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stock_volatility.py
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stock_volatility.py
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import numpy as np
import pandas as pd
import pandas_datareader.data as pdr
import fix_yahoo_finance as yf
import arch
import matplotlib.pyplot as plt
from statsmodels.graphics.tsaplots import plot_acf
yf.pdr_override()
class stock_vol:
def __init__(self, tk, start, end):
self.tk = tk
self.start = start
self.end = end
all_data = pdr.get_data_yahoo(self.tk, start=self.start, end=self.end)
self.stock_data = pd.DataFrame(all_data['Adj Close'], columns=["Adj Close"])
self.stock_data["log"] = np.log(self.stock_data)-np.log(self.stock_data.shift(1))
def mean_sigma(self):
st = self.stock_data["log"].dropna().ewm(span=252).std()
sigma = st.iloc[-1]
return sigma
def garch_sigma(self):
model = arch.arch_model(self.stock_data["log"].dropna(), mean='Zero', vol='GARCH', p=1, q=1)
model_fit = model.fit()
forecast = model_fit.forecast(horizon=1)
var = forecast.variance.iloc[-1]
sigma = float(np.sqrt(var))
return sigma
if __name__ == "__main__":
vol = stock_vol("AAPL", start="2016-01-01", end="2016-03-01")
test = vol.stock_data["log"].dropna()
print(test)
fig = plot_acf(test)
plt.show()