ICT Model Backtester

A comprehensive backtesting framework designed for testing trading strategies, investment models, and algorithmic trading systems. This tool provides advanced analytics, performance metrics, and risk assessment capabilities for quantitative analysis and strategy development.

Features

Strategy Backtesting - Comprehensive backtesting engine for trading strategies
Performance Analytics - Advanced performance metrics and risk analysis
Data Management - Historical data import and management capabilities
Risk Assessment - Comprehensive risk metrics and portfolio analysis
Visualization - Interactive charts and performance visualization
Strategy Optimization - Parameter optimization and strategy tuning
Multi-Asset Support - Support for stocks, forex, crypto, and commodities
Real-time Testing - Paper trading and real-time strategy testing

Backtesting Engine

Strategy Framework

Signal Generation - Technical indicator-based signal generation
Position Sizing - Dynamic position sizing and risk management
Order Management - Advanced order types and execution simulation
Portfolio Management - Multi-asset portfolio management
Risk Controls - Stop-loss, take-profit, and risk management rules

Data Sources

Historical Data - OHLCV data from multiple sources
Real-time Feeds - Live market data integration
Alternative Data - News, sentiment, and alternative data sources
Custom Data - Support for custom data formats and sources

Performance Metrics

Return Metrics

Total Return - Overall strategy performance
Annualized Return - Annualized performance metrics
Sharpe Ratio - Risk-adjusted return measure
Sortino Ratio - Downside risk-adjusted return
Calmar Ratio - Maximum drawdown-adjusted return

Risk Metrics

Maximum Drawdown - Largest peak-to-trough decline
Value at Risk (VaR) - Potential loss at confidence level
Expected Shortfall - Average loss beyond VaR
Volatility - Standard deviation of returns
Beta - Market correlation measure

Trading Metrics

Win Rate - Percentage of profitable trades
Profit Factor - Ratio of gross profit to gross loss
Average Trade - Average profit/loss per trade
Maximum Consecutive Losses - Longest losing streak
Recovery Factor - Total return divided by maximum drawdown

Tech Stack

Language: Python 3.8+
Data Analysis: Pandas, NumPy, SciPy
Visualization: Matplotlib, Plotly, Seaborn
Machine Learning: Scikit-learn, TensorFlow
Database: PostgreSQL, SQLite
Web Interface: Streamlit, Dash

Installation & Setup

Clone the repository:

git clone https://github.com/1cbyc/ict-model-backtester.git
cd ict-model-backtester

Install dependencies:
```
pip install -r requirements.txt
```
Set up database:
```
python setup_database.py
```

Configure data sources:

cp config.example.yaml config.yaml
# Edit configuration with your data sources

Run backtester:

python backtester.py --strategy moving_average --data data/spy.csv

Usage Examples

Basic Strategy Backtest

from backtester import Backtester
from strategies import MovingAverageStrategy

# Initialize backtester
backtester = Backtester(
    data_source='data/spy.csv',
    initial_capital=100000,
    commission=0.001
)

# Create strategy
strategy = MovingAverageStrategy(
    short_window=10,
    long_window=30
)

# Run backtest
results = backtester.run(strategy)

# Print results
print(f"Total Return: {results.total_return:.2%}")
print(f"Sharpe Ratio: {results.sharpe_ratio:.2f}")
print(f"Max Drawdown: {results.max_drawdown:.2%}")

Custom Strategy Implementation

class CustomStrategy:
    def __init__(self, rsi_period=14, oversold=30, overbought=70):
        self.rsi_period = rsi_period
        self.oversold = oversold
        self.overbought = overbought
    
    def generate_signals(self, data):
        rsi = calculate_rsi(data['close'], self.rsi_period)
        
        signals = pd.Series(index=data.index, data=0)
        signals[rsi < self.oversold] = 1  # Buy signal
        signals[rsi > self.overbought] = -1  # Sell signal
        
        return signals

Parameter Optimization

from optimizer import StrategyOptimizer

# Define parameter ranges
param_ranges = {
    'short_window': range(5, 21),
    'long_window': range(20, 51)
}

# Optimize strategy
optimizer = StrategyOptimizer(
    strategy_class=MovingAverageStrategy,
    param_ranges=param_ranges,
    metric='sharpe_ratio'
)

best_params = optimizer.optimize(data_source='data/spy.csv')
print(f"Best parameters: {best_params}")

Strategy Library

Technical Analysis Strategies

Moving Average Crossover - Simple and exponential moving averages
RSI Strategy - Relative Strength Index-based trading
MACD Strategy - Moving Average Convergence Divergence
Bollinger Bands - Mean reversion and breakout strategies
Stochastic Oscillator - Stochastic-based trading signals

Statistical Arbitrage

Pairs Trading - Cointegration-based pairs trading
Mean Reversion - Statistical mean reversion strategies
Momentum Trading - Price momentum-based strategies
Volatility Trading - Volatility-based trading strategies

Machine Learning Strategies

Classification Models - ML-based buy/sell classification
Regression Models - Price prediction and forecasting
Ensemble Methods - Combined multiple model predictions
Deep Learning - Neural network-based strategies

Data Management

Data Sources

Yahoo Finance - Free historical data
Alpha Vantage - Real-time and historical data
Quandl - Alternative data sources
Custom APIs - Integration with custom data providers

Data Processing

Data Cleaning - Handle missing data and outliers
Feature Engineering - Create technical indicators
Data Validation - Validate data quality and consistency
Data Storage - Efficient data storage and retrieval

Visualization and Reporting

Performance Charts

Equity Curve - Portfolio value over time
Drawdown Chart - Drawdown analysis
Returns Distribution - Return distribution analysis
Rolling Metrics - Rolling performance metrics

Risk Analysis

Risk-Return Scatter - Risk-return analysis
Correlation Matrix - Asset correlation analysis
VaR Analysis - Value at Risk visualization
Stress Testing - Stress test results visualization

Project Impact

This backtester has been used for:

Strategy Development - Testing and refining trading strategies
Risk Management - Portfolio risk analysis and management
Academic Research - Quantitative finance research
Professional Trading - Professional trading strategy development

Future Enhancements

Real-time Trading - Live trading integration
Advanced ML Models - Enhanced machine learning capabilities
Multi-Asset Optimization - Portfolio optimization features
Cloud Deployment - Cloud-based backtesting platform
Mobile App - Mobile monitoring and control