# Trading Bot

Python trading bot project using machine learning to decide which assets should be held. The bot currently uses the Alpaca Markets API for market data and paper trading, with any future live trading kept behind explicit safeguards.

## Project Status

This repository now has a working prototype flow for Alpaca market data, dataset generation, XGBoost training, prediction, and paper-trading rebalancing. The strategy is still intentionally narrow: it currently focuses on `SPY` exposure using a small market-regime feature set.

## Main Parts

1. Training data collection
2. Model generation and training
3. Trading bot using the trained model
4. Optional read-only web UI for trading status

## Environment

Use `mise` to manage Python and `uv` to manage Python packages.

```sh
mise install
mise exec -- uv sync
mise exec -- uv run python --version
```

The current local Python version is pinned in `.mise.toml`.

## Dependencies

Runtime dependencies are declared in `pyproject.toml`.

- `alpaca-py` for Alpaca market data and trading clients.
- `pandas` for tabular candle data handling.
- `pyarrow` for Parquet file support.
- `xgboost` and `scikit-learn` for model training and evaluation.
- `python-dotenv` for loading local Alpaca credentials from `.env`.

## Trading Safety

The default target is Alpaca paper trading. Real trading should only be added later with explicit configuration, clear documentation, and tests around order generation and broker integration.

Do not commit secrets such as Alpaca API keys, account identifiers, API tokens, or private configuration.

## Current Commands

Fetch Alpaca daily candles:

```sh
mise exec -- uv run python src/trading_bot/data/fetch_alpaca_daily.py SPY
```

Build the training dataset and train the current model:

```sh
mise exec -- uv run python src/trading_bot/data/train_pipeline.py
```

Run the current Alpaca paper-trading rebalance flow:

```sh
mise exec -- uv run python src/trading_bot/models/trade.py \
  --paper \
  --fetch-recent-data \
  --model-path models/spy_xgb_v1.json \
  --metadata-path models/spy_xgb_v1_meta.json
```

The main Python entry points are:

- `src/trading_bot/data/train_pipeline.py` for dataset generation and model training.
- `src/trading_bot/models/trade.py` for Alpaca account inspection and SPY rebalancing.

## Documentation

See [docs/README.md](docs/README.md) for the initial architecture notes and decision log.

The current data collection note is [docs/data-fetcher.md](docs/data-fetcher.md), and the training dataset contract is [docs/training-dataset.md](docs/training-dataset.md).

The first dataset generation notebook is [notebooks/spy_direction_dataset.ipynb](notebooks/spy_direction_dataset.ipynb).

Manual test instructions for the Alpaca fetcher are in [docs/manual-test/README.md](docs/manual-test/README.md).