaptability makes the Universal All Assets Strategy effective across diverse markets, providing consistent performance whether the market is trending, range-bound, or experiencing high volatility.
Customization and Flexibility
1. Directional Bias
The strategy offers traders the flexibility to set a customizable directional bias, allowing it to focus on:
Long-only trades during bullish markets.
Short-only trades during bear markets.
Bi-directional trades for those looking to capitalize on both uptrends and downtrends.
This bias can be fine-tuned based on market conditions, trader preference, or risk tolerance, without compromising the integrity of the overall signal-generation process.
2. Volatility Sensitivity
Traders can adjust the strategy’s volatility sensitivity through customizable settings. By modifying how the system reacts to volatility, traders can make the strategy more aggressive in high-volatility environments or more conservative in quieter markets, depending on their individual trading style.
Visual Representation of Component Behavior
One of the unique features of the strategy is its real-time visual representation of the eight indicators through a component table displayed on the chart. This table provides a clear overview of the current status of each indicator:
A score of 1 indicates a bullish signal.
A score of -1 indicates a bearish signal.
The table is updated at each time frame (bar), showing how each indicator is contributing to the overall trend decision. This real-time feedback allows traders to monitor the exact composition of the strategy’s signal, helping them better understand market dynamics.
Oscillator Visualization for Trend Detection
To complement the component table, the strategy includes a trend oscillator displayed beneath the price chart, offering a visual summary of the overall market direction:
Green bars represent bullish trends when the majority of indicators signal an uptrend.
Red bars represent bearish trends or a neutral (cash) position when the majority of indicators detect a downtrend.
This oscillator allows traders to quickly assess the market’s overall direction at a glance, without needing to analyze each individual indicator, providing a clear and immediate visual of the market trend.
Backtested and Forward-Tested for Real-World Conditions
The Universal All Assets Strategy has been thoroughly tested under real-world trading conditions, incorporating key factors like:
Slippage : Set at 20 ticks to represent real market fluctuations.
Order Size : Calculated as 10% of equity, ensuring appropriate risk exposure for realistic capital management.
Commission : A fee of 0.05% has been factored in to account for trading costs.
These settings ensure that the strategy’s performance metrics—such as the Sortino Ratio , Sharpe Ratio , Omega Ratio , and Profit Factor —are reflective of actual trading environments. The rigorous backtesting and forward-testing processes ensure that the strategy produces realistic results, making it compatible with the markets it is written for and demonstrating how the system would behave in live conditions. It also includes robust risk management tools to minimize drawdowns and preserve capital, making it suitable for both professional and retail traders.
Anti-Fragile Design and Realistic Expectations
The Universal All Assets Strategy is engineered to be anti-fragile, thriving in volatile markets by adjusting to turbulence rather than being damaged by it. This is a crucial feature that ensures the strategy remains effective even during times of significant market instability.
Moreover, the strategy is transparent about realistic expectations, acknowledging that no system can guarantee a 100% win rate and that past performance is not indicative of future results. This transparency fosters trust and provides traders with a realistic framework for long-term success, making it an ideal choice for traders looking to navigate complex market conditions with confidence.
Acknowledgment of External Code
Special credit goes to bii_vg, whose invite-only code was used with permission in the development of the Universal All Assets Strategy. Their contributions have been instrumental in refining certain aspects of this strategy, ensuring its robustness and adaptability across various markets.
Conclusion
The Universal All Assets Strategy by Rocheur offers traders a powerful, adaptable tool for capturing trends across a wide range of asset classes. Its eight-indicator confirmation system, combined with customizable settings and real-time visual representations, provides a comprehensive solution for traders seeking precision, flexibility, and consistency. Whether used in high-volatility markets or more stable environments, the strategy’s dynamic adaptability, transparent logic, and robust testing make it an excellent choice for traders aiming to maximize performance while managing risk effectively.
Oscillator Price Divergence & Trend Strategy (DPS) // AlgoFyreThe Oscillator Price Divergence & Trend Strategy (DPS) strategy combines price divergence and trend indicators for trend trading. It uses divergence conditions to identify entry points and a trend source for directional bias. The strategy incorporates risk management through dynamic position sizing based on a fixed risk amount. It allows for both long and short positions with customizable stop-loss and take-profit levels. The script includes visualization options for entry, stop-loss, and take-profit levels, enhancing trade analysis.
TABLE OF CONTENTS
🔶 ORIGINALITY
🔸Divergence-Trend Combination
🔸Dynamic Position Sizing
🔸Customizable Risk Management
🔶 FUNCTIONALITY
🔸Indicators
🞘 Trend Indicator
🞘 Oscillator Source
🔸Conditions
🞘 Long Entry
🞘 Short Entry
🞘 Take Profit
🞘 Stop Loss
🔶 INSTRUCTIONS
🔸Adding the Strategy to the Chart
🔸Configuring the Strategy
🔸Backtesting and Practice
🔸Market Awareness
🔸Visual Customization
🔶 CONCLUSION
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🔶 ORIGINALITY The Divergence Trend Trading with Dynamic Position Sizing strategy uniquely combines price divergence indicators with trend analysis to optimize entry and exit points. Unlike static trading strategies, it employs dynamic position sizing based on a fixed risk amount, ensuring consistent risk management. This approach allows traders to adapt to varying market conditions by adjusting position sizes according to predefined risk parameters, enhancing both flexibility and control in trading decisions. The strategy's integration of customizable stop-loss and take-profit levels further refines its risk management capabilities, making it a robust tool for both trending and volatile markets.
🔸Divergence-Trend Combination By combining trend direction with divergence conditions, the strategy enhances the accuracy of entry signals, aligning trades with prevailing market trends.
🔸Dynamic Position Sizing This strategy calculates position sizes dynamically, based on a fixed risk amount, allowing traders to maintain consistent risk exposure across trades.
🔸Customizable Risk Management Traders can set flexible risk-reward ratios and adjust stop-loss and take-profit levels, tailoring the strategy to their risk tolerance and market conditions.
🔶 FUNCTIONALITY The Divergence Trend Trading with Dynamic Position Sizing strategy leverages a combination of trend indicators and price and oscillator divergences to identify optimal trading opportunities. This strategy is designed to capitalize on medium to long-term price movements and works best on h1, h4 or D1 timeframes. It allows traders to manage risk effectively while taking advantage of both long and short positions.
🔸Indicators 🞘 Trend Indicator: A long trend is used to determine market direction, ensuring trades align with prevailing trends.
Recommendation: We recommend using the Adaptive MAs (Hurst, CVaR, Fractal) // AlgoFyre indicator with the following settings for trend detection. However, you can use any trend indicator that suits your trading style, e.g. an EMA 200.
🞘 Oscillator Source: The oscillator source is used for momentum price divergence identification. Any momentum oscillator can be used, e.g. RSI, Stochastic etc. A good oscillator is the Stochastic with the following settings:
🔸Conditions 🞘 Long Entry: A long entry condition is met if price closes above the trend AND selected divergence conditions are met, e.g. regular bullish divergence with a 10 bar lookback period with the divergence being below the 50 point mean. If the info table shows all 3 columns in the same color, the entry conditions are met and a position is opened.
🞘 Short Entry: A short entry condition is met if price closes below the trend AND selected divergence conditions are met, e.g. regular bearish divergence with a 10 bar lookback period with the divergence being above the 50 point mean.
🞘 Take Profit: Take Profit is determined by the Risk to Reward Ratio settings depending on the price distance between the entry price and the stop loss price, e.g. if stop loss is 1% away from entry and Risk Reward Ratio is 3:1 then Take Profit will be set at 3% from entry.
🞘 Stop Loss: Stop loss is a fixed level away from the trend source. For long positions, stop loss is set below the trend, and for short positions, above the trend.
