asepharyana
8c184ccae1
- Implemented Citations component to display citation data. - Created WatchlistDrawer and ChatSidebar components for managing watchlists and AI chat functionality. - Integrated API calls for watchlist management and chat interactions. - Updated index.tsx to include new components in the main application layout. - Added API client in lib/api.ts for structured API interactions. - Developed Alerts, Dashboard, Portfolio, Routines, Screener, and Report pages with relevant data fetching and UI components. - Introduced styles in tokens.css for consistent theming across the application. - Configured TypeScript and Vite for project setup and development.
2.1 KiB
2.1 KiB
Data model
SQLite for the hackathon; schema kept Postgres-compatible (serial → integer PK,
JSON → TEXT with JSON1, no SQLite-only DDL). Migrations numbered in
backend/internal/store/migrations/.
Tables
snapshots(id, ticker, date, source, payload_json, fetched_at)— raw API rows. Index (ticker, date, source). Retention: 180d, then compact to weekly.broker_activity(broker_code, ticker, date, buy, sell, net, lots, freq, avg_price)Index (ticker, date), (broker_code, date).foreign_flow(ticker, date, net_inflow)— PK (ticker, date).news_items(id, ticker, date, source, sentiment, confidence, url, title)— Index (ticker, date).filings(id, ticker, date, holder_type, txn_type, volume, price)— Index (ticker, date).routines(id, user_key, type, schedule_cron, channels_json, enabled)— 7 types (R1–R7).routine_runs(id, routine_id, started_at, status, payload_json, credits_used).alerts(id, user_key, name, rule_json, channels_json, last_fired).notification_destinations(id, user_key, kind, label, bot_token, chat_id, webhook_url, enabled, created_at)— per-user push targets.kindtelegram needs bot_token+chat_id, discord needs webhook_url (https). Secrets never leave the server in API responses.alert_events(id, alert_id, ticker, date, message, context_json, citations_json).watchlists(user_key, ticker, added_at)— PK (user_key, ticker).reports(id, ticker, generated_at, payload_json, citations_json).agent_accuracy(id, agent, ticker, prediction, predict_date, resolved, hit, actual_return).briefings(date, payload_json, citations_json)— PK date.credit_ledger(date, endpoint, calls, credits)— daily spend audit.
Seed strategy
seed.py loads one historical trading week into snapshots + derived tables so the
full demo (briefing → radar → report → interrogation) runs offline. Fixtures live in
tests/fixtures/ as JSON exports of real API shapes (field names match schema.json).
Cursors
meta(key, value):quarterly_since(universe poll cursor),news_since,filings_since— persisted so restarts resume incrementally.