advanced-algorithmic-trading

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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
# 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
tsret = tsret[tsret.index >= start_date]
return tsret
In the __main__ function the parameters are set. Firstly a random seed is defined to make
the code replicable on other work environments. n_jobs controls the number of processor cores
to use in bagging and Random Forests. Boosting is not parallelisable so does not make use of
this parameter.
n_estimators defines the total number of estimators to use in the graph of the MSE, while
the step_factor controls how granular the calculation is by stepping through the number of
estimators. In this instance axis_step is equal to 1000/10 = 100. That is, 100 separate
calculations will be performed for each of the three ensemble methods:
# Set the random seed, number of estimators
# and the "step factor" used to plot the graph of MSE
# for each method
random_state = 42
n_jobs = 1 # Parallelisation factor for bagging, random forests
n_estimators = 1000
step_factor = 10
axis_step = int(n_estimators/step_factor)
The following code downloads ten years worth of AMZN prices and converts them into a
return series with lags using the above mentioned function create_lagged_series. Missing
values are dropped (a consequence of the lag procedure) and the data is scaled to exist between
-1 and +1 for ease of comparison. This latter procedure is common in machine learning and
helps features with large differences in absolute sizes be comparable to the models:
# Download ten years worth of Amazon
# adjusted closing prices
start = datetime.datetime(2006, 1, 1)
end = datetime.datetime(2015, 12, 31)
amzn = create_lagged_series("AMZN", start, end, lags=3)
amzn.dropna(inplace=True)
# Use the first three daily lags of AMZN closing prices
# and scale the data to lie within -1 and +1 for comparison