chanmacro: Signal Expectancy Engine V1 — Market Memory System
Phase A-C complete: 4 core factors, regime detection, signal tracking, Bayesian expectancy. chanmacro/ (32 files, ~4000 lines): - models: 12 enums + 15 Pydantic v2 models (DateAwareModel, MarketStateVector, etc.) - fetchers: OHLCV + Breadth (from data_provider) + Derivatives (new endpoint) - scoring: Price Structure / Breadth (quantile buckets) / OI Matrix (5 discrete states) / Volatility Regime - regime_detector: 3-state (TREND/RANGE/PANIC), factor-locked (Price+Breadth+Vol), versioned, 2-day confirmation - expectancy: SignalTracker (record+outcomes), TimeDecay (half-life=180d), BayesianExpectancyEngine (Empirical Bayes, Leveled, SufficiencyGuard) - validation: FactorValidator (IC/ICIR/Hit Ratio), RegimeValidator (MI/KL/ANOVA), TransitionValidator (stability) - CLI: fetch|score|regime|track|backfill|expectancy|validate|serve - tests: 52 passing (models, scoring, regime, expectancy) data_provider: - /api/derivatives endpoint: funding rate, OI, OI change, basis - _derivatives storage: same persist pattern as K-line (merge→lock→snapshot→atomic write) - background refresh every 60s Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
jackyu66git
@@ -0,0 +1,218 @@
|
||||
"""
|
||||
scoring/breadth_scorer.py — Market Breadth Score.
|
||||
|
||||
The first citizen of the system. Diffusion always leads price.
|
||||
|
||||
Multi-tier: Top20 / Top30 / Top50.
|
||||
Quantile-based bucketing: EXTREME / STRONG / NORMAL / WEAK / PANIC.
|
||||
|
||||
4 sub-indicators (equal weight):
|
||||
1. Advance/Decline ratio (30%)
|
||||
2. % above EMA20 (35%)
|
||||
3. New 20d highs (20%)
|
||||
4. BTC Dominance change (15%, inverted)
|
||||
"""
|
||||
|
||||
from datetime import date as Date
|
||||
import sqlite3
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
from .base import BaseScorer
|
||||
from .constants import (
|
||||
BREADTH_W_ADVANCE, BREADTH_W_EMA20, BREADTH_W_NEW_HIGHS, BREADTH_W_BTC_DOM,
|
||||
)
|
||||
from models import FactorScore, BreadthScore, BreadthBucket, MacroDirection
|
||||
from config import config
|
||||
|
||||
|
||||
class BreadthScorer(BaseScorer):
|
||||
"""Scores market breadth with quantile-based bucketing."""
|
||||
|
||||
def compute(self, target_date: Date) -> BreadthScore:
|
||||
conn = self.get_connection()
|
||||
try:
|
||||
row = conn.execute(
|
||||
"SELECT * FROM breadth_daily WHERE date = ?", (str(target_date),)
|
||||
).fetchone()
|
||||
|
||||
if row is None:
|
||||
return BreadthScore(
|
||||
name="Breadth",
|
||||
score=50.0,
|
||||
label="No Data",
|
||||
breadth_bucket=BreadthBucket.NORMAL,
|
||||
)
|
||||
|
||||
row = dict(row)
|
||||
total = row.get("total_tracked", 50) or 50
|
||||
|
||||
# 1. Advance/Decline ratio
|
||||
advance = row.get("advance_top50", 0) or 0
|
||||
decline = row.get("decline_top50", 0) or 0
|
||||
if advance + decline > 0:
|
||||
ad_ratio = advance / (advance + decline)
|
||||
else:
|
||||
ad_ratio = 0.5
|
||||
ad_score = ad_ratio * 100
|
||||
|
||||
# 2. % above EMA20
|
||||
above_ema = row.get("above_ema20_top50", 0) or 0
|
||||
ema_pct = above_ema / total if total > 0 else 0.5
|
||||
ema_score = ema_pct * 100
|
||||
|
||||
# 3. New highs
|
||||
new_highs = row.get("new_highs_20d_top50", 0) or 0
|
||||
highs_pct = new_highs / total if total > 0 else 0
|
||||
highs_score = highs_pct * 100
|
||||
|
||||
# 4. BTC Dominance (inverted: BTC.D up = bearish for alts)
|
||||
btc_dom = row.get("btc_dominance")
|
||||
btc_dom_score = 50.0 # neutral default
|
||||
if btc_dom is not None:
|
||||
# Placeholder — needs historical comparison
|
||||
btc_dom_score = 50.0
|
||||
|
||||
# Weighted aggregate
|
||||
score = (
|
||||
ad_score * BREADTH_W_ADVANCE +
|
||||
ema_score * BREADTH_W_EMA20 +
|
||||
highs_score * BREADTH_W_NEW_HIGHS +
|
||||
btc_dom_score * BREADTH_W_BTC_DOM
|
||||
)
|
||||
|
||||
# Multi-tier breadth
|
||||
b20 = self._compute_tier_breadth(row, 20, total)
|
||||
b30 = self._compute_tier_breadth(row, 30, total)
|
||||
b50 = score # Top50 = full score
|
||||
|
||||
# Quantile bucket
|
||||
bucket = self._assign_bucket(score)
|
||||
|
||||
# Divergence
|
||||
divergence = b20 - b50
|
||||
|
||||
# Direction
|
||||
if score >= 60:
|
||||
direction = MacroDirection.BULLISH
|
||||
elif score <= 40:
|
||||
direction = MacroDirection.BEARISH
|
||||
else:
|
||||
direction = MacroDirection.NEUTRAL
|
||||
|
||||
# Narrative
|
||||
narrative = self._build_narrative(bucket, divergence, ema_pct, ad_ratio)
|
||||
|
||||
return BreadthScore(
|
||||
name="Breadth",
|
||||
score=round(score, 1),
|
||||
label=bucket.value,
|
||||
direction=direction,
|
||||
breadth_top20=round(b20, 1),
|
||||
breadth_top30=round(b30, 1),
|
||||
breadth_top50=round(b50, 1),
|
||||
breadth_bucket=bucket,
|
||||
breadth_divergence=round(divergence, 1),
|
||||
advance_pct_top50=round(ad_ratio * 100, 1),
|
||||
above_ema20_pct_top50=round(ema_pct * 100, 1),
|
||||
new_highs_top50=new_highs,
|
||||
sub_scores={
|
||||
"advance_decline": round(ad_score, 1),
|
||||
"above_ema20": round(ema_score, 1),
|
||||
"new_highs": round(highs_score, 1),
|
||||
"btc_dominance": round(btc_dom_score, 1),
|
||||
},
|
||||
narrative=narrative,
|
||||
)
|
||||
finally:
|
||||
conn.close()
|
||||
|
||||
def _compute_tier_breadth(self, row: dict, tier: int, total: int) -> float:
|
||||
"""Compute breadth score for a specific tier (Top20 or Top30)."""
