analysis-sharing
vectorbt
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analysis-sharing | vectorbt | |
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1 | 5 | |
26 | 2,668 | |
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10.0 | 8.2 | |
3 months ago | 26 days ago | |
Jupyter Notebook | Python | |
MIT License | GNU General Public License v3.0 or later |
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analysis-sharing
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Tracking mentions began in Dec 2020.
vectorbt
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Is there any python libraries to backtest buy and sell signals with dates?
For exactly this I use this https://github.com/polakowo/vectorbt it’s really a powerful tool and you can tons of things with it. Recently the developer decided to maintain it but not adding new features, which from now on will be released on the pro version. However, the free version is still very valuable, incredibly fast and suitable for basic to intermediate tasks.
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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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Looking for active python backtesting framework
However, it's not the fastest framework. If you need speed, and are good with the data science tool chain in python and the concept of flattening loops into vectorized operations, check out vector-bt. I haven't gotten a chance to play with it yet, but I'm definitely going to as soon as I find some spare time. It seems like a great option with a nicely modernized approach.
What are some alternatives?
backtrader - Python Backtesting library for trading strategies
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jesse - An advanced crypto trading bot written in Python
zipline - Zipline, a Pythonic Algorithmic Trading Library
OctoBot - Cryptocurrency trading bot using technical analysis based strategy with an advanced web interface
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Alpaca-API - The Alpaca API is a developer interface for trading operations and market data reception through the Alpaca platform.
ccxt - A JavaScript / Python / PHP cryptocurrency trading API with support for more than 100 bitcoin/altcoin exchanges
aat - Asynchronous, event-driven algorithmic trading in Python and C++
roq-samples - Demonstrates how Roq's API can support different use-cases. [C++20] [Examples]
WikiMapper - Create maps of wiki links on how they interconnect with each other.