Alpaca Trader

alpaca_trader/filters.py

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+import pandas as pd
+from datetime import datetime
+from .indicators import ema, sma, rsi, adx, atr, bollinger, macd
+from .api import AlpacaClient
+from .utils import EASTERN
+
+def check_volume(bars: pd.DataFrame, multiplier: float):
+    if len(bars) < 20 or "volume" not in bars.columns:
+        return True
+    avg = bars["volume"].rolling(window=20).mean().iloc[-1]
+    cur = bars["volume"].iloc[-1]
+    return cur >= avg * multiplier
+
+def check_candle_pattern(bars: pd.DataFrame):
+    if len(bars) < 2:
+        return False, False
+    last = bars.iloc[-1]
+    prev = bars.iloc[-2]
+    bullish = last["close"] > last["open"] and prev["close"] < prev["open"] and last["close"] > prev["open"] and last["open"] < prev["close"]
+    bearish = last["close"] < last["open"] and prev["close"] > prev["open"] and last["close"] < prev["open"] and last["open"] > prev["close"]
+    return bullish, bearish
+
+def check_macd_confirmation(bars: pd.DataFrame):
+    if len(bars) < 35:
+        return "neutral"
+    macd_line, signal_line, _ = macd(bars["close"])
+    if macd_line.iloc[-2] <= signal_line.iloc[-2] and macd_line.iloc[-1] > signal_line.iloc[-1]:
+        return "bullish"
+    if macd_line.iloc[-2] >= signal_line.iloc[-2] and macd_line.iloc[-1] < signal_line.iloc[-1]:
+        return "bearish"
+    return "neutral"
+
+def check_200_sma_filter(symbol: str, client: AlpacaClient):
+    daily = client.get_bars(symbol, "1Day", limit=210)
+    if len(daily) < 200:
+        return "neutral"
+    sma_200 = sma(daily["close"], 200).iloc[-1]
+    price = daily["close"].iloc[-1]
+    if price > sma_200 * 1.01:
+        return "bullish"
+    if price < sma_200 * 0.99:
+        return "bearish"
+    return "neutral"
+
+def check_multiframe_confluence(symbol: str, use_ema: bool, client: AlpacaClient = None):
+    if client is None:
+        from .engine import api as client
+    hourly = client.get_bars(symbol, "1Hour", limit=50)
+    if len(hourly) < 50:
+        return "neutral"
+    if use_ema:
+        short = ema(hourly["close"], 20).iloc[-1]
+        long = ema(hourly["close"], 50).iloc[-1]
+    else:
+        short = sma(hourly["close"], 20).iloc[-1]
+        long = sma(hourly["close"], 50).iloc[-1]
+    price = hourly["close"].iloc[-1]
+    if short > long and price > short:
+        return "bullish"
+    if short < long and price < short:
+        return "bearish"
+    return "neutral"
+
+def detect_market_regime(bars: pd.DataFrame, adx_threshold: float):
+    if len(bars) < 50:
+        return "unknown"
+    current_adx = adx(bars["high"], bars["low"], bars["close"]).iloc[-1]
+    current_atr = atr(bars["high"], bars["low"], bars["close"]).iloc[-1]
+    atr_series = atr(bars["high"], bars["low"], bars["close"])
+    percentile = (atr_series <= current_atr).mean() * 100
+    if percentile > 70:
+        return "high_vol"
+    if percentile < 30:
+        return "low_vol"
+    if current_adx > adx_threshold:
+        return "trend"
+    return "range"
+
+def get_vix(client: AlpacaClient, symbol: str, use_vix_filter: bool):
+    if not use_vix_filter:
+        return 0
+    try:
+        vix = client.get_bars("VIX", "1Day", limit=5)
+        if len(vix) > 0:
+            return vix["close"].iloc[-1]
+    except:
+        pass
+    try:
+        spy = client.get_bars(symbol, "1Day", limit=20)
+        if len(spy) >= 20:
+            returns = spy["close"].pct_change()
+            return returns.std() * (252 ** 0.5) * 100
+    except:
+        pass
+    return 15