Bullish B's - RSI Divergence StrategyThis indicator strategy is an RSI (Relative Strength Index) divergence trading tool designed to identify high-probability entry and exit points based on trend shifts. It utilizes both regular and hidden RSI divergence patterns to spot potential reversals, with signals for both bullish and bearish conditions.
Key Features
Divergence Detection:
Bullish Divergence: Signals when RSI indicates momentum strengthening at a lower price level, suggesting a reversal to the upside.
Bearish Divergence: Signals when RSI shows weakening momentum at a higher price level, indicating a potential downside reversal.
Hidden Divergences: Looks for hidden bullish and bearish divergences, which signal trend continuation points where price action aligns with the prevailing trend.
Volume-Adjusted Entry Signals:
The strategy enters long trades when RSI shows bullish or hidden bullish divergence, indicating an upward momentum shift.
An optional volume filter ensures that only high-volume, high-conviction trades trigger a signal.
Exit Signals:
Exits long positions when RSI reaches a customizable overbought level, typically indicating a potential reversal or profit-taking opportunity.
Also closes positions if bearish divergence signals appear after a bullish setup, providing protection against trend reversals.
Trailing Stop-Loss:
Uses a trailing stop mechanism based on ATR (Average True Range) or a percentage threshold to lock in profits as the price moves in favor of the trade.
Alerts and Custom Notifications:
Integrated with TradingView alerts to notify the user when entry and exit conditions are met, supporting timely decision-making without constant monitoring.
Customizable Parameters:
Users can adjust the RSI period, pivot lookback range, overbought level, trailing stop type (ATR or percentage), and divergence range to fit their trading style.
Ideal Usage
This strategy is well-suited for trend traders and swing traders looking to capture reversals and trend continuations on medium to long timeframes. The divergence signals, paired with trailing stops and volume validation, make it adaptable for multiple asset classes, including stocks, forex, and crypto.
Summary
With its focus on RSI divergence, trailing stop-loss management, and volume filtering, this strategy aims to identify and capture trend changes with minimized risk. This allows traders to efficiently capture profitable moves and manage open positions with precision.
This Strategy BEST works with GLD!
ابحث في النصوص البرمجية عن "alert"
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.
Simple Fibonacci Retracement Strategy This strategy uses Fibonacci retracement to identify key levels in the market and helps traders find good entry and exit points. By understanding and using this strategy, traders can improve their trading decisions and increase their chances of success in the market.
This strategy, called the "Simple Fibonacci Retracement Strategy," is designed to help traders identify potential entry and exit points in the market based on Fibonacci retracement levels. The code is written in Pine Script and runs on the TradingView platform.
Overall Function
The strategy uses Fibonacci retracement levels to identify potential support and resistance levels in the market. This helps traders find good entry and exit points for trades, as well as set stop-loss and take-profit levels to minimize risk and maximize gains.
Main Components of the Code
1. Input Parameters
Lookback Period: The number of bars used to identify the highest high and lowest low.
Fibonacci Direction: The choice of whether Fibonacci levels are calculated from top to bottom or bottom to top.
Fibonacci Levels: Specific Fibonacci levels (23.6%, 38.2%, 50%, 61.8%) used to identify important price levels.
Take Profit and Stop Loss: The number of pips used to set take profit and stop loss levels.
2. Identification of Highest and Lowest Points
The code uses the lookback period to find the highest high (highestHigh) and the lowest low (lowestLow). These levels form the basis for calculating the Fibonacci levels.
3. Calculation of Fibonacci Levels
Based on the direction chosen by the user, the code calculates the various Fibonacci levels (0%, 23.6%, 38.2%, 50%, 61.8%, 100%).
4. Trading Logic
Long Signal: Generated when the price crosses above the 61.8% Fibonacci level from bottom to top.
Short Signal: Generated when the price crosses below the 38.2% Fibonacci level from top to bottom.
When a long or short signal is generated, the strategy opens a position and sets take profit and stop loss levels based on the input parameters.
5. Visualization
The strategy plots the Fibonacci levels on the chart to provide a visual representation of the calculated levels. This helps traders see where the levels are in relation to the current price.
6. Alerts
The code also has functionality to create alerts (commented out), which can notify traders of buy or sell signals.
How to Use the Strategy
Configure Parameters: Adjust the lookback period, Fibonacci direction, and levels for take profit and stop loss to your preferences.
View the Chart: The Fibonacci levels will be plotted on the chart, providing a visual overview of potential support and resistance levels.
Trade Signals: Follow the generated buy and sell signals. Set your parameters in settings and adjust according to the generated buy and sell signals in the strategy tester. The strategy will automatically set your take profit and stop loss levels.
Evaluation and Adjustment: Monitor the performance of the strategy and make adjustments as needed to optimize the results.
Norwegian
Denne strategien, kalt "Simple Fibonacci Retracement Strategy", er designet for å hjelpe tradere med å identifisere mulige inngangs- og utgangspunkter i markedet basert på Fibonacci-retracementnivåer. Koden er skrevet i Pine Script og kjøres på TradingView-plattformen.
Overordnet Funksjon
Strategien bruker Fibonacci-retracementnivåer for å identifisere potensielle støtte- og motstandsnivåer i markedet. Dette hjelper tradere med å finne gode inngangs- og utgangspunkter for handler, samt å sette stop-loss og take-profit nivåer for å minimere risiko og maksimere gevinster.
Hovedkomponenter i Koden
1. Input Parametere
Lookback Period: Antall barer som brukes til å identifisere høyeste høydepunkt og laveste lavpunkt.
Fibonacci Direction: Valg om Fibonacci-nivåene skal beregnes fra topp til bunn eller bunn til topp.
Fibonacci Levels: Spesifikke Fibonacci-nivåer (23.6%, 38.2%, 50%, 61.8%) som brukes til å identifisere viktige prisnivåer.
Take Profit og Stop Loss: Antall pips som brukes til å sette take profit og stop loss nivåer.
2. Identifikasjon av Høyeste og Laveste Punkt
Koden bruker lookback perioden for å finne det høyeste høydepunktet (highestHigh) og det laveste lavpunktet (lowestLow). Disse nivåene er grunnlaget for å beregne Fibonacci-nivåene.
3. Beregning av Fibonacci-nivåer
Basert på retningen valgt av brukeren, beregner koden de forskjellige Fibonacci-nivåene (0%, 23.6%, 38.2%, 50%, 61.8%, 100%).
4. Handelslogikk
Long Signal: Genereres når prisen krysser over 61.8% Fibonacci-nivået fra bunn til topp.
Short Signal: Genereres når prisen krysser under 38.2% Fibonacci-nivået fra topp til bunn.
Når et long eller short signal genereres, åpner strategien en posisjon og setter take profit og stop loss nivåer basert på inputparametrene.
5. Visualisering
Strategien plottet Fibonacci-nivåene på chartet for å gi en visuell representasjon av de beregnede nivåene. Dette hjelper tradere med å se hvor nivåene er i forhold til den nåværende prisen.
6. Varsler
Koden har også funksjonalitet for å lage varsler (kommentert ut), som kan varsle tradere om kjøps- eller salgssignaler.
Slik Bruker Du Strategien
Konfigurer Parametere: Juster lookback perioden, Fibonacci-retningen, og nivåene for take profit og stop loss til dine preferanser.
Se på Chartet: Fibonacci-nivåene vil bli plottet på chartet, noe som gir deg en visuell oversikt over potensielle støtte- og motstandsnivåer.
Handle Signaler: Sett dine parametere i innstillinger og juster etter genererte kjøps- og salgssignalene i strategy testeren. Strategien vil automatisk sette dine take profit og stop loss nivåer.
Evaluering og Justering: Overvåk ytelsen til strategien og gjør justeringer etter behov for å optimalisere resultatene.
INFINITY ALGO🆕Meet the updated version of our flagship indicator, now it's INFINITY ALGO!
🏃🏻 QUICK START
In very simple terms, our indicator generates complex trading signals on your chart (buy/sell), including Entry Point, Take Profit levels, Stop Loss level
To start, you need to add our indicator to your chart , choose a timeframe (we recommend 13min,15min and 4h but you can try any, these only have the best results) and set up notifications (how to do it told below) and that's it, you can work with it even without changing the settings!
Of course, to improve the accuracy of signals you will have to choose the optimal settings of the script for each trading pair and timeframe (you can find a guide below)
📊 SIGNALS
This script will generate complex trading recommendations, both Long and Short (signals); signals include:
- Entry Point:
Calculated based on pivot levels with confirmation by EMA/SMA (you can select this in the settings); also bullish/bearish cup is checked to confirm the entry.
Additionally, in the settings you can enable Heiken Ashi calculation mode (it shows much better on some trading pairs).
Why do we mashup these components and how they work together?
- The main indicator in our script is pivot levels, it is enabled by default and cannot be disabled. Auxiliary indicators (which you can switch on and off in the script settings) are EMA/SMA and Heiken Ashi. We have used pivot levels, which mark potential support and resistance zones based on previous price action. We have also used EMA/SMA that smooth out price fluctuations and show the direction of the trend. We have added an option to use Heiken Ashi that filters out noise and highlights the trend. We have also checked for bullish/bearish cup patterns, which are reversal patterns that indicate a change in momentum. By combining these indicators, we have created a more robust entry point that considers multiple factors such as price levels, trend, noise, and momentum.
