Quantitative Strategy Lab: Institutional-Scale Backtesting & Tuning

The Optimization module in EdgeCore is the system's analytical engine for strategy evaluation, providing institutional-grade tools for backtesting, performance validation, and hyperparameter optimization. It empowers quants, researchers, and trading teams to fine-tune strategies before deployment.

Overview

This module enables:

  • Backtesting: Simulate strategies on historical market data
  • Performance Analysis: Generate detailed metrics including win rate, drawdowns, and profit factor
  • Parameter Optimization: Tune configuration variables to enhance risk-adjusted returns
  • Report Generation: Produce institutional-quality visualizations and reports

Architecture

The Optimization module interfaces with the broader EdgeCore system, including:

  • Strategy Engine (provides logic for trading decisions)
  • Historical Data Store (tick or candle-level data)
  • Exchange Simulation Layer (models fees, latency, slippage)
  • API Server (for remote control and real-time monitoring)
  • It supports parallel backtesting, strategy comparison, result caching, and full traceability of every backtest run.

Key Capabilities

Simulation Accuracy

  • Models trading fees, slippage, and time-in-force behavior
  • Replicates portfolio constraints like capital exposure and max open trades
  • Incorporates stop loss, trailing stop, and dynamic position sizing logic

Advanced Metrics

  • Profit/Loss per trade and aggregate
  • Win rate, Sharpe ratio, max drawdown
  • Monthly and per-asset breakdowns

Result Storage & Access

  • All backtest results are stored and retrievable by unique ID
  • Comparison tools identify performance deltas between versions

API & WebSocket Integration

  • Full REST API to launch, monitor, and retrieve backtest data
  • WebSocket support for real-time progress updates

Usage Modes

  • Web Console: Initiate and visualize backtests through the UI
  • REST API: Trigger tests and consume results programmatically
  • CLI: Useful for research teams automating large batch experiments

Institutional Considerations

  • Compute Efficiency: Implements caching, concurrency, and vectorization (NumPy/Pandas)
  • Auditability: Stores parameter inputs and output logs per backtest run
  • Scalability: Supports parallelized optimization across multiple cores
  • Security: Access control, rate limiting, and sandboxing of simulation processes

Integration Points

  • Data Ingestion: Leverages the same historical feed as live trading
  • Strategy Deployment: Parameter sets from top-performing backtests can be promoted to production
  • Risk & Compliance: Facilitates strategy vetting before execution

Conclusion

The Optimization module bridges research and execution. It is purpose-built for professional trading environments where model testing must be fast, accurate, and repeatable. By combining computational rigor with ease of integration, it enables systematic traders to innovate safely before going live.

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