bactester for strategies
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Ajasra
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IncrementalTrader/README.md
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IncrementalTrader/README.md
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# IncrementalTrader
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A high-performance, memory-efficient trading framework designed for real-time algorithmic trading and backtesting. Built around the principle of **incremental computation**, IncrementalTrader processes new data points efficiently without recalculating entire histories.
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## 🚀 Key Features
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- **Incremental Computation**: Constant memory usage and O(1) processing time per data point
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- **Real-time Capable**: Designed for live trading with minimal latency
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- **Modular Architecture**: Clean separation between strategies, execution, and testing
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- **Built-in Strategies**: MetaTrend, BBRS, and Random strategies included
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- **Comprehensive Backtesting**: Multi-threaded backtesting with parameter optimization
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- **Rich Indicators**: Supertrend, Bollinger Bands, RSI, Moving Averages, and more
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- **Performance Tracking**: Detailed metrics and portfolio analysis
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## 📦 Installation
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```bash
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# Clone the repository
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git clone <repository-url>
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cd Cycles
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# Install dependencies
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pip install -r requirements.txt
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# Import the module
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from IncrementalTrader import *
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```
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## 🏃♂️ Quick Start
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### Basic Strategy Usage
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```python
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from IncrementalTrader import MetaTrendStrategy, IncTrader
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import pandas as pd
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# Load your data
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data = pd.read_csv('your_data.csv')
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# Create strategy
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strategy = MetaTrendStrategy("metatrend", params={
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"timeframe": "15min",
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"supertrend_periods": [10, 20, 30],
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"supertrend_multipliers": [2.0, 3.0, 4.0]
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})
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# Create trader
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trader = IncTrader(strategy, initial_usd=10000)
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# Process data
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for _, row in data.iterrows():
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trader.process_data_point(
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timestamp=row['timestamp'],
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ohlcv=(row['open'], row['high'], row['low'], row['close'], row['volume'])
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)
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# Get results
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results = trader.get_results()
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print(f"Final Portfolio Value: ${results['final_portfolio_value']:.2f}")
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print(f"Total Return: {results['total_return_pct']:.2f}%")
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```
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### Backtesting
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```python
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from IncrementalTrader import IncBacktester, BacktestConfig
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# Configure backtest
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config = BacktestConfig(
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initial_usd=10000,
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stop_loss_pct=0.03,
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take_profit_pct=0.06,
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start_date="2024-01-01",
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end_date="2024-12-31"
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)
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# Run backtest
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backtester = IncBacktester()
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results = backtester.run_single_strategy(
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strategy_class=MetaTrendStrategy,
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strategy_params={"timeframe": "15min"},
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config=config,
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data_file="data/BTCUSDT_1m.csv"
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)
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# Analyze results
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print(f"Sharpe Ratio: {results['performance_metrics']['sharpe_ratio']:.2f}")
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print(f"Max Drawdown: {results['performance_metrics']['max_drawdown_pct']:.2f}%")
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```
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## 📊 Available Strategies
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### MetaTrend Strategy
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A sophisticated trend-following strategy that uses multiple Supertrend indicators to detect market trends.
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```python
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strategy = MetaTrendStrategy("metatrend", params={
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"timeframe": "15min",
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"supertrend_periods": [10, 20, 30],
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"supertrend_multipliers": [2.0, 3.0, 4.0],
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"min_trend_agreement": 0.6
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})
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```
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### BBRS Strategy
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Combines Bollinger Bands and RSI with market regime detection for adaptive trading.
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```python
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strategy = BBRSStrategy("bbrs", params={
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"timeframe": "15min",
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"bb_period": 20,
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"bb_std": 2.0,
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"rsi_period": 14,
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"volume_ma_period": 20
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})
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```
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### Random Strategy
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A testing strategy that generates random signals for framework validation.
