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fix: canonicalize score brackets and assessments

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This commit is contained in:
Hermes Agent
2026-07-26 23:07:24 +00:00
parent 1f754ed85d
commit 62bff348bf
6 changed files with 90 additions and 46 deletions
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backtesting/engine.py
+5 -9
View File
@@ -7,6 +7,8 @@ import sys
from collections import defaultdict
from datetime import datetime, timedelta
from scoring.policy import SCORE_BRACKETS, SCORE_VERSION, score_in_bracket
log = logging.getLogger(__name__)
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
@@ -16,14 +18,7 @@ HISTORY_PATH = os.path.join(BASE_DIR, "data", "history.json")
CACHE_PATH = os.path.join(BASE_DIR, "data", "cache.json")
# Score brackets matching the dashboard assessment levels
BRACKETS = [
(0, 20, "Extreme Caution"),
(21, 40, "Caution"),
(41, 55, "Neutral"),
(56, 70, "Moderate Opportunity"),
(71, 85, "Strong Accumulation"),
(86, 100, "Extreme Accumulation"),
]
BRACKETS = SCORE_BRACKETS
# Scoring thresholds — load from config/thresholds.json (single source of truth)
import os as _os
@@ -322,7 +317,7 @@ def run_backtest(ml_mode=False):
# --- Bracket statistics ---
bracket_stats = []
for low, high, label in BRACKETS:
days_in = [d for d in daily_scores if low <= d["score"] <= high]
days_in = [d for d in daily_scores if score_in_bracket(d["score"], (low, high, label))]
if not days_in:
bracket_stats.append({
"range": f"{low}-{high}", "label": label, "days": 0,
@@ -506,6 +501,7 @@ def run_backtest(ml_mode=False):
"current_context": current_context,
"chart_data": chart_data,
"ml_mode": ml_mode,
"score_version": SCORE_VERSION,
"computed_at": datetime.utcnow().isoformat() + "Z",
}
ml/optimizer.py
+4 -9
View File
@@ -26,6 +26,8 @@ from sklearn.metrics import (
from sklearn.model_selection import TimeSeriesSplit
from sklearn.preprocessing import StandardScaler
from scoring.policy import SCORE_BRACKETS, score_in_bracket
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s [%(name)s] %(levelname)s: %(message)s",
@@ -117,14 +119,7 @@ INTERACTION_FEATURES = ["mvrv_x_nupl", "puell_x_reserve"]
CYCLE_FEATURES = ["days_since_ath"]
FEATURE_COLS = SCORE_FEATURES + RAW_FEATURES + DELTA_FEATURES + INTERACTION_FEATURES + CYCLE_FEATURES
BRACKETS = [
(0, 20, "Extreme Caution"),
(21, 40, "Caution"),
(41, 55, "Neutral"),
(56, 70, "Moderate Opportunity"),
(71, 85, "Strong Accumulation"),
(86, 100, "Extreme Accumulation"),
]
BRACKETS = SCORE_BRACKETS
def _row_date(row):
@@ -526,7 +521,7 @@ def _composite_score(row, mode, ml_weights=None):
def _summarize_brackets(scored_rows, score_key):
results = []
for low, high, label in BRACKETS:
days_in = [r for r in scored_rows if low <= r[score_key] <= high]
days_in = [r for r in scored_rows if score_in_bracket(r[score_key], (low, high, label))]
if not days_in:
results.append({
"range": f"{low}-{high}", "label": label,
scoring/engine.py
+6 -26
View File
@@ -4,6 +4,8 @@ import json
import os
import logging
from scoring.policy import SCORE_VERSION, assessment_for_score
log = logging.getLogger(__name__)
THRESHOLDS_PATH = os.path.join(
@@ -544,19 +546,7 @@ def score_all(metrics):
else:
composite = 0
# Assessment text — calibrated for cycle-aware scoring
if composite >= 80:
assessment = "EXTREME ACCUMULATION ZONE"
elif composite >= 65:
assessment = "STRONG ACCUMULATION ZONE"
elif composite >= 50:
assessment = "MODERATE OPPORTUNITY"
elif composite >= 35:
assessment = "NEUTRAL"
elif composite >= 20:
assessment = "CAUTION — OVERHEATED"
else:
assessment = "EXTREME CAUTION"
assessment = assessment_for_score(composite)
return {
"metrics": results,
@@ -564,6 +554,7 @@ def score_all(metrics):
"assessment": assessment,
"scored_count": len(valid_scores),
"total_count": len(results),
"score_version": SCORE_VERSION,
}
@@ -646,19 +637,7 @@ def score_all_ml(metrics):
m["ml_weight"] = round(effective_weight, 4)
m["ml_contribution"] = round(m["score"] * effective_weight * 10, 2)
# Assessment text (same thresholds as classic)
if composite >= 80:
assessment = "EXTREME ACCUMULATION ZONE"
elif composite >= 65:
assessment = "STRONG ACCUMULATION ZONE"
elif composite >= 50:
assessment = "MODERATE OPPORTUNITY"
elif composite >= 35:
assessment = "NEUTRAL"
elif composite >= 20:
assessment = "CAUTION — OVERHEATED"
else:
assessment = "EXTREME CAUTION"
assessment = assessment_for_score(composite)
return {
"metrics": results,
@@ -669,4 +648,5 @@ def score_all_ml(metrics):
"ml_mode": True,
"classic_score": classic["composite_score"],
"ml_weight_total": round(weight_total, 4),
"score_version": SCORE_VERSION,
}
scoring/policy.py
+35
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@@ -0,0 +1,35 @@
"""Canonical score version, brackets, and assessment semantics."""