- 6 Take Profit levels:
It is also possible to change in the settings (It is also possible to change the values for Short or Long positions separately), it will be fixed values in % (The default Take Profits for Long&Short are as follows: TP1-0.3%; TP2-1%; TP3-2%; TP4-3%; TP5-7.5%; TP6-16.5%)
- Stop Loss Level:
As with Take Profits, this is a fixed % value that you can customise to suit your risk management needs (It is also possible to change the values for Short or Long positions separately, by default is 4.5% for Long&Short positions)
*When trading on these signals, we strongly recommend that you exit the position in parts at each take profit or close your entire position at one particular take profit. Our script was designed specifically for exiting a position on take profits
⚙️ SETTINGS
Now let's talk about the settings of this script, which allow you to customise the signals quite a lot. In general, we recommend selecting the settings for each trading pair and timeframe separately, this will allow you to achieve better targets accuracy (the default settings are universal, you can trade with them without changing them if you want)
-> IMAGE <-
1. Period - minimum value of 2. Increasing this parameter will increase the accuracy of signals, but will reduce their number (accordingly, lowering the parameter will do the opposite). For the majority of trading pairs and timeframes the optimal period will be between 5 and 10 (the default value is 5).
2. Maximum Breakout length (in bars) - for most trading pairs you can set the value from 200 to 300 and it will be optimal. Below 200 is not recommended
3. T hreshold Rate % - this value also affects the accuracy and the number of signals - the higher this value is, the more often signals will be generated, but it can negatively affect the accuracy. The minimum value is 3, and the maximum value is 10. We recommend to try values in the range from 4 to 7 for most tickers
4. Minimum Number of tests - the number of level checks is required, we recommend to try 2, and only for some timeframes increase to 3
5. MA type & MA filter - The shorter the length of moving averages, the faster they react to trend changes, and show more local trends than global ones. If the length of MAs is longer, more global trends are shown. By default, the most optimal values are set.
By the way, you can ask us for a ready-made preset for any pair and we will be happy to help you!
📄 BACKTESTING
Now let's talk about how to properly test the settings and evaluate their effectiveness. Our script has a c ustom built-in backtester that shows statistics on the current trading pair and allows you to calculate the accuracy of each take profit target, as well as calculate values such as Gross profit/loss, net profit, and the ratio of initial deposit to profit. (you can enable/disable backtester "statistics" label in main settings)
In the main settings you can change the values for: initial deposit (Deposit $), trade size $ and leverage (by the way, it also affects the display of the label "Peak profit", which is calculated with this leverage)
-> IMAGE <-
Now let's look at the backtester - it shows detailed statistics for each Take Profit level, including: accuracy in % and number of trades; gross profit & loss; net profit in % and $ (based on selected settings); deposit to profit ratio in % and $.
Why did we choose such properties in the backtest for publication?
- Well, as the initial capital we took 5000$ and deposit 3% (150$) of the initial capital in each trade. For the fee was taken the value from the exchange Binance, which is 0.06% per trade (Taker + Maker, for a user without VIP on Binance and without taking into account additional fees such as funding, leverage fees, etc).
- Please also take a look at our inbuilt backtester ( IMAGE ) which counts the accuracy to each Take Profit. Also note that our inbuilt backtester does not take any fees into account. Pay attention to the last field "Deposit with Profit" it shows the value if you would close all positions at a certain target. For example, we can see that the most optimal is TP3 at these settings for this trading pair and timeframe, as the deposit to profit ratio will be +61.2%
- Also the script is more designed for swing and long term trading, so on most trading pairs you will be able to see statistics for 60-90 trades dataset
*disclaimer: please note that past results does not guarantee future performance! The accuracy of take profit targets in our backtester is calculated on past results, keep this in mind please
📥 NOTIFICATIONS
We have provided notifications that will deliver the latest signals to you in a convenient format in TradingView. The notification looks like this: It contains the entry point, Take Profits, Stop Loss, and a bit of advice on risk management. -> IMAGE <-
To set up notifications:
1. Select the script settings, trading pair and timeframe
2. Click "add alert on InfinityAlgo", then select "alert () function calls only" in the settings
-> IMAGE <-
3. That's it, now all that's left is to wait for a fresh alert
🔑 HOW TO GET ACCESS
We hope you will like this script :) We are always ready to help you with customisation, just let us know! To learn more about our scripts & get access - check out the “Author’s instructions” below 👇🏼
Crypto Punk [Bot] (Zeiierman)█ Overview
The Crypto Punk (Zeiierman) is a trading strategy designed for the dynamic and volatile cryptocurrency market. It utilizes algorithms that incorporate price action analysis and principles inspired by Geometric Brownian Motion (GBM). The bot's core functionality revolves around analyzing differences in high and low prices over various timeframes, estimating drift (trend) and volatility, and applying this information to generate trading signals.
█ How to use the Crypto Punk Bot
Utilize the Crypto Punk Bot as a technical analysis tool to enhance your trading strategy. The signals generated by the bot can serve as a confirmation of your existing approach to entering and exiting the market. Additionally, the backtest report provided by the bot is a valuable resource for identifying the optimal settings for the specific market and timeframe you are trading in.
One method is to use the bot's signals to confirm entry points around key support and resistance levels.
█ Key Features
Let's explain how the core features work in the strategy.
⚪ Strategy Filter
The strategy filter plays a vital role in the entries and exits. By setting this filter, the bot can identify higher or lower price points at which to execute trades. Opting for higher values will make the bot target more long-term extreme points, resulting in fewer but potentially more significant signals. Conversely, lower values focus on short-term extreme points, offering more frequent signals focusing on immediate market movements.
How is it calculated?
This filter identifies significant price points within a specified dynamic range by applying linear regression to the absolute deviation of the range, smoothing out fluctuations, and determining the trend direction. The algorithm then normalizes the data and searches for extreme points.
⚪ External AI filter
The external AI filter allows traders to incorporate two external sources as signal filters. This feature is particularly useful for refining their signal accuracy with additional data inputs.
External sources can include any indicator applied to your TradingView chart that produces a plot as an output, such as a moving average, RSI, supertrend, MACD, etc. Traders can use these indicators of their choice to set filters for screening signals within the strategy.
This approach offers traders increased flexibility to select filters that align with their trading style. For instance, one trader might prefer to take trades when the price is above a moving average, while another might opt for trades when the MACD is below the MACD signal line. These external filters enable traders to choose options that best fit their trading strategies. See the example below. Note that the input sources for the External AI filter can be any indicator applied to the chart, and the input source per se does not make this strategy unique. The AI filter takes the selected input source and applies our function to it. So, if a trader selects RSI as an input filter, RSI is not unique, but how the source is computed within the AI functions is.
How is it calculated?
Once the external filters are selected and enabled within the settings panel, our AI function is applied to enhance the filter's ability to execute trades, even when the set conditions of the filter are not met. For instance, if a trader wants to take trades only when the price is above a moving average, the AI filter can actually execute trades even if the price is below the moving average.
The filter works by combining k-nearest Neighbors (KNN) with Geometric Brownian Motion (GBM) involves first using GBM to model the historical price trends of an asset, identifying patterns of drift and volatility. KNN is then applied to compare the current market conditions with historical instances, identifying the closest matches based on similar market behaviors. By examining the drift values of these nearest historical neighbors, KNN predicts the current trend's direction.
The AI adaptability value is a setting that determines how flexible the AI algorithm is when applying the external AI filter. Setting the adaptability to 10 indicates minimal adaptability, suggesting that the bot will strictly adhere to the set filter criteria. On the other hand, a higher adaptability value grants the algorithm more leeway to "think outside the box," allowing it to consider signals that may not strictly meet the filter criteria but are deemed viable trading opportunities by the AI.
█ Examples
In this example, the RSI is used to filter out signals when the RSI is below the smoothing line, indicating that prices are declining.
Note that the external filter is specifically designed to work with either 'LONG ONLY' or 'SHORT ONLY' modes; it does not apply when the bot is set to trade on 'BOTH' modes. For 'LONG ONLY' positions, the filter criteria are met when source 1 is greater than source 2 (source 1 >= source 2). Conversely, for 'SHORT ONLY' positions, the filter criteria require source 1 to be less than source 2 (source 1 <= source 2).
Examples of Filter Usage:
Long Signals: To receive long signals when the closing price is higher than a moving average, set Source 1 to the 'close' price and Source 2 to a moving average value. This setup ensures that signals are generated only when the closing price exceeds the moving average, indicating a potential upward trend.
█ Settings
⚪ Set Timeframe
Choosing the correct entry and exit timeframes is crucial for the bot's performance. The general guideline is to select a timeframe that is higher than the one currently displayed on the trading chart but still relatively close in duration. For instance, if trading on a 1-minute chart, setting the bot's Timeframe to 5 minutes is advisable.
⚪ Entry
Traders have the flexibility to configure the bot according to their trading strategy, allowing them to choose whether the bot should engage in long positions only, short positions only or both. This customization ensures that the bot aligns with the trader's market outlook and risk tolerance.
⚪ Pyramiding
Pyramiding functionality is available to enhance the bot's trading strategy. If the current position experiences a drawdown by a specified number of points, the bot is programmed to add new positions to the existing one, potentially capitalizing on lower prices to average down the entry cost. To utilize this feature, access the settings panel, navigate to 'Properties,' and look for 'Pyramiding' to specify the number of times the bot can re-enter the market (e.g., setting it to 2 allows for two additional entries).
⚪ Risk Management
The bot incorporates several risk management methods, including a regular stop loss, trailing stop, and risk-reward-based stop loss and exit strategies. These features assist traders in managing their risk.
