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>
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jackyu66git
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"""
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fetchers/breadth.py — Fetches TOP50 OHLCV and computes market breadth metrics.
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Multi-tier: Top20 / Top30 / Top50 for advance/decline, EMA20%, new highs, BTC.D.
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"""
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from datetime import date as Date, datetime
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from typing import Optional
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import logging
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import pandas as pd
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import numpy as np
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import requests
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from .base import BaseFetcher
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from config import config
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class BreadthFetcher(BaseFetcher):
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"""Fetches TOP50 coin OHLCV data and computes breadth metrics."""
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def __init__(self, provider_url: Optional[str] = None):
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super().__init__(timeout=60, max_retries=3)
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self.provider_url = provider_url or config.provider_url
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self.symbols = config.top50_symbols
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self.ema_period = config.breadth_ema_period
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self.new_high_window = config.breadth_new_high_window
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self.logger = logging.getLogger(__name__)
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def fetch(self, target_date: Optional[Date] = None) -> dict:
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"""
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Fetch daily OHLCV for all TOP50 symbols and compute breadth.
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Returns a dict suitable for storing in breadth_daily table.
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"""
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if target_date is None:
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target_date = Date.today()
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# Fetch last 60 days of daily data for each symbol to compute EMAs and new highs
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all_data = {}
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for symbol in self.symbols:
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try:
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df = self._fetch_symbol(symbol)
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if df is not None and not df.empty:
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all_data[symbol] = df
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except Exception as e:
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self.logger.debug(f"Failed to fetch {symbol}: {e}")
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if not all_data:
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self.logger.error("No symbol data fetched for breadth")
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return {}
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# Compute breadth metrics for the target date
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breadth = self._compute_breadth(all_data, target_date)
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return breadth
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def _fetch_symbol(self, symbol: str) -> Optional[pd.DataFrame]:
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"""Fetch daily OHLCV for a single symbol."""
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url = f"{self.provider_url}/api/candles"
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params = {
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"symbol": symbol,
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"tf": "1d",
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"limit": 100,
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}
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try:
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resp = requests.get(url, params=params, timeout=15)
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resp.raise_for_status()
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data = resp.json()
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if not data:
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return None
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df = pd.DataFrame(data)
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df["timestamp"] = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
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df["date"] = df["timestamp"].dt.date
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df = df.drop_duplicates(subset="date").sort_values("date").reset_index(drop=True)
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df["close"] = df["close"].astype(float)
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df["ema20"] = df["close"].ewm(span=self.ema_period, adjust=False).mean()
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return df
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except Exception:
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return None
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def _compute_breadth(self, all_data: dict, target_date: Date) -> dict:
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"""Compute breadth metrics for a specific date across all symbols."""
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total = len(all_data)
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advances_50 = declines_50 = 0
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above_ema20_50 = 0
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new_highs_50 = 0
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advances_30 = declines_30 = 0
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above_ema20_30 = 0
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new_highs_30 = 0
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advances_20 = declines_20 = 0
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above_ema20_20 = 0
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new_highs_20 = 0
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for i, (symbol, df) in enumerate(all_data.items()):
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# Get data for target date
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df["date_str"] = df["date"].astype(str)
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target_str = str(target_date)
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idx = df[df["date_str"] == target_str].index
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if len(idx) == 0:
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continue
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row_idx = idx[0]
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if row_idx < 1:
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continue
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current_close = df.loc[row_idx, "close"]
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prev_close = df.loc[row_idx - 1, "close"]
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# Advance/Decline
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if current_close > prev_close:
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if i < 50: advances_50 += 1
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if i < 30: advances_30 += 1
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if i < 20: advances_20 += 1
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elif current_close < prev_close:
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if i < 50: declines_50 += 1
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if i < 30: declines_30 += 1
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if i < 20: declines_20 += 1
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# Above EMA20
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ema20_val = df.loc[row_idx, "ema20"]
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if not pd.isna(ema20_val) and current_close > ema20_val:
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if i < 50: above_ema20_50 += 1
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if i < 30: above_ema20_30 += 1
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if i < 20: above_ema20_20 += 1
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# New 20-day highs
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lookback_start = max(0, row_idx - self.new_high_window)
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recent_highs = df.loc[lookback_start:row_idx - 1, "high"].astype(float)
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current_high = df.loc[row_idx, "high"]
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if len(recent_highs) > 0 and float(current_high) > recent_highs.max():
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if i < 50: new_highs_50 += 1
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if i < 30: new_highs_30 += 1
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if i < 20: new_highs_20 += 1
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return {
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"date": str(target_date),
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"total_tracked": total,
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"advance_top50": advances_50,
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"decline_top50": declines_50,
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"above_ema20_top50": above_ema20_50,
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"new_highs_20d_top50": new_highs_50,
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"advance_top30": advances_30,
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"advance_top20": advances_20,
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"above_ema20_top30": above_ema20_30,
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"above_ema20_top20": above_ema20_20,
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"new_highs_20d_top30": new_highs_30,
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"new_highs_20d_top20": new_highs_20,
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"btc_dominance": None, # Reserved for Coinglass API integration
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}
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def store(self, db_path: Optional[str] = None, record: Optional[dict] = None) -> int:
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"""Store a breadth record into SQLite. Returns 1 if inserted/updated."""
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import sqlite3
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db_path = db_path or config.db_path
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conn = sqlite3.connect(db_path)
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if record is None:
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conn.close()
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return 0
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try:
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conn.execute("""
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INSERT OR REPLACE INTO breadth_daily
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(date, total_tracked,
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advance_top50, decline_top50, above_ema20_top50, new_highs_20d_top50,
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advance_top30, advance_top20,
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above_ema20_top30, above_ema20_top20,
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new_highs_20d_top30, new_highs_20d_top20,
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btc_dominance)
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VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
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""", (
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record["date"], record.get("total_tracked", 50),
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record.get("advance_top50", 0), record.get("decline_top50", 0),
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record.get("above_ema20_top50", 0), record.get("new_highs_20d_top50", 0),
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record.get("advance_top30", 0), record.get("advance_top20", 0),
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record.get("above_ema20_top30", 0), record.get("above_ema20_top20", 0),
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record.get("new_highs_20d_top30", 0), record.get("new_highs_20d_top20", 0),
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record.get("btc_dominance"),
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))
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conn.commit()
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return 1
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except Exception as e:
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self.logger.error(f"Failed to store breadth: {e}")
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return 0
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finally:
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conn.close()
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