In the 21st century, we have witnessed that most manual tasks are getting automated, and artificial intelligence has the most crucial role in automation. AI has been most profoundly used in analyzing data, predicting outcomes based on that data, and automating tasks. Stock market traders have also realized the potential of harnessing data and predicting outcomes, and they are tapping into its potential. In this blog, we will discuss how to develop ai stock prediction app in 2024.
Stock market traders of all levels are looking for the best AI stock market prediction app. It has been used for hedge funds for a long time, and now individual traders want to leverage it.
You would be surprised to know that many retail traders have already started leveraging AI for their investment decisions. As per a survey conducted by Yahoo Finance-Ipsos, 20% of American investors use AI chatbots like ChatGPT to get assistance in selecting stocks over the previous year. It is expected that traders will use AI stock price prediction apps to make better decisions in the future.
The AI app for stock prediction has increased computing power, greater cloud accessibility, improved bandwidth, and generative AI services. All these technologies help in developing the best AI stock prediction app. Are you considering developing an AI app to predict the stock market? Then this article is for you. In this article, we will go through all the major aspects of how to develop an AI stock prediction app.
What is an AI Stock Market Prediction App?
An AI stock market prediction app harnesses the capabilities of AI algorithms to capture, understand, and predict financial data. It considers several parameters of the stock market, such as price movements, trade volumes, business financials, P/B ratio, P/E ratio, news sentiments, and many other intrinsic parameters.
By analyzing this data, the AI stock price prediction app can predict future prices, the upward or downward movement of stocks, and other related financial instruments. This app aims to help traders and investors make better choices by offering forecasts and insights into probable market movements. If you are considering developing an AI stock market app, then you can hire a mobile app developer like us.
Current Market Statistics According to 2024:
Why invest in an AI stock market prediction app? Is this idea worthful to invest? Here are some statistics to give you an answer:
· It has been expected that the worldwide market for online trading will grow at a CAGR of 6.4% per year and will reach a valuation of 13.3 billion USD by 2026.
· In 2023, the global online trading platform market size was valued at $9.55 billion. It is projected to grow from 10.5 billion USD in 2024 to 16.71 billion in 2032.
· In 2021, nearly 130 million people used stock trading apps, which was a 49% increase from 2020.
How Does the AI Stock Prediction App Work?
Every investor, trader, and researcher works to predict the performance of a stock. With newer technologies like AI and ML, it is now possible to predict stocks. You can build an AI Stock prediction app using these technologies. Let’s understand how an AI stock prediction app works:
Data Collection and Preparation:
The app works by building a stock prediction model. It collects historical data on stock prices along with other important data metrics like trading volume, moving averages, and technical indicators. There are many sources to collect this data, such as financial APIs, market databases, and online repositories.
After the data collection, it should be preprocessed and cleaned to remove noise, missing values, extremities, etc. In addition to this, features also need to be created to extract relevant information. A few common preprocessing steps are normalization, scaling, and feature selection.
Model Development:
For building stock prediction models, there are multiple AI techniques that you can employ, such as:
· Linear Regression: It is a simple but effective technique for the prediction of continuous values like stock prices.
· Support Vector Machines (SVM): It is best for classification and regression tasks and it can capture non-linear relationships in the data.
· Recurrent Neural Networks (RNNs): They are particularly suited for sequential data and capture temporary dependencies in the movements of stock prices.
· Long Short-Term Memory (LSTM) Networks: This RNN is particularly designed to address the vanishing gradient problem and is widely used for time series prediction tasks.
Technical Implementation:
The technical implementation is mostly done using the most popular AI framework, TensorFlow. Tensorflow comes with all the necessary libraries and modules for smooth implementation. Here are the major steps:
· Import of necessary libraries like numpy, pandas, and others.
· Loading historical stock price data and preprocessing it
· Creation of training dataset
· Compilation of model
Evaluation and Testing:
After the training of the model, it is evaluated using a separate sheet of data. There are multiple performance metrics, such as mean squared error (MSE) or root mean squared error (RMSE), to identify the accuracy of the model in predicting stock prices.
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5 Must-Have AI Features in AI Stock Prediction App:
The features and functionalities of the AI Stock prediction app can’t be ignored. You must pick the features that provide maximum user satisfaction and usability. Here are some key features to consider:
ML for Predictive Analytics:
Machine learning is the backbone of an AI-based app. The ML algorithms go through heaps of data, foreseeing market trends and moves. These algorithms sift through historical data to find the tiny patterns and signs that influence future prices and give you a strategic advantage. It contributes to high accuracy in predicting market trends and the ability to crunch diverse data.
Algorithmic Strategies:
AI-powered stock prediction systems rely on algorithmic methodologies. These tactics automate market purchasing and selling choices by adhering to predetermined criteria based on statistical analyses.
These algorithms are designed to respond to different market conditions such as price, volume, and time, and they routinely outperform human traders, completing transactions with surprising speed and accuracy. It’s like having a high-tech assistant navigate the complexities of the market for you.
