“The stock market is not a casino. It is a reflection of the economy and business.”

– Peter Lynch.

Quick Summary: Do you want to build an AI stock prediction software for your trading business? If yes, then you must read this blog post till the end. It will cover everything from steps, essential features, costs, and other essential aspects required to create an AI stock prediction software.    

 

With the rising demand for Artificial Intelligence in automating tasks, traders or investors are increasingly looking for advanced AI software that can predict stocks. While a group of investors (hedge funds) have historically leveraged such technology, individual traders now seek to build their own AI stock prediction software.

By 2025, the current market size of the worldwide stock analysis software industry is predicted to reach $8.75 billion, growing at a CAGR of 8.3%. This data depicts that the demand for stock trading software will be two-fold in the coming years.

Due to the huge popularity of stock trading platforms, businesses are looking to grab this opportunity by investing in AI stock trading software. If you are also one of those who wants to build an AI stock prediction software for your trading business, then this blog will work as a guide for you.

So, let’s delve into this blog post to unlock the potential of AI in revolutionizing your stock market strategies.

 

What is AI Stock Prediction Software?   

AI stock prediction software is a software that analyzes a variety of stock market data indications, like historical price movements, trade volumes, business financials, and news sentiment by using artificial intelligence algorithms. Based on this data, the stock market prediction software then makes an effort to predict future price movements of stocks or other financial instruments.

However, its goal is to assist investors in making better choices by offering forecasts and insights into possible market movements. Some of the popular AI stock price prediction softwares are Trade Ideas, Scanz and so on.

So, if you are planning to build an AI stock prediction software for your trading business, then you should hire a trading software development company who have expertise in AI technology.

 

Why Must Businesses Invest in AI Stock Prediction Software?

Businesses can benefit from investing in the best online trading platforms for predicting stocks with AI technology. So, let’s have a look at the market statistics of the online stock trading market.

 

 

Source: Statista

• It is projected that the worldwide market for online trading would expand at an average yearly rate of 6.4 percent per year, reaching an approximate valuation of 13.3 billion U.S. dollars by 2026.
• Between the second quarter of 2020 and the second quarter of 2021, the number of platform users at many online brokerages, including Robinhood, increased considerably.
• Early in 2021, the number of monthly downloads for popular online trading apps reached a peak.
• In the first half of 2021, SoFi had the best three-year return on stocks of all the robo-advisors we looked at around the world, while E*Trade had the best three-year return on bonds.

Let’s now look at the reasons why must businesses invest in AI stock prediction software:

1. Data-Driven Decision Making
2. Risk Mitigation
3. Competitive Advantage

Now Let’s Discuss these the reason in detail.

 

 

● Data-Driven Decision Making

Stock prediction software empowers businesses to make informed decisions based on real-time data analysis and predictive modeling, allowing them to anticipate market trends and adjust strategies accordingly.

 

● Risk Mitigation

By utilizing stock prediction software, businesses can identify potential risks in their investment portfolios, enabling them to diversify and hedge against market volatility more effectively, thus reducing the impact of adverse market movements.

 

● Competitive Advantage

Access to advanced stock prediction algorithms gives businesses a competitive edge by enabling them to stay ahead of market trends and make proactive investment decisions, potentially leading to higher returns on investment compared to competitors.

Thus, now you know why you must invest in AI stock prediction software. However, if you want a mobile app that shows the most recent trading and stocks news and updates like price quotes, indicators or volume, then it would be best to build an app like TradingView.

 

Steps to Build An AI Stock Prediction Software

Although every AI project is unique, these are the general steps involved in developing an AI stock prediction software. As per an AI development company, it is advisable to begin with a small prototype to analyze your software idea that must be in your budget. But first, you should go through essential steps to build an AI stock prediction software. Let’s take a look at the steps.

1. Define Goals of Your AI Project
2. Relevant Data Collection And Preparation
3. Choose the Best Tech Stack For Your Software
4. Select the Best AI Algorithm
5. Develop AI Stock Prediction Software
6. Test Your AI Software
7. Deploy the AI Stock Prediction Software
8. Software Maintenance & Updates

Now let’s discuss these steps in detail.

 

 

1. Define Goals of Your AI Project

Firstly, you should begin by defining the objective of your AI stock prediction project. You need to understand your main motive to build an AI stock prediction software. What happens if you are unable to identify a specific issue?

Finding the right data to gather, the right algorithms to use, and determining the project’s success all become challenging under such circumstances. Based on your challenges, your stock prediction software’s measurable goals and objectives are determined. The data collecting procedure is also started according to your particular set of difficulties.

