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KansaramandClaude Sonnet 4.6
61e145a442
Full-stack forex options analytics terminal with Bloomberg-inspired UI. Backend (FastAPI + Python): - Garman-Kohlhagen options pricing engine with full Greeks - Goldman Sachs gs-quant AI signals (RSI, MACD, Bollinger, Hurst, OU) - Monte Carlo GBM simulation and volatility surface generation - CFTC COT institutional positioning + Forex Factory economic calendar - Live data proxy: OpenSky aircraft + USGS earthquakes (CORS-safe) - Multi-leg strategy library (straddle, iron condor, butterfly, spreads) Frontend (React 18 + Vite): - NEXUS animated orbital logo (3-ring SVG) + canvas favicon animation - Bloomberg terminal design: JetBrains Mono, color-mix() tokens - 11 dashboard tabs: Greeks, Chart, AI Signals, 3D Surfaces, Breakeven, Scenarios, Monte Carlo, Institutional, Calendar, Live Map, Live Feeds - Live World Map (react-leaflet): aircraft, earthquakes, weather radar - Live Feeds: CoinGecko crypto top-12 + Windy.com global webcams - Economic calendar with filters + institutional flow (CFTC COT) - Animated landing page + session-based routing - Fully responsive dark-only terminal design system Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
31 lines
1.1 KiB
Python
31 lines
1.1 KiB
Python
from fastapi import APIRouter
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from ..schemas import ScenarioRequest
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from ..core.garman_kohlhagen import gk_price
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router = APIRouter(prefix="/scenarios", tags=["scenarios"])
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@router.post("")
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def compute_scenarios(req: ScenarioRequest):
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options = [o.model_dump() for o in req.options]
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def portfolio_value(S, sigma):
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return sum(
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gk_price(S, opt["K"], opt.get("T", req.T), req.r_d, req.r_f, sigma, opt["type"])
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* opt["qty"] for opt in options
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)
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base = portfolio_value(req.S0, req.sigma0)
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results = []
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for shock in req.shocks:
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S_s = req.S0 * (1 + shock.dS_pct)
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vol_s = max(0.005, req.sigma0 + shock.dVol)
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pnl = portfolio_value(S_s, vol_s) - base
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results.append({
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"label": shock.label,
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"dS_pct": shock.dS_pct, "dVol": shock.dVol,
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"S_shocked": round(S_s, 5), "vol_shocked": round(vol_s, 4),
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"pnl": round(pnl, 5),
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"pnl_pct": round(pnl / abs(base) * 100, 2) if base else 0,
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})
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return {"base_value": round(base, 5), "scenarios": results}
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