Stop Loss
Trailing Stop
⚪ Trading on specific days
This feature allows trading on specific days by setting which days of the week the bot can execute trades on. It enables traders to tailor their strategies according to market behavior on particular days.
⚪ Alerts
Alerts can be set for entry, exit, and risk management. This feature allows traders to automate their trading strategy, ensuring timely actions are taken according to predefined criteria.
█ How is Crypto Punk calculated?
The Crypto Punk Bot is a trading bot that utilizes a combination of price action analysis and elements inspired by Geometric Brownian Motion (GBM) to generate buy and sell signals for cryptocurrencies. The bot focuses on analyzing the difference between high and low prices over various timeframes, alongside estimates of drift (trend) and volatility derived from GBM principles.
Timeframe Analysis for Price Action
The bot examines multiple timeframes (e.g., daily, weekly) to identify the range between the highest and lowest prices within each period. This range analysis helps in understanding market volatility and the potential for significant price movements. The algorithm calculates the trading range by applying maximum and minimum functions to the set of prices over your selected timeframe. It then subtracts these values to determine the range's width. This method offers a quantitative measure of the asset's price volatility for the specified period.
Estimating Drift (Trend)
The bot estimates the drift component, which reflects the underlying trend or expected return of the cryptocurrency. The algorithm does this by estimating the drift (trend) using Geometric Brownian Motion (GBM), which involves determining an asset's average rate of return over time, reflecting the asset's expected direction of movement.
Estimating Volatility
Volatility is estimated by calculating the standard deviation of the logarithmic returns of the cryptocurrency's price over the same timeframe used for the drift calculation. Geometric Brownian Motion (GBM) involves measuring the extent of variation or dispersion in the returns of an asset over time. In the context of GBM, volatility quantifies the degree to which the price of an asset is expected to fluctuate around its drift.
Combining Drift and Volatility for Signal Generation
The bot uses the calculated drift and volatility to understand the current market conditions. A higher drift coupled with manageable volatility may indicate a strong upward trend, suggesting a potential buy signal. Conversely, a low or negative drift with increasing volatility might suggest a weakening market, triggering a sell signal.
█ Strategy Properties
This script backtest is done on the 1 hour chart Bitcoin, using the following backtesting properties:
Balance (default): 10 000 (default base currency)
Order Size: 10% of the equity
Commission: 0.05 %
Slippage: 500 ticks
Stop Loss: Risk Reward set to 1
These parameters are set to provide an accurate representation of the backtesting environment. It's important to recognize that default settings may vary for several reasons outlined below:
Order Size: The standard is set at one contract to facilitate compatibility with a wide range of instruments, including futures.
Commission: This fee is subject to fluctuation based on the specific market and financial instrument, and as such, there isn't a standard rate that will consistently yield accurate outcomes.
We advise users to customize the Script Properties in the strategy settings to match their personal trading accounts and preferred platforms. This adjustment is crucial for obtaining practical insights from the deployed strategies.
-----------------
Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Sniper [Decentrader]Bespoke Decentrader Mean Reversion / Colume based support/resistance Strategy builder.
Colour-coded mean line using price and volume
Volatility Bands (chose % or Std Dev)
Major support and resistance plotted lines
Suggested dynamic hard-stop placement
Built for all markets
A realistic strategy for multi-asset portfolio management
Complementary components to assist other indicators/strategies
Filtering for Long / Short only conditions is possible under settings.
Can be automated by including 3rd party code into the settings to be used as alerts.
Use the Mitigate lines to show previous areas of support or resistance, which have been broken.
4 main strategy options:
1. You can choose whether to enter based on the upper or lower Meanline. If the price is below the Meanline, the lower Meanline will be used for entry, while if the price is above the Meanline, the upper Meanline will be used. \n\nIf you want to use this condition to exit the position, you also need to select the "Exit at the Meanline" option as well.
2. If the selected strategy is "3. Buy/Sell Volatility Bands," you can specify which Band should trigger the position to open. The price must touch or cross the edge of the chosen Band. Additionally, if the "Exit at the Volatility Bands" option is selected, the same Band will be used for the exit criteria.
3. Buy/Sell Meanline retest": A position will be opened when the price retests the Meanline. The price must touch or wick through the Meanline without closing below/above it. (If this strategy is combined with "Exit at the Meanline" option, then in case price goes against our position, the strategy will exit if the price closes under/above the meanline
Buy/Sell Meanline breakout (UP/DOWN)": A long or short position will be opened when the price breaks above or below the Meanline
4. Buy/Sell Support/Resistance lines": A position will be opened when the price touches the support or resistance lines. This option can also be combined with the "Exit at the Meanline" option.
This tool can be used to help enter a trending asset or find entries for an asset retracing.
Please take care to test strategies before automation, which is also possible.
IU Break of any session StrategyHow this script works:
1. This script is an intraday trading strategy script which buy and sell on the bases of user-defined intraday session range breakout and gives alert(if the alert is set) message too when the new position is open.
2. It calculate the session as per the user inputs or user defined custom session.
3. The script stores the highest and lowest value of the whole session.
4. It take a long position on the first break and close above the highest value.
5. It take a short position on the break and close below the lowest value.
6. The script takes one position in one day.
7. The stop loss for this script is the previous low(if long) or high(if short).
8. Take profit is 1:2 and it's adjustable.
9. This script work on every kind of market.
How The Useful For The User :
1. User can backtest any session range breakout he wants to trade.
2. User can get alert when the new position is open.
3. User can change the Risk to Reward in order to find the best Risk to Reward.
4. User can see the highest and lowest value of the session with respect to analyzing his trading objective.
5. This strategy script highlights which session range breakout performs best and which performs worst.
DCA EMA Simple Bot [Starbots]
This is a simple idea of DCA trading on EMA crosses. Strategy is not repainting.
The difference between this and any other strategy is, that this script allows you to preset DCA buy triggers at desired levels and customize each DCA order size independently. Alerts are working, this strategy is easily used for automatic trading.
I mainly trade on Cryptohopper, Pionex, 3commas. This was created for community, alerts are working and non-repainting. Should work on any other as well.
Trading Condition:
It's buying when Fast EMA crosses up Slow EMA. Set your paramters.
It's selling if EMA's crosses back, signaling a sell. Optional.
DCA:
You can enter DCA on 20 custom levels or layers. It buys DCA when price hits the plotted blue line on the chart that's set by input % triggers. (buy 1st DCA at 2% drop, buy 2nd DCA at 5% drop,...)
Set your Inital Capital and Pyramiding in Properties tab, Initial Order Size and DCA Order Size (lot1,lot2,lot3,..), Order Type are changed in strategy inputs.
-By default you can see that we buy when EMA's cross up and signal a buy for 10% of equity, if market is dropping you will then place a first DCA order ( 20% equity) at 2% drop (lower) from initial order. If market keeps dropping you have more DCA levels where you can buy and average down your holding position. For selling you can use Take profit and Stop Loss targets that averages down multiple open positions, it will sell it once it reaches your desirable Take Profit and close a deal. You can also close your trade if EMA signals a sell.
Pyramiding - number of orders you can open at a time
Your first buy order is pyramiding 1. To allow it to buy 1 DCA or merge one time, set pyramding to 2.
Want to DCA 10 times? Set pyramiding at 11. (+1 always)
More features:
- Profit Calendar
- Show Balance label before every new trade
- DCA table - visualize how much of your investment is used in trades. If a background of the table is green you are okay, if the background color is red - you are using more money for orders than you actually have.
Buy Orders << Strategy Equity/Capital
- Show / Hide DCA lines - if your chart processing is getting slow you should hide some DCA levels to speed it up
- Backtesting Range - for testing the strategy in different time windows
- Alerts
When all trades are closed on your chart, winning rate of the strategy is 100% actually.
Win rate is shown differently as it's actually closing and opening every trade individually by default in TradingView system. We merge positions together and average it down into one big position to later sell for a profit (DCA).
You use this Trading Algorithm at your own risk. Do not trade before testing or invest something you cannot afford to lose on markets.
Bollinger Bands, RSI, and MA StrategyThe "Bollinger Bands, RSI and MA Strategy" is a trend-following strategy that combines the Bollinger Bands indicator, the Relative Strength Index (RSI), and a moving average (MA). It aims to identify potential entry and exit points in the market based on price volatility, momentum, and trend.
The strategy uses two Bollinger Bands with different standard deviations to create price channels. The default settings for the Bollinger Bands are a length of 20 periods and a standard deviation of 2.0. The upper and lower bands of the Bollinger Bands serve as dynamic resistance and support levels, respectively.
The RSI indicator is employed to gauge the strength of price momentum.
The strategy also incorporates a 50-period moving average (MA) to help identify the overall trend direction. When the price is above the MA, it suggests an uptrend, and when the price is below the MA, it suggests a downtrend.
The entry conditions for long trades are when the RSI is above the overbought level and there is no contraction in the Bollinger Bands. For short trades, the entry conditions are when the RSI is below the oversold level and there is no contraction in the Bollinger Bands.
The exit conditions for long trades are when the RSI drops below the overbought level or when the price closes below the 50-period MA.
For short trades, the exit conditions are when the RSI goes above the oversold level or when the price closes above the 50-period MA.
The strategy generates alerts for potential long and short entry signals, as well as for exit signals when the specified conditions are met. These alerts can be used to receive notifications or take further actions, such as placing trades manually or using automated trading systems.
