Commit Graph

17 Commits

Author SHA1 Message Date
OpenSquared
e2d5bebef4 feat: Rapport de Cycle — auto-généré à chaque run avec contexte IA, delta, PnL/VaR snapshot
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-06-20 07:57:41 +02:00
OpenSquared
d94052dd91 fix: Dashboard cycle coherence — scoring_run_id linkage + cycle-scoped cards
- Backend: get_status() now resolves scoring_run_id by querying
  trade_entry_prices within the cycle time window, fixing the mismatch
  between cycle run_id and the id actually written to trades
- Dashboard: Trades du cycle filters by scoring_run_id (no stale fallback)
- Dashboard: Pattern du cycle shows only patterns added in last cycle
  (created_at >= started_at), renamed from Top Patterns
- Dashboard: Dernier Cycle now shows 4 stats (patterns/scorés/loggés/fermés)
  + IA commentary snippet
- Dashboard: P&L simulated mode bottom half shows open/closed/capital/profit
- Dashboard: Régime Macro shows top 4 scenario score bars

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-06-19 20:49:44 +02:00
OpenSquared
58c3767a9d feat: simulation portfolio surveillance + patterns grid/filter UI
Portfolio Monitor (v4.4):
- New portfolio_risk.py service: concentration by asset_class, directional
  conflict detection (same underlying, opposite directions), overweight alerts
- AI agent (Step 7b) runs GPT-4o-mini after each cycle log: assessment +
  prioritized actions + rebalance suggestion, persisted in system_logs
- GET /api/journal/portfolio-risk — full risk breakdown + latest AI monitor reco
- POST /api/journal/trade-check — pre-entry conflict & concentration check
- asset_class column added to trade_entry_prices (auto-migration + populated at INSERT)
- Journal: new "Risque Sim." tab with concentration bars, conflict alerts,
  AI recommendations; red badge on tab when danger alerts exist

PatternEditor:
- Grid view default (2-3 cols responsive), list toggle
- Asset class filter chips (energy/metals/agri/equities/indices/forex/rates)
- Sort: Date (default) / Score IA / Prob.
- Period filter: Tout / 7j / 30j
- Result count badge when filters active

Doc: v4.3 → v4.4, updated Journal/PatternEditor/cycle steps/schema/glossary

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-06-19 15:49:46 +02:00
OpenSquared
d34b4043fb fix: 4 cycle errors — NameError _log, WHEAT/EUR/USD ticker normalization, 429 serial scoring
- 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>
2026-06-19 14:49:07 +02:00
OpenSquared
05a475fb04 feat: system logs page + dynamic IV watchlist with auto-add from cycle
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>
2026-06-18 13:07:35 +02:00
OpenSquared
abee090881 feat: expandable inline rows in Journal + journal/maturity params in Config
- JournalDeBord: trade rows now expand inline (full-width) instead of
  PostmortemPanel appearing below the whole table. Click anywhere on a
  row to toggle. Period selector extended to 15/30/60/90j.
- Config: added Rétention Journal (30/60/90/180j) and Seuil Maturité
  (20/30/35/50%) controls, wired to the Appliquer button.
- Backend: journal_retention_days and maturity_threshold_pct read from
  config table; seeded at startup with defaults 90d / 35%. get_status()
  now returns both values so Config page can initialise correctly.
- cycle.py: CycleConfigRequest accepts and validates both new params.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-06-18 10:08:16 +02:00
OpenSquared
7e38fd6257 fix: invalidate IV watchlist cache after cycle fetches fresh IV data
The auto-cycle was saving new IV snapshots to SQLite but not clearing
the in-memory cache in options_vol.py (TTL 1h), so the Options Lab
page kept showing stale IV Rank data until the cache naturally expired.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-06-17 21:21:25 +02:00
OpenSquared
246deaf631 feat: Phase 4 — Moteur Probabiliste & Apprentissage Automatique
Sprint 4.1 — Bayesian Updating
- database.py: update_bayesian_posteriors() — Beta(α,β) posteriors sur trades matures
- database.py: get_bayesian_posteriors() — posteriors + IC 95% + dérive prior GPT vs posterior
- Colonnes Bayésiennes ajoutées : bayesian_alpha, bayesian_beta, bayesian_win_rate, bayesian_sample_size
- auto_cycle.py: appel update_bayesian_posteriors() en Step 5.5 (après scoring)

