- Remove all built-in patterns (no proof of legitimacy); seed_builtin_patterns is now a no-op
- DB: add backtest_lab_runs table + backtest_hits/runs_count columns on patterns
- services/pattern_lab.py: build_historical_context (yfinance + RSI/MA200),
run_ai_backtest (GPT-4o as historical analyst), evaluate_outcomes (actual moves at T+horizon)
- routers/pattern_lab.py: POST /run, POST /evaluate/{id}, GET /runs, DELETE /runs/{id},
POST /save-pattern (promotes hit pattern to library with reliability counters)
- PatternLab.tsx: 34 preset events 2015-2025 (macro/geo/credit/fx/commodities/volatility/tech),
3-panel layout — preset selector + wizard + run history, market data table,
AI pattern cards with hit/miss outcome display, Save to Library button
- useApi.ts: usePatternLabRuns, useRunPatternLab, useEvaluatePatternLab, useSaveLabPattern, useDeleteLabRun
- Sidebar + App.tsx: /pattern-lab route + FlaskConical nav link
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- New institutional_reports table (DB) with importance, signals per asset class, key points, absorption tracking
- cot_fetcher.py: CFTC Socrata API (6dca-aqww), 7 instruments (Gold/Silver/Copper/WTI/NatGas/SP500/EURUSD), net positioning + 52-week z-score
- eia_fetcher.py: EIA API v2, 4 series (crude/Cushing/gasoline/distillates), WoW surprise detection
- institutional.py router: GET /reports, GET /reports/{id}, POST /refresh, GET /stats
- institutional_scheduler.py: weekly auto-fetch (COT Saturdays, EIA Wednesday afternoons)
- ai_analyzer.py: build_institutional_block() + institutional_block param injected into AI scoring prompt
- auto_cycle.py: inject institutional block into suggestion + scoring, absorption tracking via keyword overlap after each cycle commentary
- InstitutionalReports.tsx: full page with filter bar (type/category/importance/period), cards with key point bullets, EXTREME alerts highlighted, signal badges, absorption badge, trading implications, expandable detail
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Complete French→English translation across all frontend pages and backend
services — every label, button, header, empty state, toast, and nav item
is now in English. Build verified clean (tsc + vite). No i18n library
added; direct string replacement throughout.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Backend:
- get_pnl_snapshot(id) : détail complet d'un snapshot avec trades parsés
- diff_pnl_snapshots(a, b) : diff positions entre deux snapshots (nouvelles /
fermées / évolution PnL par position + delta portfolio)
- GET /api/var/pnl/snapshots/{id} : détail snapshot
- GET /api/var/pnl/diff?a=&b= : calcul du diff
Frontend PositionHistory.tsx :
- Timeline scrollable des snapshots avec sparkline PnL
- Clic snapshot → détail des positions à ce moment (prix entrée, prix actuel,
PnL %, PnL €, régime macro)
- Boutons A/B par snapshot → sélection de deux points à comparer
- Vue diff A→B : nouvelles positions, fermées, évolution PnL par trade,
delta portfolio (capital, PnL %, PnL €)
- Route /position-history + nav sidebar
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Backend:
- DB: add system_logs table (level/source/cycle_id/ticker/message) and
iv_watchlist table (ticker/added_by/is_active); seed builtin 18 tickers
- DBLogHandler attached at startup — all WARNING+ logs auto-persist to DB
- log_system_event() helper for structured manual events
- New router /api/logs: GET with filters (level, source, cycle_id, ticker,
date range), GET /sources, GET /cycles for dropdowns, DELETE /clear
- iv_watchlist now read from DB instead of hardcoded constant; options_vol
watchlist/refresh/bootstrap endpoints all use get_watchlist_tickers()
- New endpoints: POST/DELETE /options-vol/watchlist-tickers/{ticker} to
add/remove tickers; adding triggers background 1-year bootstrap
- auto_cycle: after log_trade_entries(), auto-detect new underlying proxies
not yet in watchlist, add them and bootstrap their IV history
Frontend:
- New page SystemLogs (/logs): log table with level/source/cycle/ticker/date
filters, color-coded rows, expandable JSON details, auto-refresh 30s
- Options Lab: WatchlistManager section — add ticker input, chip list with
builtin/auto/manual color coding, remove button for non-builtins
- Sidebar: Logs Système nav link (ScrollText icon)
- useApi: useSystemLogs, useLogSources, useLogCycles, useClearLogs,
useWatchlistTickers, useAddWatchlistTicker, useRemoveWatchlistTicker
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Sprint 2.1 — Pattern Reliability Score
- database.py: get_pattern_reliability() — win_rate × log(n+1) sur trades matures (≥35% horizon)
- database.py: get_all_pattern_reliability_map() pour injection rapide dans les prompts
- ai_analyzer.py: inject reliability_map dans suggest_patterns (patterns fiables mis en avant)
- auto_cycle.py: charge reliability_map avant suggestion et le passe au suggéreur
- routers/analytics.py: GET /api/analytics/reliability
- PatternEditor.tsx: ReliabilityBadge sur chaque card + usePatternReliability hook
- useApi.ts: usePatternReliability, useCalibration hooks
Sprint 2.2 — Contre-thèses & Invalidation Triggers
- database.py: migration ALTER TABLE — counter_thesis, invalidation_trigger, invalidation_probability
- database.py: save_custom_pattern() persiste les 3 nouveaux champs
- ai_analyzer.py: counter_thesis + invalidation_trigger + invalidation_probability dans le JSON schema
- auto_cycle.py: détection automatique des triggers d'invalidation contre les news (keyword match)
- routers/analytics.py: GET /api/analytics/invalidation-alerts
- PatternEditor.tsx: affichage contre-thèse dans les cards + champs dans le formulaire
- PatternEditor.tsx: affichage dans AiSuggestModal (suggestions IA)
- routers/patterns.py: PatternRequest inclut les 3 nouveaux champs
Sprint 2.3 — Calibration probabiliste & Demi-vie KB
- database.py: migration — predicted_probability sur pattern_score_history
- database.py: save_pattern_scores() stocke probability du pattern à chaque scoring run
- database.py: get_calibration_data() — Brier score + buckets de calibration par décile
- database.py: expires_at + confidence_decay_days sur knowledge_base
- database.py: decay_kb_confidence() — decay automatique + archivage à 0
- auto_cycle.py: decay_kb_confidence() appelé au début de chaque cycle (non-bloquant)
- routers/analytics.py: GET /api/analytics/calibration + POST /api/analytics/kb/decay
- frontend/src/pages/Analytics.tsx: nouvelle page — tableau fiabilité + calibration Brier
- App.tsx + Sidebar.tsx: route /analytics + entrée menu
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
