Arpeet MACDOverview
This strategy is based on the zero-lag version of the MACD (Moving Average Convergence Divergence) indicator, which captures short-term trends by quickly responding to price changes, enabling high-frequency trading. The strategy uses two moving averages with different periods (fast and slow lines) to construct the MACD indicator and introduces a zero-lag algorithm to eliminate the delay between the indicator and the price, improving the timeliness of signals. Additionally, the crossover of the signal line and the MACD line is used as buy and sell signals, and alerts are set up to help traders seize trading opportunities in a timely manner.
Strategy Principle
Calculate the EMA (Exponential Moving Average) or SMA (Simple Moving Average) of the fast line (default 12 periods) and slow line (default 26 periods).
Use the zero-lag algorithm to double-smooth the fast and slow lines, eliminating the delay between the indicator and the price.
The MACD line is formed by the difference between the zero-lag fast line and the zero-lag slow line.
The signal line is formed by the EMA (default 9 periods) or SMA of the MACD line.
The MACD histogram is formed by the difference between the MACD line and the signal line, with blue representing positive values and red representing negative values.
When the MACD line crosses the signal line from below and the crossover point is below the zero axis, a buy signal (blue dot) is generated.
When the MACD line crosses the signal line from above and the crossover point is above the zero axis, a sell signal (red dot) is generated.
The strategy automatically places orders based on the buy and sell signals and triggers corresponding alerts.
Advantage Analysis
The zero-lag algorithm effectively eliminates the delay between the indicator and the price, improving the timeliness and accuracy of signals.
The design of dual moving averages can better capture market trends and adapt to different market environments.
The MACD histogram intuitively reflects the comparison of bullish and bearish forces, assisting in trading decisions.
The automatic order placement and alert functions make it convenient for traders to seize trading opportunities in a timely manner, improving trading efficiency.
Risk Analysis
In volatile markets, frequent crossover signals may lead to overtrading and losses.
Improper parameter settings may cause signal distortion and affect strategy performance.
The strategy relies on historical data for calculations and has poor adaptability to sudden events and black swan events.
Optimization Direction
Introduce trend confirmation indicators, such as ADX, to filter out false signals in volatile markets.
Optimize parameters to find the best combination of fast and slow line periods and signal line periods, improving strategy stability.
Combine other technical indicators or fundamental factors to construct a multi-factor model, improving risk-adjusted returns of the strategy.
Introduce stop-loss and take-profit mechanisms to control single-trade risk.
Summary
The MACD Dual Crossover Zero Lag Trading Strategy achieves high-frequency trading by quickly responding to price changes and capturing short-term trends. The zero-lag algorithm and dual moving average design improve the timeliness and accuracy of signals. The strategy has certain advantages, such as intuitive signals and convenient operation, but also faces risks such as overtrading and parameter sensitivity. In the future, the strategy can be optimized by introducing trend confirmation indicators, parameter optimization, multi-factor models, etc., to improve the robustness and profitability of the strategy.
Komut dosyalarını "algo" için ara
Classic Nacked Z-Score ArbitrageThe “Classic Naked Z-Score Arbitrage” strategy employs a statistical arbitrage model based on the Z-score of the price spread between two assets. This strategy follows the premise of pair trading, where two correlated assets, typically from the same market sector, are traded against each other to profit from relative price movements (Gatev, Goetzmann, & Rouwenhorst, 2006). The approach involves calculating the Z-score of the price spread between two assets to determine market inefficiencies and capitalize on short-term mispricing.
Methodology
Price Spread Calculation:
The strategy calculates the spread between the two selected assets (Asset A and Asset B), typically from different sectors or asset classes, on a daily timeframe.
Statistical Basis – Z-Score:
The Z-score is used as a measure of how far the current price spread deviates from its historical mean, using the standard deviation for normalization.
Trading Logic:
• Long Position:
A long position is initiated when the Z-score exceeds the predefined threshold (e.g., 2.0), indicating that Asset A is undervalued relative to Asset B. This signals an arbitrage opportunity where the trader buys Asset B and sells Asset A.
• Short Position:
A short position is entered when the Z-score falls below the negative threshold, indicating that Asset A is overvalued relative to Asset B. The strategy involves selling Asset B and buying Asset A.
Theoretical Foundation
This strategy is rooted in mean reversion theory, which posits that asset prices tend to return to their long-term average after temporary deviations. This form of arbitrage is widely used in statistical arbitrage and pair trading techniques, where investors seek to exploit short-term price inefficiencies between two assets that historically maintain a stable price relationship (Avery & Sibley, 2020).
Further, the Z-score is an effective tool for identifying significant deviations from the mean, which can be seen as a signal for the potential reversion of the price spread (Braucher, 2015). By capturing these inefficiencies, traders aim to profit from convergence or divergence between correlated assets.
Practical Application
The strategy aligns with the Financial Algorithmic Trading and Market Liquidity analysis, emphasizing the importance of statistical models and efficient execution (Harris, 2024). By utilizing a simple yet effective risk-reward mechanism based on the Z-score, the strategy contributes to the growing body of research on market liquidity, asset correlation, and algorithmic trading.
The integration of transaction costs and slippage ensures that the strategy accounts for practical trading limitations, helping to refine execution in real market conditions. These factors are vital in modern quantitative finance, where liquidity and execution risk can erode profits (Harris, 2024).
References
• Gatev, E., Goetzmann, W. N., & Rouwenhorst, K. G. (2006). Pairs Trading: Performance of a Relative-Value Arbitrage Rule. The Review of Financial Studies, 19(3), 1317-1343.
• Avery, C., & Sibley, D. (2020). Statistical Arbitrage: The Evolution and Practices of Quantitative Trading. Journal of Quantitative Finance, 18(5), 501-523.
• Braucher, J. (2015). Understanding the Z-Score in Trading. Journal of Financial Markets, 12(4), 225-239.
• Harris, L. (2024). Financial Algorithmic Trading and Market Liquidity: A Comprehensive Analysis. Journal of Financial Engineering, 7(1), 18-34.
TradeShields Strategy Builder🛡 WHAT IS TRADESHIELDS?
This no-code strategy builder is designed for traders on TradingView, offering an intuitive platform to create, backtest, and automate trading strategies. While identifying signals is often straightforward, the real challenge in trading lies in managing risk and knowing when not to trade. It equips users with advanced tools to address this challenge, promoting disciplined decision-making and structured trading practices.
This is not just a collection of indicators but a comprehensive toolkit that helps identify high-quality opportunities while placing risk management at the core of every strategy. By integrating customizable filters, robust controls, and automation capabilities, it empowers traders to align their strategies with their unique objectives and risk tolerance.
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🛡 THE GOAL: SHIELD YOUR STRATEGY
The mission is simple: to shield your strategy from bad trades . Whether you're a seasoned trader or just starting, the hardest part of trading isn’t finding signals—it’s avoiding trades that can harm your account. This framework prioritizes quality over quantity , helping filter out suboptimal setups and encouraging disciplined execution.
With tools to manage risk, avoid overtrading, and adapt to changing market conditions, it protects your strategy against impulsive decisions and market volatility.
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🛡 HOW TO USE IT
1. Apply Higher Timeframe Filters
Begin by analyzing broader market trends using tools like the 200 EMA, Ichimoku Cloud, or Supertrend on higher timeframes (e.g., daily or 4-hour charts).
- Example: Ensure the price is above the 200 EMA on the daily chart for long trades or below it for short trades.
2. Identify the Appropriate Entry Signal
Choose an entry signal that aligns with your model and the asset you're trading. Options include:
Supertrend changes for trend reversals.
Bollinger Band touches for mean-reversion trades.
RSI strength/weakness for overbought or oversold conditions.
Breakouts of key levels (e.g., daily or weekly highs/lows) for momentum trades.
MACD and TSI flips.
3. Determine Take-Profit and Stop-Loss Levels
Set clear exit strategies to protect your capital and lock in profits:
Use single, dual, or triple take-profit levels based on percentages or price levels.
Choose a stop-loss type, such as fixed percentage, ATR-based, or trailing stops.
Optionally, set breakeven adjustments after hitting your first take-profit target.
4. Apply Risk Management Filters
Incorporate risk controls to ensure disciplined execution:
Limit the number of trades per day, week, or month to avoid overtrading.
Use time-based filters to trade during specific sessions or custom windows.
Avoid trading around high-impact news events with region-specific filters.
5. Automate and Execute
Leverage the advanced automation features to streamline execution. Alerts are tailored specifically for each supported platform, ensuring seamless integration with tools like PineConnector, 3Commas, Zapier, and more.
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🛡 CORE FOCUS: RISK MANAGEMENT, AUTOMATION, AND DISCIPLINED TRADING
This builder emphasizes quality over quantity, encouraging traders to approach markets with structure and control. Its innovative tools for risk management and automation help optimize performance while reducing effort, fostering consistency and long-term success.
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🛡 KEY FEATURES
General Settings
Theme Customization : Light and dark themes for a tailored interface.
Timezone Adjustment : Align session times and news schedules with your local timezone.
Position Sizing : Define lot sizes to manage risk effectively.
Directional Control : Choose between long-only, short-only, or both directions for trading.
Time Filters
Day-of-Week Selection : Enable or disable trading on specific days.
Session-Based Trading : Restrict trades to major market sessions (Asia, London, New York) or custom windows.
Custom Time Windows : Precisely control the timeframes for trade execution.
Risk Management Tools
Trade Limits : Maximum trades per day, week, or month to avoid overtrading.
Automatic Trade Closures : End-of-session, end-of-day, or end-of-week options.
Duration-Based Filters : Close trades if take-profit isn’t reached within a set timeframe or if they remain unprofitable beyond a specific duration.
Stop-Loss and Take-Profit Options : Fixed percentage or ATR-based stop-losses, single/dual/triple take-profit levels, and breakeven stop adjustments.
Economic News Filters
Region-Specific Filters : Exclude trades around major news events in regions like the USA, UK, Europe, Asia, or Oceania.
News Avoidance Windows : Pause trades before and after high-impact events or automatically close trades ahead of scheduled news releases.
Higher Timeframe Filters
Multi-Timeframe Tools : Leverage EMAs, Supertrend, or Ichimoku Cloud on higher timeframes (Daily, 4-hour, etc.) for trend alignment.
Chart Timeframe Filters
Precision Filtering : Apply EMA or ADX-based conditions to refine trade setups on current chart timeframes.
