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- Learning engine: expand default weights from 3 to all 9 strategies - Learning engine: resolve placeholder strategy_id with DB lookup - Learning engine: pass strategy_sources from trade execution - Trade executor: respect trading:paused Redis flag in RiskManager - Portfolio sync: compute actual daily P&L from day-start snapshot - Portfolio API: cumulative P&L from first snapshot, read pause flag - Portfolio metrics: compute max drawdown and avg hold duration - Add strategy_sources field to TradeExecution schema - Add dev_mode config (TRADING_DEV_MODE) to bypass auth for local dev - Dashboard: VITE_DEV_MODE bypasses ProtectedRoute and 401 redirects - Vite proxy target configurable via VITE_API_TARGET - Add top-level README.md and remaining-work-plan.md - Update CLAUDE.md with correct counts and remove stale TODOs - 404 tests passing Made-with: Cursor
81 lines
3.7 KiB
Markdown
81 lines
3.7 KiB
Markdown
# Trading Bot
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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.
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## Architecture
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```
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RSS/Reddit ─→ news_fetcher ─→ [news:raw] ─→ sentiment_analyzer ─→ [news:scored] ┐
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│
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Alpaca OHLCV ─→ market_data ─→ [market:bars] ────────────────────────────────────┤
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│
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signal_generator ←──────────────────┘
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│
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[signals:generated]
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│
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trade_executor ─→ [trades:executed] ─→ learning_engine
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│ │
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Alpaca API Redis (weights)
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```
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**Services**: news-fetcher, sentiment-analyzer, signal-generator, trade-executor, learning-engine, market-data, api-gateway, dashboard
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**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.
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## Tech Stack
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- **Backend**: Python 3.12, FastAPI, SQLAlchemy 2.0 (async), Pydantic v2, alpaca-py
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- **Frontend**: React 19, TypeScript, Vite, Tailwind CSS, TanStack Query, TradingView lightweight-charts
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- **ML**: transformers (FinBERT), Ollama (local LLM fallback)
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- **Database**: PostgreSQL 16 + TimescaleDB, Alembic migrations (16 tables)
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- **Messaging**: Redis Streams + pub/sub
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- **Auth**: WebAuthn/Passkeys + JWT sessions
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- **Observability**: OpenTelemetry + Prometheus metrics
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- **CI/CD**: Woodpecker → Docker → Kubernetes
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## Quick Start
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```bash
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# Full stack with Docker Compose
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docker compose up -d
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# Seed default strategies
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docker compose exec api-gateway python -m scripts.seed_strategies
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```
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## Development
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```bash
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# Create virtual environment
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python3 -m venv .venv && source .venv/bin/activate
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# Install all dependencies
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pip install -e ".[api,news,sentiment,trading,backtester,dev]"
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# Run unit tests (404 tests)
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python -m pytest tests/ -v -m "not integration"
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# Run integration tests (requires Redis + PostgreSQL)
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python -m pytest tests/ -v -m integration
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# Dashboard development
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cd dashboard && npm install && npm run dev
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```
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## Project Structure
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```
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trading-bot/
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├── shared/ # Shared libraries (config, DB, Redis, models, schemas, broker, strategies, fundamentals)
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├── services/ # 7 microservices (news_fetcher, sentiment_analyzer, signal_generator,
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│ # trade_executor, learning_engine, market_data, api_gateway)
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├── backtester/ # Historical replay engine with simulated broker
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├── dashboard/ # React 19 / TypeScript / Vite frontend
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├── docker/ # Dockerfiles and nginx configs
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├── scripts/ # Seed scripts and smoke tests
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├── tests/ # 404 unit + 9 integration tests
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├── alembic/ # Database migrations
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├── docker-compose.yml # Full stack orchestration
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├── .woodpecker.yml # CI/CD pipeline
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└── pyproject.toml # Python monorepo with optional dependency groups
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```
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