With the rise of digitalization and finance technologies, using APIs and open-source codes on platforms like GitHub for investment strategy automation has become increasingly popular among individual investors, especially beginners. APIs like Alpaca and Polygon provide easy access to financial data, backtesting tools like Backtrader and Zipline enable strategy simulation, FinRL offers full pipeline for deep reinforcement learning in trading. This integration of coding and investing has lowered the barrier for starter investors to develop, iterate and optimize their own trading strategies. However, sorting through information overload online and turning concepts into actual investment plans could still be challenging for many. This article aims to provide beginners with an essential guide covering the best API, dataset, backtesting framework and GitHub repositories to start implementing algorithmic trading strategies.
Alpaca and Polygon offer straightforward APIs to retrieve real-time and historical US stock market data
The Alpaca trading API provides access to real-time and historical OHLCV data across US equities and options, while the Polygon API complements with fundamentals, forex, and crypto data. Both have generous free tiers sufficient for individuals to develop basic algorithms. The APIs use simple REST interface for requesting JSON-formatted data, minimizing learning curve. Some useful features include timestamp conversion, caching, throttling control and integrated stocks/options universe lookups. With a single API key, investors could stream minute-level real-time bars for backtesting, paper-trading models before actual deployment. For security analysis enthusiasts, the wide-ranging datasets supported by Polygon API like earnings, valuation ratios, analyst estimates or insider transactions data could enable comprehensive factor research and augmentation of trading signals.
Backtrader, Zipline, FinRL – open-source backtesting frameworks for strategy iteration in Python
On the software side, Python has become the de facto option for retail algorithmic trading. Backtrader, Zipline and the more recent FinRL stand out as three popular open-source backtesting frameworks well-suited for beginners. They share similarities – integration with major data sources like Yahoo Finance or custom CSV, handling core metrics like slippage/commission, in-sample optimization and artifact generation. Unique aspects include Backtrader’s focus on simplicity and flexibility – users can fully customize indicator/order logic using Python, Zipline’s pipeline for signal generation and batch transformations, FinRL’s modeling with deep reinforcement learning. Trying all three to determine preference could be worthwhile. For extended functionality, one could further explore dedicated machine learning packages like SciKit Learn, StatsModels for statistical analysis. The hands-on experience and reproductive pipeline offered by these frameworks lay solid foundation to keep iterating strategy logics before live deployment.
Awesome-quant GitHub repository – a comprehensive collection of Quant Fin open source projects
Lastly, the awesome-quant repository on GitHub stands out as a must-bookmark reference for beginners. It catalogs over 100 critical open-source libraries across data, backtesting, modeling, visualization and more for practitioners in systematic trading and investment. Some highlights – Ricequant as an integrated Chinese stocks analysis/executional platform, Tradingene for CTA specific research needs, FinRL and Backtrader as mentioned earlier. The categorized compilation allows targeted investigation based on interests while also inspiring creative strategy ideas combining different solutions. Remarkably, all resources listed possess dedicated documentation/examples easing the initial setup. This awesome collection demonstrates the collaborative nature within Quant community to drive broader adoption of cutting-edge FinTech.
In summary, Alpaca, Polygon APIs, Python backtesting frameworks (Backtrader, Zipline, FinRL) and the Awesome-Quant GitHub repository compose an essential guide for beginners on implementing API-driven algorithmic trading strategies. Accessing data, iterating logics and integrating capabilities made agile. We encourage new investors to leverage these open-source tools for a modern, productive workflow enhancing idea incubation and sharpening financial technology skills.