How to predict the stock market with R?
This is an example of stock prediction with R using ETFs of which the stock is a composite. To get rid of seasonality in the data, we used technical indicators like RSI, ADX and Parabolic SAR that more or less showed stationarity. The goal of the project is to predict if the stock price today will go higher or lower than yesterday.
Is random walk theory suitable for a stock’s price prediction?
The random walk theory is suited for a stock’s price prediction because it is rooted in the believe that past performance is not an indicator of future results and price fluctuations can not be predicted with accuracy. I simulated the prices Amazon (AMZN)’s stock for 252*4 trading days (Since a year has ~252 trading days).
Is it possible to predict the stock market?
Note: This a stock prediction project done as part of a term assignment and clearly, is not to be taken as sound investment advice. Predicting stock prices in the market is more challenging and requires enormous effort and way more degrees and qualifications than what we currently have :) Cheers!
What is predictive modeling and why should we use it?
For example, we can build a model to predict the next day price change for a stock, or a model to predict the foreign currency exchange rates. How/Why should we use it? The power of predictive modeling can be harnessed for making the right investment decisions, and in building profitable portfolios.
Can stock price be predicted?
The stock market is known for being volatile, dynamic, and nonlinear. Accurate stock price prediction is extremely challenging because of multiple (macro and micro) factors, such as politics, global economic conditions, unexpected events, a company's financial performance, and so on.
What is the most accurate stock prediction site?
The Motley Fool is one of the most well known and best stock research sites in the world. It is one of the most popular stock advisor platforms out there when it comes to investment news and advice.
How do I get live stock prices in R?
0:242:04How to Import Stock Prices in R - YouTubeYouTubeStart of suggested clipEnd of suggested clipSimple all you have to do is first install quant mod specify the library as quant mod. And afterMoreSimple all you have to do is first install quant mod specify the library as quant mod. And after that we're just going to use this get symbols.
Can Arima predict stock price?
Stock price predictive models have been developed and run-on published stock data acquired from Yahoo Finance. The experimental results lead to the conclusion that ARIMA Model can be used to predict stock prices for a short period of time with reasonable accuracy.
What is the best tool to predict stock market?
The MACD is the best way to predict the movement of a stock. Fibonacci retracement: Fibonacci retracement is based on the assumption that markets retrace by certain predictable percentages, the most common among them being 38.2 per cent, 50 per cent and 61.8 per cent.
How do you predict when a stock will go up?
Major Indicators that Predict Stock Price MovementIncrease/Decrease in Mutual Fund Holding. ... Influence of FPI & FII on Stock Price Movement. ... Delivery Percentage in Stock Trading Volume. ... Increase/Decrease in Promoter Holding. ... Change in Business model/Promoters/Venturing into New Business.More items...•
How do I use Yahoo Finance in R?
2:209:13Finance Data in R Using getSymbols - YouTubeYouTubeStart of suggested clipEnd of suggested clipAgain one asset price and then we'll do it again with the s p 500 index and we'll get those fromMoreAgain one asset price and then we'll do it again with the s p 500 index and we'll get those from yahoo finance.
What is Tidyquant?
tidyquant: Tidy Quantitative Financial Analysis Bringing business and financial analysis to the 'tidyverse'. The 'tidyquant' package provides a convenient wrapper to various 'xts', 'zoo', 'quantmod', 'TTR' and 'PerformanceAnalytics' package functions and returns the objects in the tidy 'tibble' format.
What is Quantmod R?
The quantmod package for R is designed to assist the quantitative trader in the development, testing, and deployment of statistically based trading models.
Why Lstm is better than ARIMA?
LSTM works better if we are dealing with huge amount of data and enough training data is available, while ARIMA is better for smaller datasets (is this correct?) ARIMA requires a series of parameters (p,q,d) which must be calculated based on data, while LSTM does not require setting such parameters.
How do you use ARIMA model for prediction?
STEPSVisualize the Time Series Data.Identify if the date is stationary.Plot the Correlation and Auto Correlation Charts.Construct the ARIMA Model or Seasonal ARIMA based on the data.
How is ARIMA model used in stock market?
Determine the p and q values: Read the values of p and q from the plots in the previous step. Fit ARIMA model: Using the processed data and parameter values we calculated from the previous steps, fit the ARIMA model. Predict values on validation set: Predict the future values.