🔶 INSTRUCTIONS The Divergence Trend Trading with Dynamic Position Sizing strategy can be set up by adding it to your TradingView chart and configuring parameters such as the oscillator source, trend source, and risk management settings. This strategy is designed to capitalize on short-term price movements by dynamically adjusting position sizes based on predefined risk parameters. Enhance the accuracy of signals by combining this strategy with additional indicators like trend-following or momentum-based tools. Adjust settings to better manage risk and optimize entry and exit points.
🔸Adding the Strategy to the Chart:
Go to your TradingView chart.
Click on the "Indicators" button at the top.
Search for "Divergence Trend Trading with Dynamic Position Sizing // AlgoFyre" in the indicators list.
Click on the strategy to add it to your chart.
🔸Configuring the Strategy:
Open the strategy settings by clicking on the gear icon next to its name on the chart.
Oscillator Source: Select the source for the oscillator. An oscillator like Stochastic needs to be attached to the chart already in order to be used as an oscillator source to be selectable.
Trend Source: Choose the trend source to determine market direction. A trend indicator like Adaptive MAs (Hurst, CVaR, Fractal) // AlgoFyre needs to be attached to the chart already in order to be used as a trend source to be selectable.
Stop Loss Percentage: Set the stop loss distance from the trend source as a percentage.
Risk/Reward Ratio: Define the desired risk/reward ratio for trades.
🔸Backtesting and Practice:
Backtest the strategy on historical data to understand how it performs in various market environments.
Practice using the strategy on a demo account before implementing it in live trading.
🔸Market Awareness:
Keep an eye on market news and events that might cause extreme price movements. The strategy reacts to price data and might not account for news-driven events that can cause large deviations.
🔸Visual Customization Visualization Settings: Customize the display of entry price, take profit, and stop loss levels.
Color Settings: Switch to the AlgoFyre theme or set custom colors for bullish, bearish, and neutral states.
Table Settings: Enable or disable the information table and adjust its position.
🔶 CONCLUSION
The Divergence Trend Trading with Dynamic Position Sizing strategy provides a robust framework for capitalizing on short-term market trends by combining price divergence with dynamic position sizing. This strategy leverages divergence conditions to identify entry points and utilizes a trend source for directional bias, ensuring trades align with prevailing market conditions. By incorporating dynamic position sizing based on a fixed risk amount, traders can effectively manage risk and adapt to varying market conditions. The strategy's customizable stop-loss and take-profit levels further enhance its risk management capabilities, making it a versatile tool for both trending and volatile markets. With its strategic blend of technical indicators and risk management, the Divergence Trend Trading strategy offers traders a comprehensive approach to optimizing trade execution and maximizing potential returns.
Advanced Multi-Seasonality StrategyThe Multi-Seasonality Strategy is a trading system based on seasonal market patterns. Seasonality refers to recurring market trends driven by predictable calendar-based events. These patterns emerge due to economic cycles, corporate activities (e.g., earnings reports), and investor behavior around specific times of the year. Studies have shown that such effects can influence asset prices over defined periods, leading to opportunities for traders who exploit these patterns (Hirshleifer, 2001; Bouman & Jacobsen, 2002).
How the Strategy Works:
The strategy allows the user to define four distinct periods within a calendar year. For each period, the trader selects:
Entry Date (Month and Day): The date to enter the trade.
Holding Period: The number of trading days to remain in the trade after the entry.
Trade Direction: Whether to take a long or short position during that period.
The system is designed with flexibility, enabling the user to activate or deactivate each of the four periods. The idea is to take advantage of seasonal patterns, such as buying during historically strong periods and selling during weaker ones. A well-known example is the "Sell in May and Go Away" phenomenon, which suggests that stock returns are higher from November to April and weaker from May to October (Bouman & Jacobsen, 2002).
Seasonality in Financial Markets:
Seasonal effects have been documented across different asset classes and markets:
Equities: Stock markets tend to exhibit higher returns during certain months, such as the "January effect," where prices rise after year-end tax-loss selling (Haugen & Lakonishok, 1987).
Commodities: Agricultural commodities often follow seasonal planting and harvesting cycles, which impact supply and demand patterns (Fama & French, 1987).
Forex: Currency pairs may show strength or weakness during specific quarters based on macroeconomic factors, such as fiscal year-end flows or central bank policy decisions.
Scientific Basis:
Research shows that market anomalies like seasonality are linked to behavioral biases and institutional practices. For example, investors may respond to tax incentives at the end of the year, and companies may engage in window dressing (Haugen & Lakonishok, 1987). Additionally, macroeconomic factors, such as monetary policy shifts and holiday trading volumes, can also contribute to predictable seasonal trends (Bouman & Jacobsen, 2002).
Risks of Seasonal Trading:
While the strategy seeks to exploit predictable patterns, there are inherent risks:
Market Changes: Seasonal effects observed in the past may weaken or disappear as market conditions evolve. Increased algorithmic trading, globalization, and policy changes can reduce the reliability of historical patterns (Lo, 2004).
Overfitting: One of the risks in seasonal trading is overfitting the strategy to historical data. A pattern that worked in the past may not necessarily work in the future, especially if it was based on random chance or external factors that no longer apply (Sullivan, Timmermann, & White, 1999).
Liquidity and Volatility: Trading during specific periods may expose the trader to low liquidity, especially around holidays or earnings seasons, leading to slippage and larger-than-expected price swings.
Economic and Geopolitical Shocks: External events such as pandemics, wars, or political instability can disrupt seasonal patterns, leading to unexpected market behavior.
Conclusion:
The Multi-Seasonality Strategy capitalizes on the predictable nature of certain calendar-based patterns in financial markets. By entering and exiting trades based on well-established seasonal effects, traders can potentially capture short-term profits. However, caution is necessary, as market dynamics can change, and seasonal patterns are not guaranteed to persist. Rigorous backtesting, combined with risk management practices, is essential to successfully implementing this strategy.
References:
Bouman, S., & Jacobsen, B. (2002). The Halloween Indicator, "Sell in May and Go Away": Another Puzzle. American Economic Review, 92(5), 1618-1635.
Fama, E. F., & French, K. R. (1987). Commodity Futures Prices: Some Evidence on Forecast Power, Premiums, and the Theory of Storage. Journal of Business, 60(1), 55-73.
Haugen, R. A., & Lakonishok, J. (1987). The Incredible January Effect: The Stock Market's Unsolved Mystery. Dow Jones-Irwin.
Hirshleifer, D. (2001). Investor Psychology and Asset Pricing. Journal of Finance, 56(4), 1533-1597.
Lo, A. W. (2004). The Adaptive Markets Hypothesis: Market Efficiency from an Evolutionary Perspective. Journal of Portfolio Management, 30(5), 15-29.
Sullivan, R., Timmermann, A., & White, H. (1999). Data-Snooping, Technical Trading Rule Performance, and the Bootstrap. Journal of Finance, 54(5), 1647-1691.
This strategy harnesses the power of seasonality but requires careful consideration of the risks and potential changes in market behavior over time.
Statistical ArbitrageThe Statistical Arbitrage Strategy, also known as pairs trading, is a quantitative trading method that capitalizes on price discrepancies between two correlated assets. The strategy assumes that over time, the prices of these two assets will revert to their historical relationship. The core idea is to take advantage of mean reversion, a principle suggesting that asset prices will revert to their long-term average after deviating significantly.
Strategy Mechanics:
1. Selection of Correlated Assets:
• The strategy focuses on two historically correlated assets (e.g., equity index futures like Dow Jones Mini and S&P 500 Mini). These assets tend to move in the same direction due to similar underlying fundamentals, such as overall market conditions. By tracking their relative prices, the strategy seeks to exploit temporary mispricings.
2. Spread Calculation:
• The spread is the difference between the prices of the two assets. This spread represents the relationship between the assets and serves as the basis for determining when to enter or exit trades.
