159 lines
7.1 KiB
Python
159 lines
7.1 KiB
Python
# -*- coding: utf-8 -*-
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"""GEN HUMAN PACKAGE — buat paket annotation blind (P3-S.1 §6-7).
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HUMAN PACKAGE (human_package/):
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cases.csv : case_id, symbol, decision_timestamp, decision_tf, note
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human_annotation_template_f7.csv : template blank (60 case, format §7)
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HUMAN_ANNOTATION_PROTOCOL.md : aturan blind + chart rule + prosedur A/B + adjudikasi
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context_m15/<case_id>.csv : (opsional) 200 bar M15 terakhir s.d. decision_timestamp
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utk review offline; snapshot BERAKHIR di decision_timestamp
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MACHINE PACKAGE (machine_package/ — BLINDED, jangan dibuka annotator):
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machine_annotations_f7_v2.csv : salinan annotation machine
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machine_reasons_f7_v2.txt : alasan readable per case
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README_BLINDED.md : peringatan blinding
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Tidak ada keputusan machine di HUMAN PACKAGE. Sampling TIDAK dijalankan ulang.
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Usage: python gen_human_package.py
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"""
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import os
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import sys
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import csv
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import json
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import shutil
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import datetime as dt
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import numpy as np
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HERE = os.path.dirname(os.path.abspath(__file__))
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sys.path.insert(0, HERE)
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import smc_semantic_common as SC
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OUT = os.path.join(HERE, "output")
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HUMAN = os.path.join(HERE, "human_package")
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MACH = os.path.join(HERE, "machine_package")
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CTX = os.path.join(HUMAN, "context_m15")
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CTX_BARS = 200 # bar M15 sebelum & termasuk decision_timestamp (snapshot TIDAK melewati)
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def iso(ts):
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return dt.datetime.fromtimestamp(int(ts), tz=dt.timezone.utc).strftime("%Y-%m-%d %H:%M:%S")
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def main():
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meta = json.load(open(os.path.join(OUT, "cases_meta.json"), encoding="utf-8"))
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cases = meta["cases"]
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for p in (HUMAN, MACH, CTX):
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os.makedirs(p, exist_ok=True)
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# ---------- HUMAN PACKAGE ----------
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# cases.csv
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with open(os.path.join(HUMAN, "cases.csv"), "w", newline="", encoding="utf-8") as f:
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w = csv.writer(f)
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w.writerow(["case_id", "symbol", "decision_timestamp", "decision_tf", "note"])
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for c in cases:
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w.writerow([c["case_id"], SC.SYMBOL, c["decision_timestamp"], SC.DECISION_TF,
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"REVIEW HANYA SAMPAI decision_timestamp. Jangan lihat future."])
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# template blank
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fields = ["case_id", "liquidity_sweep", "reference", "direction", "timeframe",
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"reference_price", "sweep_price", "close_back_rejection", "reason",
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"confidence", "notes"]
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with open(os.path.join(HUMAN, "human_annotation_template_f7.csv"), "w",
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newline="", encoding="utf-8") as f:
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w = csv.writer(f)
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w.writerow(fields)
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for c in cases:
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w.writerow([c["case_id"], "", "", "", "", "", "", "", "", "", ""])
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# context pack M15 (snapshot berakhir di decision_timestamp)
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t, o, h, l, c, v, htf = SC.load_data()
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for case in cases:
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r = int(case["bar_idx"])
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s = max(0, r - CTX_BARS + 1)
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with open(os.path.join(CTX, case["case_id"] + ".csv"), "w",
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newline="", encoding="utf-8") as f:
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w = csv.writer(f)
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w.writerow(["time", "open", "high", "low", "close", "tick_volume"])
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for i in range(s, r + 1):
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w.writerow([iso(t[i]), round(o[i], 2), round(h[i], 2),
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round(l[i], 2), round(c[i], 2), int(v[i])])
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# ---------- MACHINE PACKAGE (BLINDED) ----------
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shutil.copy(os.path.join(OUT, "machine_annotations_f7_v2.csv"),
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os.path.join(MACH, "machine_annotations_f7_v2.csv"))
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rows = list(csv.DictReader(open(os.path.join(OUT, "machine_annotations_f7_v2.csv"),
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encoding="utf-8-sig")))
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with open(os.path.join(MACH, "machine_reasons_f7_v2.txt"), "w", encoding="utf-8") as f:
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for r in rows:
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f.write(f"{r['case_id']} | {r['decision_timestamp']} | "
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f"{r['machine_decision']} | {r['primary_primitive']} | "
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f"age={r['f7_state_age']} | {r['machine_reason']}\n")
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with open(os.path.join(MACH, "README_BLINDED.md"), "w", encoding="utf-8") as f:
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f.write("# MACHINE PACKAGE — BLINDED\n\n"
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"Folder ini berisi hasil machine annotation (f7_v2, source_commit b519a34).\n"
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"**DILARANG dibuka oleh annotator manusia sebelum human annotation selesai.**\n"
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"Hanya tim riset/adjudicator pasca-konsensus yang boleh membaca folder ini.\n")
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# protocol doc
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protocol = f"""# HUMAN ANNOTATION PROTOCOL — LIQUIDITY SWEEP (P3-S.1)
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## Prinsip Blind
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- Annotator HANYA menerima folder `human_package/`.
