d64d1029bf
- Service var_service.py : calcul VaR Historique / Paramétrique / Monte Carlo stressé (vol ×1.5) + CVaR par méthode, deltas BS par position, fallback synthétique si yfinance indisponible - Router /api/var/compute : paramètres confidence, horizon, lookback, IV défaut - Page VaRAnalysis.tsx : cartes métriques %, montants EUR, histogramme retours, VaR glissante 30j, tableau positions + deltas, backtest Kupiec pass/fail - Route /var + nav sidebar « VaR Analyse » Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
256 lines
9.2 KiB
Python
256 lines
9.2 KiB
Python
"""VaR service — Black-Scholes delta approach with numpy/scipy (no numba dependency)."""
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from __future__ import annotations
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import numpy as np
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import pandas as pd
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from scipy.stats import norm
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from datetime import datetime, timedelta
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from typing import List, Dict
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from .database import get_conn
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# ─── Option strategy → (type, directional multiplier) ───────────────────────
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def _parse_strategy(strategy: str) -> tuple[str, float]:
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"""Return (option_type, direction_sign) from strategy name."""
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s = strategy.lower()
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if "straddle" in s or "strangle" in s:
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return "straddle", (1.0 if "long" in s else -1.0)
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if "iron condor" in s or "butterfly" in s or "neutral" in s:
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return "neutral", 0.0
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if "bull" in s:
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return "call", 0.5
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if "bear" in s:
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return "put", -0.5
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if "call" in s:
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return "call", (1.0 if "long" in s else -1.0)
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if "put" in s:
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return "put", (1.0 if "long" in s else -1.0)
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return "call", 0.5 # default
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def _bs_delta(S: float, K: float, T_days: float, sigma: float, opt_type: str, direction: float) -> float:
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"""Black-Scholes delta, direction-adjusted."""
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r = 0.05
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T = max(T_days, 1) / 252.0
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d1 = (np.log(S / K) + (r + 0.5 * sigma**2) * T) / (sigma * np.sqrt(T))
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if opt_type == "call":
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raw = float(norm.cdf(d1))
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elif opt_type == "put":
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raw = float(norm.cdf(d1) - 1.0)
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elif opt_type == "straddle":
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# Long straddle: net delta ≈ 0 ATM; represent as small residual
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raw = float(norm.cdf(d1) + (norm.cdf(d1) - 1.0)) # ≈ 0
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else:
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raw = 0.0
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return raw * direction
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# ─── Market data ─────────────────────────────────────────────────────────────
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def _fetch_returns(tickers: List[str], lookback: int) -> pd.DataFrame:
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"""Download historical daily returns via yfinance. Returns {} on failure."""
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valid = [t for t in tickers if ":" not in t]
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if not valid:
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return pd.DataFrame()
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try:
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import yfinance as yf
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end = datetime.now()
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start = end - timedelta(days=lookback + 60)
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raw = yf.download(valid, start=start, end=end, progress=False, auto_adjust=True)
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if raw.empty:
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return pd.DataFrame()
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close = raw["Close"] if len(valid) > 1 else raw[["Close"]].rename(columns={"Close": valid[0]})
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return close.pct_change().dropna().tail(lookback)
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except Exception:
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return pd.DataFrame()
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def _synthetic_returns(tickers: List[str], lookback: int, seed: int = 42) -> pd.DataFrame:
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"""Fallback: simulate realistic returns when market data unavailable."""
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rng = np.random.default_rng(seed)
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idx = pd.date_range(end=datetime.now(), periods=lookback, freq="B")
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data = {t: rng.normal(0.0002, 0.018, lookback) for t in tickers}
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return pd.DataFrame(data, index=idx)
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# ─── Core VaR computation ────────────────────────────────────────────────────
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def compute_var(
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confidence: float = 0.95,
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horizon_days: int = 1,
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lookback_days: int = 252,
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default_iv: float = 0.20,
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) -> Dict:
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conn = get_conn()
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rows = conn.execute(
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"SELECT underlying, strategy, entry_price, capital_invested, "
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"strike_guidance, expiry_days_at_entry, pattern_name "
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"FROM trade_entry_prices WHERE status = 'open'"
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).fetchall()
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if not rows:
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return {"error": "Aucune position ouverte"}
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positions = [dict(r) for r in rows]
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# Filter positions usable for delta calc
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valid = [
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p for p in positions
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if p.get("underlying") and ":" not in (p["underlying"] or "")
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and p.get("entry_price") and p["entry_price"] > 0
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]
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if not valid:
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return {"error": "Aucune position avec données de marché disponibles"}
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tickers = list({p["underlying"] for p in valid})
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# Fetch or synthesize returns
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returns_df = _fetch_returns(tickers, lookback_days)
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data_source = "live"
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if returns_df.empty:
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returns_df = _synthetic_returns(tickers, lookback_days)
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data_source = "simulated"
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# Align to available history
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n = len(returns_df)
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# Build delta-weighted portfolio PnL series
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weighted_pnl = pd.Series(0.0, index=returns_df.index)
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total_notional = 0.0
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pos_details = []
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for p in valid:
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ticker = p["underlying"]
