hftbacktest 2.4.4

A high-frequency trading and market-making backtesting tool accounts for limit orders, queue positions, and latencies, utilizing full tick data for trades and order books.

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

Installation

hftbacktest supports Python 3.11+. 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.

You can also find some data here, hosted by the supporter.

A Quick Example

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

@njit
def market_making_algo(hbt):
    asset_no = 0
    tick_size = hbt.depth(asset_no).tick_size
    lot_size = hbt.depth(asset_no).lot_size

    # in nanoseconds
    while hbt.elapse(10_000_000) == 0:
        hbt.clear_inactive_orders(asset_no)

        a = 1
        b = 1
        c = 1
        hs = 1

        # Alpha, it can be a combination of several indicators.
        forecast = 0
        # In HFT, it can be various measurements of short-term market movements,
        # such as the high-low range in the last X minutes.
        volatility = 0
        # Delta risk, it can be a combination of several risks.
        position = hbt.position(asset_no)
        risk = (c + volatility) * position
        half_spread = (c + volatility) * hs

        max_notional_position = 1000
        notional_qty = 100

        depth = hbt.depth(asset_no)

        mid_price = (depth.best_bid + depth.best_ask) / 2.0

        # fair value pricing = mid_price + a * forecast
        #                      or underlying(correlated asset) + adjustment(basis + cost + etc) + a * forecast
        # risk skewing = -b * risk
        reservation_price = mid_price + a * forecast - b * risk
        new_bid = reservation_price - half_spread
        new_ask = reservation_price + half_spread

        new_bid_tick = min(np.round(new_bid / tick_size), depth.best_bid_tick)
        new_ask_tick = max(np.round(new_ask / tick_size), depth.best_ask_tick)

        order_qty = np.round(notional_qty / mid_price / lot_size) * lot_size

        # Elapses a process time.
        if not hbt.elapse(1_000_000) != 0:
            return False

        last_order_id = -1
        update_bid = True
        update_ask = True
        buy_limit_exceeded = position * mid_price > max_notional_position
        sell_limit_exceeded = position * mid_price < -max_notional_position
        orders = hbt.orders(asset_no)
        order_values = orders.values()
        while order_values.has_next():
            order = order_values.get()
            if order.side == BUY:
                if order.price_tick == new_bid_tick or buy_limit_exceeded:
                    update_bid = False
                if order.cancellable and (update_bid or buy_limit_exceeded):
                    hbt.cancel(asset_no, order.order_id, False)
                    last_order_id = order.order_id
            elif order.side == SELL:
                if order.price_tick == new_ask_tick or sell_limit_exceeded:
                    update_ask = False
                if order.cancellable and (update_ask or sell_limit_exceeded):
                    hbt.cancel(asset_no, order.order_id, False)
                    last_order_id = order.order_id

        # It can be combined with a grid trading strategy by submitting multiple orders to capture better spreads and
        # have queue position.
        # This approach requires more sophisticated logic to efficiently manage resting orders in the order book.
        if update_bid:
            # There is only one order at a given price, with new_bid_tick used as the order ID.
            order_id = new_bid_tick
            hbt.submit_buy_order(asset_no, order_id, new_bid_tick * tick_size, order_qty, GTX, LIMIT, False)
            last_order_id = order_id
        if update_ask:
            # There is only one order at a given price, with new_ask_tick used as the order ID.
            order_id = new_ask_tick
            hbt.submit_sell_order(asset_no, order_id, new_ask_tick * tick_size, order_qty, GTX, LIMIT, False)
            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:
            # Waits for the order response for a maximum of 5 seconds.
            timeout = 5_000_000_000
            if not hbt.wait_order_response(asset_no, last_order_id, timeout):
                return False

    return True

Tutorials

• Data Preparation

• Getting Started

• Working with Market Depth and Trades

• Integrating Custom Data

• Making Multiple Markets - Introduction

• High-Frequency Grid Trading

• High-Frequency Grid Trading - Comparison Across Other Exchanges

• High-Frequency Grid Trading - Simplified from GLFT

• 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

• Level-3 Backtesting

• Market Making with Alpha - Order Book Imbalance

• Market Making with Alpha - Basis

• Market Making with Alpha - APT

• Queue-Based Market Making in Large Tick Size Assets

• Fusing Depth Data

• Accelerated Backtesting

• Pricing Framework

Examples

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

The complete process of backtesting Binance Futures

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

Migration to V2

Please see the migration guide.

Roadmap

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.