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>
This commit is contained in:
OpenSquared
297
backend/routers/ai.py
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297
backend/routers/ai.py
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from fastapi import APIRouter, HTTPException
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from pydantic import BaseModel
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from typing import Optional, List, Dict, Any
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import json
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from services.ai_analyzer import (
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analyze_speech, evaluate_pattern, suggest_pattern_from_context,
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rank_trade_ideas, analyze_news_item, get_client, score_patterns_with_context,
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suggest_patterns_from_market_context, ai_score_news_batch,
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DEFAULT_ANALYSIS_TEMPLATE, _chat,
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)
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from services.data_fetcher import fetch_geo_news, get_all_quotes
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from services.geo_analyzer import compute_geo_risk_score, match_patterns
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from services.database import get_config, get_custom_patterns, get_analysis_config, save_pattern_scores, get_pattern_scores, get_score_deltas, compute_pattern_similarity, log_geo_alert, log_trade_entries
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router = APIRouter(prefix="/api/ai", tags=["ai"])
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def require_ai():
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key = get_config("openai_api_key") or ""
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if not key:
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raise HTTPException(400, "Clé OpenAI non configurée — aller dans Config")
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import os
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os.environ["OPENAI_API_KEY"] = key
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class SpeechRequest(BaseModel):
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text: str
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speaker: Optional[str] = ""
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class PatternEvalRequest(BaseModel):
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pattern: Dict[str, Any]
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class PatternSuggestRequest(BaseModel):
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context: str
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class NewsAnalyzeRequest(BaseModel):
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title: str
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summary: str
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class ScorePatternsRequest(BaseModel):
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top_n: Optional[int] = None # override config default
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category_filter: Optional[str] = None # "all"|"energy"|"metals"|"agriculture"|"forex"|"indices"|"equities"
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template: Optional[str] = None # override config template
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@router.get("/status")
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def ai_status():
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key = get_config("openai_api_key") or ""
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return {
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"enabled": bool(key),
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"key_configured": bool(key),
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"model": "gpt-4o",
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"fast_model": "gpt-4o-mini",
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}
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@router.post("/analyze-speech")
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def analyze_speech_endpoint(req: SpeechRequest):
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require_ai()
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return analyze_speech(req.text, req.speaker)
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@router.post("/analyze-news")
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def analyze_news_endpoint(req: NewsAnalyzeRequest):
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require_ai()
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return analyze_news_item(req.title, req.summary)
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@router.post("/evaluate-pattern")
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def evaluate_pattern_endpoint(req: PatternEvalRequest):
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require_ai()
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return evaluate_pattern(req.pattern)
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@router.post("/suggest-pattern")
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def suggest_pattern_endpoint(req: PatternSuggestRequest):
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require_ai()
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return suggest_pattern_from_context(req.context)
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@router.get("/top-ideas")
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def top_ideas():
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require_ai()
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from routers.geopolitical import _news_cache
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news = _news_cache.get("data") or fetch_geo_news()
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matches = match_patterns(news)
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geo_score = compute_geo_risk_score(news)
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ideas = rank_trade_ideas(matches, geo_score, news, {})
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return {"ideas": ideas, "count": len(ideas)}
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@router.post("/score-patterns")
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def score_patterns(req: ScorePatternsRequest):
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"""Score all patterns with rich context (news + prices + IV) via GPT-4o."""
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require_ai()
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# Load config defaults
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cfg = get_analysis_config()
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top_n = req.top_n or cfg.get("top_n", 10)
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category_filter = req.category_filter or cfg.get("category_filter", "all")
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template = req.template or cfg.get("template") or DEFAULT_ANALYSIS_TEMPLATE
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# Gather context
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from routers.geopolitical import _news_cache
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news = _news_cache.get("data") or fetch_geo_news()
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# AI-rescore news: get accurate impact magnitudes + directional signals per asset class
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# This enables contra-signal detection in pattern scoring (e.g. peace deal → oil bearish)
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news = ai_score_news_batch(news)
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_news_cache["data"] = news # propagate AI-enriched scores back to news feed
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geo_score = compute_geo_risk_score(news)
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quotes = get_all_quotes()
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# Macro regime context (use cached value from /api/market/macro-regime if available)
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from routers.market_data import _macro_cache
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macro_regime = _macro_cache.get("data")
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if not macro_regime:
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from services.data_fetcher import get_macro_gauges, score_macro_scenarios
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gauges = get_macro_gauges()
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macro_regime = {"gauges": gauges, "scenarios": score_macro_scenarios(gauges)}
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# All patterns from DB (builtin-seeded + custom)
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all_patterns = get_custom_patterns()
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# Score all active patterns
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scored = score_patterns_with_context(
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patterns=all_patterns,
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recent_news=news,
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quotes_by_class=quotes,
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geo_score=geo_score,
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template=template,
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top_n=len(all_patterns),
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category_filter=None,
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macro_regime=macro_regime,
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)
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# Persist scores for later retrieval
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run_id = save_pattern_scores(scored, meta={"geo_score": geo_score.get("score"), "total": len(scored)})
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# Journal de Bord — log geo alert + trade entry prices at this moment
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top_patterns_for_log = sorted(
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[{"pattern_id": sp.get("pattern_id"), "name": sp.get("geo_trigger"), "score": sp.get("score", 0)}
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for sp in scored if sp.get("score", 0) > 0],
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key=lambda x: -x["score"]
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)[:10]
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log_geo_alert(
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geo_score=int(geo_score.get("score") or 0),
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top_patterns=top_patterns_for_log,
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news_count=len(news),
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run_id=run_id,
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)
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log_trade_entries(run_id=run_id, scored_patterns=scored, quotes=quotes)
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return {
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"scored_patterns": scored,
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"count": len(scored),
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"top_n": top_n,
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"category_filter": category_filter,
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"geo_score": geo_score.get("score"),
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}
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@router.get("/last-scores")
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def last_scores():
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"""Return last persisted AI pattern scores with inter-run score deltas."""
