FrederikBussler

| Name          | Time         | Deaths                       |
|---------------|--------------|------------------------------|
| Spanish Flu   | 1918-1919    | 40-50M                       |
| Asian Flu     | 1957-1958    | 1.1M                         |
| Hong Kong Flu | 1968-1970    | 1M                           |
| HIV/AIDS      | 1981-present | 25-35M                       |
| Swine Flu     | 2009-2010    | 200,000                      |
| SARS          | 2002-2003    | 770                          |
| Ebola         | 2014-2016    | 11,000                       |
| MERS          | 2015-present | 912                          |

FrederikBussler

strats = ['returns']  # 19

for col in cols:  # 20
    strat = 'strategy_%s' % col.split('_')[1]  # 21
    df[strat] = df[col].shift(1) * df['returns']  # 22
    strats.append(strat)  # 23

df[strats].dropna().cumsum().apply(np.exp).plot()  # 24

FrederikBussler

df['returns'] = np.log(df['closeAsk'] / df['closeAsk'].shift(1))  # 12

cols = []  # 13

for momentum in [15, 30, 60, 120]:  # 14
    col = 'position_%s' % momentum  # 15
    df[col] = np.sign(df['returns'].rolling(momentum).mean())  # 16
    cols.append(col)  # 17

FrederikBussler

data = oanda.get_history(instrument='EUR_USD',  # our instrument
                         start='2016-12-08',  # start data
                         end='2016-12-10',  # end date
                         granularity='M1')  # minute bars  # 7

FrederikBussler

import pandas as pd # get your dependencies
df = pd.read_csv(‘example.csv’) # read the data
df.head(1) # look at the data and make sure you’re changing the right column
df.rename(columns={'oldName': 'newName'}, inplace=True) # rename the column