TradeForge is an all-in-one Python-based trading platform that combines real-time market data visualization, technical analysis, ML predictions, strategy backtesting, live charts, and simulated trade execution — all in a user-friendly Streamlit dashboard.
- Real-time OHLCV data dashboard with multiple timeframes
- SMA, EMA, RSI, MACD technical indicators
- ML-based buy/sell signal predictions (RandomForest & XGBoost)
- Strategy backtesting with portfolio simulation and performance metrics
- Simulated trade execution with testnet logging
- Live candlestick charts with auto-refresh
- Equity curve visualization and buy/sell markers
- Configurable strategy parameters (RSI thresholds, ML model selection)
- Simulated auto-trading toggle with cooldown handling (Testnet support)
- Centralized logging with color-coded console output and file logs
- Multi-page modular Streamlit design for easy navigation
Data Layer:
- Real-time OHLCV data from SQL database or CSV fallback
- Labeled datasets for ML predictions
Indicator & Analytics Layer:
- SMA, EMA, RSI, MACD calculations
- Signal generation for trading strategies
ML Prediction Layer:
- RandomForest / XGBoost models for buy/sell predictions
- Integrated with visualization for marker overlays
Backtest Engine:
- Strategy simulation with portfolio, metrics, and charts
Trade Execution Layer:
- Testnet trade placement with logging
- Cooldown management to prevent rapid orders
Visualization Layer:
- Candlestick charts with indicators, signals, and equity curve
- Plotly & Matplotlib integration
Config & Logger:
- Auto-trading toggle and strategy parameter adjustments via Streamlit
- Centralized, color-coded logging with emoji indicators
- OHLCV CSV files (e.g., BTCUSDT_15m.csv)
- ML-labeled CSVs for signal predictions
- Python 3.13
- Streamlit (UI)
- Pandas, NumPy (data processing)
- Plotly, Matplotlib (charts)
- scikit-learn
- Joblib (model serialization)
- Logging & config management
- SQL / CSV data sources
- Random Forest, XG Boost (ML models)
- User selects trading pair, interval, and chart settings from the sidebar.
- App fetches OHLCV data from SQL database or CSV fallback.
- Technical indicators (SMA, EMA, RSI, MACD) are computed.
- ML model predicts buy/sell signals and overlays them on charts.
- Users can run strategy backtests and view portfolio performance.
- Simulated trades can be executed via the Trade Executor page.
- Live charts auto-refresh with updated data and predictions.
- Strategy parameters and auto-trading status can be configured in Settings.
- Amil Gauri — Contributor, Developer, Maintainer
This project is open-source under the MIT License.
You are free to use, modify, and distribute it with proper credit.
See the full license details here.