investpy
backtesting.py
Our great sponsors
| investpy | backtesting.py | |
|---|---|---|
| 49 | 25 | |
| 1,561 | 4,825 | |
| - | - | |
| 0.0 | 0.0 | |
| 28 days ago | about 1 month ago | |
| Python | Python | |
| MIT License | GNU Affero General Public License v3.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
investpy
backtesting.py
-
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.
-
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/
-
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.
-
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.
-
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.
-
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?
-
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?
What are some alternatives?
tvdatafeed - A simple TradingView historical Data Downloader
backtrader - Python Backtesting library for trading strategies
yfinance - Download market data from Yahoo! Finance's API
vectorbt - Find your trading edge, using the fastest engine for backtesting, algorithmic trading, and research.
stock_screener - Picking stocks through various screening methods. Focus on Northern Europe.
PyTrader-python-mt4-mt5-trading-api-connector-drag-n-drop - Open Source Trading Strategies & End-to-End solution connecting Metatrader4 & Metatrader5 💹 with Python with a simple drag and drop EA. Fully tested bug free & efficient solution for live & paper trading⭐ Full Documentation ready. Lightweight, efficient and stable implementation 🔥 [UnavailableForLegalReasons - Repository access blocked]
portfolio - Track and evaluate the performance of your investment portfolio across stocks, cryptocurrencies, and other assets.
ccxt - A JavaScript / TypeScript / Python / C# / PHP cryptocurrency trading API with support for more than 100 bitcoin/altcoin exchanges
valinvest - A value investing tool based on Warren Buffett, Joseph Piotroski and Benjamin Graham thoughts
lumibot - Backtesting and Trading Bots Made Easy for Crypto, Stocks, Options, Futures, FOREX and more
cryptoCMD - Cryptocurrency historical price data library in Python. Data from https://coinmarketcap.com.
gym-anytrading - The most simple, flexible, and comprehensive OpenAI Gym trading environment (Approved by OpenAI Gym)