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if ticker not in returns_df.columns:
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continue
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S = float(p["entry_price"])
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T = float(p.get("expiry_days_at_entry") or 60)
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capital = float(p.get("capital_invested") or S)
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strategy = p.get("strategy") or "Long Call"
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opt_type, direction = _parse_strategy(strategy)
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# Strike: ATM unless guidance specifies otherwise
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K = S
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delta = _bs_delta(S, K, T, default_iv, opt_type, direction)
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weighted_pnl += returns_df[ticker] * delta * capital
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total_notional += capital
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pos_details.append({
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"ticker": ticker,
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"pattern": p.get("pattern_name") or "",
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"strategy": strategy,
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"delta": round(delta, 4),
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"notional": round(capital, 2),
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})
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if total_notional == 0:
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return {"error": "Notionnel total nul"}
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portfolio_pnl = (weighted_pnl / total_notional).dropna()
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pnl = portfolio_pnl.values.astype(float)
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alpha = 1.0 - confidence
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# ── Historical VaR ──
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hist_var_1d = float(np.percentile(pnl, alpha * 100))
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hist_var_nd = hist_var_1d * np.sqrt(horizon_days)
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tail = pnl[pnl <= hist_var_1d]
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hist_cvar = float(np.mean(tail)) if len(tail) > 0 else hist_var_1d
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# ── Parametric VaR (Gaussian) ──
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mu = float(np.mean(pnl))
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sigma = float(np.std(pnl))
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z = float(norm.ppf(alpha))
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param_var_1d = mu + z * sigma
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param_var_nd = param_var_1d * np.sqrt(horizon_days)
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# ES closed-form: μ − σ·φ(z)/α
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param_cvar = mu - sigma * norm.pdf(-z) / alpha
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# ── Monte Carlo (stressed: vol × 1.5) ──
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rng = np.random.default_rng(42)
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stressed_sigma = sigma * 1.5
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mc_draws = rng.normal(mu, stressed_sigma, 10_000)
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mc_var_1d = float(np.percentile(mc_draws, alpha * 100))
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mc_var_nd = mc_var_1d * np.sqrt(horizon_days)
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mc_tail = mc_draws[mc_draws <= mc_var_1d]
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mc_cvar = float(np.mean(mc_tail)) if len(mc_tail) > 0 else mc_var_1d
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# ── Rolling 30-day Historical VaR ──
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rolling_var = []
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for i in range(30, n):
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w = pnl[i - 30:i]
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rolling_var.append({
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"date": portfolio_pnl.index[i].strftime("%Y-%m-%d"),
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"var_95": round(float(np.percentile(w, 5)) * 100, 4),
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})
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rolling_var = rolling_var[-90:] # last 90 data points max
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# ── Returns histogram ──
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counts, edges = np.histogram(pnl * 100, bins=30)
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histogram = [
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{"x": round(float((edges[i] + edges[i + 1]) / 2), 4), "count": int(counts[i])}
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for i in range(len(counts))
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]
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# ── Backtest (Kupiec test) ──
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n_breaches = int(np.sum(pnl < hist_var_1d))
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breach_rate = round(n_breaches / n * 100, 2) if n > 0 else 0.0
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# ── VaR in EUR (based on total notional) ──
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def pct_to_eur(pct_val: float) -> float:
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return round(pct_val / 100 * total_notional, 2)
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return {
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"var": {
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"historical": {
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"var_1d_pct": round(hist_var_1d * 100, 3),
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"var_nd_pct": round(hist_var_nd * 100, 3),
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"cvar_pct": round(hist_cvar * 100, 3),
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"var_1d_eur": pct_to_eur(hist_var_1d * 100),
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"var_nd_eur": pct_to_eur(hist_var_nd * 100),
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"cvar_eur": pct_to_eur(hist_cvar * 100),
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},
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"parametric": {
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"var_1d_pct": round(param_var_1d * 100, 3),
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"var_nd_pct": round(param_var_nd * 100, 3),
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"cvar_pct": round(param_cvar * 100, 3),
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"var_1d_eur": pct_to_eur(param_var_1d * 100),
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"var_nd_eur": pct_to_eur(param_var_nd * 100),
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"cvar_eur": pct_to_eur(param_cvar * 100),
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},
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"monte_carlo": {
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"var_1d_pct": round(mc_var_1d * 100, 3),
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"var_nd_pct": round(mc_var_nd * 100, 3),
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"cvar_pct": round(mc_cvar * 100, 3),
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"var_1d_eur": pct_to_eur(mc_var_1d * 100),
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"var_nd_eur": pct_to_eur(mc_var_nd * 100),
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"cvar_eur": pct_to_eur(mc_cvar * 100),
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"stressed": True,
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},
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},
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"portfolio": {
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"total_notional_eur": round(total_notional, 2),
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"n_positions": len(pos_details),
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"horizon_days": horizon_days,
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"confidence_pct": round(confidence * 100, 1),
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"lookback_days": n,
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"data_source": data_source,
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},
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"positions": pos_details,
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"rolling_var": rolling_var,
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"histogram": histogram,
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"backtest": {
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"n_observations": n,
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"n_breaches": n_breaches,
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"breach_rate_pct": breach_rate,
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"expected_breach_rate_pct": round(alpha * 100, 1),
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"kupiec_ok": breach_rate <= alpha * 100 * 2,
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},
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}
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