advanced-algorithmic-trading

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seen whether it is feasible to produce a robust strategy that can be profitable above the higher
frequency transaction costs necessary to carry it out.
18.6 Bibliographic Note
A gentle introduction to tree-based methods can be found in James et al (2013)[59], which covers
the basics of both DTs and their associated ensemble methods.
A more rigourous account, pitched at the late undergraduate/early graduate mathematics/statistics
level can be found in Hastie et al (2009)[51].
Murphy (2012)[71] provides a discussion of Adaptive Basis Function Models, of which DT/-
CART models are a subset. The book covers both the frequentist and Bayesian approach to
these models.
For the practitioner working on "real world" data (such as quants!), Kuhn et al (2013)[67] is
an appropriate text pitched at a simpler level.
18.7 Full Code
# ensemble_prediction.py
import datetime
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pandas_datareader.data as web
import seaborn as sns
import sklearn
from sklearn.ensemble import (
BaggingRegressor, RandomForestRegressor, AdaBoostRegressor
)
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import scale
from sklearn.tree import DecisionTreeRegressor
def create_lagged_series(symbol, start_date, end_date, lags=3):
"""
This creates a pandas DataFrame that stores
the percentage returns of the adjusted closing
value of a stock obtained from Yahoo Finance,
along with a number of lagged returns from the
prior trading days (lags defaults to 3 days).
Trading volume is also included.