673ce08d84
- Add velocity-based entry signal in strategy.py (VELOCITY_THRESHOLD=0.10, VELOCITY_MIN_MOVE=0.5%, VELOCITY_MIN_AGE_M=5) - Add fast-poll thread in daemon/runner.py (SIGNAL_POLL_INTERVAL=15s) for sub-minute velocity event detection - Add vol_ratio tiered condition and get_active_signals() to strategy.py - Change BEAR_THRESHOLD -1.0 → -0.5 in market_regime.py to catch slow downtrends earlier (weighted 2h score) - Expand sell_reason VARCHAR2(500) in price_db.py DDL - Add velocity_backtest.py and sim10m.py for strategy experimentation - Update STRATEGY.md: correct regime algorithm description (weighted 2h score, not BTC 1h ±5%), add fast-poll/velocity sections, add backtest section D, add change history table Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
111 lines
3.2 KiB
Python
111 lines
3.2 KiB
Python
"""시장 레짐(Bull/Neutral/Bear) 판단.
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BTC·ETH·SOL·XRP 가중 평균 2h 추세로 레짐을 결정하고
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매수 조건 파라미터(trend_pct, vol_mult)를 동적으로 반환한다.
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계산된 현재가는 price_history DB에 저장해 재활용한다.
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"""
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from __future__ import annotations
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import logging
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import time
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import pyupbit
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from .price_db import get_price_n_hours_ago, insert_prices
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logger = logging.getLogger(__name__)
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# 대장 코인 가중치
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LEADERS: dict[str, float] = {
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"KRW-BTC": 0.40,
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"KRW-ETH": 0.30,
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"KRW-SOL": 0.15,
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"KRW-XRP": 0.15,
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}
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TREND_HOURS = 2 # 2h 추세 기준
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BULL_THRESHOLD = 1.5 # score ≥ 1.5% → Bull
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BEAR_THRESHOLD = -0.5 # score < -0.5% → Bear
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# 레짐별 매수 조건 파라미터
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REGIME_PARAMS: dict[str, dict] = {
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"bull": {"trend_pct": 3.0, "vol_mult": 1.5, "emoji": "🟢"},
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"neutral": {"trend_pct": 5.0, "vol_mult": 2.0, "emoji": "🟡"},
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"bear": {"trend_pct": 8.0, "vol_mult": 3.5, "emoji": "🔴"},
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}
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# 10분 캐시 (스캔 루프마다 API 호출 방지)
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_cache: dict = {}
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_cache_ts: float = 0.0
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_CACHE_TTL = 600
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def get_regime() -> dict:
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"""현재 시장 레짐 반환.
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Returns:
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{
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'name': 'bull' | 'neutral' | 'bear',
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'score': float, # 가중 평균 2h 추세(%)
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'trend_pct': float, # 매수 추세 임계값
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'vol_mult': float, # 거래량 배수 임계값
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'emoji': str,
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}
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"""
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global _cache, _cache_ts
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if _cache and (time.time() - _cache_ts) < _CACHE_TTL:
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return _cache
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score = 0.0
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current_prices: dict[str, float] = {}
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for ticker, weight in LEADERS.items():
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try:
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current = pyupbit.get_current_price(ticker)
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if not current:
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continue
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current_prices[ticker] = current
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# DB에서 2h 전 가격 조회 → 없으면 API 캔들로 대체
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past = get_price_n_hours_ago(ticker, TREND_HOURS)
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if past is None:
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df = pyupbit.get_ohlcv(ticker, interval="minute60", count=4)
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if df is not None and len(df) >= 3:
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past = float(df["close"].iloc[-3])
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if past:
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trend = (current - past) / past * 100
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score += trend * weight
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logger.debug(f"[레짐] {ticker} {trend:+.2f}% (기여 {trend*weight:+.3f})")
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except Exception as e:
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logger.warning(f"[레짐] {ticker} 오류: {e}")
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# 현재가 DB 저장 (다음 레짐 계산 및 추세 판단에 재활용)
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if current_prices:
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try:
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insert_prices(current_prices)
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except Exception as e:
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logger.warning(f"[레짐] 가격 저장 오류: {e}")
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# 레짐 결정
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if score >= BULL_THRESHOLD:
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name = "bull"
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elif score < BEAR_THRESHOLD:
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name = "bear"
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else:
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name = "neutral"
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params = REGIME_PARAMS[name]
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result = {"name": name, "score": round(score, 3), **params}
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logger.info(
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f"[레짐] score={score:+.3f}% → {params['emoji']} {name.upper()} "
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f"(TREND≥{params['trend_pct']}% / VOL≥{params['vol_mult']}x)"
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)
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_cache = result
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_cache_ts = time.time()
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return result
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