Get a high performance trading simulator for realistic strategy testing, built for founders, using Python.
intermediateโฑ 1-2 hours๐ต Free + LLM API costs
134 stars29 forksPythonQuality 8/10Updated 5/3/2024100% free ยท open source
What it is
Use Python to create fast and realistic trading simulations for strategy testing.
What you can make with it
High-performance trading simulations that mimic real market conditions, allowing you to test and optimize your trading strategies.
How it helps
This helps you save time and lower risk by simulating different market conditions, allowing you to refine your strategies before putting them into practice.
Real use case example
"A founder wants to test a new trading strategy for a cryptocurrency, so she builds a high-performance simulation using this skill, running 1,000 iterations in just 30 minutes, and refining her strategy to increase expected returns by 15%."
If you're new
Pick this up when you want to build your first trading simulation and learn how to use Python for AI tasks.
If you're senior
Use this when developing and testing complex trading strategies that require high-performance simulations.
Common confusion cleared up
This tool is built specifically for high-frequency trading strategies, not for general market analysis or data visualization.
Why we list it on WorkflowStacks: This is a free and open-source tool, saving you costs compared to paid alternatives and providing a high performance trading simulator.
What it does
The PythonMatchingEngine simulates a high-performance trading environment for testing and validating high-frequency trading strategies using Level 3 market data.
โขTesting trading strategies before deploying them in live markets
โขValidating the performance of trading algorithms under various market conditions
โขComparing the effectiveness of different trading strategies
Quick start
1Modify the `config.json` file to set up your trading environment, including defining the trading strategy and market data feed
2Run the `matching_engine.py` script to start the simulation, using a command like `python matching_engine.py -c config.json`
3Use the `backtest.py` script to backtest your trading strategy against historical market data, with a command like `python backtest.py -c config.json -d historical_data.csv`
4Analyze the output reports and logs to evaluate the performance of your trading strategy
5Adjust the trading strategy parameters and repeat the simulation to refine and optimize your strategy
Heads up: Ensure you have the necessary dependencies installed, including `pandas`, `numpy`, and `yfinance`, and that your system meets the minimum requirements for running the simulation, including sufficient CPU and memory resources.
Saves to your device
What's inside โ free to inspect
No purchase needed
Read the entire source before you build โ unlike paid marketplaces that hide it behind a buy button.