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data = get_pattern_scores()
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deltas = get_score_deltas()
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scored = data.get("scores", [])
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for sp in scored:
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pid = sp.get("pattern_id", "")
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sp["score_trend"] = deltas.get(pid) # None = first run, int = change vs previous run
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return {
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"scored_patterns": scored,
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"scored_at": data.get("scored_at"),
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"meta": data.get("meta", {}),
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"count": len(scored),
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}
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@router.get("/pattern-similarity")
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def pattern_similarity():
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"""Return pairs of patterns with high keyword overlap (Jaccard >= 0.25)."""
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patterns = get_custom_patterns()
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pairs = compute_pattern_similarity(patterns, threshold=0.25)
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return {"pairs": pairs, "count": len(pairs)}
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@router.post("/suggest-new-patterns")
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def suggest_new_patterns():
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"""Ask GPT-4o to propose new patterns from current geo/market context (no text input needed)."""
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require_ai()
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from routers.geopolitical import _news_cache
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news = _news_cache.get("data") or fetch_geo_news()
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quotes = get_all_quotes()
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from services.data_fetcher import get_economic_calendar
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calendar = get_economic_calendar()
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# Macro regime context
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from routers.market_data import _macro_cache
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macro_regime = _macro_cache.get("data")
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if not macro_regime:
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from services.data_fetcher import get_macro_gauges, score_macro_scenarios
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gauges = get_macro_gauges()
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macro_regime = {"gauges": gauges, "scenarios": score_macro_scenarios(gauges)}
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# Geo risk score for additional context
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geo_score = compute_geo_risk_score(news)
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patterns = suggest_patterns_from_market_context(news, quotes, calendar, macro_regime=macro_regime, geo_score=geo_score)
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return {"suggested_patterns": patterns, "count": len(patterns)}
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@router.get("/analysis-template")
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def get_template():
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cfg = get_analysis_config()
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return {
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"template": cfg.get("template") or DEFAULT_ANALYSIS_TEMPLATE,
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"top_n": cfg.get("top_n", 10),
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"category_filter": cfg.get("category_filter", "all"),
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}
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class MacroNarrationRequest(BaseModel):
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macro_regime: Dict[str, Any]
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@router.post("/macro-narration")
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def macro_narration(req: MacroNarrationRequest):
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"""Ask GPT-4o to narrate the current macro regime for the MacroRegime page."""
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require_ai()
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sc = req.macro_regime.get("scenarios", {})
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gauges = req.macro_regime.get("gauges", {})
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dom = sc.get("dominant", "incertain")
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scores = sc.get("scores", {})
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reasons = sc.get("reasons", {})
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def gv(key: str) -> str:
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v = gauges.get(key, {}).get("value")
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return str(round(v, 3)) if v is not None else "N/A"
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def gc(key: str) -> str:
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v = gauges.get(key, {}).get("change_pct")
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return f"{v:+.2f}%" if v is not None else "N/A"
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user = f"""Tu es un stratège macro senior utilisant un framework à 3 axes : Inflation / Croissance / Liquidité.
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Scénario dominant: {dom.upper()}
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Scores des 8 scénarios: {json.dumps(scores, ensure_ascii=False)}
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Raisons du scénario dominant: {json.dumps(reasons.get(dom, []), ensure_ascii=False)}
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AXE INFLATION (énergie + taux réels):
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- Brent (var J-1): {gc('brent')}
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- Gaz naturel (var J-1): {gc('ng')}
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- TIPS ETF (var J-1): {gc('tips')}
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AXE CROISSANCE (cycle réel):
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- Cuivre (var J-1): {gc('copper')} — "Dr Copper"
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- S&P vs 200j MA: {gv('spx_vs_200d')}%
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- Russell vs S&P (breadth): {gv('iwm_spx_ratio')} pts%
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- Industriels XLI (proxy ISM, var J-1): {gc('xli')}
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AXE LIQUIDITÉ / STRESS (conditions financières):
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- VIX: {gv('vix')}
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- Pente 10Y–3M: {gv('slope_10y3m')}% (négatif = récession probable)
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- HYG spreads HY (var J-1): {gc('hyg')}
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- LQD spreads IG (var J-1): {gc('lqd')}
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- Dollar DXY (var J-1): {gc('dxy')}
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SIGNAUX DÉRIVÉS:
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- Ratio Or/Cuivre: {gv('gold_copper_ratio')} (>700 = peur, <500 = expansion)
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- Or (var J-1): {gc('gold')}
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- IEF Trésor (var J-1): {gc('ief')}
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Donne une analyse narrative COURTE (5-7 phrases) en français pour un trader options:
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1. Confirme le régime dominant et ses 2-3 signaux les plus forts
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2. Identifie 1-2 compteurs en contradiction ou tension (signal ambigu)
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3. Évalue si la LIQUIDITÉ confirme ou contredit le régime inflation/croissance
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4. Cite les 2-3 classes d'actifs les plus favorisées/défavorisées
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5. Donne 1 biais tactique concret options pour les prochains jours
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Réponds UNIQUEMENT en JSON: {{"narration": "<ton texte 5-7 phrases>"}}"""
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result = _chat(
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"Tu es un stratège macro senior. Analyse concise et actionnable pour traders options. JSON uniquement.",
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user,
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model="gpt-4o",
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json_mode=True,
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max_tokens=600,
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)
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if not result:
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return {"narration": "IA non disponible — vérifier la clé OpenAI."}
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return {"narration": result.get("narration", "")}
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