nkaz001/hftbacktest
Free, open source, a high frequency trading and market making backtesting and trading bot, which accounts for limit orders, queue positions, and latencies, utilizing full tick data for trades and order books(Level-2 and Level-3), with real-world crypto trading examples for Binance and Bybit observed · 2026-08-28
Health v2 · maintenance only
63/100
- Activity 58
- Release rhythm 48
- Longevity 100
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: 52.5
- age_days: 1469
- days_rel: 266
- days_push: 253
- n_releases_24m: 11
Adoption not part of the score
4516 stars · 880 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
A high-frequency trading and market-making backtesting framework written in Rust with Python bindings, which accurately simulates limit orders, queue positions, and feed/order latencies using full tick-by-tick order book data (Level-2 and Level-3). It also supports deploying live trading bots for Binance Futures and Bybit using the same algorithm code.
Use cases
- backtest high-frequency trading strategies with realistic latency and queue position modeling
- develop and test market making strategies on crypto exchanges
- reconstruct full order books from Level-2 and Level-3 tick data
- simulate order fills accounting for queue position
- prototype a trading bot and deploy it live to Binance or Bybit with the same code
- backtest multi-asset and multi-exchange strategies
- research market making with order book imbalance alpha
When to choose
- you need accurate HFT or market-making backtesting that models latencies and queue positions rather than naive fills
- you have full tick data for trades and order books and want tick-by-tick simulation
- you trade crypto on Binance Futures or Bybit and want a path from backtest to live bot
- you want fast backtests via Numba JIT in Python or native Rust performance
When to avoid
- you only need simple bar-based or daily backtesting for lower-frequency strategies
- you trade equities or other asset classes without crypto exchange connectors
- you lack tick-level order book data, which the framework requires for accurate simulation
Facets
framework · maturity active
simulation trading machine-learning fintech python rust cross-platform backtesting high-frequency-trading market-making limit-order-book crypto-trading tick-data latency-modeling queue-position binance bybit cryptocurrency quantitative-trading
2 sources
- readme: https://github.com/nkaz001/hftbacktest · fetched 2026-08-28 · f37d247335e1
- registry_crates: https://crates.io/api/v1/crates/hftbacktest · fetched 2026-08-29 · 509bd3d2c5a6
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| nkaz001/hftbacktest | main | 63 |
For agents
markdown · JSON · MCP: product_card(name="nkaz001/hftbacktest")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem