Objective

HDFC bank's stock prices are considered for this project. The objective is to predict the next day opening price on the basis of open(the price at which the stock opened on a specific day), high(highest price the stock had on a specific day), low(lowest price the stock had on a specific day), close(the price at which the stock closed on a day), volume(number of transaction that occured for this company. i.e. HDFC bank on a specific day), 5DMA(5 days moving average of the opening price), 10DMA(10 days moving average of the opening price), 20DMA(20 days moving average of the opening price), 50DMA(50 days moving average of the opening price). A comparative study is also conducted to check which moving average contirbutes to better accuracy.

Data

Data contains date, open, high, low, close, volume, 5 days moving average, 10 days moving average, 20 days moving average, 50 days moving average and the next day open.
Last attribute will be used as label and all the remaining attributes will be used as features.

date	open	high	low	close	volume	5 DMA	10 DMA	20 DMA	50 DMA	next day open
2018-01-01	1873	1881	1851	1855	1645129	1870	1872	1866	1847	1859
2018-01-02	1859	1875	1859	1872	1194079	1870	1870	1869	1848	1875
2018-01-03	1875	1878	1851	1853	1132822	1869	1871	1873	1850	1853
2018-01-04	1853	1866	1853	1860	593444	1865	1868	1872	1852	1863
2018-01-05	1863	1868	1856	1864	717717	1865	1866	1872	1853	1865
2018-01-08	1865	1871	1858	1861	1142577	1860	1865	1872	1854	1862
2018-01-09	1862	1870	1855	1864	1326382	1863	1864	1869	1855	1865
2018-01-10	1865	1868	1857	1864	1153972	1864	1864	1868	1856	1864
2018-01-11	1864	1876	1856	1873	1014774	1864	1863	1867	1856	1873
2018-01-12	1873	1878	1860	1865	1084177	1866	1865	1867	1857	1870

Data collected from yahoo finance.
Different moving average and next day open are programatically calculated. Above table is stored in this csv file

Visualisation

The visualisation is performed using Japnese Candle Stick Pattern and an addional line graph to plot the 5 days moving average. This plot demonstrates the entire data since 2015. It plot the OHLC and 5SMA. Since this graph is difficult to understand, this can be seen (also mentioned below). This graph plots the OHLC price and 5SMA. Green color indicates a positive day and red color indicates a negative day.

Code

python3 Analysis/LinearRegression.py

This file will develop the Linear Regression model and plot a graph containing the following plots.

1. Predicted opening price for the next day by linear regression.
   
2. Value of next day opening on the basis of 5DMA, 10DMA, 20DMA, 50DMA respectively. Generally this 5SMA is also used as a method to predict the next day open.
   
3. Actual opening price for the next day.
   The graph contains the respective labels for the plots.

Outcome

Linear Regression Model predicts the next day opening price with an accuracy of 88% when computed using 5 DMA.

Other Accuracies computed using 5 DMA, 10DMA, 20DMA and 50DMA are as follows

Moving Average Used	Accuracy
5 days	88%
10 days	87%
20 days	85%
50 days	84%

Graphs for othe rmoving averages are as follows.



A collective graph of all the results is as follows.

This linear regression model has an accuracy of 88%

References

1. Predicting stock market using machine learning
2. Japnese Candlestick Pattern in python
3. Linear Regression in sklearn