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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>
50 lines
1.6 KiB
Python
50 lines
1.6 KiB
Python
import numpy as np
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from scipy.stats import norm
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def _d1_d2(S, K, T, r, sigma):
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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 + 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 bs_price(S, K, T, r, sigma, option_type="call"):
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d1, d2 = _d1_d2(S, K, T, r, sigma)
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if option_type == "call":
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return float(np.asarray(S) * norm.cdf(d1) - K * np.exp(-r * T) * norm.cdf(d2))
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return float(K * np.exp(-r * T) * norm.cdf(-d2) - np.asarray(S) * norm.cdf(-d1))
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def bs_delta(S, K, T, r, sigma, option_type="call"):
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d1, _ = _d1_d2(S, K, T, r, sigma)
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return float(norm.cdf(d1) if option_type == "call" else norm.cdf(d1) - 1.0)
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def bs_gamma(S, K, T, r, sigma):
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d1, _ = _d1_d2(S, K, T, r, sigma)
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return float(norm.pdf(d1) / (np.asarray(S) * sigma * np.sqrt(T)))
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def bs_vega(S, K, T, r, sigma):
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d1, _ = _d1_d2(S, K, T, r, sigma)
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return float(np.asarray(S) * norm.pdf(d1) * np.sqrt(T))
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def bs_theta(S, K, T, r, sigma, option_type="call"):
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d1, d2 = _d1_d2(S, K, T, r, sigma)
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S = np.asarray(S)
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decay = -(S * norm.pdf(d1) * sigma) / (2 * np.sqrt(T))
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if option_type == "call":
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return float(decay - r * K * np.exp(-r * T) * norm.cdf(d2))
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return float(decay + r * K * np.exp(-r * T) * norm.cdf(-d2))
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def bs_rho(S, K, T, r, sigma, option_type="call"):
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"""Rho — sensitivity to interest rate changes."""
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_, d2 = _d1_d2(S, K, T, r, sigma)
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if option_type == "call":
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return float(K * T * np.exp(-r * T) * norm.cdf(d2))
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return float(-K * T * np.exp(-r * T) * norm.cdf(-d2))
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