Conclusion

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()

		

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