strategy_optimisation_train_test_split.py

# train_test_split.py
from __future__ import print_function 

import datetime

import sklearn

from sklearn.cross_validation import train_test_split 
from sklearn.ensemble import RandomForestClassifier 
from sklearn.linear_model import LogisticRegression 
from sklearn.lda import LDA
from sklearn.metrics import confusion_matrix 
from sklearn.qda import QDA
from sklearn.svm import LinearSVC, SVC

from create_lagged_series import create_lagged_series

if __name__ == "__main__":
  # Create a lagged series of the S&P500 US stock market index 
  snpret = create_lagged_series("^GSPC", datetime.datetime(2010,1,1),
                                datetime.datetime(2005,12,31), lags=5
                               )
  # Use the prior two days of returns as predictor 
  # values, with direction as the response
  X = snpret[["Lag1","Lag2"]]
  y = snpret["Direction"]
  
  # Train/test split
  X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.8, random_state=42)
  
  # Create the (parametrised) models
  print("Hit Rates/Confusion Matrices:\n") 
  models = [("LR", LogisticRegression()),
            ("LDA", LDA()),
            ("QDA", QDA()),
            ("LSVC", LinearSVC()),
            ("RSVM", SVC(C=1000000.0, cache_size=200, 
                         class_weight=None, coef0=0.0, 
                         degree=3, gamma=0.0001, kernel=’rbf’, 
                         max_iter=-1, probability=False,
                         random_state=None,
                         shrinking=True, tol=0.001, verbose=False)),
            ("RF", RandomForestClassifier(
              n_estimators=1000, criterion=’gini’,
              max_depth=None, min_samples_split=2,
              min_samples_leaf=1, max_features=’auto’,
              bootstrap=True, oob_score=False, n_jobs=1,
              random_state=None, verbose=0)
            )]
  
  # Iterate through the models
  for m in models:
    
    # Train each of the models on the training set
    m[1].fit(X_train, y_train)
    
    # Make an array of predictions on the test set
    pred = m[1].predict(X_test)
    
    # Output the hit-rate and the confusion matrix for each model
    print("%s:\n%0.3f" % (m[0], m[1].score(X_test, y_test))) 
    print("%s\n" % confusion_matrix(pred, y_test))