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347 lines
10 KiB
Python
347 lines
10 KiB
Python
"""Rule-based entry score model for the backtester.
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Computes a composite score in [0, 1] from alpha-only features available at
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entry time (no forward-looking data). Higher score = more favorable entry
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conditions for a long swing trade.
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Alpha features (5 components, 100% weight):
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Event Quality (35%):
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- Event quality (35%) — parser confidence + signal strength + guidance
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Market Confirmation (65%):
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- Reaction quality (25%) — moderate positive return is ideal
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- Close strength (18%) — close near high = buyers won the day
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- Volume conviction (14%) — above-average but not exhaustion
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- Gap quality (8%) — small positive gap = orderly strength
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Non-alpha features (removed from composite, retained for analysis scripts):
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- Earnings surprise — eps_growth_qoq is sequential growth, not real surprise
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- Risk penalty — already hard-gated at Gate 10
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- Parse confidence — already hard-gated at Gate 11
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- Direction clarity — already hard-gated at Gates 12-13
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- LM sentiment — 70-word dictionary is noise for SEC filings
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"""
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from __future__ import annotations
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from typing import Any
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from libs.common.logging import get_logger
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logger = get_logger(__name__)
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def compute_entry_score(row: dict[str, Any]) -> float:
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"""Compute composite entry score from alpha-only features.
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Components and weights:
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Event Quality (35%):
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1. Event quality (35%) — parser confidence + signal strength
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Market Confirmation (65%):
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2. Reaction quality (25%) — moderate positive return is ideal
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3. Close strength (18%) — close near high = buyers won the day
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4. Volume conviction (14%) — above-average but not exhaustion
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5. Gap quality (8%) — small positive gap = orderly strength
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Returns float in [0.0, 1.0].
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"""
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# Alpha features only
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event = _event_quality_score(row)
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reaction = _reaction_score(row)
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close = _close_strength_score(row)
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volume = _volume_score(row)
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gap = _gap_score(row)
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raw = (
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# Event Quality (35%)
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event * 0.35
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# Market Confirmation (65%)
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+ reaction * 0.25
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+ close * 0.18
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+ volume * 0.14
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+ gap * 0.08
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)
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return max(0.0, min(1.0, raw))
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# ---------------------------------------------------------------------------
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# Market feature scoring
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# ---------------------------------------------------------------------------
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def _reaction_score(row: dict[str, Any]) -> float:
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"""Score based on reaction_day_return.
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Sweet spot: moderate positive return (0.5-3%) suggests post-event
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continuation without being "already priced in".
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Mapping:
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+0.5% to +3% -> 0.9 (ideal PEAD zone)
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+0% to +0.5% -> 0.65 (flat, uncertain direction)
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+3% to +8% -> 0.45 (getting priced in)
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> +8% -> 0.2 (extreme — mean reversion risk)
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-2% to 0% -> 0.4 (mild negative)
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-5% to -2% -> 0.25 (moderate negative)
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< -5% -> 0.1 (strongly bearish)
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"""
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rdr = row.get("reaction_day_return")
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if rdr is None:
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return 0.5
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r = float(rdr)
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if 0.005 <= r <= 0.03:
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return 0.9
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elif 0.0 <= r < 0.005:
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return 0.65
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elif 0.03 < r <= 0.08:
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return 0.45
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elif r > 0.08:
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return 0.2
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elif -0.02 <= r < 0.0:
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return 0.4
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elif -0.05 <= r < -0.02:
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return 0.25
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else: # r < -0.05
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return 0.1
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def _close_strength_score(row: dict[str, Any]) -> float:
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"""Score based on close_location [0=low, 1=high].
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Linear mapping: 0.0 -> 0.1, 1.0 -> 1.0.
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Close near session high = buyers controlled the day.
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"""
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cl = row.get("close_location")
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if cl is None:
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return 0.5
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c = max(0.0, min(1.0, float(cl)))
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return 0.1 + 0.9 * c
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def _volume_score(row: dict[str, Any]) -> float:
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"""Score based on volume_ratio_20d.
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Above-average volume confirms conviction, but extreme volume
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(>3x) can signal exhaustion or panic, so it gets discounted.
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Mapping:
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1.2x-2.0x -> 0.8 (healthy conviction)
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1.0x-1.2x -> 0.6 (normal)
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2.0x-3.0x -> 0.55 (high — possible exhaustion)
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> 3.0x -> 0.4 (extreme — likely exhaustion)
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< 1.0x -> 0.3 (below average — no conviction)
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"""
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vr = row.get("volume_ratio_20d")
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if vr is None:
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return 0.5
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v = float(vr)
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if 1.2 <= v <= 2.0:
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return 0.8
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elif 1.0 <= v < 1.2:
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return 0.6
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elif 2.0 < v <= 3.0:
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return 0.55
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elif v > 3.0:
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return 0.4
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else: # v < 1.0
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return 0.3
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def _gap_score(row: dict[str, Any]) -> float:
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"""Score based on gap_size (open vs previous close).
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Small positive gap (0-2%) = orderly bullish opening.
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Large gap (>5%) = potential exhaustion gap.
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Negative gap = bearish opening pressure.