Entry Signals
Customizable Options : Choose from signals like Supertrend, Bollinger Bands, RSI, MACD, Ichimoku Cloud, or EMA pullbacks.
Indicator Parameter Overrides : Fine-tune default settings for specific signals.
Exit Settings
Flexible Take-Profit Targets : Single, dual, or triple targets. Exit at significant levels like daily/weekly highs or lows.
Stop-Loss Variability : Fixed, ATR-based, or trailing stop-loss options.
Alerts and Automation
Third-Party Integrations : Seamlessly connect with platforms like PineConnector, 3Commas, Zapier, and Capitalise.ai.
Precision-Formatted Alerts : Alerts are tailored specifically for each platform, ensuring seamless execution. For example:
- PineConnector alerts include risk-per-trade parameters.
- 3Commas alerts contain bot-specific configurations.
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🛡 PUBLISHED CHART SETTINGS: 15m COMEX:GC1!
Time Filters : Trades are enabled from Tuesday to Friday, as Mondays often lack sufficient data coming off the weekend, and weekends are excluded due to market closures. Custom time sessions are turned off by default, allowing trades throughout the day.
Risk Filters : Risk is tightly controlled by limiting trades to a maximum of 2 per day and enabling a mechanism to close trades if they remain open too long and are unprofitable. Weekly trade closures ensure that no positions are carried over unnecessarily.
Economic News Filters : By default, trades are allowed during economic news periods, giving traders flexibility to decide how to handle volatility manually. It is recommended to enable these filters if you are creating strategies on lower timeframes.
Higher Timeframe Filters : The setup incorporates confluence from higher timeframe indicators. For example, the 200 EMA on the daily timeframe is used to establish trend direction, while the Ichimoku cloud on the 30-minute timeframe adds additional confirmation.
Entry Signals : The strategy triggers trades based on changes in the Supertrend indicator.
Exit Settings : Trades are configured to take partial profits at three levels (1%, 2%, and 3%) and use a fixed stop loss of 2%. Stops are moved to breakeven after reaching the first take profit level.
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🛡 WHY CHOOSE THIS STRATEGY BUILDER?
This tool transforms trading from reactive to proactive, focusing on risk management and automation as the foundation of every strategy. By helping users avoid unnecessary trades, implement robust controls, and automate execution, it fosters disciplined trading.
Auto Harmonic Pattern - Backtester [Trendoscope]We are finally here with the implementation of backtesting tool for Auto-Harmonic-Pattern-UltimateX .
CAUTION: THIS IS NOT A STRATEGY AND SHOULD NOT BE FOLLOWED BLINDLY. WE ENCOURAGE USERS TO UTILISE THIS AS BACKTESTING TOOL FOR BUILDING THEIR STRATEGY BASED ON HARMONIC PATTERNS
This script is based on our premium indicator - Auto-Harmonic-Pattern-UltimateX . In this script, along with implementation of scanning harmonic patterns, we provide various options via settings which enables users to build their own strategy based on harmonic patterns, use them with custom coded filters, backtest them on various tickers and timeframes.
Harmonic Patterns is concept and we can trade harmonic pattern in many ways. While general interest around harmonic patterns is to find reversal zones and use them for short term swing trades. But, using it along trend following strategies can also be very rewarding. Here is one of the educational idea I shared about using harmonic patterns for trend following. These are just few possibilities where users can explore further on how they want to trade this. The settings of this script are crafted in such a way that it enables users to explore all these possibilities.
🎲 Components
Chart components of this script is lighter compared to Auto Harmonic Pattern - UltimateX. This is because we want to keep lighter interface in order to support seamless execution of emulator. Since pine strategy framework does most of the things such as calculating profitability, keeping track of trades and results etc, display with respect to - "Closed Trade Stats" are removed from this script and "Open Trade Stats" are made lighter.
🎲 Settings
🎯 Trade Settings : Few important settings under this section are
Due to pine limitations, we will not be able to support both long and short in a same setup. Hence, users need to chose either long or short trade setup.
Entry/Base/Target play important role in defining your strategy.
Confluence is another important factor which lets users use multiple patterns at once as confirmation.
🎯 Zigzag Settings : Zigzag settings determine the size of patterns being formed.
Please note that smaller patterns may not yield very good results and larger patterns may take time to complete trade. Similarly higher depth can cause runtime issues. Recursive zigzag option is alternative to deep search algorithm.
🎯 Filters :
Filters enable users to select trades based on specific conditions. Ability to use external filter even allows writing and using custom filters to be used with this algorithm. Here is a video which explains how this can be done. HOW-TO-Use-external-filters
Pattern filters allow users to pick and chose patterns they want to trade. This can be done either individually or based on category
🎯 Alerts :
Apart from strategy specific alerts, the script also implements customisable alerts via pine alert() function. Alerts can be configured to send upon three conditions
When new pattern is created
When an existing pattern updates entry/stop/target due to safe repaint of D (Only happens when Trail Entry Price is selected)
When a pattern in trade closes either due to hitting stop or target
Important Note: Alerts fired via this method may not match the trades shown on chart as trades which are controlled via pine strategy emulator depends on various other factors such as pyramiding.
Alert template is customisable and users can make use of available placeholders to get dynamic data in alerts. Valid placeholders are
{alertType} - Alert type - New/Update/Close
{id} - Pattern Id
{ticker} - Ticker
{timeframe} - Chart timeframe
{price} - Current price
{patterns} - Identified pattern names
{direction} - Direction - Long/Short
{entry} - Entry Price
{stop} - Stop Price
{target} - Target Price
{orderType} - Limit/Stop - applicable for only New and Update types
{status} - Trade status. Valid values are Pending/Cancelled/Stopped/Success
Template is common for all custom alert types. Hence, updating the template will impact all custom alerts - New/Update/Close
{
"alert" : "{alertType}",
"id" : {id},
"ticker" : "{ticker}",
"timeframe" : "{timeframe}",
"price" : {price},
"patterns" : "{patterns}",
"direction" : "{direction}",
"entry" : {entry},
"stop" : {stop},
"target" : {target},
"orderType" : {orderType}
"status" : {status}
}
Here is a video on how to customise the alerts using templates and placeholders - HOW-TO-Customize-Alerts-With-Placeholders
🎯 Miscellaneous :
These are simple settings to control display and backtest bars. If you are running alerts, we suggest turning of Open Trades and Drawings and limit backtest to minimal value in order to improve efficiency of
🎯 Backtest Engine Parameters :
Default settings are optimised for trend following. Users are encouraged to play around with settings and filters to build strategy out of this tool.
Position sizing is not leveraged. Margin settings makes sure that trades cannot exceed capital.
All measures are taken to avoid repainting. Script does not use request.security and real time bars. This drastically reduces the risk of repainting in scripts.
If you are premium user, please select "Bar Magnifier".
gangood bot for FinandyGangood is a mean reversion algorithm currently optimized for trading the ETH/USDT pair on the 1 hour chart time frame. All indicator inputs use the closing price of the period, and all trades are executed at the open of the period following the period in which the trading signal was generated.
To take into account slippage, the commission costs 0.15%.
Backtest result from 2020.
Result since 2019 2,500,000%, maximum drawdown 18%
This bot uses 11 indicators:
1) ADX
2) RANGE FILTER
3) SAR
4) RSI
5) TWAP
6) JMA
7) MACD
8) VOLUME DELTA
9) VOLUME WEIGHT
10) MA
11) TSI
Pattern 1:
There are 3 main components that make up Gangood: I. Trend Filter. The algorithm uses a version of the ADX indicator as a trend filter to only trade during certain time periods when price is most likely to be range-bound (i.e., average retracement). This indicator consists of a fast ADX and a slow ADX both using the same lookback period.
The ADX is smoothed with a 6-period EMA and the slow ADX is smoothed with a 12-period EMA. When the fast ADX is above the slow ADX , the algorithm does not trade because it indicates that the price is most likely trending, which is bad for a mean reversion system. Conversely, when the fast ADX is below the slow ADX, the price is likely to be in a range, so this is the only time the algorithm is allowed to trade. II. Bollinger Bands When the trend filter allows trading, the algorithm uses Bollinger Bands.
Indicator for opening long and short positions. The Bolliger Bands indicator has a 20 lookback period and a 1.5 standard deviation for both the upper and lower bands. When the price crosses the lower band, a buy signal is generated and a long position is opened. When the price crosses the upper band, a sell signal is generated and a short position is opened.
Pattern 2:
Based on RSI which is commonly used as a trend reversal indicator. However, here it is used as a trend-setting indicator, often with great success. This pattern only takes long trades, which is quite successful in a bull market.
Pattern 3:
Long or short trades are determined by the intersection of the fast EMA with the slow EMA for long positions and vice versa for short positions. Trades should only occur close to intersections. We then use the MACD for the long position. an indicator with a 10-minute time frame where we look for high peaks in negative values for longs and vice versa for shorts. They should be significantly higher than the other peaks.
Capital Management:
The maximum leverage in this strategy, I would recommend 2x, in order to trade without unnecessary risks and keep your nerves in order.
Bot setup:
I use the Finandy terminal, in which you can easily trade with this strategy.
1. We go to binance and turn on the hedging mode, this is necessary so that if tradingview sends a webhook for buying later than for selling.
2. Adding a new signal to Finandy
2.1. Open tab
2.1.1. "Order side" Strategy
2.1.2. "Amount" Balance% x Leverage
2.1.3. We set the percentage of the order two times less than the one you want
2.1.4. "Shoulder" is twice as large as the one you want
2.2.Close tab
2.2.1. "Enebaled" tick
2.2.2. "Reverse / Close" Disable
3. Set a notification for this strategy.
4. Copy "Signal URL" and paste it into webhook on tradingview
5. Copy "Signal Message" and paste it into the message on tradingview
CryptoNite - Machine Learning Strategy (15Min Timeframe)Greeting Traders! I am back with another ML strategy. :D I kept my word with combining my machine learning algorithms from Python and integrating them into Tradingview. Thanks to Tradingview's new release of Pinescript v5 it is now possible. This strategy respects the Sortino Ratio and was created using 2 years of data for 50 different cryptocurrencies. That is a total of 100 years of data and 44,849 trades to create this strategy. Now let me tell you, my computer and I are exhausted. We both been at it non-stop for about two months everyday. I refine the strategy, and the computer runs 24/7 for a few days to spit out the best results into the terminal. It's been a good run so my computer will finally get some sleep tonight.
