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Manus Machiene Learning Beast – Indicator Description
Overview
Manus Machiene Learning Beast is an advanced TradingView indicator that combines Machine Learning (Lorentzian Classification) with trend, volatility, and market regime filters to generate high-quality long and short trade signals.
The indicator is designed for rule-based, disciplined trading and works especially well for set-and-forget, semi-automated, or fully automated execution workflows.
⸻
Core Concept
At its core, the indicator uses a machine-learning model based on a modified K-Nearest Neighbors (KNN) approach.
Instead of standard Euclidean distance, it applies Lorentzian distance, which:
• Reduces the impact of outliers
• Accounts for market distortions caused by volatility spikes and major events
• Produces more robust predictions in real market conditions
The model does not attempt to predict exact tops or bottoms.
Instead, it estimates the probable price direction over the next 4 bars.
⸻
Signal Logic
Long Signals
A long signal is generated when:
• The ML model predicts a positive directional bias
• All enabled filters are satisfied
• A new directional change is detected (non-repainting)
• Optional trend filters (EMA / SMA) confirm the direction
• Optional kernel regression confirms bullish momentum
📍 Displayed as a green label below the bar
Short Signals
A short signal is generated when:
• The ML model predicts a negative directional bias
• Filters confirm bearish conditions
• A new directional change occurs
• Trend and kernel filters align
📍 Displayed as a red label above the bar
⸻
Filters & Components
All filters are modular and can be enabled or disabled individually.
1. Volatility Filter
• Avoids trading during extremely low or chaotic volatility conditions
2. Regime Filter (Trend vs Range)
• Attempts to filter out sideways markets
• Especially important for ML-based systems
3. ADX Filter (Optional)
• Trades only when sufficient trend strength is present
4. EMA / SMA Trend Filters
• Classic trend confirmation (e.g., 200 EMA / 200 SMA)
• Ensures trades are aligned with the higher-timeframe trend
5. Kernel Regression (Nadaraya-Watson)
• Smooths price behavior
• Acts as a momentum and trend confirmation filter
• Can be used in standard or smoothed mode
⸻
Moving Average Overlays
For visual market context, the indicator includes optional overlays:
• ✅ SMA 200
• ✅ HMA 200
Both can be toggled via checkboxes and are visual aids only, unless explicitly enabled as filters.
⸻
Exit Logic
Two exit methods are available:
1. Fixed Exit
• Trades close after 4 bars
• Matches the ML model’s training horizon
2. Dynamic Exit
• Uses kernel regression and signal changes
• Designed to let profits run in strong trends
⚠️ Recommended only when no additional trend filters are active.
⸻
Backtesting & Trade Statistics
The indicator includes an on-chart statistics panel showing:
• Win rate
• Total trades
• Win/Loss ratio
• Early signal flips (useful for identifying choppy markets)
⚠️ This is intended for calibration and optimization only, not as a replacement for full strategy backtesting.
⸻
Typical Use Cases
• Swing trading (M15 – H4)
• Rule-based discretionary trading
• Set-and-forget trading
• TradingView alerts → MT4/MT5 → EA execution
• Prop-firm trading (e.g. FTMO), with proper risk management
⸻
Important Disclaimer
This indicator:
• ❌ does not guarantee profits
• ❌ is not a “holy grail”
• ✅ is a decision-support and structure tool
It performs best when:
• Combined with strict risk management (e.g. ATR-based stops)
• Used in trending or expanding markets
• Executed with discipline and consistency
Overview
Manus Machiene Learning Beast is an advanced TradingView indicator that combines Machine Learning (Lorentzian Classification) with trend, volatility, and market regime filters to generate high-quality long and short trade signals.
The indicator is designed for rule-based, disciplined trading and works especially well for set-and-forget, semi-automated, or fully automated execution workflows.
⸻
Core Concept
At its core, the indicator uses a machine-learning model based on a modified K-Nearest Neighbors (KNN) approach.
Instead of standard Euclidean distance, it applies Lorentzian distance, which:
• Reduces the impact of outliers
• Accounts for market distortions caused by volatility spikes and major events
• Produces more robust predictions in real market conditions
The model does not attempt to predict exact tops or bottoms.
Instead, it estimates the probable price direction over the next 4 bars.
⸻
Signal Logic
Long Signals
A long signal is generated when:
• The ML model predicts a positive directional bias
• All enabled filters are satisfied
• A new directional change is detected (non-repainting)
• Optional trend filters (EMA / SMA) confirm the direction
• Optional kernel regression confirms bullish momentum
📍 Displayed as a green label below the bar
Short Signals
A short signal is generated when:
• The ML model predicts a negative directional bias
• Filters confirm bearish conditions
• A new directional change occurs
• Trend and kernel filters align
📍 Displayed as a red label above the bar
⸻
Filters & Components
All filters are modular and can be enabled or disabled individually.
1. Volatility Filter
• Avoids trading during extremely low or chaotic volatility conditions
2. Regime Filter (Trend vs Range)
• Attempts to filter out sideways markets
• Especially important for ML-based systems
3. ADX Filter (Optional)
• Trades only when sufficient trend strength is present
4. EMA / SMA Trend Filters
• Classic trend confirmation (e.g., 200 EMA / 200 SMA)
• Ensures trades are aligned with the higher-timeframe trend
5. Kernel Regression (Nadaraya-Watson)
• Smooths price behavior
• Acts as a momentum and trend confirmation filter
• Can be used in standard or smoothed mode
⸻
Moving Average Overlays
For visual market context, the indicator includes optional overlays:
• ✅ SMA 200
• ✅ HMA 200
Both can be toggled via checkboxes and are visual aids only, unless explicitly enabled as filters.
⸻
Exit Logic
Two exit methods are available:
1. Fixed Exit
• Trades close after 4 bars
• Matches the ML model’s training horizon
2. Dynamic Exit
• Uses kernel regression and signal changes
• Designed to let profits run in strong trends
⚠️ Recommended only when no additional trend filters are active.
⸻
Backtesting & Trade Statistics
The indicator includes an on-chart statistics panel showing:
• Win rate
• Total trades
• Win/Loss ratio
• Early signal flips (useful for identifying choppy markets)
⚠️ This is intended for calibration and optimization only, not as a replacement for full strategy backtesting.
⸻
Typical Use Cases
• Swing trading (M15 – H4)
• Rule-based discretionary trading
• Set-and-forget trading
• TradingView alerts → MT4/MT5 → EA execution
• Prop-firm trading (e.g. FTMO), with proper risk management
⸻
Important Disclaimer
This indicator:
• ❌ does not guarantee profits
• ❌ is not a “holy grail”
• ✅ is a decision-support and structure tool
It performs best when:
• Combined with strict risk management (e.g. ATR-based stops)
• Used in trending or expanding markets
• Executed with discipline and consistency
开源脚本
秉承TradingView的精神,该脚本的作者将其开源,以便交易者可以查看和验证其功能。向作者致敬!您可以免费使用该脚本,但请记住,重新发布代码须遵守我们的网站规则。
免责声明
这些信息和出版物并非旨在提供,也不构成TradingView提供或认可的任何形式的财务、投资、交易或其他类型的建议或推荐。请阅读使用条款了解更多信息。
开源脚本
秉承TradingView的精神,该脚本的作者将其开源,以便交易者可以查看和验证其功能。向作者致敬!您可以免费使用该脚本,但请记住,重新发布代码须遵守我们的网站规则。
免责声明
这些信息和出版物并非旨在提供,也不构成TradingView提供或认可的任何形式的财务、投资、交易或其他类型的建议或推荐。请阅读使用条款了解更多信息。