A comprehensive stock analysis and prediction system that combines multiple AI approaches:
-
Stock Analysis & Prediction
- Real-time stock data fetching using yfinance and Tiingo APIs
- Technical analysis with candlestick visualization
- Price prediction using LSTM-CNN hybrid models
- Interactive dashboards for market monitoring
-
AI Integration
- LLM-powered fundamental analysis using GPT-4
- Custom LangChain agents for market research
- Deep learning models for price prediction
- Q-Learning implementation for trading decisions
-
Technical Implementation
- Streamlit-based interactive web interface
- Asynchronous data processing for real-time updates
- GPU-accelerated computations with PyTorch
- Custom neural network architectures with attention mechanisms
The system allows users to:
- Search and analyze any publicly traded company
- Get real-time price updates and predictions
- Access AI-powered fundamental analysis
- Visualize market data through interactive charts
A state-of-the-art algorithmic trading system leveraging deep reinforcement learning for autonomous trading strategies. This system combines advanced ML techniques with high-frequency trading capabilities to optimize portfolio performance across diverse market conditions.
- 🔹 Autonomous trading via deep reinforcement learning
- 🔹 Multi-agent system for market interaction
- 🔹 Dynamic portfolio optimization
- 🔹 Real-time market microstructure analysis
- 🔹 Risk-adjusted return optimization
├── PyTorch 2.0+ : Neural network framework
│ ├── CUDA optimization
│ └── Distributed training
├── Ray/RLlib : RL framework
│ ├── PPO, DDPG, SAC algorithms
│ └── Multi-agent support
└── TensorBoard : Training visualization
├── pandas-ta : Technical analysis
├── scikit-learn : Feature preprocessing
├── NumPy/CuPy : GPU computation
├── yfinance : Market data
└── Tiingo : Financial data API
├── Streamlit : Web interface
├── FastAPI : REST endpoints
└── LangChain : LLM integration
├── Plotly/Dash : Interactive dashboards
├── mplfinance : Candlestick charts
└── NetworkX : Agent interaction graphs
MarketAnalysis
├── Order Book Processing
│ ├── Bid-ask imbalance
│ ├── Order flow toxicity
│ └── Liquidity analysis
│
├── Technical Indicators
│ ├── Adaptive moving averages
│ ├── Volatility metrics
│ └── Volume profiles
│
└── Sentiment Analysis
├── News processing
├── Social media signals
└── Market sentiment indexTradingStrategy
├── Signal Generation
│ ├── Multi-timeframe analysis
│ ├── Regime detection
│ └── Alpha factor creation
│
├── Position Management
│ ├── Dynamic sizing
│ ├── Risk allocation
│ └── Correlation analysis
│
└── Execution Engine
├── Smart order routing
├── Transaction cost analysis
└── Slippage optimizationRiskEngine
├── Portfolio Analytics
│ ├── Value at Risk (VaR)
│ ├── Expected Shortfall
│ └── Beta exposure
│
├── Position Controls
│ ├── Dynamic stop-loss
│ ├── Take-profit optimization
│ └── Exposure limits
│
└── Market Impact
├── Liquidity analysis
├── Impact modeling
└── Cost estimationRLFramework
├── Environment
│ ├── Market simulator
│ ├── Reward shaping
│ └── State space design
│
├── Agent Architecture
│ ├── LSTM-CNN hybrid
│ ├── Attention mechanism
│ └── Multi-head output
│
└── Training Pipeline
├── Experience replay
├── Curriculum learning
└── Model validation├── Deep Learning : PyTorch with CUDA optimization
├── RL Framework : Ray/RLlib for distributed training
├── Data Pipeline : pandas-ta, scikit-learn
├── Computation : NumPy/CuPy (GPU-accelerated)
├── Visualization : Plotly/Dash
├── Database : MongoDB, Redis
└── API Layer : FastAPI
- 🧠 LSTM networks for temporal pattern recognition
- 🔄 Conv1D/Conv2D layers for feature extraction
- 📊 Custom attention mechanisms
- 🛡️ Dropout regularization (0.2)
- ⚡ ReLU activation with orthogonal initialization
├── Real-time Data Processing
│ ├── Order book reconstruction
│ ├── Market regime detection (HMM)
│ └── Sentiment analysis
│
├── Technical Analysis
│ ├── Custom indicators
│ ├── Multi-timeframe analysis
│ └── Feature engineering
│
└── Risk Management
├── Position sizing
├── Dynamic stop-loss
└── Exposure controls
- ⚡ Microsecond-precision execution
- 🔄 Smart order routing
- 📊 Real-time risk monitoring
- 🛡️ Transaction cost optimization
Input Layer (OHLCV + Features)
↓
Conv1D/Conv2D Feature Extraction
↓
LSTM Temporal Processing
↓
Attention Layer
↓
Dense Strategy Layer
↓
Action Output (Trading Decisions)- 📈 Real-time market data integration
- 🔄 Continuous feature engineering
- 📊 90/10 train-validation split
- 🎯 20-day rolling window
- Environment Setup
conda create -n rl-trading python=3.9
conda activate rl-trading
pip install -r requirements.txt- Configuration
cp config/example.yaml config/local.yaml
# Edit local.yaml with your settings- Launch Training
python train.py --config configs/ppo_config.yaml- Window Size: 20 days
- Batch Size: 64
- Learning Rate: 0.01
- Training Epochs: 150
- Prediction Horizon: 10 days
.
├── 📂 agents/ # RL implementations
├── 📂 data/ # Data pipeline
├── 📂 environments/ # Trading environments
├── 📂 models/ # Trained models
├── 📂 risk/ # Risk management
├── 📂 execution/ # Order execution
└── 📂 analysis/ # Research & reports