pyalgotrade
backtesting.py
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| pyalgotrade | backtesting.py | |
|---|---|---|
| 1 | 23 | |
| 4,025 | 3,668 | |
| - | - | |
| 0.0 | 8.4 | |
| about 1 month ago | 15 days ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | GNU Affero General Public License v3.0 |
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pyalgotrade
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Best Backtesting Libraries (Python)
pyalgotrade – PyAlgoTrade is an event driven algorithmic trading Python library. Although the initial focus was on backtesting, paper trading is now possible
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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Backtest libraries
https://kernc.github.io/backtesting.py/ Nice analytics/visualisation but you need to do tricks to buy half a bitcoin. No native support of strategies based on multiple assets.
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Any advice on where to start?
Hey, love the learning attitude! Some things to consider include (how are you actually going to write your code). If you want to spend more time experimenting with a signal (which with your background, seems like you do), try using a framework out there like backtesting.py, backtrader, or blankly (the last one is my favorite because I'm able to actually deploy live in one line whereas the others don't). These frameworks have built-in backtesting, data connections, etc. that should help you speed your dev up.
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Why Building a Trading Algorithm is More Than Just the Algorithm - 3 Things
It’s super easy to get up and running with code. With the rise of data science as a field, datasets are far and wide. Accessible from just about any venue. Take a look at Kaggle, QuiverQuant, Yahoo Finance, or even directly from the brokerages and exchanges. Developers can easily download data directly as a .csv or .json and quickly get up and running by utilizing frameworks like backtesting.py or vectorbt. “Great, it seems like I can get up and running and I’ll have an awesome money making trading algorithm in no time”.... unfortunately, wrong. Why is this wrong? Well, simulation is NOT the real world. The real world is not a CSV file—the real world is a stream of events. Cause and effect. The real world works in a fashion where new data comes in, you make a decision, and then you figure it out, not “I have all of this data, let me run this all through time and figure it out”. Indeed, the data sources that you get in real-time are almost completely different from the data sources you use in simulation. Rather than .csv you use WebSockets; rather than QuiverQuant you use APIs; rather than backtesting frameworks you use more robust, event driven packages. Without it, you’re stuck duplicating code, rewriting it into an event-based system, and ultimately using that to go into production, and who knows if your code is going to change along the way.
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Backtesting for Python
I highly recommend using the Backtesting (https://github.com/kernc/backtesting.py) library. With some small modifications to data, you can quickly backtest any trading strategy.
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Custom forex backtester
backtesting.py is a good option. It should be pretty easy to make strategy to suite your needs.
What are some alternatives?
backtrader - Python Backtesting library for trading strategies
vectorbt - Find your trading edge, using the fastest engine for backtesting, algorithmic trading, and research.
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 🔥
backtrader - Python Backtesting library for trading strategies [Moved to: https://github.com/mementum/backtrader]
ccxt - A JavaScript / Python / PHP cryptocurrency trading API with support for more than 100 bitcoin/altcoin exchanges
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.
StockSharp - Algorithmic trading and quantitative trading open source platform to develop trading robots (stock markets, forex, crypto, bitcoins, and options).
lumibot - Backtesting and Trading Bots Made Easy for Crypto, Stocks, Options, Futures, FOREX and more
td-ameritrade-client - TD Ameritrade Java Client
zipline - Zipline, a Pythonic Algorithmic Trading Library