So let's talk a little about the features of the strategy. In the settings window, you'll see the Stoploss, Take Profit Parameters, and Date Range. You can change the Date Range, but I recommend to leave the SL/TP parameters how they are because the machine learning algo chose those input. If you wish to change them you are always welcome to do so but backtest results will change. For the Take Profit parameters you'll see on the left side you something labeled time duration(displayed in minutes) and on the right side you'll see take profit values. Let's talk a little bit how they work.
TP_values = {
"0": 0.102,
"133": 0.051,
"431": 0.039,
"963": 0
}
In python, the table looks like this but it is quite easy to understand in Tradingview.
From 0-133 minutes, the strategy is looking to the reach target point 1 at 10.2% profit.
From 133-431 minutes, the strategy is looking to the reach target point 2 at 5.1% profit.
From 431-963 minutes, the strategy is looking to the reach target point 3 at 3.9% profit.
From 963+ minutes, the strategy is looking to break even at 0% profit on target point 4.
Through each target point a sell trigger is active. It will look for the best time to sell even if TP has not been reached.
This helps the trade not stay open too long.
The last thing I need to mention is the textbox displayed on the right side of your chart. This textbox displays the current Take Profit value in dollar amount. So when you're in a trade you'll know what TP target has to be reached when the open trade is active. Throughout time, the target price changes depending how long the trade has been open. If you have any questions feel free to comment down below, and enjoy this strategy!
hamster-bot HD preset_2presets for users
// DESCRIPTION OF STRATEGY ver. 2
HiDeep Strategy
Author foresterufa
This is a counter-trend strategy that is gradually gaining a position against the trend at the best price.
A prerequisite for completing a position is the price exit from the internal channel on the chart and the appearance of the HiDeep indicator.
The condition for closing the position is touching the opposite side of the internal channel.
A condition for facilitating closure along the middle line of the channel, with high price volatility , is that the price touches the border of the external channel.
Input signals are generated by HiDeep indicators. Closing a position by moving averages.
HigherHigh LowerLow RATALGOHi Traders,
This is Trend following strategy.
This strategy calculates the higher high or lower low of a look back period. If the previous high or low is breached, a signal to enter market is given.
This strategy works well with regular candles and line charts if you find the right settings and chart time frame.
Give it a try with your settings & post your feedback and suggestion if any for improvement.
I had automate this strategy with broker using Trading view Alert feature to get some live results on NSE:Banknifty1!
MTF - Box Trading StrategyMultiTime Frame - Box Trading Strategies (MTF-BT))
How does it work ? The code uses dynamic levels and crossovers on higher time frames to identify trade calls.
Model 1 (Default) Uses a low risk model and Model 2 (Optional) Uses an aggressive model
How to Deploy / Use
As part of the Indicator there are a few choices the user can opt for
Box Resolution - The resolution of the higher time frame for analysis , typically set at 90 , can be customized by the users.
Use Long Strategy 1 - This would add long trades based on Model1 Algorithm for the users
Use Short Strategy 1 - This would add short trades based on Model1 Algorithm for the users
Use Long Strategy 2 - This would add long trades based on Model2 Algorithm for the users
Use Short Strategy 2 - This would add short trades based on Model2 Algorithm for the users
Check Range Val Validate the width of the channel on higher timeframe and trade only when the channel is wider than the value provided ,
The value of 0.14 is determined using series of back test across various assets
Use Stop Loss : Flag to check if Stop Loss should be done by the strategy
Stop Loss Limit : Stop Loss in Absolute terms
Use Profit Booking : Flag to check if Profit Booking should be done by the strategy
Stop Loss Limit : Profit Target in Absolute terms
Do Intraday Exit :Flag to check if trade should be taken as an Intraday only
Exit Window : Session time during which the trade should be closed , like 15:00 - 15:30 for NSE , 22:30 - 23:00 for MCX etc ,
it should be wide enough to accommodate the resolution the use has on the screen
Visual Checks - The user could manually validate the back test results on various assets they would like to use this strategy on before putting it live.
Usage/Markets : Index Trading / Equities and also well with Commodities and Currencies
Time Frame : works well between 3 and 30 , keep the Box resolution to at least 45 for 3/5 mins TF and you could move upto 180 (3 hrs ) for a 30 mins TF.
Strategy Settings Used/Assumed : All of this values are provided in the Properties Tab of the Indicator Settings
and the users can customize it to suit the broker or the product they are charting it against
Initial Capital : 100 000
Order Size : 10 Quantities for Equities , you may change it to 1 lot for Future contracts based on capital deployed
Commission : is set at 0.05%
Slippage : 20 ticks
Recalculate Option : After the Order is filled is selected by default
Disclaimer : There could be scenarios when the breakout/breakdown candle is rejected , especially when it is long one
so it is always recommended to have a confirmation candle that open-closes above the breakout candle / open-closes below the breakdown candle
If you like it and find it useful or if you find a defect or bug , Please let us know in the comments .. that would encouraging !! for us to develop it further
Thank you and have a beautiful and Profitable trading session !
How to get access
Please click on the link / email in the signature or send me a private message to get access
Feedback
Please click on the link/email in the signature or send me a private message for suggestions/feedbacks
GreenCrypto Strategy
This strategy majorly uses MA, Tilson and S&R. MA is used for predicting the trend, Instead of normal cross-over of the MA, we are calculating the trend of the MA itself (whether MA is moving upward or downward by comparing the previous and current value of MA), along with MA we also use Tilson to calculate the MA.
Once we have MA and Tilson we take average and merge both MA and Tilson MA to get a double confirmation on the trend of the market. for entry and exit we use S&R with the merged MA, if the trend change is at the support or resistance level we go for LONG/SHORT respectively. Here we are doing continuous LONG+SHORT position, this provides more opportunity to capture unexpected market trend.
Enter a Long Trade when the script shows "Long" and exit either when you get "Short" signal or when it meets your target.
Parameters:
"Use 1:EST, 2:SST, 3:HST ?" : Select EMA , SMA or HullMA (works best on HullMA)
Length: Length of the EMA / SMA /HullmA
Factor: Used for calculation of Tilson and the Support and resistance .
Date/month/day : for selecting the right backtesting the period (currently it set to Jan 2018 to current day )
for this backtesting i have used 1000$ capital and 0.02% commission for each trade.
This strategy works best on 4H time fram but you can also use it on 1 day or higher timeframe charts
The default config present in this script is designed for ETH but it will also work with other coins)
Config for Specific Crypto coins (Please feel free to try out other configs also) :
ADA, BNB, EOS : "Use 1:EST, 2:SST, 3:HST ?" = 3
"Length" = 8
"Factor" = 0.9
ETC, XLM : "Use 1:EST, 2:SST, 3:HST ?" = 3
"Length" = 8
"Factor" = 0.85
Please DM me if you would like to tryout 7 Days free trail.
The Profit Gate | Tier 1 Script | v1.0.0This script is used to optimized the trend of the stock based on volume , and many kind of moving average. You can use this to swing, or get the idea of long hold play. This work for Crypto as well as penny stock.
This script is best for Penny Stock, Big Cap, Crypto. It is generally based on the idea of averaging move of previous candles as well as current volume . This means if we have our candles at 15m, it will capture bunch of previous candles up to 10 years ahead to get an average move. This will give us a prediction of whether or not a stock will move up (Buy), or go down (Sell).
General Buy|Sell Tier 1
This script is used to optimized the trend of the stock based on volume , and many kind of moving average. You can use this to swing, or get the idea of long hold play. This work for Crypto as well as penny stock.
This script is best for Penny Stock, Big Cap, Crypto. It is generally based on the idea of averaging move of previous candles as well as current volume . This means if we have our candles at 15m, it will capture bunch of previous candles up to 10 years ahead to get an average move. This will give us a prediction of whether or not a stock will move up (Buy), or go down (Sell).
We also use Binary entropy function to optimize the original MACD .
This indicator should be able to tell you where to get in, out, or start to set trailing stop loss on the current position. I will constantly update this algorithm.
Trend analysis, This is ridge model that take in past data from the nearest certain number of candles then predict the next trend by an algorithm.
We also have standard deviation so we can apply it to find the best strike price with the highest probability to get ITM
Please DM me for access to this script
TC Chart Score StrategyThis is My Call Confidence Strategy
The Strategy is designed to help confirm a bullish reversal after a downtrend.
This uses custom weighted algorithm
The Algorithm combines directional movement, volume over average, and moving averages to formulate a score.
The score is then used in conjunction with a smoothed score of the same criteria to initiate a buy signal on a cross over.
The settings are designed to help you customize how you weight directional movement, and the moving averages to further finetune the algorithm to your timelines.
The default settings are designed to be used on a 1 hour time frame.
You can change the settings for other time frames to further increase effectiveness.
This script will be updated as needed if a better algorithm is designed.
RAT Moving Average Crossover StrategyThis is based on general moving average crossovers but some modifications made to generate buy sell signals.
[B] hamster-bot ZZ Breakout reversal strategyAttention! This is a beta version of the strategy script >> <<
A backtest should only be done if you understand how the options work. Otherwise, do a test in the release version
Wildfire [v1]Lower time frame trading strategy with a very simple algorithm and adjustable parameters.
Backtest result shown is from 1st Jan 2018.
Tested with BTCUSD 30m Bitfinex and ETHUSD 30m. Approaches to addressing the drawdown are in development, however the algo in general seems very workable. Prelim tests in other markets encouraging. I have another bot called WARBASTARD which operates in higher timeframes (4hrs) and has far more acceptable drawdown figures.
Invite only, sorry.
PMA Cross [LePasha]Strategy Overview: Trading with the LePasha Moving Average (PMA)
This strategy is built upon the LePasha Moving Average (PMA) — a custom-designed moving average indicator that offers advanced adaptability to market conditions. Unlike traditional moving averages such as SMA or EMA, the LePasha PMA integrates volatility and momentum sensitivity through a sophisticated calculation involving ATR normalization and adaptive smoothing. This results in a moving average that is both smooth and highly responsive to price action.
You can explore the full PMA indicator here on TradingView:
Core Strategy Logic
The trading algorithm takes positions based on price crossing the PMA line:
Long trades trigger after the price stays above the PMA for a predefined number of bars, confirming an upward trend.
Short trades trigger after the price stays below the PMA for the same confirmation period, indicating a downtrend.
This confirmation period filters out noise and false breakouts, allowing trades to be placed only when the trend is likely stable.