How accurate is Google stock prediction?
In this paper, we have used Kaggle data of google stock price from the year 2012 to 2016. To predict the stock price of the first two months of 2017 based on the last two months of 2016. For this purpose, we used the Recurrent Neural Network (RNN) as a deep learning model and obtained an accuracy of 87.32%.
Is Walletinvestor accurate?
Not always 100% accurate predictions but much better than competitors. Overall great tool to monitor coins, NFT and market. I would recommend to get Vip if you can.
Is tickertape forecast accurate?
No. They are scamming uninformed investors.
What is stock Rover?
Stock Rover is a comprehensive investment research platform that runs in your Internet browser. Stock Rover provides detailed current and historical data covering financial, operational, price and analyst information for stocks, ETFs and funds listed on the major North American exchanges.
What is the optimum K value of KNN?
To verify this claim and to further test our model, we ran KNN classification on the data set. Using a handy script I wrote, we arrived at a optimum K value of 8.
What is DiagrammeR R?
The DiagrammeR R package allows us to visualise the tree structure generated by xgboost. Here’s the entire structure.
What does RSI mean?
RSI - Relative Stregth Index (A measure of how the stock performed scaled to 0-100 w.r.t the Weighted Moving Average)
Is a stock prediction project considered sound investment advice?
Note: This a stock prediction project done as part of a term assignment and clearly, is not to be taken as sound investment advice. Predicting stock prices in the market is more challenging and requires enormous effort and way more degrees and qualifications than what we currently have :) Cheers!
Why is random walk theory suited for a stock's price prediction?
The random walk theory is suited for a stock’s price prediction because it is rooted in the believe that past performance is not an indicator of future results and price fluctuations can not be predicted with accuracy.
Which stock has the lowest risk?
Google (GOOGL) stock has the lowest risk and the lowest return. Facebook (FB) and Amazon (AMZN) are equally as risky but the latter has stronger returns. If you are risk-inclined, Tesla (TSLA) is a good investment as it has high risk and high returns. But if you are risk-adverse, like me, Apple (AAPL) is the best choice.
What is the difference between the mean and standard deviation of a log return?
The mean is assumed as the consistent rate of return while standard deviation is the risk that comes with purchasing the stock. I used plotly, an interactive visualization tool, to illustrate my findings.
Why add lambda parameter to model building and forecasting?
Let’s add the lambda parameter to the model building and forecasting to help reduce the correlation in the residuals and bring the residual mean closer to 0, and in effect improve the accuracy on the test set. I found that setting lambda to 1.9 for this dataset yielded the best results. I also found that setting robust to TRUE and biasadj to TRUE yielded the best results in reducing the mean of the residuals and minimizing the RMSE (balanced between the training set and the test set).
Why are there spikes in the remainder?
The spikes in the remainder are from the unusual spikes in the prices.
Is the RMSE test set worse with extra month information?
If we look at the RMSE, the test set is worse with the extra month information.
Can forecasting predict the future?
Given a time series set of data with numerical values, we often immediately lean towards using forecasting to predict the future.
What is predictive modeling in trading?
The predictive modeling in trading is a modeling process wherein we predict the probability of an outcome using a set of predictor variables . In this post, we will be illustrating predictive modeling in R.
What is the most significant pre-requisite for predictive modeling?
The most significant pre-requisite for predictive modeling is to have a good understanding of the dataset. The understanding helps in:
How many machine learning algorithms are there in R?
There are hundreds of machine learning algorithms available in R, and determining which model to use can be confusing for beginners. Modelers are expected to try different algorithms based on the problem at hand and with more experience & practice you will be able to determine the right set.
How accurate is KNN?
As we can observe from the accuracy metric, all the models have accuracy between 50-54%. Ideally we should try to tune models with highest accuracies. However, for the example’s sake, we will select the KNN algorithm and try to improve its accuracy by tuning the parameters.
What packages are needed for RStudio?
Note: Before you begin, make sure that you have the following packages installed and selected on your RStudio: Quantmode, PRoc, TTR, Caret, Corrplot, FSelector, rJava, kLar, randomforest, kernlab, rpart
What statistics can be used to understand a dataset?