It is important to note that this strategy serves as a starting point and should be thoroughly backtested and validated with historical data before applying it to live trading. Additionally, it is recommended to consider risk management techniques, including setting appropriate stop-loss and take-profit levels, to effectively manage trades.
Kioseff Trading - AI-Optimized RSIAI-Optimized RSI
Introducing AI-Optimized RSI: a streamlined solution for traders of any skill level seeking to rapidly test and optimize RSI. Capable of analyzing thousands of strategies, this tool cuts through the complexity to identify the most profitable, reliable, or efficient approaches.
Paired with TradingView's native backtesting capabilities, the AI-Optimized RSI learns from historical performance data. Set up is easy for all skill levels, and it makes fine-tuning trading alerts and RSI straightforward.
Features
Purpose : Uncover optimal RSI settings and entry levels with precision. Say goodbye to random guesses and arbitrary indicator use—this tool provides clear direction based on data.
Target Performance : You set the goal, and AI-RSI seeks it out, whether it's maximizing profits, efficient trading, or achieving the highest win rate.
AI-Powered : With intelligent AI recommendations, the tool dynamically fine-tunes your RSI approach, steering you towards ideal strategy performance.
Rapid Testing : Evaluate thousands of RSI strategies.
Dual Direction : Perfect both long and short RSI strategies with equal finesse.
Deep Insights : Access detailed metrics including profit factor, PnL, win rate, trade counts, and more, all within a comprehensive strategy script.
Instant Alerts : Set alerts and trade.
Full Customization : Test and optimize all RSI settings, including cross levels, profit targets and stop losses.
Simulated Execution : Explore the impact of limit orders and other trade types through simulation.
Integrative Capability : Combine your own custom indicators or others from the TradingView community for a personalized optimization experience.
Flexible Timeframes : Set your optimization and backtesting to any date range.
Key Settings
The image above shows explanations for a list of key settings for the optimizer.
Direction : This setting controls trade direction: Long or Short.
Entry Condition : Define RSI entry: Select whether to trigger trades on RSI crossunders or crossovers.
RSI Lengths Range : Choose the range of RSI periods to test and find the best one.The AI will find the best RSI period for you.
RSI Cross Range : Set the range for RSI levels where crosses trigger trade signals. The AI will find the best level for you.
Combinations : Select how many RSI strategies to compare.
Optimization Type : Choose the goal for optimization and the AI: profit, win rate, or efficiency.
Profit Target : Set your profit target with this setting.
Stop Loss : Decide your maximum allowable loss (stop loss) per trade.
Limit Order : Specify whether to include limit orders in the strategy.
Stop Type : Choose your stop strategy: a fixed stop loss or a trailing stop.
How to: Find the best RSI for trading
It's important to remember that merely having the AI-Optimized RSI on your chart doesn't automatically provide you with the best strategy. You need to follow the AI's guidance through an iterative process to discover the optimal RSI settings and strategy.
1.Starting Your Strategy Setup
Begin by deciding your goals for each trade: your profit target and stop loss. You'll also choose how to manage your stops – whether they stay put (fixed) or move with the price (trailing), and whether you want to exit trades at a specific price (limit orders). Keep the initial settings for RSI lengths and cross ranges at their default to give the tool a broad testing field. The AI's guidance will refine these settings to pinpoint the most effective ones through a process of comprehensive testing.
The image above shows our chart prior to any optimization efforts.
Note: the settings shown above in the key settings section will be used to start our demonstration.
2. Follow AI’s suggestions
Optimization Prompt: After loading your strategy, the indicator will prompt you to change the RSI length range and RSI level range to a better performing range.
Continue changing the RSI length range and RSI level range to match the indicator's suggestions until "Best Found" is displayed!
The image above shows results after we applied the tool’s suggestions. New suggestions have appeared, and we will continue to apply them.
Continue to adjust settings as recommended by the optimizer. If no better options are found, the optimizer will suggest increasing the number of combinations. Repeat this process until the optimizer indicates that the optimal setting has been identified.
Success! With the "Best Found" notification, an optimized RSI is now active. The AI will keep refining the strategy based on ongoing performance, ensuring continuous optimization.
AI Mode
AI Mode incorporates Heuristic-Based Adaptive Learning to fine-tune trading strategies in a continuous manner. This feature consists of two main components:
Heuristic-Based Decision Making: The algorithm evaluates multiple RSI-based trading strategies using specific metrics such as Profit and Loss (PNL), Win Rate, and Most Efficient Profit. These metrics act as heuristics to assist the algorithm in identifying suitable strategies for trade execution.
Online Learning: The algorithm updates the performance evaluations of each strategy based on incoming market data. This enables the system to adapt to current market conditions.
Incorporating both heuristic-based decision-making and online learning, this feature aims to provide a framework for trading strategy optimization.
Settings
AI Mode Aggressiveness:
Description: The "AI Mode Aggressiveness" setting allows you to fine-tune the AI's trading behavior. This setting ranges from “Low” to “High”, with “High” indicating a more assertive trading approach.
Functionality: This feature filters trading strategies based on a proprietary evaluation method. A higher setting narrows down the strategies that the AI will consider, leaning towards more aggressive trading. Conversely, a lower setting allows for a more conservative approach by broadening the pool of potential strategies.
Adaptive Learning Aggressiveness:
Description: When Adaptive Learning is enabled, the "Adaptive Learning Aggressiveness" setting controls how dynamically the AI adapts to market conditions using selected performance metrics.
Functionality: This setting impacts the AI's responsiveness to shifts in strategy performance. By adjusting this setting, you can control how quickly the AI moves away from strategies that may have been historically successful but are currently underperforming, towards strategies that are showing current promise.
Optimization
Trading system optimization is immensely advantageous when executed with prudence.
Technical-oriented, mechanical trading systems work when a valid correlation is methodical to the extent that an objective, precisely-defined ruleset can consistently exploit it. If no such correlation exists, or a technical-oriented system is erroneously designed to exploit an illusory correlation (absent predictive utility), the trading system will fail.
Evaluate results practically and test parameters rigorously after discovery. Simply mining the best-performing parameters and immediately trading them is unlikely a winning strategy. Put as much effort into testing strong-performing parameters and building an accompanying system as you would any other trading strategy. Automated optimization involves curve fitting - it's the responsibility of the trader to validate a replicable sequence or correlation and the trading system that exploits it.
MACD Optimizer Pro [Kioseff Trading]Massive update! This script now includes 12 different moving averages and 30+ built-in technical indicators to enhance your trading strategy optimization! (:
This script (MACD Optimizer Pro) allows the user to optimize and test hundreds of MACD strategies, simultaneously, in under 40 seconds. Of course, theoretically, an unlimited number of trading strategies can be tested with the MACD Optimizer Pro. After the optimization period - the MACD Optimizer Pro will show the most profitable MACD strategy or, should you choose, the highest win-rate MACD strategy or the most-efficient MACD strategy!
Optimization results can be backtested and verified using the native TradingView backtester - which is included in the MACD Optimizer Pro - and made easy to use! This feature makes settings alerts a simple practice!
Features
Test hundreds of MACD strategies, simultaneously, in under 40 seconds.
Optimize long MACD strategies and short MACD strategies.
12 different built-in moving averages included to improve your MACD strategy.
30+ built-in technical indicators to improve your MACD strategy.
Runs as a strategy script - profit factor, PnL , win-rate, number of trades, max drawdown, equity curve and other pertinent statistics shown.
Alerts
Optimize any MACD setting
Profit targets, trailing stops, fixed stop losses, and a binary MACD strategy can all be tested.
Strategies can be optimized for highest win rate, highest net profit, most efficient profit.
Limit orders can be simulated.
External indicators can be used for optimization i.e. your own, custom-built indicator, an indicator from your favorite author, or almost any publicly available
TradingView indicator.
Date range for optimization and backtesting are configurable.
Explanation
The image above shows a list of configurations for the optimizer. You can
You can test hundreds of different MACD settings in under 40 seconds on any timeframe, asset, etc.
The image above shows additional settings to filter the outcome of your optimization testing. Additionally, you can test an unlimited number of profit targets and stop losses!
You can add one of several built-in TradingView indicators to filter trade entries.
The image above shows all built-in moving averages and TradingView indicators that can be incorporated into your MACD strategy.
Additionally, you can add your own, custom indicator to the optimization test, your favorite indicator by your favorite author or almost any publicly available indicator on TradingView.
The image above shows the settings section in which you can implement this feature.
The image above shows an example of the custom indicator feature! In this instance, I am using the public indicator titled "Self-Optimizing" RSI and requiring it to measure below a level prior to entry! Almost any custom indicator, your favorite indicator, etc. is compatible with this feature!
The MACD Optimizer has improved user friendliness over previous versions. The optimizer can be as simple or complex as you'd like - capable of handling both "easy" and "difficult" tasks at your discretion.
Additionally, you can configure the optimizer to prioritize MACD strategies that earn profit most efficiently!
The image above shows this feature in action.
You can also configure the optimizer to prioritize MACD strategies that achieve the highest win rate!
The image above shows this feature in action.
Instructions
The instructions below show a rudimentary approach to using the optimizer.
1. Build your strategy in the settings.
You should also disable the "Run a Backtest" feature to improve load times during optimization.
The image above shows my custom strategy settings.
Now that you've got some data on your chart - you should try "Freezing" the "Smoothing" setting for MACD . When doing this, the optimizer will test hundreds of MACD settings with a fixed "Smoothing" setting. Try using the best "Smoothing" setting you were able to find for your initial testing.
