mirror of
https://github.com/Mihirkansara/nexus-quant-terminal.git
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Kansaram
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>
275 lines
12 KiB
Python
275 lines
12 KiB
Python
"""
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quant_analysis.py — Quantitative signals for forex pairs.
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Core analytics powered by:
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• gs-quant (Goldman Sachs, 2024) — volatility, RSI, MACD, Bollinger Bands,
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max drawdown, z-scores, rolling statistics
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• Custom implementations for models not in gs-quant:
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1. EWMA Vol Forecast — RiskMetrics λ=0.94 (JP Morgan, 1994)
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2. VaR / CVaR — Parametric Normal + Historical ES (Basel III)
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3. Hurst Exponent — Variance-scaling (Hurst 1951)
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4. Ornstein-Uhlenbeck — OLS AR(1) (Uhlenbeck & Ornstein 1930)
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5. Carry Signal — Uncovered Interest Parity (Fama 1984)
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6. Momentum — Price momentum (Jegadeesh & Titman 1993)
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"""
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import numpy as np
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import pandas as pd
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from scipy import stats
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# GS-Quant timeseries — all work offline, no credentials required
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from gs_quant.timeseries import econometrics as gseco
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from gs_quant.timeseries import statistics as gsstat
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from gs_quant.timeseries import technicals as gstech
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# ─── helpers ──────────────────────────────────────────────────────────────────
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def _to_series(prices) -> pd.Series:
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arr = np.array(prices, dtype=float)
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idx = pd.date_range(end=pd.Timestamp.today().normalize(), periods=len(arr), freq='D')
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return pd.Series(arr, index=idx)
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def _safe_last(series: pd.Series, default=0.0) -> float:
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try:
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v = series.dropna()
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return float(v.iloc[-1]) if len(v) else default
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except Exception:
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return default
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def _hurst_exponent(prices: np.ndarray) -> float:
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"""Variance-scaling H estimator: Var[ΔX_τ] ~ τ^(2H). (Hurst 1951)"""
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prices = np.array(prices, dtype=float)
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max_lag = min(len(prices) // 3, 30)
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if max_lag < 3:
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return 0.5
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lags = range(2, max_lag)
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variances = [np.var(np.diff(prices, n=lag)) for lag in lags]
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try:
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slope, *_ = stats.linregress(np.log(list(lags)), np.log(variances))
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return float(np.clip(slope / 2, 0.01, 0.99))
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except Exception:
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return 0.5
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def _estimate_ou_params(prices: np.ndarray) -> dict:
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"""OLS AR(1) → OU parameters. (Uhlenbeck & Ornstein 1930)"""
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X = np.array(prices, dtype=float)
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Xl, Xc = X[:-1], X[1:]
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n = len(Xl)
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b_num = n * np.dot(Xl, Xc) - Xl.sum() * Xc.sum()
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b_den = n * np.dot(Xl, Xl) - Xl.sum() ** 2
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b = float(np.clip(b_num / b_den if b_den else 0.999, 0.001, 0.9999))
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a = float(Xc.mean() - b * Xl.mean())
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resid_sigma = float(np.std(Xc - (a + b * Xl)) * np.sqrt(252))
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kappa = float(-np.log(b) * 252)
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theta = float(a / (1 - b))
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half_life = float(np.log(2) / max(kappa, 1e-6) / 252 * 365)
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return {"kappa": round(kappa, 4), "theta": round(theta, 6),
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"sigma": round(resid_sigma, 6), "half_life_days": round(half_life, 1)}
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# ─── main signal engine ───────────────────────────────────────────────────────
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def compute_signals(prices: list, r_d: float = 0.05, r_f: float = 0.04) -> dict:
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"""
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Full quant signal suite — core analytics via gs-quant (Goldman Sachs),
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extended with OU, Hurst, VaR and carry trade signals.
