StockPulse

 

1. Introduction:

          Stock Pulse basic and most important feature is that it works well and has the ability to handle and analysis data and the data is not static it is dynamic and coming from different location and like stock websites the chart is on the live data so it is also dynamic and there are more features which are like first the data come in raw form , it will be cleaned and send to the analysis section to be converted into  different charts and then processed and calculate all the missing values and remove it then user can download the cleaned data it can also upload it then their data will go through the same process then he or she can see data from different dates months or even  years and of their favorite companies and can track their performance . The data is of different companies like Apple, Microsoft and Sysco etc.

  2.The Critical Need for Anomaly Detection in Modern Markets:

             In this huge world of different companies you cannot handle and cannot track performance of every company and their movement  and crashes and recovery in stocks today and it is important to make an application to handle data of different companies and their stocks and performance is very important in today’s world to get rid of different types of fraud which is common in daily stock market and local departments across the world so to put a stop at this we have make an application to do it like it can analyze the data and shows in different charts and can track their information and performance as well.

3.Understanding Stock Market Anomalies: Spikes, Dips, and Irregularities:

             In the world there are many companies and people buy their stock and can have anomalies like a sudden change in the stocks like spikes (it is when the stocks goes high from its normal behavior suddenly) and Dips (it is when the stocks go at Low from its normal behavior suddenly) that’s why it is important to remember all the sudden behavior of companies to track their performs. Irregularities include seasonal effects irregularities can make market act weird, think of a person who always wins but suddenly starts losing for a period of time that’s seasonal effect. Unexpected volatility is also a kind of irregularities which means that stocks skyrocket in a moment for no clear reason sometimes people making big bets on stocks can mess up its normal price. To trade, investors and traders should know every trends to make a trade or make a bet these trends can offer to make a lot of money on the other hand its also too risky for making a move.

 

 

4.  How Stock Pulse works: 

Stock pulse is the solution for such hazards and anomalies it takes data from API’s such as Yahoo finance and alpha vantage. It takes the news about firm and industry and then detect anomalies and shows the user analyses charts indicating a possible investing opportunity and for unplanned decline in prices can indicate problems or shifts in market stability. The solution for all these problems is this website STOCK PULSE. Stock pulse is made to detect anomalies in financial stock market data by identifying abrupt behavior of stock market.

It all starts with collecting market data from sources like yahoo finance and alpha vintage. Alpha vantage offers free api service on financial data it provides real time and  historical stocks data and show prices and tell insides of forex, cryptocurrency and technical indicators and it only requires a free API key for signup it uses formats like JSON and csv. Yahoo finance is an unofficial API or third party API because Yahoo has shut down the official old API but its scrape libraries and hidden end points are available on internet. It also uses historical prices, company fundamentals and market news to predict or analyze the data, the format it uses is typically JSON, pandas data frame when libraries are used but its not an official API so end point can be changed without the user noticing it.

The app itself uses EDA( exploratory data analysis) which begins with data exploration to learn trends, seasonality and volatility in stocks value . It picks the size of data and the types of variables used in the data provided by the API’s and differentiate between them in terms of numeric , categorical data and texts also detects the pattern in that data uses that data to identifying the issue such as missing values errors and unusual activities. Generating hypotheses is also the part of EDA it consists of insights that guides the user for further analysis and model building through the charts. Basically EDA is to spot the anomalies and problems in the data.

Stock pulse handles data preprocessing by observing the missing values like drops, fills and impute in the stocks by removing the duplicate entries and correcting the unusual formats like texts and units. It transforms data by scaling or normalizing the numeric ranges such as high, low, open, close and volume also splits the data and divide it into training validation and test. Data preprocessing improves model accuracy and it saves time in later analysis and production.

For anomalies detection Stock pulse uses isolation forest that splits the data repeatedly it does not requires labeled data and best used with high dimensional data and gives anomalies score as output on the other hand SVM build powerful models using margins

Stock pulse is developed to detect the anomalies in financial time series data by stock prices movement over the time.

Stock pulse also have features that can give free hand to user for testing his/her to find anomalies and provide them results using charts and in the form of JSON, SQL and csv file to export anomaly reports for further decision making also provide visual summaries. This application is designed with a clear interface ensuring ease for all users. Stock pulse provides consistent results with high accuracy in anomaly detection and visualizations among the data segments. This application is compatible with the most common (OS) operating system and supports standard data formats for external visualization tools like tableau. The software technologies used in this project are, for storing data csv file , for frontend python and its libraries streamlit and for backend python and its libraries like scikit-learn, Pytorch, matplotlib and seaborn.    