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```python
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strategy = RandomStrategy("random", params={
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"timeframe": "15min",
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"buy_probability": 0.1,
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"sell_probability": 0.1
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})
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```
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## 🔧 Technical Indicators
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All indicators are designed for incremental computation:
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```python
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from IncrementalTrader.strategies.indicators import *
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# Moving Averages
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sma = MovingAverageState(period=20)
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ema = ExponentialMovingAverageState(period=20, alpha=0.1)
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# Volatility
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atr = ATRState(period=14)
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# Trend
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supertrend = SupertrendState(period=10, multiplier=3.0)
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# Oscillators
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rsi = RSIState(period=14)
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bb = BollingerBandsState(period=20, std_dev=2.0)
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# Update with new data
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for price in price_data:
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sma.update(price)
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current_sma = sma.get_value()
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```
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## 🧪 Parameter Optimization
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```python
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from IncrementalTrader import OptimizationConfig
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# Define parameter ranges
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param_ranges = {
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"supertrend_periods": [[10, 20, 30], [15, 25, 35], [20, 30, 40]],
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"supertrend_multipliers": [[2.0, 3.0, 4.0], [1.5, 2.5, 3.5]],
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"min_trend_agreement": [0.5, 0.6, 0.7, 0.8]
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}
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# Configure optimization
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opt_config = OptimizationConfig(
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base_config=config,
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param_ranges=param_ranges,
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max_workers=4
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)
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# Run optimization
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results = backtester.optimize_strategy(
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strategy_class=MetaTrendStrategy,
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optimization_config=opt_config,
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data_file="data/BTCUSDT_1m.csv"
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)
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# Get best parameters
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best_params = results['best_params']
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best_performance = results['best_performance']
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```
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## 📈 Performance Analysis
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```python
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# Get detailed performance metrics
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performance = results['performance_metrics']
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print(f"Total Trades: {performance['total_trades']}")
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print(f"Win Rate: {performance['win_rate']:.2f}%")
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print(f"Profit Factor: {performance['profit_factor']:.2f}")
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print(f"Sharpe Ratio: {performance['sharpe_ratio']:.2f}")
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print(f"Max Drawdown: {performance['max_drawdown_pct']:.2f}%")
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print(f"Calmar Ratio: {performance['calmar_ratio']:.2f}")
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# Access trade history
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trades = results['trades']
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for trade in trades[-5:]: # Last 5 trades
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print(f"Trade: {trade['side']} at {trade['price']} - P&L: {trade['pnl']:.2f}")
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```
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## 🏗️ Architecture
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IncrementalTrader follows a modular architecture:
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```
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IncrementalTrader/
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├── strategies/ # Trading strategies and indicators
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│ ├── base.py # Base classes and framework
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│ ├── metatrend.py # MetaTrend strategy
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│ ├── bbrs.py # BBRS strategy
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│ ├── random.py # Random strategy
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│ └── indicators/ # Technical indicators
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├── trader/ # Trade execution and position management
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│ ├── trader.py # Main trader implementation
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│ └── position.py # Position management
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├── backtester/ # Backtesting framework
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│ ├── backtester.py # Main backtesting engine
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│ ├── config.py # Configuration management
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│ └── utils.py # Utilities and helpers
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└── docs/ # Documentation
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```
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## 🔍 Memory Efficiency
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Traditional batch processing vs. IncrementalTrader:
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| Aspect | Batch Processing | IncrementalTrader |
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|--------|------------------|-------------------|
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| Memory Usage | O(n) - grows with data | O(1) - constant |
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| Processing Time | O(n) - recalculates all | O(1) - per data point |
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| Real-time Capable | No - too slow | Yes - designed for it |
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| Scalability | Poor - memory limited | Excellent - unlimited data |
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## 📚 Documentation
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- [Architecture Overview](docs/architecture.md) - Detailed system design
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- [Strategy Development Guide](docs/strategies/strategies.md) - How to create custom strategies
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- [Indicator Reference](docs/indicators/base.md) - Complete indicator documentation
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- [Backtesting Guide](docs/backtesting.md) - Advanced backtesting features
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- [API Reference](docs/api/api.md) - Complete API documentation
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## 🤝 Contributing
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1. Fork the repository
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2. Create a feature branch
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3. Make your changes
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4. Add tests for new functionality
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5. Submit a pull request
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## 📄 License
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This project is licensed under the MIT License - see the LICENSE file for details.
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## 🆘 Support
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For questions, issues, or contributions:
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- Open an issue on GitHub
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- Check the documentation in the `docs/` folder
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- Review the examples in the `examples/` folder
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---
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**IncrementalTrader** - Efficient, scalable, and production-ready algorithmic trading framework.
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