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"""
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px_raw = np.array(prices, dtype=float)
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n = len(px_raw)
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if n < 10:
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return {"error": "Need ≥10 observations."}
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px = _to_series(px_raw)
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w20 = min(20, n)
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w60 = min(60, n)
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rets_raw = np.diff(np.log(px_raw))
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# ── 1. Volatility (gs-quant econometrics.volatility) ────────────────────
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# GS implementation: annualized realized vol over rolling window
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hv20_s = gseco.volatility(px, w20)
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hv60_s = gseco.volatility(px, w60)
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hv20 = _safe_last(hv20_s, np.std(rets_raw[-w20:]) * np.sqrt(252))
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hv60 = _safe_last(hv60_s, np.std(rets_raw[-w60:]) * np.sqrt(252))
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vol_regime = ("HIGH" if hv20 > hv60 * 1.25 else
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"LOW" if hv20 < hv60 * 0.80 else "NORMAL")
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# EWMA forecast (RiskMetrics λ=0.94, JP Morgan 1994)
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lam, ewma_var = 0.94, float(np.var(rets_raw[-w20:]))
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for r in rets_raw[-w20:]:
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ewma_var = lam * ewma_var + (1 - lam) * r ** 2
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ewma_vol = float(np.sqrt(ewma_var * 252))
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# GS max-drawdown (institutional risk metric)
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dd_s = gseco.max_drawdown(px, w60)
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max_dd = _safe_last(dd_s, -0.01)
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# ── 2. VaR / CVaR (custom — Parametric Normal + Basel III) ─────────────
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mu_d, sig_d = float(np.mean(rets_raw[-w20:])), float(np.std(rets_raw[-w20:]))
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var95 = float(-(mu_d + sig_d * stats.norm.ppf(0.05)))
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var99 = float(-(mu_d + sig_d * stats.norm.ppf(0.01)))
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tail = np.sort(rets_raw[-w60:])[:max(1, int(0.05 * w60))]
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cvar95 = float(-np.mean(tail))
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# ── 3. Hurst Exponent (custom — Hurst 1951) ─────────────────────────────
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hurst = _hurst_exponent(px_raw[-w60:])
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hurst_regime = ("MEAN-REVERTING" if hurst < 0.45 else
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"TRENDING" if hurst > 0.55 else "RANDOM WALK")
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hurst_action = {"MEAN-REVERTING": "FADE extreme moves — OU strategies apply",
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"TRENDING": "FOLLOW momentum — trend-following applies",
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"RANDOM WALK": "No structural edge — vol strategies apply"}[hurst_regime]
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# ── 4. Ornstein-Uhlenbeck (custom — Uhlenbeck & Ornstein 1930) ──────────
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ou = _estimate_ou_params(px_raw[-w60:])
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ou_std = ou["sigma"] / np.sqrt(max(ou["kappa"], 0.01) * 252)
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# Z-score via gs-quant statistics (institutional grade)
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z_s = gsstat.zscores(px, w20)
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gs_z = _safe_last(z_s, 0.0)
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ou_z = float((px_raw[-1] - ou["theta"]) / max(ou_std, 1e-9))
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ou_signal = "SELL" if ou_z > 2 else ("BUY" if ou_z < -2 else "NEUTRAL")
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ou_conf = min(92, 50 + int(abs(ou_z) * 15)) if ou_signal != "NEUTRAL" else 40
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# ── 5. RSI (gs-quant technicals.relative_strength_index) ────────────────
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rsi_s = gstech.relative_strength_index(px, 14)
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rsi = _safe_last(rsi_s, 50.0)
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rsi_signal = ("OVERBOUGHT" if rsi > 70 else
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"OVERSOLD" if rsi < 30 else "NEUTRAL")
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rsi_bias = ("BEARISH" if rsi > 70 else
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"BULLISH" if rsi < 30 else "NEUTRAL")
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# ── 6. MACD (gs-quant technicals.macd) ──────────────────────────────────
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macd_s = gstech.macd(px)
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macd = _safe_last(macd_s, 0.0)
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macd_signal = "BULLISH" if macd > 0 else "BEARISH"
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# ── 7. Bollinger Bands (gs-quant technicals.bollinger_bands) ────────────
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bb_s = gstech.bollinger_bands(px, w20)
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current_px = float(px_raw[-1])
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sma_s = gstech.moving_average(px, w20)
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sma = _safe_last(sma_s, current_px)
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std_s = gsstat.std(px, w20)
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gsstd = _safe_last(std_s, float(np.std(px_raw[-w20:])))
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bb_upper = sma + 2.0 * gsstd
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bb_lower = sma - 2.0 * gsstd
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bb_pct = float((current_px - bb_lower) / max(bb_upper - bb_lower, 1e-9)) # 0=lower,1=upper
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bb_signal = ("NEAR UPPER BAND" if bb_pct > 0.85 else
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"NEAR LOWER BAND" if bb_pct < 0.15 else "MID BAND")
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bb_bias = ("BEARISH" if bb_pct > 0.85 else
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"BULLISH" if bb_pct < 0.15 else "NEUTRAL")
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# ── 8. Momentum (Jegadeesh & Titman 1993) ───────────────────────────────
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ret5 = float(px_raw[-1] / px_raw[max(-5, -n)] - 1) if n >= 5 else 0.0
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ret20 = float(px_raw[-1] / px_raw[max(-20, -n)] - 1) if n >= 20 else 0.0
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mom_signal = ("BULLISH" if ret5 > 0 and ret20 > 0 else
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"BEARISH" if ret5 < 0 and ret20 < 0 else "MIXED")
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# ── 9. Carry (Uncovered Interest Parity, Fama 1984) ─────────────────────
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carry_diff = r_d - r_f
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carry_signal = ("BUY BASE" if carry_diff > 0.005 else
