advanced-algorithmic-trading

258
"""
# Obtain stock information from Yahoo Finance
ts = web.DataReader(
symbol, "yahoo", start_date, end_date
)
# Create the new lagged DataFrame
tslag = pd.DataFrame(index=ts.index)
tslag["Today"] = ts["Adj Close"]
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
# 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
if __name__ == "__main__":
# 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)
# Download ten years worth of Amazon
# adjusted closing prices
start = datetime.datetime(2006, 1, 1)
end = datetime.datetime(2015, 12, 31)