New Agents tab in the sidebar (Ops group) for viewing, editing, and
creating AI agent configurations:
Database (migration 026):
- ai_agents table: editable configs for each LLM agent (model, prompts,
temperature, tokens, retries). source='system' for built-in,
source='user' for custom. Seeds 3 system agents (Document Extractor,
Event Classifier, Thesis Rewriter) using WHERE NOT EXISTS to never
overwrite user edits across reinstalls.
- agent_performance_log table: per-invocation metrics (duration,
confidence, retries, tokens, errors) linked to agent config.
API endpoints:
- GET/POST /api/agents — list and create agents
- GET/PUT/DELETE /api/agents/{id} — view, edit, delete (system agents
can be edited but not deleted)
- GET /api/agents/{id}/performance — aggregated metrics (success rate,
avg/p95 latency, confidence, token usage)
- GET /api/agents/{id}/performance/history — hourly time series
Frontend:
- AgentsPage with sidebar list + detail panel
- Agent detail: config display, system prompt viewer, performance
dashboard with metrics cards and time-series chart
- Edit form: all config fields editable including system prompt,
model, temperature, tokens, retries
- Create form: new user-defined agents with auto-slug generation
- System agents show blue badge, user agents show green badge
Migration 023 was deleting all but the latest trend_windows row per
entity before 024 could save them to trend_history. On reinstall,
this wiped the entire history every time.
Fixed by restructuring:
- 023 now creates trend_history FIRST and copies all trend_windows
rows into it before deduplicating trend_windows down to latest-only.
Uses NOT EXISTS to avoid duplicating rows on re-runs.
- 024 is now idempotent: ensures table/indexes exist and backfills
from recommendations (last 7 days, 1 point per ticker/window/hour)
to reconstruct approximate history even if trend_windows was sparse.
Both migrations are safe to re-run on existing databases.
- New 'intraday_bars' endpoint in PolygonMarketAdapter: fetches hourly
bars for today using range_bars URL with timespan=hour, sort=asc
- Scheduler expands intraday_bars global source into per-ticker jobs
for all active companies (every 15 minutes via polling_interval)
- Migration 025 inserts the intraday source with 900s cadence
- Frontend price matching uses closest-timestamp instead of date-string
matching, with 2h tolerance for intraday and 36h for daily windows
- Bumped market price fetch limit to 200 for intraday granularity
- New GET /api/market/prices/{ticker} endpoint serving OHLCV data from
market_snapshots, deduped by bar_timestamp
- New useMarketPrices hook in frontend
- Trend chart now shows price (purple line) on a right Y axis ($)
alongside trend metrics (%) on the left Y axis
- Custom tooltip formats price as dollars, metrics as percentages
- Price line uses connectNulls for days with missing bar data
Replaced Recharts default Tooltip with formatter prop (broken in
Recharts v3 with explicit type annotations) with a custom
TrendTooltip component matching the SQL Explorer pattern. Shows
each series name, value, and color on hover.
/api/patterns/{ticker} returns {ticker, patterns, count} but
useHistoricalPatterns typed its response as HistoricalPattern[].
The .map() call on the object caused 'e.map is not a function'.
Fixed by unwrapping resp.patterns in the hook's queryFn.
Trend charts blank:
- trend_windows uses upsert (1 row per ticker/window), so charts had
at most 1 data point. Added trend_history table (migration 024) that
appends every snapshot. New /api/trends/history endpoint serves the
time series. Frontend now uses useTrendHistory for charts and
useTrends for the latest summary card.
Competitor GUIDs:
- list_competitors query returned raw company_b_id UUIDs without
joining companies table. Added LEFT JOIN with CASE to resolve the
other company's ticker and legal_name. Updated Pydantic model to
include enriched fields. Frontend fallback changed from truncated
UUID to ticker/legal_name/Unknown.
- ID mismatch: API generated a throwaway UUID while BacktestReplay
generated its own internally. Frontend polled with wrong ID and
never found the DB row. Now pre-generate ID in endpoint and pass
it to BacktestReplay.
- Field name: API returned 'backtest_id' but frontend read 'data.id'.
Unified to 'id' everywhere.
- No polling: useBacktestResult fired once and never refreshed.
Added refetchInterval that polls every 2s while status is running.
- Response shape: GET endpoint nested results under 'result' object
but frontend expected flat fields. Flattened response to match
BacktestResult type.
- Added running/failed/completed status indicators in BacktestPanel.
- Add dedup check in recommendation worker: skip generation when latest
rec for same ticker+window has identical action/mode/confidence
- Widen position sizing range (1-10% portfolio, 0.3-2% max loss) and
factor in trend strength + evidence count for differentiated sizing
- API returns only latest recommendation per ticker by default (DISTINCT ON)
to eliminate duplicate rows in the frontend list view
- Sell path: looks up existing position, sells full quantity, returns proceeds to pool
- Correlation matrix: computed from 30-day market_snapshots on startup + every 5min
- Holidays: 10 major US market holidays for 2026 checked in trading window functions
All 152 tasks across both phases are now marked complete:
- Phase 1 (1-26): pure computation modules, property tests, API, frontend, infra
- Phase 2 (27-37): live decision loop, stop-loss monitor, performance metrics,
risk tier scheduler, rebalancer, notification dispatch, backtest replay,
real DB connections, paper trading config, integration tests
Hover over any bar, line point, or scatter dot to see every column
value for that data point. The Y-axis column is highlighted in
brand color, X-axis in white, and other columns in gray. Works
for all chart types (bar, line, scatter, auto).
Adds an '✨ Auto' button that analyzes query results and picks the
best chart type and column mapping:
- Date/time column + numeric → line chart (time series)
- Categorical + numeric → bar chart (categories)
- Two numeric columns → scatter plot
- Shows detected type and column names as a label
Click Auto, run any query, and it figures out the rest.
Deploy scripts live on gremlin-1 at ~/sources/kube/stonks-oracle/,
not in the git repo. They reference local secret files and should
not be version controlled.
The pg-query API returns all values as strings. The chart builder
was using Number() which returns NaN for non-numeric strings.
Now uses parseFloat with NaN fallback to 0.
Celes Renata