# Trading Bot

Automated stock trading bot combining news sentiment analysis with technical strategies. Built as event-driven Python microservices communicating via Redis Streams, with a React/TypeScript dashboard and Alpaca paper trading.

## Architecture

```
RSS/Reddit ─→ news_fetcher ─→ [news:raw] ─→ sentiment_analyzer ─→ [news:scored] ┐
                                                                                  │
Alpaca OHLCV ─→ market_data ─→ [market:bars] ────────────────────────────────────┤
                                                                                  │
                                              signal_generator ←──────────────────┘
                                                    │
                                              [signals:generated]
                                                    │
                                              trade_executor ─→ [trades:executed] ─→ learning_engine
                                                    │                                       │
                                                Alpaca API                             Redis (weights)
```

**Services**: news-fetcher, sentiment-analyzer, signal-generator, trade-executor, learning-engine, market-data, api-gateway, dashboard

**9 Trading Strategies**: Momentum, Mean Reversion, News-Driven, Value, MACD Crossover, Bollinger Breakout, VWAP, Liquidity, MA Stack — combined via weighted ensemble with multi-armed bandit weight adjustment.

## Tech Stack

- **Backend**: Python 3.12, FastAPI, SQLAlchemy 2.0 (async), Pydantic v2, alpaca-py
- **Frontend**: React 19, TypeScript, Vite, Tailwind CSS, TanStack Query, TradingView lightweight-charts
- **ML**: transformers (FinBERT), Ollama (local LLM fallback)
- **Database**: PostgreSQL 16 + TimescaleDB, Alembic migrations (16 tables)
- **Messaging**: Redis Streams + pub/sub
- **Auth**: WebAuthn/Passkeys + JWT sessions
- **Observability**: OpenTelemetry + Prometheus metrics
- **CI/CD**: Woodpecker → Docker → Kubernetes

## Quick Start

```bash
# Full stack with Docker Compose
docker compose up -d

# Seed default strategies
docker compose exec api-gateway python -m scripts.seed_strategies
```

## Development

```bash
# Create virtual environment
python3 -m venv .venv && source .venv/bin/activate

# Install all dependencies
pip install -e ".[api,news,sentiment,trading,backtester,dev]"

# Run unit tests (404 tests)
python -m pytest tests/ -v -m "not integration"

# Run integration tests (requires Redis + PostgreSQL)
python -m pytest tests/ -v -m integration

# Dashboard development
cd dashboard && npm install && npm run dev
```

## Project Structure

```
trading-bot/
├── shared/              # Shared libraries (config, DB, Redis, models, schemas, broker, strategies, fundamentals)
├── services/            # 7 microservices (news_fetcher, sentiment_analyzer, signal_generator,
│                        #   trade_executor, learning_engine, market_data, api_gateway)
├── backtester/          # Historical replay engine with simulated broker
├── dashboard/           # React 19 / TypeScript / Vite frontend
├── docker/              # Dockerfiles and nginx configs
├── scripts/             # Seed scripts and smoke tests
├── tests/               # 404 unit + 9 integration tests
├── alembic/             # Database migrations
├── docker-compose.yml   # Full stack orchestration
├── .woodpecker.yml      # CI/CD pipeline
└── pyproject.toml       # Python monorepo with optional dependency groups
```