advanced-algorithmic-trading

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tslag["Volume"] = ts["Volume"]
# Create the shifted lag series of
# prior trading period close values
for i in range(0,lags):
tslag["Lag%s" % str(i+1)] = ts["Adj Close"].shift(i+1)
# Create the returns DataFrame
tsret = pd.DataFrame(index=tslag.index)
tsret["Volume"] = tslag["Volume"]
tsret["Today"] = tslag["Today"].pct_change()*100.0
# If any of the values of percentage
# returns equal zero, set them to
# a small number (stops issues with
# QDA model in scikit-learn)
for i,x in enumerate(tsret["Today"]):
if (abs(x) < 0.0001):
tsret["Today"][i] = 0.0001
# Create the lagged percentage returns columns
for i in range(0,lags):
tsret["Lag%s" % str(i+1)] = tslag[
"Lag%s" % str(i+1)
].pct_change()*100.0
# Create the "Direction" column
# (+1 or -1) indicating an up/down day
tsret["Direction"] = np.sign(tsret["Today"])
tsret = tsret[tsret.index >= start_date]
return tsret
Note that we are not storing the direct close price values in the "Today" or "Lag" columns.
Instead we are storing the close-to-close percentage return from the previous day.
We need to obtain the data for Amazon daily prices along some suitable time frame. I have
chosen Jan 1st 2004 to Oct 27th 2016. However this is an arbitrary choice. You can adjust the
time frame as you see fit. To obtain the data and place it into a Pandas DataFrame called lags
we can use the following code:
if __name__ == "__main__":
symbol = "AMZN"
start_date = datetime.datetime(2004, 1, 1)
end_date = datetime.datetime(2016, 10, 27)
lags = create_lagged_series(
symbol, start_date, end_date, lags=10
)