Performance Insights and Practical Impact
Backtests reveal that this PMA-based strategy achieves a win rate exceeding 70%, which is a strong edge in trading. To contextualize:
A trader using a risk-to-reward ratio of 1:2 (risking 1 unit to gain 2 units) needs at least a 34% win rate just to break even.
Achieving more than 70% wins means the strategy is not only profitable but robust, offering significant statistical confidence for traders.
Why Incorporate the LePasha PMA into Your Trading System?
Higher Win Probability: The PMA’s volatility-adjusted smoothing reduces whipsaws, resulting in fewer losing trades.
Adaptivity: Unlike fixed-length MAs, the PMA adapts dynamically to market volatility and momentum, making it suitable across different asset classes and timeframes.
Improved Entry Signals: By confirming the trend direction over multiple bars, it ensures higher-quality trade entries and reduces premature signals.
Conclusion
The LePasha Moving Average (PMA) represents a meaningful advancement in moving average design. Integrating this indicator into your trading algorithm can significantly enhance your edge, increasing both win rate and consistency. Whether used alone or alongside other tools, the PMA’s unique methodology and strong backtested results offer traders a powerful resource for navigating markets effectively.
Discover more and try the PMA indicator here:
🔗
Bober XM v2.0# ₿ober XM v2.0 Trading Bot Documentation
**Developer's Note**: While our previous Bot 1.3.1 was removed due to guideline violations, this setback only fueled our determination to create something even better. Rising from this challenge, Bober XM 2.0 emerges not just as an update, but as a complete reimagining with multi-timeframe analysis, enhanced filters, and superior adaptability. This adversity pushed us to innovate further and deliver a strategy that's smarter, more agile, and more powerful than ever before. Challenges create opportunity - welcome to Cryptobeat's finest work yet.
## !!!!You need to tune it for your own pair and timeframe and retune it periodicaly!!!!!
## Overview
The ₿ober XM v2.0 is an advanced dual-channel trading bot with multi-timeframe analysis capabilities. It integrates multiple technical indicators, customizable risk management, and advanced order execution via webhook for automated trading. The bot's distinctive feature is its separate channel systems for long and short positions, allowing for asymmetric trade strategies that adapt to different market conditions across multiple timeframes.
### Key Features
- **Multi-Timeframe Analysis**: Analyze price data across multiple timeframes simultaneously
- **Dual Channel System**: Separate parameter sets for long and short positions
- **Advanced Entry Filters**: RSI, Volatility, Volume, Bollinger Bands, and KEMAD filters
- **Machine Learning Moving Average**: Adaptive prediction-based channels
- **Multiple Entry Strategies**: Breakout, Pullback, and Mean Reversion modes
- **Risk Management**: Customizable stop-loss, take-profit, and trailing stop settings
- **Webhook Integration**: Compatible with external trading bots and platforms
### Strategy Components
| Component | Description |
|---------|-------------|
| **Dual Channel Trading** | Uses either Keltner Channels or Machine Learning Moving Average (MLMA) with separate settings for long and short positions |
| **MLMA Implementation** | Machine learning algorithm that predicts future price movements and creates adaptive bands |
| **Pivot Point SuperTrend** | Trend identification and confirmation system based on pivot points |
| **Three Entry Strategies** | Choose between Breakout, Pullback, or Mean Reversion approaches |
| **Advanced Filter System** | Multiple customizable filters with multi-timeframe support to avoid false signals |
| **Custom Exit Logic** | Exits based on OBV crossover of its moving average combined with pivot trend changes |
### Note for Novice Users
This is a fully featured real trading bot and can be tweaked for any ticker — SOL is just an example. It follows this structure:
1. **Indicator** – gives the initial signal
2. **Entry strategy** – decides when to open a trade
3. **Exit strategy** – defines when to close it
4. **Trend confirmation** – ensures the trade follows the market direction
5. **Filters** – cuts out noise and avoids weak setups
6. **Risk management** – controls losses and protects your capital
To tune it for a different pair, you'll need to start from scratch:
1. Select the timeframe (candle size)
2. Turn off all filters and trend entry/exit confirmations
3. Choose a channel type, channel source and entry strategy
4. Adjust risk parameters
5. Tune long and short settings for the channel
6. Fine-tune the Pivot Point Supertrend and Main Exit condition OBV
This will generate a lot of signals and activity on the chart. Your next task is to find the right combination of filters and settings to reduce noise and tune it for profitability.
### Default Strategy values
Default values are tuned for: Symbol BITGET:SOLUSDT.P 5min candle
Filters are off by default: Try to play with it to understand how it works
## Configuration Guide
### General Settings
| Setting | Description | Default Value |
|---------|-------------|---------------|
| **Long Positions** | Enable or disable long trades | Enabled |
| **Short Positions** | Enable or disable short trades | Enabled |
| **Risk/Reward Area** | Visual display of stop-loss and take-profit zones | Enabled |
| **Long Entry Source** | Price data used for long entry signals | hl2 (High+Low/2) |
| **Short Entry Source** | Price data used for short entry signals | hl2 (High+Low/2) |
The bot allows you to trade long positions, short positions, or both simultaneously. Each direction has its own set of parameters, allowing for fine-tuned strategies that recognize the asymmetric nature of market movements.
### Multi-Timeframe Settings
1. **Enable Multi-Timeframe Analysis**: Toggle 'Enable Multi-Timeframe Analysis' in the Multi-Timeframe Settings section
2. **Configure Timeframes**: Set appropriate higher timeframes based on your trading style:
- Timeframe 1: Default is now 15 minutes (intraday confirmation)
- Timeframe 2: Default is 4 hours (trend direction)
3. **Select Sources per Indicator**: For each indicator (RSI, KEMAD, Volume, etc.), choose:
- The desired timeframe (current, mtf1, or mtf2)
- The appropriate price type (open, high, low, close, hl2, hlc3, ohlc4)
### Entry Strategies
- **Breakout**: Enter when price breaks above/below the channel
- **Pullback**: Enter when price pulls back to the channel
- **Mean Reversion**: Enter when price is extended from the channel
You can enable different strategies for long and short positions.
### Core Components
### Risk Management
- **Position Size**: Control risk with percentage-based position sizing
- **Stop Loss Options**:
- Fixed: Set a specific price or percentage from entry
- ATR-based: Dynamic stop-loss based on market volatility
- Swing: Uses recent swing high/low points
- **Take Profit**: Multiple targets with percentage allocation
- **Trailing Stop**: Dynamic stop that follows price movement
## Advanced Usage Strategies
### Moving Average Type Selection Guide
- **SMA**: More stable in choppy markets, good for higher timeframes
- **EMA/WMA**: More responsive to recent price changes, better for entry signals
- **VWMA**: Adds volume weighting for stronger trends, use with Volume filter
- **HMA**: Balance between responsiveness and noise reduction, good for volatile markets
### Multi-Timeframe Strategy Approaches
- **Trend Confirmation**: Use higher timeframe RSI (mtf2) for overall trend, current timeframe for entries
- **Entry Precision**: Use KEMAD on current timeframe with volume filter on mtf1
- **False Signal Reduction**: Apply RSI filter on mtf1 with strict KEMAD settings
### Market Condition Optimization
| Market Condition | Recommended Settings |
|------------------|----------------------|
| **Trending** | Use Breakout strategy with KEMAD filter on higher timeframe |
| **Ranging** | Use Mean Reversion with strict RSI filter (mtf1) |
| **Volatile** | Increase ATR multipliers, use HMA for moving averages |
| **Low Volatility** | Decrease noise parameters, use pullback strategy |
## Webhook Integration
The strategy features a professional webhook system that allows direct connectivity to your exchange or trading platform of choice through third-party services like 3commas, Alertatron, or Autoview.
The webhook payload includes all necessary parameters for automated execution:
- Entry price and direction
- Stop loss and take profit levels
- Position size
- Custom identifier for webhook routing
## Performance Optimization Tips
1. **Start with Defaults**: Begin with the default settings for your timeframe before customizing
2. **Adjust One Component at a Time**: Make incremental changes and test the impact
3. **Match MA Types to Market Conditions**: Use appropriate moving average types based on the Market Condition Optimization table
4. **Timeframe Synergy**: Create logical relationships between timeframes (e.g., 5min chart with 15min and 4h higher timeframes)
5. **Periodic Retuning**: Markets evolve - regularly review and adjust parameters
## Common Setups
### Crypto Trend-Following
- MLMA with EMA or HMA
- Higher RSI thresholds (75/25)
- KEMAD filter on mtf1
- Breakout entry strategy
### Stock Swing Trading
- MLMA with SMA for stability
- Volume filter with higher threshold
- KEMAD with increased filter order
- Pullback entry strategy
### Forex Scalping
- MLMA with WMA and lower noise parameter
- RSI filter on current timeframe
- Use highest timeframe for trend direction only
- Mean Reversion strategy
## Webhook Configuration
- **Benefits**:
- Automated trade execution without manual intervention
- Immediate response to market conditions
- Consistent execution of your strategy
- **Implementation Notes**:
- Requires proper webhook configuration on your exchange or platform
- Test thoroughly with small position sizes before full deployment
- Consider latency between signal generation and execution
### Backtesting Period
Define a specific historical period to evaluate the bot's performance:
| Setting | Description | Default Value |
|---------|-------------|---------------|
| **Start Date** | Beginning of backtest period | January 1, 2025 |
| **End Date** | End of backtest period | December 31, 2026 |
- **Best Practice**: Test across different market conditions (bull markets, bear markets, sideways markets)
- **Limitation**: Past performance doesn't guarantee future results
## Entry and Exit Strategies
### Dual-Channel System
A key innovation of the Bober XM is its dual-channel approach:
- **Independent Parameters**: Each trade direction has its own channel settings
- **Asymmetric Trading**: Recognizes that markets often behave differently in uptrends versus downtrends
- **Optimized Performance**: Fine-tune settings for both bullish and bearish conditions
This approach allows the bot to adapt to the natural asymmetry of markets, where uptrends often develop gradually while downtrends can be sharp and sudden.
