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, each of them is series
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 nd array
pred_test = model.predict(x_test)
print("Error Rate is {0}".format((y_test != pred_test).sum() * 1. / len(y_test)))
return model
def create_symbol_forecast_model(self):
snpret = create_lagged_series(self.symbol_list[0],
self.model_start_date,
self.model_end_date,
lags=5)
X = snpret[["Lag1", "Lag2"]]
y = snpret["Direction"]
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 = QDA()
model.fit(X_train, y_train)
return model
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 = QDA()
model.fit(X_train, y_train)
return model
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 = QDA()
model.fit(X_train, y_train)
return model
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.discriminant_analysis import (LinearDiscriminantAnalysis as LDA,
QuadraticDiscriminantAnalysis as
QDA)
from sklearn.metrics import confusion_matrix
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("SPY",
dt(2016, 1, 10),
dt(2017, 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")
# -*- coding: utf-8 -*-
# k_fold_cross_val.py
import datetime
import pandas as pd
import sklearn
from sklearn import cross_validation
from sklearn.metrics import confusion_matrix
from sklearn.svm import 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(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"]
# Create a k-fold cross validation object
kf = cross_validation.KFold(len(snpret),
n_folds=10,
shuffle=True,
random_state=42)
# Use the kf object to create index arrays that
# state which elements have been retained for training
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(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=0.8, random_state=42
)
# Create the (parametrised) models
print "Hit Rates/Confusion Matrices:\n"
models = [("LR", LogisticRegression()),
# train_test_split.py
import datetime
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
from sklearn.metrics import confusion_matrix
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis as 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(2000, 1, 1),
datetime.date.today(),
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")