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ALI YESILKAYA
73e2ad30db
Panneau « Actions rapides » sur le dashboard : 6 boutons one-click qui renvoient une analyse finlab instantanée (déterministe, gratuite, sans LLM), dans une modale. - 📊 Analyse (action sélectionnée) : technique (RSI/MACD/MM/signaux) + fondamental (PER/PEG/ROE/marges/dette/reco/cible) + plan R:R, combinés en une fiche - 📐 Plan R:R, 📈 Fondamentaux (action sélectionnée) ; 📋 Digest, 🔭 Opportunités, 🔔 Alertes (global) - Backend : endpoints /api/digest, /api/fundamentals, /api/analyze - Fix : fundamental.compare_portfolio() utilisait l'ancien schéma mono-compte (pf["positions"]) → cassé par le multi-comptes ; passe par data.portfolio_tickers() Vérifié : endpoints OK (TestClient), rendu modale au navigateur (Playwright) — fiche Analyse NVDA complète (technique + fondamental + plan). JS node --check OK. Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
65 lines
1.9 KiB
Python
65 lines
1.9 KiB
Python
"""Analyse fondamentale : ratios clés et résultats via yfinance."""
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from __future__ import annotations
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import pandas as pd
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from . import data
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def _b(x: float | None) -> str:
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"""Formate un grand nombre en milliards."""
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return f"{x/1e9:.1f} Md" if isinstance(x, (int, float)) else "—"
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def snapshot(symbol: str) -> dict:
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i = data.info(symbol)
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def g(*keys):
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for k in keys:
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v = i.get(k)
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if v is not None:
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return v
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return None
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return {
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"ticker": symbol,
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"nom": g("shortName", "longName"),
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"secteur": g("sector"),
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"cours": g("currentPrice", "regularMarketPrice"),
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"PER": g("trailingPE"),
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"PER_fwd": g("forwardPE"),
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"PEG": g("pegRatio"),
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"P/S": g("priceToSalesTrailing12Months"),
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"P/B": g("priceToBook"),
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"marge_nette_%": round(g("profitMargins") * 100, 1) if g("profitMargins") else None,
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"ROE_%": round(g("returnOnEquity") * 100, 1) if g("returnOnEquity") else None,
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"croiss_CA_%": round(g("revenueGrowth") * 100, 1) if g("revenueGrowth") else None,
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"dette/FP": g("debtToEquity"),
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"div_%": round(g("dividendYield") * 100, 2) if g("dividendYield") else None,
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"capi": _b(g("marketCap")),
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"reco": g("recommendationKey"),
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"cible_moy": g("targetMeanPrice"),
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}
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def compare(symbols: list[str]) -> pd.DataFrame:
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rows = []
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for s in symbols:
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try:
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rows.append(snapshot(s))
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except Exception as e:
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rows.append({"ticker": s, "nom": f"erreur: {e}"})
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return pd.DataFrame(rows)
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def compare_portfolio() -> pd.DataFrame:
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return compare(data.portfolio_tickers())
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if __name__ == "__main__":
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import sys
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syms = sys.argv[1:]
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df = compare(syms) if syms else compare_portfolio()
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pd.set_option("display.max_columns", None, "display.width", 240)
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print(df.to_string(index=False))
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