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>
67 lines
2.4 KiB
Python
67 lines
2.4 KiB
Python
"""
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fetchers/derivatives.py — Fetches derivatives data from data_provider API.
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Clean consumer: no direct ccxt dependency. Just HTTP GET /api/derivatives.
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"""
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from datetime import date as Date
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from typing import Optional
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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 DerivativesFetcher(BaseFetcher):
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"""Fetches derivatives snapshot from data_provider /api/derivatives."""
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def __init__(self, provider_url: Optional[str] = None):
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super().__init__(timeout=15, max_retries=3)
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self.provider_url = provider_url or config.provider_url
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def fetch(self, target_date: Optional[Date] = None) -> list[dict]:
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"""Fetch derivatives data. Returns list with one record dict."""
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url = f"{self.provider_url}/api/derivatives"
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params = {"symbol": config.btc_symbol}
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try:
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data = self._get(url, params=params)
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record = {
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"date": str(target_date or Date.today()),
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"symbol": config.btc_symbol,
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"funding_rate": data.get("funding_rate"),
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"open_interest": data.get("open_interest"),
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"oi_24h_change_pct": data.get("oi_change_pct"),
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"basis_annualised_pct": data.get("basis"),
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"source": "data_provider",
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}
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return [record]
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except Exception:
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return []
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def store(self, db_path: Optional[str] = None, records: Optional[list[dict]] = None) -> int:
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"""Store derivatives records into SQLite."""
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import sqlite3
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db_path = db_path or config.db_path
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records = records or []
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conn = sqlite3.connect(db_path)
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count = 0
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for r in records:
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try:
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conn.execute("""
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INSERT OR REPLACE INTO derivatives
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(date, symbol, funding_rate, open_interest, oi_24h_change_pct,
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long_liquidations, short_liquidations, basis_annualised_pct)
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VALUES (?, ?, ?, ?, ?, NULL, NULL, ?)
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""", (
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r["date"], r.get("symbol", config.btc_symbol),
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r.get("funding_rate"), r.get("open_interest"),
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r.get("oi_24h_change_pct"), r.get("basis_annualised_pct"),
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))
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count += 1
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except Exception:
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continue
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conn.commit()
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conn.close()
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return count
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