zhaoli
c0eb65efa7
Mined from book/ on the 'book' branch (HEAD 436692a). 11 queued runs,
each = hypothesis + one-variable change vs the exp-26 reference + acceptance
metric, per the ch.02 isolation/falsification discipline.
- workflows/: 5 runnable config-only YAMLs (Q01 M2 repro, Q02 seed10, Q03 topk20,
Q04 label10d, Q05 label22d) byte-derived from the exp-26 reference
- designs/: 6 design docs needing custom strategy modules or tool-only A/B
(Q06 Kelly, Q07 weekly rebalance, Q08 risk-limit A/B, Q09 long-short,
Q10 HMM overlay, Q11 standalone reversal)
33 lines
1.8 KiB
Markdown
33 lines
1.8 KiB
Markdown
# QUEUE-06 — Fractional-Kelly sizing vs equal-weight top-k (re-run exp 15 on clean lake)
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**Status:** QUEUED · **Priority:** P1 · **Effort:** custom strategy module + run
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## Hypothesis (prove)
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Fractional-Kelly sizing — sizing each name by the edge magnitude of its score
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instead of equal-weight × risk_degree — is a sizing rule (not a strategy) that
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throws away less edge and beats equal-weight top-k **net of costs** on the clean
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lake. Source: `book/README.md` open questions (exp 15 run never finished),
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`book/references/chat-ideas.md` ("Kelly sizing is a sizing rule, not a strategy").
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## Change vs exp-26 reference (ONE variable)
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- **Strategy**: equal-weight `TopkDropoutStrategy` (topk 10, n_drop 1) →
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custom `FractionalKellyDropoutStrategy` (same topk/n_drop selection, sizing ∝
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score magnitude, capped at a fraction f of the equal-weight notional; f as a
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parameter, e.g. 0.5).
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- All signal/config unchanged (compact stochastic features, 5-seed RankIC
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ensemble, 5d label, train/valid/test, SPY benchmark, 5bp/15bp/$5 costs).
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## Acceptance
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- `net_IR > 0.21` AND `net_ann_return > +2.13%` (exp-26 reference), with
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`total_cost` not higher than the reference book.
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- If sizing flattens the book (over-concentration) and net degrades → REFUTED
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(recorded negative; equal-weight stays canonical).
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## Execution prerequisites
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1. New contrib module `tac_qlib/contrib/strategy/kelly_dropout.py`
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(`FractionalKellyDropoutStrategy` subclassing
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`qlib.contrib.strategy.signal_strategy.TopkDropoutStrategy`), copy to the
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venv site-packages copy (`/opt/venv/lib/python3.12/site-packages/tac_qlib/...`).
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2. Workflow YAML with `strategy.class=FractionalKellyDropoutStrategy`,
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`module_path=tac_qlib.contrib.strategy.kelly_dropout`.
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3. Trace (rd_trace_start → run → rd_trace_finish), snapshot the new module. |