Hftbacktest

=========== HftBacktest ===========

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High-Frequency Trading Backtesting Tool =======================================

This framework is designed for developing high frequency trading and market making strategies. It focuses on accounting for both feed and order latencies, as well as the order queue position for order fill simulation. The framework aims to provide more accurate market replay-based backtesting, based on full order book and trade tick feed data.

Key Features ============

• Working in Numba _ JIT function (Python).
• Complete tick-by-tick simulation with a customizable time interval or based on the feed and order receipt.
• Full order book reconstruction based on Level-2 Market-By-Price and Level-3 Market-By-Order feeds.
• Backtest accounting for both feed and order latency, using provided models or your own custom model.
• Order fill simulation that takes into account the order queue position, using provided models or your own custom model.
• Backtesting of multi-asset and multi-exchange models
• Deployment of a live trading bot for quick prototyping and testing using the same algorithm code: currently for Binance Futures and Bybit. (Rust-only)

Documentation =============

See full document here _.

Tutorials you’ll likely find interesting:

• High-Frequency Grid Trading - Simplified from GLFT _
• Market Making with Alpha - Order Book Imbalance _
• Market Making with Alpha - APT _
• Accelerated Backtesting _
• Pricing Framework _

Why Accurate Backtesting Matters — Not Just Conservative Approach =================================================================

Trading is a highly competitive field where only the small edges usually exist, but they can still make a significant difference. Because of this, backtesting must accurately simulate real-world conditions.: It should neither rely on an overly pessimistic approach that hides these small edges and profit opportunities, nor on an overly optimistic one that overstates them through unrealistic simulation. Or at the very least, you should clearly understand what differs from live trading and by how much, since sometimes fully accurate backtesting is not practical due to the time it requires.

This is not about overfitting at the start—before you even consider issues like overfitting, you need confidence that your backtesting truly reflects real-world execution. For example, if you run a live trading strategy in January 2025, the backtest for that exact period should produce results that closely align with the actual results. Once you’ve validated that your backtesting can accurately reproduce live trading results, then you can proceed to deeper research, optimization, and considerations around overfitting.

Accurate backtesting is the foundation. Without it, all further analysis—whether conservative or aggressive—becomes unreliable.

Getting started ===============