Previous refactor (89f01ad) moved to OpenRouter because no sk-ant-api-* key
was found in Vault. Turns out claude-agent-service-spare-{1,2} hold
sk-ant-oat01-* OAuth tokens (108 chars, scope user:inference, 1-year TTL,
minted via 'claude setup-token' — see memory id=832).
These tokens work with the Anthropic SDK via the auth_token= constructor
argument (routes to Authorization: Bearer ... instead of x-api-key: ...).
They consume the Enterprise Claude subscription quota rather than
per-call billing, so the OpenRouter zero-credit problem goes away.
- llm_analyzer.py: revert OpenAI client to AsyncAnthropic; tool-use API
+ cache_control restored
- config.py: openrouter_api_key -> anthropic_oauth_token; model slug
reverted from anthropic/claude-sonnet-4.5 -> claude-sonnet-4-5
- main.py: AsyncOpenAI -> AsyncAnthropic(auth_token=...), drop OpenRouter
attribution headers
- pyproject: openai>=1.50 -> anthropic>=0.40 in meet_kevin extras
- tests: mocks ported back to messages.create + tool_use blocks
User's Vault has openrouter_api_key but no direct sk-ant-* Anthropic key.
OpenRouter passes through Claude Sonnet 4.6 (~3% markup over Anthropic
list pricing) and matches the existing gpt_mini_endpoint pattern used
by recruiter-responder.
- Replace anthropic.AsyncAnthropic with openai.AsyncOpenAI + base_url
- Convert Anthropic tool-use API to OpenAI function-calling
- System prompt unchanged (analyst instructions are model-agnostic)
- Drop cache_control (not in OpenAI API); revisit later if cost matters
- Model slug: anthropic/claude-sonnet-4.5 (OpenRouter's current Claude tier)
- Pricing: $3.10/M input, $15.50/M output (OpenRouter pass-through)
- Config field anthropic_api_key -> openrouter_api_key
- pyproject extras: anthropic>=0.40 -> openai>=1.50
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
- Implement CaptionResult frozen dataclass for structured caption data
- Add parse_srt() to parse SubRip format with flexible timestamp handling
- Add extract_captions() async function using yt-dlp subprocess wrapper
- Prefer manual captions over auto-generated; clean up SRT files after parsing
- Add 16 comprehensive tests covering edge cases (empty input, malformed SRT,
timestamp variations, language extraction, manual vs auto selection)
- Type-safe implementation with full mypy --strict compliance
- Add sample.srt fixture with 3 segments mentioning NVDA for test reference
- 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
- Point Ollama to local instance via host.docker.internal, use gemma3 model
- Remove Docker Ollama service (using host's Ollama instead)
- Add company-name-to-ticker mapping (Apple→AAPL, Tesla→TSLA, etc.) for RSS articles
- Lower signal thresholds for faster feedback with paper trading:
- FinBERT confidence: 0.6→0.4, signal strength: 0.3→0.15
- News strategy: article_count 2→1, confidence 0.5→0.3, score ±0.3→±0.15
- Fix market data BarSet access bug (BarSet.__contains__ returns False incorrectly)
- Fix market data SIP feed error by switching to IEX feed for free Alpaca accounts
- Fix nginx proxy routing for /api/auth/* to api-gateway /auth/*
- Add seed_sample_data script
- Update tests for new thresholds and alpaca mock modules
Wire the trading bot to real Alpaca market data and persist pipeline
state to the database so the dashboard displays live information.
- Add market-data service fetching OHLCV bars from Alpaca, publishing
to market:bars Redis Stream; signal generator consumes bars and
injects current_price into signals for position sizing
- Sentiment analyzer now persists Article + ArticleSentiment rows to
DB after scoring, with duplicate and error handling
- API gateway runs a background portfolio sync task that snapshots
Alpaca account state into PortfolioSnapshot/Position DB tables
during market hours
- TradeSignal carries a signal_id UUID; signal generator and trade
executor both persist their records to DB with cross-references
- 303 unit tests pass (57 new tests added)
