9.7 KiB
9.7 KiB
SESSION HANDOVER — SNIPERGOLD_ML P3: SMC SEMANTIC VALIDATION
Date : 2026-08-22
Session : P3.1 -> P3.2.1 -> P3.2.2 (feature/label forensic + de-overlap/survival) — CLOSED
Status : RESEARCH / DIAGNOSTIC ONLY — NO production change
Forge : https://forge.mql5.io/chiki2bum2/SniperGold_ML
1. SESSION SUMMARY
This session completed three consecutive forensic research phases:
| Phase | Output | Core result |
|---|---|---|
| P3.1 | Feature + Label Forensic | 19 runtime-consistent features ≈ uninformative vs the 24-bar/0.75 ATR label (max |AUC−0.5|=0.014); label misaligned (t_hit ~2 bars, typical MFE 3.8 ATR); no temporal/regime evidence; root cause = MULTIPLE FACTORS (A dominant, B secondary) |
| P3.2.1 | Event-based label forensic | The 24-bar label measures directional drift, not setup validation; the existing event stream is heavily overlapped (98.5% within 16 bars); f9-confirm events LOSS > WIN; the apparent E_EQH asymmetry = overlap artifact |
| P3.2.2 | Formal de-overlap + survival | Retention after de-overlap ~1.5%; asymmetry gone after de-overlap; serial dependence gone (ac1 0.50 → 0); decision C — EVENT STREAM INVALID AS INDEPENDENT SETUP UNIT |
Commit & push complete:
dc1faa9 research: checkpoint P3.1 feature-label audit and P3.2 event forensic (P3.2.1)
b41059d research: P3.2.2 formal event de-overlap and survival diagnostic
CURRENT FORGE HEAD = b41059d814c3dd812c8227dc0d34462f000af2b3
CURRENT LOCAL HEAD = b41059d814c3dd812c8227dc0d34462f000af2b3
WORKING TREE = CLEAN (no uncommitted/untracked in the publish mirror)
P3.2.2 remains the reference checkpoint. No artifacts were deleted.
2. P3.2.2 STATUS — COMPLETE
P3.2.2 = COMPLETE
event retention after de-overlap : ≈ 1.5%
97–99% of existing events : FOLLOW-ON / clustered
median cluster : ≈ 46 bars (max 473)
lead-event outcomes : LOSS >> WIN (SL ~2x to 4.7x TP)
median time-to-event : ≈ 1–2 bars
~90% of outcomes within 8 bars ; >98% within 16 bars
H16: TP ≈ 0.31 | SL ≈ 0.67 | censoring ≈ 2% | ambiguity ≈ 0.5–1.6%
ALL outcome autocorrelation : ≈ 0.50
LEAD outcome autocorrelation : ≈ 0 (de-overlap successful)
DECISION: C — EVENT STREAM ITSELF INVALID (as an independent setup unit)
Clarification:
SGML_L3_EVENT mechanism = technically viable as a labeling mechanism
existing event stream = NOT a valid independent setup unit
Evidence stored in docs/P3_2_2_DEOVERLAP_SURVIVAL.md + ml/p3/output/p3_2_deoverlap_h{8,16,24}.json + p3_2_survival.json (committed at b41059d).
3. IMPORTANT NEW RESEARCH CONCLUSION
Do not state anymore:
"SMC has no edge" — NOT YET PROVEN
"SMC setups are invalid" — NOT YET PROVEN
The correct conclusion:
The current SMC event implementation has not yet been proven to semantically represent one discrete trading setup.
New hypotheses to carry into the next session:
A = coding semantic error
B = SMC definition too simple
C = timeframe semantics incorrect
D = event-state treated as event onset
E = combination of A/B/C/D
Special note: the findings "median cluster 46 bars" and "events persist as long state" are consistent with hypothesis D (event-state treated as event onset) — events that should be discrete (onset) actually persist as long states, causing repeated counting.
4. MQL LITE CAPABILITY CONSTRAINT
MQL Lite / AI Assistant built into MT5
= DOES NOT have image analysis capability
Therefore the next session is FORBIDDEN from designing:
MT5 AI -> visually reading screenshots/charts
as a validation method.
5. NEW VALIDATION ARCHITECTURE
Historical MT5 data
↓
MQL5 SMC Engine
↓
Machine Annotation
│
├───────────────┐
│ │
↓ ↓
MT5 chart OHLC/context
│ │
↓ ↓
Human Annotation
│
└───────┬───────┘
↓
Python Comparator
↓
Semantic agreement/error analysis
↓
Regression tests
Principle:
MQL Lite is not a vision model.
Humans look at the chart.
The machine reads OHLC/SMC state.
Python compares the two.
6. GOLDEN DATASET PLAN
The next session starts:
SMC SEMANTIC GOLDEN DATASET
Objective: test whether the algorithm truly implements the concepts:
Liquidity Sweep | CHoCH / MSS | BOS | FVG | Order Block
Displacement | MTF Alignment | Candidate Setup
DO NOT audit all concepts at once.
First phase: LIQUIDITY SWEEP.
7. FIRST GOLDEN DATASET SCOPE
Initial target : 50–100 cases
Must not contain only signal-positive examples.
