Timing parameters set by values ​​from python trend

Question

I have daily stock price data from yahoo finance in a data frame called price_data .

I would like to add a column to this that provides the set value from the time trend in the Adj Close column.

Here is the data structure that I use:

 In [41]: type(price_data) Out[41]: pandas.core.frame.DataFrame In [42]: list(price_data.columns.values) Out[42]: ['Open', 'High', 'Low', 'Close', 'Volume', 'Adj Close'] In [45]: type(price_data.index) Out[45]: pandas.tseries.index.DatetimeIndex 

What is the best way to achieve this in Python?

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Aside, the following is achieved in R-language

 all_time_fitted <- function(data) { all_time_model <- lm(Adj.Close ~ Date, data=data) fitted_value <- predict(all_time_model) return(fitted_value) } 

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Here are some sample data:

 In [3]: price_data Out[3]: Open High Low Close Volume Adj Close Date 2005-09-27 21.05 21.40 19.10 19.30 961200 19.16418 2005-09-28 19.30 20.53 19.20 20.50 5747900 20.35573 2005-09-29 20.40 20.58 20.10 20.21 1078200 20.06777 2005-09-30 20.26 21.05 20.18 21.01 3123300 20.86214 2005-10-03 20.90 21.75 20.90 21.50 1057900 21.34869 2005-10-04 21.44 22.50 21.44 22.16 1768800 22.00405 2005-10-05 22.10 22.31 21.75 22.20 904300 22.04377 

Answer 1

Quick and dirty ...

 # get some data import pandas.io.data as web import datetime start = datetime.datetime(2015, 1, 1) end = datetime.datetime(2015, 4, 30) df=web.DataReader("F", 'yahoo', start, end) # a bit of munging - better column name - Day as integer df = df.rename(columns={'Adj Close':'AdjClose'}) dayZero = df.index[0] df['Day'] = (df.index - dayZero).days # fit a linear regression import statsmodels.formula.api as sm fit = sm.ols(formula="AdjClose ~ Day", data=df).fit() print(fit.summary()) predict = fit.predict(df) df['fitted'] = predict # plot import matplotlib.pyplot as plt fig, ax = plt.subplots(figsize=(8,4)) ax.scatter(df.index, df.AdjClose) ax.plot(df.index, df.fitted, 'r') ax.set_ylabel('$') fig.suptitle('Yahoo') plt.show()