file: ../README.rst
Project description
The master branch is switched to hftbacktest-2.0.0-alpha, which uses the Rust implementation as the backend. If you want to see the current version 1.8.4, please check out the corresponding tag.
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
The experimental features are currently in the early stages of development, having been completely rewritten in Rust to support the following 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 L2 Market-By-Price and L3 Market-By-Order (Rust-only, 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. (Rust-only)
Example: 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.
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):
max_position = 5
half_spread = hbt.tick_size * 20
skew = 1
order_qty = 0.1
last_order_id = -1
order_id = 0
asset_no = 0
# Checks every 0.1s
while hbt.elapse(100_000_000) == 0:
# Clears cancelled, filled or expired orders.
hbt.clear_inactive_orders(asset_no)
# Gets the market depth.
depth = hbt.depth(asset_no)
# Obtains the current mid-price and computes the reservation price.
mid_price = (depth.best_bid + depth.best_ask) / 2.0
reservation_price = mid_price - skew * hbt.position(asset_no) * depth.tick_size
buy_order_price = reservation_price - half_spread
sell_order_price = reservation_price + half_spread
last_order_id = -1
# Cancel all outstanding orders
orders = hbt.orders(asset_no)
values = orders.values()
while True:
order = values.next()
if order is None:
break
if order.cancellable:
hbt.cancel(asset_no, 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(asset_no, last_order_id)
# Clears cancelled, filled or expired orders.
hbt.clear_inactive_orders(asset_no)
last_order_id = -1
if hbt.position < max_position:
# Submits a new post-only limit bid order.
order_id += 1
hbt.submit_buy_order(
asset_no,
order_id,
buy_order_price,
order_qty,
GTX,
LIMIT,
False
)
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(
asset_no,
order_id,
sell_order_price,
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:
hbt.wait_order_response(asset_no, last_order_id)
Tutorials
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.
Project details
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