How to Develop an AI Stock Prediction Software?

Investing in stocks is one of the most popular investment strategies that the majority population has been using for ages. While it looks like a lucrative opportunity to make money, not everyone can understand its complexities, resulting in choosing poor investment strategies and financial losses. 

Yes, it is true! Even an online study predicts approximately 90% of stock traders are estimated to lose money in the markets. And one of the major reasons behind this is being unable to predict the right stock to invest in. 

But, what if stock market investors get assistance from artificial intelligence to choose the right stocks to invest in and turn their losses into profits? Yes, we are talking about AI stock prediction software. 

Such software helps investors analyze several stocks based on various factors and helps them choose the best ones to invest in. In this blog, you will read about AI stock prediction software, how it works, and how you can also develop similar software. So, let’s start. 

What is AI Stock Prediction? 

AI stock prediction is a process of using artificial intelligence and machine learning to analyze a large amount of financial data and predict the right stocks to invest in.

It also helps investors understand market trends and patterns with speed and accuracy, enabling them to predict whether the market would be bearish or bullish so that they can make informed stock investment decisions. 

How AI Stock Prediction Software Works? 

Before moving forward, understand how AI stock prediction software works. Below is the simplified breakdown of its working process: 

1. Data Collection

The working of AI stock prediction software starts with collecting data from various sources. The software collects vast amounts of data, such as historical stock prices, their performances, financial statements, market news, economic indicators, and more. 

2. Data Processing

After collecting the data, the AI-powered stock prediction software processes identify the key features/factors that influence stock price movements. These factors include technical indicators, such as moving averages, emotional factors, like market sentiments, or real-life indicators, such as economic events. 

3. Model Training 

The AI stock forecasting software uses machine learning algorithms, majorly, neural networks, decision trees, or deep learning models, to analyze the data. With the help of processed data, machine-learning algorithms can learn patterns and relationships between variables and stock price changes. 

4. Prediction Generation

After training, the AI model in the stock prediction software uses real-time market data to make predictions about the movement of stock prices and stock markets. 

To make accurate predictions, these models analyze current market conditions and trends to forecast future stock prices. 

5. Trade Execution 

Some AI stock prediction software not only predicts the stock prices but also executes the trade automatically, based on the set factors and numbers. This process of AI-powered trade execution is called algo-trading. 

Read more: How to Develop An Algorithmic Trading App?

How to Build an AI Stock Prediction Software

You can develop AI stock prediction software by following the development process mentioned below: 

Step#1. Define the Scope and Objective 

The first step in developing a custom AI-powered stock price estimation software is to define the purpose of the software you want to build. 

You must determine the specific objective of the software, its features and functionalities, the design, the technology stack you are going to use to develop the software, and more. 

You must also set measurable goals to determine AI stock prediction app’s accuracy and performance.  

Step#2. Collect and Preprocess Data  

At this step, collect the stock market data, especially prices and volumes, from various sources, and other data, such as economic indicators, financial reports, and sentiment data from news and social media. 

Now, preprocess the collected data to remove irrelevant and corrupted data. Also, handle missing values to make the data consistent to improve the accuracy of the predictions. 

Step#3. Choose the Predictive Model 

After you have preprocessed the data, you need to select the machine-learning algorithms that will empower the predictive model in the software. Some common algorithms include linear regression, recurrent neural networks (RNNs), decision trees, and other deep learning models. 

Read More: How Predictive Analytics Powered by AI is Revolutionizing Decision-Making?

Step#4: Design the User Interface 

Start designing the user interfaces for your AI stock prediction software. Users usually like simple and intuitive user interfaces when it comes to stock prediction apps and software. 

So, keep the UI design minimalistic yet interactive and eye-catching to grab the attention of the users. Watch our guide on the Importance of UI/UX in Mobile App Development to learn about tips for designing UI/UX for apps and software. 

Step#5. Develop the Software 

Once the UI/UX designing is done, start developing the core software. Integrate the predictive model that will analyze the data and predict the right stocks to invest in. 

Also, you need to connect the software to real-time market data providers through APIs to ensure that the AI-powered stock prediction software processes live market data for up-to-date predictions. 

Moreover, you can also choose to integrate APIs for automated stock execution so that the software can automatically buy or sell stocks, based on the AI predictions. 

Step#6. Ensure Compliance and Security

If any regulations and data security standards need to be followed in your country, then you must ensure that your AI stock prediction software complies with those particular regulations. 

Step#7. Testing and Quality Assurance 

Post-development, rigorously test the AI-powered stock prediction software to identify and fix bugs and glitches, if there are any. Also, use the software on different devices to ensure that it delivers a constant user experience on different screen sizes. 

Step#8. Deploy the Software 

Once the testing is done, deploy the AI-driven stock prediction software. If your software is meant to be for general investors, publish it on app stores like Play Store and App Store. 

If the software is for your business’s internal use, integrate it with the existing systems and train your workforce to use the software efficiently. 

Technology Stack For AI Stock Prediction Software Development  

The following is the suggested technology to build custom AI-powered stock estimation software. 

* Please note that it is only for recommendation purposes. You can use other tools and technologies to build your software if they align with your requirements. 

Top Features to Integrate into AI Stock Prediction Software

While developing your AI-enabled stock prediction software, you must integrate the following features into the software, to provide enhanced stock trading experiences. 