 

2. Relevant Data Collection And Preparation

As we know that data is essential when creating an AI stock prediction software because it assists to instruct software to grasp patterns and do predictions. Once you have collected the data, it is vital to execute the data presentation.

Choosing the appropriate tech stack for your AI stock prediction software is crucial for carrying out data preparation as it affects the pipeline used for data pretreatment. The next stage will provide you with detailed data about the technology stack.

 

3. Choose the Best Tech Stack For Your Software

The selection of a technology platform is a critical decision that has an immediate impact on the reliability, scalability, and efficacy of your AI solution. Which technology platform should be chosen for a given AI in stock trading marketplace?

There are many aspects to consider while developing your AI stock prediction software, including project goals, data characteristics, flexibility, and security requirements. All the steps involved, from choosing the correct AI programming language to building the framework for your AI stock prediction software, are crucial.

 

4. Select the Best AI Algorithm

Your AI project will either succeed or fail based on your choice of AI algorithm. There are numerous AI algorithms accessible for various kinds of issues. So how are the right algorithms chosen for the creation of AI systems?

Choosing the best algorithm for your AI stock prediction software involves considering a number of variables, including the kind of problem you want to answer, the data that is available, the algorithm’s precision and efficiency, and the knowledge and resources needed to build an AI stock prediction software.

 

5. Develop AI Stock Prediction Software

The generative AI services team incorporates trained AI models to predict stock markets into your stock prediction software development process through the use of the agile software development life cycle. Architecture, UI, front-end, and back-end software development for apps based on AI are carried out simultaneously.

On the other hand, your software’s combined AI and ML models are constantly being trained to increase the accuracy of the integrated model. Furthermore, developers verify predictive models to see if the AI system is producing the desired outcomes.

Finally, your AI stock trading app development solution’s efficiency is tuned to meet your business needs. Your AI systems will be tested and evaluated after development is finished.

 

                              

6. Test Your AI Software

The software testing services team utilizes this method to ensure that your AI solution is free of any errors. Testing also gives you the chance to evaluate the effectiveness, consistency, bias or fairness, and feedback of your AI system. Before deploying AI-powered structures, you have to verify all of the results of your AI solution. Your software for artificial intelligence is error-free and perfect.

 

7. Deploy the AI Stock Prediction Software

The first step in deploying an AI product is setting up the infrastructure, which involves choosing the networking components and the deployment location—cloud or on-premise, depending on your needs. AI systems are deployed using the Continuous Integration/Continuous Deployment methodology. Your AI system is deployed into cloud platforms using either automatic or manual technologies.

 

8. Software Maintenance & Updates

Updating and keeping an eye on the usefulness and reliability of your AI system is crucial if you want precise results from your software. The AI software maintenance and support services team therefore keeps an eye on things and updates your AI software with fresh data as needed in the future.

This is how you can build an AI stock prediction software from start to finish. Now, let’s have a look at the essential features to integrate in your AI stock prediction software.

 

Essential Features To Build An AI Stock Prediction Software

Do you want to build AI stock prediction software? Well, creating an AI software requires essential features that play a significant role in making your software successful. So, as per a custom software development company, below are the crucial features of AI stock prediction software that you must incorporate in it.

i. ML for Predictive Analysis
ii. Real-time Market Data Integration
iii. Integrating NLP for Filtering
iv. Automated Reporting
v. Add Metrics
vi. Data Protection Standards
vii. BackTesting Tools
viii. Risk Management Protocols

 

Now, Let’s discuss these features in detail.

 

● ML for Predictive Analysis

The software may analyze historical stock data, spot patterns, and forecast future movements by using past patterns by implementing machine learning algorithms. This improves the process of predicting future stock fluctuations, which helps investors make decisions.

 

● Real-time Market Data Integration  

Users can get up-to-date insights and make fast investment decisions thanks to the software’s seamless integration with real-time market data streams, which guarantees that the software only processes the most recent data.

 

● Integrating NLP for Filtering

By utilizing NLP, news, social media, and other textual data might be analyzed. With the assistance of a digital transformation services provider, you should integrate cutting-edge technologies like NLP, AI and sentiment analysis in your software, pertinent data filtering, and the effect of news on stock prices into a more thorough prediction model is the goal of this feature.

 

● Automated Reporting

Automated reporting features produce clear, concise summaries of trends, insights, and projections. Because of this, consumers can make better-informed decisions with less effort put into the analytical process.

 

● Add Metrics  

You can also build a stock market website and include a variety of performance metrics that enhance the website’s analytical capabilities. Metrics like accuracy, precision, and recall provide users with a quantitative measure of the website’s predictive accuracy.

