jackyu66gitandClaude
71951019fb
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>
29 lines
745 B
Python
29 lines
745 B
Python
"""
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scoring/base.py — Abstract base class for all scoring modules.
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"""
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from abc import ABC, abstractmethod
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from datetime import date as Date
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from typing import Optional
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import sqlite3
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from models import FactorScore
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from config import config
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class BaseScorer(ABC):
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"""Abstract base for all factor scorers."""
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def __init__(self, db_path: Optional[str] = None):
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self.db_path = db_path or config.db_path
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def get_connection(self) -> sqlite3.Connection:
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conn = sqlite3.connect(self.db_path)
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conn.row_factory = sqlite3.Row
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return conn
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@abstractmethod
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def compute(self, target_date: Date) -> FactorScore:
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"""Compute factor score for a given date from database records."""
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...
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