forked from mnbvc188199/Warrior_EA
5a4b36bb61
- Implemented AFML part A for testing the dip-z book against search artifacts, including PBO, DSR, and CPCV metrics. - Developed AFML part B to generate time and tick bars from M1 broker data, including return distribution statistics. - Created AFML part C to build a pipeline for dip-z primary analysis, incorporating features and a random forest model for classification. - Added HCC history decoder to read and process broker M1 `.hcc` files, ensuring proper handling of data structure and integrity.
59 lines
3.3 KiB
Markdown
59 lines
3.3 KiB
Markdown
# AFML campaign - pre-registered plan (2026-09-27)
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Lopez de Prado, *Advances in Financial Machine Learning*, applied to the validated
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book (4-index H4 vol-gated dip-z, long, risk 0.25%). Criteria written BEFORE any
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result was seen. Nothing below is tuned after the fact; any change is logged as a
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deviation with its reason.
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## A. Is the book a product of the search? (AFML ch.11-14)
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Trial family = the neighbourhood the book was picked from, on real broker H4
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(2021-01..2026-08), 4 indices, one equity curve per config:
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z threshold {-1.0,-1.25,-1.5,-1.75,-2.0} x z window {10,15,20,30,40} x
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max bars {5,10,15,20} x vol gate {none, >=.30, >=.50, >=.70} x stop ATR {2,3,4}
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= 1,200 configs.
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1. **PBO by CSCV** (Bailey, Borwein, Lopez de Prado, Zhu 2015), 16 monthly-block
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partitions, selection metric = Sharpe of monthly returns.
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PASS: PBO <= 0.20.
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2. **Deflated Sharpe Ratio** of the chosen config, with the variance of trial
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Sharpes measured on the 1,200-config family and N reported at 1,200 and at a
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project-wide 5,000 (style scan + FX families + state scans).
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PASS: DSR >= 0.95 at N = 1,200.
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3. **CPCV** (AFML ch.12): 10 time groups, 2 test groups, purge = 20 H4 bars,
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embargo = 1%; select best config on train by ret/DD, score on test; rebuild the
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9 backtest paths. Report the distribution of path Sharpe and path maxDD at 0.25%.
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PASS: every path maxDD <= 5% and median path ret/DD >= 2.
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## B. Sampling outside the clock (AFML ch.2)
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Broker M1 `.hcc` (2022-01..2026-08, the only M1 years for indices) aggregated into
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(a) time H4 bars and (b) TICK bars (fixed count of broker tick-volume per bar),
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threshold set per symbol so the average bar count equals the time-H4 count. Volume
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and dollar bars are NOT possible: CFD feeds carry no traded volume. M1
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granularity means a tick bar closes on the M1 bar where the count is crossed.
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1. Statistical claim: tick-bar log returns closer to normal (lower excess
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kurtosis, lower Jarque-Bera) and lower |lag-1 autocorr| of squared returns.
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Reported, not a gate - this is the book's claim, checked on our data.
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2. Trading claim: the identical dip-z rule (+ gate), same bar-count parameters,
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same constant cost (median broker spread), on tick bars vs time bars.
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PASS: tick bars improve portfolio ret/DD at >= 90% of time-bar cadence, in BOTH
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halves (2022-23, 2024-26).
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## C. The whole pipeline combined (ch.3, 4, 5, 7, 8)
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On whichever bars won B (time bars if tick bars did not win):
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primary = dip-z signal; label = triple barrier (stop 3 ATR / exit at SMA / 10
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bars) -> 1 if net R > 0; features = FFD log price at the minimum d passing ADF,
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FFD of the z-window SMA gap, vol percentile, bar duration (tick bars), recent
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return / range, cross-index agreement (how many of the 4 indices are in a dip);
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model = random forest, sample weights = average uniqueness (ch.4), purged k-fold
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+ embargo (ch.7), 5 folds, plus walk-forward refits.
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PASS: pooled OOS AUC >= 0.55 with bootstrap 95% CI excluding 0.50, AND the
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filtered book beats the unfiltered one on ret/DD in the walk-forward test.
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Priors (logged so the result can be judged against them): C has failed three
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times in this project on bar-geometry features (AUC 0.50-0.56); the new inputs
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here are FFD memory and tick sampling. Expect A to show a real but search-inflated
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Sharpe; B is genuinely unknown.
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