 

● Data Protection Standards    

Strong data protection guidelines guarantee the confidentiality and security of sensitive financial data, fostering user confidence and satisfying legal compliance obligations.

 

● BackTesting Tools

Users are able to gauge the predictive model’s efficacy by running it against previously collected data using back testing tools. Insights regarding the model’s dependability can be gained by evaluating its performance in the past.

 

● Risk Management Protocols

Implementing risk management protocols helps users assess and mitigate potential risks associated with their investment strategies. Its feature adds an extra layer of protection and aids in making informed and risk-aware decisions.

So, these are some of the crucial features that you should integrate in your AI stock prediction software. However, you can also incorporate the above features when you build an app like StockEdge. Now, let’s determine the cost to build an AI stock prediction software.

 

Cost to Build An AI Stock Prediction Software

The AI stock prediction software development cost may vary which can range between $8,000-$30,000. There are some major factors that are responsible for the cost fluctuation. For example, software complexity, functionality, coding process, platform choice, testing, UI/UX design, AI/ML integration technology and so on.

Analyzing the actual cost to build an AI stock prediction software can be challenging due to AI project complexity and requires a mobile app development company to assist in knowing the core facts on the basis of your project needs. Let’s now look at the table that showcases the cost on the basis of complexity.

 

Software Complexity	Cost Estimations
Basic AI stock prediction software	$8000-$14000
Moderate AI stock prediction software	$15000-$22000
Advanced AI stock prediction software	$30000

 

Now that you clearly know the cost estimation of AI stock prediction software as per complexity level, let’s now look at the formula to measure the cost.

 

Total software development cost = Hourly Rate of AI Developers * Total Development Time

Moreover, if you want to know the cost to build a stock prediction app with AI technology, then you must consult with an AI app development services provider like Dev Technosys. They would facilitate an overview on cost estimation and let you know the budget.

How Much Does It Cost To Maintain An AI Stock Prediction Software?  

Now that you know the cost to build an AI stock prediction software, it is time to examine the software maintenance cost. Basically, the cost to maintain a software can be around 15-20% of total development cost. For example, if your AI stock market prediction solution cost is $20,000, then you will be charged maintenance costs around $3000-$4000 annually.

Additionally, there are some factors that affect the maintenance cost of a software. Let’s have a look:

• Hosting
• Data acquisition and processing
• Model Monitoring and Support
• Security and compliance
• New features updates
• Level of maintenance required

So, these are some of the aspects that are responsible for the fluctuation in the software maintenance cost. It’s crucial to budget for data, infrastructure, and ongoing maintenance to ensure the software remains effective and competitive in the dynamic financial landscape.

Let’s Build Your AI Stock Prediction Solution With Dev Technosys!    

This blog post informed you about the whole procedure to make an AI stock prediction software solution, such as the general time frame and the AI software development steps. As you can see, creating AI stock prediction software is an exhausting and long procedure. Hence, if you want to get the results you want, it’s a good idea to hire AI developers that are very proficient and competent.

If you are planning to build an AI stock prediction software solution, Get in touch with Dev Technosys. As a top ML development company, we rely on our team of highly skilled developers, designers, and quality assurance experts. Let us know about your requirements, and we’ll begin developing your AI stock prediction software.

Frequently Asked Questions!

1. How To Build An App Like Stock Trainer?

To build an app like Stock Trainer, you need to follow the below steps:

• Define app goals
• Research market & competitors
• Choose a platform
• Design user-friendly UI/UX
• Develop features
• Integrate API for market data
• Test rigorously, fix bugs
• Launch & market app

2. How Long Does It Take to Build An AI Stock Prediction Software?

The complexity, features, and team experience all affect how long it takes to develop an AI stock prediction software. Depending on the amount of data needed, developing, testing, and refining the model might take anything from 2-8 months or more.

3. How Much Does It Cost To Build a Trading Platform Like the eToro?

The cost to build a trading platform like the eToro may fluctuate depending on factors like project complexity, features, design, platform type and so on. Generally, it can be around $10000-$25000 or more. To know the exact cost estimation, share your requirements with us.

4. What Are The Top 5 AI Stock Prediction Apps?

There are many best AI app for stock trading that predict stocks, however, we have jotted the best ones for you. So, have a look:

• Tickeron
• Trade Ideas
• TrendSpider
• Scanz
• StockHero

5. How Does AI Stock Prediction Software Work?

AI stock prediction software uses complex algorithms to analyze vast amounts of historical stock data and identify patterns. Users input the stocks they’re interested in, and the software generates predictions based on its analysis.

Also, users can view these stock predictions to inform their investment decisions. The software will provide users with regular updates to adapt to changing market conditions.