rsohlot / hftbacktest

A high-frequency trading and market-making backtesting tool in Python and Rust, which accounts for limit orders, queue positions, and latencies, utilizing full tick data for trades and order books, with real-world crypto market-making examples for Binance Futures

HftBacktest

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.

Rust implementation with experimental features

The experimental features are currently in the early stages of development, having been completely rewritten in Rust to support the following features.

• Complete tick-by-tick simulation with a customizable time interval or based on the feed and order receipt.
• Full order book reconstruction based on L2 Market-By-Price and L3 Market-By-Order (WIP) 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 using the same algorithm code: currently for Binance Futures and Bybit (WIP).

Please see rust directory.

Example: The complete process of backtesting Binance Futures

gridtrading: The complete process of backtesting Binance Futures using a high-frequency grid trading strategy implemented in Rust.

Key Features

• Working in Numba JIT function.
• Complete tick-by-tick simulation with a variable time interval.
• Full order book reconstruction based on L2 feeds(Market-By-Price).
• 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.

Documentation

See full document here.

Getting started

Installation

hftbacktest supports Python 3.10+. You can install hftbacktest using pip:

pip install hftbacktest

Or you can clone the latest development version from the Git repository with:

git clone https://github.com/nkaz001/hftbacktest

Data Source & Format

Please see Data or Data Preparation.

A Quick Example

Get a glimpse of what backtesting with hftbacktest looks like with these code snippets:

@njit
def simple_two_sided_quote(hbt, stat):
    max_position = 5
    half_spread = hbt.tick_size * 20
    skew = 1
    order_qty = 0.1
    last_order_id = -1
    order_id = 0

    # Checks every 0.1s
    while hbt.elapse(100_000):
        # Clears cancelled, filled or expired orders.
        hbt.clear_inactive_orders()

        # Obtains the current mid-price and computes the reservation price.
        mid_price = (hbt.best_bid + hbt.best_ask) / 2.0
        reservation_price = mid_price - skew * hbt.position * hbt.tick_size

        buy_order_price = reservation_price - half_spread
        sell_order_price = reservation_price + half_spread

        last_order_id = -1
        # Cancel all outstanding orders
        for order in hbt.orders.values():
            if order.cancellable:
                hbt.cancel(order.order_id)
                last_order_id = order.order_id

        # All order requests are considered to be requested at the same time.
        # Waits until one of the order cancellation responses is received.
        if last_order_id >= 0:
            hbt.wait_order_response(last_order_id)

        # Clears cancelled, filled or expired orders.
        hbt.clear_inactive_orders()

            last_order_id = -1
        if hbt.position < max_position:
            # Submits a new post-only limit bid order.
            order_id += 1
            hbt.submit_buy_order(
                order_id,
                buy_order_price,
                order_qty,
                GTX
            )
            last_order_id = order_id

        if hbt.position > -max_position:
            # Submits a new post-only limit ask order.
            order_id += 1
            hbt.submit_sell_order(
                order_id,
                sell_order_price,
                order_qty,
                GTX
            )
            last_order_id = order_id

        # All order requests are considered to be requested at the same time.
        # Waits until one of the order responses is received.
        if last_order_id >= 0:
            hbt.wait_order_response(last_order_id)

        # Records the current state for stat calculation.
        stat.record(hbt)

Tutorials

• Data Preparation
• Getting Started
• Working with Market Depth and Trades
• Integrating Custom Data
• Making Multiple Markets - Introduction
• High-Frequency Grid Trading
• Impact of Order Latency
• Order Latency Data
• Guéant–Lehalle–Fernandez-Tapia Market Making Model and Grid Trading
• Making Multiple Markets
• Risk Mitigation through Price Protection in Extreme Market Conditions

Examples

You can find more examples in examples directory and Rust examples.

Roadmap

Currently, new features are being implemented in Rust due to the limitations of Numba, as performance is crucial given the size of the high-frequency data. The imminent task is to integrate hftbacktest in Python with hftbacktest in Rust by using the Rust implementation as the backend. Meanwhile, the data format, which is currently different, needs to be unified. On the pure Python side, the performance reporting tool should be improved to provide more performance metrics with increased speed.

Please see the roadmap.

Contributing

Thank you for considering contributing to hftbacktest! Welcome any and all help to improve the project. If you have an idea for an enhancement or a bug fix, please open an issue or discussion on GitHub to discuss it.

The following items are examples of contributions you can make to this project:

Please see the roadmap.