To attain this understanding of the dataset, you can use descriptive statistics like standard deviation, mean, skewness, along with graphical understanding of the data.
Which function provides k features with the highest importance?
Now, in order to choose the best features using the importance values returned by random.forest.importance, we use the cutoff.k function which provides k features with the highest importance values.
How accurate is the predictive model of Apple stock?
After comparing all models we have built for the predictive model of Apple stock price, we have found out that the predictive model trained with stock price between 2010–2018 using Facebook Prophet is the most accurate as it achieved 44% R-square. The predictive model trained with Holt-winters method achieved 41% R-square, while the models trained with the Box-Jerkins method were both having negative R-square. We would use the model trained with stock price between 2010–2018 using Facebook Prophet to predict future Apple stock price.
What does negative R square mean?
A negative R-square means the model performs worse than taking the average. Time series models are very sensitive to historical trend pattern; building stock price predictive model is very sensitive to stock price performance that selecting a time frame for training data set is can change the trend pattern by a lot.
What is the column of prediction called?
The prediction made by Prophet returns prediction, confidence interval. The column of prediction is called ‘yhat’.
What are the algorithms used to make predictions?
In my mind, there are 3 algorithms to make predictions: Adaptive model, Box-Jerkins method (ARIMA model), and Holt-Winters method; in Python, we can use Facebook Prophet, pmdarima, and statsmodels to help us. Let’s use these packages to make predictions on Apple stock price for a prototype predictive model.
How sensitive is a time series model?
Time series models are very sensitive to historical trend pattern ; building stock price predictive model is very sensitive to stock price performance that selecting a time frame for training data set is can change the trend pattern by a lot. We know that the stock price of Apple hits the lowest area in 1997 and suffer until the release of ipod in 2000s. The stock price of Apple did not gain momentum until the iPhone gained popularity. If we select the time frame between 2010 and 2018 as training data set. The R-squared raised up to 44.73%. In this example, we can see training data select is an important step to boost accuracy. Let’s call the model trained with 1997–2018 data “model_max” and the model trained with 2010–2018 be “model_8yr”.
Can you predict Apple stock price with Box-Jerkins method?
As both models trained with pmdarima achieved negative R-square, we may conclude that it is not useful to predict Apple stock price with the Box-Jerkins method.
Stock Prediction with R
Packages Required
- xgboost
- quantmod
- highcharter
- psych
Dataset Description
- Data used in this project is obtained from Yahoo Finance API using quantmod built in function getSymbols(). This gives us data in the form of time series xts objects. Using the last()function we can specify our time range. I’m using the last 5 years of data for this project.The following stocks/ETFs were used: 1. Response Variables: JPMorgan - Open, Close 2. Predictor Variables: …
Visualisation of Price History
- The highcharterlibrary is a brilliant tool for generating visually appeasing and interactive charts. Although it’s free for non-commercial/academic use, it requires a license for commercial use though. This is the first time I’m playing with this library and I gotta say, it’s really neat. The following chart was generated using highcharter. Check out the whole chart here
Prediction Model Description
- Our goal in this project is to use ETFs to predict the value of one composite stock. The premise for this is that, we can think of an ETF as a representative for the entire industry. Banking and financial firms are all pretty much correlated to each other as even a minor policy change could potentially affect all of them. Thus, by using the performance of the ETF to train our Machine Le…
Machine Learning Algorithm
- We will be using the xgboost algorithm with the goal of binary logistic regression. After data preparation into training (approx 70% )and test (approx 30%) sets, we then feed it to the algorithm. Here’s the ROC Curve for our first run on 10 rounds. We achieved an AUC of : 0.591939755047997 To verify this claim and to further test our model, we ran KNN classification on the data set. Usin…
Xgboost Visualisation
- The DiagrammeR R package allows us to visualise the tree structure generated by xgboost. Here’s the entire structure. IMO, it looks really cool. This is what we get when we zoom into one tree
Codebase and License
- Here’s the full github repo for this project. This project is licensed under the MIT License - see the LICENSE.mdfile located in my github repo for more details.
Acknowledgments
- Project Collaborator : Suman Kumar
- R Core Team (2013). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/.
- R Packages used : xgboost, quantmod, highcharter, psych, pROC