Backtesting
Backtesting validates your trading strategies using historical market data before deploying real capital.
What is Backtesting?
Backtesting simulates your strategy's performance against historical data to:
Validate Strategy Logic: Ensure your strategy works as intended
Assess Risk: Understand potential drawdowns and volatility
Optimize Parameters: Fine-tune strategy settings
Build Confidence: Deploy with historical validation
Key Features
High-Quality Data: Tick-level precision with multiple timeframes
Realistic Simulation: Includes trading fees, slippage, and market impact
Performance Analytics: Comprehensive metrics and risk analysis
Cross-Exchange Data: Aggregate data from major exchanges
Getting Started
Create Your Strategy: Describe your trading approach
Configure Parameters: Set testing period and risk limits
Run Simulation: Execute backtest across historical data
Analyze Results: Review performance metrics
Optimize: Refine strategy based on insights
Use Cases
Common scenarios where backtesting provides valuable insights:
Strategy Development
Test new trading ideas before risking capital
Compare multiple strategy variants
Validate theoretical concepts with real market data
Risk Management
Assess maximum drawdown potential
Determine optimal position sizing
Evaluate strategy behavior during market stress
Portfolio Optimization
Test asset allocation strategies
Evaluate correlation effects across instruments
Optimize rebalancing frequencies
Performance Analysis
Compare strategy performance to benchmarks
Identify optimal market conditions for your strategy
Analyze seasonal or cyclical patterns
Step-by-step tutorials for each use case will be added soon.
Last updated