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<a href="https://tradingstrategy.ai">
<img src="https://raw.githubusercontent.com/tradingstrategy-ai/trading-strategy/master/logo.svg" width="384">
</a>
# Trading Strategy framework for Python
Trading Strategy framework is a Python framework for algorithmic trading on decentralised exchanges.
It is using [backtesting data](https://tradingstrategy.ai/trading-view/backtesting) and [real-time price feeds](https://tradingstrategy.ai/trading-view)
from [Trading Strategy Protocol](https://tradingstrategy.ai/).
# Use cases
* Analyse cryptocurrency investment opportunities on [decentralised exchanges (DEXes)](https://tradingstrategy.ai/trading-view/exchanges)
* Creating trading algorithms and trading bots that trade on DEXes
* Deploy trading strategies as on-chain smart contracts where users can invest and withdraw with their wallets
# Features
* Supports multiple blockchains like [Ethereum mainnet](https://tradingstrategy.ai/trading-view/ethereum),
[Binance Smart Chain](https://tradingstrategy.ai/trading-view/binance) and
[Polygon](https://tradingstrategy.ai/trading-view/polygon)
* Access trading data from on-chain decentralised exchanges like
[SushiSwap](https://tradingstrategy.ai/trading-view/ethereum/sushi), [QuickSwap](https://tradingstrategy.ai/trading-view/polygon/quickswap) and [PancakeSwap](https://tradingstrategy.ai/trading-view/binance/pancakeswap-v2)
* Integration with Jupyter Notebook for easy manipulation of data.
See [example notebooks](https://tradingstrategy.ai/docs/programming/code-examples/index.html).
* Write [algorithmic trading strategies](https://tradingstrategy.ai/docs/programming/strategy-examples/index.html) for decentralised exchange
# Getting started
See [the Getting Started tutorial](https://tradingstrategy.ai/docs/programming/code-examples/getting-started.html) and the rest of the [Trading Strategy documentation](https://tradingstrategy.ai/docs/).
The easiest way to get started is ready [Dev Container for Visual Studio Code](https://tradingstrategy.ai/docs/programming/setting-up-development-environment/dev-container/visual-studio-code-docker-dev-container-for-jupyter-notebooks.html).
# Prerequisites
* Python 3.10
* [Understanding Python package management and installation](https://packaging.python.org/en/latest/guides/installing-using-pip-and-virtual-environments/)
(unless using Dev Container from teh above)
# Installing the package
You can install this package with
[Poetry](https://python-poetry.org/) as a dependency:
```shell
poetry add trading-strategy -E direct-feed
```
Poetry, local development:
```shell
poetry install -E direct-feed
```
Pip:
```shell
pip install "trading-strategy[direct-feed]"
```
**Note**: `trading-strategy` package provides trading data
download and management functionality only. If you want to [developed
automated trading strategies you need to install trade-executor package as well](https://github.com/tradingstrategy-ai/trade-executor/).
# Documentation
- [Documentation](https://tradingstrategy.ai/docs/).
- [Getting started](https://tradingstrategy.ai/docs/getting-started.html).
Community
---------
* [Trading Strategy website](https://tradingstrategy.ai)
* [Blog](https://tradingstrategy.ai/blog)
* [Twitter](https://twitter.com/TradingProtocol)
* [Discord](https://tradingstrategy.ai/community#discord)
* [Telegram channel](https://t.me/trading_protocol)
* [Changelog and version history](https://github.com/tradingstrategy-ai/trading-strategy/blob/master/CHANGELOG.md)
[Read more documentation how to develop this package](https://tradingstrategy.ai/docs/programming/development.html).
# License
GNU AGPL 3.0.
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