Vix Spx Seasonality By Month, Even Keel

While seeing some “sell in may” headlines a while ago, thought i’d pull up the monthly mean returns for spx and vix. I wanted to see them on an even keel, so that each month starts at 0%, to better gauge their monthly behaviour

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import quandl
import calendar

%matplotlib inline
sns.set(style="whitegrid")

Since yahoo data went dark, had to pull it in manually. There is a more intelligent solution posted in Trading With Python blog

spx = pd.read_csv("../Data/Spx.csv", index_col="Date")
spx.index = pd.to_datetime(spx.index, format="%Y-%m-%d")
spx = spx.apply(pd.to_numeric, errors="coerce")

vix = pd.read_csv("../Data/Vix.csv", index_col="Date")
vix.index = pd.to_datetime(vix.index, format="%Y-%m-%d")
vix = vix.apply(pd.to_numeric, errors="coerce")

Adding dates and stuff to dataframes

def DatesAndPct(df):
 df["day"] = df.index.dayofyear
 df["month"] = df.index.month
 df["year"] = df.index.year
 df["pct"] = np.log(df["Adj Close"]).diff()
 return df

spx = DatesAndPct(spx)
vix = DatesAndPct(vix)

Making a function for plots and plotting e‘m all at once

plt.figure(figsize=(16, 9))
label_props = dict(boxstyle="square", facecolor="w", edgecolor="none", alpha=0.69)

def plotMonths(df, color, line=1, m_names=True):
 df.index = pd.to_datetime(df.index, format="%Y-%m-%d")
 df.set_index(df["day"], inplace=True, drop=True)

 for i in range(1, 13):
 month_name = calendar.month_abbr[i] # Adding month name

 data = df[df["month"] == i]
 out = data["pct"].groupby(data.index).mean()
 out.iloc[0] = 0 # Setting returns to start from zero

 # Getting coordinates for month name labels
 x = out.index[-1]+2
 y = out.cumsum().iloc[-1]-0.01

 # Plotting
 plt.plot(out.cumsum(), linewidth=line, color=color, label="_nolabel_")
 if m_names == True:
 plt.text(x, y, month_name, size=13, bbox=label_props)

plotMonths(spx, "#555555", 2, m_names=False)
plotMonths(vix, "crimson", m_names=True)

plt.title("Vix and Spx mean returns from month start (log scale)") 
plt.plot([], [], label="Vix (since 1990)", color="crimson") # Adding custom legends
plt.plot([], [], label="Spx (since 1950)", color="#555555") # Adding custom legends
plt.axhline(linestyle="--", color="#555555", alpha=0.55)
plt.grid(alpha=0.21)
plt.legend(loc="upper left")
plt.xlabel("Day of year")
plt.ylabel("Log cumulative monthly return")