Developing profitable investment apps requires comprehensive frameworks and strategies. The open-source libraries introduced cover backtesting, trading bots, data sources etc. QuantConnect, Zipline and Backtrader provide backtesting capabilities. Freqtrade and OctoBot enable crypto trading automation. The examples showcase DRL algorithms for stock trading optimization. With reusable components and tutorials, developers can build performant investment apps for free.
Backtesting frameworks like Backtrader and Zipline accelerate strategy iteration
The backtesting frameworks like Backtrader, Zipline and QuantConnect implemented in Python and C# simplify historical viability evaluation. They incorporate event-driven architecture, modular plugins and visualization tools. The reusable technical indicators and seamless connectivity to brokerages ensure rapid prototyping. With thousands of strategies testable in seconds, these frameworks enable high-turnover optimization.
Trading bots like Freqtrade automate crypto exchange operations
Open-source trading bots like Freqtrade, OctoBot and Hummingbot provide automation for crypto exchanges. They support algorithmic trading, triangular arbitrage, market making etc. Built on Python and Node.js, these bots offer backtesting, plotting, and risk management modules. The exchange connectivity APIs and web interfaces give flexibility. The customizable strategies coupled with auto-tuning find profitable opportunities.
DRL powered FinRL optimizes stock trading strategies
As a proof-of-concept, FinRL pioneers deep reinforcement learning for quantitative trading. Its simulations spanning global indexes and tutorials on DQN, PPO demonstrate practical usages. The incorporated state-of-the-art algorithms, reward functions and evaluation baselines speed up development. By automatically directing data processing, strategy building and backtesting, FinRL enables high-performant financial applications.
The integrated libraries form comprehensive toolchain
The covered open-source projects provide building blocks spanning use cases – from backtesting to live trading, exchanges to simulations. Their integration enables an automated pipeline – ingesting data, optimizing strategies with deep learning and deploying bots. With reusable interfaces and modular architecture, developers can customize components like environments, algorithms and indicators for free.
The introduced open-source investment app development frameworks and tools accelerate building through reusable interfaces, tutorials and modular architecture. They cover backtesting, algorithm implementation, data management and exchange connectivity. With comprehensive coverage and integration capabilities, developers can optimize and automate quantitative trading strategies for free.