backtesting.py
finance_ml
| backtesting.py | finance_ml | |
|---|---|---|
| 25 | 15 | |
| 4,846 | 726 | |
| - | - | |
| 0.0 | 0.0 | |
| about 1 month ago | over 1 year ago | |
| Python | Jupyter Notebook | |
| GNU Affero General Public License v3.0 | MIT License |
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backtesting.py
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Python developers -- what broker and api do you use?
We chose backtesting.py for a backtesting framework. There are several to choose from but that one seems like the most well-supported and actively worked on at the moment.
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How do you backtest with IBKR Data?
The process might vary a bit based on what you want to trade, but I've had some success with back-testing by using the IBKR API to download historical data for the stocks I want, then plugging in that data to some other back-testing framework, like https://kernc.github.io/backtesting.py/
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Anyone here built backtest/alpha visualization/exploration dashboard(s)?
https://kernc.github.io/backtesting.py/ offers nice way to zoom backtesting. It has some bugs, but good for visualization.
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What platform do you use to backtest historical millisecond tick data?
Are there any free trading platform options to import and backtest millisecond historical tick data? Do you use something like https://github.com/kernc/backtesting.py and then translate algorithms into mql4/5 or pinescript?
- GitHub - kernc/backtesting.py: Backtest trading strategies in Python.
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Where/how do I “import backtesting” and other programs? TY!
But even if you don't know that, you can Google the library to find its documentation and installation instructions - in this case, here where it does indeed tell you to run pip install backtesting.
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What are your 2022 success stories? It's been a long year. You deserve to brag.
Sure. My strategy is actually very simple using common indicators like MACD and RSI. Most of the code is actually based on https://github.com/kernc/backtesting.py and I use it to check whether I should buy or sell on a particular day. Hope this helps!
- Backtesting.py - interpreting the generated chart
- How hard would this be?
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Bot development best practices for making backtesting easier?
I'm leaning towards using the backtesting.py library, but from the example on their main page, it looks like you need to program your strategy using their library?
finance_ml
- finance_ml: unofficial repo based on [Advances in Financial Machine Learning](https://www.amazon.com/Advances-Financial-Machine-Learning-Marcos/dp/1119482089). It's incomplete and not updated Other Models - star count:542.0
- finance_ml: unofficial repo based on [Advances in Financial Machine Learning](https://www.amazon.com/Advances-Financial-Machine-Learning-Marcos/dp/1119482089). It's incomplete and not updated Other Models - star count:446.0
What are some alternatives?
backtrader - Python Backtesting library for trading strategies
AlgoTradingRepoList - This repository contains basic information about different algotrading repo I like.
vectorbt - Find your trading edge, using the fastest engine for backtesting, algorithmic trading, and research.
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ccxt - A JavaScript / TypeScript / Python / C# / PHP cryptocurrency trading API with support for more than 100 bitcoin/altcoin exchanges
lumibot - Backtesting and Trading Bots Made Easy for Crypto, Stocks, Options, Futures, FOREX and more
gym-anytrading - The most simple, flexible, and comprehensive OpenAI Gym trading environment (Approved by OpenAI Gym)
blankly - 🚀 💸 Easily build, backtest and deploy your algo in just a few lines of code. Trade stocks, cryptos, and forex across exchanges w/ one package.
ta4j - A Java library for technical analysis.
zipline - Zipline, a Pythonic Algorithmic Trading Library
StockSharp - Algorithmic trading and quantitative trading open source platform to develop trading robots (stock markets, forex, crypto, bitcoins, and options).
investpy - Financial Data Extraction from Investing.com with Python