Trading Strategy framework is a Python framework for algorithmic trading on decentralised exchanges.
- Download decentralised finance market data sets
- Develop and backtest trading strategies in Jupyter Notebook
- Live trade execution for onchain trading
- Smart contract vault support for turning your trading strategy to a third-party investable vault
The trading-strategy library provides data fetching for backtesting and live trading.
It is using backtesting data and real-time price feeds from Trading Strategy Protocol.
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Analyse cryptocurrency investment opportunities on decentralised exchanges (DEXes)
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Creating trading algorithms and trading bots that trade on DEXes
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Deploy trading strategies as on-chain smart contracts where users can invest and withdraw with their wallets
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Supports multiple blockchains like Ethereum mainnet, Binance Smart Chain and Polygon
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Access trading data from on-chain decentralised exchanges like SushiSwap, QuickSwap and PancakeSwap
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Integration with Jupyter Notebook for easy manipulation of data. See example notebooks.
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Write algorithmic trading strategies for decentralised exchange
See the Getting Started repository and the rest of the Trading Strategy documentation.
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Python 3.10
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Understanding Python package management and installation (unless using Dev Container from teh above)
You can install this package with
Poetry as a dependency:
poetry add trading-strategy -E direct-feedPoetry, local development:
poetry install -E direct-feedPip:
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.
Read more documentation how to develop this package.
GNU AGPL 3.0.