Machine Learning + Traditional TA Hybrid

Entry Logic

• Long entry: A machine learning model gives a buy signal (e.g., based on a classification algorithm predicting an up move). The signal is confirmed by a traditional technical analysis setup (e.g., a breakout from a bull flag).
• Short entry: The ML model gives a sell signal, confirmed by a TA setup (e.g., a breakdown from a head and shoulders pattern).
• Confirmation: The ML signal and the TA setup must align.
• Timeframe: Any, depending on the model and the TA setup.
• Location: At the point of TA confirmation.
• Market Condition: Any.

Exit Logic

• Profit Target: A target determined by the ML model or by traditional TA (e.g., a measured move).
• Scaling Out: As per the strategy rules.
• Trailing Stop: As per the strategy rules.
• Signal Failure: If the TA pattern fails.
• Opposite Signal: If the ML model gives an opposite signal.
• Time Expiration: As per the strategy rules.
• Momentum Loss: As per the strategy rules.

Stop Loss Structure

• Hard Stop: Based on the TA pattern.
• Soft Stop: If the ML model changes its prediction.
• Max Dollar Loss: As per the strategy rules.
• Max Percent Loss: As per the strategy rules.
• Structural Stop: Based on the TA pattern.

Risk Management Framework

• Risk Per Trade: As per the strategy rules.
• Daily Limit: As per the strategy rules.
• Weekly Limit: As per the strategy rules.
• Max Drawdown: As per the strategy rules.
• R:R Requirement: As per the strategy rules.

Position Sizing Model

• Sizing Approach: As per the strategy rules.
• Volatility Adjustment: As per the strategy rules.
• Conviction Sizing: Can be based on the confidence score of the ML model.
• Scaling In: As per the strategy rules.
• Scaling Out: As per the strategy rules.

Trade Filtering

• Market Conditions: As per the ML model and TA rules.
• Setups: The ML signal and TA setup must align.
• Instruments: Any.
• Time Restrictions: Any.
• Chop/News Avoidance: As per the strategy rules.

Context Framework

• Trend Direction: As per the ML model and TA rules.
• VWAP Relationship: Can be a feature in the ML model.
• MA Relationship: Can be a feature in the ML model.
• Range Location: Can be a feature in the ML model.
• Higher TF Alignment: Can be a feature in the ML model.

Trade Management Rules

• Breakeven: As per the strategy rules.
• Scale Out: As per the strategy rules.
• Add Size: As per the strategy rules.
• Fast vs Slow Moves: As per the strategy rules.

Time Rules

• Optimal Window: Any.
• Times to Avoid: Any.
• Session Notes: A systematic, data-driven approach.

Setup Classification

• A+ Setup: A high-confidence ML signal with a textbook TA pattern.
• A Setup: A good ML signal with a decent TA pattern.
• B Setup: A weak signal or pattern.
• C Setup: No alignment.

Market Selection Criteria

• Instruments: Any that the ML model is trained on.
• Volume: High.
• Volatility: Any.

Statistical Edge Metrics

• Win Rate: Varies depending on the model.
• Avg Win: Varies.
• Avg Loss: Varies.
• Profit Factor: Varies.
• Expectancy: Varies.

Failure Conditions

• Market Conditions: When the market regime changes and the ML model is no longer effective (model decay).
• Specific Scenarios: Overfitting of the ML model to historical data.

Psychological Rules

• Mental Discipline: Requires trust in the system and the ability to execute signals without emotion.

Advanced Components

• Regime Detection: The ML model can have a regime detection component built-in.
• Filters: The ML model can incorporate various filters.
• Correlation: The ML model can account for correlations.
• MTF Alignment: The ML model can use multi-timeframe features.

Location

• Strongest: In market conditions that are similar to the data the model was trained on.
• Weakest: In new, unprecedented market conditions.