Tobias Zimmermann
39 lines
1.2 KiB
Python
39 lines
1.2 KiB
Python
"""Technische Indikatoren (reine pandas/numpy, ohne TA-Lib)."""
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from __future__ import annotations
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import numpy as np
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import pandas as pd
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def ema(series: pd.Series, period: int) -> pd.Series:
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return series.ewm(span=period, adjust=False).mean()
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def rsi(close: pd.Series, period: int = 14) -> pd.Series:
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delta = close.diff()
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gain = delta.clip(lower=0.0)
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loss = -delta.clip(upper=0.0)
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avg_gain = gain.ewm(alpha=1 / period, adjust=False).mean()
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avg_loss = loss.ewm(alpha=1 / period, adjust=False).mean()
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rs = avg_gain / avg_loss.replace(0.0, np.nan)
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out = 100 - (100 / (1 + rs))
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return out.fillna(50.0)
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def atr(df: pd.DataFrame, period: int = 14) -> pd.Series:
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high, low, close = df["high"], df["low"], df["close"]
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prev_close = close.shift(1)
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tr = pd.concat(
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[(high - low), (high - prev_close).abs(), (low - prev_close).abs()], axis=1
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).max(axis=1)
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return tr.ewm(alpha=1 / period, adjust=False).mean()
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def crossover(a: pd.Series, b: pd.Series) -> pd.Series:
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return (a > b) & (a.shift(1) <= b.shift(1))
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def crossunder(a: pd.Series, b: pd.Series) -> pd.Series:
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return (a < b) & (a.shift(1) >= b.shift(1))
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