### Channel Types
#### 1. Keltner Channels
Traditional volatility-based channels using EMA and ATR:
| Setting | Long Default | Short Default |
|---------|--------------|---------------|
| **EMA Length** | 37 | 20 |
| **ATR Length** | 13 | 17 |
| **Multiplier** | 1.4 | 1.9 |
| **Source** | low | high |
- **Strengths**:
- Reliable in trending markets
- Less prone to whipsaws than Bollinger Bands
- Clear visual representation of volatility
- **Weaknesses**:
- Can lag during rapid market changes
- Less effective in choppy, non-trending markets
#### 2. Machine Learning Moving Average (MLMA)
Advanced predictive model using kernel regression (RBF kernel):
| Setting | Description | Options |
|---------|-------------|--------|
| **Source MA** | Price data used for MA calculations | Any price source (low/high/close/etc.) |
| **Moving Average Type** | Type of MA algorithm for calculations | SMA, EMA, WMA, VWMA, RMA, HMA |
| **Trend Source** | Price data used for trend determination | Any price source (close default) |
| **Window Size** | Historical window for MLMA calculations | 5+ (default: 16) |
| **Forecast Length** | Number of bars to forecast ahead | 1+ (default: 3) |
| **Noise Parameter** | Controls smoothness of prediction | 0.01+ (default: ~0.43) |
| **Band Multiplier** | Multiplier for channel width | 0.1+ (default: 0.5-0.6) |
- **Strengths**:
- Predictive rather than reactive
- Adapts quickly to changing market conditions
- Better at identifying trend reversals early
- **Weaknesses**:
- More computationally intensive
- Requires careful parameter tuning
- Can be sensitive to input data quality
### Entry Strategies
| Strategy | Description | Ideal Market Conditions |
|----------|-------------|-------------------------|
| **Breakout** | Enters when price breaks through channel bands, indicating strong momentum | High volatility, emerging trends |
| **Pullback** | Enters when price retraces to the middle band after testing extremes | Established trends with regular pullbacks |
| **Mean Reversion** | Enters at channel extremes, betting on a return to the mean | Range-bound or oscillating markets |
#### Breakout Strategy (Default)
- **Implementation**: Enters long when price crosses above the upper band, short when price crosses below the lower band
- **Strengths**: Captures strong momentum moves, performs well in trending markets
- **Weaknesses**: Can lead to late entries, higher risk of false breakouts
- **Optimization Tips**:
- Increase channel multiplier for fewer but more reliable signals
- Combine with volume confirmation for better accuracy
#### Pullback Strategy
- **Implementation**: Enters long when price pulls back to middle band during uptrend, short during downtrend pullbacks
- **Strengths**: Better entry prices, lower risk, higher probability setups
- **Weaknesses**: Misses some strong moves, requires clear trend identification
- **Optimization Tips**:
- Use with trend filters to confirm overall direction
- Adjust middle band calculation for market volatility
#### Mean Reversion Strategy
- **Implementation**: Enters long at lower band, short at upper band, expecting price to revert to the mean
- **Strengths**: Excellent entry prices, works well in ranging markets
- **Weaknesses**: Dangerous in strong trends, can lead to fighting the trend
- **Optimization Tips**:
- Implement strong trend filters to avoid counter-trend trades
- Use smaller position sizes due to higher risk nature
### Confirmation Indicators
#### Pivot Point SuperTrend
Combines pivot points with ATR-based SuperTrend for trend confirmation:
| Setting | Default Value |
|---------|---------------|
| **Pivot Period** | 25 |
| **ATR Factor** | 2.2 |
| **ATR Period** | 41 |
- **Function**: Identifies significant market turning points and confirms trend direction
- **Implementation**: Requires price to respect the SuperTrend line for trade confirmation
#### Weighted Moving Average (WMA)
Provides additional confirmation layer for entries:
| Setting | Default Value |
|---------|---------------|
| **Period** | 15 |
| **Source** | ohlc4 (average of Open, High, Low, Close) |
- **Function**: Confirms trend direction and filters out low-quality signals
- **Implementation**: Price must be above WMA for longs, below for shorts
### Exit Strategies
#### On-Balance Volume (OBV) Based Exits
Uses volume flow to identify potential reversals:
| Setting | Default Value |
|---------|---------------|
| **Source** | ohlc4 |
| **MA Type** | HMA (Options: SMA, EMA, WMA, RMA, VWMA, HMA) |
| **Period** | 22 |
- **Function**: Identifies divergences between price and volume to exit before reversals
- **Implementation**: Exits when OBV crosses its moving average in the opposite direction
- **Customizable MA Type**: Different MA types provide varying sensitivity to OBV changes:
- **SMA**: Traditional simple average, equal weight to all periods
- **EMA**: More weight to recent data, responds faster to price changes
- **WMA**: Weighted by recency, smoother than EMA
- **RMA**: Similar to EMA but smoother, reduces noise
- **VWMA**: Factors in volume, helpful for OBV confirmation
- **HMA**: Reduces lag while maintaining smoothness (default)
#### ADX Exit Confirmation
Uses Average Directional Index to confirm trend exhaustion:
| Setting | Default Value |
|---------|---------------|
| **ADX Threshold** | 35 |
| **ADX Smoothing** | 60 |
| **DI Length** | 60 |
- **Function**: Confirms trend weakness before exiting positions
- **Implementation**: Requires ADX to drop below threshold or DI lines to cross
## Filter System
### RSI Filter
- **Function**: Controls entries based on momentum conditions
- **Parameters**:
- Period: 15 (default)
- Overbought level: 71
- Oversold level: 23
- Multi-timeframe support: Current, MTF1 (15min), or MTF2 (4h)
- Customizable price source (open, high, low, close, hl2, hlc3, ohlc4)
- **Implementation**: Blocks long entries when RSI > overbought, short entries when RSI < oversold
### Volatility Filter
- **Function**: Prevents trading during excessive market volatility
- **Parameters**:
- Measure: ATR (Average True Range)
- Period: Customizable (default varies by timeframe)
- Threshold: Adjustable multiplier
- Multi-timeframe support
- Customizable price source
- **Implementation**: Blocks trades when current volatility exceeds threshold × average volatility
### Volume Filter
- **Function**: Ensures adequate market liquidity for trades
- **Parameters**:
- Threshold: 0.4× average (default)
- Measurement period: 5 (default)
- Moving average type: Customizable (HMA default)
- Multi-timeframe support
- Customizable price source
- **Implementation**: Requires current volume to exceed threshold × average volume
### Bollinger Bands Filter
- **Function**: Controls entries based on price relative to statistical boundaries
- **Parameters**:
- Period: Customizable
- Standard deviation multiplier: Adjustable
- Moving average type: Customizable
- Multi-timeframe support
- Customizable price source
- **Implementation**: Can require price to be within bands or breaking out of bands depending on strategy
### KEMAD Filter (Kalman EMA Distance)
- **Function**: Advanced trend confirmation using Kalman filter algorithm
- **Parameters**:
- Process Noise: 0.35 (controls smoothness)
- Measurement Noise: 24 (controls reactivity)
- Filter Order: 6 (higher = more smoothing)
- ATR Length: 8 (for bandwidth calculation)
- Upper Multiplier: 2.0 (for long signals)
- Lower Multiplier: 2.7 (for short signals)
- Multi-timeframe support
- Customizable visual indicators
- **Implementation**: Generates signals based on price position relative to Kalman-filtered EMA bands
## Risk Management System
### Position Sizing
Automatically calculates position size based on account equity and risk parameters:
| Setting | Default Value |
|---------|---------------|
| **Risk % of Equity** | 50% |
- **Implementation**:
- Position size = (Account equity × Risk %) ÷ (Entry price × Stop loss distance)
- Adjusts automatically based on volatility and stop placement
- **Best Practices**:
- Start with lower risk percentages (1-2%) until strategy is proven
- Consider reducing risk during high volatility periods
### Stop-Loss Methods
Multiple stop-loss calculation methods with separate configurations for long and short positions:
| Method | Description | Configuration |
|--------|-------------|---------------|
| **ATR-Based** | Dynamic stops based on volatility | ATR Period: 14, Multiplier: 2.0 |
| **Percentage** | Fixed percentage from entry | Long: 1.5%, Short: 1.5% |
| **PIP-Based** | Fixed currency unit distance | 10.0 pips |
- **Implementation Notes**:
- ATR-based stops adapt to changing market volatility
- Percentage stops maintain consistent risk exposure
- PIP-based stops provide precise control in stable markets
### Trailing Stops
Locks in profits by adjusting stop-loss levels as price moves favorably:
| Setting | Default Value |
|---------|---------------|
| **Stop-Loss %** | 1.5% |
| **Activation Threshold** | 2.1% |
| **Trailing Distance** | 1.4% |
- **Implementation**:
- Initial stop remains fixed until profit reaches activation threshold
- Once activated, stop follows price at specified distance
- Locks in profit while allowing room for normal price fluctuations
### Risk-Reward Parameters
Defines the relationship between risk and potential reward:
| Setting | Default Value |
|---------|---------------|
| **Risk-Reward Ratio** | 1.4 |
| **Take Profit %** | 2.4% |
| **Stop-Loss %** | 1.5% |
- **Implementation**:
- Take profit distance = Stop loss distance × Risk-reward ratio
- Higher ratios require fewer winning trades for profitability
- Lower ratios increase win rate but reduce average profit
### Filter Combinations
The strategy allows for simultaneous application of multiple filters:
- **Recommended Combinations**:
- Trending markets: RSI + KEMAD filters
- Ranging markets: Bollinger Bands + Volatility filters
- All markets: Volume filter as minimum requirement
- **Performance Impact**:
- Each additional filter reduces the number of trades
- Quality of remaining trades typically improves
- Optimal combination depends on market conditions and timeframe
### Multi-Timeframe Filter Applications
| Filter Type | Current Timeframe | MTF1 (15min) | MTF2 (4h) |
|-------------|-------------------|-------------|------------|
| RSI | Quick entries/exits | Intraday trend | Overall trend |
| Volume | Immediate liquidity | Sustained support | Market participation |
| Volatility | Entry timing | Short-term risk | Regime changes |
| KEMAD | Precise signals | Trend confirmation | Major reversals |
## Visual Indicators and Chart Analysis
The bot provides comprehensive visual feedback on the chart:
- **Channel Bands**: Keltner or MLMA bands showing potential support/resistance
- **Pivot SuperTrend**: Colored line showing trend direction and potential reversal points
- **Entry/Exit Markers**: Annotations showing actual trade entries and exits
- **Risk/Reward Zones**: Visual representation of stop-loss and take-profit levels
These visual elements allow for:
- Real-time strategy assessment
- Post-trade analysis and optimization
- Educational understanding of the strategy logic
## Implementation Guide
### TradingView Setup
1. Load the script in TradingView Pine Editor
2. Apply to your preferred chart and timeframe
3. Adjust parameters based on your trading preferences
4. Enable alerts for webhook integration
### Webhook Integration
1. Configure webhook URL in TradingView alerts
2. Set up receiving endpoint on your trading platform
3. Define message format matching the bot's output
4. Test with small position sizes before full deployment
### Optimization Process
1. Backtest across different market conditions
2. Identify parameter sensitivity through multiple tests
3. Focus on risk management parameters first
4. Fine-tune entry/exit conditions based on performance metrics
5. Validate with out-of-sample testing
## Performance Considerations
### Strengths
- Adaptability to different market conditions through dual channels
- Multiple layers of confirmation reducing false signals
- Comprehensive risk management protecting capital
- Machine learning integration for predictive edge
### Limitations
- Complex parameter set requiring careful optimization
- Potential over-optimization risk with so many variables
- Computational intensity of MLMA calculations
- Dependency on proper webhook configuration for execution
### Best Practices
- Start with conservative risk settings (1-2% of equity)
- Test thoroughly in demo environment before live trading
- Monitor performance regularly and adjust parameters
- Consider market regime changes when evaluating results
## Conclusion
The ₿ober XM v2.0 represents a significant evolution in trading strategy design, combining traditional technical analysis with machine learning elements and multi-timeframe analysis. The core strength of this system lies in its adaptability and recognition of market asymmetry.
