added Rmd

ARIMA_2_1_2_simulation.Rmd

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+---
+title: "Simulate Time Series"
+runtime: shiny
+output: html_document
+---
+
+This is an interactive document.  It interactively simmulates a Moving average order 2 model.
+The same code that could simulate the series non interactively is also included.
+
+Make sure you have the following packages installed on your system.
+```{r,echo=FALSE,message=FALSE}
+# load packages ####
+# if you do not have these packages - install.packages('package name')
+library(tidyquant)
+library(forecast)
+library(quantmod)
+library(dplyr)
+library(tidyverse)
+library(ggplot2)
+library(lubridate)
+library(xts)
+library(shiny)
+```
+
+```{r,echo=FALSE}
+inputPanel(
+  sliderInput("phi_1","Correlation in First MA component: ",min=0, max=0.99999,value=0.5,step = 0.01),
+  sliderInput("phi_2","Correlation in Second MA component: ",min=0, max=0.99999,value=0.5,step = 0.01)
+)
+``` 
+
+```{r,echo=FALSE}
+x <- reactive({ 
+  date <- seq.Date(as.Date("2001/01/01"),as.Date("2013/01/01"),"days") 
+  l <- length(date) 
+  y <-arima.sim(model=list(ma=c(input$phi_1,input$phi_2)),n=l) 
+  d <- data.frame(date=date,y=y) 
+  x <- xts(d[,-1],order.by = d[,1]) 
+})
+```
+
+```{r,echo=FALSE,message=FALSE}
+renderPlot({
+  plot(x())
+})
+``` 
+```{r,echo=FALSE}
+renderPlot({
+acf(x())
+})
+```
+```{r,echo=FALSE}
+renderPlot({
+pacf(x())
+})
+```
+
+```{r}
+renderPrint({
+fit_2 <- auto.arima(x())
+broom::tidy(fit_2) 
+})
+
+```
+
+
+```{r}
+renderPrint({
+  arima(x(),order = c(0,0,2)) 
+})
+```
+
+```{r,echo=FALSE,include=FALSE,eval=FALSE}
+  date <- seq.Date(as.Date("2001/01/01"),as.Date("2013/01/01"),"days") 
+  l <- length(date) 
+  y <-arima.sim(model=list(ma=c(0.9)),n=l) 
+  x <- data.frame(date=date,y=y) 
+  x <- xts(x[,-1],order.by = x[,1]) 
+```
+
+```{r,echo=FALSE,message=FALSE,include=FALSE,eval=FALSE}
+  plot(x)
+``` 
+
+```{r,echo=FALSE,include=FALSE,eval=FALSE}
+print(acf(x))
+print(pacf(x) )
+print(arima(x,order=c(0,0,1)))
+```
+

Introduction to tidyquant.Rmd

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+---
+title: "Introduction to tidyquant"
+runtime: shiny
+output: html_notebook
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(
+	echo = FALSE,
+	message = FALSE,
+	warning = FALSE
+)
+```
+
+```{r}
+library(tidyverse)
+library(magrittr)
+library(tidyquant)
+```
+
+
+Copied from Matt Dancho
+
+tidyquant is a package that integrates other R package resourses for collecting and analyzing financial 
+data into a tidy framework.
+
+Three packages integrated
+: xts, zoo, quantmod, TTR, PerfomanceAnalytics
+
+## tq_index()
+Financial index data can be collected using `tq_index`.  A list of the indexes available can 
+be listed with `tq_index_options()`. 
+
+```{r,include=FALSE}
+index_options <- tq_index_options()
+inputPanel(
+  selectInput("index_options", label = "tq_index_options:",
+              choices = index_options, selected = index_options[1])
+)
+```
+
+```{r,include=FALSE}
+renderDataTable({
+  tq_index(input$index_options)
+})
+```
+
+
+A list of the exchanges available through `tq_exchange()` can be listed using `tq_exchange_options()`
+
+```{r}
+exchange_options <- tq_exchange_options()
+inputPanel(
+  selectInput("exchange_options", label = "tq_exchange_options:",
+              choices = exchange_options, selected = exchange_options[1])
+)
+actionButton("list_exchange","List Selected Exchange")
+```
+
+```{r,include=TRUE}
+exchange_list <-eventReactive(input$list_exchange,{ 
+  tq_exchange(input$exchange_options)  
+})
+exchange_options <-eventReactive(input$list_exchange,{ 
+    exchange_list() %>%  
+    select(symbol,company)  %$% 
+    structure(as.character(symbol), names = as.character(company))
+})
+
+renderDataTable({   exchange_list()  })  
+```
+
+```{r,include=FALSE}
+renderPrint({ exchange_options() })
+```
+
+```{r,include=FALSE}
+renderUI({
+   selectInput("exchange_index", label = "Exchange Index:",
+              choices = exchange_options(), selected = exchange_options()[1]) 
+})
+
+```
+
+
+
+