1. User-friendly Dashboard

One of the features of the AI stock prediction software must be an intuitive dashboard that displays stock trends, AI predictions, performance insights, risk analysis and management, and more. 

2. Machine Learning-Based Predictions

Another feature to add to the software is advanced machine learning algorithms that will forecast stock prices based on past data and market trends. 

3. Technical Analysis Tools

In the AI-powered stock prediction software, integrate technical indicators and charting tools so that users can analyze top stock trends and patterns along with getting AI-driven insights.

4. Performance Analytics and Reporting

This feature provides detailed performance metrics, including prediction accuracy, portfolio returns, and risk analysis, to help users track their progress.

5. Real-time Data Integration

This feature fetches live stock market data from multiple sources to ensure up-to-the-minute predictions.

6. Portfolio Management

The portfolio management feature in the AI-powered stock prediction application enables users to build and manage their diverse portfolios. 

7. Alerts and Notifications

This feature will send real-time alerts and notifications for significant market events, trade signals, or changes in stock performance based on AI predictions.

8. Multi-Asset Support

In AI stock prediction software, the multi-asset support feature allows predictions and trading across different asset types, such as stocks, cryptocurrencies, ETFs, and more. 

9. Security and Privacy

You must implement strong security and privacy measures, such as end-to-end encryption, two-factor authentication, and secure API connections, to protect user data and financial information.

10.  Automated Trading

If you are developing an AI stock prediction software with algo-trading capabilities, you can add this feature to your software to allow for the automatic execution of trades based on AI-generated predictions. 

Top Strategies to Monetize Artificial Intelligence-Based Stock Prediction Software 

You need to monetize your AI stock prediction software to generate revenue from it. Here are some proven strategies to monetize AI-powered stock prediction software. 

1. Subscription Model 

By using the subscription model, you provide access to your software at different subscription plans. Users need to buy a subscription plan to use the software. By leveraging this model, you can get continuous income from users, monthly or annually. 

2. Freemium Model 

By implementing the freemium monetizing strategy, you allow users to access the basic features of your AI stock prediction software for free, and pay to use advanced features, such as algo-trading, advanced technical analysis, AI-powered portfolio management, and more. 

3. Data Licensing 

You can sell AI-generated insights and stock market analytics to financial institutions, hedge funds, or individual traders who want access to high-quality predictions, and generate revenue from it. 

4. In-app Purchases

The next proven monetization strategy is in-app purchases. In this, you can offer additional features, such as enhanced AI algorithms, market sentiment analysis tools, or exclusive data feeds, that users can buy and expand their software’s capabilities.

5. Advertising and Sponsorships

You can also run advertisements from financial services companies, brokerages, or fintech firms within the software, and charge them on the impression or clicks basis. 

6. Commission on Automated Trading

In case, your AI stock prediction software has an automated trading feature, you can partner with brokerage firms and earn a commission on trades executed through the platform. You can share a percentage of profits generated by users from AI-powered trades. 

7. Performance-Based Fees

Lastly, you can also charge users based on the performance of the AI predictions or trades made using the software. For instance, you can take a small percentage of profits made on the successful trades, from the users. 

So, these are the top monetization strategies you can implement for your AI-powered stock prediction software. You can use two or more strategies simultaneously to generate decent revenue from the software. 

Benefits of Developing an AI-powered Stock Prediction Software 

There are numerous advantages to building an AI stock prediction software, such as: 

1. Improved Accuracy in Prediction of Stock Prices 

The artificial intelligence algorithms in the stock prediction software can process vast amounts of past and real-time data accurately, compared to other traditional methods. 

The major benefit of developing the software is that it can predict the prices accurately, therefore, users will incur fewer losses.

2. Real-Time Market Analysis

Stock trading requires quick actions. If you think too much, you will lose the opportunity to make profits. 

Therefore, another benefit of building a custom AI prediction software is that it can analyze the market in real-time and allow users to take necessary actions to book their profits on time. 

3. Data-Driven Decision Making

The third advantage of building AI-powered stock prediction software is that it allows users to make informed decisions based on data, rather than emotions and sentiments. 

Data-backed decisions are much better than emotion-backed ones in the stock markets. 

4. Increased Efficiency 

The AI in the stock prediction software takes care of the repetitive and time-consuming tasks, such as analyzing market data, monitoring trends, and executing trades. 

This allows stock traders to execute more trades in less time, hence increasing their overall efficiencies. 

5. Competitive Advantage

The stock traders who use AI-powered stock prediction software have a competitive edge over the competitors who still use traditional methods. 

The AI helps traders uncover hidden trends, predict market shifts, and execute trades at the right moment for better outcomes, compared to traditional methods. 

Conclusion 

AI stock prediction software is the next big thing in the stock analysis and trading industry. The AI can analyze the data fast and accurately, and even access those insights which otherwise might be left unseen by the human traders. By developing a custom AI prediction software, you can take your stock analysis game to the next level. 

If you prefer to develop it for the users, it will be a ground-breaking business idea as it can help less-experienced traders too, to make profits. And you can generate revenue by implementing the monetization strategies mentioned in this blog. 

So, if you want to develop AI-powered stock prediction software, you can follow the development process given above, or reach out to Quytech, the leading AI-first software development company. 

We have developed numerous AI-powered stock trading apps, such as Project10, and other custom software for clients tailored to their specific requirements.