### Market Asymmetry and Adaptive Approach
The strategy acknowledges a fundamental truth about markets: bullish and bearish phases behave differently and should be treated as distinct environments. The dual-channel system with separate parameters for long and short positions directly addresses this asymmetry, allowing for optimized performance regardless of market direction.
### Targeted Backtesting Philosophy
It's counterproductive to run backtests over excessively long periods. Markets evolve continuously, and strategies that worked in previous market regimes may be ineffective in current conditions. Instead:
- Test specific market phases separately (bull markets, bear markets, range-bound periods)
- Regularly re-optimize parameters as market conditions change
- Focus on recent performance with higher weight than historical results
- Test across multiple timeframes to ensure robustness
### Multi-Timeframe Analysis as a Game-Changer
The integration of multi-timeframe analysis fundamentally transforms the strategy's effectiveness:
- **Increased Safety**: Higher timeframe confirmations reduce false signals and improve trade quality
- **Context Awareness**: Decisions made with awareness of larger trends reduce adverse entries
- **Adaptable Precision**: Apply strict filters on lower timeframes while maintaining awareness of broader conditions
- **Reduced Noise**: Higher timeframe data naturally filters market noise that can trigger poor entries
The ₿ober XM v2.0 provides traders with a framework that acknowledges market complexity while offering practical tools to navigate it. With proper setup, realistic expectations, and attention to changing market conditions, it delivers a sophisticated approach to systematic trading that can be continuously refined and optimized.
Fusion Sniper X [ Crypto Strategy]📌 Fusion Sniper X — Description for TradingView
Overview:
Fusion Sniper X is a purpose-built algorithmic trading strategy designed for cryptocurrency markets, especially effective on the 1-hour chart. It combines advanced trend analysis, momentum filtering, volatility confirmation, and dynamic trade management to deliver a fast-reacting, high-precision trading system. This script is not a basic mashup of indicators, but a fully integrated strategy with logical synergy between components, internal equity management, and visual trade analytics via a customizable dashboard.
🔍 How It Works
🔸 Trend Detection – McGinley Dynamic + Gradient Slope
McGinley Dynamic is used as the baseline to reflect adaptive price action more responsively than standard moving averages.
A custom gradient filter, calculated using the slope of the McGinley line normalized by ATR, determines if the market is trending up or down.
trendUp when slope > 0
trendDown when slope < 0
🔸 Momentum Confirmation – ZLEMA-Smoothed CCI
CCI (Commodity Channel Index) is used to detect momentum strength and direction.
It is further smoothed with ZLEMA (Zero Lag EMA) to reduce noise while keeping lag minimal.
Entry is confirmed when:
CCI > 0 (Bullish momentum)
CCI < 0 (Bearish momentum)
🔸 Volume Confirmation – Relative Volume Spike Filter
Uses a 20-period EMA of volume to calculate the expected average.
Trades are only triggered if real-time volume exceeds this average by a user-defined multiplier (default: 1.5x), filtering out low-conviction signals.
🔸 Trap Detection – Wick-to-Body Reversal Filter
Filters out potential trap candles using wick-to-body ratio and body size compared to ATR.
Avoids entering on manipulative price spikes where:
Long traps show large lower wicks.
Short traps show large upper wicks.
🔸 Entry Conditions
A trade is only allowed when:
Within selected date range
Cooldown between trades is respected
Daily drawdown guard is not triggered
All of the following align:
Trend direction (McGinley slope)
Momentum confirmation (CCI ZLEMA)
Volume spike active
No trap candle detected
🎯 Trade Management Logic
✅ Take Profit (TP1/TP2 System)
TP1: 50% of the position is closed at a predefined % gain (default 2%).
TP2: Remaining 100% is closed at a higher profit level (default 4%).
🛑 Stop Loss
A fixed 2% stop loss is enforced per position using strategy.exit(..., stop=...) logic.
Stop loss is active for both TP2 and primary entries and updates the dashboard if triggered.
❄️ Cooldown & Equity Protection
A user-defined cooldown period (in bars) prevents overtrading.
A daily equity loss guard blocks new trades if portfolio drawdown exceeds a % threshold (default: 2.5%).
📊 Real-Time Dashboard (On-Chart Table)
Fusion Sniper X features a futuristic, color-coded dashboard with theme controls, showing:
Current position and entry price
Real-time profit/loss (%)
TP1, TP2, and SL status
Trend and momentum direction
Volume spike state and trap candle alerts
Trade statistics: total, win/loss, drawdown
Symbol and timeframe display
Themes include: Neon, Cyber, Monochrome, and Dark Techno.
📈 Visuals
McGinley baseline is plotted in orange for trend bias.
Bar colors reflect active positions (green for long, red for short).
Stop loss line plotted in red when active.
Background shading highlights active volume spikes.
✅ Why It’s Not Just a Mashup
Fusion Sniper X is an original system architecture built on:
Custom logic (gradient-based trend slope, wick trap rejection)
Synergistic indicator stacking (ZLEMA-smoothed momentum, ATR-based slope)
Position and equity tracking (not just signal-based plotting)
Intelligent risk control with take-profits, stop losses, cooldown, and max loss rules
An interactive dashboard that enhances usability and transparency
Every component has a distinct role in the system, and none are used as-is from public sources without modification or integration logic. The design follows a cohesive and rule-based structure for algorithmic execution.
⚠️ Disclaimer
This strategy is for educational and informational purposes only. It does not constitute financial advice. Trading cryptocurrencies involves substantial risk, and past performance is not indicative of future results. Always backtest and forward-test before using on a live account. Use at your own risk.
📅 Backtest Range & Market Conditions Note
The performance results displayed for Fusion Sniper X are based on a focused backtest period from December 1, 2024 to May 10, 2025. This range was chosen intentionally due to the dynamic and volatile nature of cryptocurrency markets, where structural and behavioral shifts can occur rapidly. By evaluating over a shorter, recent time window, the strategy is tuned to current market mechanics and avoids misleading results that could come from outdated market regimes. This ensures more realistic, forward-aligned performance — particularly important for high-frequency systems operating on the 1-hour timeframe.
Praetor Sentinel V11.2 NOLOOSE BETA📈 Praetor Sentinel V11.2 – "NOLOOSE BETA"
Algorithmic Trading Strategy for Trend Markets with Adaptive Risk Management
Praetor Sentinel V11.2 is an advanced algorithmic trading strategy for TradingView, specifically designed to operate in strong trend conditions. It combines multiple technical systems—including dynamic trend filters, multi-layer EMA structures, ADX-based volatility control, and adaptive trailing stops—into a powerful and automated trading framework.
🔧 Core Features
Multi-EMA Trend Detection: Two EMA pairs (short/long) to identify and confirm directional trends.
XO-EMA Breakout Logic: Fast EMA crossover to detect breakout opportunities.
ADX Trend Filter: Trades only during strong market trends (above custom ADX threshold).
HTF Filter: Optional higher timeframe trend confirmation (e.g. Daily 50 EMA).
VWAP Validation: Ensures entries aren't taken against the volumetric average.
RSI Filter: Adds a momentum filter (e.g. RSI > 50 for long trades).
🎯 Entry Signals
The strategy uses two entry types:
Breakout Entries: Based on XO-EMA cross and multi-EMA trend alignment.
Pullback Entries: Configurable via various methods such as EMA21 reentry, RSI reversal, engulfing candles, or VWAP reclaim.
All entries can be delayed via confirmation candle logic, requiring a bullish or bearish follow-up bar.
🛡️ Risk Management & Exit Logic
Dynamic ATR Trailing Stop: Adjusts stop distance according to market volatility with optional swing high/low protection.
Break-Even Logic: Locks in trades at breakeven once a defined profit is reached.
Hard Stop-Loss: Caps potential loss per trade with a fixed % (e.g. 1%).
Safe Mode ("NOLOOSE"): Exits early if price moves too far against the position — ideal for automated bots that must avoid drawdowns.
🤖 Automation & Alerts
This strategy is fully automatable with services like 3Commas using built-in alert messages for entries and exits.
All parameters are fully configurable to adapt to different assets, timeframes, and trading styles.
⚙️ Additional Features
Configurable leverage & position sizing
Time-based trading window
Built-in Anchored VWAP
Modular design for easy extension
📌 Summary
Praetor Sentinel V11.2 is a professional-grade tool for trend traders who want rule-based entry/exit logic, adaptive stop systems, and robust protection features. When paired with automation tools, it offers a reliable, low-maintenance setup that emphasizes safety, structure, and scalability.
🛠 How to Use Praetor Sentinel V11.2 – NOLOOSE BETA
🔍 1. Basic Configuration (Required)
Setting Description
Enable Long Trades Enables long (buy) positions.
Enable Short Trades Enables short (sell) positions.
Leverage Used for position sizing calculations.
Position Size % Defines % of capital to be used per trade.
⏰ 2. Time Filter (Optional)
Restricts trading to a defined time range.
Setting Description
Start Date Start date for strategy to be active.
End Date End date for strategy to stop.
Time Zone Time zone for above settings.
📊 3. Trend Setup (Essential for Entry Signals)
Setting Description
MA Type Type of moving average: EMA or SMA.
EMA1/2 Short & Long Two EMA-based systems to determine trend.
Fast/Slow EMA (XO) Used for crossover breakout detection.
