With the rise of algorithmic and quantitative trading, open-source trading frameworks have become invaluable tools for developing and backtesting investment strategies. Github hosts some of the most popular python-based backtesting engines like zipline, backtrader, quantopian which are well-documented and have abundant strategy examples that can serve as templates. These platforms support fetching market data from public APIs, running vectorized backtesting for faster optimizations, machine learning plugins, live trading integration etc. For crypto trading bots, CCXT library implements unified APIs for 100+ exchanges. Overall, leveraging such open-source stacks helps quants rapidly research and prototype stratgies at low cost.
Zipline and Quantopian provide solid backtesting foundations with pandas for data analysis
The zipline and quantopian python libraries are both built on pandas datastructures under the hood which makes them very convenient for analysts already using the pandas/numpy stack for modeling. Zipline uses a pipeline API to fetch, clean and transform market data which can then be fed into algos. The pipeline architecture ensures a clean separation of data and model. Quantopian takes a similar approach by separating out the research environment for development from the backtester. Both platforms allow fetching free market data from sources like Quandl or doing live trading by integrating with brokers. The modularity and extendability of these libraries is a major advantage for collaboration.
Backtrader framework has C++ speed and flexibility for custom extensions
Backtrader is yet another popular open-source backtesting framework but implemented in python/C++ for additional speed. It follows an event-driven architecture that is closer to how live trading systems work. The ability to write custom components by subclassing base classes makes it easier to prototype new indicators or brokers. Backtrader also allows vectorization across multiple securities which enables fast optimizations of a strategy. Some valuable strategy examples in the Backtrader github project use crossover moving averages, Ichimoku clouds for entries etc. that serve as good starting points.
CCXT crypto trading library standardizes access across 100+ exchanges
As crypto assets trade on hundreds of exchanges worldwide, traders face major complexity in managing connections, authentication, rate limits etc. CCXT solves this by implementing a unified API in javascript/python/php for connecting to 100+ exchanges. It handles all the nitty-gritty REST API intricacies behind a simple facade. The CCXT library tracks orderbooks, trades, manages rate limits and retries etc. so developers can focus on strategy logic. This makes it trivial to run arbitrage or market-making bots across exchanges. CCXT is under active development and supports most major crypto exchanges – it’s an invaluable tool for crypto algo trading.
To summarize, leveraging open-source quantitative trading frameworks like zipline, quantopian, backtrader and CCXT drastically reduces the costs for strategists to research and develop investmet ideas. The availability of strategy examples in these projects provides a solid foundation to build upon. Integrations with data sources and brokers also smooths out the path to live trading.