Add comprehensive project documentation and fix data pipeline

- Add detailed README with architecture diagram and usage instructions
- Add API, configuration, and development documentation
- Fix price data column handling for yfinance auto_adjust=True
- Fix model feature dimension indexing and temporal attention batching
- Add missing imports and position tracking in paper broker
- Add python-dotenv support for environment variables
- Update .gitignore with Python artifacts and environment files

docs/DEVELOPMENT.md

@@ -0,0 +1,194 @@
+# Development Guide
+
+## Setting Up Development Environment
+
+```bash
+# Clone repo
+git clone <repository-url>
+cd stock_gnn_r9700
+
+# Create virtual environment
+python -m venv venv
+source venv/bin/activate
+
+# Install in editable mode + dev dependencies
+pip install -r requirements.txt
+pip install black isort mypy pytest pytest-asyncio
+```
+
+## Code Style
+
+Format with `black` and `isort`:
+
+```bash
+black src/ config.py main.py live_trading.py benchmark.py
+isort src/ config.py main.py live_trading.py benchmark.py
+```
+
+Type-check with `mypy`:
+
+```bash
+mypy src/
+```
+
+## Running Tests
+
+```bash
+pytest tests/ -v
+```
+
+## Adding a New Data Processor
+
+1. Create file in `src/data/`:
+
+```python
+# src/data/earnings_processor.py
+import logging
+from typing import Dict, List
+
+logger = logging.getLogger(__name__)
+
+class EarningsProcessor:
+    def __init__(self):
+        self.earnings_data = {}
+
+    def fetch_earnings(self, tickers: List[str], start_date: str, end_date: str):
+        """Fetch earnings data."""
+        pass
+
+    def get_earnings_features(self, ticker: str, date: str) -> Dict:
+        """Return earnings-based features."""
+        return {"earnings_surprise": 0.0, "eps_growth": 0.0}
+```
+
+2. Import and initialize in `StockDataPipeline.__init__`:
+
+```python
+from src.data.earnings_processor import EarningsProcessor
+
+class StockDataPipeline:
+    def __init__(self):
+        ...
+        self.earnings_processor = EarningsProcessor()
+```
+
+3. Call `fetch_earnings` in `update_alternative_data` and include features in `create_training_dataset`.
+
+## Adding a New Model
+
+1. Create file in `src/models/`:
+
+```python
+# src/models/my_model.py
+import torch
+import torch.nn as nn
+from config import config
+
+class MyModel(nn.Module):
+    def __init__(self, num_features: int):
+        super().__init__()
+        self.fc = nn.Linear(num_features, 1)
+
+    def forward(self, data):
+        return self.fc(data.x)
+```
+
+2. Wrap with `AMDOptimizer` before training:
+
+```python
+from src.amd.optimizations import AMDOptimizer
+
+model = MyModel(num_features)
+model = AMDOptimizer().optimize_model(model)
+```
+
+## Adding a New Trading Strategy
+
+1. Subclass `Broker` in `src/trading/`:
+
+```python
+from src.trading.broker import Broker
+
+class AlpacaBroker(Broker):
+    def submit_order(self, order):
+        # Implementation
+        pass
+```
+
+2. Instantiate in `live_trading.py` and pass to `RealTimeTrader`.
+
+## Adding API Endpoints
+
+1. Create router in `src/web/api/`:
+
+```python
+from fastapi import APIRouter
+
+router = APIRouter()
+
+@router.get("/health")
+async def health():
+    return {"status": "ok"}
+```
+
+2. Include in `src/web/app.py`:
+
+```python
+from src.web.api import my_endpoints
+app.include_router(my_endpoints.router, prefix="/api/my")
+```
+
+## Environment Variables
+
+Set in `.env` file (not committed):
+
+```bash
+POLYGON_API_KEY=your_key
+ALPHA_VANTAGE_API_KEY=your_key
+IB_CLIENT_ID=1
+```
+
+Load with `python-dotenv` if needed.
+
+## Git Workflow
+
+```bash
+# Feature branch
+git checkout -b feature/my-feature
+
+# Commit
+git add .
+git commit -m "feat: add my feature"
+
+# Push
+git push origin feature/my-feature
+```
+
+## Debugging
+
+### GPU Memory Issues
+
+```python
+from src.utils.memory_manager import MemoryManager
+
+mm = MemoryManager()
+print(mm.get_memory_stats())
+```
+
+### Model Inspection
+
+```python
+from src.models.gnn_model import CorporateActionAwareGNN
+
+model = CorporateActionAwareGNN(num_features=10)
+print(sum(p.numel() for p in model.parameters()))  # parameter count
+```
+
+### Data Inspection
+
+```python
+from src.data.pipeline import StockDataPipeline
+
+pipe = StockDataPipeline()
+print(pipe.price_data['AAPL'].tail())
+```