advanced-algorithmic-trading

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produce a final truth column where both conditions are met.
Since these are written with Python True and False operators they need to be converted to
+1 and -1 integers, which the final two lines carry out.
Note that in the following code the "up/down factor" is commented out in favour of the
directional change, but is still kept in the same column name of UpDown. If you wish to use the
"up/down factor" instead of the directional change as response, you will need to uncomment out
ts["UpDown"] = down_tot & up_tot and comment out the line below it:
# Determine if the stock has gone up at least by
# ’up_down_factor’ x ’percent_factor’ and down no more
# then ’percent_factor’
up = up_down_factor*percent_factor
down = percent_factor
# Create the list of True/False entries for each date
# as to whether the up/down logic is true
down_cols = [
ts["Lookforward%s" % str(i+1)] > -down
for i in range(0, lookforward_minutes)
]
up_cols = [
ts["Lookforward%s" % str(i+1)] > up
for i in range(0, lookforward_minutes)
]
# Carry out the bitwise and, as well as bitwise or
# for the down and up logic
down_tot = down_cols[0]
for c in down_cols[1:]:
down_tot = down_tot & c
up_tot = up_cols[0]
for c in up_cols[1:]:
up_tot = up_tot | c
#ts["UpDown"] = down_tot & up_tot
ts["UpDown"] = np.sign(ts["Lookforward1"])
# Convert True/False into 1 and 0
ts["UpDown"] = ts["UpDown"].astype(int)
ts["UpDown"].replace(to_replace=0, value=-1, inplace=True)
return ts
This function carries out the data preparation work of the script. In the __main__ function
the above function is called for the AREX data.
n_estimators controls the number of decision trees to use in the RandomForestClassifier,
while n_jobs controls the number of CPU cores to use for the training.
Make sure to change your path from csv_filepath = "/path/to/your/AREX.csv" to the
path where your AREX (or other) data resides before running this script: