# Stock Prediction-Intraday Trading

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Stock Prediction-Intraday is one of the trading norms of the stock market, buy shares at the opening time of the market and then sell the same at the closing time of the same day.

Today we are dealing with one of the data sets, based on daily data of seven years from 2014 to 2021.

We are going to use a simple machine learning algorithm to understand the data, analyze and make predictions based on an algorithm.

If you want to read more on stock market secrets click here.

In this tutorial, we took randomly one of the stocks for analysis and prediction. You can try some other stocks based on your view and interest.

One of the suggestions is that you need to watch the stocks for at least 3 months closely and make your own conclusions with the help of these predictions.

These prediction ideas you can make use of long-term investment. For intraday, you need to know about some kind of strategies.

Suppose if you are going against the market trend chances higher for losing money. One of the simple strategies we already explained in one of our old posts click here to read.

## Load Library

library(prophet) library(lubridate) library(ggplot2) library(pacman) pacman::p_load(data.table, fixest, BatchGetSymbols, finreportr, ggplot2, lubridate)

## Set parameters

first.date <- Sys.Date() - 2500 last.date <- Sys.Date() freq.data <- "daily" tickers <- c("BALKRISIND.NS")

We are taking daily data from 2014-07-01 to 2021-05-05.

How to choose lottery numbers?

## Getting Data

stocks <- BatchGetSymbols(tickers = tickers, first.date = first.date, last.date = last.date, freq.data = freq.data, do.cache = FALSE, thresh.bad.data = 0) data<-stocks$df.tickers data<-na.omit(data) head(data)

Following details will get for analysis.

price.open price.high price.low price.close volume price.adjusted ref.date 2 367.2603 371.1934 357.9189 364.2366 124870 342.4004 2014-07-02 3 363.8187 367.3586 358.1648 361.9259 30469 340.2281 2014-07-03 4 359.0743 369.2268 359.0743 365.3428 29728 343.4402 2014-07-04 5 362.8600 367.9485 354.9691 358.0419 74821 336.5770 2014-07-07 6 356.7390 360.8688 347.5944 348.8972 79854 327.9806 2014-07-08 7 346.1194 348.5285 330.4850 341.2030 402494 320.7476 2014-07-09 ticker ret.adjusted.prices ret.closing.prices 2 BALKRISIND.NS 0.004678743 0.004678642 3 BALKRISIND.NS -0.006344423 -0.006344118 4 BALKRISIND.NS 0.009441102 0.009441052 5 BALKRISIND.NS -0.019983830 -0.019983871 6 BALKRISIND.NS -0.025540681 -0.025540653 7 BALKRISIND.NS -0.022053048 -0.022053126 str(data)

The dataset contains total 1680 observations and 10 variables.

## Q plot

Let’s plot the dataset for understanding.

qplot(data$ref.date, data$price.close,data=data)

It is clearly evident that the data set is not stationary. Let make use of log transformation and convert it into stationary data.

What is mean by best standard deviation?

### Log transformation

ds <- data$ref.date y <- data$price.close df <- data.frame(ds, y) head(df)

After log transforamtion the data set should like this.

ds y 1 2014-07-02 364.2366 2 2014-07-03 361.9259 3 2014-07-04 365.3428 4 2014-07-07 358.0419 5 2014-07-08 348.8972 6 2014-07-09 341.2030

Stock forecasting we are using prophet package

m <- prophet(df) future <- make_future_dataframe(m, periods = 30)

periods indicate the number of days need to forecast.

Minimum number of units in an experimental design

forecast <- predict(m, future)

## Model performance & Stock Prediction

pred <- forecast$yhat[1:dim(df)[1]] actual <- m$history$y plot(actual, pred)

summary(lm(pred~actual))

Call:

lm(formula = pred ~ actual) Residuals: Min 1Q Median 3Q Max -261.419 -41.156 -4.332 39.322 304.031 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 27.155044 3.728168 7.284 4.97e-13 *** actual 0.965945 0.004164 231.989 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 69.56 on 1678 degrees of freedom Multiple R-squared: 0.9698, Adjusted R-squared: 0.9697 F-statistic: 5.382e+04 on 1 and 1678 DF, p-value: < 2.2e-16

Adjusted R square is 96% quite good model.

When you are dealing with time series you need to get an idea about some of the trends like weekly and yearly.

## Plot forecast

prophet_plot_components(m, forecast)

Now you can see some of the trends like weekly Monday price is going down and Thursday and Friday it’s going up and some seasonal trend based on yearly data.

tail(forecast)

You can see predicted values in yhat

ds trend additive_terms additive_terms_lower additive_terms_upper 1705 2021-05-29 1811.291 -20.84209 -20.84209 -20.84209 1706 2021-05-30 1813.041 -20.02460 -20.02460 -20.02460 1707 2021-05-31 1814.791 -24.12734 -24.12734 -24.12734 1708 2021-06-01 1816.542 -22.08570 -22.08570 -22.08570 1709 2021-06-02 1818.292 -20.96707 -20.96707 -20.96707 1710 2021-06-03 1820.042 -17.90472 -17.90472 -17.90472 weekly weekly_lower weekly_upper yearly yearly_lower yearly_upper 1705 2.092700 2.092700 2.092700 -22.93479 -22.93479 -22.93479 1706 2.092701 2.092701 2.092701 -22.11730 -22.11730 -22.11730 1707 -2.826402 -2.826402 -2.826402 -21.30094 -21.30094 -21.30094 1708 -1.585587 -1.585587 -1.585587 -20.50011 -20.50011 -20.50011 1709 -1.239506 -1.239506 -1.239506 -19.72756 -19.72756 -19.72756 1710 1.089445 1.089445 1.089445 -18.99416 -18.99416 -18.99416 multiplicative_terms multiplicative_terms_lower multiplicative_terms_upper 1705 0 0 0 1706 0 0 0 1707 0 0 0 1708 0 0 0 1709 0 0 0 1710 0 0 0 yhat_lower yhat_upper trend_lower trend_upper yhat 1705 1702.054 1876.785 1811.189 1811.291 1790.449 1706 1702.299 1882.703 1812.854 1813.041 1793.017 1707 1699.517 1875.155 1814.448 1814.791 1790.664 1708 1707.877 1885.060 1816.108 1816.543 1794.456 1709 1702.521 1889.921 1817.741 1818.366 1797.325 1710 1708.005 1898.301 1819.334 1820.132 1802.137 plot(m, forecast)

The plot is showing an increasing trend for the next 30 days.

Applying the knowledge of machine learning and algorithms to daily life allows us to make better decisions instead of random guesses.

Disclaimer:- For any kind of investment please consult your financial advisor, we are not recommending any stocks or trading ideas.

The post Stock Prediction-Intraday Trading appeared first on finnstats.

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