3. Mean and Standard Deviation:
• The historical average (mean) of the spread is calculated using a Simple Moving Average (SMA) over a chosen period. The strategy also computes the standard deviation (volatility) of the spread, which measures how far the spread has deviated from the mean over time. This allows the strategy to define statistically significant price deviations.
4. Entry Signal (Mean Reversion):
• A buy signal is triggered when the spread falls below the mean by a multiple (e.g., two) of the standard deviation. This indicates that one asset is temporarily undervalued relative to the other, and the strategy expects the spread to revert to its mean, generating profits as the prices converge.
5. Exit Signal:
• The strategy exits the trade when the spread reverts to the mean. At this point, the mispricing has been corrected, and the profit from the mean reversion is realized.
Academic Support:
Statistical arbitrage has been widely studied in finance and economics. Gatev, Goetzmann, and Rouwenhorst’s (2006) landmark study on pairs trading demonstrated that this strategy could generate excess returns in equity markets. Their research found that by focusing on historically correlated stocks, traders could identify pricing anomalies and profit from their eventual correction.
Additionally, Avellaneda and Lee (2010) explored statistical arbitrage in different asset classes and found that exploiting deviations in price relationships can offer a robust, market-neutral trading strategy. In these studies, the strategy’s success hinges on the stability of the relationship between the assets and the timely execution of trades when deviations occur.
Risks of Statistical Arbitrage:
1. Correlation Breakdown:
• One of the primary risks is the breakdown of correlation between the two assets. Statistical arbitrage assumes that the historical relationship between the assets will hold in the future. However, market conditions, company fundamentals, or external shocks (e.g., macroeconomic changes) can cause these assets to deviate permanently, leading to potential losses.
• For instance, if two equity indices historically move together but experience divergent economic conditions or policy changes, their prices may no longer revert to the expected mean.
2. Execution Risk:
• This strategy relies on efficient execution and tight spreads. In volatile or illiquid markets, the actual price at which trades are executed may differ significantly from expected prices, leading to slippage and reduced profits.
3. Market Risk:
• Although statistical arbitrage is designed to be market-neutral (i.e., not dependent on the overall market direction), it is not entirely risk-free. Systematic market shocks, such as financial crises or sudden shifts in market sentiment, can affect both assets simultaneously, causing the spread to widen rather than revert to the mean.
4. Model Risk:
• The assumptions underlying the strategy, particularly regarding mean reversion, may not always hold true. The model assumes that asset prices will return to their historical averages within a certain timeframe, but the timing and magnitude of mean reversion can be uncertain. Misestimating this timeframe can lead to extended drawdowns or unrealized losses.
5. Overfitting:
• Over-reliance on historical data to fine-tune the strategy parameters (e.g., the lookback period or standard deviation thresholds) may result in overfitting. This means that the strategy works well on past data but fails to perform in live markets due to changing conditions.
Conclusion:
The Statistical Arbitrage Strategy offers a systematic and quantitative approach to trading that capitalizes on temporary price inefficiencies between correlated assets. It has been proven to generate returns in academic studies and is widely used by hedge funds and institutional traders for its market-neutral characteristics. However, traders must be aware of the inherent risks, including correlation breakdown, execution risks, and the potential for prolonged deviations from the mean. Effective risk management, diversification, and constant monitoring are essential for successfully implementing this strategy in live markets.
ICT Master Suite [Trading IQ]Hello Traders!
We’re excited to introduce the ICT Master Suite by TradingIQ, a new tool designed to bring together several ICT concepts and strategies in one place.
The Purpose Behind the ICT Master Suite
There are a few challenges traders often face when using ICT-related indicators:
Many available indicators focus on one or two ICT methods, which can limit traders who apply a broader range of ICT related techniques on their charts.
There aren't many indicators for ICT strategy models, and we couldn't find ICT indicators that allow for testing the strategy models and setting alerts.
Many ICT related concepts exist in the public domain as indicators, not strategies! This makes it difficult to verify that the ICT concept has some utility in the market you're trading and if it's worth trading - it's difficult to know if it's working!
Some users might not have enough chart space to apply numerous ICT related indicators, which can be restrictive for those wanting to use multiple ICT techniques simultaneously.
The ICT Master Suite is designed to offer a comprehensive option for traders who want to apply a variety of ICT methods. By combining several ICT techniques and strategy models into one indicator, it helps users maximize their chart space while accessing multiple tools in a single slot.
Additionally, the ICT Master Suite was developed as a strategy . This means users can backtest various ICT strategy models - including deep backtesting. A primary goal of this indicator is to let traders decide for themselves what markets to trade ICT concepts in and give them the capability to figure out if the strategy models are worth trading!
What Makes the ICT Master Suite Different
There are many ICT-related indicators available on TradingView, each offering valuable insights. What the ICT Master Suite aims to do is bring together a wider selection of these techniques into one tool. This includes both key ICT methods and strategy models, allowing traders to test and activate strategies all within one indicator.
Features
The ICT Master Suite offers:
Multiple ICT strategy models, including the 2022 Strategy Model and Unicorn Model, which can be built, tested, and used for live trading.
Calculation and display of key price areas like Breaker Blocks, Rejection Blocks, Order Blocks, Fair Value Gaps, Equal Levels, and more.
The ability to set alerts based on these ICT strategies and key price areas.
A comprehensive, yet practical, all-inclusive ICT indicator for traders.
Customizable Timeframe - Calculate ICT concepts on off-chart timeframes
Unicorn Strategy Model
2022 Strategy Model
Liquidity Raid Strategy Model
OTE (Optimal Trade Entry) Strategy Model
Silver Bullet Strategy Model
Order blocks
Breaker blocks
Rejection blocks
FVG
Strong highs and lows
Displacements
Liquidity sweeps
Power of 3
ICT Macros
HTF previous bar high and low
Break of Structure indications
Market Structure Shift indications
Equal highs and lows
Swings highs and swing lows
Fibonacci TPs and SLs
Swing level TPs and SLs
Previous day high and low TPs and SLs
And much more! An ongoing project!
How To Use
Many traders will already be familiar with the ICT related concepts listed above, and will find using the ICT Master Suite quite intuitive!
Despite this, let's go over the features of the tool in-depth and how to use the tool!
The image above shows the ICT Master Suite with almost all techniques activated.
ICT 2022 Strategy Model
The ICT Master suite provides the ability to test, set alerts for, and live trade the ICT 2022 Strategy Model.
The image above shows an example of a long position being entered following a complete setup for the 2022 ICT model.
A liquidity sweep occurs prior to an upside breakout. During the upside breakout the model looks for the FVG that is nearest 50% of the setup range. A limit order is placed at this FVG for entry.
The target entry percentage for the range is customizable in the settings. For instance, you can select to enter at an FVG nearest 33% of the range, 20%, 66%, etc.
The profit target for the model generally uses the highest high of the range (100%) for longs and the lowest low of the range (100%) for shorts. Stop losses are generally set at 0% of the range.
The image above shows the short model in action!
Whether you decide to follow the 2022 model diligently or not, you can still set alerts when the entry condition is met.
ICT Unicorn Model
The image above shows an example of a long position being entered following a complete setup for the ICT Unicorn model.
A lower swing low followed by a higher swing high precedes the overlap of an FVG and breaker block formed during the sequence.
During the upside breakout the model looks for an FVG and breaker block that formed during the sequence and overlap each other. A limit order is placed at the nearest overlap point to current price.
The profit target for this example trade is set at the swing high and the stop loss at the swing low. However, both the profit target and stop loss for this model are configurable in the settings.
For Longs, the selectable profit targets are:
Swing High
Fib -0.5
Fib -1
Fib -2
For Longs, the selectable stop losses are:
Swing Low
Bottom of FVG or breaker block
The image above shows the short version of the Unicorn Model in action!
For Shorts, the selectable profit targets are:
Swing Low
Fib -0.5
Fib -1
Fib -2
For Shorts, the selectable stop losses are:
Swing High
Top of FVG or breaker block
The image above shows the profit target and stop loss options in the settings for the Unicorn Model.
