🧠 AI-Powered Trading Intelligence System
A complete, modular AI trading system with market prediction, risk modeling, trader behavior analysis, and decision intelligence.
Architecture
┌─────────────────────────────────────────────────────────────────┐
│ TRADING INTELLIGENCE SYSTEM │
│ │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────────────┐ │
│ │ Feature │ │ Sentiment │ │ Portfolio │ │
│ │ Engine │ │ Engine │ │ Encoder │ │
│ │ (69 feats) │ │ (NLP) │ │ (Positions+Account) │ │
│ └──────┬───────┘ └──────┬───────┘ └──────────┬───────────┘ │
│ │ │ │ │
│ ▼ ▼ ▼ │
│ ┌──────────────────────────────┐ ┌─────────────────────────┐ │
│ │ PREDICTION MODEL │ │ RISK MODEL │ │
│ │ (PatchTST + iTransformer) │ │ (Portfolio-aware) │ │
│ │ • Direction probability │ │ • Risk score │ │
│ │ • Expected return │ │ • Position sizing │ │
│ │ • Uncertainty estimation │ │ • SL/TP levels │ │
│ │ • Multi-horizon (1/5/20d) │ │ • Drawdown probs │ │
│ └──────────────┬───────────────┘ └───────────┬─────────────┘ │
│ │ │ │
│ │ ┌───────────────────────┐ │ │
│ │ │ PERSONALIZATION LAYER │ │ │
│ │ │ • Trader profiling │ │ │
│ │ │ • Behavior alerts │ │ │
│ │ │ • Strategy adaptation │ │ │
│ │ └───────────┬───────────┘ │ │
│ │ │ │ │
│ ▼ ▼ ▼ │
│ ┌─────────────────────────────────────────┐ │
│ │ DECISION ENGINE │ │
│ │ BUY / SELL / HOLD + Confidence Score │ │
│ └─────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────┘
Research Foundation
| Paper | Key Contribution | How We Use It |
|---|---|---|
| PatchTST (ICLR 2023) | Channel-independent patch-based Transformer | Core architecture: patch embedding, channel-independence |
| Chronos (Amazon 2024) | Language model paradigm for time series | Probabilistic prediction heads |
| Kronos (2025) | Financial K-line tokenization | OHLCVA candlestick encoding, hierarchical loss |
| iTransformer (2024) | Inverted attention across variates | ChannelMixer cross-feature attention |
| FinMultiTime (2025) | Multi-modal financial dataset | Multi-modal fusion design |
5 Components
- Feature Engine - 69 features: price, technical indicators (RSI, MACD, ATR, EMA, Bollinger), volatility (Garman-Klass, Parkinson), volume (OBV, VWAP, MFI), market regime detection
- Prediction Model - PatchTST-based Transformer with multi-task heads for direction, return, and uncertainty
- Risk Model - Portfolio-aware with position encoding, behavior analysis, VaR estimation
- Personalization - Trader profiling (5 archetypes), behavior alerts (overtrading, revenge trading)
- Decision Engine - Combines all signals into BUY/SELL/HOLD with confidence scores
Quick Start
from trading_intelligence.feature_engine import FeatureEngine
from trading_intelligence.prediction_model import TradingTransformer
from trading_intelligence.decision_engine import DecisionEngine
# Compute features
fe = FeatureEngine(lookback_window=60, prediction_horizons=[1, 5, 20])
features = fe.compute_all_features(ohlcv_df)
# Create model
model = TradingTransformer(num_channels=69, seq_len=60, d_model=128, n_heads=8, n_layers=3)
# Get decision
engine = DecisionEngine(prediction_model=model)
decision = engine.make_decision(features, current_atr=0.015)
print(decision.signal) # BUY / SELL / HOLD
Evaluation Metrics
| Metric | 1-Day | 5-Day | 20-Day |
|---|---|---|---|
| Direction Accuracy | 50.2% | 47.8% | 46.4% |
| Information Coefficient | -0.07 | 0.01 | 0.36 |
| Sharpe Ratio | -0.18 | 0.53 | -1.69 |
| Profit Factor | 0.97 | 1.09 | 0.80 |
Training
- Multi-task loss: BCE (direction) + Gaussian NLL (returns) + Sharpe penalty (risk)
- Uncertainty-weighted task combination (Kendall et al. 2018)
- Walk-forward temporal split (no look-ahead bias)
- CosineAnnealing LR schedule with warm restarts
Disclaimer
For research and educational purposes only. Not financial advice.