Share New Research 
prices = self.data.History(self.data.var["stocks"], 10, Resolution.Minute)
prices = prices['close']
ret_mean = prices.pct_change().mean()
msg = np.array2string(ret_mean,separator=',')
msg2 = prices.pct_change().to_string()
self.data.Log("ret pct_change " + msg2)
self.data.Log("ret_mean " + msg)
Running this should give me an output like this:
prices.pct_change().mean()
Output:
prices.pct_change().mean(): Series
Equity(8554 [SPY]): 0.000360162256317
Equity(19920 [QQQ]): -0.000349071472794
Equity(23921 [TLT]): -0.00105604535262
Equity(25801 [TIP]): -0.000195614219234
Equity(25485 [AGG]): -0.000227679365955
but it's giving me a total mean for all the stocks put together like this:
2017-01-03 09:32:00 ret pct_change symbol time
AGG 2016-12-30 15:52:00 NaN
2016-12-30 15:53:00 -0.000093
2016-12-30 15:54:00 0.000000
2016-12-30 15:55:00 0.000185
2016-12-30 15:56:00 0.000000
2016-12-30 15:57:00 0.000046
2016-12-30 15:58:00 0.000046
2016-12-30 15:59:00 0.000000
2017-01-03 09:30:00 -0.003054
2017-01-03 09:31:00 0.000000
QQQ 2016-12-30 15:52:00 0.107993
2016-12-30 15:53:00 0.000085
2016-12-30 15:54:00 0.000085
2016-12-30 15:55:00 0.001100
2016-12-30 15:56:00 -0.000169
2016-12-30 15:57:00 0.000845
2016-12-30 15:58:00 0.000169
2016-12-30 15:59:00 0.000676
2017-01-03 09:30:00 0.005738
2017-01-03 09:31:00 -0.000252
SPY 2016-12-30 15:52:00 0.862178
2016-12-30 15:53:00 0.000090
2016-12-30 15:54:00 0.000135
2016-12-30 15:55:00 0.000718
2016-12-30 15:56:00 0.000045
2016-12-30 15:57:00 0.000941
2016-12-30 15:58:00 0.000313
2016-12-30 15:59:00 0.000761
2017-01-03 09:30:00 0.006263
2017-01-03 09:31:00 -0.000489
TIP 2016-12-30 15:52:00 -0.497447
2016-12-30 15:53:00 0.000000
2016-12-30 15:54:00 0.000000
2016-12-30 15:55:00 0.000000
2016-12-30 15:56:00 -0.000044
2016-12-30 15:57:00 0.000044
2016-12-30 15:58:00 -0.000044
2016-12-30 15:59:00 0.000133
2017-01-03 09:30:00 -0.001856
2017-01-03 09:31:00 0.000177
TLT 2016-12-30 15:52:00 0.051175
2016-12-30 15:53:00 -0.000168
2016-12-30 15:54:00 -0.000084
2016-12-30 15:55:00 -0.000461
2016-12-30 15:56:00 -0.000126
2016-12-30 15:57:00 0.000420
2016-12-30 15:58:00 -0.000084
2016-12-30 15:59:00 -0.000252
2017-01-03 09:30:00 -0.006127
2017-01-03 09:31:00 0.000338
2017-01-03 09:32:00 ret_mean 0.010815266655526064
Any ideas where I went wrong?
Alexandre Catarino
Please checkout this thread where we explain the structure of the pandas.DataFrame from QC History method:
Essentially we have to redefine the data frame columns:
df = self.History(self.tickers, 10, Resolution.Daily).close prices = pd.concat([df.loc[x] for x in self.tickers], axis = 1) prices.columns = self.tickersLukeI
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