trading/docs/plans/2026-02-22-sprint-plan.md

Trading Bot — Sprint Plan

Sprint 1: Foundation

Goal: Working project skeleton with database, shared libraries, and infrastructure containers.

Acceptance Criteria:

• docker compose up -d postgres redis boots healthy containers
• python -m alembic upgrade head applies all migrations cleanly
• python -m pytest tests/ — all unit tests pass
• Shared libraries importable: shared.config, shared.redis_streams, shared.telemetry, shared.models, shared.schemas, shared.db

Deliverables:

• pyproject.toml with all dependency groups
• docker-compose.yml with postgres+timescaledb, redis, ollama
• .env.example, .gitignore
• shared/ package: config, redis_streams, telemetry, db
• shared/models/ — all SQLAlchemy models (trading, news, learning, auth, timeseries)
• shared/schemas/ — all Pydantic schemas
• alembic/ — initial migration with hypertables
• Unit tests for redis_streams, models, schemas

Tasks (Plan Tasks 1-4):

• Task 1: Python monorepo setup + shared utilities
• Task 2: Docker Compose infrastructure
• Task 3: Database models + Alembic migrations
• Task 4: Pydantic schemas

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Sprint 2: Data Pipeline

Goal: News articles flow from RSS/Reddit through sentiment analysis into scored Redis stream.

Acceptance Criteria:

• News fetcher polls RSS feeds and publishes RawArticle to news:raw stream
• Sentiment analyzer consumes news:raw, scores with FinBERT, publishes to news:scored
• Broker abstraction interface defined, Alpaca implementation can fetch account/positions
• All unit tests pass

Deliverables:

• shared/broker/ — abstract interface + Alpaca implementation
• services/news-fetcher/ — RSS + Reddit sources
• services/sentiment-analyzer/ — FinBERT + Ollama fallback + ticker extraction
• Unit tests for broker, news fetcher, sentiment analyzer

Tasks (Plan Tasks 5-7):

• Task 5: Brokerage abstraction layer
• Task 6: News fetcher service
• Task 7: Sentiment analyzer service

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Sprint 3: Trading Core

Goal: System generates trade signals from market data + sentiment and executes paper trades.

Acceptance Criteria:

• 3 strategies (momentum, mean reversion, news-driven) produce correct signals for known inputs
• Signal generator combines strategies via weighted ensemble, publishes to signals:generated
• Trade executor applies risk checks, submits orders via broker, records trades in DB
• All unit tests pass

Deliverables:

• shared/strategies/ — base interface + 3 strategy implementations
• services/signal-generator/ — market data consumer + weighted ensemble
• services/trade-executor/ — risk manager + execution loop
• Unit tests for strategies, signal generator, trade executor

Tasks (Plan Tasks 8-10):

• Task 8: Strategy implementations
• Task 9: Signal generator service
• Task 10: Trade executor service

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Sprint 4: Intelligence

Goal: System learns from trade outcomes and can replay historical data for backtesting.

Acceptance Criteria:

• Learning engine computes P&L, adjusts strategy weights respecting all guardrails
• Backtester replays historical data through same strategies, produces equity curves + metrics
• Weight adjustments logged in learning_adjustments table
• All unit tests pass

Deliverables:

• services/learning-engine/ — evaluator + weight adjuster
• backtester/ — engine, simulated broker, data loader, metrics
• Unit tests for learning engine, backtester

Tasks (Plan Tasks 11-12):

• Task 11: Learning engine service
• Task 12: Backtesting engine

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Sprint 5: API & Dashboard

Goal: Web interface for monitoring and controlling the trading bot.

Acceptance Criteria:

• Passkey registration and login works end-to-end
• All API endpoints return correct data (portfolio, trades, signals, strategies, news)
• Control endpoints pause/resume trading, force-close positions
• Dashboard renders all 5 views with real-time WebSocket updates
• All tests pass (backend + frontend)

Deliverables:

• services/api-gateway/ — FastAPI with passkey auth, all routes, WebSocket
• dashboard/ — React app with auth, portfolio, trades, strategies, news, backtest views
• Unit tests for API routes, auth flow

Tasks (Plan Tasks 13-16):

• Task 13: API Gateway — Auth
• Task 14: API Gateway — Trading endpoints
• Task 15: Dashboard — Setup & Auth
• Task 16: Dashboard — Trading views

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Sprint 6: Integration & Deployment

Goal: Full system runs in Docker Compose, passes integration tests.

Acceptance Criteria:

• docker compose up starts all services, all health checks pass
• Smoke test script passes (hits key endpoints, verifies responses)
• Integration tests pass (news pipeline end-to-end, trading flow end-to-end)
• Default strategies seeded in database

Deliverables:

• docker/ — Dockerfiles for all services
• Updated docker-compose.yml with all application services
• scripts/smoke-test.sh, scripts/seed_strategies.py
• tests/integration/ — pipeline and trading flow tests

Tasks (Plan Tasks 17-18):

• Task 17: Dockerfiles & full Docker Compose
• Task 18: Integration testing & seed data