| Win Rate | Sharpe Ratio | Total Return | Max Drawdown | Profit Factor |
|---|---|---|---|---|
| 88.9% | 6.62 | 1.31% | 0.05% | 29.27 |
9 trades · NSE:SONATSOFTW-EQ · Nov-Dec 2025 · INR 100,000 → INR 101,314
Warmup: Jan 1 - Oct 31, 2025 (indicators only, no training)
Training: Nov 1 - Dec 31, 2025
Prediction: Jan 1 - 8, 2026 (out-of-sample)
- Triple-barrier labeling with T+2 to T+5 forward returns
- Walk-forward cross-validation
- Data integrity verified via
audit_bias.py
| Step | Action |
|---|---|
| Trend ID | EMA(10)/SMA(20) crossover + ADX strength |
| Entry | Wait for 6% pullback within trend |
| Validate | ML ensemble confirms direction |
| Size | Scale position by confidence |
| Exit | ATR-based stops (1.5x SL, 3x TP) |
| Control | Rule |
|---|---|
| Position Size | risk = base_risk * (confidence / 0.70) |
| Max Position | 15% of capital |
| Stop Loss | 1.5x ATR (EOD exit if not hit) |
| Regime Filter | ADX < 15 → skip trade |
| Signal Gate | Technical + ML must align |
| Metric | Value | Metric | Value |
|---|---|---|---|
| Total Trades | 9 | Win Rate | 88.9% |
| Winners | 8 | Sharpe Ratio | 6.62 |
| Losers | 1 | Calmar Ratio | 180.43 |
| Avg Win | INR 170 | Profit Factor | 29.27 |
| Avg Loss | INR 46 | Max Drawdown | 0.05% |
All trades exited at EOD (daily bars); stop/target rarely reached intraday.
Feature Importance: Volatility features dominate (ATR z-score: 39%, ATR percentile: 28%, returns_std: 20%). The model identifies regime conditions rather than predicting direction directly.
| Date | Signal | Direction | Confidence | Regime |
|---|---|---|---|---|
| Jan 1 | HOLD | DOWN | 77.6% | Uptrend Strong |
| Jan 2 | HOLD | DOWN | 77.6% | Uptrend Strong |
| Jan 5 | HOLD | DOWN | 54.6% | Transitional |
| Jan 6 | HOLD | DOWN | 54.6% | Uptrend Weak |
| Jan 7 | HOLD | DOWN | 54.6% | Transitional |
| Jan 8 | HOLD | DOWN | 50.4% | Transitional |
Signal Logic: All HOLD signals because ML predicts DOWN while regime shows Uptrend/Transitional. Conservative approach preserves capital rather than betting against trend.
# Clone repository
git clone <repository>
cd finstreet
# Run automated setup
chmod +x setup.sh
./setup.sh
# Activate environment
source venv/bin/activate
# Run full pipeline
python run.py allThe setup.sh script automatically:
- Creates Python virtual environment
- Installs all dependencies from
requirements.txt - Creates necessary directories (
data/,models/,reports/) - Generates
.envtemplate for FYERS API credentials
# Build image
docker build -t finstreet .
# Run full pipeline (mount volumes for output)
docker run -v $(pwd)/data:/app/data -v $(pwd)/reports:/app/reports finstreet all
# Run specific commands
docker run -v $(pwd)/data:/app/data finstreet backtest
docker run -v $(pwd)/reports:/app/reports finstreet visualize
docker run finstreet predictThe Dockerfile provides:
- Multi-stage build for optimized image size
- Python 3.11 slim base image
- All dependencies pre-installed
- Health check for container monitoring
python run.py fetch # Fetch data from FYERS API
python run.py train # Train XGBoost model only
python run.py ensemble # Train XGBoost + LightGBM ensemble
python run.py backtest # Run backtest simulation
python run.py predict # Generate Jan 1-8 predictions
python run.py visualize # Create performance charts
python run.py all # Full pipeline┌─────────────────────────────────────────────────────────────────┐
│ TRADING PIPELINE │
├─────────────────────────────────────────────────────────────────┤
│ FYERS API ──► Features ──► ML Ensemble ──► Signal Gate ──► Trade
│ │ │ │ │ │
│ OHLCV RSI, MACD XGBoost + Tech + ML Risk
│ Daily ADX, ATR LightGBM Aligned Mgmt
│ KER, Z-Score (60/40) │
└─────────────────────────────────────────────────────────────────┘
finstreet/
├── run.py # Entry point
├── audit_bias.py # Data leakage verification
├── sensitivity_analysis.py # Parameter robustness testing
├── Dockerfile # Container configuration
├── setup.sh # Local setup script
├── requirements.txt # Python dependencies
├── config/settings.py # Configuration
├── src/
│ ├── data/ # FYERS API integration
│ ├── features/ # Technical indicators, preprocessing
│ ├── model/ # XGBoost + LightGBM ensemble
│ ├── strategy/ # Trading logic
│ ├── backtest/ # Simulation engine
│ ├── forecast/ # Daily predictions
│ ├── execution/ # Trade execution
│ └── visualization/ # Chart generation
├── data/
│ ├── raw/ # OHLCV from FYERS
│ └── processed/ # Trades, equity curve
├── models/ # Trained model artifacts
└── reports/
├── figures/ # Performance charts
└── daily_predictions.csv
| Requirement | Status |
|---|---|
| FYERS API for data | src/data/fyers_client.py |
| FYERS API for execution | src/execution/trader.py |
| Daily OHLCV only | No intraday data |
| Nov-Dec 2025 trading window | All trades in window |
| Jan 1-8, 2026 predictions | reports/daily_predictions.csv |
| No look-ahead bias | Verified via audit_bias.py |
| Walk-forward validation | Chronological execution |
MIT License - See LICENSE for details.