Optimal Trade Entry (OTE) Model
The image above shows an example of a long position being entered following a complete setup for the OTE model.
Price retraces either 0.62, 0.705, or 0.79 of an upside move and a trade is entered.
The profit target for this example trade is set at the -0.5 fib level. This is also adjustable in the settings.
For Longs, the selectable profit targets are:
Swing High
Fib -0.5
Fib -1
Fib -2
The image above shows the short version of the OTE Model in action!
For Shorts, the selectable profit targets are:
Swing Low
Fib -0.5
Fib -1
Fib -2
Liquidity Raid Model
The image above shows an example of a long position being entered following a complete setup for the Liquidity Raid Modell.
The user must define the session in the settings (for this example it is 13:30-16:00 NY time).
During the session, the indicator will calculate the session high and session low. Following a “raid” of either the session high or session low (after the session has completed) the script will look for an entry at a recently formed breaker block.
If the session high is raided the script will look for short entries at a bearish breaker block. If the session low is raided the script will look for long entries at a bullish breaker block.
For Longs, the profit target options are:
Swing high
User inputted Lib level
For Longs, the stop loss options are:
Swing low
User inputted Lib level
Breaker block bottom
The image above shows the short version of the Liquidity Raid Model in action!
For Shorts, the profit target options are:
Swing Low
User inputted Lib level
For Shorts, the stop loss options are:
Swing High
User inputted Lib level
Breaker block top
Silver Bullet Model
The image above shows an example of a long position being entered following a complete setup for the Silver Bullet Modell.
During the session, the indicator will determine the higher timeframe bias. If the higher timeframe bias is bullish the strategy will look to enter long at an FVG that forms during the session. If the higher timeframe bias is bearish the indicator will look to enter short at an FVG that forms during the session.
For Longs, the profit target options are:
Nearest Swing High Above Entry
Previous Day High
For Longs, the stop loss options are:
Nearest Swing Low
Previous Day Low
The image above shows the short version of the Silver Bullet Model in action!
For Shorts, the profit target options are:
Nearest Swing Low Below Entry
Previous Day Low
For Shorts, the stop loss options are:
Nearest Swing High
Previous Day High
Order blocks
The image above shows indicator identifying and labeling order blocks.
The color of the order blocks, and how many should be shown, are configurable in the settings!
Breaker Blocks
The image above shows indicator identifying and labeling order blocks.
The color of the breaker blocks, and how many should be shown, are configurable in the settings!
Rejection Blocks
The image above shows indicator identifying and labeling rejection blocks.
The color of the rejection blocks, and how many should be shown, are configurable in the settings!
Fair Value Gaps
The image above shows indicator identifying and labeling fair value gaps.
The color of the fair value gaps, and how many should be shown, are configurable in the settings!
Additionally, you can select to only show fair values gaps that form after a liquidity sweep. Doing so reduces "noisy" FVGs and focuses on identifying FVGs that form after a significant trading event.
The image above shows the feature enabled. A fair value gap that occurred after a liquidity sweep is shown.
Market Structure
The image above shows the ICT Master Suite calculating market structure shots and break of structures!
The color of MSS and BoS, and whether they should be displayed, are configurable in the settings.
Displacements
The images above show indicator identifying and labeling displacements.
The color of the displacements, and how many should be shown, are configurable in the settings!
Equal Price Points
The image above shows the indicator identifying and labeling equal highs and equal lows.
The color of the equal levels, and how many should be shown, are configurable in the settings!
Previous Custom TF High/Low
The image above shows the ICT Master Suite calculating the high and low price for a user-defined timeframe. In this case the previous day’s high and low are calculated.
To illustrate the customizable timeframe function, the image above shows the indicator calculating the previous 4 hour high and low.
Liquidity Sweeps
The image above shows the indicator identifying a liquidity sweep prior to an upside breakout.
The image above shows the indicator identifying a liquidity sweep prior to a downside breakout.
The color and aggressiveness of liquidity sweep identification are adjustable in the settings!
Power Of Three
The image above shows the indicator calculating Po3 for two user-defined higher timeframes!
Macros
The image above shows the ICT Master Suite identifying the ICT macros!
ICT Macros are only displayable on the 5 minute timeframe or less.
Strategy Performance Table
In addition to a full-fledged TradingView backtest for any of the ICT strategy models the indicator offers, a quick-and-easy strategy table exists for the indicator!
The image above shows the strategy performance table in action.
Keep in mind that, because the ICT Master Suite is a strategy script, you can perform fully automatic backtests, deep backtests, easily add commission and portfolio balance and look at pertinent metrics for the ICT strategies you are testing!
Lite Mode
Traders who want the cleanest chart possible can toggle on “Lite Mode”!
In Lite Mode, any neon or “glow” like effects are removed and key levels are marked as strict border boxes. You can also select to remove box borders if that’s what you prefer!
Settings Used For Backtest
For the displayed backtest, a starting balance of $1000 USD was used. A commission of 0.02%, slippage of 2 ticks, a verify price for limit orders of 2 ticks, and 5% of capital investment per order.
A commission of 0.02% was used due to the backtested asset being a perpetual future contract for a crypto currency. The highest commission (lowest-tier VIP) for maker orders on many exchanges is 0.02%. All entered positions take place as maker orders and so do profit target exits. Stop orders exist as stop-market orders.
A slippage of 2 ticks was used to simulate more realistic stop-market orders. A verify limit order settings of 2 ticks was also used. Even though BTCUSDT.P on Binance is liquid, we just want the backtest to be on the safe side. Additionally, the backtest traded 100+ trades over the period. The higher the sample size the better; however, this example test can serve as a starting point for traders interested in ICT concepts.
Community Assistance And Feedback
Given the complexity and idiosyncratic applications of ICT concepts amongst its proponents, the ICT Master Suite’s built-in strategies and level identification methods might not align with everyone's interpretation.
That said, the best we can do is precisely define ICT strategy rules and concepts to a repeatable process, test, and apply them! Whether or not an ICT strategy is trading precisely how you would trade it, seeing the model in action, taking trades, and with performance statistics is immensely helpful in assessing predictive utility.
If you think we missed something, you notice a bug, have an idea for strategy model improvement, please let us know! The ICT Master Suite is an ongoing project that will, ideally, be shaped by the community.
A big thank you to the @PineCoders for their Time Library!
Thank you!
Stochastic RSI OHLC StrategyThe script titled "Stochastic RSI High Low Close Bars" is a versatile trading strategy implemented in Pine Script, designed for TradingView. Here's an overview of its features:
Description
This strategy leverages the Stochastic RSI to determine entry and exit signals in the market, focusing on high, low, and close values of the indicator. It incorporates various trading styles, stop-loss mechanisms, and multi-timeframe analysis to adapt to different market conditions.
Key Features
Stochastic RSI Analysis:
Uses the Stochastic RSI to identify potential entry points for long and short positions.
Tracks high, low, and close values for more granular analysis.
Multiple Trading Styles:
Supports diverse trading styles like Volume Color Swing, RSI Divergence, RSI Pullback, and more.
Allows switching between these styles to suit market dynamics.
Session-Based Trading:
Offers session control, limiting trades to specific hours (e.g., NY sessions).
Can close all positions at the end of the trading day.
Stop-Loss and Take-Profit Mechanisms:
Includes both static and dynamic stop-losses, with options for time-based stops, trailing stops, and momentum-based exits.
Customizable take-profit levels ensure efficient trade management.
Volume Analysis:
Integrates volume indicators to add a bias for trade entries and exits, enhancing signal reliability.
Multi-Timeframe Integration:
Employs multi-timeframe RSI analysis, allowing the strategy to capture broader trends and optimize entries.
This script is designed to provide flexibility and adaptability, making it useful for different trading strategies and market conditions. It is suitable for traders looking to refine their entries and exits with a focus on the Stochastic RSI.
