Initial release: TradeMind crypto trading bot with paper/live modes and strategy training

trademind/indicators.py

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