The distribution must include:
clear positive, clear negative, borderline,
false-positive candidates, false-negative candidates,
trend, range,
high-volatility, low-volatility
Case selection NOT based on profit results.
8. CASE DEFINITION
Every case must have:
case_id
symbol
decision_timestamp
decision_timeframe
available_data_end
H4 context
M30 context
M15 context
M3 context
Human data MUST stop at decision_timestamp. No future candles allowed.
9. MACHINE ANNOTATION
Produced from the existing code (no new Sweep definition; measure what the code currently does).
For Liquidity Sweep at minimum:
liquidity_reference
reference_type
reference_price
sweep_high/low
excess_distance
close_after_sweep
rejection
timeframe
algorithm_decision
algorithm_reason
10. HUMAN ANNOTATION
Separate from the machine output. The reviewer answers:
Liquidity Sweep: YES / NO / AMBIGUOUS
Reference: Equal High / Equal Low / Swing High / Swing Low / Other
Reason: free text
Confidence: HIGH / MEDIUM / LOW
Machine decisions are NOT shown before the human annotation is complete.
11. MULTI-ANNOTATOR RULE
Minimum 2 independent human annotators (when possible).
Order: Human A vs Human B first.
Disagreement -> ADJUDICATION REQUIRED (do not treat either human as
absolute truth).
Record human agreement as the practical upper bound of agreement.
12. MACHINE VS HUMAN COMPARISON
HUMAN vs MACHINE -> TP / FP / FN / TN / Precision / Recall / F1
Do not stop at numbers: also compare machine_reason vs human_reason.
13. FALSE AGREEMENT CHECK
Human = YES & Machine = YES but different reasons
-> SEMANTIC FALSE AGREEMENT
Example: Human "equal-high swept & rejected" vs Machine "current high > previous swing".
Do not count YES==YES as full semantic success.
14. TIMEFRAME VALIDATION
Compare where the human sees liquidity vs where the machine detects it:
H4 / M30 / M15 / M3.
Goal: find timeframe mismatches
(e.g. human: sweep = M30 event; machine: sweep = M15 event)
-> can indicate an architecture/implementation mismatch.
15. SEMANTIC AGREEMENT LEVELS
Level 1 — Outcome agreement : Human YES & Machine YES
Level 2 — Structural agreement : reference level, timeframe, sweep direction,
rejection, event context IDENTICAL
Level 3 — Reason agreement : human reasoning & machine primitives
substantively IDENTICAL
ONLY Level 3 = STRONG SEMANTIC AGREEMENT.
16. NO PRODUCTION CHANGE
The next session (initial phase) only: research / annotation / comparison / diagnostic.
Forbidden:
modify production SMC logic
modify the feature contract
modify the MLP
modify runtime semantics
train LSTM
train a regime model
implement a meta-label gate
If a bug is found:
DOCUMENT -> REPRODUCE -> CLASSIFY -> PROPOSE FIX
No hot-fixes during the annotation phase.
17. NEXT-SESSION ENTRY POINT
First tasks of the next session:
1. Restore this state (Forge HEAD = b41059d8, working tree clean).
2. Read this handover + docs/P3_2_2_DEOVERLAP_SURVIVAL.md.
3. Start the SMC SEMANTIC GOLDEN DATASET — LIQUIDITY SWEEP:
a. define case selection (50–100 cases, balanced distribution, no
future candles, case_id + timestamps + 4-TF context);
b. extract machine annotation from the existing code (no new definitions);
c. separate human annotation (multi-annotator, adjudication if needed);
d. Python comparator (TP/FP/FN/TN, Precision/Recall/F1 + reason comparison);
e. false-agreement check + timeframe validation (Levels 1/2/3);
f. classify hypotheses A–E (section 3).
4. Do not touch production; do not start P3.3 model work.
18. COMMIT & PUSH STATUS
Commits made this session:
dc1faa9 research: checkpoint P3.1 feature-label audit and P3.2 event forensic (P3.2.1)
b41059d research: P3.2.2 formal event de-overlap and survival diagnostic
Push: complete to https://forge.mql5.io/chiki2bum2/SniperGold_ML.git
FINAL FORGE HEAD = b41059d814c3dd812c8227dc0d34462f000af2b3
HANDOVER COMMIT SHA = (filled after this handover is committed)
19. APPENDIX — P3.2.2 EVIDENCE LOCATION
docs/P3_FEATURE_LABEL_FORENSIC.md (P3.1)
docs/P3_2_LABEL_FORENSIC.md (P3.2.1)
docs/P3_2_2_DEOVERLAP_SURVIVAL.md (P3.2.2)
ml/p3/*.py (7+2 diagnostic scripts)
ml/p3/output/*.json (feature/label/candidate/temporal/regime/deoverlap/survival)
Large local artifacts (not committed, still available):
ml/p3/output/p3_2_label_events.json (17.8 MB, per-event H16 detail)
Key provenance: P2 commit 8d330343 | contract C44CC6F2 | model 06df8452
| dataset e85a0861 | checkpoint dc1faa9 | P3.2.2 b41059d