- Trading Bot – TopBot Anomaly Robot Strategy -- Introduction -
This strategy is based on a search for abnormal market price movements relative to a time-shifted main moving average. Different variations of the main moving average are created and shifted proportionally rather than linearly, giving the strategy greater reactivity and serving as position entry points. What's more ? This strategy stands out with a major innovation, allowing position exits to be set on variations in the moving average (and not on the moving average itself, like all strategies that close positions on return to the moving average), which greatly improves actual results.
- Detailed operation of the strategy -
It defines a function that calculates various moving averages (depending on the type of moving average defined by the user) and the chosen length. The function takes into account different types of moving averages: SMA, PCMA, EMA, WMA, DEMA, ZLEMA and HMA, and is offset in time so that it can be an entry or exit condition in real time (otherwise you'd have to wait for the next candle for the moving average to be calculated).
It calculates shifted variants (semi-parallel) as a percentage of this main moving average, high and low, to define position entry points (depending on user settings, up to 10 shifted levels for ten position entries for each direction). By calculating shifts as percentages rather than fixed values, the resulting deviations are not parallel to the main moving average, but can be used to detect sudden price contractions. By adjusting these deviations proportionally, we can observe variations relative to the main moving average more clearly, enabling us to detect dynamic support and resistance zones that adapt to market fluctuations. The fact that they are not strictly parallel avoids too rigid an interpretation and gives a more nuanced reading of trends, capturing small divergences that could indicate more subtle changes in market dynamics.
The most distinctive feature of this strategy concerns position exits: the script calculates two new moving averages shifted in proportion to the main moving average (adjustable) to define position exit price levels.
The strategy enters position when one of the deviations from the position entry moving average is crossed, and exits position when the deviation from the position exit moving average is crossed.
Position entry can be single or up to ten entry levels per direction to smooth trades. Differentiated settings are available for Longs and Shorts.
In this type of strategy, the return to the moving average is generally used as the position exit point, but this strategy incorporates a unique feature: the position exit can be made on a deviation from the moving average, adjustable and differentiated for Long and Short positions.
This is a major change compared to other strategies using a moving-average position exit, since the result is thatchanging the position exit point considerably improves the strategy's results .
Backtest with a classic exit back to the moving average :
Backtest with an exit back on an (adjustable) derivative of the moving average :
- “Ready to use” and user-adjustable parameters -
The strategy interface has been optimized for easy creation of trading robots, with all settings underlying the calculations and numerous options for optimization. Here are the contents of the strategy parameters interface:
In addition, important information about strategy settings and results is displayed directly on the chart. The percentage profit displayed may differ slightly from that of the backtest, as it includes potential profits from open trades (strategy.openprofit) in its calculation.
- Conditions, options and settings for graph and backtest presentation -
Here are the conditions and settings for the graph presented on the screen:
The strategy is set for 10 possible LONG and SHORT entries
10% of capital in x2 leverage is invested at each position entry (i.e. 20% of capital under backtest conditions)
The backtest runs for 14 months: from 08/17/2023 to 08/19/2024
It is carried out on PENDLEUSDT.P on BitGet Swap in 4H
LONGS strategy settings: 0.18 - 0.19 - 0.2 - 0.21 - 0.22 - 0.23 - 0.24 - 0.25 - 0.26 - 0.275 - LONGS output deviation: 0.03 (3%)
Strategy settings for SHORTS: 0.21 - 0.22 - 0.23 - 0.24 - 0.25 - 0.26 - 0.27 - 0.28 - 0.29 - 0.3 - LONGS output deviation: 0.032 (3.2%)
All other settings are strategy defaults - Broker fees + spread are set at 0.13% per trade
We can see several interesting points:
The strategy has very high winrate if set to this objective
The settings here have not been “over-optimized”, i.e. all 10 entries are unused, leaving room for larger-than-expected market movements in the future. In this particular case, it is set to favor safety over profitability optimization, but other approaches are possible to maximize profitability.
The result is 277.75% , thanks to the strategy's adjustment of position exit levels. With a conventional exit at the moving average, results are only 204.47%, a significant difference.
- How to adjust and apply the strategy? -
Generally speaking, the strategy works well on a large proportion of cryptocurrencies, especially for LONG positions. The recommended timeframes are: 30M-45M-1H-2H-3H-4H and the most appropriate timeframe will vary according to the cryptocurrency. It is also possible, with certain assets, to run the strategy on shorter timeframes such as 5M or 15M with success.
The strategy can be used with a single position entry level, maximizing capital utilization on each trade and/or having several strategies active on a single account at the same time
It can also be used in a “safe” way, using up to ten successive entries to smooth out unforeseen market movements and minimize risk as much as possible. In this case, enter positions with 1/10 of the capital each time, for a setting of ten entries, and give preference to a single active bot per account so that all positions can be covered (a fixed dollar amount, not a percentage, is then recommended)
The recommended leverage is x1 or x2 for controlled long-term trading, especially with ten entry levels, although sometimes higher leverage could be considered with controlled risk.
Here's how to set up the strategy:
Start by finding a cryptocurrency displaying a nice curve with the default settings
Then try out the default settings on all timeframes, and select the timeframe with the best curve or the best result
Deactivate shorts
Set the first long triggerlevel to the value that gives the best result
(optional): Change the moving average type, period and data source to find the most optimized setting before proceeding to the next step
Set the 10thlong inputlevel to the last value modifying the result
Set the 8 intermediate input levels, distributing them as evenly as possible
Then adjust the output level of the longs, which can greatly improve the results
Temporarily deactivate the longs, activate the shorts and follow the same process
Reactivate longs and shorts
- How to program robots for automated trading using this strategy -
If you want to use this strategy for automated trading, it's very simple. All you need is an account with a cryptocurrency broker that allows APIs, and an intermediary between TradinView and your broker who will manage your orders.
Here's how it works:
On your intermediary, create a bot that will manage the details of your orders (amount, single or multiple entries, exit conditions). This bot is linked to the broker via an API and will be able to place real orders. Each bot has four different signals that enable it to be activated via a webhook. When one of the signals is received, it executes the orders for you.
On TradingView, set the strategy to a suitable asset and timeframe. Once set, enter in the strategy parameters the signals specific to the bot you've created. Confirm and close the parameters.
Still on TradingView, create an alarm based on your set strategy (on the strategy tester). Give the alarm the name of your choice and in “Message” enter only{{strategy.order.comment}}.
In alarm notifications, activate the webhook and enter the webhook of your trading intermediary. Confirm the alarm.
As long as the alarm is activated in TradingView, the strategy will monitor the market and send an order to enter or exit a position as soon as the conditions are met. Your bot will receive the instruction and place orders with your broker. Subsequent changes to the strategy settings do not change those stored in the alarm. If you wish to change the settings for one of your bots, simply delete the old alarm and create a new one.
Note: In your bot settings, on your intermediary, make sure to allow: - Multiple inputs - A single output signal to close all positions - Stoploss disabled (if necessary, use the strategy one)
NNFX RSI EMA FVMA MACD ALGOThis Pine Script introduces a cutting-edge trading strategy that seamlessly integrates multiple technical indicators—namely, the Flexible Variable Moving Average ( FVMA ), Relative Strength Index ( RSI ), Moving Average Convergence Divergence ( MACD ), and Exponential Moving Average ( EMA )—to deliver a sophisticated trading experience. This script stands out due to its comprehensive approach, robust risk management, and the inclusion of crucial data tables for various timeframes, making it an invaluable tool for traders seeking to enhance their market performance.
Originality of the Strategy:
The originality of this script lies in its unique combination of multiple powerful indicators, enabling traders to benefit from diverse perspectives on market dynamics. This mashup enhances decision-making processes, providing multiple layers of confirmation for trade entries and exits. The strategy is designed to offer an innovative solution for traders looking to improve their performance through well-defined rules and a solid framework.
