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fix: add gs-quant to requirements, graceful fallback if unavailable

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- Added gs-quant to backend/requirements.txt
- All gs_quant calls now conditional on GS_QUANT_AVAILABLE flag
- Pure numpy/pandas fallbacks for volatility, RSI, MACD, Bollinger, z-score
- Fixes ModuleNotFoundError crash on Render deployment

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
Kansaram
2026-06-07 19:16:30 +05:30
co-authored by Claude Sonnet 4.6
parent 7f299873cf
commit 84bde8c664
2 changed files with 38 additions and 17 deletions
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backend/app/core/quant_analysis.py
+37 -17
View File
@@ -18,9 +18,14 @@ import pandas as pd
from scipy import stats
# GS-Quant timeseries — all work offline, no credentials required
from gs_quant.timeseries import econometrics as gseco
from gs_quant.timeseries import statistics as gsstat
from gs_quant.timeseries import technicals as gstech
try:
from gs_quant.timeseries import econometrics as gseco
from gs_quant.timeseries import statistics as gsstat
from gs_quant.timeseries import technicals as gstech
GS_QUANT_AVAILABLE = True
except ImportError:
gseco = gsstat = gstech = None
GS_QUANT_AVAILABLE = False
# ─── helpers ──────────────────────────────────────────────────────────────────
@@ -90,8 +95,8 @@ def compute_signals(prices: list, r_d: float = 0.05, r_f: float = 0.04) -> dict:
# ── 1. Volatility (gs-quant econometrics.volatility) ────────────────────
# GS implementation: annualized realized vol over rolling window
hv20_s = gseco.volatility(px, w20)
hv60_s = gseco.volatility(px, w60)
hv20_s = gseco.volatility(px, w20) if GS_QUANT_AVAILABLE else None
hv60_s = gseco.volatility(px, w60) if GS_QUANT_AVAILABLE else None
hv20 = _safe_last(hv20_s, np.std(rets_raw[-w20:]) * np.sqrt(252))
hv60 = _safe_last(hv60_s, np.std(rets_raw[-w60:]) * np.sqrt(252))
vol_regime = ("HIGH" if hv20 > hv60 * 1.25 else
@@ -104,7 +109,7 @@ def compute_signals(prices: list, r_d: float = 0.05, r_f: float = 0.04) -> dict:
ewma_vol = float(np.sqrt(ewma_var * 252))
# GS max-drawdown (institutional risk metric)
dd_s = gseco.max_drawdown(px, w60)
dd_s = gseco.max_drawdown(px, w60) if GS_QUANT_AVAILABLE else None
max_dd = _safe_last(dd_s, -0.01)
# ── 2. VaR / CVaR (custom — Parametric Normal + Basel III) ─────────────
@@ -127,32 +132,47 @@ def compute_signals(prices: list, r_d: float = 0.05, r_f: float = 0.04) -> dict:
ou_std = ou["sigma"] / np.sqrt(max(ou["kappa"], 0.01) * 252)
# Z-score via gs-quant statistics (institutional grade)
z_s = gsstat.zscores(px, w20)
gs_z = _safe_last(z_s, 0.0)
z_s = gsstat.zscores(px, w20) if GS_QUANT_AVAILABLE else None
gs_z = _safe_last(z_s, float((px_raw[-1] - np.mean(px_raw[-w20:])) / max(np.std(px_raw[-w20:]), 1e-9)))
ou_z = float((px_raw[-1] - ou["theta"]) / max(ou_std, 1e-9))
ou_signal = "SELL" if ou_z > 2 else ("BUY" if ou_z < -2 else "NEUTRAL")
ou_conf = min(92, 50 + int(abs(ou_z) * 15)) if ou_signal != "NEUTRAL" else 40
# ── 5. RSI (gs-quant technicals.relative_strength_index) ────────────────
rsi_s = gstech.relative_strength_index(px, 14)
rsi = _safe_last(rsi_s, 50.0)
if GS_QUANT_AVAILABLE:
rsi_s = gstech.relative_strength_index(px, 14)
rsi = _safe_last(rsi_s, 50.0)
else:
d = np.diff(px_raw[-15:])
gain = np.mean(d[d > 0]) if np.any(d > 0) else 1e-9
loss = np.mean(-d[d < 0]) if np.any(d < 0) else 1e-9
rsi = float(100 - 100 / (1 + gain / loss))
rsi_signal = ("OVERBOUGHT" if rsi > 70 else
"OVERSOLD" if rsi < 30 else "NEUTRAL")
rsi_bias = ("BEARISH" if rsi > 70 else
"BULLISH" if rsi < 30 else "NEUTRAL")
# ── 6. MACD (gs-quant technicals.macd) ──────────────────────────────────
macd_s = gstech.macd(px)
macd = _safe_last(macd_s, 0.0)
if GS_QUANT_AVAILABLE:
macd_s = gstech.macd(px)
macd = _safe_last(macd_s, 0.0)
else:
ema12 = float(pd.Series(px_raw).ewm(span=12).mean().iloc[-1])
ema26 = float(pd.Series(px_raw).ewm(span=26).mean().iloc[-1])
macd = ema12 - ema26
macd_signal = "BULLISH" if macd > 0 else "BEARISH"
# ── 7. Bollinger Bands (gs-quant technicals.bollinger_bands) ────────────
bb_s = gstech.bollinger_bands(px, w20)
current_px = float(px_raw[-1])
sma_s = gstech.moving_average(px, w20)
sma = _safe_last(sma_s, current_px)
std_s = gsstat.std(px, w20)
gsstd = _safe_last(std_s, float(np.std(px_raw[-w20:])))
if GS_QUANT_AVAILABLE:
bb_s = gstech.bollinger_bands(px, w20)
sma_s = gstech.moving_average(px, w20)
sma = _safe_last(sma_s, current_px)
std_s = gsstat.std(px, w20)
gsstd = _safe_last(std_s, float(np.std(px_raw[-w20:])))
else:
sma = float(np.mean(px_raw[-w20:]))
gsstd = float(np.std(px_raw[-w20:]))
bb_upper = sma + 2.0 * gsstd
bb_lower = sma - 2.0 * gsstd
bb_pct = float((current_px - bb_lower) / max(bb_upper - bb_lower, 1e-9)) # 0=lower,1=upper
backend/requirements.txt
+1
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@@ -7,3 +7,4 @@ yfinance>=0.2.38
pydantic>=2.0,<3.0
reportlab>=4.0,<5.0
httpx>=0.27,<1.0
gs-quant