87 行
3 KiB
Python
87 行
3 KiB
Python
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# -*- coding: utf-8 -*-
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"""Uji region klasik GARCH + winsorize pada return padat-train XAUUSD M15."""
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import os
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import sys
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import math
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import numpy as np
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import datetime as dt
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from scipy.optimize import minimize
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HERE = os.path.dirname(os.path.abspath(__file__))
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SRC = os.path.normpath(os.path.join(HERE, "..", "..", "SniperGold_ML"))
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sys.path.insert(0, SRC)
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import train_regime as TR
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BASE = os.path.normpath(os.path.join(HERE, "..", "..", "..", "Files", "AlgoForge", "Data"))
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z = np.load(os.path.join(BASE, "XAUUSD_M15.npz"))
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t = z["time"].astype(np.int64)
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c = z["close"].astype(np.float64)
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r = np.zeros(len(c))
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r[1:] = np.log(np.maximum(c[1:], 1e-12) / np.maximum(c[:-1], 1e-12))
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lo = int(np.argmax(t >= dt.datetime(2018, 1, 1).timestamp()))
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hi = int(np.argmax(t > dt.datetime(2024, 7, 30).timestamp()))
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rr = r[lo:hi].copy()
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n = len(rr)
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var0 = float(np.var(rr) + 1e-12)
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rl = [float(x) for x in rr]
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def negll_p(p, data):
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w, a, b, nu = p
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if w <= 0 or a < 0 or b < 0 or a + b >= 0.999 or nu <= 2.05:
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return 1e12
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l1 = math.log(math.pi * (nu - 2.0))
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l2 = 2.0 * math.lgamma(nu / 2.0) - 2.0 * math.lgamma((nu + 1.0) / 2.0)
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s2 = var0
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ll = 0.0
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for rv in data:
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s2 = w + a * rv * rv + b * s2
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if s2 < 1e-12:
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s2 = 1e-12
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e2 = rv * rv / s2
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ll += math.log(s2) + (nu + 1.0) * math.log1p(e2 / (nu - 2.0))
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return 0.5 * (ll + len(data) * (l1 + l2))
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def fit(data, x0, bnd, tag, winsor=None):
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d = np.clip(data, -winsor, winsor) if winsor else data
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res = minimize(negll_p, x0, args=(d,), method="L-BFGS-B", bounds=bnd,
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options=dict(maxiter=4000))
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w, a, b, nu = res.x
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print(f" [{tag}] omega={w:.3e} alpha={a:.4f} beta={b:.4f} "
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f"persist={a+b:.4f} nu={nu:.2f} nfev={res.nfev}")
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return res.x
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print(f"data: n={n} std={rr.std():.6f} |r|max={np.abs(rr).max():.4f} "
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f"|r|>1%: {int((np.abs(rr) > 0.01).sum())}")
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CLS = [(1e-10, 1e-4), (0.001, 0.15), (0.80, 0.98), (2.1, 30.0)]
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X0 = [1e-7, 0.06, 0.92, 7.0]
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print("\nA. region klasik (alpha<=0.15, beta>=0.80):")
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fit(rr, X0, CLS, "klasik, data penuh")
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for wl in (0.01, 0.005):
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fit(rr, X0, CLS, f"klasik, winsor={wl}", winsor=wl)
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print("\nB. region sedang (alpha<=0.25, beta>=0.70):")
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MID = [(1e-10, 1e-4), (0.001, 0.25), (0.70, 0.98), (2.1, 30.0)]
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fit(rr, X0, MID, "sedang, data penuh")
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fit(rr, X0, MID, "sedang, winsor=0.01", winsor=0.01)
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# evaluasi standardisasi: z = r/sigma; cek std(z) & HMM sederhana
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print("\nC. evaluasi z (harus std ~1, non-konstan) utk solusi terbaik di atas:")
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for tag, par, wl in [
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("klasik winsor=0.005", fit(rr, X0, CLS, "eval", winsor=0.005), 0.005),
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]:
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w, a, b, nu = par
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d = np.clip(rr, -wl, wl)
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s2 = np.empty(n)
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s2[0] = w / (1 - a - b)
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for i in range(1, n):
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s2[i] = w + a * d[i - 1] ** 2 + b * s2[i - 1]
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sig = np.sqrt(np.maximum(s2, 1e-12))
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zz = rr / sig
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print(f" z: std={zz.std():.3f} mean={zz.mean():+.4f} |z|max={np.abs(zz).max():.2f} "
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f"q99={np.quantile(np.abs(zz), 0.99):.2f}")
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