Flexible Variable Moving Average (FVMA):
The FVMA adapts dynamically to market conditions, offering a more responsive trend line than traditional moving averages. This flexibility allows for quick identification of trends and reversals, crucial for fast-paced trading environments.
Exponential Moving Average (EMA):
By giving greater weight to recent price data, the EMA enhances sensitivity to price changes, allowing for more accurate entries and exits when used alongside the FVMA. This combination maximizes the effectiveness of the strategy in identifying optimal trading opportunities.
Relative Strength Index (RSI):
The RSI helps identify overbought or oversold conditions, integrating seamlessly with other indicators to enhance the strategy's ability to pinpoint potential reversal points. This aspect of the strategy ensures that traders can make informed decisions based on market momentum.
Moving Average Convergence Divergence (MACD):
The MACD serves as an essential confirmation tool, providing insights into trend strength and momentum. This enhances the accuracy of entry and exit signals, allowing traders to make more informed decisions based on robust technical analysis.
Multi-Take Profit (TP) and Stop Loss (SL) Levels:
The strategy supports multiple TPs, allowing traders to lock in profits at various levels while effectively managing risk through a robust SL system. This flexibility caters to diverse trading styles and risk profiles, ensuring that the strategy can adapt to individual trader needs.
Default Properties:
Take Profit Levels: TP1 is set to 2.0, and TP2 is set to 2.9, which is designed to enhance profit potential while maintaining a solid risk-reward ratio.
Stop Loss: A SL is set at 2% of the 5% account balance, which helps to preserve capital and manage risk effectively, adhering to the guideline of not risking more than 5-10% of the account balance per trade.
Labeling System for Exits: Automatic labeling of TP and SL exits on the chart provides clear visualization of trading outcomes. This feature supports informed decision-making and performance tracking, aligning with the guideline of providing transparent results.
Custom Alerts System:
The inclusion of customizable alerts for trade entries, exits, and SL/TP hits keeps traders informed in real-time, enabling prompt actions without constant market monitoring. This is crucial for effective trade management and helps traders respond quickly to market changes.
API Boxes for Automated Trading:
The strategy features API boxes, allowing traders to set up automated trading based on indicator signals. This functionality enables seamless integration with trading platforms, enhancing efficiency and streamlining the trading process, which is particularly valuable for traders looking to optimize their execution.
Data Tables for Enhanced Analysis:
The script includes data tables displaying critical insights across various timeframes: 2-hour, daily, weekly, and monthly. These tables provide a comprehensive overview of market conditions, allowing traders to analyze trends and make informed decisions based on a broad spectrum of data. By leveraging this information, traders can identify high-probability setups and align their strategies with prevailing market trends, significantly increasing their chances of success.
Default Properties:
Initial Capital: £1,000, ensuring a realistic starting point for traders.
Risk per Trade: 5% of the account balance, promoting sustainable trading practices.
Commission: 0.1%, reflecting realistic transaction costs that traders may encounter.
Slippage: 1%, accounting for potential market volatility during trade execution.
Take Profit Levels:
TP1: 2.0
TP2: 2.9
Stop Loss (SL): 2% of the 5% account balance, which is well within acceptable risk parameters.
Compliance with TradingView Guidelines:
This script fully complies with TradingView's guidelines, specifically:
Strategy Results:
The strategy is designed to publish backtesting results that do not mislead traders. The realistic parameters outlined in the default properties ensure that traders have a clear understanding of potential outcomes.
The dataset used for backtesting has sufficient trades to produce a reliable sample size, aligning with the guideline of ideally having more than 100 trades.
Any deviations from recommended practices are justified in the script description, ensuring transparency and adherence to best practices.
The script explains the default properties in detail, providing a thorough understanding of how these settings influence performance.
Why This Script is Worth Paying For:
This Pine Script offers an unparalleled trading experience through its unique combination of technical indicators, comprehensive trade management features, and detailed data tables for multiple timeframes. Here are compelling reasons to invest in this strategy:
Holistic Approach: The integration of multiple indicators ensures a well-rounded perspective on market conditions, increasing the likelihood of successful trades.
Advanced Risk Management: The flexibility of multiple TPs and SLs empowers traders to tailor their risk profiles according to individual strategies, enhancing overall profitability.
Automated Trading Capability: The inclusion of API boxes for automated trading streamlines execution, allowing traders to capitalize on opportunities without the need for manual intervention.
Comprehensive Data Analysis: The detailed data tables provide invaluable insights across different timeframes, enabling traders to make informed decisions based on robust market analysis.
In summary, this innovative Pine Script represents a powerful tool designed to empower traders at all levels. Its originality, synergistic functionality, and comprehensive features create a dynamic and effective trading environment, justifying its value and positioning it as a must-have for anyone serious about achieving consistent trading success.
Super GBPJPY 30 (ausama raid)
### Strategy Description: Super GBPJPY 30 (ausama raid)
FX:GBPJPY
**Overview**:
The "Super GBPJPY 30" trading strategy utilizes SuperTrend indicators to identify overall trends and entry/exit signals specifically on the GBP/JPY currency pair, operating on a 30-minute timeframe. This strategy aims to enhance profit opportunities by leveraging financial leverage and advanced take-profit settings.
**Strategy Settings**:
1. **Leverage**: Users can specify an appropriate leverage (1 or higher).
2. **Enable Advanced Take Profit**: This allows users to activate or deactivate the advanced take profit level.
3. **Show Monthly Performance Table**: Displays the strategy's performance across previous months and years.
**Timeframe**:
- The strategy is designed for the **GBP/JPY currency pair** on the **30-minute timeframe**, providing a balance between timely entries and risk management.
**Entry and Exit Indicators**:
- **Overall Trend Indicator**: The overall trend is determined using the SuperTrend indicator, with specific settings for the ATR length and factor used.
- **Entry Indicator**: A second SuperTrend indicator is employed to signal entry and exit points, improving decision-making accuracy.
### How It Works:
1. **Buy Signals**: A buy order is placed when the overall trend is bullish, and the entry indicator gives a buy signal.
2. **Sell Signals**: A sell order is executed when the overall trend is bearish, and the entry indicator provides a sell signal.
3. **Trade Management**:
- Half of the position can be closed when a specific profit level is reached.
- The position will be exited if the entry indicator's trend changes.
### Chart Illustrations:
- **Indicator Lines**: The indicator lines are displayed on the chart, with trends indicated in blue (bullish) or red (bearish).
- **Candle Colors**: The candle colors change based on the entry indicator's signals, making it easier to visualize current trends.
### Performance:
- **Performance Table**: The current year's monthly performance is displayed, allowing users to view past results.
- **Profit Percentage**: The strategy offers good risk management by defining the profit percentage for each trade.
**Note**: Ensure to test the strategy on a demo account before applying it in the real market.
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Gabriel's Witcher Strategy [65 Minute Trading Bot]Strategy Description: Gabriel's Witcher Strategy
Author: Gabriel
Platform: TradingView Pine Script (Version 5)
Backtested Asset: Avalanche (Coinbase Brokage for Volume adjustment)
Timeframe: 65 Minutes
Strategy Type: Comprehensive Trend-Following and Momentum Strategy with Scalping and Risk Management Features
Overview
Gabriel's Witcher Strategy is an advanced trading bot designed for the Avalanche pair on a 65-minute timeframe. This strategy integrates a multitude of technical indicators to identify and execute high-probability trading opportunities. By combining trend-following, momentum, volume analysis, and range filtering, the strategy aims to capitalize on both long and short market movements. Additionally, it incorporates scalping mechanisms and robust risk management features, including take-profit (TP) levels and commission considerations, to optimize trade performance and profitability.
====Key Components====
Source Selection:
Custom Source Flexibility: Allows traders to select from a wide range of price and volume sources (e.g., Close, Open, High, Low, HL2, HLC3, OHLC4, VWAP, On-Balance Volume, etc.) for indicator calculations, enhancing adaptability to various trading styles.
