Predicting Stock Movements
The tool uses the yfinance library to fetch historical stock market data, providing a comprehensive dataset for analysis and modeling. With this data, we can train models to predict closing prices. With a deep understanding of the data and its underlying dynamics, you can choose the right modeling approach for your needs. The tool provides a range of models, including ARIMA, SARIMA, FB Prophet and more. The model that you will get would be the ones built with ideal set of parameters.
The predictions made by this tool are for informational purposes only and should not be taken as investment advice.
Investing in the stock market involves risks, and you should not make investment decisions based solely on the predictions made by this tool.
Please do your own research and consult with a financial advisor before making any investment decisions. The creators of this tool are not responsible for any losses or damages that may result from the use of this tool.
Use at your own risk
What can you do with this app?
⇛ Predict closing prices : Use our tool to predict the closing prices of your favorite stocks based on historical data.
⇛ Explore stock market trends : Analyze historical stock market data to identify trends and patterns.
Get started
To get started, simply select a stock ticker symbol from the dropdown menu, and our app will fetch the historical data and provide you with a dataset which you can download and use further for your analysis.
Before deciding on the best modeling option for the data, you can perform several task (provided in the tool) to better understand the stock pattern and prepare the data for modeling.
Data Analysis and Preparation for Modeling
The following sections are designed to guide you through the process of understanding and preparing your stock data for modeling. By following these steps, you’ll be able to gain valuable insights into your data, identify trends and patterns, and ultimately choose the right modeling option for your analysis.
Analyze Your Data
Upon selecting the ticker symbol, the dataset is loaded, and a series of necessary data validation checks are performed to ensure data integrity. This process involves replacing or deleting missing values, verifying data types, and other essential checks. Once the data validation is complete, a graphical representation of the dataset is generated to facilitate comparison of prices.
Time Series Decomposition
This section focuses on decomposing the time series data into its constituent components, including trend, seasonality, and residuals. The method of decomposition is chosen based on the characteristics of the time series data. The goal is to identify and separate these components to better understand the underlying patterns and anomalies in the data.
Stationarity Check
The Stationarity tab is a crucial step in our analysis pipeline. Under the hood Augmented Dickey-Fuller (ADF) test is performed to check if the selected stock data is stationary.
How Does it Work?
When a user selects a stock ticker symbol, our system internally performs an ADF test to check for stationarity. If the test indicates that the data is stationary, you can proceed with modeling. However, if the test suggests that the data is not stationary, you can download the stationarized data to ensure accurate results when modeling.
ACF and PACF Plots
After checking for stationarity, you can proceed to analyze the autocorrelation structure of the data using Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots. The slider helps you choose no the of lags dynamically which can help you understand the data better.
Once you are done with analzying and stationarizing data then you can move forward choosing the right model for your data.