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helper_functions.R
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load_and_preprocess_data = function() {
setwd("D:/4th Sem Work/Research Work_Rahul/Market-Timing/")
.libPaths("D:/4th Sem Work/Research Work_Rahul/Market-Timing/libs/")
suppressMessages(suppressWarnings({
library(dplyr)
library(vars)
library(lubridate)
library(ggplot2)
library(tseries)
library(xts)
library(PerformanceAnalytics)
library(tree)
library(openxlsx)
library(fredr)
library(usethis)
}))
cfnai = read.csv("./data/cfnai.csv")
cfnai$Date = as.Date(parse_date_time(cfnai$Date,"dmy"))
cfnai = tibble(cfnai);
prices_ts = as.xts(cfnai$SP500.Values,order.by = cfnai$Date)
cfnai_ts = as.xts(cfnai$CFNAI,order.by = cfnai$Date)
cfnaima3_ts = as.xts(cfnai$CFNAI_MA3,order.by = cfnai$Date)
diffusion_ts = as.xts(cfnai$DIFFUSION,order.by = cfnai$Date)
df = merge(cfnai_ts,cfnaima3_ts,diffusion_ts,prices_ts)
df$returns_ts = Return.calculate(df$prices_ts,"discrete")
return (df)
}
generic_expanding_window = function(sdate,edate,method_,df){
func_ = match.fun(method_)
dates = index(df)
walkforward_df = data.frame(Date=as.Date("2029-09-01"),Predicted_Returns=3.5,Actual_Returns=5.5)
for(idx in seq_along(dates)){
date = dates[[idx]]
if(date >= edate && date < "2022-12-31"){
training_data = df[paste0(sdate,"::",date)]
next_date = dates[[idx+1]]
model = func_(returns_ts ~ diffusion_ts + cfnaima3_ts,data = training_data)
loc_prediction = predict(model,df[next_date])
g = data.frame(next_date,as.numeric(loc_prediction),as.numeric(df[next_date]$returns_ts))
names(g) = c("Date","Predicted_Returns","Actual_Returns")
walkforward_df = rbind(walkforward_df,g)
}
}
walkforward_df.ts = as.xts(cbind(walkforward_df$Predicted_Returns,walkforward_df$Actual_Returns),
order.by=walkforward_df$Date)
names(walkforward_df.ts) = c("Predicted_Returns","Actual_Returns")
return (walkforward_df.ts["::2022"])
}
generic_rolling_window = function(sdate,edate,method_,df){
func_ = match.fun(method_)
dates = index(df)
rollingwindow_df = data.frame(Date=as.Date("2029-09-01"),Predicted_Returns=3.5,Actual_Returns=5.5)
for(idx in seq_along(dates)){
if(idx + 133 < length(dates)){
sdate_ = as.Date(dates[[idx]])
edate_ = as.Date(dates[[idx+133]])
training_data_rolling = df[paste0(sdate_,"::",edate_)]
if(method_ == "tree"){
model_rolling = func_(returns_ts ~ diffusion_ts + cfnaima3_ts,data = training_data_rolling)
}
else{
model_rolling = func_(returns_ts ~ diffusion_ts + cfnaima3_ts,data = training_data_rolling,method = "svmLinear")
}
next_date = dates[[idx+134]]
pred_returns = predict(model_rolling,df[next_date])
actual_returns = df[next_date]$returns_ts
loc_df = data.frame(next_date,as.numeric(actual_returns),as.numeric(pred_returns))
names(loc_df) = c("Date","Actual_Returns","Predicted_Returns")
rollingwindow_df = rbind(rollingwindow_df,loc_df)
}
}
rollingwindow_df.ts = as.xts(cbind(rollingwindow_df$Predicted_Returns,rollingwindow_df$Actual_Returns),
order.by=rollingwindow_df$Date)
names(rollingwindow_df.ts) = c("Predicted_Returns","Actual_Returns")
return (rollingwindow_df.ts["::2022"])
}
plot_ts = function(df_,title){
plot(df_)
addLegend(
legend.loc = "topleft",
legend.names = c("Predicted Returns","Actual Returns"),
col = c("red","black")
)
}
varmodel_expanding_window = function(sdate,edate,df_cfnai,technique="dynamic"){
dates = index(df_cfnai)
walkforward_df = data.frame(Date=as.Date("2029-09-01"),Predicted_Returns=3.5)
for(idx in seq_along(dates)){
date = dates[[idx]]
# print(date)
if(date >= edate && date < "2022-12-31"){
training_data = df_cfnai[paste0(sdate,"::",date)]
next_date = dates[[idx+1]]
# model = func_(returns_ts ~ diffusion_ts + cfnaima3_ts,data = training_data)
if(is.numeric(technique)){
model.lag.order = technique
}
else{
if(technique == "dynamic")
model.lag.order = VARselect(training_data,lag.max = 10)$selection[[1]]
}
model = VAR(training_data,p = model.lag.order)
loc_prediction = predict(model,n.ahead=1)[[1]]$logrets_ts[[1]]
g = data.frame(next_date,exp(as.numeric(loc_prediction)))
names(g) = c("Date","Predicted_Returns")
#
walkforward_df = rbind(walkforward_df,g)
}
}
walkforward_df.ts = as.xts(walkforward_df$Predicted_Returns,
order.by=walkforward_df$Date)
names(walkforward_df.ts) = c("Predicted_Returns")
return (walkforward_df.ts["::2022"])
}
varmodel_rolling_window = function(sdate,edate,df_cfnai,technique="dynamic"){
dates = index(df_cfnai)
rollingwindow_df = data.frame(Date=as.Date("2029-09-01"),Predicted_Returns=3.5)
for(idx in seq_along(dates)){
if(idx + 133 < length(dates)){
sdate_ = as.Date(dates[[idx]])
edate_ = as.Date(dates[[idx+133]])
training_data_rolling = df_cfnai[paste0(sdate_,"::",edate_)]
next_date = dates[[idx+134]]
if(is.numeric(technique)){
model.lag.order = technique
}
else{
if(technique == "dynamic")
model.lag.order = VARselect(training_data_rolling,lag.max = 10)$selection[[1]]
}
model_rolling = VAR(training_data_rolling,p = model.lag.order)
pred_returns= predict(model_rolling,n.ahead=1)[[1]]$logrets_ts[[1]]
loc_df = data.frame(next_date,exp(as.numeric(pred_returns)))
names(loc_df) = c("Date","Predicted_Returns")
rollingwindow_df = rbind(rollingwindow_df,loc_df)
}
}
rollingwindow_df.ts = as.xts(rollingwindow_df$Predicted_Returns,
order.by=rollingwindow_df$Date)
names(rollingwindow_df.ts) = c("Predicted_Returns")
return (rollingwindow_df.ts["::2022"])
}
load_predictors = function(){
wb = createWorkbook()
list_of_predictors = tibble(read.xlsx("./data/consolidated_data.xlsx"))
for(idx in seq_along(list_of_predictors$Predictor)){
pred = list_of_predictors$Predictor[[idx]]
addWorksheet(wb,paste0("Sheet_",idx))
data_series = fredr(
series_id = pred,
observation_start = as.Date("1985-01-01"),
observation_end = as.Date("2022-12-31"),
frequency = 'm'
)
writeData(wb, paste0("Sheet_",idx), data_series, startRow = 1, startCol = 1)
print(paste(pred,"written successfully.."))
}
saveWorkbook(wb,"./data/new_data_consolidated.xlsx")
}