hftbacktest - High-Frequency Trading and Market-Making Backtesting Tool

Quant Finance Tool Monday - High-Frequency Trading and Market-Making Backtesting Tool

Happy Monday, Friends!

A 5-minute introduction to a Quantitative Finance tool.

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This week’s Monday’s tool:

• hftbacktest - High-Frequency Trading and Market-Making Backtesting Tool.¹

Img Src: Sigmund

Backtesting is useful

Backtesting aims to experiment with your trading strategies by applying them to historical market data. This process allows you to evaluate the potential effectiveness of your strategies by analyzing their performance in past market conditions. Essentially, it serves as a predictive tool, offering valuable insights into the profitability and robustness of your approaches. By doing so, you can refine your strategies, identify any vulnerabilities, and optimize your decision-making processes without the immediate financial risks of real-time trading.

Backtesting is usually behind a paywall

Given the importance of backtesting tools, prop desks, banks, and hedge funds will all have proprietary backtesting tools that focus on their specific markets. So what’s interesting about this tool is that it’s a) open source, b) MIT license, and c) written in Python so it’s relatively straightforward to understand and build on top of it.

What this tool offers

This Python 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 of the Tool

• 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.

Example from the webpage

Some Python code²

@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)

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That’s all for today :) Have a great one!

For more Quant Finance treats, check out our archives.

Stay quanty!

All the best,
Sebastian

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1. https://github.com/nkaz001/hftbacktest

2. https://hftbacktest.readthedocs.io/en/latest/index.html