Backend: assemble_trend_with_evidence now deduplicates document IDs
via dict.fromkeys() (the rollup code already did this, but the base
assembly didn't — same doc could appear multiple times from different
intelligence extractions).
Frontend: Trends.tsx deduplicates via Set before rendering as a safety
net for existing data already stored with duplicates.
- EvidenceRef component now fetches document details via useDocument()
hook and displays the title instead of 'doc:43156423…'
- TanStack Query deduplicates and caches lookups for repeated doc IDs
- Pattern IDs still render as before (e.g. 'pattern META other (1d)')
- Override Trade button changed from brand-600 to red-600
The handler for /api/macro/impacts/:ticker was returning the impacts
array directly instead of { exposure_profile, impacts }. The frontend
destructures macroData.impacts which was undefined, falling back to
an empty array — so the Macro tab always showed 'No active macro impacts'
even with mock data present.
- recommendation worker: filter out non-UUID document IDs (synthetic
pattern:* IDs from competitive signals) before inserting into
recommendation_evidence table — the uuid cast was failing and
silently dropping all evidence rows
- wrap executemany in try/except so partial failures don't lose all evidence
- SqlExplorer: wrap Lucide icons in <span title=...> instead of passing
title prop directly (not supported by lucide-react, broke CI build)
- Add GET/PUT /api/admin/trading/approval-config endpoints
- Add POST/DELETE /api/admin/trading/lockouts endpoints
- Add useApprovalConfig, useUpdateApprovalConfig, useCreateLockout, useDeleteLockout hooks
- Add Paper Order Approval toggle card with confirmation dialog
- Add lockout creation form and delete button to Active Lockouts card
- Add MSW handlers for all new endpoints
- Add property-based tests for bug condition exploration and preservation
- Pattern IDs (pattern:META:other:1d) shown as 'pattern META other (1d)'
- Document UUIDs shown as clickable 'doc:43156423…' links to document detail
- Unknown formats shown truncated as fallback
API was returning a flat array but frontend expects CompanyMacroImpacts
wrapper with exposure_profile and impacts fields. Also queries the
exposure_profiles table for the company's active profile.
- Trading page: added conservative/moderate/aggressive selector that
updates the trading engine config via PUT /api/trading/config
- Recommendations page: added risk tier dropdown that defaults to the
engine's current tier and filters recs by the tier's min_confidence
- Backend: added min_confidence query param to GET /api/recommendations
- Risk tier thresholds: conservative ≥0.75, moderate ≥0.55, aggressive ≥0.40
- Removed PUT /api/trading/capital (set capital) — only touched in-memory state
- Removed POST /api/trading/capital/adjust (add/withdraw) — same problem
- Reset endpoint now: liquidates Alpaca positions, cancels orders, clears DB,
then queries Alpaca for real portfolio_value to set engine capital
- Frontend: replaced CapitalCard with simple ResetCard (one button)
- Removed useSetTradingCapital and useAdjustCapital hooks
- Added cancel_all_orders() and close_all_positions() to AlpacaBrokerAdapter
- Reset endpoint creates a temporary adapter to call Alpaca DELETE /v2/orders
and DELETE /v2/positions before clearing DB and engine state
- Also clears positions table and processed_recommendation_ids on reset
- Broker reset is best-effort — DB/engine reset proceeds even if Alpaca fails
Agreement of 1-2 signals was inflating confidence to paper-eligible
levels (0.575) even with low credibility sources. Added log2-based
dampener that scales agreement contribution by unique source count,
saturating at n=7. Single signals now cap at 0.39 confidence,
2 signals at 0.49 — both correctly below paper threshold (0.50).
- Recommendation worker now resolves thesis-rewriter config from DB
and passes ollama_config to generate_recommendation. Thesis rewriting
is now active when the thesis-rewriter agent exists in ai_agents.
Refreshes config every 50 jobs.
- Event classifier now resolves its own config separately from the
document extractor via 'event-classifier' slug. Uses a separate
OllamaClient when the model differs from the extractor. Refreshes
alongside the extractor every 100 jobs.
- Document extractor was already wired (existing code).
- Added 8 unit tests for AgentConfigResolver covering: DB resolution,
variant override, not-found, DB errors, TTL caching, cache refresh,
and invalidation.
- Migration 026 and OllamaConfig now default to qwen3.5:9b instead of
llama3.1:8b. Existing deployments keep their current model (qwen3.5:9b-fast)
since the migration uses WHERE NOT EXISTS on slug.
- Event classifier system prompt expanded with macro-vs-company filtering:
explicitly instructs the model to NOT classify single-company news
(lawsuits, earnings, management changes, debt crises) as macro events.
Sets severity=low and confidence<0.3 for company-specific articles.
Reserves 'critical' severity for multi-country/global market events.
Prevents over-tagging event_types by requiring direct description.
- Updated test_system_prompt_is_concise threshold to accommodate the
expanded prompt (300 → 1000 chars).
Three distinct capital operations on the Trading Controls page:
- Set Capital: overwrites pool balances to a new amount (existing)
- Add/Withdraw: adjusts active pool by a delta without touching
positions, orders, or history. Validates sufficient balance for
withdrawals. Logged to reserve_pool_ledger as manual_adjustment.
- Reset Everything: nuclear option — deletes all positions, orders,
trading decisions, stop levels, snapshots, backtests, notifications,
and circuit breaker events, then resets capital fresh. Red button
with double-confirmation dialog.
Backend: POST /api/trading/capital/adjust and POST /api/trading/reset
Frontend: CapitalCard rebuilt with three sections and confirmation UIs
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
Celes Renata