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.
37 lines
1.9 KiB
Markdown
37 lines
1.9 KiB
Markdown
# Data model
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SQLite for the hackathon; schema kept Postgres-compatible (serial → integer PK,
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JSON → TEXT with JSON1, no SQLite-only DDL). Migrations numbered in
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`backend/app/store/migrations/`.
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## Tables
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- `snapshots(id, ticker, date, source, payload_json, fetched_at)` — raw API rows.
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Index (ticker, date, source). Retention: 180d, then compact to weekly.
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- `broker_activity(broker_code, ticker, date, buy, sell, net, lots, freq, avg_price)`
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Index (ticker, date), (broker_code, date).
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- `foreign_flow(ticker, date, net_inflow)` — PK (ticker, date).
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- `news_items(id, ticker, date, source, sentiment, confidence, url, title)` —
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Index (ticker, date).
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- `filings(id, ticker, date, holder_type, txn_type, volume, price)` — Index (ticker, date).
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- `routines(id, user_key, type, schedule_cron, channels_json, enabled)` — 7 types (R1–R7).
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- `routine_runs(id, routine_id, started_at, status, payload_json, credits_used)`.
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- `alerts(id, user_key, name, rule_json, channels_json, last_fired)`.
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- `alert_events(id, alert_id, ticker, date, message, context_json, citations_json)`.
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- `watchlists(user_key, ticker, added_at)` — PK (user_key, ticker).
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- `reports(id, ticker, generated_at, payload_json, citations_json)`.
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- `agent_accuracy(id, agent, ticker, prediction, predict_date, resolved, hit, actual_return)`.
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- `briefings(date, payload_json, citations_json)` — PK date.
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- `credit_ledger(date, endpoint, calls, credits)` — daily spend audit.
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## Seed strategy
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`seed.py` loads one historical trading week into snapshots + derived tables so the
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full demo (briefing → radar → report → interrogation) runs offline. Fixtures live in
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`tests/fixtures/` as JSON exports of real API shapes (field names match schema.json).
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## Cursors
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- `meta(key, value)`: `quarterly_since` (universe poll cursor), `news_since`,
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`filings_since` — persisted so restarts resume incrementally.
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