Calculating cumulative returns with pandas dataframe
If they are daily simple returns and you want a cumulative return, surely you must want a daily compounded number?
df['perc_ret'] = (1 + df.Daily_rets).cumprod() - 1
# Or:
# df.Daily_rets.add(1).cumprod().sub(1)
>>> df
Poloniex_DOGE_BTC Poloniex_XMR_BTC Daily_rets perc_ret
172 0.006085 -0.000839 0.003309 0.003309
173 0.006229 0.002111 0.005135 0.008461
174 0.000000 -0.001651 0.004203 0.012700
175 0.000000 0.007743 0.005313 0.018080
176 0.000000 -0.001013 -0.003466 0.014551
177 0.000000 -0.000550 0.000772 0.015335
178 0.000000 -0.009864 0.001764 0.017126
If they are log returns, then you could just use cumsum
.
you just cannot simply add them all by using cumsum
for example, if you have array [1.1, 1.1], you supposed to have 2.21, not 2.2
import numpy as np
# daily return:
df['daily_return'] = df['close'].pct_change()
# calculate cumluative return
df['cumluative_return'] = np.exp(np.log1p(df['daily_return']).cumsum())