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total return. The Sharpe Ratio is higher than that for LDA as the returns have increased but
the drawdowns have stayed approximately the same. The equity curve also looks more visually
consistent. Despite this, it also remains underwater for the latter three months of 2013 and only
begins to recover at the start of 2014.
29.6 Next Steps
There is a lot of work to be done to turn this into an effective strategy that would be placed in
production.
The first step would be to replicate this model across many tens or hundreds of equities
(depending upon available capital!) as a means of attempting to generate many uncorrelated
"bets" on, say, separate sectors of the market.
In addition a more robust hyperparameter study should be carried out for many of the
classifiers. This will optimise the bias-variance trade-off in the models. To some extent this is
controlled by the maximum depth of the tree in the Random Forest classifier.
Taking this from a backtested system to a consistently working production model will require
accounting for slippage, market impact, average daily volume of shares traded and more realistic
commissions.
Running at minutely frequency is clearly a lot harder than carrying out daily strategies. This
is the "price to pay" for obtaining larger Sharpe ratios and thus strategies that possess improved
statistical significance.
29.7 Full Code
# intraday_ml_model_fit.py
import datetime
import numpy as np
import pandas as pd
import sklearn
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.ensemble import (
BaggingClassifier, RandomForestClassifier, GradientBoostingClassifier
)
from sklearn.externals import joblib
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
def create_up_down_dataframe(
csv_filepath,
lookback_minutes=30,