SCORE_VERSION = "accumulation-score-v2"
# Half-open intervals [low, high), except the final bracket includes 100.
# Keep labels canonical because they are persisted in live and backtest output.
SCORE_BRACKETS = [
(0, 20, "EXTREME CAUTION"),
(20, 35, "CAUTION — OVERHEATED"),
(35, 50, "NEUTRAL"),
(50, 65, "MODERATE OPPORTUNITY"),
(65, 80, "STRONG ACCUMULATION ZONE"),
(80, 100, "EXTREME ACCUMULATION ZONE"),
]
def score_in_bracket(score, bracket):
"""Return whether a 0-100 score belongs to a canonical bracket."""
low, high, _ = bracket
if not 0 <= score <= 100:
return False
return low <= score < high or (high == 100 and score == 100)
def bracket_for_score(score):
"""Return the one canonical bracket for a 0-100 score."""
for bracket in SCORE_BRACKETS:
if score_in_bracket(score, bracket):
return bracket
raise ValueError(f"score must be between 0 and 100, got {score!r}")
def assessment_for_score(score):
"""Return the canonical assessment label for a score."""
return bracket_for_score(score)[2]
tests/test_ml_optimizer_validation.py
+2 -2
View File
@@ -66,9 +66,9 @@ def test_run_out_of_sample_comparison_scores_only_validation_rows_with_fold_weig
assert sum(bucket["days"] for bucket in comparison["ml_weighted"]) == 2
assert sum(bucket["days"] for bucket in comparison["equal_weight"]) == 2
extreme_ml = next(bucket for bucket in comparison["ml_weighted"] if bucket["label"] == "Extreme Accumulation")
extreme_ml = next(bucket for bucket in comparison["ml_weighted"] if bucket["label"] == "EXTREME ACCUMULATION ZONE")
assert extreme_ml["days"] == 2
assert extreme_ml["avg_365d"] == 110.0
caution_equal = next(bucket for bucket in comparison["equal_weight"] if bucket["label"] == "Caution")
caution_equal = next(bucket for bucket in comparison["equal_weight"] if bucket["label"] == "CAUTION — OVERHEATED")
assert caution_equal["days"] == 2
tests/test_score_policy.py
+38
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@@ -0,0 +1,38 @@
import pytest
from backtesting import engine as backtest_engine
from ml import optimizer
from scoring import engine as scoring_engine
from scoring import policy
def test_score_brackets_are_contiguous_and_shared_by_all_scoring_paths():
assert backtest_engine.BRACKETS is policy.SCORE_BRACKETS
assert optimizer.BRACKETS is policy.SCORE_BRACKETS
for left, right in zip(policy.SCORE_BRACKETS, policy.SCORE_BRACKETS[1:]):
assert left[1] == right[0]
for tenth in range(0, 1001):
score = tenth / 10
matches = [bracket for bracket in policy.SCORE_BRACKETS if policy.score_in_bracket(score, bracket)]
assert len(matches) == 1, f"score {score} matched {matches}"
assert policy.assessment_for_score(score) == matches[0][2]
@pytest.mark.parametrize(
("score", "assessment"),
[
(0, "EXTREME CAUTION"),
(19.999, "EXTREME CAUTION"),
(20, "CAUTION — OVERHEATED"),
(35, "NEUTRAL"),
(50, "MODERATE OPPORTUNITY"),
(65, "STRONG ACCUMULATION ZONE"),
(80, "EXTREME ACCUMULATION ZONE"),
(100, "EXTREME ACCUMULATION ZONE"),
],
)
def test_assessment_boundaries_match_live_scoring(score, assessment):
assert policy.assessment_for_score(score) == assessment
assert scoring_engine.assessment_for_score(score) == assessment