 

 

5. From Raw Data to Actionable Insights: The Data Preprocessing Pipeline: 

             The most and essential step of getting data dynamically it will come in raw data if its live also it will come in raw form and it has different missing values and have data which is not cleaned sand cannot perform analysis so it is important for the data to first go through cleaning process and then the anomalies should go through the data and is very important process to track the company stocks and analysis how much it go in losses or profit over days months and even years so we have made a complete application for this purpose so you can track the performance of the company you loved to track   . In our project the data is coming from API then converted into csv them it goes to the models for cleaning process after the cleaning it will check for the anomalies and detect the missing values then it will remove the missing then it will show us what it has removed and then it will send the data to the analysis it will show different charts so you can see it in user friendly UI and track the performance of your company.

 

6. The Data Science Engine: Algorithms Powering Stock Pulse:

                In our application we have used python with some libraries like streamlit, fastapi, uvicorn, pandas, numpy, scikit-learn, yfinance. alpha vantage, plotly, joblib, matplotlib, pytest, python-dotenv, torch, shap, tenseflow,  tsfresh, statsmodels, pandas-profiling ,xlrd ,openpyxl etc. We have use these libraries in our project it would do different things in our project and the algorithms we have used is IsoForest etc. It will helps in data cleaning anomalies  and after this process the data is then presented to the user in the form of charts and different UI which will be user friendly so everyone can see and track the performance easily and can export it and also user can add the data and check the charts and same process on their data too check ttheir data analysis

7. Seeing the Unseen: A Guide to the Stock Pulse Dashboard & Visualization:

 In this flow chart it shows first the application starts then the data is integrated then the data goes to the processing area then the data goes to feature engineering then thee data goes to feature selection  then to the model training area  then anomalies detection area from there it goes to two options

Option 1: In this option if anomalies is not detected then it ends

Option 2: If the anomalies is detected then it goes to the store dashboard and report section then after that flow chart ends

I explain further than when you come to Stock Pulse first of have a side bar with all the pages navigation and at the bottom there is a dropdown from which have option of two dataset and upload csv where you can add your own data after that the form has column option if you select it after which you have to start anomalies and after that it shows that this is the missing values and then you can go to different pages and see different charts and can track the changes

8. Stock Pulse, How it is beautiful and easy to use interface:

   Stock Pulse comes out with the sleek and beautiful user friendly interface which make discovering stocks trend both simple and clear for engaging. The moment you open the web app the sleek and beautiful layout helps you focus on the data without distractions clear and accurate charts well organized UI and smooth interaction between user and the web app create seamless experience for rookies as well as seasonal traders and investors Stock Pulse provides powerful tools that can be used for visual efficiency and are very easy to handle.

First of all we come to the first page it is a black layout theme on the left side it has a sidebar with option of two dataset of yahoo finance and alpha vantage and the third option is upload file if you choose to press option one or two the form will appear at the bottom of sidebar from which you can see data Apple, Google, Microsoft and Tesla etc. and the date from which the data start to come and the date till the data come and then there is a button when you click it. It start it will show a new section there are different option from which you can select and go to that section and can see different charts like up down in stock and ratio how much the stock had raised or has dropped. There are different types of charts you can view to track the performance of your chosen company and has the section of anomalies detected when you go to this page you see the data of anomalies which the model has detected and you can the whole information about it how much high it risen and how much dropped and track it and they there is a feature from where you can download the data into a csv format file of the dataset you have chosen.

Now we will come to upload csv section in the sidebar there you will go to the page there you will upload your file in which the data the file format can be different from csv or it can be csv after that the form will open where user can map  the column of their data if the column is different from the existing column or the name is changed so the user can map the column which they had then it will process thee data which will be provided by the user and delete the missing values then it will move the data into file where it will make different charts of the data then go to the anomalies section their he will find the different anomalies and you can see the information about how much data has anomalies how much it is had raised and how much it is has dropped then you can export the cleaned form of your given format data into csv format file

9. Problem faced during storing csv file and visualization

The main problem that occurred and fixed while creating Stock pulse is that all the data ends up in one column it usually occurs because the data provided sometimes doesn’t have comma and semi colons and when we opened that file all the data collapsed into a single column and also split to overcome this problem we used separators these inspection saved us but the other problem was data mismatch and it basically occurs because the numeric data also occurs in the text format. This error occurred when we were working on data analysis we fixed this by using library pandas, the use of this library ensured us that later we are free from this bug in the project for stock prices. While we were dealing with these problems the problem that occurred was chart readability the most common and stubborn error the main cause of this is overcrowded chart data and having so many points in the chart were hard to interpret we fixed this by using plotting libraries which helped us a lot. All of these problem or bugs helped us to learn cleaning the data and making visualization more effective and making the results.