|
||||
advance = row.get(f"advance_top{tier}", 0) or 0
|
||||
above_ema = row.get(f"above_ema20_top{tier}", 0) or 0
|
||||
new_highs = row.get(f"new_highs_20d_top{tier}", 0) or 0
|
||||
|
||||
tier_actual = min(tier, total)
|
||||
if tier_actual == 0:
|
||||
return 50.0
|
||||
|
||||
ad_ratio = advance / tier_actual if tier_actual > 0 else 0.5
|
||||
ema_ratio = above_ema / tier_actual if tier_actual > 0 else 0.5
|
||||
highs_ratio = new_highs / tier_actual if tier_actual > 0 else 0
|
||||
|
||||
return (
|
||||
ad_ratio * 100 * BREADTH_W_ADVANCE +
|
||||
ema_ratio * 100 * BREADTH_W_EMA20 +
|
||||
highs_ratio * 100 * BREADTH_W_NEW_HIGHS +
|
||||
50 * BREADTH_W_BTC_DOM # neutral for BTC.D
|
||||
)
|
||||
|
||||
def _assign_bucket(self, score: float) -> BreadthBucket:
|
||||
"""Assign quantile-based bucket. V1 uses fixed thresholds until history accumulated."""
|
||||
# V1: fixed thresholds (will switch to quantile when enough history)
|
||||
if score >= 80:
|
||||
return BreadthBucket.EXTREME
|
||||
elif score >= 60:
|
||||
return BreadthBucket.STRONG
|
||||
elif score >= 40:
|
||||
return BreadthBucket.NORMAL
|
||||
elif score >= 20:
|
||||
return BreadthBucket.WEAK
|
||||
else:
|
||||
return BreadthBucket.PANIC
|
||||
|
||||
@staticmethod
|
||||
def compute_quantile_boundaries(db_path: str) -> dict:
|
||||
"""Compute quantile boundaries from historical breadth data.
|
||||
|
||||
This should be called after accumulating enough history (> 1 year).
|
||||
Returns boundaries for pd.qcut.
|
||||
"""
|
||||
conn = sqlite3.connect(db_path)
|
||||
df = pd.read_sql_query(
|
||||
"SELECT date, advance_top50, decline_top50, above_ema20_top50 FROM breadth_daily",
|
||||
conn
|
||||
)
|
||||
conn.close()
|
||||
|
||||
if len(df) < 100:
|
||||
return {"boundaries": [0, 20, 40, 60, 80, 100], "is_quantile": False}
|
||||
|
||||
df["ad_ratio"] = df["advance_top50"] / (df["advance_top50"] + df["decline_top50"])
|
||||
df["ema_ratio"] = df["above_ema20_top50"] / 50
|
||||
df["breadth_raw"] = (
|
||||
df["ad_ratio"] * BREADTH_W_ADVANCE * 100 +
|
||||
df["ema_ratio"] * BREADTH_W_EMA20 * 100 +
|
||||
40 * BREADTH_W_NEW_HIGHS +
|
||||
50 * BREADTH_W_BTC_DOM
|
||||
)
|
||||
|
||||
boundaries = list(np.percentile(df["breadth_raw"].dropna(), [10, 30, 70, 90]))
|
||||
return {
|
||||
"boundaries": [0] + boundaries + [100],
|
||||
"is_quantile": True,
|
||||
"n_samples": len(df),
|
||||
}
|
||||
|
||||
@staticmethod
|
||||
def _build_narrative(bucket: BreadthBucket, divergence: float,
|
||||
ema_pct: float, ad_ratio: float) -> str:
|
||||
parts = []
|
||||
if bucket == BreadthBucket.EXTREME:
|
||||
parts.append(f"全市场极度扩散({ema_pct:.0%}站上EMA20)")
|
||||
elif bucket == BreadthBucket.STRONG:
|
||||
parts.append("市场广度强势")
|
||||
elif bucket == BreadthBucket.NORMAL:
|
||||
parts.append("市场广度中性")
|
||||
elif bucket == BreadthBucket.WEAK:
|
||||
parts.append("市场广度疲弱")
|
||||
else:
|
||||
parts.append("市场广度恐慌")
|
||||
|
||||
if divergence > 10:
|
||||
parts.append("资金集中于大市值(Top20>>Top50)")
|
||||
elif divergence < -10:
|
||||
parts.append("垃圾币狂欢(Top50>>Top20)")
|
||||
|
||||
return ", ".join(parts)
|
||||
Reference in New Issue
Block a user