advanced-algorithmic-trading

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The next steps consist of filtering the DataFrame via starting and ending dates as well as
dropping the non-essential columns. The shift method is used to create all of the future and
historical minutely lags. The rows with generated NaN values are dropped. All of the prices are
then converted into returns using the pct_change method. Once again the rows with generated
NaN values are dropped:
# Filter on start/end dates
if start is not None:
ts = ts[ts.index >= start]
if end is not None:
ts = ts[ts.index <= end]
# Drop the non-essential columns
ts.drop(
[
"Open", "Low", "High",
"Volume", "OpenInterest"
],
axis=1, inplace=True
)
# Create the lookback and lookforward shifts
for i in range(0, lookback_minutes):
ts["Lookback%s" % str(i+1)] = ts["Close"].shift(i+1)
for i in range(0, lookforward_minutes):
ts["Lookforward%s" % str(i+1)] = ts["Close"].shift(-(i+1))
ts.dropna(inplace=True)
# Adjust all of these values to be percentage returns
ts["Lookback0"] = ts["Close"].pct_change()*100.0
for i in range(0, lookback_minutes):
ts["Lookback%s" % str(i+1)] = ts[
"Lookback%s" % str(i+1)
].pct_change()*100.0
for i in range(0, lookforward_minutes):
ts["Lookforward%s" % str(i+1)] = ts[
"Lookforward%s" % str(i+1)
].pct_change()*100.0
ts.dropna(inplace=True)
The following code carries out the calculation of the "up/down factor". It creates a list of up
and down truth columns for each historical or future lag.
The "down" columns are iterated over and bitwise-added (&) to generate a final column,
which is True only when all columns have not dropped below the down percent factor.
The "up" columns use bitwise-or (|) to generate a final column, which is True when at least
one of the columns exceed the up percent factor. These two columns are then bitwise-added to