Sprint 4.2 — Détection Automatique de Régimes (K-Means numpy pur)
- database.py: detect_and_save_regime_clusters() — K-Means sur 7 gauges macro (VIX, slope, DXY…)
- database.py: get_regime_cluster_history() — timeline des clusters
- database.py: get_regime_transition_matrix() — P(cluster j | cluster i) sur N transitions
- Table regime_clusters avec anomaly_flag (points > 3σ)
- auto_cycle.py: appel detect_and_save_regime_clusters() en Step 5.6

Sprint 4.3 — Embeddings Sémantiques (remplace Jaccard)
- database.py: get_or_create_pattern_embedding() — OpenAI text-embedding-3-small, stocké en DB
- database.py: max_cosine_similarity_vs_existing() — similarité cosinus vs patterns existants
- Table pattern_embeddings avec vecteur JSON + model_version
- auto_cycle.py: _is_duplicate_pattern() — cosinus seuil 0.75 avec fallback Jaccard automatique

Sprint 4.4 — Tableau de Bord Analytique Avancé
- AnalyticsAdvanced.tsx: nouvelle page /analytics-advanced
  • BayesianTable : prior GPT vs WR bayésien ± IC 95%, dérive, niveau de confiance
  • ClusterTimeline : timeline colorée des clusters + anomalies
  • TransitionMatrix : heatmap P(j|i) avec diagonale auto-transition
  • EmbeddingsSummary : liste des patterns vectorisés
  • Boutons "Bayesian update" et "Détecter régime" avec mutation React Query
- analytics.py router : 5 nouveaux endpoints (bayesian, regime-clusters, transitions, detect, embeddings)
- useApi.ts : 4 nouveaux hooks (useBayesianPosteriors, useRegimeClusters, useRegimeTransitions, usePatternEmbeddings)
- App.tsx + Sidebar.tsx : route /analytics-advanced + entrée menu

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-06-17 17:46:34 +02:00
OpenSquared
e44c8799b9 feat: Phase 3 — Portfolio Risk Engine (Exposition, Clusters, Kelly, Risk Dashboard)
Sprint 3.1 — Vue Portefeuille Consolidée
- database.py: get_portfolio_exposure() — exposition par classe d'actif + facteur de risque
- database.py: get_pnl_timeline() — courbe P&L cumulé pour equity curve
- Alertes concentration automatiques (>40% par classe, >50% par facteur)
- _RISK_FACTOR_MAP: classification géopolitique/inflation/récession/liquidité/dollar

Sprint 3.2 — Risk Cluster Engine
- database.py: get_risk_clusters() — saturation par facteur + risk_prompt_context
- database.py: get_pattern_correlations() — matrice Pearson sur trades matures
- auto_cycle.py: injection du contexte risque dans le prompt de scoring (Step 3.5)
- ai_analyzer.py: paramètre risk_context dans score_patterns_with_context()
- Pénalisation automatique des patterns sur facteurs saturés dans le scoring GPT

Sprint 3.3 — Position Sizing Kelly Fractionnel
- database.py: compute_kelly_sizing() — f* = (p×G - (1-p))/G, Kelly ×33% par défaut
- Ajustement cluster: sizing ÷2 si facteur saturé
- Ajustement fiabilité: sizing ÷2 si win_rate historique <40% (≥5 trades)
- JournalDeBord.tsx: colonne "Kelly" avec KellyCell (% + €, ajustements signalés)
- routers/risk.py: GET /api/risk/kelly/{pattern_id}