Time_Series_Analysis.Rmd

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+---
+title: "Time Series Analysis"
+author: "Harlan A Nelson"
+output:
+  html_document:
+    df_print: paged
+---
+
+This notebook compiles information from a variety of web sources regarding time series analysis.
+This code is from the tidyquant tutorial.
+
+
+There are a few packages related to time series analysis.  
+* zoo
+* xts
+* quantmod
+* TTR
+* PerformanceAnalytics
+* tidyquant
+
+This notebook starts with tidyquant
+```{r}
+library(tidyquant)
+```
+
+```{r}
+tq_transmute_fun_options()
+```
+```{r}
+data("FANG")
+```
+
+```{r}
+FANG
+```
+```{r}
+FANG_annual_returns <- FANG %>% 
+  group_by(symbol) %>% 
+  tq_transmute(select = adjusted,
+               mutate_fun = periodReturn,
+               period = "yearly",
+               type = "arithmetic")
+FANG_annual_returns
+```
+
+```{r}
+FANG_annual_returns %>% 
+  ggplot(aes(x = date, y = yearly.returns, fill = symbol)) +
+  geom_bar(stat = "identity") +
+  geom_hline(yintercept = 0, color = palette_light()[[1]]) +
+  scale_y_continuous(labels = scales::percent) +
+  labs(title = "FANG: Annual Returns",
+       subtitle = "Get annual returns quickly with tq_transmute!",
+       y = "Annual Returns", x = "") +
+  facet_wrap(~ symbol, ncol = 2) +
+  theme_tq()  +
+  scale_fill_tq()
+```
+
+
+```{r}
+FANG_daily_log_returns <- FANG %>% 
+  group_by(symbol) %>% 
+  tq_transmute(select = adjusted,
+               mutate_fun = periodReturn,
+               period = "daily",
+               type = "log",
+               col_rename = "monthly.returns")
+```
+```{r}
+FANG_daily_log_returns %>% 
+  ggplot() +
+  aes(x = monthly.returns, fill = symbol) +
+  geom_density(alpha = 0.5) +
+  labs(title = "FANG: Charting the Daily Log Returns",
+       x = "Monthly Returns",
+       y = "Density") +
+  theme_tq() +
+  scale_fill_tq() +
+  facet_wrap(~ symbol, ncol = 2)
+```
+
+
+```{r}
+FANG %>% 
+  group_by(symbol) %>% 
+  tq_transmute(select = open:volume,
+               mutate_fun = to.period,
+               period = "months")
+```
+```{r}
+FANG_daily <- FANG %>% 
+  group_by(symbol)
+
+FANG_daily %>% 
+  ggplot() +
+  aes(x = date, y = adjusted, color = symbol) +
+  geom_line(size = 1) +
+  labs(title = "Daily Stock Prices",
+       x     = "", 
+       y     = "Adjusted Proces", 
+       color = "") +
+  facet_wrap( ~ symbol, ncol = 2, scales = "free_y") +
+  scale_y_continuous(labels = scales::dollar) +
+  theme_tq() + 
+  scale_color_tq()
+```
+```{r}
+FANG_monthly <- FANG %>% 
+  group_by(symbol) %>% 
+  tq_transmute(select = adjusted,
+               mutate_fun = to.period,
+               period = "months")
+
+FANG_monthly %>% 
+  ggplot() +
+  aes(x = date, y = adjusted, color = symbol) +
+  geom_line(size = 1) +
+  labs(title = "Monthly Stock Prices",
+       x = "", y = "Adjusted Prices", color = "") +
+  facet_wrap(~ symbol, ncol = 2, scales = "free_y") +
+  scale_y_continuous(labels = scales::dollar) + 
+  theme_tq() + 
+  scale_color_tq()
+
+```
+
+
+```{r}
+FANG_returns_monthly <- FANG %>% 
+  group_by(symbol) %>% 
+  tq_transmute(select     = adjusted,
+               mutate_fun = periodReturn,
+               period     = "monthly")
+```
+```{r}
+baseline_returns_monthly <- "XLK"  %>% 
+  tq_get(get   = "stock.prices",
+         from  = "2013-01-01",
+         to    = "2016-12-31") %>% 
+  tq_transmute(select = adjusted,
+               mutate_fun = periodReturn,
+               period = "monthly")  
+```
+
+```{r}
+returns_joined <- left_join(FANG_returns_monthly,
+                            baseline_returns_monthly,
+                            by = "date")
+returns_joined
+```
+
+
+```{r}
+FANG_rolling_corr <- returns_joined %>% 
+  tq_transmute_xy(x = monthly.returns.x,
+                  y = monthly.returns.y,
+                  mutate_fun = runCor,
+                  n = 6,
+                  col_rename = "rolling.corr.6")
+```
+
+```{r}
+FANG_rolling_corr %>% 
+  ggplot() +
+  aes(x = date, y = rolling.corr.6, color = symbol) +
+  geom_hline(yintercept = 0, color = palette_light()[1]) +
+  geom_line(size = 1) +
+  labs(title = "FANG: Six Month Rolling Correlations to XLK",
+       x = "", y = "Correlation", color = "") +
+  facet_wrap(~ symbol, ncol = 2) +
+  theme_tq() +
+  scale_color_tq()
+```
+```{r}
+names(returns_joined )
+FANG_rolling_corr <- returns_joined %>% 
+  tq_transmute_xy(x=monthly.returns.x,y=monthly.returns.y,
+                  mutate_fun = runCor,
+                  n = 6,
+                  col_rename = "rolling.corr.6")
+```
+
+```{r}
+FANG_rolling_corr %>% 
+  ggplot() +
+  aes(x = date, y = rolling.corr.6, color = symbol) +
+  geom_hline(yintercept = 0, color = palette_light()[1]) +
+  geom_line(size = 1) +
+  labs(title = "FANG: Six Month Rolling Correlations to XLK",
+       x = "", y = "Correlation", color = "") +
+  facet_wrap(~ symbol, ncol = 2) +
+  theme_tq() +
+  scale_color_tq()
+```