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https://github.com/Mihirkansara/nexus-quant-terminal.git
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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>
106 lines
3.9 KiB
Python
106 lines
3.9 KiB
Python
"""
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garman_kohlhagen.py — Garman-Kohlhagen (1983) model for European forex options.
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Extension of Black-Scholes that accounts for BOTH the domestic and foreign
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risk-free interest rates — essential for currency options pricing.
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Reference: Garman, M.B. & Kohlhagen, S.W. (1983). "Foreign currency option values."
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Journal of International Money and Finance, 2(3), 231–237.
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Model:
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d1 = [ln(S/K) + (r_d − r_f + σ²/2)·T] / (σ·√T)
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d2 = d1 − σ·√T
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C = S·e^(−r_f·T)·N(d1) − K·e^(−r_d·T)·N(d2)
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P = K·e^(−r_d·T)·N(−d2) − S·e^(−r_f·T)·N(−d1)
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"""
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import numpy as np
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from scipy.stats import norm
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def _d1_d2(S: float, K: float, T: float, r_d: float, r_f: float, sigma: float):
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"""Compute GK d1 and d2 intermediate values."""
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S = np.asarray(S, dtype=float)
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sqrt_T = np.sqrt(T)
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d1 = (np.log(S / K) + (r_d - r_f + 0.5 * sigma ** 2) * T) / (sigma * sqrt_T)
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d2 = d1 - sigma * sqrt_T
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return d1, d2
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def gk_price(S, K, T, r_d, r_f, sigma, option_type="call"):
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d1, d2 = _d1_d2(S, K, T, r_d, r_f, sigma)
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S = np.asarray(S, dtype=float)
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if option_type == "call":
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return float(S * np.exp(-r_f * T) * norm.cdf(d1) - K * np.exp(-r_d * T) * norm.cdf(d2))
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return float(K * np.exp(-r_d * T) * norm.cdf(-d2) - S * np.exp(-r_f * T) * norm.cdf(-d1))
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def gk_delta(S, K, T, r_d, r_f, sigma, option_type="call"):
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"""
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Delta — sensitivity of option price to spot rate change.
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Call: e^(−r_f·T)·N(d1) Put: −e^(−r_f·T)·N(−d1)
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"""
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d1, _ = _d1_d2(S, K, T, r_d, r_f, sigma)
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factor = np.exp(-r_f * T)
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if option_type == "call":
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return float(factor * norm.cdf(d1))
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return float(-factor * norm.cdf(-d1))
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def gk_gamma(S, K, T, r_d, r_f, sigma):
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"""
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Gamma — rate of change of delta.
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Γ = e^(−r_f·T)·N'(d1) / (S·σ·√T) (same sign for calls and puts)
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"""
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d1, _ = _d1_d2(S, K, T, r_d, r_f, sigma)
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return float(np.exp(-r_f * T) * norm.pdf(d1) / (np.asarray(S) * sigma * np.sqrt(T)))
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def gk_vega(S, K, T, r_d, r_f, sigma):
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"""
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Vega — sensitivity to implied volatility.
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ν = S·e^(−r_f·T)·N'(d1)·√T (same for calls and puts)
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"""
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d1, _ = _d1_d2(S, K, T, r_d, r_f, sigma)
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return float(np.asarray(S) * np.exp(-r_f * T) * norm.pdf(d1) * np.sqrt(T))
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def gk_theta(S, K, T, r_d, r_f, sigma, option_type="call"):
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"""
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Theta — time decay (per year).
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Call: −S·σ·e^(−r_f·T)·N'(d1)/(2√T) − r_d·K·e^(−r_d·T)·N(d2) + r_f·S·e^(−r_f·T)·N(d1)
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Put: −S·σ·e^(−r_f·T)·N'(d1)/(2√T) + r_d·K·e^(−r_d·T)·N(−d2) − r_f·S·e^(−r_f·T)·N(−d1)
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"""
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d1, d2 = _d1_d2(S, K, T, r_d, r_f, sigma)
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S = np.asarray(S, dtype=float)
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decay = -(S * sigma * np.exp(-r_f * T) * norm.pdf(d1)) / (2 * np.sqrt(T))
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if option_type == "call":
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return float(decay - r_d * K * np.exp(-r_d * T) * norm.cdf(d2)
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+ r_f * S * np.exp(-r_f * T) * norm.cdf(d1))
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return float(decay + r_d * K * np.exp(-r_d * T) * norm.cdf(-d2)
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- r_f * S * np.exp(-r_f * T) * norm.cdf(-d1))
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def gk_rho_d(S, K, T, r_d, r_f, sigma, option_type="call"):
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"""
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Rho_d — sensitivity to DOMESTIC interest rate.
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Call: K·T·e^(−r_d·T)·N(d2) Put: −K·T·e^(−r_d·T)·N(−d2)
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"""
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_, d2 = _d1_d2(S, K, T, r_d, r_f, sigma)
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factor = K * T * np.exp(-r_d * T)
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if option_type == "call":
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return float(factor * norm.cdf(d2))
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return float(-factor * norm.cdf(-d2))
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def gk_phi(S, K, T, r_d, r_f, sigma, option_type="call"):
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"""
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Phi (ρ_f) — sensitivity to FOREIGN interest rate.
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Call: −S·T·e^(−r_f·T)·N(d1) Put: S·T·e^(−r_f·T)·N(−d1)
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"""
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d1, _ = _d1_d2(S, K, T, r_d, r_f, sigma)
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factor = np.asarray(S) * T * np.exp(-r_f * T)
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if option_type == "call":
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return float(-factor * norm.cdf(d1))
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return float(factor * norm.cdf(-d1))
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