2. Take the best "Smoothing" setting and test various MACD and Signal Lengths.
The image above shows me configuring the MACD Optimizer to test different MACD line lengths and Signal line lengths with a fixed "smoothing" setting.
From the results, we can see that there are better MACD settings than what was shown in our initial test!
With this information we can execute a TradingView backtest.
3. Execute a TradingView Backtest.
You must enable the "Run a Backtest" feature to perform a TradingView backtest. Additionally, it's advised to enable the "STOP OPTIMIZATION" feature when performing a TradingView backtest. Enabling this feature will improve load times for the backtest to only a few seconds (since the optimizer won't look for the best setting when this feature is enabled).
The image above shows completion of the process!
From here, you can perform further testing, set alerts, etc.
Backtest Settings Shown
Initial Capital: The initial capital used for the shown backtests is $3,500 USD. Set the initial capital to replicate your true starting capital (: PnL for the MACD strategies (listed in table) is calculated using a starting capital of $10,000 USD.
Slippage: The slippage settings for the displayed backtest was set to 2 ticks.
Commission: Commission was adjusted to 0.1%.
Verify Price for Limit Orders was set to 2 ticks.
Optimization
Trading system optimization is immensely advantageous when executed with prudence.
Technical-oriented, mechanical trading systems work when a valid correlation is methodical to the extent that an objective, precisely-defined ruleset can consistently exploit it. If no such correlation exists, or a technical-oriented system is erroneously designed to exploit an illusory correlation (absent predictive utility), the trading system will fail.
Evaluate results practically and test parameters rigorously after discovery. Simply mining the best-performing parameters and immediately trading them is unlikely a winning strategy. Put as much effort into testing strong-performing parameters and building an accompanying system as you would any other trading strategy. Automated optimization involves curve fitting - it's the responsibility of the trader to validate a replicable sequence or correlation and the trading system that exploits it.
Thanks for checking this out!
Kioseff Trading - AI-Optimized Supertrend
AI-Optimized Supertrend
Introducing AI-Optimized Supertrend: a streamlined solution for traders of any skill level seeking to rapidly test and optimize Supertrend. Capable of analyzing thousands of strategies, this tool cuts through the complexity to identify the most profitable, reliable, or efficient approaches.
Paired with TradingView's native backtesting capabilities, the AI-Optimized Supertrend learns from historical performance data. Set up is easy for all skill levels, and it makes fine-tuning trading alerts and Supertrend straightforward.
Features
Rapid Supertrend Strategy Testing : Quickly evaluate thousands of Supertrend strategies to find the most effective ones.
AI-Assisted Optimization : Leverage AI recommendations to fine-tune strategies for superior results.
Multi-Objective Optimization : Prioritize Supertrend based on your preference for the highest win rate, maximum profit, or efficiency.
Comprehensive Analytics : The strategy script provides an array of statistics such as profit factor, PnL, win rate, trade counts, max drawdown, and an equity curve to gauge performance accurately.
Alerts Setup : Conveniently set up alerts to be notified about critical trade signals or changes in performance metrics.
Versatile Stop Strategies : Experiment with profit targets, trailing stops, and fixed stop losses.
Binary Supertrend Exploration : Test binary Supertrend strategies.
Limit Orders : Analyze the impact of limit orders on your trading strategy.
Integration with External Indicators : Enhance strategy refinement by incorporating custom or publicly available indicators from TradingView into the optimization process.
Key Settings
The image above shows explanations for a list of key settings for the optimizer.
Set the Factor Range Limits : The AI suggests optimal upper and lower limits for the Factor range, defining the sensitivity of the Supertrend to price fluctuations. A wider range tests a greater variety, while a narrower range focuses on fine-tuning.
Adjust the ATR Range : Use the AI's recommendations to establish the upper and lower bounds for the Average True Range (ATR), which influences the Supertrend's volatility threshold.
ATR Flip : This option lets you interchange the order of ATR and Factor values to quicky test different sequences, giving you the flexibility to explore various combinations and their impact on the Supertrend indicator's performance.
Strategies Evaluated : Adjust this setting to determine how many Supertrend strategies you want to assess and compare.
Enable AI Mode : Turn this feature on to allow the AI to determine and employ the optimal Supertrend strategy with the desired performance metric, such as the highest win rate or maximum profitability.
Target Metric : Adjust this to direct the AI towards optimizing for maximum profit, top win rates, or the most efficient profits.
AI Mode Aggressiveness : Set how assertively the AI pursues the chosen performance goal, such as highest profit or win rate.
Strategy Direction : Choose to focus the AI's testing and optimization on either long or short Supertrend strategies.
Stop Loss Type : Specify the stop loss approach for optimization—fixed value, a trailing stop, or Supertrend direction changes.
Limit Order : Decide if you want to execute trades using limit orders for setting your profit targets, stop losses, or apply them to both.
Profit Target : Define your desired profit level when using either a fixed stop loss or a trailing stop.
Stop Loss : Define your desired stop loss when using either a fixed stop loss or a trailing stop.
How to: Find the best Supertrend for trading
It's important to remember that merely having the AI-Optimized Supertrend on your chart doesn't automatically provide you with the best strategy. You need to follow the AI's guidance through an iterative process to discover the optimal Supertrend settings and strategy.
Optimizing Supertrend involves adjusting two key parameters: the Factor and the Average True Range (ATR). These parameters significantly influence the Supertrend indicator's sensitivity and responsiveness to price movements.
Factor : This parameter multiplies the ATR to determine the distance of the Supertrend line from the price. Higher values will create a wider band, potentially leading to fewer trade signals, while lower values create a narrower band, which may result in more signals but also more noise.
ATR (Average True Range) : ATR measures market volatility. By using the ATR, the Supertrend adapts to changing market volatility; a higher ATR value means a more volatile market, so the Supertrend adjusts accordingly.
During the optimization process, these parameters are systematically varied to determine the combination that yields the best performance based on predefined criteria such as profitability, win rate, or risk management efficiency. The optimization aims to find the optimal Factor and ATR settings.
1.Starting Your Strategy Setup
Begin by deciding your goals for each trade: your profit target and stop loss, or if all trades exit when Supertrend changes direction. You'll also choose how to manage your stops – whether they stay put (fixed) or move with the price (trailing), and whether you want to exit trades at a specific price (limit orders). Keep the initial settings for Supertrend Factor Range and Supertrend ATR Range at their default to give the tool a broad testing field. The AI's guidance will refine these settings to pinpoint the most effective ones through a process of comprehensive testing.
Demonstration Start: We'll begin with the settings outlined in the key settings section, using Supertrend's direction change to the downside as our exit signal for all trades.
2. Continue applying the AI’s suggestions
Keep updating your optimization settings based on the AI's recommendations. Proceed with this iterative optimization until the "Best Found" message is displayed, signaling that the most effective strategy has been identified.
While following the AI's suggestions, we've been prompted with a new suggestion: increase the
number of strategies evaluated. Keep following the AI's new suggestions to evaluate more strategies. Do this until the "Best Found" message shows up.
Success! We continued to follow the AI’s suggestions until “Best Found” was indicated!
AI Mode
AI Mode incorporates Heuristic-Based Adaptive Learning to fine-tune trading strategies in a continuous manner. This feature consists of two main components:
Heuristic-Based Decision Making: The algorithm evaluates multiple Supertrend-based trading strategies using metrics such as Profit and Loss (PNL), Win Rate, and Most Efficient Profit. These metrics act as heuristics to assist the algorithm in identifying suitable strategies for trade execution.
Online Learning: The algorithm updates the performance evaluations of each strategy based on incoming market data. This enables the system to adapt to current market conditions.
Incorporating both heuristic-based decision-making and online learning, this feature aims to provide a framework for trading strategy optimization.
AI Mode Settings
AI Mode Aggressiveness:
Description: The "AI Mode Aggressiveness" setting allows you to fine-tune the AI's trading behavior. This setting ranges from “Low” to “High”, with “High” indicating a more assertive trading approach.
Functionality: This feature filters trading strategies based on a proprietary evaluation method. A higher setting narrows down the strategies that the AI will consider, leaning towards more aggressive trading. Conversely, a lower setting allows for a more conservative approach by broadening the pool of potential strategies.
Optimization
Trading system optimization is immensely advantageous when executed with prudence.
Technical-oriented, mechanical trading systems work when a valid correlation is methodical to the extent that an objective, precisely-defined ruleset can consistently exploit it. If no such correlation exists, or a technical-oriented system is erroneously designed to exploit an illusory correlation (absent predictive utility), the trading system will fail.
Evaluate results practically and test parameters rigorously after discovery. Simply mining the best-performing parameters and immediately trading them is unlikely a winning strategy. Put as much effort into testing strong-performing parameters and building an accompanying system as you would any other trading strategy. Automated optimization involves curve fitting - it's the responsibility of the trader to validate a replicable sequence or correlation and the trading system that exploits it.
Quantitative mean reversion v4The code uses the concept of mean reversion. Mean reversion suggests that price over a period of time reverts back to its statistical mean. In simple terms, it means if a price has drifted apart from the statistical mean, after a certain amount of time, it will revert back to its statistical mean. This drift is measured via z-score. When the z-score value is high, the price is expected to revert. Besides, the higher the time frame you use, the lesser the drift is, so reduce the z-score in the tabs if you use higher time frames, else, vice-versa.
