momentum devandClaude Opus 4.8
97e3bf91a6
The float audit and the labeler both point at the SAME free, available-today selection signal the bot underuses: gap magnitude + cheap absolute price separate runners from faders, while the bot's gap%×RVOL rank leans on RVOL (labeler-proven FLAT) and CAPS gap. - strategy/evaluation/runner_propensity.py: pure, transparent, FROZEN-threshold primitive -> a coarse runner-propensity tier = gap_tier(0-3) + price_tier(0-2). Gap is weighted heavier BY CONSTRUCTION because the data says it dominates; premarket gap preferred over RTH gap; cheap price is a secondary tie-breaker (the two are correlated). 6 unit tests. - research/labeler.py runner_rank_report + `runner-rank` CLI: measures the lever on the v1 ORB labeled set. SHADOW/measurement only, not wired into live selection. MEASURED (v1 gappers, gap>=10%, n=187, 19 autocorrelated days -- small, caveated): the combined tier ladders monotonically: tier1 2% -> tier3 35% -> tier5 (cheap+big-gap) 48% runner (1.63x base). Gap dominates (>=50% -> 48%, 1.64x); cheap<$2 a real secondary (1.29x). OPERATIONAL KICKER: the live ~35% TRIGGER_GAP_MAX would EXCLUDE 73 gappers whose runner rate is 44% vs 20% for the kept <=35% -- the cap discards the best runners (this is how NXTS at +81% got excluded). Caveat: +1R/+2R separate far less than the +20% runner label, and gap/price are correlated; a hypothesis to shadow-confirm + adversarially validate before touching the live cap, not a validated rank. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
82 lines
3.6 KiB
Python
82 lines
3.6 KiB
Python
"""Runner-propensity selection signal — rank gappers by how likely they are to RUN.
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The bot ranks gappers on gap%×RVOL, but the labeler proved RVOL is FLAT (~1.0x, does
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not separate winners) while two FREE signals do separate runners from faders:
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- GAP MAGNITUDE (the dominant axis): premarket-gap high-tercile ~4.46x runner lift
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(labeler), and in the v1 gapper set gap>=50% runs 48% vs 21% for <50%.
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- CHEAP ABSOLUTE PRICE (a secondary, correlated co-signal): cheap<$5 runs 35% vs 17%
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for >=$5 (and the float audit's runner open median $1.19 vs fader $3.48). Big gappers
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are usually cheap microcaps, so price largely CO-OCCURS with gap and adds little on
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top of it -- it is a tie-breaker, not an independent edge.
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This is a PURE, transparent, FROZEN-threshold primitive (no fitted weights, n is small
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and autocorrelated). It returns a coarse runner-propensity TIER for use as a SELECTION
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rank / size lever -- SHADOW/measurement first (see research.labeler runner-rank), never
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a hard gate. Gap is weighted heavier than price BY CONSTRUCTION (gap_tier 0-3 vs
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price_tier 0-2) because the data says gap dominates. premarket-gap is preferred over the
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RTH-open gap when available (it is the stronger, earlier signal).
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NOTE vs the live caps: the bot's TRIGGER_GAP_MAX (~35%) EXCLUDES exactly the gap>=50%
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names that run most (it excluded NXTS at +81%). This primitive scores them HIGHEST --
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the lever is as much about not CAPPING the big gappers as about ranking them.
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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@dataclass
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class RunnerPropensity:
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tier: int # 0 (low) .. 5 (high) combined runner-propensity rank
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gap_tier: int # 0-3 from gap magnitude (the dominant axis)
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price_tier: int # 0-2 from cheapness (secondary co-signal)
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gap_used: float | None # the gap fraction actually used
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gap_source: str # "premarket" | "rth" | "none"
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reason: str
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def runner_propensity(
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price: float | None,
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gap_pct: float | None = None,
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*,
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premarket_gap_pct: float | None = None,
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cheap_below: float = 2.0,
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mid_below: float = 5.0,
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) -> RunnerPropensity:
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"""Coarse runner-propensity tier from cheap-price + gap magnitude. Pure.
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tier = gap_tier (0-3) + price_tier (0-2); higher = more runner-prone. gap is the
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dominant axis (wider range); price is a secondary co-signal. premarket gap preferred."""
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# --- gap: prefer the premarket gap (stronger/earlier), fall back to the RTH gap ---
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if premarket_gap_pct is not None:
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gap, src = premarket_gap_pct, "premarket"
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elif gap_pct is not None:
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gap, src = gap_pct, "rth"
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else:
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gap, src = None, "none"
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# gap tiers (frozen bands matching the labeler's separation: >=50% is the strong cut)
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if gap is None or gap < 0.10:
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gap_tier = 0 # not a gapper
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elif gap < 0.25:
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gap_tier = 1
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elif gap < 0.50:
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gap_tier = 2
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else:
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gap_tier = 3 # >=50% -- the strongest runner band
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# price tiers (cheaper = higher; a secondary tie-breaker)
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if price is None or price <= 0 or price >= mid_below:
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price_tier = 0
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elif price < cheap_below:
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price_tier = 2 # < $2
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else:
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price_tier = 1 # $2 - $5
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tier = gap_tier + price_tier
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reason = (f"gap_tier={gap_tier}({src}{'' if gap is None else f' {gap*100:.0f}%'})"
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f"+price_tier={price_tier}")
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return RunnerPropensity(tier=tier, gap_tier=gap_tier, price_tier=price_tier,
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gap_used=gap, gap_source=src, reason=reason)
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