What it does Downloads historical stock data from Yahoo Finance, preprocesses it into sequences, and trains either a multilayer perceptron or LSTM to predict prices. Then it backtests against historical data so you can see how wrong (or occasionally right) the model is. The author is explicit: this is a learning scaffold, not a trading system.

The interesting bit The README spends more time warning you what the project cannot do than selling what it can. That candor is the feature. The author also leaves deliberate gaps—no hyperparameter tuning, no position sizing in backtests, no live trading logic—effectively turning the repo into a homework assignment with starter code.

Key highlights

• Two model architectures included: basic MLP and LSTM via Keras
• Full pipeline from data download (pandas_datareader / fix-yahoo-finance) through preprocessing to backtesting
• Python 3.6+ required; f-strings are used throughout
• Backtesting only evaluates prediction accuracy, not strategy profitability
• Accompanying blog posts explain the theory behind each model choice

Caveats

• Yahoo Finance data source has already broken once and required a workaround; the author expects it may break again
• The preprocessing is a simple numpy array reshape with a for loop—no feature engineering beyond raw prices
• No code for the “extensions” section is actually implemented; it’s a to-do list disguised as a roadmap

Verdict Grab this if you’re a student or junior developer who wants to trace a complete ML workflow without getting buried in abstractions. Skip it if you already understand train/test splits and want production-grade quantitative finance tooling—you’ll outgrow it in an afternoon.