Various curves of Volume Analysis are employed:
Tick Volume Calculation: Utilizes tick volume as a fallback when actual volume data is unavailable, ensuring consistency across different data feeds.
Volume Indicators: Incorporates multiple volume-based indicators such as On-Balance Volume (OBV), Accumulation/Distribution (AccDist), Negative Volume Index (NVI), Positive Volume Index (PVI), and Price Volume Trend (PVT) for comprehensive market analysis.
Trend Indicators:
ADX (Average Directional Index): Measures trend strength using either the Classic or Masanakamura method, with customizable length and threshold settings. It's used to open positions when the mesured trend is strong, or exit when its weak.
Jurik Moving Average (JMA): A smooth moving average that reduces lag, configurable with various parameters including source, resolution, and repainting options.
Parabolic SAR: Identifies potential reversals in market trends with adjustable start, increment, and maximum settings.
Custom Trend Indicator: Utilizes highest and lowest price points over a specified timeframe to determine current and previous trend bases, visually represented with color-filled areas.
Momentum Indicators:
Relative Strength Index (RSI): Evaluates the speed and change of price movements, smoothed with a custom length and source. It's used to not enter the market for shorts in oversold or longs for overbought conditions, and to enter for long in oversold or shorts for overboughts.
Momentum-Based Calculations: Employs both Double Exponential Moving Averages (DEMA) on a MACD-based RSI to enhance momentum signal accuracy which is then further accelerated by a Hull MA. This is the technical analysis tool that determines bearish or bullish momentum.
OBV-Based Momentum Conditions: Uses two exponential moving averages of OBV to determine bullish or bearish momentum shifts, anomalities, breakouts where banks flow their funds in or Smart Money Concepts trade.
Moving Averages (MA):
Multiple MA Types: Includes Simple Moving Average (SMA), Exponential Moving Average (EMA), Weighted Moving Average (WMA), Hull Moving Average (HMA), and Volume-Weighted Moving Average (VWMA), selectable via input parameters.
MA Speed Calculation: Measures the percentage change in MA values to determine the direction and speed of the trend.
Range Filtering:
Variance-Based Filter: Utilizes variance and moving averages to filter out trades during low-volatility periods, enhancing trade quality.
Color-Coded Range Indicators: Visualizes range filtering with color changes on the chart for quick assessment.
Scalping Mechanism:
Heikin-Ashi Candles: Optionally uses Heikin-Ashi candles for smoother price action analysis.
EMA-Based Trend Detection: Employs fast, medium, and slow EMAs to determine trend direction and potential entry points.
Fractal-Based Filtering: Detects regular or BW (Black & White) fractals to confirm trade signals.
Take Profit (TP) Management:
Dynamic TP Levels: Calculates TP levels based on the number of consecutive long or short entries, adjusting targets to maximize profits.
TP Signals and Re-Entry: Plots TP signals on the chart and allows for automatic re-entry upon TP hit, maintaining continuous trade flow.
Risk Management:
Commission Integration: Accounts for trading commissions to ensure net profitability.
Position Sizing: Configured to use a percentage of equity for each trade, adjustable via input parameters.
Pyramiding: Allows up to one additional position per direction to enhance gains during strong trends.
Alerts and Visual Indicators:
Buy/Sell Signals: Plots visual indicators (triangles and flags) on the chart to signify entry and TP points.
Bar Coloring: Changes bar colors based on ADX and trend conditions for immediate visual cues.
Price Levels: Marks significant price levels related to TP and position entries with cross styles.
Input Parameters
Source Settings:
Custom Sources (srcinput): Choose from various price and volume sources to tailor indicator calculations.
ADX Settings:
ADX Type (ADX_options): Select between 'CLASSIC' and 'MASANAKAMURA' methods.
ADX Length (ADX_len): Defines the period for ADX calculation.
ADX Threshold (th): Sets the minimum ADX value to consider a strong trend.
RSI Settings:
RSI Length (len_3): Period for RSI calculation.
RSI Source (src_3): Source data for RSI.
Trend Strength Settings:
Channel Length (n1): Period for trend channel calculation.
Average Length (n2): Period for smoothing trend strength.
Jurik Moving Average (JMA) Settings:
JMA Source (inp): Source data for JMA.
JMA Resolution (reso): Timeframe for JMA calculation.
JMA Repainting (rep): Option to allow JMA to repaint.
JMA Length (lengths): Period for JMA.
Parabolic SAR Settings:
SAR Start (start): Initial acceleration factor.
SAR Increment (increment): Acceleration factor increment.
SAR Maximum (maximum): Maximum acceleration factor.
SAR Point Width (width): Visual width of SAR points.
Trend Indicator Settings:
Trend Timeframe (timeframe): Period for trend indicator calculations.
Momentum Settings:
Source Type (srcType): Select between 'Price' and 'VWAP'.
Momentum Source (srcPrice): Source data for momentum calculations.
RSI Length (rsiLen): Period for momentum RSI.
Smooth Length (sLen): Smoothing period for momentum RSI.
OBV Settings:
OBV Line 1 (e1): EMA period for OBV line 1.
OBV Line 2 (e2): EMA period for OBV line 2.
Moving Average (MA) Settings:
MA Length (length): Period for MA calculations.
MA Type (matype): Select MA type (1: SMA, 2: EMA, 3: HMA, 4: WMA, 5: VWMA).
Range Filter Settings:
Range Filter Length (length0): Period for range filtering.
Range Filter Multiplier (mult): Multiplier for range variance.
Take Profit (TP) Settings:
TP Long (tp_long0): Percentage for long TP.
TP Short (tp_short0): Percentage for short TP.
Scalping Settings:
Scalping Activation (ACT_SCLP): Enable or disable scalping.
Scalping Length (HiLoLen): Period for scalping indicators.
Fast EMA Length (fastEMAlength): Period for fast EMA in scalping.
Medium EMA Length (mediumEMAlength): Period for medium EMA in scalping.
Slow EMA Length (slowEMAlength): Period for slow EMA in scalping.
Filter (filterBW): Enable or disable additional fractal filtering.
Pullback Lookback (Lookback): Number of bars for pullback consideration.
Use Heikin-Ashi Candles (UseHAcandles): Option to use Heikin-Ashi candles for smoother trend analysis.
Strategy Logic
Indicator Calculations:
Volume and Source Selection: Determines the primary data source based on user input, ensuring flexibility and adaptability.
ADX Calculation: Computes ADX using either the Classic or Masanakamura method to assess trend strength.
RSI Calculation: Evaluates market momentum using RSI, further smoothed with custom periods.
Trend Strength Assessment: Utilizes trend channel and average lengths to gauge the robustness of current trends.
Jurik Moving Average (JMA): Smooths price data to reduce lag and enhance trend detection.
Parabolic SAR: Identifies potential trend reversals with adjustable parameters for sensitivity.
Momentum Analysis: Combines RSI with DEMA and OBV-based conditions to confirm bullish or bearish momentum.
Moving Averages: Employs multiple MA types to determine trend direction and speed.
Range Filtering: Filters out low-volatility periods to focus on high-probability trades.
Trade Conditions:
Long Entry Conditions:
ADX Confirmation: ADX must be above the threshold, indicating a strong uptrend.
RSI and Momentum: RSI below 70 and positive momentum signals.
JMA and SAR: JMA indicates an uptrend, and Parabolic SAR is below the price.
Trend Indicator: Confirms the current trend direction.
Range Filter: Ensures market is in an upward range.
Scalping Option: If enabled, additional scalping conditions must be met.
Short Entry Conditions:
ADX Confirmation: ADX must be above the threshold, indicating a strong downtrend.
RSI and Momentum: RSI above 30 and negative momentum signals.
JMA and SAR: JMA indicates a downtrend, and Parabolic SAR is above the price.
