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.
- Strip SQL comments (-- and /* */) before checking for SELECT,
so queries with leading comments don't get rejected
- Show the actual error detail from the API response instead of
generic 'API error 400' in the SQL Explorer UI
The migration ran on every deploy, inserting duplicate queries each
time (96 instead of 12). Added UNIQUE constraint on name and changed
ON CONFLICT to reference it. Cleaned up 84 duplicates in DB.
The trading engine network policy only allowed egress on ports 443
(HTTPS) and 53 (DNS). Gmail SMTP uses port 587 (STARTTLS), causing
'Network is unreachable' when sending notifications.
Replaced the Gmail API (OAuth2) notification delivery with plain
SMTP using a Gmail app password. Much simpler setup — no Google
Cloud project, no OAuth2 flow, no extra dependencies.
- Rewrote _send_gmail() to use smtplib with smtp.gmail.com:587 TLS
- Added stonks-gmail-secrets to Helm chart (GMAIL_SENDER,
GMAIL_RECIPIENT, GMAIL_APP_PASSWORD)
- Added gmail secret to trading-engine deployment
- Updated runmefirst.sh to read gmail.app from kube dir
- Sender/recipient: celes@celestium.life
The API returns macro_enabled/competitive_enabled but the TypeScript
interfaces expected 'enabled'. The toggles always showed disabled.
Now handles both field names with fallback.
Alpaca returns 404 when you don't hold a position in a ticker.
The ingestion worker was logging this as an error and incrementing
the failure count. Now returns an empty items list instead, since
'no position' is a valid state, not an error.
The ingestion worker creates an AlpacaBrokerAdapter but the pod
didn't have BROKER_API_KEY/BROKER_API_SECRET env vars, causing
401 Unauthorized on every broker source fetch. Added
stonks-broker-secrets to the ingestion service's secrets list.
The SQL Explorer was querying Trino which has zero tables. Rewrote to
use PostgreSQL directly:
Backend:
- GET /api/analytics/pg-schema: returns all public tables with column
names, types, and nullability from information_schema
- POST /api/analytics/pg-query: read-only SQL execution against
PostgreSQL with SELECT-only enforcement, auto LIMIT, and descriptive
error messages for syntax/table/query errors
Frontend:
- Schema browser shows all PostgreSQL tables with columns and types
- Click a table name → generates SELECT * FROM table LIMIT 100
- Pre-built Queries section with 12 seeded queries covering companies,
recommendations, trends, market prices, documents, global events,
trading decisions, ingestion health, reserve pool, sector exposure
- User-saved queries shown separately with delete buttons
- Chart builder, Monaco editor, and save functionality preserved
Migration 021: seeds 12 pre-built saved queries
The Trino/Iceberg lakehouse has zero tables, so all Trino-backed
dashboards showed 'No data available'. Rewrote all four to use
existing PostgreSQL-backed API endpoints:
- Sentiment Heatmap: useTrends + useCompanies → sector and ticker
trend strength bar charts (30k trend_windows in DB)
- Prediction Accuracy: useRecommendations → confidence distribution
and action distribution charts (30k recommendations in DB)
- Paper PnL: useTradingMetrics + useTradingMetricsHistory → equity
curve, daily returns, win/loss stats from trading engine
- Model Quality: useModelPerformance + useModelFailures → success
rate, latency, retries, and failure table from ops API
Removed unused Trino query function and ScatterChart imports.
The throughput API returns one row per source_type per time bucket,
but the chart was mapping each row as a separate bar. With 5 source
types × 24 hours, the bars were tiny and overlapping. Now aggregates
completed/failed/items across source types per time bucket so the
chart shows meaningful totals.
The alpaca.url config file contains https://paper-api.alpaca.markets/v2
but the adapter code also prepends /v2/ to all paths, resulting in
/v2/v2/positions which returns 404. Now strips trailing /v2 or /v1
from the configured base URL since the adapter manages API versioning.