Based on the parameters, the code will provide a trade signal - both long and short, and entry and exit. You can use notifications for alerts. Please use the parameters in the options to find the best combinations for your stocks.
In the properties, you can use your own brokers commission, capital, to see if the strategy is profitable for your ticker in the long run or not. This code has been tested for profits for various assets in both crypto - Bitcoin futures , Ethereum futures -, and stocks - AMD , Apple , MSFT , etc.
This is not get rich quick scheme, and you have to be patient with it for the long run.
If you have any query, please feel free to ask in the comments sections.
If you want some new changes, please feel free to suggest
Currently, I am optimising the maximum time for holding a trade. Till that's completed, use this and please feel free to leave a feedback to make it better
Trend Follower Intraday [ Adjustable TF ]Trend Follower Intraday for 3 minute Time-Frame (Adjustable) , that has the time condition for Indian Markets as well.
Unlike the Free Scripts - Risk Management , Position Sizing , Partial Exit etc. are also included .
Send us a Message to know more about the strategy.
// ══════════════════════════════════════════════════════════════════════════ //
The Timing can be changed to fit other markets, scroll down to "TIME CONDITION" to know more.
The commission is also included in the strategy .
The basic idea is when ,
1) EMA1 crosses above EMA2 , is a Long condition .
2) EMA1 crosses below EMA2 , is a Short condition .
3) Green Section indicates Long position.
4) Red Section indicates Short position.
5) Allowed hours specifies the trade entry timing.
6) ATR STOP is the stop-loss value on chart , can be adjusted in INPUTS.
7) Target 1 is the 1st target value on chart , can be adjusted in INPUTS.
8) RISK is Maximum Risk per trade for the intraday trade can be changed .
9) Total Capital used can be adjusted under INPUTS.
10) ATR TRAIL is used for trailing after entry, as mentioned in the inputs below.
11) Check trades under the list of trades .
12) Trade only in liquid stocks .
13) Risk only 1-5% of total capital.
14) Inputs can be changed for better back-test results, but also manually check the trades before setting alerts
15) SQUARE OFF TIME - As you change the time frame , also change the square-off time to the candle's closing time.
Eg: For 3min Time-frame , Hour = 2Hrs | Minute = 57min
16) Strategy stops for the day if you have a loss .
17) COMMISSION value is set to 20Rs and SLIPPAGE value is set to 2 . Go to properties to change it .
*The input values and the results are mentioned under "BACKTEST RESULTS" below*
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// ————————> RISK MANAGEMENT <——————— //
// ══════════════════════════════ //
Risk management is done based on max loss per trade and can be adjusted in the INPUTS.
// ═══════════════════════════ //
// ————————> POSITION SIZE <——————— //
// ═══════════════════════════ //
Quantity of each trade is different based on the loss
// ═════════════════════════ //
// ————————> PROPERTIES <——————— //
// ═════════════════════════ //
COMMISSION , SLIPPAGE ,RECALCULATE is already mentioned in the code.
COMMISSION can be charges , based on the broker charges.
// ═══════════════════════════════//
// ————————> TIME CONDITION <————————— //
// ═══════════════════════════════//
The time can be changed in the INPUT.
The Indian Markets open at 9:15am and closes at 3:30pm.
The 'Allowed hours' under Inputs specifies the time at which Entries should happen .
"Close All" function closes all the trades before 3pm , at the open of the next candle.
To change the time to close all trades , check INPUT.
All open trades get closed by 3pm , because some brokers don't allow you to place fresh intraday orders after 3pm .
// ═══════════════════════════════════════════════ //
// ————————> BACKTEST RESULTS ( 123 CLOSED TRADES ) <————————— //
// ═══════════════════════════════════════════════ //
INPUTS can be changed for better Back-Test results.
The strategy applied to NSE:JSWENERGY (3 min Time-Frame and with a capital of 3,00,000 ) gives us 81% profitability , as shown below
It was tested for a period a 6 months with a Profit Factor of 1.957 ,net Profit of 43,000Rs .
Sharpe Ratio = 0.745
Sortino Ratio = 2.091
No strategy in the world promises 100% profits in all market conditions , so always define your risk before trading.
Also check Back-Test results manually ,before setting Alerts
The Graph has a Linear Curve with Consistent Profits.
The INPUTS are as follows,
1) EMA1 ————————————————> 38
2) EMA2 ————————————————> 118
3) ALLOWED HRS ———————————> 9:35 TO 14:30
4) ATR STOP ——————————————> 3.2
5) RISK ——————————————————> 3000
6) ATR TRAIL ———————————————> 2.6
7) TARGET 1 ————————————————> 2.4
8) MAX POSITION VALUE ——————————> 3,00,000
8) MAX DRAWDOWN —————————————> 9,000
8) SQUARE-OFF ————————————————> 14:57
NSE:JSWENERGY
Apply it to your charts Now !
NSE:JSWENERGY
Send us a message for FREE TRIALS | Instant Access
Thank You ☺
APIBridge Nifty Options Algo StrategyUsing Pinescript, we will use charts of Cash/Future to trade in Options. Note this strategy works well with even the free version of TradingView.
The Relative Strength Index ( RSI ). Is a momentum oscillator that measures the speed and change of price movements. The RSI oscillates between zero and 100. Increasing RSI shows increasing bullish momentum. Decreasing RSI shows increasing bearish momentum. We take RSI upper bound as 80 to indicate bullish momentum and RSI lower bound as 20 to indicate bearish momentum.
We use the above premise to create options buy-only strategy which trades in ATM strikes by default. This strategy requires very less margin (Minimum Rs . 15000).
Since this strategy uses underlying data (cash/future) to place trades in Options, please ignore the backtest of this strategy given by TradingView. TradingView does not provide options data but this strategy bypasses it.
Strategy Premise
The Relative Strength Index (RSI) is a momentum oscillator that measures the speed and change of price movements. The RSI oscillates between zero and 100. Increasing RSI shows increasing bullish momentum. Decreasing RSI shows increasing bearish momentum. We take RSI upper bound as 80 to indicate bullish momentum and RSI lower bound as 20 to indicate bearish momentum.
We use the above premise to create options buy-only strategy which trades in ATM strikes by default. This strategy requires very less margin (Rs. 15000 should be sufficient).
NSE Options Algo Strategy Logic
Long Entry: When RSI goes above 80, send LE in an auto-calculated option strike Call. When RSI goes below 20, send LE in auto-calculated option strike Put.
Long Exit: When we hit Stop loss or Target. In case SL/TGT does not hit and reverse RSI goes above 80 send Long Exit in auto-calculated option. Put as per last trade; RSI goes below 20, send LX in auto-calculated option call as per last trade.
For Long and Short entry the order is fired in the option buying side with auto strike price selection.
Option Strategy Parameters for TraingView Charts
RSI Length(Mandatory): Number of bars used to calculated RSI.
Upper Band(Mandatory): To specify upper band of RSI.
Lower Band(Mandatory): For specifying lower band of RSI.
Use reversal from Upper Band (Optional): This will enable short entry when RSI is falling below 80 from upper band. Recommended to keep unchecked initially.
Use reversal from Lower Band (Optional): This will enable long entry when RSI is raising above 20 from lower band. Recommended to keep unchecked initially.
Quantity: We use this specify the trade quantity (for Nifty min 75)
Custom Stop Loss in Points: Movement in chart price against the momentum which will trigger exit in options positions
Custom Target in Points: Movement in chart price against the momentum which will trigger exit in options positions
Base symbol: This is the base instrument symbol like NIFTY or BANK NIFTY.
Strike distance from ATM: Our default strike selection is considered as first ATM option (with nearest distance, only 100s are considered ). This strike distance allows to calculate ATM options which are at fixed distance.
Expiry: Expiry of option. Weekly and monthly both expiry are allowed.
Instrument: For index instrument will be OPTIDX, for stock instrument will be OPTSTK
Strategy Tag: The Strategy of Nifty options configured in Api bridge.
Setting Up Alert
Before setting up the alert make sure that you have selected desired script, time frame, strategy settings, and APIbridge configuration. Click in settings add alert and paste {{strategy.order.comment}} in message box.
Important: Do not change any settings during live trading. It may break the sequence of exit for the correct call/put.
[XRP][1h] Chanu Delta inspired — Breakeven StrategyHello, this is my first TV contribution. I usually don't publish anything but the script is a quick review of an other contributor (Chanu Delta V3 script )
I reverse engineered this indicator today as I wanted to test it on other contracts. The original version (which aims to be traded on BTC) has been ported to XRP (as btc and xrp prices are narrowly correlated) then modified with a couple of what I believe are improvements:
- No backtest bias even with `security` function.
- Extra backtest bias validation, always trading on next bar as Crossover/under bias is confirmed
- Backtest with 2 ajustable TP, ajustable equity and breakeven option
- The current version is not design to use pyramiding as it would require extra logic to monitor the lifecycle of the position in the context of a study.
- Commented alerts examples with variables available in script scope so you can use them in alerts (just replace strategy with indicator and remove backtest related code block).
- Trade filling assumption set to 10, fees to 0.02 as the are default bybit maker fees and I advice to enter with trailing orders using a max of 2 ticks as offset to lower fees rather than a market order!
- Backtest and Alerts happen on barclose.
- No repaint guaranteed.
There are a thousand ways to improve it (adx/bb based dynamic TP/SL, order lifecycle, pyramiding...) but it seems to be a cool starting point.
Don't forget to have fun!
TTP Kent Strat PROKent Strat PRO trades breakouts using Bollinger Bands together with SuperTrend.
