asepharyana
3882c5aef0
- Introduced AGENT-SPECS.md detailing the specifications for seven agents including their inputs, processing steps, and outputs. - Created API-REFERENCE.md outlining the Sectors API v2 endpoints, parameters, costs, and usage. - Developed API.md to specify backend routes, request/response structures, and error handling. - Established ARCHITECTURE.md to describe the project layout, conventions, scheduler, and citation pipeline. - Added DATA-MODEL.md to define the database schema, tables, and seed strategy. - Compiled PLAN.md to outline the project concept, problem statement, unique features, and implementation timeline. - Created README.md as an index for documentation with links to all relevant files. - Documented ROUTINES.md detailing the seven automated routines, their schedules, inputs, detection logic, and delivery formats. - Introduced TECH-STACK.md to specify the technology choices and rationale for both backend and frontend components.
1.9 KiB
1.9 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/app/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).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.