d256b65d30
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>
112 lines
3.3 KiB
Python
112 lines
3.3 KiB
Python
from fastapi import APIRouter, Query
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from typing import Optional
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from services.options_pricer import (
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black_scholes, compute_pnl_curve, bull_call_spread,
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bear_put_spread, long_straddle, implied_vol_surface
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)
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from services.data_fetcher import get_quote, compute_historical_iv
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router = APIRouter(prefix="/api/options", tags=["options"])
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@router.get("/price")
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def price_option(
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symbol: str = Query(...),
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strike: float = Query(...),
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expiry_days: int = Query(90),
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option_type: str = Query("call"),
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rate: float = Query(0.05),
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):
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q = get_quote(symbol)
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S = q["price"] if q and "price" in q else strike
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sigma = compute_historical_iv(symbol)
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T = expiry_days / 365
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result = black_scholes(S, strike, T, rate, sigma, option_type)
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result["underlying_price"] = S
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result["sigma"] = sigma
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return result
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@router.get("/pnl-curve")
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def pnl_curve(
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symbol: str = Query(...),
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strike: float = Query(...),
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expiry_days: int = Query(90),
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option_type: str = Query("call"),
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quantity: int = Query(1),
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premium_paid: float = Query(...),
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rate: float = Query(0.05),
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):
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q = get_quote(symbol)
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S = q["price"] if q and "price" in q else strike
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sigma = compute_historical_iv(symbol)
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T = expiry_days / 365
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return compute_pnl_curve(S, strike, T, rate, sigma, option_type, quantity, premium_paid)
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@router.get("/strategy/bull-call-spread")
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def bull_spread(
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symbol: str = Query(...),
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strike_low: float = Query(...),
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strike_high: float = Query(...),
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expiry_days: int = Query(90),
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rate: float = Query(0.05),
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):
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q = get_quote(symbol)
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S = q["price"] if q and "price" in q else strike_low
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sigma = compute_historical_iv(symbol)
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T = expiry_days / 365
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result = bull_call_spread(S, strike_low, strike_high, T, rate, sigma)
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result["underlying_price"] = S
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result["sigma"] = sigma
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return result
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@router.get("/strategy/bear-put-spread")
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def bear_spread(
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symbol: str = Query(...),
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strike_high: float = Query(...),
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strike_low: float = Query(...),
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expiry_days: int = Query(90),
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rate: float = Query(0.05),
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):
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q = get_quote(symbol)
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S = q["price"] if q and "price" in q else strike_high
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sigma = compute_historical_iv(symbol)
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T = expiry_days / 365
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result = bear_put_spread(S, strike_high, strike_low, T, rate, sigma)
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result["underlying_price"] = S
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result["sigma"] = sigma
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return result
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@router.get("/strategy/straddle")
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def straddle(
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symbol: str = Query(...),
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strike: float = Query(...),
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expiry_days: int = Query(90),
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rate: float = Query(0.05),
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):
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q = get_quote(symbol)
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S = q["price"] if q and "price" in q else strike
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sigma = compute_historical_iv(symbol)
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T = expiry_days / 365
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result = long_straddle(S, strike, T, rate, sigma)
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result["underlying_price"] = S
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result["sigma"] = sigma
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return result
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@router.get("/iv-surface")
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def iv_surface(
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symbol: str = Query(...),
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rate: float = Query(0.05),
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):
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q = get_quote(symbol)
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S = q["price"] if q and "price" in q else 100.0
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sigma = compute_historical_iv(symbol)
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strikes_pct = [0.80, 0.85, 0.90, 0.95, 1.00, 1.05, 1.10, 1.15, 1.20]
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expiries = [7, 14, 30, 60, 90, 180]
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surface = implied_vol_surface(S, strikes_pct, expiries, rate, sigma)
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return {"symbol": symbol, "spot": S, "surface": surface}
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