| from __future__ import annotations | |
| import pandas as pd | |
| def ensure_cols(df: pd.DataFrame, cols: list[str]) -> None: | |
| missing = [c for c in cols if c not in df.columns] | |
| if missing: | |
| raise ValueError(f"DataFrame missing columns: {missing}") | |
| def rsi(df: pd.DataFrame, price_col: str = "close", period: int = 14) -> pd.DataFrame: | |
| ensure_cols(df, [price_col]) | |
| delta = df[price_col].diff() | |
| gain = delta.clip(lower=0) | |
| loss = -delta.clip(upper=0) | |
| avg_gain = gain.rolling(window=period, min_periods=period).mean() | |
| avg_loss = loss.rolling(window=period, min_periods=period).mean() | |
| rs = avg_gain / avg_loss | |
| df["RSI"] = 100 - (100 / (1 + rs)) | |
| return df | |
| def macd( | |
| df: pd.DataFrame, | |
| price_col: str = "close", | |
| short_window: int = 12, | |
| long_window: int = 26, | |
| signal_window: int = 9, | |
| ) -> pd.DataFrame: | |
| ensure_cols(df, [price_col]) | |
| short_ema = df[price_col].ewm(span=short_window, adjust=False).mean() | |
| long_ema = df[price_col].ewm(span=long_window, adjust=False).mean() | |
| macd_line = short_ema - long_ema | |
| signal_line = macd_line.ewm(span=signal_window, adjust=False).mean() | |
| df["MACD_Line"] = macd_line | |
| df["Signal_Line"] = signal_line | |
| return df | |