Description
Create a comprehensive backtesting framework that allows users to test trading strategies against historical data and validate signal performance.
Tasks
Design backtesting engine architecture with event-driven simulation
Implement historical data replay with realistic execution conditions
Create slippage and fee modeling for accurate backtesting
Build strategy performance metrics calculation
Implement walk-forward analysis and out-of-sample testing
Create backtesting UI for strategy configuration
Add Monte Carlo simulation for strategy robustness testing
Implement benchmark comparison (buy-and-hold, market indices)
Create detailed backtesting reports with visualizations
Add strategy optimization and parameter tuning
Acceptance Criteria
Backtesting results correlate with live trading performance within 10%
Framework processes 1 year of historical data in <5 minutes
Slippage and fee modeling reflects real market conditions
Performance metrics provide comprehensive strategy evaluation
Monte Carlo simulations test strategy robustness across market conditions
Optimization features help users improve strategy parameters
Reports provide actionable insights for strategy improvement
Dependencies
Issue #11 (Historical Data Backfill)
Issue #7 (Trading Signal System)
Description
Create a comprehensive backtesting framework that allows users to test trading strategies against historical data and validate signal performance.
Tasks
Design backtesting engine architecture with event-driven simulation
Implement historical data replay with realistic execution conditions
Create slippage and fee modeling for accurate backtesting
Build strategy performance metrics calculation
Implement walk-forward analysis and out-of-sample testing
Create backtesting UI for strategy configuration
Add Monte Carlo simulation for strategy robustness testing
Implement benchmark comparison (buy-and-hold, market indices)
Create detailed backtesting reports with visualizations
Add strategy optimization and parameter tuning
Acceptance Criteria
Backtesting results correlate with live trading performance within 10%
Framework processes 1 year of historical data in <5 minutes
Slippage and fee modeling reflects real market conditions
Performance metrics provide comprehensive strategy evaluation
Monte Carlo simulations test strategy robustness across market conditions
Optimization features help users improve strategy parameters
Reports provide actionable insights for strategy improvement
Dependencies
Issue #11 (Historical Data Backfill)
Issue #7 (Trading Signal System)