trading/.claude/CLAUDE.md

Trading Bot — Project Knowledge

Project Overview

Automated stock trading bot combining news sentiment analysis with technical strategies. Event-driven Python microservices communicating via Redis Streams, with a React/TypeScript dashboard.

Architecture

• 7 microservices: news-fetcher, sentiment-analyzer, signal-generator, trade-executor, learning-engine, api-gateway, dashboard
• Shared libraries in shared/: config, redis_streams, telemetry, db, models, schemas, broker abstraction, strategies
• Infra: PostgreSQL+TimescaleDB, Redis Streams, Ollama (local LLM), Docker Compose
• Brokerage: Alpaca (paper trading) via abstraction layer in shared/broker/

Key Design Decisions

• Services use underscores in Python package names (e.g., services/news_fetcher/) but hyphens in docker-compose service names (e.g., news-fetcher)
• Redis Streams for inter-service messaging: news:raw, news:scored, signals:generated, trades:executed
• Sentiment analysis is tiered: FinBERT first, Ollama fallback when confidence < threshold
• Learning uses multi-armed bandit style weight adjustment with guardrails (min trades, max shift, weight floor)
• Auth is passkey/WebAuthn (py-webauthn on backend, @simplewebauthn/browser on frontend) with JWT sessions
• OpenTelemetry + Prometheus /metrics endpoints (no Prometheus/Grafana deployed — uses existing infra)

Tech Stack

• Backend: Python 3.12, FastAPI, SQLAlchemy 2.0 (async, mapped_column style), Pydantic v2, alpaca-py
• Frontend: React 18, TypeScript, Vite, Tailwind CSS, TanStack Query, TradingView lightweight-charts, Recharts
• ML: transformers (FinBERT), ollama-python
• Testing: pytest, pytest-asyncio (asyncio_mode="auto"), React Testing Library
• Database: PostgreSQL 16 + TimescaleDB extension, Alembic for migrations

Project Structure

trading-bot/
├── shared/              # Shared Python libraries
│   ├── config.py        # Pydantic BaseSettings (env_prefix="TRADING_")
│   ├── redis_streams.py # StreamPublisher + StreamConsumer
│   ├── telemetry.py     # OpenTelemetry setup
│   ├── db.py            # Async engine + sessionmaker
│   ├── models/          # SQLAlchemy models (14 tables)
│   ├── schemas/         # Pydantic v2 schemas (message types)
│   ├── broker/          # BaseBroker ABC + AlpacaBroker
│   └── strategies/      # BaseStrategy + 3 implementations
├── services/
│   ├── news_fetcher/    # RSS + Reddit → news:raw
│   ├── sentiment_analyzer/ # FinBERT + Ollama → news:scored
│   ├── signal_generator/   # Weighted ensemble → signals:generated
│   ├── trade_executor/     # Risk mgmt + Alpaca → trades:executed
│   ├── learning_engine/    # Multi-armed bandit weight adjustment
│   └── api_gateway/        # FastAPI + WebAuthn + WebSocket
├── backtester/          # Historical replay engine
├── dashboard/           # React/TypeScript (Vite)
├── docker/              # Dockerfile.service, Dockerfile.dashboard, nginx.conf
├── scripts/             # seed_strategies.py, smoke_test.sh
├── tests/               # Unit + integration tests
├── alembic/             # Database migrations
├── docker-compose.yml
└── pyproject.toml       # Monorepo with optional dep groups

Development Workflow

• Use .venv: python3 -m venv .venv && source .venv/bin/activate
• Install all deps: pip install -e ".[api,news,sentiment,trading,backtester,dev]"
• Run unit tests: python -m pytest tests/ -v -m "not integration"
• Run integration tests (requires docker): python -m pytest tests/ -v -m integration
• Dashboard dev: cd dashboard && npm install && npm run dev
• Dashboard build: cd dashboard && npm run build
• Full stack: docker compose up -d

Test Coverage (246 unit tests)

• tests/test_redis_streams.py — 5 tests
• tests/test_models.py — 21 tests
• tests/test_schemas.py — 49 tests
• tests/test_broker.py — 18 tests
• tests/test_strategies.py — 24 tests
• tests/test_backtester.py — 13 tests
• tests/services/test_news_fetcher.py — 10 tests
• tests/services/test_sentiment_analyzer.py — 19 tests
• tests/services/test_signal_generator.py — 17 tests
• tests/services/test_trade_executor.py — 16 tests
• tests/services/test_learning_engine.py — 28 tests
• tests/services/test_api_auth.py — 13 tests
• tests/services/test_api_routes.py — 13 tests
• tests/integration/ — 9 integration tests (news pipeline + trading flow)

Important Patterns

• Each service extends shared.config.BaseConfig for its own settings
• Services use StreamConsumer/StreamPublisher for Redis Streams messaging
• Broker abstraction: all trading goes through BaseBroker interface (swap Alpaca for another broker without changing services)
• Strategy abstraction: all strategies implement BaseStrategy.evaluate() returning TradeSignal | None
• The pyproject.toml uses optional dependency groups per service: [api], [news], [sentiment], [trading], [backtester]
• The webauthn package on PyPI is called webauthn (not py-webauthn)

Known Issues / TODO

• Code review was started but not completed — should be done before production use
• JWT test warnings about short HMAC keys (test-only, production keys should be 32+ bytes)
• Integration tests mock FinBERT and Alpaca — need real integration tests with live services
• No CI/CD pipeline yet
• Twitter/X source not implemented in news fetcher (only RSS + Reddit)
• Dashboard has no unit tests (only build verification)
• Strategy conflict during Sprint 3 merge — both signal-generator and strategies agents created shared/strategies/; resolved by keeping the dedicated strategies agent's versions

How This Was Built

• Built in 6 sprints using parallel subagent worktrees
• Each sprint had 2-3 agents working independently on non-overlapping files
• Worktree branches merged into master after each sprint
• Tests verified after every merge (246 tests, 0 failures)
• Agent teams didn't work well (idle notification issues) — switched to direct subagent Task calls with worktree isolation