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>
115 lines
4.6 KiB
Python
115 lines
4.6 KiB
Python
"""
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config.py — Global configuration for ChanMacro.
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All weights, thresholds, and paths are configurable.
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V1 weights are deliberately simple; they will be tuned via Phase 0 validation.
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"""
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from dataclasses import dataclass, field
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from pathlib import Path
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from typing import Optional
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@dataclass
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class Config:
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"""Global configuration. Override via config.json or env vars."""
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# ── Paths ──────────────────────────────────────────────
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db_path: str = "data/macro.db"
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data_dir: str = "data"
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# ── Data Provider ──────────────────────────────────────
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provider_url: str = "http://127.0.0.1:80"
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btc_symbol: str = "BTC/USDT:USDT"
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top50_symbols: list[str] = field(default_factory=lambda: [
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"BTC/USDT:USDT", "ETH/USDT:USDT", "SOL/USDT:USDT",
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"BNB/USDT:USDT", "XRP/USDT:USDT", "DOGE/USDT:USDT",
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"ADA/USDT:USDT", "AVAX/USDT:USDT", "DOT/USDT:USDT",
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"LINK/USDT:USDT", "MATIC/USDT:USDT", "UNI/USDT:USDT",
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"ATOM/USDT:USDT", "LTC/USDT:USDT", "ETC/USDT:USDT",
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"FIL/USDT:USDT", "APT/USDT:USDT", "ARB/USDT:USDT",
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"OP/USDT:USDT", "NEAR/USDT:USDT",
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"INJ/USDT:USDT", "TIA/USDT:USDT", "SUI/USDT:USDT",
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"SEI/USDT:USDT", "RUNE/USDT:USDT",
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])
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# ── Breadth ────────────────────────────────────────────
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breadth_top_n: list[int] = field(default_factory=lambda: [20, 30, 50])
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breadth_ema_period: int = 20
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breadth_new_high_window: int = 20
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# ── Regime (factor-locked: Price + Breadth + Vol) ─────
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regime_version: str = "v1_price_breadth_vol"
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# Weights for trend_score within regime detection
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regime_w_price: float = 0.35
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regime_w_breadth: float = 0.50
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regime_w_vol: float = 0.15
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# Weights for panic_score
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regime_panic_w_anti_trend: float = 0.60
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regime_panic_w_vol_extreme: float = 0.40
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# ── Price Structure ────────────────────────────────────
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ps_ema_fast: int = 20
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ps_ema_mid: int = 60
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ps_ema_slow: int = 120
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ps_adx_period: int = 14
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ps_adx_threshold: int = 25
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ps_atr_period: int = 14
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ps_bb_period: int = 20
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ps_roc_periods: list[int] = field(default_factory=lambda: [5, 10, 20])
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# ── OI Matrix ──────────────────────────────────────────
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oi_price_threshold_pct: float = 0.5 # min price change% to classify
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oi_oi_threshold_pct: float = 0.5 # min OI change% to classify
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# ── Volatility Regime ──────────────────────────────────
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vol_atr_period: int = 14
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vol_hv_short: int = 20
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vol_hv_long: int = 60
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# Thresholds (ATR/Close %)
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vol_low_threshold: float = 2.0
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vol_high_threshold: float = 5.0
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vol_explosive_threshold: float = 10.0
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# ── Trend (L2 aggregation) ─────────────────────────────
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trend_w_price: float = 0.30
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trend_w_breadth: float = 0.70
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# ── Maturity Score ─────────────────────────────────────
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maturity_w_trend: float = 0.50
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maturity_w_breadth: float = 0.30
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maturity_w_vol: float = 0.20
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# ── Expectancy ─────────────────────────────────────────
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half_life_days: int = 180
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sufficiency_min_effective: int = 30
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sufficiency_low: int = 50
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sufficiency_medium: int = 100
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level_min_samples: int = 50
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knn_max_distance: float = 0.35
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knn_k: int = 200
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# ── Validation ─────────────────────────────────────────
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min_history_days: int = 365
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regime_min_avg_duration: int = 5
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regime_max_flip_rate: float = 0.15
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@classmethod
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def from_json(cls, path: str = "config.json") -> "Config":
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"""Load config from JSON file, overriding defaults."""
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import json
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config = cls()
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try:
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with open(path) as f:
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data = json.load(f)
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for key, value in data.items():
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if hasattr(config, key):
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setattr(config, key, value)
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except FileNotFoundError:
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pass
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return config
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# Global singleton
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config = Config()
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