HTF Filter Uses higher timeframe trend for additional confirmation.
RSI Filter Confirms entries only if momentum (RSI) supports it.
ADX Threshold Ensures trades only occur during strong trends.
🎯 4. Entry Logic
Setting Description
Pullback Entry Type Enables optional entry setups:
"Off"
"EMA21"
"RSI"
"Engulfing"
"VWAP"
| Use Confirmation Candle | Entry is delayed until a confirmation bar appears. |
| VWAP Confirmation | Trade only if price is above/below the VWAP (based on direction). |
Note: You can combine breakout + pullback signals. Only one has to trigger.
🧯 5. Risk Control & Exit Settings
Setting Description
Trailing Stop Mode
"Standard": Classic trailing stop
"Dynamic ATR": Adjusts to current volatility
"Dynamic ATR + Swing": Adds swing high/low buffer
| Enable Break-Even | Moves SL to breakeven once a target % gain is reached. |
| Enable Hard Stop-Loss | Fixed stop-loss (e.g. 1%) to cap trade risk. |
| Enable Safe Mode | Exits trade early if price moves against it beyond defined % (e.g. 0.3%). |
🔔 6. Alerts & Bot Automation
Setting Description
Entry Long/Short Msg Text message sent via alert when a position opens.
Exit Long/Short Msg Alert message for stop-loss/exit logic.
How to automate with 3Commas:
Load the strategy on your chart.
Manually create alerts using "Create Alert" in TradingView.
Use the built-in alert_message values for bot integration.
✅ Recommended Settings (Example for BTC/ETH on 1H)
Long & Short: ✅ Enabled
Leverage: 2.0
Timeframe: 1H
Pullback Entry: "EMA21"
MA Type: EMA
HTF Filter: Enabled (Daily EMA50)
RSI Filter: Enabled
VWAP Filter: Enabled
Break-Even: On at 0.5%
Hard SL: 1.0%
Safe Mode: On at -0.3%
Trailing Stop: "Dynamic ATR + Swing"
📘 Pro Tips for Testing & Customization
Use the Strategy Tester in TradingView to analyze performance over different assets.
Experiment with timeframes and entry modes.
Ideal for trending assets like BTC, ETH, SOL, etc.
You can expand it with take-profit logic, fixed TPs, indicator exits, etc.
Cycle Biologique Strategy // (\_/)
// ( •.•)
// (")_(")
//@fr33domz
Experimental Research: Cycle Biologique Strategy
Overview
The "Cycle Biologique Strategy" is an experimental trading algorithm designed to leverage periodic cycles in price movements by utilizing a sinusoidal function. This strategy aims to identify potential buy and sell signals based on the behavior of a custom-defined biological cycle.
Key Parameters
Cycle Length: This parameter defines the duration of the cycle, set by default to 30 periods. The user can adjust this value to optimize the strategy for different asset classes or market conditions.
Amplitude: The amplitude of the cycle influences the scale of the sinusoidal wave, allowing for customization in the sensitivity of buy and sell signals.
Offset: The offset parameter introduces phase shifts to the cycle, adjustable within a range of -360 to 360 degrees. This flexibility allows the strategy to align with various market rhythms.
Methodology
The core of the strategy lies in the calculation of a periodic cycle using a sinusoidal function.
Trading Signals
Buy Signal: A buy signal is generated when the cycle value crosses above zero, indicating a potential upward momentum.
Sell Signal: Conversely, a sell signal is triggered when the cycle value crosses below zero, suggesting a potential downtrend.
Execution
The strategy executes trades based on these signals:
Upon receiving a buy signal, the algorithm enters a long position.
When a sell signal occurs, the strategy closes the long position.
Visualization
To enhance user experience, the periodic cycle is plotted visually on the chart in blue, allowing traders to observe the cyclical nature of the strategy and its alignment with market movements.
Hierarchical + K-Means Clustering Strategy===== USER GUIDE =====
Hierarchical + K-Means Clustering Strategy
OVERVIEW:
This strategy combines hierarchical clustering and K-means algorithms to analyze market volatility patterns
and generate trading signals. It uses a modified SuperTrend indicator with ATR-based volatility clustering
to identify potential trend changes and market conditions.
KEY FEATURES:
- Advanced volatility analysis using hierarchical clustering and K-means algorithms
- Modified SuperTrend indicator for trend identification
- Multiple filter options including moving average and ADX trend strength
- Volume-based exit mechanism to protect profits
- Customizable appearance settings
SETTINGS EXPLANATION:
1. SuperTrend Settings:
- ATR Length: Period for ATR calculation (default: 11)
- SuperTrend Factor: Multiplier for ATR to determine trend bands (default: 3)
2. Hierarchical Clustering Settings:
- Training Data Length: Number of bars used for clustering analysis (default: 200)
3. Appearance Settings:
- Transparency 1 & 2: Control the opacity of trend lines and fills
- Bullish/Bearish Color: Colors for uptrend and downtrend visualization
4. Time Settings:
- Start Year/Month: Define when the strategy should start executing trades
5. Filter Settings:
- Moving Average Filter: Uses SMA to filter trades (only enter when price is on correct side of MA)
- Trend Strength Filter: Uses ADX to ensure trades are taken in strong trend conditions
6. Volume Stop Loss Settings:
- Volume Ratio Threshold: Controls sensitivity of volume-based exits
- Monitoring Delay Bars: Number of bars to wait before monitoring volume for exit signals
HOW TO USE:
1. Apply the indicator to your chart
2. Adjust settings according to your trading preferences and timeframe
3. Long signals appear when price crosses above the SuperTrend line (▲k marker)
4. Short signals appear when price crosses below the SuperTrend line (▼k marker)
5. The strategy automatically manages exits based on volume balance conditions
INTERPRETATION:
- Green line/area: Bullish trend - consider long positions
- Red line/area: Bearish trend - consider short positions
- Yellow line: Moving average for additional trend confirmation
- Volume balance exits occur when buying/selling pressure equalizes
RECOMMENDED TIMEFRAMES:
This strategy works best on 1H, 4H, and daily charts for most markets.
For highly volatile assets, shorter timeframes may also be effective.
RISK MANAGEMENT:
Always use proper position sizing and consider setting additional stop losses
beyond the strategy's built-in exit mechanisms.
===== END OF USER GUIDE =====
Boilerplate Configurable Strategy [Yosiet]This is a Boilerplate Code!
Hello! First of all, let me introduce myself a little bit. I don't come from the world of finance, but from the world of information and communication technologies (ICT) where we specialize in data processing with the aim of automating it and eliminating all human factors and actors in the processes. You could say that I am an algotrader.
That said, in my journey through trading in recent years I have understood that this world is often shown to be incomplete. All those who want to learn about trading only end up learning a small part of what it really entails, they only seek to learn how to read candlesticks. Therefore, I want to share with the entire community a fraction of what I have really understood it to be.
As a computer scientist, the most important thing is the data, it is the raw material of our work and without data you simply cannot do anything. Entropy is simple: Data in -> Data is transformed -> Data out.
The quality of the outgoing data will directly depend on the incoming data, there is no greater mystery or magic in the process. In trading it is no different, because at the end of the day it is nothing more than data. As we often say, if garbage comes in, garbage comes out.
Most people focus on the results only, on the outgoing data, because in the end we all want the same thing, to make easy money. Very few pay attention to the input data, much less to the process.
Now, I am not here to delude you, because there is no bigger lie than easy money, but I am here to give you a boilerplate code that will help you create strategies where you only have to concentrate on the quality of the incoming data.
To the Point
The code is a strategy boilerplate that applies the technique that you decide to customize for the criteria for opening a position. It already has the other factors involved in trading programmed and automated.
1. The Entry
This section of the boilerplate is the one that each individual must customize according to their needs and knowledge. The code is offered with two simple, well-known strategies to exemplify how the code can be reused for your own benefits.
For the purposes of this post on tradingview, I am going to use the simplest of the known strategies in trading for entries: SMA Crossing
// SMA Cross Settings
maFast = ta.sma(close, length)
maSlow = ta.sma(open, length)
The Strategy Properties for all cases published here:
For Stock TSLA H1 From 01/01/2025 To 02/15/2025
For Crypto XMR-USDT 30m From 01/01/2025 To 02/15/2025
For Forex EUR-USD 5m From 01/01/2025 To 02/15/2025
But the goal of this post is not to sell you a dream, else to show you that the same Entry decision works very well for some and does not for others and with this boilerplate code you only have to think of entries, not exits.
2. Schedules, Days, Sessions
As you know, there are an infinite number of markets that are susceptible to the sessions of each country and the news that they announce during those sessions, so the code already offers parameters so that you can condition the days and hours of operation, filter the best time parameters for a specific market and time frame.
3. Data Filtering
The data offered in trading are numerical series presented in vectors on a time axis where an endless number of mathematical equations can be applied to process them, with matrix calculation and non-linear regressions being the best, in my humble opinion.
4. Read Fundamental Macroeconomic Events, News
The boilerplate has integration with the tradingview SDK to detect when news will occur and offers parameters so that you can enable an exclusion time margin to not operate anything during that time window.
5. Direction and Sense
In my experience I have found the peculiarity that the same algorithm works very well for a market in a time frame, but for the same market in another time frame it is only a waste of time and money. So now you can easily decide if you only want to open LONG, SHORT or both side positions and know how effective your strategy really is.
6. Reading the money, THE PURPOSE OF EVERYTHING
The most important section in trading and the reason why many clients usually hire me as a financial programmer, is reading and controlling the money, because in the end everyone wants to win and no one wants to lose. Now they can easily parameterize how the money should flow and this is the genius of this boilerplate, because it is what will really decide if an algorithm (Indicator: A bunch of math equations) for entries will really leave you good money over time.
7. Managing the Risk, The Ego Destroyer
Many trades, little money. Most traders focus on making money and none of them know about statistics and the few who do know something about it, only focus on the winrate. Well, with this code you can unlock what really matters, the true success criteria to be able to live off of trading: Profit Factor, Sortino Ratio, Sharpe Ratio and most importantly, will you really make money?
8. Managing Emotions
Finally, the main reason why many lose money is because they are very bad at managing their emotions, because with this they will no longer need to do so because the boilerplate has already programmed criteria to chase the price in a position, cut losses and maximize profits.
In short, this is a boilerplate code that already has the data processing and data output ready, you only have to worry about the data input.
“And so the trader learned: the greatest edge was not in predicting the storm, but in building a boat that could not sink.”