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- Folder `machine_package/` berisi keputusan machine — **DILARANG dibuka sebelum annotasi selesai**.
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- Human A (primary) dan Human B (independent reviewer) mengerjakan secara independen.
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## Chart Data Rule (WAJIB)
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- Buka chart XAUUSD (M15 utk keputusan; H4/M30/M3 sbg konteks) di terminal MT5.
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- Scroll HINGGA `decision_timestamp` pada kolom `cases.csv`.
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- **TIDAK boleh melihat candle setelah decision_timestamp** (future).
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- Snapshot/context `context_m15/<case_id>.csv` berakhir TEPAT di decision_timestamp.
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## Kolom Template (human_annotation_template_f7.csv)
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| Kolom | Nilai |
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|---|---|
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| liquidity_sweep | YES / NO / AMBIGUOUS |
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| reference | EQH / EQL / Swing High / Swing Low / Other / None |
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| direction | Bullish / Bearish / None |
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| timeframe | H4 / M30 / M15 / M3 / None |
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| reference_price | (numeric opsional) |
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| sweep_price | (numeric opsional) |
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| close_back_rejection | YES / NO / AMBIGUOUS / N/A |
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| reason | teks bebas — fakta yang teramati, bukan opini |
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| confidence | HIGH / MEDIUM / LOW |
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## Definisi kerja utk annotator (bukan definisi final — utk konsistensi labeling)
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- Liquidity Sweep = harga menembus (wick) level likuiditas (equal high/low / swing) lalu
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menunjukkan rejection (close kembali) — nilai apakah ini TERJADI pada window sekitar
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decision_timestamp.
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- Reference = jenis level likuiditas yang disapu.
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- Direction = arah intent setelah sweep (Bullish = sell-side swept; Bearish = buy-side swept).
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## Prosedur
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1. Salin `human_annotation_template_f7.csv` -> `human_A_f7.csv` (Human A).
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2. Human B mengerjakan salinan -> `human_B_f7.csv` (tanpa melihat A).
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3. Simpan hasil di `ml/p3/smc_semantic/output/`.
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4. Setelah A & B selesai: hitung inter-rater (human_interrater_f7_report.json).
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5. Kasus berbeda -> adjudikasi -> `human_adjudicated_f7.csv` (adjudicator melihat chart,
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protokol, metadata — TIDAK melihat machine result).
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6. Baru setelah consensus: machine vs human (human_machine_f7_comparison.json).
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## Larangan
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- Jangan mengubah f7/threshold/InpSeqWindow berdasarkan hasil annotasi (validation, bukan optimasi).
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- Jangan menampilkan machine result ke annotator sebelum selesai.
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## Case Set
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- 60 kasus (SGML_SWEEP_001..060), XAUUSD M15, 2018-2026, seed 42 (case set hash
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{__import__('hashlib').sha256(open(os.path.join(OUT, 'cases_meta.json'),'rb').read()).hexdigest()[:16]}).
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"""
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with open(os.path.join(HUMAN, "HUMAN_ANNOTATION_PROTOCOL.md"), "w", encoding="utf-8") as f:
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f.write(protocol)
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print("[saved] human_package/ (cases.csv, template, protocol, context_m15/)")
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print("[saved] machine_package/ (BLINDED: annotations + reasons)")
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if __name__ == "__main__":
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main()
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