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Hi
I have tried to work out a way to loop over a list of fundamental ratios, and get it nice and smooth into a pandas DataFrame, so I can implement a multivariable linear regression model on the data. My lack of data manipulation skills in pandas unfortunately stops me from making it work. I have worked out a way to do it without a loop, but i takes up a lot of space, and it is not flexible at all (see code).
qb = QuantBook()
from sklearn.decomposition import FactorAnalysis
tickers = ("AAPL", "MSFT")
symbols = [qb.AddEquity(ticker, Resolution.Daily).Symbol for ticker in tickers]
start_time = datetime(2017, 1, 2)
end_time = datetime(2020, 1, 2)
funda_start = datetime(2020, 1, 1)
funda_end = datetime(2020, 1, 2)
fundamentals = ["ValuationRatios.SustainableGrowthRate", "ValuationRatios.PayoutRatio"]
pe_ratios = qb.GetFundamental(symbols, "ValuationRatios.SustainableGrowthRate", funda_start, funda_end)
pay_ratio = qb.GetFundamental(symbols, "ValuationRatios.PayoutRatio", funda_start, funda_end)
dataframe = pd.concat([pe_ratios, pay_ratio], axis=0)
dataframe = dataframe.transpose()
dataframe.columns = fundamentals
returns = qb.History(symbols, start_time, end_time, Resolution.Daily)['close']
returns = returns.to_frame()
returns.reset_index(["time"], inplace=True)
final_returns = returns.groupby(level=0)
final_returns = final_returns.agg(lambda x: x.iloc[-1]).close
final_returns = final_returns.to_frame()
final_data = pd.concat([dataframe, final_returns], axis=1)
y = final_data.close
X = dataframe
from sklearn import linear_model
regr = linear_model.LinearRegression()
regr.fit(X, y)
print(regr.coef_)
The problem I am encountering is that a loop over the list, and appending to a empty DataFrame does not work (see code)
qb = QuantBook()
tickers = ("AAPL", "MSFT")
symbols = [qb.AddEquity(ticker, Resolution.Daily).Symbol for ticker in tickers]
start_time = datetime(2017, 1, 2)
end_time = datetime(2020, 1, 2)
funda_start = datetime(2020, 1, 1)
funda_end = datetime(2020, 1, 2)
fundamentals = ["ValuationRatios.SustainableGrowthRate", "ValuationRatios.PayoutRatio"]
ratios = pd.DataFrame()
for fundamental in fundamentals:
ratio = qb.GetFundamental(symbols, fundamental, funda_start, funda_end)
ratio1 = ratio.transpose()
ratio1.set_axis([fundamental], axis=1)
ratios.append(ratio1)
ratios
I was wondering if any knew how to solve this problem, as it becomes more irritating, the more variables is being used in the program
Have a nice day
Lucas
Varad Kabade
Hi Lucas
We recommend using pandas.concat to combine the different fundamentals dataframe. While it is not necessary for linear regression, it is also a good practice to rename your columns for easy management. The below snippet change would do the trick:
Best,
Varad Kabade
Lucas
Hej Varad
As always, thank you for the help. When running the code, is produces a dataframe with 552 columns and 1 row. This is fine, and can be fixed with the following code, to make it nice and easy to read, for anyone with the same issue:
Have a good day
Lucas
Lucas
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