elephann

#!/usr/bin/env python
"""
Example of using Keras to implement a 1D convolutional neural network (CNN) for timeseries prediction.
"""

from __future__ import print_function, division

import numpy as np
from keras.layers import Convolution1D, Dense, MaxPooling1D, Flatten
from keras.models import Sequential

elephann

# In my portfolio, I show how the popular Fama-MacBeth (1973) procedure is constructed in R.
# The procedure is used to estimate risk premia and determine the validity of asset pricing models.
# Google shows that the original paper has currently over 9000 citations (Mar 2015), making the methodology one of the most  
# influential papers in asset pricing studies. It's used by thousands of finance students each year, but I'm unable to find a 
# complete description of it from the web. 
#
# While the methodology is not statistically too complex (although the different standard errors can get complex),
# it can pose some serious data management challenges to students and researchers.
#
# The goal of the methodology is to estimate risk premia in the financial markets. While newer, more sophisticated methods for

elephann

# Time Series Testing 
import keras.callbacks
from keras.models import Sequential  
from keras.layers.core import Dense, Activation, Dense, Dropout
from keras.layers.recurrent import LSTM

# Call back to capture losses 
class LossHistory(keras.callbacks.Callback):
    def on_train_begin(self, logs={}):
        self.losses = []

elephann

par(mar=c(0,0,0,0))
plot(0,0,xlim=c(0,28),ylim=c(0,1),
       xaxt="n",yaxt="n",bty="n",xlab="",ylab="",type="n")
i <- 1
for(j in 1:20)
{
  test <- (6+j):26
  train <- 1:(5+j)
  arrows(0,1-j/20,27,1-j/20,0.05)
  points(train,rep(1-j/20,length(train)),pch=19,col="blue")

elephann

library(quantmod)
library(PerformanceAnalytics)
library(TTR)

###############################################################
### User Input ################################################
###############################################################

getSymbols("SPY", from = "1900-01-01", to="2012-06-01")
close <- SPY$SPY.Close

elephann

#' Read xlsx files
#'
#' @param file The path to xlsx file
#' @param keep_sheets A vector of sheet name
#' @param header Whether include the head in the sheet
#' @param empty_row Whether to remove the empty rows
#' @export
xlsxToR <- function(file, keep_sheets = NULL, header = TRUE, empty_row = TRUE) 
{  
    suppressWarnings(file.remove(tempdir()))

elephann

# The MIT License (MIT)
#
# Copyright (c) 2012 Schaun Jacob Wheeler
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:

elephann

#' Read xlsx files
#'
#' @param file The path to xlsx file
#' @param keep_sheets A vector of sheet name
#' @param header Whether include the head in the sheet
#' @param empty_row Whether to remove the empty rows
#' @export
xlsxToR <- function(file, keep_sheets = NULL, header = TRUE, empty_row = TRUE) 
{  
    suppressWarnings(file.remove(tempdir()))