102 lines
3.7 KiB
Python
102 lines
3.7 KiB
Python
import pandas as pd
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from datetime import datetime
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import logging
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from .indicators import ema, sma, rsi, adx, atr, bollinger, macd
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from .api import AlpacaClient
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from .utils import EASTERN
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logger = logging.getLogger(__name__)
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def check_volume(bars: pd.DataFrame, multiplier: float):
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if len(bars) < 20 or "volume" not in bars.columns:
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return True
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avg = bars["volume"].rolling(window=20).mean().iloc[-1]
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cur = bars["volume"].iloc[-1]
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return cur >= avg * multiplier
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def check_candle_pattern(bars: pd.DataFrame):
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if len(bars) < 2:
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return False, False
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last = bars.iloc[-1]
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prev = bars.iloc[-2]
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bullish = last["close"] > last["open"] and prev["close"] < prev["open"] and last["close"] > prev["open"] and last["open"] < prev["close"]
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bearish = last["close"] < last["open"] and prev["close"] > prev["open"] and last["close"] < prev["open"] and last["open"] > prev["close"]
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return bullish, bearish
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def check_macd_confirmation(bars: pd.DataFrame):
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if len(bars) < 35:
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return "neutral"
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macd_line, signal_line, _ = macd(bars["close"])
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if macd_line.iloc[-2] <= signal_line.iloc[-2] and macd_line.iloc[-1] > signal_line.iloc[-1]:
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return "bullish"
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if macd_line.iloc[-2] >= signal_line.iloc[-2] and macd_line.iloc[-1] < signal_line.iloc[-1]:
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return "bearish"
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return "neutral"
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def check_200_sma_filter(symbol: str, client: AlpacaClient):
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daily = client.get_bars(symbol, "1Day", limit=210)
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if len(daily) < 200:
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return "neutral"
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sma_200 = sma(daily["close"], 200).iloc[-1]
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price = daily["close"].iloc[-1]
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if price > sma_200 * 1.01:
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return "bullish"
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if price < sma_200 * 0.99:
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return "bearish"
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return "neutral"
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def check_multiframe_confluence(symbol: str, use_ema: bool, client: AlpacaClient = None):
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if client is None:
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from .engine import api as client
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hourly = client.get_bars(symbol, "1Hour", limit=50)
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if len(hourly) < 50:
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return "neutral"
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if use_ema:
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short = ema(hourly["close"], 20).iloc[-1]
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long = ema(hourly["close"], 50).iloc[-1]
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else:
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short = sma(hourly["close"], 20).iloc[-1]
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long = sma(hourly["close"], 50).iloc[-1]
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price = hourly["close"].iloc[-1]
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if short > long and price > short:
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return "bullish"
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if short < long and price < short:
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return "bearish"
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return "neutral"
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def detect_market_regime(bars: pd.DataFrame, adx_threshold: float):
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if len(bars) < 50:
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return "unknown"
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current_adx = adx(bars["high"], bars["low"], bars["close"]).iloc[-1]
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current_atr = atr(bars["high"], bars["low"], bars["close"]).iloc[-1]
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atr_series = atr(bars["high"], bars["low"], bars["close"])
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percentile = (atr_series <= current_atr).mean() * 100
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if percentile > 70:
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return "high_vol"
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if percentile < 30:
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return "low_vol"
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if current_adx > adx_threshold:
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return "trend"
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return "range"
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def get_vix(client: AlpacaClient, symbol: str, use_vix_filter: bool):
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if not use_vix_filter:
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return 0
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try:
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vix = client.get_bars("VIX", "1Day", limit=5)
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if len(vix) > 0:
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return vix["close"].iloc[-1]
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except Exception as e:
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logger.warning(f"VIX data unavailable: {e}")
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try:
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spy = client.get_bars(symbol, "1Day", limit=20)
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if len(spy) >= 20:
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returns = spy["close"].pct_change()
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calculated_vix = returns.std() * (252 ** 0.5) * 100
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logger.info(f"Using calculated volatility as VIX proxy: {calculated_vix:.1f}")
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return calculated_vix
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except Exception as e:
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logger.warning(f"Could not calculate volatility: {e}")
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logger.warning("VIX data unavailable, skipping VIX filter for this iteration")
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return 0
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