Machine Learning for Trading: The Overhyped & The Practical

💡 The Aha Moment

ML isn't a standalone strategy. It's a FILTER. You already have setups (Janus sweeps). ML predicts which ones have 75% vs 50% probability. Trade the 75% ones, skip the 50%. That's the edge.

🎯 Practice Exercise: Implement ML Feature Engineering for Trade Filtering

Objective: Build an ML model that filters your existing setups, improving expectancy by 10-15% through selective trade-taking.

Part 1: Feature Engineering (The Most Important Step)

For each of your historical trades, calculate these features AT TIME OF SIGNAL (no look-ahead!):

Feature Set Template (20+ features recommended):

PRICE FEATURES:
1. Distance from VWAP (%): (Price - VWAP) / VWAP
2. Distance from 50 EMA (%): (Price - EMA50) / EMA50
3. Distance from daily high (%): (High - Price) / High
4. Distance from daily low (%): (Price - Low) / Low

MOMENTUM FEATURES:
5. RSI (14): Value 0-100
6. ADX (14): Trend strength
7. +DI / -DI ratio: Directional indicator
8. Rate of Change (10): Price change last 10 candles

VOLATILITY FEATURES:
9. ATR / Price ratio: Normalized volatility
10. Bollinger Band Width %: (Upper - Lower) / Middle
11. Recent range expansion: Current ATR / 20-period avg ATR

VOLUME FEATURES:
12. Volume vs avg: Current / 20-period average
13. CVD (Cumulative Volume Delta): Net buying pressure
14. VWAP vs POC distance: Fair value alignment

TIME FEATURES:
15. Time of day: Minutes since open (normalize 0-390)
16. Day of week: Mon=1, Fri=5
17. Time since last signal: Minutes

REGIME FEATURES:
18. VIX level: Current VIX reading
19. Regime score: ADX + ATR + BB Width composite
20. DXY change %: Macro headwind/tailwind

YOUR FEATURES (calculate for 50+ historical trades):
Trade 1: [Feature 1: ___, Feature 2: ___, ..., Feature 20: ___, Outcome: Win/Loss]
Trade 2: [...]

Part 2: Train/Test Split (Time-Based ONLY)

NEVER shuffle time series data. Use rolling windows:

Walk-Forward ML Validation:

Window 1:
Train: Jan-Jun 2023 (trades 1-30)
Test: Jul-Sep 2023 (trades 31-40)
Model: Random Forest, max_depth=5
Test Accuracy: ___%
Test Performance: ___%

Window 2:
Train: Apr-Sep 2023 (trades 15-50)
Test: Oct-Dec 2023 (trades 51-65)
Model: Re-train on new window
Test Accuracy: ___%
Test Performance: ___%

Window 3:
Train: Jul-Dec 2023 (trades 35-75)
Test: Jan-Mar 2024 (trades 76-90)
Test Accuracy: ___%
Test Performance: ___%

Average Test Performance: ___% success rate
Compare to Baseline (no filter): ___% success rate
Improvement: +___% (goal: +10% minimum)

Part 3: Model Selection and Overfitting Prevention

Test 3 models. Simplest one that works = winner:

Red Flag: If train accuracy is 90% but test is 60%, you're overfitting. Reduce model complpotential exity or add more data.

Part 4: Feature Importance Analysis

Which features actually matter? Use model's feature importance:

Random Forest Feature Importance:

Top 10 Features (by importance score):
1. VIX level: 0.18 (most important)
2. Distance from VWAP: 0.15
3. ADX: 0.12
4. Time of day: 0.10
5. CVD: 0.09
6. RSI: 0.07
7. ATR ratio: 0.06
8. Volume vs avg: 0.05
9. BB Width: 0.04
10. DXY change: 0.03

Bottom Features (< 0.02): Day of week, Time since last signal
→ Remove these features (noise, not signal)

Simplified Model (top 6 features only):
Test Accuracy: ___% (compare to 20-feature model)
If within 2%, use simpler model (less overfitting)

Part 5: Production Deployment with Confidence Thresholds

Don't trade ALL predictions. Only trade high-confidence ones:

Model Output = Probability (0.0 to 1.0)

Confidence Thresholds:
Probability > 0.65 = High confidence (take trade)
Probability 0.45-0.65 = Neutral (skip trade)
Probability < 0.45 = Low confidence (skip or inverse)

Backtest Results by Threshold:
All Trades (no filter): 55% expectancy, 1.8R avg
Confidence > 0.60: ___% expectancy, ___R avg
Confidence > 0.65: ___% expectancy, ___R avg
Confidence > 0.70: ___% expectancy, ___R avg (fewer trades)

Optimal Threshold: 0.___ (maximize expectancy, not accuracy)

YOUR RESULTS:
Trades Taken with ML Filter: ___ / 100 total signals
Success Rate Improvement: From ___% → ___% (+___%)
Expectancy Improvement: From $___/trade → $___/trade

Part 6: Monitoring and Retraining Protocol

ML models degrade over time. Monitor and retrain:

Monitoring Schedule:

Week 1-4: Track live performance vs test predictions
Expected: 60% accuracy
Actual: ___% accuracy
Drift: ___% (< 5% OK)

Week 5-8: Continue tracking
Actual: ___% accuracy
Drift: ___% (if > 10%, retrain)

Retraining Trigger:
1. Accuracy drops > 10% below test baseline
2. Market regime shifts (VIX spikes, new cycle)
3. Every 3 months minimum (refresh with recent data)

Retrain Process:
- Add last 3 months of new trade data
- Re-run walk-forward validation
- Compare new model to old model on same test set
- Deploy only if new model outperforms by 3%+

Implementation Goal: Build ML filter over 4-6 weeks using your last 50-100 trades. Deploy in paper trading for 20 signals. Example target: Improve expectancy by 10%+ through selective filtering. If successful, ML just added 15-20% to your annual returns by helping you skip low-probability setups. This is how professionals use ML—not as magic, but as systematic edge enhancement.