This was causing 1,017 consecutive broker sync failures.
When multiple recommendations for the same ticker produce 'act'
decisions, the second one would overwrite the first in
simulated_positions, losing the first position's value and causing
incorrect portfolio value calculations. Now skips if already holding.
- Map DB 'id' field to 'recommendation_id' for evaluate_recommendation()
- Ensure confidence is cast to float (asyncpg may return Decimal)
- Add per-day logging showing rec count, act/skip, positions, pool balance
- Helps diagnose why backtests produce 0 trades
Two tiers of market data:
1. Per-ticker prev bars (existing 50 sources, 15-min cadence) for
watchlist detail — trading decisions, stop-loss, position sizing
2. Grouped daily (new single source, once per day) for broad market
context — correlation analysis, sector rotation, competitive intel
Changes:
- Add grouped_daily endpoint to PolygonMarketAdapter with auto date
calculation (previous trading day, skip weekends)
- Add fetch_global_market_sources() to scheduler for sources without
company_id, scheduled once daily (86400s cadence)
- Update _persist_market_items to use item-level ticker from T field
and look up company_id dynamically for grouped daily bars
- Migration 020: make company_id nullable on sources and
market_snapshots tables, add grouped daily source row
- Fix backtest replay to query market_snapshots data->>'c' for prices
- Increase market_api polling cadence from 60s to 900s (15 min).
The prev-day bar endpoint returns the same data all day, so polling
every minute wastes API quota. 50 tickers at 15-min cadence = ~3.3
req/min, well within the 5/min rate limit.
- Reduce market_api rate limit from 30/min to 5/min to match.
- Fix backtest replay to query market_snapshots with data->>'c' for
close prices instead of nonexistent market_data.close_price column.
- Enrich backtest recommendations with prices from market_snapshots
and sectors from companies table.
The simulated timestamp was 10:00 UTC (6:00 AM ET) which is outside
the trading window. Changed to 11:00 AM ET so backtested decisions
actually pass the trading window check.
Phase 2 of the autonomous trading engine:
- Replace start()/stop() stubs with real async implementations
- Decision loop: polls recommendations from PostgreSQL, deduplicates
via Redis, evaluates through the full pipeline, submits orders to
stonks:queue:broker_orders
- Stop-loss monitor: fetches prices from Polygon API, checks crossings,
submits immediate sell orders, safety sell after 15 min without data
- Performance loop: computes metrics every 5 min during market hours,
persists daily snapshots at market close
- Risk tier scheduler: evaluates daily at 16:00 ET, persists tier changes
- Rebalance scheduler: evaluates Monday 09:45 ET, respects circuit breaker
- Notification dispatch: SNS + Gmail with rate limiting and retry
- Backtest replay: fetches historical data, simulates decisions, persists
- Real asyncpg/redis connections in FastAPI lifespan (graceful degradation)
- Migration 019: enable paper trading with conservative tier, 5 cap
- Added max_open_positions to TradingConfig with env var loading
- Phase 2 tasks added to autonomous-trading-engine spec
When on /trading/engine, the /trading nav item also matched via
startsWith. Now checks if a more specific child route matches
first and uses exact match in that case.
When the LLM returns empty summary and no key facts, raise ValueError
so the retry logic kicks in instead of persisting an empty event.
Also strip whitespace from summary and filter empty key_facts entries.
Cleaned up 17 empty events from the database.
Macro news documents have no ticker, causing upload_normalized_text
and upload_parser_output to produce paths like parsed//2026/...
which MinIO rejects as XMinioInvalidObjectName. Use '_global' as
the path segment when ticker is empty, matching the existing
macro prefix pattern in upload_raw_document.
TypeScript strict mode in CI rejects explicit parameter types on
Recharts formatter/tickFormatter callbacks. Use inference with
'as number' casts on the value instead. Also fix unsafe cast in
PortfolioComposition and handle possibly-undefined percent.
Celes Renata