Automated Reporting:
Automated reporting is vital for increasing efficiency in stock prediction software. The real-time reports will provide you with rapid updates on what is occurring in the market, while historical analysis will provide pearls of knowledge gleaned from previous performance. There will also be customized dashboards that adjust the stats to your preferences.
Important Metrics:
There must be a variety of performance metrics that can improve the analytical capabilities of the website. Some of the important metrics are:
· Profit and Loss to see the gains and losses
· Risk/Reward Ratio for weighing risks against gains
· Win Rate to track the success percentage
· Sharpe Ratio to balance return and risk
· Volatility for the price changes
Data Protection Standards:
For any app that involves financial transactions or data, security becomes the foremost feature. The AI stock prediction app should have top-notch security features guided by heavyweights like GDPR. Here are the major features:
· Encryption: Advanced encryption techniques must be used to transmit data between different modules.
· Access Control: There must be strict protocols for who will get access to view or use the data.
· Data Anonymization: If required, the app should remove or masquerade the data details.
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Revenue Models of AI Stock Prediction App:
Although the market of AI stock prediction apps and online trading is booming, how would you be able to make money through it? Basically, “How to monetize an AI stock prediction app” is the question that comes to mind. Here are the major ways of monetizing your app:
Freemium Model:
This is the most common way of making money through an AI stock prediction app. In the freemium model, the users will have access to the app’s basic features for a limited period. If they want to continue using the app, they have to subscribe or buy a membership.
There will also be premium features in the app, which will be accessible only after paying a certain amount as a fee. This is the most proven and reliable method of monetizing a mobile app.
Paid Apps:
Another common way of making money through a mobile app is through paid apps. It is amongst the oldest ways to monetize a mobile app. The users have to pay an upfront amount to download and install the app. However, it is slightly more difficult to convince users that the app is worth paying for without any trial. Thus, you must offer robust and unique features in the app.
In-app Advertising:
Advertising is another common way of making through a mobile app. You can make money by allowing ads of several financial institutions or any other finance apps on your AI stock prediction app.
Furthermore, this model does not necessitate the deployment of a single revenue model. You may combine in-app advertising with various monetization models. The freemium model is an excellent example of this. Users can initially use the app in the freemium format, with the premium version being adless.
Process To Develop AI Stock Prediction App:
Although it is possible to hire in-house developers to develop an AI Stock Prediction App, seeking advice from an expert mobile app development company is the best way. This is because they have all the required resources and expertise working in technologies such as Tensorflow, AI & ML algorithms, mobile app development technologies, and others. Also, they will have prior experience working in fintech and similar projects. Here is the process of developing an AI stock prediction app:
Define Scope and Objective:
Before the development begins, you have to clarify your objective for the app. Determine the scope along with key features, target audience, platform, and the level of accuracy you want to achieve.
Data Collection and Preparation:
As we have already mentioned above, you have to collect historical data on stock prices along with other important data metrics like trading volume, moving averages, and technical indicators.
Choose a machine learning model:
Initially, we give the example of Tensorflow, but you can select any machine learning model for stock prediction, like Recurrent neural networks. We also deliver quality AI development services so you can contact us.
Data Splitting and Model Training:
After deciding on the machine learning model and collecting data, the data should be split for training. AI professionals will train your selected model using the training dataset, changing parameters iteratively to boost accuracy. Validate the model against the testing dataset to guarantee generalization.
Integration with Mobile App Framework:
The mobile app developers will develop the backend of your mobile app using a framework like React Native or Flutter. They will integrate the trained model into the app’s backend to manage predictions.
UI Design:
After the backend part, the design team will work on the user interface. They have to create a user-friendly and captivating UI for your AI stock prediction app. It will require knowledge of front-end technologies like React or Angular.
Third-party integrations:
The app will require third-party integrations to fetch data in real-time. It will ensure that the predictions are based on the latest information.
Testing and Quality Assurance:
Here comes the last phase of development, i.e., testing. The QA experts will test the app for any persisting issues, speed, UI, features & functionalities, performance, and security.
Deployment:
After the sign-off from the testing team, the app will be ready to launch in an app store. You can get the help of experts to launch your app on the Google Play Store or Apple App Store.
Maintenance:
Even after the launch, the app can have certain issues related to newer upgrades of devices. Thus, regular maintenance will be required to ensure good performance, security, and new updates within the app.
Cost to Develop AI Stock Prediction App:
The cost of developing an AI stock prediction app will depend upon a lot of factors like features & functionalities, platform, complexity, development team location, etc. The location of the development team plays a critical role in determining cost. To read about mobile app development cost in detail you can explore it.
If you outsource your project to a development company located in North America or Central Europe, then you will charge hourly rates of $80-120 per hour. However, you can get a similar quality of development in Southeast Asian countries at a much lower rate. There, the hourly Rate is $25-40 per hour.
The overall rough cost of the AI stock prediction app can lie between $60,000-$80,000 with standard features. However, the final price can be calculated only after obtaining proper requirements.
Conclusion:
Octal IT Solution is a top stock trading app development company that has expertise in developing AI-based mobile apps. We have expertise in machine learning, data science, and mobile app development. The combination of these technologies results in a futuristic solution on the table. Let us know your requirements.
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