DISCLAIMER
This post is intended for programmers and quantitative traders who already have a certain level of knowledge and experience. It is not intended to be financial advice or to sell you any money-making script, if you use it, you do so at your own risk.
MultiLayer Acceleration/Deceleration Strategy [Skyrexio]Overview
MultiLayer Acceleration/Deceleration Strategy leverages the combination of Acceleration/Deceleration Indicator(AC), Williams Alligator, Williams Fractals and Exponential Moving Average (EMA) to obtain the high probability long setups. Moreover, strategy uses multi trades system, adding funds to long position if it considered that current trend has likely became stronger. Acceleration/Deceleration Indicator is used for creating signals, while Alligator and Fractal are used in conjunction as an approximation of short-term trend to filter them. At the same time EMA (default EMA's period = 100) is used as high probability long-term trend filter to open long trades only if it considers current price action as an uptrend. More information in "Methodology" and "Justification of Methodology" paragraphs. The strategy opens only long trades.
Unique Features
No fixed stop-loss and take profit: Instead of fixed stop-loss level strategy utilizes technical condition obtained by Fractals and Alligator to identify when current uptrend is likely to be over (more information in "Methodology" and "Justification of Methodology" paragraphs)
Configurable Trading Periods: Users can tailor the strategy to specific market windows, adapting to different market conditions.
Multilayer trades opening system: strategy uses only 10% of capital in every trade and open up to 5 trades at the same time if script consider current trend as strong one.
Short and long term trend trade filters: strategy uses EMA as high probability long-term trend filter and Alligator and Fractal combination as a short-term one.
Methodology
The strategy opens long trade when the following price met the conditions:
1. Price closed above EMA (by default, period = 100). Crossover is not obligatory.
2. Combination of Alligator and Williams Fractals shall consider current trend as an upward (all details in "Justification of Methodology" paragraph)
3. Acceleration/Deceleration shall create one of two types of long signals (all details in "Justification of Methodology" paragraph). Buy stop order is placed one tick above the candle's high of last created long signal.
4. If price reaches the order price, long position is opened with 10% of capital.
5. If currently we have opened position and price creates and hit the order price of another one long signal, another one long position will be added to the previous with another one 10% of capital. Strategy allows to open up to 5 long trades simultaneously.
6. If combination of Alligator and Williams Fractals shall consider current trend has been changed from up to downtrend, all long trades will be closed, no matter how many trades has been opened.
Script also has additional visuals. If second long trade has been opened simultaneously the Alligator's teeth line is plotted with the green color. Also for every trade in a row from 2 to 5 the label "Buy More" is also plotted just below the teeth line. With every next simultaneously opened trade the green color of the space between teeth and price became less transparent.
Strategy settings
In the inputs window user can setup strategy setting: EMA Length (by default = 100, period of EMA, used for long-term trend filtering EMA calculation). User can choose the optimal parameters during backtesting on certain price chart.
Justification of Methodology
Let's explore the key concepts of this strategy and understand how they work together. We'll begin with the simplest: the EMA.
The Exponential Moving Average (EMA) is a type of moving average that assigns greater weight to recent price data, making it more responsive to current market changes compared to the Simple Moving Average (SMA). This tool is widely used in technical analysis to identify trends and generate buy or sell signals. The EMA is calculated as follows:
1.Calculate the Smoothing Multiplier:
Multiplier = 2 / (n + 1), Where n is the number of periods.
2. EMA Calculation
EMA = (Current Price) × Multiplier + (Previous EMA) × (1 − Multiplier)
In this strategy, the EMA acts as a long-term trend filter. For instance, long trades are considered only when the price closes above the EMA (default: 100-period). This increases the likelihood of entering trades aligned with the prevailing trend.
Next, let’s discuss the short-term trend filter, which combines the Williams Alligator and Williams Fractals. Williams Alligator
Developed by Bill Williams, the Alligator is a technical indicator that identifies trends and potential market reversals. It consists of three smoothed moving averages:
Jaw (Blue Line): The slowest of the three, based on a 13-period smoothed moving average shifted 8 bars ahead.
Teeth (Red Line): The medium-speed line, derived from an 8-period smoothed moving average shifted 5 bars forward.
Lips (Green Line): The fastest line, calculated using a 5-period smoothed moving average shifted 3 bars forward.
When the lines diverge and align in order, the "Alligator" is "awake," signaling a strong trend. When the lines overlap or intertwine, the "Alligator" is "asleep," indicating a range-bound or sideways market. This indicator helps traders determine when to enter or avoid trades.
Fractals, another tool by Bill Williams, help identify potential reversal points on a price chart. A fractal forms over at least five consecutive bars, with the middle bar showing either:
Up Fractal: Occurs when the middle bar has a higher high than the two preceding and two following bars, suggesting a potential downward reversal.
Down Fractal: Happens when the middle bar shows a lower low than the surrounding two bars, hinting at a possible upward reversal.
Traders often use fractals alongside other indicators to confirm trends or reversals, enhancing decision-making accuracy.
How do these tools work together in this strategy? Let’s consider an example of an uptrend.
When the price breaks above an up fractal, it signals a potential bullish trend. This occurs because the up fractal represents a shift in market behavior, where a temporary high was formed due to selling pressure. If the price revisits this level and breaks through, it suggests the market sentiment has turned bullish.
The breakout must occur above the Alligator’s teeth line to confirm the trend. A breakout below the teeth is considered invalid, and the downtrend might still persist. Conversely, in a downtrend, the same logic applies with down fractals.
In this strategy if the most recent up fractal breakout occurs above the Alligator's teeth and follows the last down fractal breakout below the teeth, the algorithm identifies an uptrend. Long trades can be opened during this phase if a signal aligns. If the price breaks a down fractal below the teeth line during an uptrend, the strategy assumes the uptrend has ended and closes all open long trades.
By combining the EMA as a long-term trend filter with the Alligator and fractals as short-term filters, this approach increases the likelihood of opening profitable trades while staying aligned with market dynamics.
Now let's talk about Acceleration/Deceleration signals. AC indicator is calculated using the Awesome Oscillator, so let's first of all briefly explain what is Awesome Oscillator and how it can be calculated. The Awesome Oscillator (AO), developed by Bill Williams, is a momentum indicator designed to measure market momentum by contrasting recent price movements with a longer-term historical perspective. It helps traders detect potential trend reversals and assess the strength of ongoing trends.
The formula for AO is as follows:
AO = SMA5(Median Price) − SMA34(Median Price)
where:
Median Price = (High + Low) / 2
SMA5 = 5-period Simple Moving Average of the Median Price
SMA 34 = 34-period Simple Moving Average of the Median Price
The Acceleration/Deceleration (AC) Indicator, introduced by Bill Williams, measures the rate of change in market momentum. It highlights shifts in the driving force of price movements and helps traders spot early signs of trend changes. The AC Indicator is particularly useful for identifying whether the current momentum is accelerating or decelerating, which can indicate potential reversals or continuations. For AC calculation we shall use the AO calculated above is the following formula:
AC = AO − SMA5(AO), where SMA5(AO)is the 5-period Simple Moving Average of the Awesome Oscillator
When the AC is above the zero line and rising, it suggests accelerating upward momentum.
When the AC is below the zero line and falling, it indicates accelerating downward momentum.
When the AC is below zero line and rising it suggests the decelerating the downtrend momentum. When AC is above the zero line and falling, it suggests the decelerating the uptrend momentum.
Now we can explain which AC signal types are used in this strategy. The first type of long signal is when AC value is below zero line. In this cases we need to see three rising bars on the histogram in a row after the falling one. The second type of signals occurs above the zero line. There we need only two rising AC bars in a row after the falling one to create the signal. The signal bar is the last green bar in this sequence. The strategy places the buy stop order one tick above the candle's high, which corresponds to the signal bar on AC indicator.
After that we can have the following scenarios:
Price hit the order on the next candle in this case strategy opened long with this price.
Price doesn't hit the order price, the next candle set lower high. If current AC bar is increasing buy stop order changes by the script to the high of this new bar plus one tick. This procedure repeats until price finally hit buy order or current AC bar become decreasing. In the second case buy order cancelled and strategy wait for the next AC signal.
If long trades are initiated, the strategy continues utilizing subsequent signals until the total number of trades reaches a maximum of 5. All open trades are closed when the trend shifts to a downtrend, as determined by the combination of the Alligator and Fractals described earlier.
Why we use AC signals? If currently strategy algorithm considers the high probability of the short-term uptrend with the Alligator and Fractals combination pointed out above and the long-term trend is also suggested by the EMA filter as bullish. Rising AC bars after period of falling AC bars indicates the high probability of local pull back end and there is a high chance to open long trade in the direction of the most likely main uptrend. The numbers of rising bars are different for the different AC values (below or above zero line). This is needed because if AC below zero line the local downtrend is likely to be stronger and needs more rising bars to confirm that it has been changed than if AC is above zero.
Why strategy use only 10% per signal? Sometimes we can see the false signals which appears on sideways. Not risking that much script use only 10% per signal. If the first long trade has been open and price continue going up and our trend approximation by Alligator and Fractals is uptrend, strategy add another one 10% of capital to every next AC signal while number of active trades no more than 5. This capital allocation allows to take part in long trades when current uptrend is likely to be strong and use only 10% of capital when there is a high probability of sideways.
Backtest Results
Operating window: Date range of backtests is 2023.01.01 - 2024.11.01. It is chosen to let the strategy to close all opened positions.
Commission and Slippage: Includes a standard Binance commission of 0.1% and accounts for possible slippage over 5 ticks.
Initial capital: 10000 USDT
Percent of capital used in every trade: 10%
Maximum Single Position Loss: -5.15%
Maximum Single Profit: +24.57%
Net Profit: +2108.85 USDT (+21.09%)
Total Trades: 111 (36.94% win rate)
Profit Factor: 2.391
Maximum Accumulated Loss: 367.61 USDT (-2.97%)
Average Profit per Trade: 19.00 USDT (+1.78%)
Average Trade Duration: 75 hours
How to Use
Add the script to favorites for easy access.
Apply to the desired timeframe and chart (optimal performance observed on 3h BTC/USDT).
Configure settings using the dropdown choice list in the built-in menu.
Set up alerts to automate strategy positions through web hook with the text: {{strategy.order.alert_message}}
Disclaimer:
Educational and informational tool reflecting Skyrex commitment to informed trading. Past performance does not guarantee future results. Test strategies in a simulated environment before live implementation
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters.