Sprint 3.4 — Tableau de Bord Risque Global
- database.py: get_risk_dashboard() — HHI, score diversification, drawdown attendu, recommandation
- database.py: _build_risk_recommendation() — alerte Risk Committee automatique
- RiskDashboard.tsx: nouvelle page — jauges concentration, courbe P&L, corrélations, recommandation
- Dashboard.tsx: banner d'alerte concentration sur le Cockpit avec lien vers /risk
- routers/risk.py: GET /api/risk/exposure|timeline|clusters|correlations|dashboard
- App.tsx + Sidebar.tsx: route /risk + entrée menu Risk Dashboard

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-06-17 17:18:36 +02:00
OpenSquared
f09c5b8ee7 feat: Phase 2 — Pattern Reliability, Contre-thèses & Calibration probabiliste
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>
2026-06-17 16:50:53 +02:00
OpenSquared
9a6b6f70b1 feat: Phase 1 — IV Rank, Term Structure, Skew, Options Flow (Sprint 1.1/1.2/1.3)
Backend:
- iv_engine.py: ATM IV, term structure (30/60/90/180j), put/call skew,
  options flow (P/C OI ratio, unusual strikes, gamma bias), proxy map for futures→ETFs
- database.py: iv_history table + save_iv_snapshot, get_iv_rank_percentile, get_iv_history
- routers/options_vol.py: /api/options-vol/ endpoints (snapshot, batch, watchlist, history)
- auto_cycle.py: inject IV context string into scoring prompt (step 3.5)
- ai_analyzer.py: score_patterns_with_context accepts iv_context param
- main.py: register options_vol router

Frontend:
- pages/OptionsLab.tsx: full IV dashboard (watchlist by IVR, term structure, skew, flow, sparkline)
- pages/JournalDeBord.tsx: IvRankCell component + IV Rank column per trade
- hooks/useApi.ts: useIvSnapshot, useIvWatchlist, useIvBatch, useIvHistory, useIvForTrade

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-06-17 16:29:33 +02:00
OpenSquared
27d6b598e8 feat: next run countdown in auto-cycle config
- auto_cycle.py: next_run_at now set to future timestamp (now + interval)
  instead of current time — was always showing wrong value
- Config.tsx: NextRunCountdown component shows live countdown (updates
  every second) + absolute local time, only visible when auto-cycle enabled

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-06-17 12:02:50 +02:00
OpenSquared
3818544832 fix: auto_cycle — NameError 'meaningful' + Super Contexte bloqué par gate rapport
- Fix NameError: len(meaningful) → len(meaningful_mature) (ligne 624)
- _auto_synthesize_knowledge() appelé même si pas assez de trades matures,
  pour que le Super Contexte se mette à jour à chaque cycle (gate 6h suffit)

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-06-17 09:15:12 +02:00
OpenSquared
7b50a9b339 fix: KeyError 'stats' in cycle log line — use .get() defensively
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-06-17 08:43:10 +02:00
OpenSquared
9075762dd5 feat: time-aware trade maturity classification
- Add _trade_maturity() helper: classifies trades by % of horizon elapsed
  (trop_tot <10%, debut 10-35%, mature 35-75%, fin_horizon >75%)
- Fix horizon_days fallback chain in log_trade_entries (default 30→90)
- journal.py: enrich each MTM trade with maturity dict + horizon_days
- reasoning.py: portfolio report segments trades by maturity; GPT-4o
  draws lessons only from matures (≥35% elapsed), never from trop_tot
- auto_cycle.py: 90d window, maturity-aware prompt with timing rules
- JournalDeBord.tsx: maturity badge with emoji, label, progress bar
  and day counter (Xj / Yj Z%) replacing plain days_held column

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-06-16 23:49:33 +02:00
OpenSquared
929283045f fix: score differentiation + auto Super Contexte synthesis
- 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>
2026-06-16 20:43:48 +02:00
OpenSquared
d256b65d30 Initial commit — GeoOptions Intelligence Cockpit v2.0
Stack: FastAPI + React/TypeScript + SQLite + GPT-4o
Features: Radar géopolitique, Marchés, Régime Macro, Journal de Bord MTM,
Rapport IA, Super Contexte (base de raisonnement évolutive), Boucle feedback IA.
Deploy: Docker + docker-compose + nginx pour openfin.open-squared.tech

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-06-16 20:29:59 +02:00