185 lines
6.9 KiB
Python
185 lines
6.9 KiB
Python
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# -*- coding: utf-8 -*-
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"""P3.1d TEMPORAL DIAGNOSTIC — SniperGold_ML.
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Diagnostic ONLY. Answers the fourth research question:
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"Apakah informasi yang hilang pada static feature memang bersifat temporal?"
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No neural sequence model is used. Evidence built from:
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* feature autocorrelation (lags 1..10) for continuous features
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* state persistence & transition matrices for discrete features
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* run-length of persistent states
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* LAGGED INFORMATION: univariate AUC of feature[t-k] vs label[t]
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(k = 0..5) -> if delayed information is retained, temporal dependency exists
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* label conditional persistence: P(Y_t=1 | Y_{t-k}=1) for k = 1..24
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* state-age analysis: does P(Y=1) change with the age of the current
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HTF-bias / swing-trend run? (i.e., is "state age" itself informative?)
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All metrics are descriptive; nothing is promoted to a model.
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"""
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import os
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import sys
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import json
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import numpy as np
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HERE = os.path.dirname(os.path.abspath(__file__))
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if HERE not in sys.path:
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sys.path.insert(0, HERE)
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import p3_common as P3
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import train_model as TM
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CONT_FEATURES = [6, 13, 14, 15, 16, 17, 18]
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DISC_FEATURES = [0, 1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12]
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LAGS = [1, 2, 3, 5, 8, 12, 16, 24]
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def main():
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t, o, h, l, c, v, htf = P3.load_data()
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F = P3.load_F()
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A = P3.atr_series(h, l, c)
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n = len(c)
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lab = P3.make_label(c, A)
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labeled = lab != 0
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midx = np.where(labeled)[0]
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y = lab[midx]
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ypos = (y == 1).astype(int)
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report = {"provenance": P3.provenance(),
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"dataset_hash": P3.dataset_hash(F, lab, A, t)}
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# ---- 1) feature autocorrelation (continuous) ----
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print("=== FEATURE AUTOCORRELATION (continuous, lags 1..10) ===")
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ac = {}
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for j in CONT_FEATURES:
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x = F[:, j].astype(float)
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xc = x - x.mean()
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var = (xc * xc).mean()
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row = {}
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for k in range(1, 11):
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row[str(k)] = float((xc[:-k] * xc[k:]).mean() / var) if var > 0 else 0.0
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ac[j] = row
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print(f" {P3.FEAT_NAMES[j]:<16s} " +
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" ".join(f"l{k}:{row[str(k)]:+.3f}" for k in (1, 3, 5, 10)))
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report["feature_autocorr"] = {P3.FEAT_NAMES[j]: ac[j] for j in CONT_FEATURES}
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# ---- 2) discrete state persistence ----
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print("\n=== DISCRETE STATE PERSISTENCE (P(state_t == state_{t-1})) ===")
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dp = {}
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for j in DISC_FEATURES:
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xs = F[:, j]
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dp[j] = float((xs[1:] == xs[:-1]).mean())
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print(f" {P3.FEAT_NAMES[j]:<16s} persist={dp[j]:.4f}")
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report["discrete_persistence"] = {P3.FEAT_NAMES[j]: dp[j] for j in DISC_FEATURES}
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# ---- 3) run lengths of persistent states ----
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print("\n=== RUN LENGTH (bars) of non-zero states ===")
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rl = {}
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for j in (0, 1, 2, 3, 4, 5, 7, 8):
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xs = F[:, j]
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runs = []
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cur = 0
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curv = 0
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for vv in xs:
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if vv != 0 and vv == curv:
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cur += 1
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else:
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if cur:
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runs.append(cur)
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cur = 1 if vv != 0 else 0
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curv = vv
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if cur:
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runs.append(cur)
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rl[j] = {"mean": float(np.mean(runs)) if runs else 0.0,
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"median": float(np.median(runs)) if runs else 0.0,
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"p95": float(np.percentile(runs, 95)) if runs else 0.0,
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"max": float(np.max(runs)) if runs else 0.0}
