With the advancement of data science and technology, there are more and more high-quality resources available for investing beginners nowadays. Especially on github, a popular open source platform, many data science libraries, backtesting frameworks and educational materials can be downloaded for free, which lower the barriers for beginners who want to get into quantitative investing. By leveraging data science in investing, beginners can test trading hypothesis, build strategy prototypes rapidly and evaluate risks in a scientific way.
Popular Python backtesting frameworks enable fast trading strategy validation
As one of the most popular programming languages for data analytics, Python has many great open source backtesting frameworks like Backtrader, Zipline and Quantopian. These frameworks provide features to download market data, define strategy logic, simulate trades, analyze risks/returns automatically. So with just a few lines of Python code, investing beginners can quickly validate any trading idea historically without really trading it live. This is perfect for beginners to get feedback and build up more practical experience.
Abundant machine learning libraries to enhance strategy signals
Machine learning has become an integral part of quantitative investing given its predictive power. And thanks to open source projects, many Python machine learning libraries are free to use like Sklearn, Pytorch, Tensorflow. Investing beginners can leverage these to extract predictive signals from market data and improve strategy performance. For example, LSTM neural networks are commonly used to model price series and generate profitable entry/exit signals. With just basic Python and coding skills, investing beginners can incorporate advanced machine learning models easily.
Various high quality datasets broaden backtesting coverage
Robust backtesting requires large amounts of historical data like prices, fundamentals, sentiment indicators etc. Many open data platforms offer free investing datasets covering global stocks, futures, forex, commodities, even alternative data like social media sentiments, satellite images etc. By combining different datasets, investing beginners can test strategies under diverse market conditions and achieve solid out-of-sample performance. This is the key to develop long-term winning strategies.
In summary, data science and github enable investing beginners to access many free resources like backtesting frameworks, machine learning libraries and market data. By leveraging these, investing beginners can lower barriers, save costs, speed up learning curves and become profitable quant investors. The advance of open source ecosystem truly empowers more retail investors through data science.