AlphaPredictor

AlphaPredictor is a stock analytics web app built with Streamlit. It downloads 5 years of historical price data, engineers return-based features, trains three machine learning models, and predicts the next-day closing price — all with an interactive dashboard.

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Features

• Fetches 5 years of historical market data via yfinance
• Trains three independent models:
  • Linear Regression — fast baseline
  • Random Forest Regressor — ensemble tree model
  • XGBoost Regressor — gradient-boosted trees
• Predicts next-day closing price as a 3-model ensemble average
• Confidence score derived from how closely the three models agree
• Bullish / Bearish trend signal (▲ / ▼)
• Model Comparison table — MAE, RMSE, and MAPE on a held-out test set
• Actual vs Predicted chart — visual comparison of all three models on the test period
• Historical price chart with 20-day and 50-day moving averages (Plotly)
• Modular codebase, clean error handling, and type-annotated throughout

Project Structure

AlphaPredictor/
├── app.py                 # Streamlit entry point
├── modules/
│   ├── data_loader.py     # Fetch & clean historical stock data (yfinance)
│   ├── model.py           # Feature engineering + LR / RF / XGBoost training
│   └── visualization.py   # Historical price chart (Plotly)
├── data/                  # Optional folder for cached CSVs
├── requirements.txt
└── README.md

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Installation

python -m venv .venv
# Windows
.venv\Scripts\activate
# macOS / Linux
source .venv/bin/activate

pip install -r requirements.txt

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Run the App

streamlit run app.py

Open the URL shown in the terminal (usually http://localhost:8501).

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How It Works

1. Enter a ticker such as AAPL, TSLA, or MSFT and click Predict →.
2. The app downloads 5 years of daily OHLCV data.
3. Feature engineering converts raw prices to stationary daily returns, then builds:
   • Three lag features (lag_1, lag_2, lag_3)
   • Two rolling-average features (ma_5, ma_10)
4. Train / test split — oldest 80% for training, newest 20% for held-out evaluation (no shuffle — time-series integrity is preserved).
5. Each model is evaluated on the test window → MAE, RMSE, MAPE reported.
6. All three models are retrained on 100% of data before predicting tomorrow, so the most recent market behaviour is captured.
7. Tomorrow's predicted return is converted back to a price: price = latest_close × (1 + predicted_return).
8. The ensemble prediction is the simple average of all three model prices.
9. Confidence is estimated from the standard deviation of the three predicted returns — lower spread = higher confidence.

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Output Explained

Field	Description
Latest Close	Most recent closing price
Predicted Next Close	Ensemble (average of LR + RF + XGB) price forecast
Expected Change	% difference between ensemble prediction and latest close
Confidence	0–100 score; higher = more agreement between models
Signal	▲ Bullish if prediction > latest close, ▼ Bearish otherwise

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Model Comparison Table

After prediction, the app shows a per-model breakdown on the held-out test set:

Model	MAE	RMSE	MAPE (%)
Linear Regression	—	—	—
Random Forest	—	—	—
XGBoost	—	—	—

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Design Decisions

• Returns instead of raw prices — keeps the target stationary; Random Forest and XGBoost can extrapolate correctly even at all-time highs.
• Two-phase training — eval phase on the test split gives honest error metrics; final phase retrains on all data before forecasting.
• No data leakage — the latest row (for tomorrow's forecast) is captured before dropna() removes it from the training set.
• MultiIndex compatibility — newer yfinance versions return a MultiIndex column structure; columns are flattened automatically after download.

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Ideas for Extension

• Add RSI, MACD, Bollinger Bands as features
• Experiment with LSTM or Transformer-based models
• Add ticker autocomplete / search
• Cache downloaded data with st.cache_data to speed up repeated runs
• Deploy to Streamlit Community Cloud

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Disclaimer

This project is for educational purposes only and is not financial advice. Stock markets are inherently unpredictable; never make investment decisions based solely on model output.