Trend Indicator: Confirms the current trend direction.
Range Filter: Ensures market is in a downward range.
Scalping Option: If enabled, additional scalping conditions must be met.
Position Management:
Entry Execution: Places long or short orders based on the identified conditions and user-selected position types (Longs, Shorts, or Both).
Take Profit (TP): Automatically sets TP levels based on predefined percentages, adjusting dynamically with consecutive trades.
Re-Entry Mechanism: Allows for automatic re-entry upon TP hit, maintaining active trading positions.
Exit Conditions: Closes positions when TP levels are reached or when opposing trend signals are detected.
Visual Indicators:
Bar Coloring: Highlights bars in green for bullish conditions, red for bearish, and orange for neutral.
Plotting Price Levels: Marks significant price levels related to TP and trade entries with cross symbols.
Signal Shapes: Displays triangle and flag shapes on the chart to indicate trade entries and TP hits.
Alerts:
Custom Alerts: Configured to notify traders of long entries, short entries, and TP hits, enabling timely trade management and execution.
Usage Instructions
Setup:
Apply the Strategy: Add the script to your TradingView chart set to BTCUSDT with a 65-minute timeframe.
Configure Inputs: Adjust the input parameters under their respective groups (e.g., Source Settings, ADX, RSI, Trend Strength, etc.) to match your trading preferences and risk tolerance.
Position Selection:
Choose Position Type: Use the Position input to select Longs, Shorts, or Both based on your market outlook.
Execution: The strategy will automatically execute and manage positions according to the selected type, ensuring targeted trading actions.
Signal Interpretation:
Buy Signals: Blue triangles below the bars indicate potential long entry points.
Sell Signals: Red triangles above the bars indicate potential short entry points.
Take Profit Signals: Flags above or below the bars signify TP hits for long and short positions, respectively.
Bar Colors: Green bars suggest bullish conditions, red bars indicate bearish conditions, and orange bars represent neutral or consolidating markets.
Risk Management:
Default Position Size: Set to 100% of equity. Adjust the default_qty_value as needed for your risk management strategy.
Commission: Accounts for a 0.1% commission per trade. Adjust the commission_value to match your broker's fees.
Pyramiding: Allows up to one additional position per direction to enhance gains during strong trends.
Backtesting and Optimization:
Historical Testing: Utilize TradingView's backtesting features to evaluate the strategy's performance over historical data.
Parameter Tuning: Optimize input parameters to align the strategy with current market dynamics and personal trading objectives.
Alerts Configuration:
Set Up Alerts: Enable and configure alerts based on the predefined alertcondition statements to receive real-time notifications of trade signals and TP hits.
Additional Features
Comprehensive Indicator Integration: Combines multiple technical indicators to provide a holistic view of market conditions, enhancing trade signal accuracy.
Scalping Options: Offers an optional scalping mechanism to capitalize on short-term price movements, increasing trading flexibility.
Dynamic Take Profit Levels: Adjusts TP targets based on the number of consecutive trades, maximizing profit potential during favorable trends.
Advanced Volume Analysis: Utilizes various volume indicators to confirm trend strength and validate trade signals.
Customizable Range Filtering: Filters trades based on market volatility, ensuring trades are taken during optimal conditions.
Heikin-Ashi Candle Support: Optionally uses Heikin-Ashi candles for smoother price action analysis and reduced noise.
====Recommendations====
Thorough Backtesting:
Historical Performance: Before deploying the strategy in a live trading environment, perform comprehensive backtesting to understand its performance under various market conditions. These are the premium settings for Avalanche Coinbase.
Optimization: Regularly review and adjust input parameters to ensure the strategy remains effective amidst changing market volatility and trends. Backtest the strategy for each crypto and make sure you are in the right brokage when using the volume sources as it will affect the overall outcome of the trading strategy.
Risk Management:
Position Sizing: Adjust the default_qty_value to align with your risk tolerance and account size.
Stop-Loss Implementation: Although the strategy includes TP levels, they're also consided to be a stop-loss mechanisms to protect against adverse market movements.
Commission Adjustment: Ensure the commission_value accurately reflects your broker's fees to maintain realistic backtesting results. Generally, 0.1~0.3% are most of the average broker's comission fees.
Slipage: The slip comssion is 1 Tick, since the strategy is adjusted to only enter/exit on bar close where most positions are available.
Continuous Monitoring:
Strategy Performance: Regularly monitor the strategy's performance to ensure it operates as expected and make adjustments as needed. A max-drawndown hit has been added to operate in case the premium Avalanche settings go wrong, but you can turn it off an adjust the equity percentage to 50% if you are confortable with the high volatile max-drown or even 100% if your account allows you to borrow cash.
Customization:
Indicator Parameters: Tailor indicator settings (e.g., ADX length, RSI period, MA types) to better fit your specific trading style and market conditions.
Scalping Options: Enable or disable scalping based on your trading preferences and risk appetite.
Conclusion
Gabriel's Witcher Strategy is a robust and versatile trading solution designed to navigate the complexities of the Crypto market. By integrating a wide array of technical indicators and providing extensive customization options, this strategy empowers traders to execute informed and strategic trades. Its comprehensive approach, combining trend analysis, momentum detection, volume evaluation, and range filtering, ensures that trades are taken during optimal market conditions. Additionally, the inclusion of scalping features and dynamic take-profit management enhances the strategy's adaptability and profitability potential. Unlike any trading strategy, with both diligent testing and continuous monitoring under the strategy tester, it's possible to achieve sustained success by adjusting the settings to the individual Crypto that need it, for example this one is preset for Avalanche Coinbase 65 Miinutes but it can be adjust for BTCUSD or Etherium if you backtest and search for the right settings.
G-Channel with EMA StrategyThe G-Channel is a custom channel with an upper (a), lower (b), and average (avg) line. These lines are dynamically calculated based on the current and previous closing prices, using the length input (default 100) to smooth the values:
Upper Line (a): This is the maximum value of the current price or the previous upper value, adjusted by the difference between the upper and lower lines divided by the length.
Lower Line (b): This is the minimum value of the current price or the previous lower value, similarly adjusted by the difference between the upper and lower lines.
The average line (avg) is simply the midpoint between the upper and lower lines. The G-Channel signals trend direction:
Bullish Condition: The system looks for the condition when the price crosses over the lower line (b), indicating a potential upward trend.
Bearish Condition: When the price crosses under the upper line (a), it signals a potential downward trend.
Exponential Moving Average (EMA)
The strategy also incorporates an EMA with a default length of 200. The EMA serves as a trend filter to determine whether the market is trending upward or downward:
Price below EMA: Indicates a bearish trend.
Price above EMA: Indicates a bullish trend.
Buy/Sell Conditions
The strategy generates buy or sell signals based on the interaction between the G-Channel signals and the price relative to the EMA:
Buy Signal: The strategy triggers a buy when:
A bullish condition (recent crossover of price over the lower G-Channel line) is detected.
The price is below the EMA, indicating that despite the recent bullish signal, the market might still be undervalued or in a temporary downturn.
Sell Signal: The strategy triggers a sell when:
A bearish condition (recent crossunder of price below the upper G-Channel line) is detected.
The price is above the EMA, suggesting that the market might be overextended and poised for a downturn.
Visualization
The strategy plots:
The upper, lower, and average lines of the G-Channel, with the average line colored based on bullish (green) or bearish (red) conditions.
The EMA (orange) line to provide context on the general trend direction.
Markers for Buy and Sell signals to visually indicate the strategy's entry points.
Strategy Execution
When a buy or sell signal is detected:
Buy Entry: If the bullish condition and price < EMA condition are met, a long (buy) position is opened.
Sell Entry: If the bearish condition and price > EMA condition are met, a short (sell) position is opened.
Purpose
This strategy aims to catch price reversals at critical points (when the price moves through the G-Channel) while filtering trades using the EMA to avoid entering during unfavorable market trends.
