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We have had some questions regarding the structure of the pandas.DataFrame that we get from a History call:
df = self.History(["SPY","TQQQ"], 1000)df is a multi-index pandas.DataFrame where the symbols ("SPY" and "TQQQ") are index 0 and date/time is index 1:
self.Log(str(hist.index))
self.Log(str(hist.columns))For some applications, it is more convenient to have the symbols in the columns:
# Select the closing prices
prices = hist.close
# Get data frame for each symbol
spy = prices.loc['SPY']
tqqq = prices.loc['TQQQ']
# Concat the data frames and define the columns
prices = pd.concat([spy,tqqq],axis = 1)
prices.columns = self.stocksPlease checkout a working example below where we can find the application that needs a different data frame structure.
Thanks to Yan Xiaowei for the proposed solution.
We welcome alternative solutions.
Yan Xiaowei
If your stock list is very long, you can do the conversion in two lines:
# prices is the historical DataFrame with symbols on the first-level index, and self.stocks is your symbol list.
prices = pd.concat([prices.loc[x] for x in self.stocks],axis = 1)
prices.columns = self.stocks
Alexandre Catarino
LukeI proposed yet another solution (my favorite so far!):
prices = hist.close.unstack(level=0)HanByul P
Hi,
@Lukel and @Alexandre Catarino, Thank you for helping me.
Here's another problem I have:
import numpy as np
import pandas as pd
class MyAlgo(QCAlgorithm):
def Initialize(self):
self.SetCash(100000)
self.SetStartDate(2017,7,1)
self.SetEndDate(2017,9,1)
# Add assets you'd like to see
self.AddEquity("AAPL")
self.AddEquity("AMZN")
self.AddEquity("UPS")
self.security_list = ["AAPL", "AMZN", "UPS"]
def OnData(self, data):
pass
def rebalance(self):
history = self.History(self.security_list, 10, Resolution.Daily)
price_history = history["close"].unstack(level=0)
self.Log(str(price_history))
My logs showed as below:
2017-07-03 10:30:00 :symbol AAPL AMZN UPS time 2017-06-19 145.767708 995.13 110.021668 2017-06-20 144.442909 992.58 108.820602 2017-06-21 145.299546 1001.79 108.403703 2017-06-22 145.060485 1001.34 108.254810 2017-06-23 145.717904 1003.74 109.445950 2017-06-26 145.229820 993.65 110.190413 2017-06-27 143.217720 977.41 108.890085 2017-06-28 145.259703 990.03 109.237501 2017-06-29 145.259703 990.03 109.237501 2017-06-30 143.466742 968.00 109.763588
I was expecting something like below:
AAPL AMZN UPS2017-8-1 00:00 100.00 200.00 150.0
2017-8-2 00:00 100.00 200.00 150.0
2017-8-3 00:00 100.00 200.00 150.0
What went wrong in my code?
Thanks.
HanByul P
Hi ~ @Lukel and @Alexandre Catarino, Discard the above writing I posted. I downloaded the logs and now see the below. The logs in the baktest screen made me confused. Thanks a lot.
2017-07-03 10:30:00 symbol AAPL AMZN UPS time 2017-06-19 145.767708 995.13 110.021668 2017-06-20 144.442909 992.58 108.820602 2017-06-21 145.299546 1001.79 108.403703 2017-06-22 145.060485 1001.34 108.254810 2017-06-23 145.717904 1003.74 109.445950 2017-06-26 145.229820 993.65 110.190413 2017-06-27 143.217720 977.41 108.890085 2017-06-28 145.259703 990.03 109.237501 2017-06-29 145.259703 990.03 109.237501 2017-06-30 143.466742 968.00 109.763588Alexandre Catarino
Your question is not clear. Are you talking about the prices in QuantConnect not being round numbers?
In QuantConnect, prices are adjusted by default. while they are raw in Quantopian.
When we want to work with raw prices, we can set the data normalization mode:
self.AddEquity("AAPL").SetDataNormalizationMode(DataNormalizationMode.Raw) self.AddEquity("AMZN").SetDataNormalizationMode(DataNormalizationMode.Raw) self.AddEquity("UPS").SetDataNormalizationMode(DataNormalizationMode.Raw)HanByul P
@Alexandre Catarino, Thanks a lot :)
Alexandre Catarino
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