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Viktor Barzin 5f5529ef09 feat(dashboard): 3 components for the strategy page 
TickerScorecardTable: bridge-status badges (WOULD-TRADE/HOLDING/etc),
conviction bar, unrealised P&L, manual-close button.
BacktestRunHistory: sortable run list with return/sharpe/alpha columns,
row click selects a run for detail view.
StrategyVsBenchmarkCurve: dual lightweight-charts line (strategy blue,
SPY dashed grey) with legend.
2026-05-24 00:48:22 +00:00
.claude fix: resolve all remaining TODOs, add dev mode auth bypass 2026-02-25 22:02:25 +00:00
.planning/codebase docs: map existing codebase 2026-02-23 20:04:05 +00:00
alembic feat: add Alembic migration for Meet Kevin tables 2026-05-21 19:31:21 +00:00
backtester fix: resolve 13 important issues from code review 2026-02-22 17:58:01 +00:00
dashboard feat(dashboard): 3 components for the strategy page 2026-05-24 00:48:22 +00:00
docker fix(docker): drop sentiment extras from runtime image 2026-05-22 20:15:06 +00:00
docs/plans add Meet Kevin v2 implementation plan (3 phases, 22 tasks) 2026-05-24 00:40:20 +00:00
scripts feat: wire 6 new strategies and fundamentals into signal generator 2026-02-23 21:55:59 +00:00
services feat(meet-kevin): throttle inter-video LLM calls (30s) to stay under Anthropic RPM 2026-05-22 20:25:19 +00:00
shared feat(kevin): KevinDecision + KevinAccountState schemas 2026-05-24 00:44:57 +00:00
tests feat(kevin): KevinDecision + KevinAccountState schemas 2026-05-24 00:44:57 +00:00
.env.example feat: productionize local service — fix signal pipeline, lower thresholds, add company-name ticker extraction 2026-02-22 22:17:26 +00:00
.gitignore feat: docker compose infrastructure — postgres+timescaledb, redis, ollama 2026-02-22 15:11:50 +00:00
.woodpecker.yml ci: include meet_kevin extras in test step (yt-dlp, feedparser, anthropic, httpx) 2026-05-22 20:00:59 +00:00
alembic.ini feat: database models and alembic migrations — all tables per design 2026-02-22 15:17:07 +00:00
docker-compose.yml feat: productionize local service — fix signal pipeline, lower thresholds, add company-name ticker extraction 2026-02-22 22:17:26 +00:00
pyproject.toml refactor(meet-kevin): switch LLM back to native Anthropic SDK with OAuth bearer 2026-05-22 19:24:40 +00:00
README.md fix: resolve all remaining TODOs, add dev mode auth bypass 2026-02-25 22:02:25 +00:00

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