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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 |
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| .claude | ||
| .planning/codebase | ||
| alembic | ||
| backtester | ||
| dashboard | ||
| docker | ||
| docs/plans | ||
| scripts | ||
| services | ||
| shared | ||
| tests | ||
| .env.example | ||
| .gitignore | ||
| .woodpecker.yml | ||
| alembic.ini | ||
| docker-compose.yml | ||
| pyproject.toml | ||
| README.md | ||
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
# Full stack with Docker Compose
docker compose up -d
# Seed default strategies
docker compose exec api-gateway python -m scripts.seed_strategies
Development
# 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