def fit_model(self):
"""Fits a Quadratic Discriminat Analyser to the US
sock market index (^GPSC in Yahoo)."""
# Create a laggged series of the S&P500 US stock market index
snpret = create_lagged_series(self.symbol, self.start_train,
self.end_period, lags=5)
# Use the prior two days of returns as
# predictor value, with direction as the response
X = snpret[["Lag1", "Lag2"]]
y = snpret["Direction"]
# Create training and test sets
X_train = X[X.index < self.start_test]
y_train = y[y.index < self.start_test]
# Create the prediciting factors for use
# in direction forecasting.
self.predictors = X[X.index >= self.start_test]
# Create the Quadractic Discriminant Analysis model
# and the forcasting strategy
self.model = QDA()
self.model.fit(X_train, y_train)
def fit_model(self):
"""Fits a Quadratic Discriminant Analyser to the
US stock market index (^GPSC in Yahoo)."""
# Create a lagged series of the S&P500 US stock market index
snpret = create_lagged_series(self.symbol,
self.start_train,
self.end_period,
lags=5)
# Use the prior two days of returns as
# predictor values, with direction as the response
x = snpret[["Lag1", "Lag2"]]
y = snpret["Direction"]
# Create training and test sets
x_train = x[x.index < self.start_test]
y_train = y[y.index < self.start_test]
# Create the predicting factors for use
# in direction forecasting
self.predictors = x[x.index >= self.start_test]
# Create the Quadratic Discriminant Analysis model
# and the forecasting strategy
self.model = QDA()
self.model.fit(x_train, y_train)
def create_symbol_forecast_model(self):
# Create a lagged series of the S&P500 US stock market index
snpret = create_lagged_series(self.symbol_list[0], self.model_start_date, self.model_end_date, lags=5)
# Use the prior two days of returns as predictor
# values, with direction as the response
X = snpret[["Lag1","Lag2"]]
y = snpret["Direction"]
# Create training and test sets
start_test = self.model_start_test_date
X_train = X[X.index < start_test]
X_test = X[X.index >= start_test]
y_train = y[y.index < start_test]
y_test = y[y.index >= start_test]
model = QuadraticDiscriminantAnalysis()
model.fit(X_train, y_train)
return model
def fit_model(self):
snpret = create_lagged_series(self.symbol,
self.start_train,
self.end_period,
lags=5)
X = snpret[["Lag1", "Lag2"]]
y = snpret["Direction"]
X_train = X[X.index < self.start_test]
y_train = y[y.index < self.start_test]
self.predictors = X[X.index >= self.start_test]
# print self.predictors.head()
self.model = QDA()
self.model.fit(X_train, y_train)
def create_symbol_forecast_model(self):
# Create a lagged series of the market index
snpret = create_lagged_series( self.symbol_list[0],
self.model_start_date, self.model_end_date, lags = 5
)
# Use the prior X days of returns as predictor values with direction
# as the response.
X = snpret[['Lag1','Lag2']]
y = snpret["Direction"]
# Create training and test sets
start_test = self.model_start_test_date
X_train = X[X.index < start_test]
X_test = X[X.index >= start_test]
y_train = y[y.index < start_test]
y_test = y[y.index >= start_test]
#model to use is Quadratic Discriminant Analysis
model = QDA()
model.fit(X_train, y_train)
return model
import datetime
import sklearn
from sklearn import cross_validation
from sklearn.cross_validation import train_test_split
from sklearn.grid_search import GridSearchCV
from sklearn.metrics import classification_report
from sklearn.svm import SVC
from forecast 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(2001,1,10),
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=0.5, random_state=42
)
# Set the parameters by cross-validation
tuned_parameters = [
{'kernel': ['rbf'], 'gamma': [1e-3, 1e-4], 'C': [1, 10, 100, 1000]}
# -*- coding: utf-8 -*-
# grid_search.py
import sklearn
from sklearn.svm import SVC
from forecast import create_lagged_series
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
if __name__ == "__main__":
# Create a lagged series of the S&P500 US stock market index
snpret = create_lagged_series("SPX", '2001-01-10', '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=0.5, random_state=42)
# Set the parameters by cross-validation
tuned_parameters = [{'kernel': ['rbf'], 'gamma': [1e-3, 1e-4], 'C': [1, 10, 100, 1000]}]
# Perform the grid search on the tuned parameters
model = GridSearchCV(SVC(C=1), tuned_parameters, cv=10)
model.fit(X_train, y_train)
print("Optimised parameters found on training set:")
print(model.best_estimator_, "\n")
print("Grid scores calculated on training set:")
#means = model.cv_results_['mean_test_score']
#params = model.cv_results_['params']
print(model.cv_results_)
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 forecast import create_lagged_series
if __name__ == "__main__":
#Create lagged series
snpret = create_lagged_series(
"^GSPC", datetime.datetime(2001,1,10),
datetime.datetime(2008,12,31), lags=5
)
#use prior two days of returns as predictor
X = snpret[["Lag1", "Lag2", "Lag3"]]
# direction as response
y = snpret["Direction"]
# Train/test split
# 70% of data used for training and 20% used for testing
# random_state - data is not sequentially divided, but sampled randomly
X_train, X_test, y_train, y_test = train_test_split(
X, y, train_size=0.7, random_state=42
)
#create parametrised models
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import forecast
import datetime
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis as QDA
from sklearn.metrics import confusion_matrix
from sklearn.svm import LinearSVC, SVC
#create lag series of S&P500
snpret = forecast.create_lagged_series('^GSPC', datetime.datetime(2001, 1, 10),
datetime.datetime(2005, 12, 31))
#use prior 2 days of return as predictor values,
#with direction as response
X = snpret[['Lag1', 'Lag2']]
y = snpret['Direction']
#test data is split into 2 parts: before & after 2005,1,1
start_test = datetime.datetime(2005, 1, 1)
#create training & data set
X_train = X[X.index < start_test]
X_test = X[X.index >= start_test]
y_train = y[y.index < start_test]
y_test = y[y.index >= start_test]
#create parametrised models
