- auto_cycle.py: replace _log with logger (NameError at lines 484/489)
- auto_cycle.py: normalize underlying via _normalize_ticker before _resolve_ticker
so WHEAT→ZW=F→WEAT and EUR/USD→EURUSD=X→FXE reach the IV watchlist correctly
- iv_engine.py: _resolve_ticker now strips slash-format forex (EUR/USD→EURUSD=X)
before _PROXY lookup, fixing yfinance 500/404 spam from get_atm_iv
- database.py: _fetch in log_trade_entries uses _normalize_ticker (not _normalize_yf_ticker)
so commodity aliases like WHEAT→ZW=F are applied at price-fetch time
- ai_analyzer.py: max_workers=1 for batch scorer — parallel workers both slept and
retried simultaneously after 429, causing repeated bursts; sequential fixes the pattern
- journal.py + JournalDeBord.tsx: add price_warning field (no_price_data/no_entry_price/
no_live_price) with visible ⚠ badge and amber color on affected ticker/price cells
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Ticker normalization (_normalize_ticker):
- EUR/USD slash-format → EURUSD=X (was passed raw to yfinance → 500/404 spam)
- bare 6-char forex pairs EURUSD/USDJPY etc → append =X
- commodity alias table: WHEAT→ZW=F, CORN→ZC=F, WTI→CL=F, BRENT→BZ=F,
GOLD→GC=F, SILVER→SI=F, NATGAS→NG=F, SUGAR→SB=F, + 15 others
- also normalize underlying at log_trade_entries time so stored tickers
are already canonical before MtM lookups
GPT-4o 429 rate limit:
- _chat() retries up to 3× on rate_limit errors, respects retry-after hint
from error message (e.g. "try again in 12.37s"), falls back to 2^n×5s
- batch scorer: parallel workers 4→2 to halve the token burst per cycle
(2 concurrent batches × ~6K tokens vs 4 × ~6K = 24K burst at 30K limit)
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>
- ai_analyzer: add explicit calibration rules to SYSTEM_SCORER and batch
prompt so GPT-4o produces a spread of scores rather than defaulting
to 50 for all patterns (0-news patterns capped at 35, contra patterns
at 40, high-signal patterns can reach 70-85)
- auto_cycle: add _auto_synthesize_knowledge() called after each auto
portfolio snapshot; skips if last synthesis < 6h old to avoid
redundant GPT-4o calls — Super Contexte now updates automatically
every cycle without manual intervention
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
