adds realized volatility cones to volatility_model.py

openbb_terminal/common/technical_analysis/volatility_model.py

@@ -2,7 +2,7 @@
 __docformat__ = "numpy"
 
 import logging
-
+import numpy as np
 import pandas as pd
 import pandas_ta as ta
 
@@ -166,3 +166,75 @@ def atr(
             offset=offset,
         ).dropna()
     )
+
+@log_start_end(log=logger)
+def rvol(
+    data:pd.DataFrame,
+    lower_q:float = 0.25,
+    upper_q: float = 0.75
+) -> pd.DataFrame:
+    """Returns a DataFrame of realized volatility quantiles.
+    
+    Paramters
+    ---------
+    data: pd.DataFrame
+        DataFrame of the OHLC data.
+    lower_q: float (default = 0.25)
+        The lower quantile to calculate the realized volatility over time for.
+    upper_q: float (default = 0.75)
+        The upper quantile to calculate the realized volatility over time for.
+    
+    Returns
+    -------
+    pd.DataFrame: rvol_cones
+        DataFrame of realized volatility quantiles.
+    
+    Examples
+    --------
+    df = get_rvol_cones(symbol = "AAPL")
+    
+    df = get_rvol_cones(symbol = "AAPL", lower_q = 0.10, upper_q = 0.90)
+    """
+    lower_q_label = str((int(lower_q*100)))
+    upper_q_label = str((int(upper_q*100)))
+    
+    rvol_cones: DataFrame = pd.DataFrame()
+    quantiles = [lower_q, upper_q]
+    windows = [3,10,30,60,90,120,150,180,210,240]
+    data = data
+    min_ = []
+    max_ = []
+    median = []
+    top_q = []
+    bottom_q = []
+    realized = []
+    data.index = data.index.date
+    data = pd.DataFrame(data).sort_index(ascending = False)
+
+    def realized_vol(data, window=30):
+        """Helper function for calculating realized volatility."""
+
+        log_return = (data["Close"] / data["Close"].shift(1)).apply(np.log)
+
+        return log_return.rolling(window=window, center=False).std() * np.sqrt(252)
+    
+    for window in windows:
+        
+            # Looping to build a dataframe with realized volatility over each window.
+        
+        estimator = realized_vol(window=window,data=data)
+        min_.append(estimator.min())
+        max_.append(estimator.max())
+        median.append(estimator.median())
+        top_q.append(estimator.quantile(quantiles[1]))
+        bottom_q.append(estimator.quantile(quantiles[0]))
+        realized.append(estimator[-1])
+    
+    df = [realized, min_, bottom_q, median, top_q, max_]
+    pd.DataFrame(df).columns = windows
+    df_windows = list(windows)
+    df = pd.DataFrame(df, columns=df_windows)
+    df = df.rename(index = {0:'Realized', 1: 'Min', 2:'Lower 'f"{lower_q_label}"'%', 3:'Median', 4:'Upper 'f"{upper_q_label}"'%', 5: 'Max'})
+    rvol_cones = df.copy()
+
+    return rvol_cones.transpose()