Price difference

#Create a new column PriceDiff in the DataFrame df
df['PriceDiff'] = df['Close'].shift(-1) - df['Close']

Daily return

df['Return'] = df['PriceDiff'] / df['Close']

List Comprehension

Direction

df['Direction'] = [1 if df['PriceDiff'].loc[ei] > 0 else 0  for ei in df.index]

Moving average

df['ma9'] = df['Close'].rolling(9).mean()
df['ma26'] = df['Close'].rolling(26).mean()

Exponentially weighted

df['EMA9'] = df['Close'].ewm(span=9,adjust=False).mean()
df['EMA26'] = df['Close'].ewm(span=26,adjust=False).mean()

Shares

“Shares” column to make decisions base on the strategy

# if EMA9 > EMA26, denote as 1 (long one share of stock), otherwise, denote as 0 (do nothing)

df['Shares'] = [1 if df.loc[ei, 'EMA9']>df.loc[ei, 'EMA26'] else 0 for ei in df.index]

Profit

“Profit” using List Comprehension, for any rows in df, if Shares=1, the profit is calculated as the close price of tomorrow df['Close1']- the close price of today. Otherwise the profit is 0.

df['Close1'] = df['Close'].shift(-1)
df['Profit'] = [df.loc[ei, 'Close1'] - df.loc[ei, 'Close'] if df.loc[ei, 'Shares']==1 else 0 for ei in df.index]

Wealth

Use .cumsum() to calculate the accumulated wealth over the period

df['Wealth'] = df['Profit'].cumsum()
...
df['Wealth'].plot()
plt.title('Total money you win is {}'.format(df.loc[df.index[-2], 'Wealth']))