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print(f" {P3.FEAT_NAMES[j]:<16s} run mean={rl[j]['mean']:.1f} "
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f"med={rl[j]['median']:.0f} p95={rl[j]['p95']:.0f} max={rl[j]['max']:.0f}")
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report["run_length"] = {P3.FEAT_NAMES[j]: rl[j] for j in rl}
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# ---- 4) LAGGED INFORMATION: AUC(feature[t-k], label[t]) ----
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print("\n=== LAGGED INFORMATION: univariate AUC(feature[t-k] vs label[t]) ===")
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key_feats = [0, 5, 7, 9, 16, 18]
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lag_auc = {}
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for j in key_feats:
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row = {}
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for k in LAGS:
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# feature value k bars before each labeled bar
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xk = F[midx - k, j]
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valid = midx - k >= 0
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if valid.sum() < 1000:
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row[str(k)] = None
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continue
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row[str(k)] = float(TM.auc(ypos[valid].astype(int), xk[valid]))
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lag_auc[j] = row
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print(f" {P3.FEAT_NAMES[j]:<16s} " +
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" ".join(f"k{k}:{(row[str(k)] if row[str(k)] is not None else float('nan')):.4f}"
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for k in LAGS))
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report["lagged_auc"] = {P3.FEAT_NAMES[j]: lag_auc[j] for j in key_feats}
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# ---- 5) label conditional persistence ----
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print("\n=== LABEL CONDITIONAL PERSISTENCE: P(Y_t=1 | Y_{t-k}=1) ===")
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lcp = {}
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li = midx # labeled bar indices (ascending)
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yv = y
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p1_uncond = float((yv == 1).mean())
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for k in (1, 2, 3, 5, 8, 12, 16, 24):
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# find pairs (i, i-k) both labeled with Y_{i-k}=1
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# labeled bars are not contiguous; use absolute time index mapping
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pos_idx = li[yv == 1]
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n_prev = 0
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n_same = 0
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# vectorized: for each labeled bar, check if bar-k bars ago was labeled +1
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valid = li - k >= 0
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prev_lab = np.full(len(li), 0)
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# map bar index -> label
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labmap = np.zeros(n, dtype=int)
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labmap[li] = yv
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prev = labmap[li - k]
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n_prev = int((prev == 1).sum())
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n_same = int(((prev == 1) & (yv == 1)).sum())
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p = float(n_same / n_prev) if n_prev else float("nan")
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lcp[str(k)] = {"n_prev_pos": n_prev, "P_same": p,
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"unconditional_P1": float(p1_uncond),
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"lift": float(p / p1_uncond) if n_prev else float("nan")}
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print(f" k={k:>2}: P(Y_t=1 | Y_{{t-{k}}}=1)={p:.4f} (uncond {p1_uncond:.4f}, "
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f"lift={p / p1_uncond:.3f})")
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report["label_cond_persistence"] = lcp
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# ---- 6) STATE-AGE ANALYSIS: P(Y=1) vs age of current run ----
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print("\n=== STATE-AGE ANALYSIS: P(Y=1) by age of current HTF-bias run ===")
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age = {}
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for j in (0, 1, 5):
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xs = F[:, j]
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# run age at each bar (bars since run started)
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age_arr = np.zeros(n, dtype=int)
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cur_age = 0
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prev = 0
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for i in range(n):
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if xs[i] != 0 and xs[i] == prev:
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cur_age += 1
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else:
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cur_age = 1 if xs[i] != 0 else 0
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age_arr[i] = cur_age
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prev = xs[i]
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# P(Y=1 | age bucket) on labeled bars
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buckets = [(1, 3), (4, 10), (11, 30), (31, 100), (101, 10 ** 9)]
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rows = []
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for lo, hi in buckets:
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m = (age_arr[midx] >= lo) & (age_arr[midx] <= hi)
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if m.sum() < 100:
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continue
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rows.append({"age_bucket": f"{lo}-{hi}", "n": int(m.sum()),
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"P1": float(ypos[m].mean())})
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print(f" {P3.FEAT_NAMES[j]:<16s} age {lo:>3}-{hi:<4}: "
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f"P(Y=1)={ypos[m].mean():.4f} n={int(m.sum())}")
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age[j] = rows
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report["state_age"] = {P3.FEAT_NAMES[j]: age[j] for j in age}
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P3.save_json("temporal_diagnostic.json", report)
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print("\nTemporal diagnostic selesai. Output: ml/p3/output/temporal_diagnostic.json")
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if __name__ == "__main__":
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main()
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