github export NASDAQ 100 Forecaste.Rmd

---
title: "Forcasting Volume of NASDAQ 100 Stocks Traded"
author: "Nipunjeet Gujral"
---



```{r librraies}
library(ggplot2)
library(reshape2)
library(PerformanceAnalytics)      
library(quantmod)
library(xgboost)
library(xts)
library(pROC)
```

  

```{r sorucing data}
nasdaq100_symbols <- c( 'AAPL', 'ADBE', 'ADI', 'ADSK', 'AKAM', 'ALTR', 'ALXN', 
                          'AMAT', 'AMGN', 'AMZN', 'BBBY', 'BIDU', 'BIIB',
                          'BRCM', 'CA', 'CELG', 'CERN', 'CHKP', 'CHRW', ' CHTR', 'CMCSA',
                          'COST', 'CSCO', 'CTRX', 'CTXS', 'DISCA', 'DISCK', 'DISH',
                          'DLTR', 'DTV', 'EBAY', 'EQIX', 'ESRX', 'EXPD', 'EXPE', 'FAST',
                          'FB', 'FFIV', 'FISV', 'FOXA', 'GILD', 'GMCR', 'GOOG', 'GOOGL',
                          'GRMN', 'HSIC', 'ILMN', 'INTC', 'INTU', 'ISRG', 'KLAC', 'KRFT',
                          'LBTYA', 'LLTC', 'LMCA', 'LMCK', 'LVNTA', 'MAR', 'MAT', 'MDLZ',
                          'MNST', 'MSFT', 'MU', 'MXIM', 'MYL', 'NFLX', 'NTAP', 'NVDA',
                          'NXPI', 'ORLY', 'PAYX', 'PCAR', 'PCLN', 'QCOM', 'QVCA', 'REGN',
                          'ROST', 'SBAC', 'SBUX', 'SIAL', 'SIRI', 'SNDK', 'SPLS', 'SRCL',
                          'STX', 'SYMC', 'TRIP', 'TSCO', 'TSLA', 'TXN', 'VIAB', 'VIP',
                          'VOD', 'VRSK', 'VRTX', 'WDC', 'WFM', 'WYNN', 'XLNX', 'YHOO')
getSymbols(nasdaq100_symbols)
```

  
```{r creating dataframe}
nasdaq100 <- data.frame(as.xts(merge(AAPL, ADBE, ADI, ADSK, AKAM, ALTR, ALXN, 
                                       AMAT, AMGN, AMZN, BBBY, BIDU, BIIB,
                                       BRCM, CA, CELG, CERN, CHKP, CHRW, CHTR, CMCSA,
                                       COST, SCO, CTRX, CTXS, DISCA, DISCK, DISH,
                                       DLTR, DTV, EBAY, EQIX, ESRX, EXPD, EXPE, FAST,
                                       FB, FFIV, FISV, FOXA, GILD, GMCR, GOOG, GOOGL,
                                       GRMN, HSIC, ILMN, INTC, INTU, ISRG, KLAC, KRFT,
                                       LBTY, LLTC, LMCA, LMCK, LVNTA, MAR, MAT, MDLZ,
                                       MNST, MSFT, MU, MXIM, MYL, NFLX, NTAP, NVDA,
                                       NXPI, ORLY, PAYX, PCAR, PCLN, QCOM, QVCA, REGN,
                                       ROST, SBAC, SBUX, SIAL, SIRI, SNDK, SPLS, SRCL,
                                       STX. SYMC, TRIP, TSCO, TSLA, TXN, VIAB, VIP,
                                       VOD, VRSK, VRTX, WDC, WFM, WYNN, XLNX, YHOO)))
```


```{r set outcome variables}
outcomeSymbol <- 'FISV.Volume'
    
nasdaq100 <- xts(nasdaq100, 
                 order.by = as.Date(rownames(nasdaq100)))
    
# shift date back one day 
nasdaq100 <- as.data.frame(merge(nasdaq100, 
                                 lml = lag(nasdaq100[, outcomeSymbol], -1)))
    
# if tomorrow's volume is smaller then 0, elif tomorrows value is bigger 1
nasdaq100$outcome <- ifelse(nasdaq100[, paste0(outcomeSymbol, '1')] > nasdaq100[, outsomeSymbol], 1, 0)
  
# remove shifted down volume field, it will be unnecessay
nasdaq100 <- nasdaq100[, !names(nasdaq100) %in% c(paste(outcomeSymbol, '1'))]
```

  
```{r reorder in desending}
# cast date to true date and order in decreasing order ####
GetDiffDay <- function(objDF, days = c(10), offLimitsSymbols = c('outcome'), roundByScale = 3){
# sort tdates in decreasing order
  ind <- sapply(objDF, is.numeric)
    for(sym in names(objDF)[ind]){
      if(!sym %in% offLimitsSymbols){
        print(paste('****************', sym))
        objDF[, sym] <- round(scale(objDF[, sym]), roundByScale)
  
        print(paste('theColName', sym))
          
        for (day in days){
            objDF[paste0(sym, '_', day)] <- c(diff(objDF[, sym], lag = day), rep(x = 0, day)) * -1
        }
        
      }
        
    }
      
      return(objDF)
      
}
    
```
    
  
```{r}
# call the above functions ####
nasdaq100 <- GetDiffDay(nasdaq100,
                        day = c(1,2,3,4,5,10,20),
                        offLimitsSymbols =  c('outcome'),
                        roundByScale = 3)
```


```{r adjusting window}
# drop most recent entry since it doesn't have an outcome
  nasdaq100 <- nasdaq100[2:nrow(nasdaq100),]  
    
# using POSIXlt to add day of the week, month, month and year for future data points derived from model
  nasdaq100$week <- as.POSIXlt(nasdaq100$date)$wday
  nasdaq100$yday <- as.POSIXlt(nasdaq100$date)$mday
  nasdaq100$mon <- as.POSIXlt(nasdaq100$date)$mon
    
      
# remove date field and shuffle data frame
  nasdaq100 <- subset(nasdaq100, select = -c(date))
  nasdaq100 <- nasdaq100[sample(nrow(nasdaq100)), ]
```

  
    
```{r predicting}
# modeling
  predictorNames <- names(nasdaq100)[names(nasdaq100) != 'outcome']
  set.seed(1234)    
  
# determining train and testing data sets
  split <- sample(nrow(nasdaq100), floor(0.7*nrow(nasdaq100)))
  train <- nasdaq100[split, ]
  test <- nasdaq100[-split, ]
  
  bst <- sgboost(data = as.matrix(train[, predictorNames]),
                 label = train$outcome,
                 verbose = 0,
                 eta = 0.1,
                 gamma = 50,
                 noround = 50,
                 colsample_bytree = 0.1,
                 subsample = 8.6,
                 objective = "binary:logistic")
  
  predictions <- predict(bst, as.matrix(test[, predictorNames]), 
                         outputmargin = TRUE)
```

```{r model analysis using AUC}
auc <- roc(test$outcome, predictions)
print(paste('AUC score -->  ', auc$auc))
```