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"SELL BASE" if carry_diff < -0.005 else "NEUTRAL")
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# ── Composite signal (8 inputs, gs-quant enhanced) ────────────────────────
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bull = sum([
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ret5 > 0, ret20 > 0,
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ou_signal == "BUY",
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carry_signal == "BUY BASE",
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macd_signal == "BULLISH",
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rsi_bias == "BULLISH",
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bb_bias == "BULLISH",
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hurst_regime == "TRENDING" and ret5 > 0,
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])
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bear = sum([
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ret5 < 0, ret20 < 0,
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ou_signal == "SELL",
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carry_signal == "SELL BASE",
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macd_signal == "BEARISH",
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rsi_bias == "BEARISH",
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bb_bias == "BEARISH",
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hurst_regime == "TRENDING" and ret5 < 0,
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])
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if bull >= bear + 3:
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composite, conf = "BULLISH", min(95, 50 + bull * 6)
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elif bear >= bull + 3:
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composite, conf = "BEARISH", min(95, 50 + bear * 6)
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elif bull > bear:
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composite, conf = "BULLISH", min(70, 50 + bull * 4)
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elif bear > bull:
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composite, conf = "BEARISH", min(70, 50 + bear * 4)
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else:
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composite, conf = "NEUTRAL", 45
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return {
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"composite": composite,
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"confidence": conf,
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"n_observations": n,
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"powered_by": "gs-quant (Goldman Sachs) + custom quant models",
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"volatility": {
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"hv_20_pct": round(hv20, 2),
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"hv_60_pct": round(hv60, 2),
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"regime": vol_regime,
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"ewma_forecast_pct": round(ewma_vol * 100, 2),
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"max_drawdown_pct": round(max_dd * 100, 2),
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"method": "gs-quant econometrics.volatility() + EWMA λ=0.94 (RiskMetrics 1994)",
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},
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"risk": {
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"var_95_pct": round(var95 * 100, 3),
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"var_99_pct": round(var99 * 100, 3),
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"cvar_95_pct": round(cvar95 * 100, 3),
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"method": "Parametric Normal VaR / Historical CVaR (Basel III)",
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},
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"hurst": {
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"exponent": round(hurst, 3),
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"regime": hurst_regime,
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"action": hurst_action,
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"method": "Variance-scaling estimator (Hurst 1951)",
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},
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"mean_reversion": {
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"kappa": ou["kappa"],
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"theta": ou["theta"],
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"half_life_days": ou["half_life_days"],
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"zscore": round(ou_z, 2),
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"gs_zscore": round(gs_z, 2),
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"signal": ou_signal,
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"confidence": ou_conf,
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"method": "OLS AR(1) → OU SDE (Uhlenbeck & Ornstein 1930) + gs-quant zscores",
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},
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"rsi": {
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"value": round(rsi, 2),
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"signal": rsi_signal,
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"bias": rsi_bias,
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"method": "gs-quant technicals.relative_strength_index(14) (Wilder 1978)",
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},
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"macd": {
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"value": round(macd, 6),
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"signal": macd_signal,
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"method": "gs-quant technicals.macd() — 12/26/9 EMA crossover (Appel 1979)",
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},
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"bollinger": {
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"upper": round(bb_upper, 5),
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"lower": round(bb_lower, 5),
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"sma": round(sma, 5),
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"pct_b": round(bb_pct, 3),
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"signal": bb_signal,
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"bias": bb_bias,
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"method": "gs-quant technicals.bollinger_bands(20,2σ) (Bollinger 1983)",
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},
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"momentum": {
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"return_5d_pct": round(ret5 * 100, 3),
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"return_20d_pct": round(ret20 * 100, 3),
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"signal": mom_signal,
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"method": "Price momentum (Jegadeesh & Titman 1993)",
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},
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"carry": {
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"r_d": round(r_d * 100, 2),
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"r_f": round(r_f * 100, 2),
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"differential_pct": round(carry_diff * 100, 2),
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"signal": carry_signal,
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"method": "Uncovered Interest Parity (Fama 1984)",
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},
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}
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