Owner question: can score and confidence be combined into one number that is
easier to understand and for the system to decide on?
Research (pre-registered, nothing ships to the signal path):
- signals.grading_mode: "pair" (default, the shipped two-floor rule, byte-
identical: all five reference MD5s reproduce on both scripts), "min"
(min(|score|, 0.6*conf)), "product" (|score|*conf/100); thresholds fixed from
the control's corner points, no tuning. Helpers _final_signal_and_grade /
_transitioning_boost / conviction / CONVICTION_THRESHOLDS in signal_aggregator;
mirrored in K2 (GRADING_MODE_CODES, PF_conv_* packed from the live module);
twin refuses other values; M15 OOS MD5 equal on both scripts for both modes.
- Verdict: FAILED on M5 and M15. K-MIN moves 43-54 trades for -1.2R / -2.9R
(clauses a,b,e / a,b,d); K-PROD -29R on M15 (PF 1.29, W3 DD 28.9R), neutral
on M5. H1/H4 product +11.9R / +17.0R on W3 from extra low-score trades
(H1 PF 1.98 -> 1.75, DD doubles): hypothesis only.
- Code review (high) before the runs: 6 findings fixed, 1 recorded
(engine_config_fingerprint omits the switch under "pair", so live rows keep
their fingerprint).
Delivery (books untouched): alerts lead with the grade and print the two raw
numbers in words (main.plain_words: "Direction: strong buy · Reliability: good
· timeframes agree", cut-offs = the grade table's own) on Telegram, /status,
the CLI and the dashboard card (plain_words in the snapshot); "MTF" label
replaced by "Other timeframes"; guides updated. Suite 566 passed.
Evidence: backtest/research/conviction_campaign_2026-09-27.md
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Owner decisions: research only (live scans stay on the CPU code), gpu_backtest.py
stays the base reference, every research flag supported, the one exemption from
instructions item 6 recorded in backtest/instructions_for_backtest.txt.
- backtest/cuda_signal_kernels.py: the CUDA decision stage. K1 = engine.py + the
GPUTechnicalEngine overrides (one thread per bar), K2 = analyze_from_dataframes
end to end incl. gates 6b-6i, the four setup generators and the regime filter
(one thread per primary bar), K3 = position_sizing.build_features, K4 cooldown,
K5 prefix median, K6 recorder bar rows. Config packer with generated #defines;
leaf module; --fmad=false; Python evaluation order kept (ternary min/max,
pairwise sums, Neumaier sum(), exact round(x, 2)).
- backtest/cudaBasedBacktesting.py: same CLI, cache files and writers as the
reference; the host turns kernel codes into the exact strings; no CPU path.
- test_all.py Module 37 (16 tests): every stage pinned against the live code;
suite 552 / 552 on the GPU, without CUDA and with FB2_BACKEND=cpu.
- Proof: five reference MD5s, four 6-month acceptance books (M5/M15, $0.65/$0.30),
M15 OOS --export-features and --record --record-bars tables, ten research-flag
variants, and the owner's five-year M5 and M15 books, all byte-identical to
gpu_backtest.py. Wall time 0.8-6.6 s per book against 13-353 s.
- CLAUDE.md sections 1, 3, 6, 7, 9, 10, 11 and the instructions addendum: any
signal change is now a parity event for the twin.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Harwinder Sekhon