# Horizontal Resistance Breakout Strategy Backtester

A modular backtesting system for a horizontal resistance breakout trading strategy using Python and backtrader.

## Strategy Overview

This strategy identifies and trades breakouts above horizontal resistance levels with the following conditions:

### Entry Criteria:
1. **Resistance Level**: Price breaks above a horizontal resistance level (20-day high with 1% tolerance)
2. **Volume Confirmation**: Breakout occurs on 2x average volume (20-bar average)
3. **Higher Lows Pattern**: Price was making at least 2 higher lows before breakout
4. **Retracement Entry**: Wait for price to retrace back to resistance level with:
   - Low volume (< 50% of breakout volume)
   - Small body candles (body < 30% of total range)
   - Entry within 1% of resistance level
5. **Trend Filter**: Only trade when price is above 200 SMA

### Risk Management:
- **Stop Loss**: Below the lowest low of the retracement
- **Target**: 2:1 risk/reward ratio
- **Position Size**: Risk 2% of account per trade

## Installation

1. Clone or download this repository
2. Install TA-Lib (required for technical indicators):
   - **Windows**: Download from [TA-Lib website](https://www.ta-lib.org/hdr_dw.html)
   - **Mac**: `brew install ta-lib`
   - **Linux**: `sudo apt-get install ta-lib`
3. Install Python dependencies:
   ```bash
   pip install -r requirements.txt
   ```

## Project Structure

```
├── config.py          # Strategy configuration parameters
├── indicators.py      # Custom indicators (resistance, higher lows, etc.)
├── utils.py          # Helper functions and trade logging
├── strategy.py       # Main strategy implementation
├── data_loader.py    # Data loading and validation utilities
├── backtest.py       # Main backtest runner
├── example_usage.py  # Example scripts
└── requirements.txt  # Python dependencies
```

## Usage

### Basic Usage

Run a backtest on a single stock:

```bash
python backtest.py AAPL --start 2023-01-01 --end 2023-12-31
```

### Command Line Options

```bash
python backtest.py [SYMBOL] [OPTIONS]

Arguments:
  SYMBOL              Stock symbol to backtest (e.g., AAPL, MSFT)

Options:
  --start DATE        Start date (YYYY-MM-DD) [default: 2022-01-01]
  --end DATE          End date (YYYY-MM-DD) [default: 2023-12-31]
  --data-source       Data source: 'yahoo' or 'csv' [default: yahoo]
  --csv-path PATH     Path to CSV file (if using csv source)
  --no-plot           Disable chart plotting
```

### Examples

1. **Single Stock Backtest**:
   ```bash
   python backtest.py TSLA --start 2023-06-01 --end 2023-12-31
   ```

2. **Using CSV Data**:
   ```bash
   python backtest.py CUSTOM --data-source csv --csv-path data/custom_data.csv
   ```

3. **Run Without Plotting**:
   ```bash
   python backtest.py SPY --no-plot
   ```

4. **Run Example Scripts**:
   ```bash
   python example_usage.py
   ```

## Configuration

Edit `config.py` to adjust strategy parameters:

```python
# Strategy Parameters
RESISTANCE_LOOKBACK = 20      # Days to look back for resistance
RESISTANCE_TOLERANCE = 0.01   # 1% tolerance for horizontal resistance
VOLUME_MULTIPLIER = 2.0       # Required volume multiplier for breakout
# ... etc
```

## Output

The backtest provides comprehensive performance metrics:

- **Portfolio Performance**: Starting/ending value, total return
- **Risk Metrics**: Sharpe ratio, maximum drawdown
- **Trade Statistics**: Win rate, average trade duration
- **Strategy-Specific Metrics**: Number of breakouts detected, entries made

### Sample Output:
```
Starting Portfolio Value: $100,000.00
Ending Portfolio Value: $112,543.21
Total Return: 12.54%

==============================================================
BACKTEST ANALYSIS
==============================================================
Sharpe Ratio: 1.45
Max Drawdown: 8.32%
Max Drawdown Duration: 45 bars
Total Return: 12.54%
Average Annual Return: 12.54%

Total Trades: 28
Winning Trades: 18
Losing Trades: 10
Win Rate: 64.29%
Average Trade Duration: 15.3 bars
Total P&L: $12,543.21
Total Commission: $156.78
==============================================================
```

## Data Requirements

### CSV Format
If using CSV data, ensure it has the following columns:
- Date/DateTime (as index)
- open
- high
- low
- close
- volume

### Data Quality
The system includes data validation that checks for:
- Missing values
- Invalid price relationships (high < low, etc.)
- Negative volumes
- Time gaps

## Extending the Strategy

### Adding New Indicators
Create new indicators in `indicators.py`:

```python
class MyCustomIndicator(bt.Indicator):
    lines = ('my_line',)
    params = (('period', 20),)
    
    def __init__(self):
        # Indicator logic here
        pass
    
    def next(self):
        # Calculate indicator value
        self.lines.my_line[0] = calculated_value
```

### Modifying Entry/Exit Logic
Edit the strategy logic in `strategy.py`:
- `check_for_breakout()`: Modify breakout detection
- `check_for_entry()`: Adjust entry conditions
- `manage_position()`: Change exit rules

## Troubleshooting

1. **TA-Lib Installation Issues**:
   - Ensure TA-Lib C library is installed before pip installing the Python wrapper
   - On Windows, use the appropriate .whl file for your Python version

2. **No Data Found**:
   - Check internet connection for Yahoo Finance
   - Verify the symbol exists and has data for the specified period
   - For hourly data, note that Yahoo Finance has limitations on historical data

3. **Memory Issues**:
   - Reduce the backtest period
   - Use daily instead of hourly data for longer periods

## Performance Optimization Tips

1. **Parameter Optimization**: Use `example_usage.py` as a template to test different parameters
2. **Multiple Timeframes**: Modify data compression in `backtest.py`
3. **Stock Universe**: Test on different market sectors and cap sizes
4. **Market Conditions**: Analyze performance in trending vs ranging markets

## License

This project is for educational purposes. Always perform your own due diligence before trading with real money.

## Contributing

Feel free to submit issues, fork the repository, and create pull requests for any improvements.