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What is clawby-quant?

openclawby/clawby-quant — explained in plain English

Analysis updated 2026-05-18

26PythonAudience · developerComplexity · 4/5Setup · moderate

In one sentence

A self-hosted crypto perpetual futures trading bot with 56 market data factors, a replay-accurate backtester, and strict paper versus live trading separation.

Mindmap

mindmap
  root((Clawby Quant))
    What it does
      Automated crypto futures trading
      Factor driven strategies
      Replay accurate backtesting
    Tech stack
      Python
      FastAPI
      React
      SQLite
    Use cases
      Backtest a trading strategy on history
      Run paper trading with virtual balance
      Route live orders across exchanges
    Audience
      Crypto traders
      Quant researchers

Code map

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filefunction / class

What do people build with it?

USE CASE 1

Backtest a crypto perpetual futures strategy against historical data before risking real money.

USE CASE 2

Run automated paper trading with a virtual balance to observe how strategies perform.

USE CASE 3

Monitor 56 live market and on chain factors that feed into the trading strategies.

USE CASE 4

Route live trading orders across Binance, Bitget, and OKX from a single dashboard.

What is it built with?

PythonFastAPIReactSQLiteWebSockets

How does it compare?

openclawby/clawby-quantaevella/sky-pc-mcp-companionalicankiraz1/gemma-4-31b-mtp-vllm-server
Stars262626
LanguagePythonPythonPython
Setup difficultymoderatemoderatehard
Complexity4/53/54/5
Audiencedevelopervibe coderops devops

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · moderate Time to first run · 1h+

Requires Python 3.10+, Node.js 20+, a Binance futures API key, and a Clawby API key for aggregated data factors.

So what is it?

Clawby Quant is a self-hosted trading system for automatically buying and selling crypto perpetual futures contracts, a type of margin traded crypto derivative traded against a US dollar equivalent stablecoin. Everything runs on your own machine, including your exchange API keys, which never get sent anywhere else. The project comes with a clear warning that it is meant for research and education, that derivatives trading carries real risk of loss, and that past backtest results do not guarantee future performance. The system ships with four ready made trading strategies, each of which can be cloned into multiple independent copies with their own settings, coins, and capital. Decisions are driven by a library of 56 data factors, ranging from official Binance exchange statistics like open interest and long/short ratios to aggregated data such as cross exchange liquidations, funding rates, and on chain metrics. Before risking real money, strategies can be tested against historical data using a backtesting engine that runs the exact same strategy code used in live trading, so there is no gap between what was tested and what actually runs, and it accounts for real trading costs like fees, slippage, and funding payments. Live trading orders can be routed to Binance, Bitget, or OKX, while market data and the factor library are based on Binance. The system strictly separates paper trading, which uses a virtual balance and is the default mode, from live trading, which requires typing a confirmation word before it activates. Several layers of risk controls are built in, including position sizing limits, a maximum margin multiplier cap, per coin exposure caps, a daily loss circuit breaker, and a quiet period around major market events. The dashboard is available in both English and Chinese and includes an overview of account performance, a trade journal, and factor and risk monitoring pages. Setup requires Python, Node.js, a Binance futures API key, and an API key from the related Clawby data service.

Copy-paste prompts

Prompt 1
Help me set up my .env file and Binance API keys to run Clawby Quant in paper mode.
Prompt 2
Explain how the replay-accurate backtester avoids lookahead bias in this project.
Prompt 3
Walk me through cloning one of the four built in strategies with different parameters.
Prompt 4
Explain the risk control layers in this project before I consider enabling live trading.

Frequently asked questions

What is clawby-quant?

A self-hosted crypto perpetual futures trading bot with 56 market data factors, a replay-accurate backtester, and strict paper versus live trading separation.

What language is clawby-quant written in?

Mainly Python. The stack also includes Python, FastAPI, React.

How hard is clawby-quant to set up?

Setup difficulty is rated moderate, with roughly 1h+ to a first successful run.

Who is clawby-quant for?

Mainly developer.

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