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Mapping:
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+0.5% to +2% -> 0.8 (orderly strength)
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0% to +0.5% -> 0.6 (neutral-to-mild)
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+2% to +5% -> 0.5 (getting extended)
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> +5% -> 0.3 (exhaustion gap risk)
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-2% to 0% -> 0.4 (mild weakness)
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< -2% -> 0.2 (bearish gap)
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"""
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gs = row.get("gap_size")
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if gs is None:
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return 0.5
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g = float(gs)
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if 0.005 <= g <= 0.02:
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return 0.8
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elif 0.0 <= g < 0.005:
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return 0.6
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elif 0.02 < g <= 0.05:
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return 0.5
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elif g > 0.05:
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return 0.3
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elif -0.02 <= g < 0.0:
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return 0.4
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else: # g < -0.02
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return 0.2
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# ---------------------------------------------------------------------------
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# Earnings surprise (SUE) scoring
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# ---------------------------------------------------------------------------
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def _earnings_surprise_score(row: dict[str, Any]) -> float:
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"""Score based on earnings surprise (eps_growth_qoq as naive SUE proxy).
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Only active for earnings_release events; returns neutral 0.5 for others.
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Bernard & Thomas (1989): drift magnitude is proportional to surprise.
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Mapping:
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> +20% EPS growth -> 0.9 (strong beat)
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> +5% -> 0.75 (moderate beat)
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> -5% -> 0.5 (in-line)
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> -20% -> 0.25 (moderate miss)
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<= -20% -> 0.1 (severe miss)
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"""
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event_type = row.get("event_type", "")
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if event_type != "earnings_release":
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return 0.5
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sue = row.get("eps_growth_qoq")
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if sue is None:
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return 0.5
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s = float(sue)
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if s > 0.20:
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return 0.9
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if s > 0.05:
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return 0.75
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if s > -0.05:
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return 0.5
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if s > -0.20:
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return 0.25
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return 0.1
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# ---------------------------------------------------------------------------
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# Event feature scoring
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# ---------------------------------------------------------------------------
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def _event_quality_score(row: dict[str, Any]) -> float:
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"""Score based on event parsing quality and signal strength.
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Combines:
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- document_quality_score [0-1]: parser confidence in the extraction
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- signal_strength_score [0-1]: strength of business fundamentals signals
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- guidance_direction_score: 1.0=raised, 0.5=inline, 0.25=unclear, 0.0=lowered
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When event features are absent (market_v1 only Parquet), returns 0.5.
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"""
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doc_q = row.get("document_quality_score")
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sig_s = row.get("signal_strength_score")
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guid = row.get("guidance_direction_score")
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# If no event features at all, return neutral
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if doc_q is None and sig_s is None and guid is None:
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return 0.5
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# Weight: signal_strength 40%, guidance 35%, document_quality 25%
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scores = []
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weights = []
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if sig_s is not None:
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scores.append(float(sig_s))
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weights.append(0.40)
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if guid is not None:
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scores.append(float(guid))
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weights.append(0.35)
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if doc_q is not None:
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scores.append(float(doc_q))
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weights.append(0.25)
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if not scores:
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return 0.5
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total_weight = sum(weights)
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return sum(s * w for s, w in zip(scores, weights)) / total_weight
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def _risk_penalty_score(row: dict[str, Any]) -> float:
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"""Score based on oneoff_penalty (risk flags).
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oneoff_penalty [0-1]: fraction of active risk flags.
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Higher penalty = lower score (more risk = less favorable entry).
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Inverted: 0.0 penalty -> 0.9 score, 1.0 penalty -> 0.2 score.
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When absent, returns neutral 0.5.
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"""
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penalty = row.get("oneoff_penalty")
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if penalty is None:
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return 0.5
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p = max(0.0, min(1.0, float(penalty)))
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# Linear inversion: 0 -> 0.9, 1.0 -> 0.2
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return 0.9 - 0.7 * p
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def _parse_confidence_score(row: dict[str, Any]) -> float:
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"""Score based on parse_confidence_overall [0-1]."""
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conf = row.get("parse_confidence_overall")
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if conf is None:
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return 0.5
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c = float(conf)
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if c > 0.8:
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return 0.9
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if c > 0.6:
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return 0.7
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if c > 0.5:
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return 0.5
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if c > 0.4:
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return 0.3
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return 0.2
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def _direction_clarity_score(row: dict[str, Any]) -> float:
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"""Score based on event_direction categorical field."""
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direction = row.get("event_direction")
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if direction is None:
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return 0.5
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return {"bullish": 0.9, "mixed": 0.4, "neutral": 0.3,
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"bearish": 0.1, "unknown": 0.2}.get(str(direction).lower(), 0.5)
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# ---------------------------------------------------------------------------
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# Text sentiment scoring
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# ---------------------------------------------------------------------------
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def _text_sentiment_score(row: dict[str, Any]) -> float:
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"""Score based on Loughran-McDonald text sentiment features.
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Uses lm_net_sentiment (positive - negative word fraction).
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Typical range is [-0.02, +0.02] for SEC filings.
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Mapping:
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> +0.005 -> 0.8 (noticeably positive tone)
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> +0.001 -> 0.65 (mildly positive)
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> -0.001 -> 0.5 (neutral)
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> -0.005 -> 0.35 (mildly negative)
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<= -0.005 -> 0.2 (noticeably negative tone)
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When absent, returns neutral 0.5.
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"""
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net = row.get("lm_net_sentiment")
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if net is None:
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return 0.5
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n = float(net)
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if n > 0.005:
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return 0.8
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if n > 0.001:
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return 0.65
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if n > -0.001:
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return 0.5
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if n > -0.005:
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return 0.35
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return 0.2
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