Introduction
Strategy development is hard enough, but then comes the deployment gap between backtesting and live trading. Built a strategy in VectorBT or backtesting.py? You face a complete rewrite for live trading.
Two days ago, I launched StrateQueue to solve this. The response has been incredible: 26 GitHub stars and 1,300 downloads in 48 hours from the quant community on Reddit.
The Problem
Every quant hits the same wall: your backtesting strategy works perfectly, but going live means starting over. The frameworks we love for research: VectorBT, backtesting.py, backtrader, and Zipline, aren't designed for real-time execution. You end up rewriting everything from scratch, introducing bugs, and losing weeks of development time. I've been through this cycle too many times.
The Solution
StrateQueue acts as a bridge between your existing backtesting code and live brokers. No rewrites, no framework changes, just point it at your strategy file and specify your broker. It handles the real-time data feeds, order management, and execution logic while your strategy code stays exactly the same. The whole deployment process takes under 10 seconds.
Demo
Quick-Start
pip install stratequeue
stratequeue deploy \
--strategy examples/strategies/backtestingpy/sma.py \
--symbol AAPL \
--timeframe 1m
Contribution and Feedback
Looking for feedback from real traders on what features matter most. Contributors are welcomed, especially for optimization, advanced order types, and aiding in the development of a dashboard stratequeue webui. Happy to answer questions!
Top comments (0)