PRO features:
- 3commas bot alerts for long/short bots
- Custom JSON bots alerts
Features:
- Risk/reward ratio parameter
- Longs, shorts and combined positions.
- Breakout settings
- Trailing SL, trailing TP
- Use of latest candles to place the SL using a lookback parameter (how many candles to look back for a low/high price)
- Select your SL between the ATR trendline and the latest candle: the closest or furthest away value
- Show the trendline
- Backtest mode for accurate backtests
- Signal mode for live price accurate signals
- Date range backtesting
Filters:
- EMA 200 filter and timeframe selector. This filter can be used to trade with the trend: open longs on an uptrend and shorts on a downtrend.
- ADX filter using threshold. This filter can be used to filter entries where the trend is not very strong.
- ADX pointing up. ADX values pointing up and above certain threshold can improve entries.
- Relative volume filter based on the volume being X% above the MA of the Volume. Trading with volume can help filtering out bad trades.
Example setup:
1) pick BINANCE:ETHUSDT chart, 15 min chart
2) trade longs + shorts
3) pick ratio 3
4) trailing SL checked
5) trailing TP unchecked
7) stop loss "furthest"
8) candle loopback 30
9) BB period 21, dev 1, ATR filter on, atr period 5
10) EMA filter on, 15 min
11) ADX off
12) Volume filter on set to 60%
RSI Mean Reversion StrategyThis is a scalping strategy designed to be used for crypto trading. It uses an Exponential Moving Average with a default length of 100 in order to identify the trend of the market. If the price is trading above 100, it will only take long trades, and vice versa for shorts. It places long orders when the RSI value closes below 40, and the price is also above the 100 EMA. It places short orders when the RSI value is above 60, and the price is below the 100 EMA.
*Note: for custom alert messages to be read, "{{strategy.order.alert_message}}" must be placed into the alert dialogue box when the alert is set.
STD-Filterd, R-squared Adaptive T3 w/ Dynamic Zones BT [Loxx]STD-Filterd, R-squared Adaptive T3 w/ Dynamic Zones BT is the backtest strategy for "STD-Filterd, R-squared Adaptive T3 w/ Dynamic Zones " seen below:
Included:
This backtest uses a special implementation of ATR and ATR smoothing called "True Range Double" which is a range calculation that accounts for volatility skew.
You can set the backtest to 1-2 take profits with stop-loss
Signals can't exit on the same candle as the entry, this is coded in a way for 1-candle delay post entry
This should be coupled with the INDICATOR version linked above for the alerts and signals. Strategies won't paint the signal "L" or "S" until the entry actually happens, but indicators allow this, which is repainting on current candle, but this is an FYI if you want to get serious with Pinescript algorithmic botting
You can restrict the backtest by dates
It is advised that you understand what Heikin-Ashi candles do to strategies, the default settings for this backtest is NON Heikin-Ashi candles but you have the ability to change that in the source selection
This is a mathematically heavy, heavy-lifting strategy with multi-layered adaptivity. Make sure you do your own research so you understand what is happening here. This can be used as its own trading system without any other oscillators, moving average baselines, or volatility/momentum confirmation indicators.
What is the T3 moving average?
Better Moving Averages Tim Tillson
November 1, 1998
Tim Tillson is a software project manager at Hewlett-Packard, with degrees in Mathematics and Computer Science. He has privately traded options and equities for 15 years.
Introduction
"Digital filtering includes the process of smoothing, predicting, differentiating, integrating, separation of signals, and removal of noise from a signal. Thus many people who do such things are actually using digital filters without realizing that they are; being unacquainted with the theory, they neither understand what they have done nor the possibilities of what they might have done."
This quote from R. W. Hamming applies to the vast majority of indicators in technical analysis . Moving averages, be they simple, weighted, or exponential, are lowpass filters; low frequency components in the signal pass through with little attenuation, while high frequencies are severely reduced.
"Oscillator" type indicators (such as MACD , Momentum, Relative Strength Index ) are another type of digital filter called a differentiator.
Tushar Chande has observed that many popular oscillators are highly correlated, which is sensible because they are trying to measure the rate of change of the underlying time series, i.e., are trying to be the first and second derivatives we all learned about in Calculus.
We use moving averages (lowpass filters) in technical analysis to remove the random noise from a time series, to discern the underlying trend or to determine prices at which we will take action. A perfect moving average would have two attributes:
It would be smooth, not sensitive to random noise in the underlying time series. Another way of saying this is that its derivative would not spuriously alternate between positive and negative values.
It would not lag behind the time series it is computed from. Lag, of course, produces late buy or sell signals that kill profits.
The only way one can compute a perfect moving average is to have knowledge of the future, and if we had that, we would buy one lottery ticket a week rather than trade!
Having said this, we can still improve on the conventional simple, weighted, or exponential moving averages. Here's how:
Two Interesting Moving Averages
We will examine two benchmark moving averages based on Linear Regression analysis.
In both cases, a Linear Regression line of length n is fitted to price data.
I call the first moving average ILRS, which stands for Integral of Linear Regression Slope. One simply integrates the slope of a linear regression line as it is successively fitted in a moving window of length n across the data, with the constant of integration being a simple moving average of the first n points. Put another way, the derivative of ILRS is the linear regression slope. Note that ILRS is not the same as a SMA ( simple moving average ) of length n, which is actually the midpoint of the linear regression line as it moves across the data.
We can measure the lag of moving averages with respect to a linear trend by computing how they behave when the input is a line with unit slope. Both SMA (n) and ILRS(n) have lag of n/2, but ILRS is much smoother than SMA .
Our second benchmark moving average is well known, called EPMA or End Point Moving Average. It is the endpoint of the linear regression line of length n as it is fitted across the data. EPMA hugs the data more closely than a simple or exponential moving average of the same length. The price we pay for this is that it is much noisier (less smooth) than ILRS, and it also has the annoying property that it overshoots the data when linear trends are present.
However, EPMA has a lag of 0 with respect to linear input! This makes sense because a linear regression line will fit linear input perfectly, and the endpoint of the LR line will be on the input line.
These two moving averages frame the tradeoffs that we are facing. On one extreme we have ILRS, which is very smooth and has considerable phase lag. EPMA has 0 phase lag, but is too noisy and overshoots. We would like to construct a better moving average which is as smooth as ILRS, but runs closer to where EPMA lies, without the overshoot.
A easy way to attempt this is to split the difference, i.e. use (ILRS(n)+EPMA(n))/2. This will give us a moving average (call it IE /2) which runs in between the two, has phase lag of n/4 but still inherits considerable noise from EPMA. IE /2 is inspirational, however. Can we build something that is comparable, but smoother? Figure 1 shows ILRS, EPMA, and IE /2.
Filter Techniques
Any thoughtful student of filter theory (or resolute experimenter) will have noticed that you can improve the smoothness of a filter by running it through itself multiple times, at the cost of increasing phase lag.
There is a complementary technique (called twicing by J.W. Tukey) which can be used to improve phase lag. If L stands for the operation of running data through a low pass filter, then twicing can be described by:
L' = L(time series) + L(time series - L(time series))
That is, we add a moving average of the difference between the input and the moving average to the moving average. This is algebraically equivalent to:
2L-L(L)
This is the Double Exponential Moving Average or DEMA , popularized by Patrick Mulloy in TASAC (January/February 1994).
In our taxonomy, DEMA has some phase lag (although it exponentially approaches 0) and is somewhat noisy, comparable to IE /2 indicator.
We will use these two techniques to construct our better moving average, after we explore the first one a little more closely.
Fixing Overshoot
An n-day EMA has smoothing constant alpha=2/(n+1) and a lag of (n-1)/2.
Thus EMA (3) has lag 1, and EMA (11) has lag 5. Figure 2 shows that, if I am willing to incur 5 days of lag, I get a smoother moving average if I run EMA (3) through itself 5 times than if I just take EMA (11) once.
This suggests that if EPMA and DEMA have 0 or low lag, why not run fast versions (eg DEMA (3)) through themselves many times to achieve a smooth result? The problem is that multiple runs though these filters increase their tendency to overshoot the data, giving an unusable result. This is because the amplitude response of DEMA and EPMA is greater than 1 at certain frequencies, giving a gain of much greater than 1 at these frequencies when run though themselves multiple times. Figure 3 shows DEMA (7) and EPMA(7) run through themselves 3 times. DEMA^3 has serious overshoot, and EPMA^3 is terrible.
The solution to the overshoot problem is to recall what we are doing with twicing:
DEMA (n) = EMA (n) + EMA (time series - EMA (n))
The second term is adding, in effect, a smooth version of the derivative to the EMA to achieve DEMA . The derivative term determines how hot the moving average's response to linear trends will be. We need to simply turn down the volume to achieve our basic building block:
EMA (n) + EMA (time series - EMA (n))*.7;
This is algebraically the same as:
EMA (n)*1.7-EMA( EMA (n))*.7;
I have chosen .7 as my volume factor, but the general formula (which I call "Generalized Dema") is:
GD (n,v) = EMA (n)*(1+v)-EMA( EMA (n))*v,
Where v ranges between 0 and 1. When v=0, GD is just an EMA , and when v=1, GD is DEMA . In between, GD is a cooler DEMA . By using a value for v less than 1 (I like .7), we cure the multiple DEMA overshoot problem, at the cost of accepting some additional phase delay. Now we can run GD through itself multiple times to define a new, smoother moving average T3 that does not overshoot the data:
T3(n) = GD ( GD ( GD (n)))
In filter theory parlance, T3 is a six-pole non-linear Kalman filter. Kalman filters are ones which use the error (in this case (time series - EMA (n)) to correct themselves. In Technical Analysis , these are called Adaptive Moving Averages; they track the time series more aggressively when it is making large moves.
