# Alpha Lab

Quantitative research experiments for qshare library. This repository contains Jupyter notebooks and analysis scripts for exploring trading strategies and machine learning models.

## Philosophy

- **Notebook-centric**: Experiments are interactive notebooks, not rigid scripts
- **Minimal abstraction**: Simple functions over complex class hierarchies
- **Self-contained**: Each task directory is independent
- **Ad-hoc friendly**: Easy to modify for exploration

## Structure

```
alpha_lab/
├── common/              # Shared utilities (keep minimal!)
│   ├── __init__.py
│   ├── paths.py         # Path management
│   └── plotting.py      # Common plotting functions
│
├── cta_1d/             # CTA 1-day return prediction
│   ├── __init__.py     # Re-exports from src/
│   ├── config.yaml     # Task configuration
│   ├── src/            # Implementation modules
│   │   ├── __init__.py
│   │   ├── loader.py   # CTA1DLoader
│   │   ├── train.py    # Training functions
│   │   ├── backtest.py # Backtest functions
│   │   └── labels.py   # Label blending utilities
│   ├── 01_data_check.ipynb
│   ├── 02_label_analysis.ipynb
│   ├── 03_baseline_xgb.ipynb
│   └── 04_blend_comparison.ipynb
│
├── stock_15m/          # Stock 15-minute return prediction
│   ├── __init__.py     # Re-exports from src/
│   ├── config.yaml     # Task configuration
│   ├── src/            # Implementation modules
│   │   ├── __init__.py
│   │   ├── loader.py   # Stock15mLoader
│   │   └── train.py    # Training functions
│   ├── 01_data_exploration.ipynb
│   └── 02_baseline_model.ipynb
│
└── results/            # Output directory (gitignored)
    ├── cta_1d/
    └── stock_15m/
```

## Setup

```bash
# Install dependencies
pip install -r requirements.txt

# Create environment file
cp .env.template .env
# Edit .env with your settings
```

## Usage

### Interactive (Notebooks)

Start Jupyter and run notebooks interactively:

```bash
jupyter notebook
```

Each task directory contains numbered notebooks:
- `01_*.ipynb` - Data loading and exploration
- `02_*.ipynb` - Analysis and baseline models
- `03_*.ipynb` - Advanced experiments
- `04_*.ipynb` - Comparisons and ablations

### Command Line

Train models from config files:

```bash
# CTA 1D
python -m cta_1d.train --config cta_1d/config.yaml --output results/cta_1d/exp01

# Stock 15m
python -m stock_15m.train --config stock_15m/config.yaml --output results/stock_15m/exp01

# CTA Backtest
python -m cta_1d.backtest \
    --model results/cta_1d/exp01/model.json \
    --dt-range 2023-01-01 2023-12-31 \
    --output results/cta_1d/backtest_01
```

### Python API

```python
# Import from task root (re-exports from src/)
from cta_1d import CTA1DLoader, train_model, TrainConfig
from stock_15m import Stock15mLoader, train_model, TrainConfig
from common import create_experiment_dir
```

## Experiment Tracking

Experiments are tracked manually in `results/{task}/README.md`:

```markdown
## 2025-01-15: Baseline XGB
- Notebook: `cta_1d/03_baseline_xgb.ipynb` (cells 1-50)
- Config: eta=0.5, lambda=0.1
- Train IC: 0.042
- Test IC: 0.038
- Notes: Dual normalization, 4 trades/day
```

## Adding a New Task

1. Create directory: `mkdir my_task`
2. Add `src/` subdirectory with:
   - `__init__.py` - Export public APIs
   - `loader.py` - Dataset loader class
   - Other modules as needed
3. Add root `__init__.py` that re-exports from `src/`
4. Create numbered notebooks
5. Add entry to `results/my_task/README.md`

## Git Worktrees

This repository uses git worktrees for parallel experiment development:

| Worktree | Branch | Purpose |
|----------|--------|---------|
| `alpha_lab` | `master` | Main repo (reference) |
| `alpha_lab_cta_1d` | `cta_1d_exp` | CTA 1-day experiments |
| `alpha_lab_stock_1d` | `stock_1d_exp` | Stock 1-day experiments |
| `alpha_lab_stock_15m` | `stock_15m_exp` | Stock 15-min experiments |
| `alpha_lab_data_ops` | `data_ops_exp` | Data ops research |

```bash
# Create a new worktree
git worktree add ../alpha_lab_new_exp -b new_exp

# List all worktrees
git worktree list

# Remove a worktree when done
git worktree remove ../alpha_lab_new_exp
```

## Best Practices

1. **Keep it simple**: Only add to `common/` after 3+ copies
2. **Module organization**: Place implementation in `src/`, re-export from root `__init__.py`
3. **Notebook configs**: Define CONFIG dict in first cell for easy modification
4. **Document results**: Update results README after significant runs
5. **Git discipline**: Don't commit large files, results, or credentials