Development of AI Trend Direction Prediction Model
Predicting price direction (binary classification: up/down) is simpler than precise magnitude estimation. Even moderate 52-55% accuracy over several days gives positive expected value with proper risk management.
Classification Task Formulation
Binary Target:
df['target'] = (df['close'].shift(-N) > df['close']).astype(int)
# 1 = price rises in N days, 0 = falls or stays flat
Class Imbalance Problem: markets often have bias (e.g., stocks in long-term growth trend). Need calibration or balancing.
Alternative Formulation: 3-class (up / sideways / down) with ±0.2% uncertainty zone — allows abstaining when signal is weak.
Feature Engineering
Momentum Features (most predictive):
- Relative Strength: returns for 1/3/6/12 months
- Rate of Change (ROC): log return for various horizons
- Acceleration: change in momentum (momentum of momentum)
Mean Reversion Features:
- Deviation from SMA: (Close - SMA_20) / SMA_20
- Bollinger %B: (Close - Lower) / (Upper - Lower)
- RSI: overbought/oversold levels
Volatility-Adjusted Features:
- Sharp/Smooth: ratio of volatility on short vs. long window
- Price position in N-day range (Williams %R)
Regime Features:
- VIX level (risk-on / risk-off)
- Market breadth: % of stocks above SMA200
- Treasury yield curve slope (10y-2y)
Ensemble Models
Base Classifiers:
from sklearn.ensemble import VotingClassifier
from lightgbm import LGBMClassifier
from xgboost import XGBClassifier
from sklearn.linear_model import LogisticRegression
ensemble = VotingClassifier(
estimators=[
('lgbm', LGBMClassifier(n_estimators=300, class_weight='balanced')),
('xgb', XGBClassifier(n_estimators=300, scale_pos_weight=ratio)),
('lr', LogisticRegression(C=0.1, class_weight='balanced'))
],
voting='soft' # probabilistic voting
)
Why Ensemble: Different models capture different predictability aspects. LightGBM — nonlinear interactions. Logistic Regression — linear signals. Ensemble stabilizes, reduces overfit.
Probability Calibration
Raw model predictions are often poorly calibrated. For trading strategies this matters: predicted 0.6 probability should mean actual 60% frequency.
Signal Density: Focus on trading rule: trade only when model confidence > threshold. Precision-Recall curve helps choose threshold.
from sklearn.calibration import CalibratedClassifierCV
calibrated = CalibratedClassifierCV(ensemble, method='isotonic', cv=3)
calibrated.fit(X_train, y_train)
Risk Management in Signal Trading
Trading Conditions:
- P(up) > 0.55: long
- P(up) < 0.45: short
- 0.45-0.55: no position
Position Sizing by Confidence:
Position = (2 × P - 1) × Max_Position_Size × Volatility_Adjustment
Kelly-like formula: higher P → larger position.
Stop-loss: for long, stop at -2 × ATR(14). Mechanical exit, independent of overfitted model.
Metrics and Evaluation
| Metric | Value | Interpretation |
|---|---|---|
| Accuracy | 52-56% | Better than random |
| Precision Long | > 55% | Longs profitable |
| AUC-ROC | > 0.55 | Ranking works |
| IC (prediction correlation) | > 0.03 | Weak but stable edge |
Backtesting with Real Parameters:
- Slippage: 0.05-0.1% per execution
- Commission: 0.02-0.05% per side
- Short financing: annual rate / 365 per day
After accounting for TC, model should show Sharpe > 0.8 on out-of-sample.
Common Pitfalls:
- Overfitting to historical patterns: CPCV (Combinatorial Purged CV) helps
- Instability: overfitted model degrades in 1-3 months
- Regime change: model trained on sideways market fails in trend
Timeline: baseline model with momentum features — 3-4 weeks. Full system with ensemble, calibration, backtesting and monitoring — 8-12 weeks.