What is R-squared Adaptive?
One tool available in forecasting the trendiness of the breakout is the coefficient of determination ( R-squared ), a statistical measurement.
The R-squared indicates linear strength between the security's price (the Y - axis) and time (the X - axis). The R-squared is the percentage of squared error that the linear regression can eliminate if it were used as the predictor instead of the mean value. If the R-squared were 0.99, then the linear regression would eliminate 99% of the error for prediction versus predicting closing prices using a simple moving average .
R-squared is used here to derive a T3 factor used to modify price before passing price through a six-pole non-linear Kalman filter.
What are Dynamic Zones?
As explained in "Stocks & Commodities V15:7 (306-310): Dynamic Zones by Leo Zamansky, Ph .D., and David Stendahl"
Most indicators use a fixed zone for buy and sell signals. Here’ s a concept based on zones that are responsive to past levels of the indicator.
One approach to active investing employs the use of oscillators to exploit tradable market trends. This investing style follows a very simple form of logic: Enter the market only when an oscillator has moved far above or below traditional trading lev- els. However, these oscillator- driven systems lack the ability to evolve with the market because they use fixed buy and sell zones. Traders typically use one set of buy and sell zones for a bull market and substantially different zones for a bear market. And therein lies the problem.
Once traders begin introducing their market opinions into trading equations, by changing the zones, they negate the system’s mechanical nature. The objective is to have a system automatically define its own buy and sell zones and thereby profitably trade in any market — bull or bear. Dynamic zones offer a solution to the problem of fixed buy and sell zones for any oscillator-driven system.
An indicator’s extreme levels can be quantified using statistical methods. These extreme levels are calculated for a certain period and serve as the buy and sell zones for a trading system. The repetition of this statistical process for every value of the indicator creates values that become the dynamic zones. The zones are calculated in such a way that the probability of the indicator value rising above, or falling below, the dynamic zones is equal to a given probability input set by the trader.
To better understand dynamic zones, let's first describe them mathematically and then explain their use. The dynamic zones definition:
Find V such that:
For dynamic zone buy: P{X <= V}=P1
For dynamic zone sell: P{X >= V}=P2
where P1 and P2 are the probabilities set by the trader, X is the value of the indicator for the selected period and V represents the value of the dynamic zone.
The probability input P1 and P2 can be adjusted by the trader to encompass as much or as little data as the trader would like. The smaller the probability, the fewer data values above and below the dynamic zones. This translates into a wider range between the buy and sell zones. If a 10% probability is used for P1 and P2, only those data values that make up the top 10% and bottom 10% for an indicator are used in the construction of the zones. Of the values, 80% will fall between the two extreme levels. Because dynamic zone levels are penetrated so infrequently, when this happens, traders know that the market has truly moved into overbought or oversold territory.
Calculating the Dynamic Zones
The algorithm for the dynamic zones is a series of steps. First, decide the value of the lookback period t. Next, decide the value of the probability Pbuy for buy zone and value of the probability Psell for the sell zone.
For i=1, to the last lookback period, build the distribution f(x) of the price during the lookback period i. Then find the value Vi1 such that the probability of the price less than or equal to Vi1 during the lookback period i is equal to Pbuy. Find the value Vi2 such that the probability of the price greater or equal to Vi2 during the lookback period i is equal to Psell. The sequence of Vi1 for all periods gives the buy zone. The sequence of Vi2 for all periods gives the sell zone.
In the algorithm description, we have: Build the distribution f(x) of the price during the lookback period i. The distribution here is empirical namely, how many times a given value of x appeared during the lookback period. The problem is to find such x that the probability of a price being greater or equal to x will be equal to a probability selected by the user. Probability is the area under the distribution curve. The task is to find such value of x that the area under the distribution curve to the right of x will be equal to the probability selected by the user. That x is the dynamic zone.
Included:
Bar coloring
Signals
Alerts
Loxx's Expanded Source Types
Miyagi (10 in 1) + DCA StrategyMiyagi: The attempt at mastering something for the best results.
Miyagi indicators combine multiple trigger conditions and place them in one toolbox for traders to easily use, produce alerts, backtest, reduce risk and increase profitability.
Miyagi (10 in 1) + DCA Strategy built for the Miyagi (10 in 1) + Alerts found here:
The DCA Strategy was designed to help visualize, backtest and improve users' DCA strategies and overall profitability.
Users can backtest different trading timeframes using the start and end date inputs.
Users can backtest different take profit and stoploss percents, both long and short.
Users can choose whether or not to use DCA on the backtester via a selectable input.
Input the DCA as you would normally using the Wick Hunter bot.
Happy trading!
Miyagi (4 in 1) + DCA StrategyMiyagi: The attempt at mastering something for the best results.
Miyagi indicators combine multiple trigger conditions and place them in one toolbox for traders to easily use, produce alerts, backtest, reduce risk and increase profitability.
Miyagi (4 in 1) + DCA Strategy built for the Miyagi (4 in 1) + Alerts found here:
The DCA Strategy was designed to help visualize, backtest and improve users' DCA strategies and overall profitability.
Users can backtest different trading timeframes using the start and end date inputs.
Users can backtest different take profit and stoploss percents, both long and short.
Users can choose whether or not to use DCA on the backtester via a selectable input.
Input the DCA as you would normally using the Wick Hunter bot.
Happy trading!
Miyagi STrend StrategyMiyagi: The attempt at mastering something for the best results.
Miyagi indicators combine multiple trigger conditions and place them in one toolbox for traders to easily use, produce alerts, backtest, reduce risk and increase profitability.
Miyagi STrend was created to allow traders the ability to both scalp and swing trade from as singular indicator. STrend aims to help traders catch more of the move.
STrend Strategy built for the Miyagi STrend found here:
It would be best suited to utilize a stoploss when trading with Miyagi STrend to minimize risk.
Alerts are meant to fire on "Once per Bar Close" to confirm entry and exit signals.
Happy Trading!
Market First - Relative Strength/Weakness (the ZenBot strategy)This market-first trading strategy gives BUY, SHORT, and CLOSE signals based on volume, trend, and relative strength or weakness to the market (SPY by default, can be customized). This indicator is useful for signaling day-trade entries and exits for tickers that are strong (or weak) against the market.
Stocks that are showing relative strength (or weakness) to the market, are trending, and have decent movement generate a buy (or short) signal. When the trend runs out, a CLOSE signal is fired.
Potential profit (based on ATR) and actual profit is calculated, predicting the type of move expected
Unique 'stay in trade' logic helps prevent unnecessary CLOSE signals if a trend is likely to continue
A colored plot indicates the strength of the current trend and turns orange/red when the strength is weakened.
Crypto traders can uncheck 'Trade during market hours' for 24-hour trading, and should change the comparison ticker from SPY to BTCUSD or something similar for their market.
Enjoy!
KEY CONCEPTS
The three- and five-minute timeframes are used to establish and verify trend ( ADX /DI with custom logic)
Entries and exits are based on Parabolic SAR and confirmed on multiple timeframes, trend, and relative volume
Relative strength /weakness to the market compares ticker to SPY
Chop is avoided at all costs. I've experimented with choppiness indicator below 38, but found that the ADX DI+/- readings work even better.
Trend is established using ADX DI+/- readings over 20, confirmed by EMA 5/13 crossover and EMA5 slope
Signals will fire only if the average volume for the current 5-min bar is above normal
Only tickers with a five-bar / 13 period ATR of 1% the ticker's price generate signal.
Only longs above daily-anchored VWAP , shorts below daily-anchored VWAP
Signals fire on bar close to prevent repainting / look-ahead bias
Indicator labels and alerts generated
SIGNALS
BUY: up-trending tickers showing relative strength are bought on the three-minute PSAR
SELL: when the close price falls below the 1, 3, and 5-minute PSAR, or the ADX DI- falls below 20
SHORT: down-trending tickers with relative weakness are shorted on the three-minute PSAR
COVER: when the close price moves above the 1, 3, and 5-minute PSAR, or the ADX DI- falls below 20
ALERTS
Alerts are generated on BUY, SELL, SHORT, and COVER signals, as well as optional LOST RELATIVE STRENGTH and LOST RELATIVE WEAKNESS
INPUTS
Use relative strength /weakness comparison with the market : trigger trades based on the ticker's strength or weakness to the selected comparison ticker (usually SPY for equities or BTCUSD for crypto)
[* ]Comparison Ticker for relative strength /weakness : Ticker to compare against for relative strength /weakness
Trade during market hours only : Take buy/sells during specified hours. Disable this for crypto trading.
[* ]Market hours (market time) : Customize market hours - defaults to 9:30 to 16:00 EST
[* ]"Only trade very strong trends" : take trades only if an established trend is very strong ( ADX over 40 ) (DEFAULT = ON)
"Limit trade direction to VWAP" : Long trades only above VWAP , shorts below (DEFAULT = ON)
"Limit trade direction to Market direction" : Long trades only if SPY (or selected comparison ticker) is up, shorts if the market is down. (DEFAULT= ON)
"Limit trades based on a ticker's green/red status for the day" : Long trades if the ticker is green for the day, shorts if red. (DEFAULT = ON)
