Social-Sentiment-Driven Crypto Predictor (Baseline)

This Streamlit app implements core functionality from the attached PRD using the provided dataset.csv (market data). Since the dataset includes no sentiment columns, the app focuses on market-driven features but supports uploading a merged CSV that contains sentiment fields for extended modelling.

Features

• Roles: Trader, Analyst, Researcher (role-appropriate controls and views)
• EDA: time-series price chart, summary stats, correlation matrix
• Sentiment: detects sentiment-like columns; shows correlation vs returns; what-if slider
• Feature engineering: returns, moving averages, volatility, RSI, lags
• Models: Linear Regression, Random Forest, KNN with auto-select best (by RMSE)
• Hyperparameters: RF trees and KNN neighbors (Researcher role)
• Forecast horizons: 1 day and 7 days; next-period forecast shown with date and return
• Metrics: MAE, RMSE, MAPE, R²; latency for prediction path
• Interpretability: feature importance/coefficients; permutation importance fallback
• Backtest: simple thresholded long-only vs buy-and-hold
• Exports: predictions, filtered dataset, engineered features; optional model saving

Project Structure

• streamlit_app.py — Streamlit UI and workflow
• features.py — data parsing, feature engineering, modelling, backtesting helpers
• dataset.csv — provided historical OHLCV data (example: AAVE)
• requirements.txt — dependencies

Run Locally

1. Install dependencies

   pip install -r requirements.txt

2. Launch the app

   streamlit run streamlit_app.py

3. In the app sidebar, optionally upload a merged dataset with sentiment columns to extend the feature set.

Mapping to PRD

• User-friendly interface: Streamlit UI with roles and clear tabs.
• Transparency/interpretability: linear/RF/KNN, coefficients/importances, permutation importance.
• Real-time responsiveness: shows prediction latency; lightweight models and caching.
• Visualization suite: price charts, correlation matrix, sentiment vs return table.
• Back-testing & simulated trading: naive long-only vs buy-and-hold.
• Telemetry: local CSV logging for export actions (extend as needed).

Open items (for a v2):

• Live social ingestion pipeline and alignment (e.g., Twitter/Reddit APIs).
• Scenario analysis UX beyond a single slider; multi-asset support.
• Strategy backtests with transaction costs and risk metrics.

Notes

• The PRD calls for sentiment integration and role-based UX. This baseline covers market-only modelling with interpretable algorithms and extensible inputs.
• For larger datasets or more sophisticated strategies (e.g., transaction costs, risk metrics), extend the simple_backtest logic.
• For feature interpretability on KNN, consider permutation importance in a future iteration.