hezebang/pyalgotrade — explained in plain English
Analysis updated 2026-08-12 · repo last pushed 2023-11-13
Backtest a moving average and RSI strategy against years of historical stock data to see if it would have been profitable.
Run simulated paper trades using live market data to practice trading without risking real capital.
Trade Bitcoin automatically on the Bitstamp exchange using a custom coded strategy.
Learn how algorithmic trading systems process market events and evaluate buy or sell decisions.
| hezebang/pyalgotrade | 000madz000/rfid-attendance | 00kaku/gallery-slider-block | |
|---|---|---|---|
| Language | — | TypeScript | JavaScript |
| Last pushed | 2023-11-13 | 2024-07-22 | 2021-05-19 |
| Maintenance | Dormant | Dormant | Dormant |
| Setup difficulty | moderate | easy | easy |
| Complexity | 3/5 | 2/5 | 2/5 |
| Audience | developer | developer | general |
Figures from each repo's GitHub metadata at analysis time.
Requires an older Python version (2.7 or 3.7) and the project is deprecated with no maintenance, so dependency issues are likely on modern systems.
PyAlgoTrade is a free Python toolkit that helps you test and run automated trading strategies. Instead of manually buying and selling stocks or cryptocurrencies, you write code that defines your trading rules, and the software applies them to market data on your behalf. The most common use is "backtesting", replaying historical market data through your strategy to see how it would have performed before you risk any real money. The project works by processing market events in sequence: each new price tick or data point triggers your code to evaluate whether to buy, sell, or hold. You can feed it historical price data from common CSV files like those from Yahoo Finance or Google Finance. Beyond simulated backtesting, it also supports "paper trading" (simulated trades using live market data) and actual live trading of Bitcoin through the Bitstamp exchange. The target users are individual algorithmic traders, finance students, or hobbyists who want to experiment with automated trading strategies using Python. For example, if you believed a certain combination of moving averages and the RSI indicator could predict stock movements, you could encode that logic, test it against years of historical data, and measure the results using built-in performance metrics like the Sharpe ratio. A major caveat: the project is officially deprecated and no longer maintained. The developers recommend looking at an alternative project called Basana for new work. PyAlgoTrade also relies on older versions of Python (2.7 and 3.7), so its real-world viability for modern live trading is limited. It is best treated as a learning resource for understanding how algorithmic trading systems work rather than a production-ready tool for managing live capital today.
A free Python toolkit for testing and running automated stock and crypto trading strategies. You write trading rules in code and replay historical market data to see how they would have performed before risking real money.
Dormant — no commits in 2+ years (last push 2023-11-13).
Use freely for any purpose, including commercial use, as long as you keep the copyright notice.
Setup difficulty is rated moderate, with roughly 30min to a first successful run.
Mainly developer.
This repo across BitVibe Labs
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