import datetime
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import style
import numpy as np
import time
style.use('ggplot')
def results():
df = pd.read_csv('performance_data_sp500ish.csv', index_col='time', parse_dates=True)
#sort by date, since it's currently by stock.
df.sort_index(inplace=True)
# now we want to perform an average..
#df['mean'] = df['pc'].mean()
df['x_mean'] = pd.expanding_mean(df['pc'], 0)
#df['ma'] = pd.rolling_mean(df['pc'], 10)
return df['x_mean']
x = results()
x.plot(label='Wholistic Performance')
plt.axhline(0, color='k', linewidth = 4)
plt.legend()
plt.show()
The next tutorial:
Python and Pandas with Sentiment Analysis Database