# Lowkey Backtest

A backtesting framework supporting multiple market types (spot, perpetual) with realistic trading simulation including leverage, funding, and shorts.

## Requirements

- Python 3.12+
- Package manager: `uv`

## Installation

```bash
uv sync
```

## Quick Reference

| Command | Description |
|---------|-------------|
| `uv run python main.py download -p BTC-USDT` | Download data |
| `uv run python main.py backtest -s meta_st -p BTC-USDT` | Run backtest |
| `uv run python main.py wfa -s regime -p BTC-USDT` | Walk-forward analysis |
| `uv run python train_model.py --download` | Train/retrain ML model |
| `uv run python research/regime_detection.py` | Run research script |

---

## Backtest CLI

The main entry point is `main.py` which provides three commands: `download`, `backtest`, and `wfa`.

### Download Data

Download historical OHLCV data from exchanges.

```bash
uv run python main.py download -p BTC-USDT -t 1h
```

**Options:**
- `-p, --pair` (required): Trading pair (e.g., `BTC-USDT`, `ETH-USDT`)
- `-t, --timeframe`: Timeframe (default: `1m`)
- `-e, --exchange`: Exchange (default: `okx`)
- `-m, --market`: Market type: `spot` or `perpetual` (default: `spot`)
- `--start`: Start date in `YYYY-MM-DD` format

**Examples:**
```bash
# Download 1-hour spot data
uv run python main.py download -p ETH-USDT -t 1h

# Download perpetual data from a specific date
uv run python main.py download -p BTC-USDT -m perpetual --start 2024-01-01
```

### Run Backtest

Run a backtest with a specific strategy.

```bash
uv run python main.py backtest -s <strategy> -p <pair> [options]
```

**Available Strategies:**
- `meta_st` - Meta Supertrend (triple supertrend consensus)
- `regime` - Regime Reversion (ML-based spread trading)
- `rsi` - RSI overbought/oversold
- `macross` - Moving Average Crossover

**Options:**
- `-s, --strategy` (required): Strategy name
- `-p, --pair` (required): Trading pair
- `-t, --timeframe`: Timeframe (default: `1m`)
- `--start`: Start date
- `--end`: End date
- `-g, --grid`: Run grid search optimization
- `--plot`: Show equity curve plot
- `--sl`: Stop loss percentage
- `--tp`: Take profit percentage
- `--trail`: Enable trailing stop
- `--fees`: Override fee rate
- `--slippage`: Slippage (default: `0.001`)
- `-l, --leverage`: Leverage multiplier

**Examples:**
```bash
# Basic backtest with Meta Supertrend
uv run python main.py backtest -s meta_st -p BTC-USDT -t 1h

# Backtest with date range and plot
uv run python main.py backtest -s meta_st -p BTC-USDT --start 2024-01-01 --end 2024-12-31 --plot

# Grid search optimization
uv run python main.py backtest -s meta_st -p BTC-USDT -t 4h -g

# Backtest with risk parameters
uv run python main.py backtest -s meta_st -p BTC-USDT --sl 0.05 --tp 0.10 --trail

# Regime strategy on ETH/BTC spread
uv run python main.py backtest -s regime -p ETH-USDT -t 1h
```

### Walk-Forward Analysis (WFA)

Run walk-forward optimization to avoid overfitting.

```bash
uv run python main.py wfa -s <strategy> -p <pair> [options]
```

**Options:**
- `-s, --strategy` (required): Strategy name
- `-p, --pair` (required): Trading pair
- `-t, --timeframe`: Timeframe (default: `1d`)
- `-w, --windows`: Number of walk-forward windows (default: `10`)
- `--plot`: Show WFA results plot

**Examples:**
```bash
# Walk-forward analysis with 10 windows
uv run python main.py wfa -s meta_st -p BTC-USDT -t 1d -w 10

# WFA with plot output
uv run python main.py wfa -s regime -p ETH-USDT --plot
```

---

## Research Scripts

Research scripts are located in the `research/` directory for experimental analysis.

### Regime Detection Research

Tests multiple holding horizons for the regime reversion strategy using walk-forward training.

```bash
uv run python research/regime_detection.py
```

**Options:**
- `--days DAYS`: Number of days of historical data (default: 90)
- `--start DATE`: Start date (YYYY-MM-DD), overrides `--days`
- `--end DATE`: End date (YYYY-MM-DD), defaults to now
- `--output PATH`: Output CSV path

**Examples:**
```bash
# Use last 90 days (default)
uv run python research/regime_detection.py

# Use last 180 days
uv run python research/regime_detection.py --days 180

# Specific date range
uv run python research/regime_detection.py --start 2025-07-01 --end 2025-12-31
```

**What it does:**
- Loads BTC and ETH hourly data
- Calculates spread features (Z-score, RSI, volume ratios)
- Trains RandomForest classifier with walk-forward methodology
- Tests horizons from 6h to 150h
- Outputs best parameters by F1 score, Net PnL, and MAE

**Output:**
- Console: Summary of results for each horizon
- File: `research/horizon_optimization_results.csv`

---

## ML Model Training

The `regime` strategy uses a RandomForest classifier that can be trained with new data.

### Train Model

Train or retrain the ML model with latest data:

```bash
uv run python train_model.py [options]
```

**Options:**
- `--days DAYS`: Days of historical data (default: 90)
- `--pair PAIR`: Base pair for context (default: BTC-USDT)
- `--spread-pair PAIR`: Trading pair (default: ETH-USDT)
- `--timeframe TF`: Timeframe (default: 1h)
- `--market TYPE`: Market type: `spot` or `perpetual` (default: perpetual)
- `--output PATH`: Model output path (default: `data/regime_model.pkl`)
- `--train-ratio R`: Train/test split ratio (default: 0.7)
- `--horizon H`: Prediction horizon in bars (default: 102)
- `--download`: Download latest data before training
- `--dry-run`: Run without saving model

**Examples:**
```bash
# Train with last 90 days of data
uv run python train_model.py

# Download fresh data and train
uv run python train_model.py --download

# Train with 180 days of data
uv run python train_model.py --days 180

# Train on spot market data
uv run python train_model.py --market spot

# Dry run to see metrics without saving
uv run python train_model.py --dry-run
```

### Daily Retraining (Cron)

To automate daily model retraining, add a cron job:

```bash
# Edit crontab
crontab -e

# Add entry to retrain daily at 00:30 UTC
30 0 * * * cd /path/to/lowkey_backtest_live && uv run python train_model.py --download >> logs/training.log 2>&1
```

### Model Files

| File | Description |
|------|-------------|
| `data/regime_model.pkl` | Current production model |
| `data/regime_model_YYYYMMDD_HHMMSS.pkl` | Versioned model snapshots |

The model file contains:
- Trained RandomForest classifier
- Feature column names
- Training metrics (F1 score, sample counts)
- Training timestamp

---

## Output Files

| Location | Description |
|----------|-------------|
| `backtest_logs/` | Trade logs and WFA results |
| `research/` | Research output files |
| `data/` | Downloaded OHLCV data and ML models |
| `data/regime_model.pkl` | Trained ML model for regime strategy |

---

## Running Tests

```bash
uv run pytest tests/
```

Run a specific test file:

```bash
uv run pytest tests/test_data_manager.py
```