# Financial-Engineering

**Repository Path**: email4reg/Financial-Engineering

## Basic Information

- **Project Name**: Financial-Engineering
- **Description**: understanding concepts of econophysics with stock market data
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-02-14
- **Last Updated**: 2020-12-18

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Stock Market-Analysis  
  
  **Source**: stocks from google finance : https://www.google.com/finance  
  
  **Data**: Historical prices from NASDAQ and NYSE for last 10 years. Which includes variables : *Open,High,Low,Close,Volume*.  
            10 stock sources from NASDAQ - AAPL(Apple), AMD, AMZN(Amazon),ATVI,FB(Facebook),GOOG,GOOGL(Google),INTC(Intel),  
            MSFT(Microsoft),TSLA(Tesla).  
            10 stock sources from NYSE - ABB, AFG, ALK, BAC, C, GCH, GE, IBM, JPM(JP Morgan), LVS, T.  
            
  ![alt text](https://github.com/jishu1989/Financial-Analysis/blob/master/screenshots/nasdaq_stock_sources.JPG) 
   
  **Methods** :Preprocessing the variables of the datasets. The datasets are stock sources which are loaded into the R enviroment from csv format.  
  * Date: Originally date format in the dataset is *16-Mar-17*,and its data type is factor, which is converted to date type using ```as.date()``` to 2017-03-16. We write a function to transform date column for all the sources.  
  
  ![alt text](https://github.com/jishu1989/Financial-Analysis/blob/master/screenshots/date_change.JPG)   
  
  * Columns: Here we are working with 10 stock sources from NASDAQ, each stock source dataset consists of 5 columns *Date,Open,High,Low,Close,Volume*. Below is a descriptive view of the dataframe of one of the sources.  
  
  ![alt text](https://github.com/jishu1989/Financial-Analysis/blob/master/screenshots/desc_data.JPG)   
  ![alt text](https://github.com/jishu1989/Financial-Analysis/blob/master/screenshots/data_tab.JPG)    
  
Listing the stocks and their individual length.  
```
tickers= temp
j=0
for(t in tickers)
{
  j=j+1
  a =get(noquote(t))[,1]
  print(c(t,length(a)))

}  

[1] "aapl.csv" "4000"    
[1] "amd.csv" "4000"   
[1] "amzn.csv" "4000"    
[1] "atvi.csv" "4000"    
[1] "fb.csv" "1215"  
[1] "goog.csv" "3165"    
[1] "googl.csv" "3165"     
[1] "intc.csv" "4000"    
[1] "msft.csv" "4000"    
[1] "tsla.csv" "1691"    

```

Considering sources which has longest historical price range, based on their number of rows we select the sources having 4000 rows. We create a datasets *Open,High,Low,Close* . For instance Open dataset consists of open prices for all the stock sources having 4000 rows, so on for Close, High and Low.

**Open**  

![alt text](https://github.com/jishu1989/Financial-Analysis/blob/master/screenshots/open.JPG) ![alt text](https://github.com/jishu1989/Financial-Analysis/blob/master/screenshots/open_graph.JPG)   


**High**  

![alt text](https://github.com/jishu1989/Financial-Analysis/blob/master/screenshots/high.JPG) ![alt text](https://github.com/jishu1989/Financial-Analysis/blob/master/screenshots/high_graph.JPG)    

**Low**  

![alt text](https://github.com/jishu1989/Financial-Analysis/blob/master/screenshots/low.JPG)  ![alt text](https://github.com/jishu1989/Financial-Analysis/blob/master/screenshots/low_graph.JPG)    

**Close**  

![alt text](https://github.com/jishu1989/Financial-Analysis/blob/master/screenshots/close.JPG)![alt text](https://github.com/jishu1989/Financial-Analysis/blob/master/screenshots/close_graph.JPG)