Younger digital-native generations are reaching the time of their lives when they have to think about their long-term financial health. Many are looking for efficient money management strategies, including trading. Stock trading app development targeted at novice traders has exploded in popularity recently. Currently, over two-thirds of people who buy stocks are trading digitally [1]—a figure that continues to rise as more people seek the convenience and accessibility of online platforms. At the same time, as executing trades become more convenient, day trading is increasing in popularity. Financial software development aims to recreate the success of apps such as Robinhood and Stash.
We have compiled a list of nine innovative features offered by the top 10 most popular stock trading apps on the App Store. These features deliver significant value to users by improving their experience, democratizing information, or directly increasing the profitability of their trading. Let's examine each of these features to understand how they work, what value they bring to users, and what you'll need to implement them.
Trending features to incorporate in a trading app
The increasing number of tech-savvy consumers interested in trading across both developed and emerging markets creates an excellent time for stock trading app development. The market value for trading apps is expected to double by 2035 [2]. Recent trends, such as commission-free trading, API integration, and data analytics tools, are helping democratize the trading process. Additionally, AI has the potential to enhance efficiency and convenience across nearly all features. It offers personalized recommendations and automates trades, allowing even novice investors to make better-informed decisions.
The features in this list are sorted from the most prevalent that are included in most apps to the least that only a few on the top 10 offer.
Educational resources
Younger generations are increasingly engaged in trading, with 56% of Gen Z in the US and 74% in Canada reporting involvement in trading [3]. While over half of young consumers state that they prioritize improving their financial health when selecting a financial institution [4], for many of them, the first point of contact with trading is high-risk investment markets like cryptocurrency and NFTs. They explain this contradiction by the lack of financial literary education, particularly when it comes to identifying and understanding lower-risk investment opportunities. Despite the growing popularity of financial tools and trading apps, many young investors seem to lack the guidance or resources to move toward investments that align with their long-term financial goals.
Leaders in stock trading app development recognize this disparity, with nine out of the top ten trading apps offering in-app educational content. They typically provide short articles, podcasts, or comprehensive courses. Robinhood, for example, particularly caters to novice investors by encouraging them to start small and gradually increase their trading volumes as they gain confidence. The app also embeds educational information in its push notifications, explaining how certain events affect stock prices or what economic indicators mean for investments.
AI can enhance trading education by offering personalized, real-time learning experiences. Gamified elements like levels, challenges, and rewards—such as earning a free trade after completing successful paper trades—can engage novice traders. With AI, education adapts to each user's experience, allowing them to learn at their own pace while reducing mistakes.
Newsflash integration
The second most popular feature is an embedded newsflash that explains stock movements or alerts users to real-time economic events. Users can filter the news they receive based on sectors or specific stocks of interest, and many apps offer customization options like setting alerts for criteria such as stock volatility or macroeconomic events. Tailored alerts further enhance this by filtering out unnecessary noise, allowing users to focus on information that aligns with their trading strategy. Some platforms even include social features, allowing users to share or comment on articles. Real-time news is crucial in fast-moving markets, where timely information can be the key to executing profitable trades.
Building a newsflash feature relies on integrating APIs from news aggregators like Bloomberg or Reuters, which pull data from various sources, including financial blogs and government agencies.
In more advanced versions, Natural Language Processing (NLP) algorithms can analyze news content to assess its relevance to a user’s portfolio or watchlist. These systems can evaluate sentiment, categorize the news, and rank its importance, providing users with the most pertinent information for their investments.
Cash sweep
Seven out of ten most popular trading apps allow the user to automatically transfer funds from their bank account to the trading platform. A cash sweep automatically moves idle cash in the user's trading account into an interest-bearing or money market fund. Typically, this is a feature users set up when choosing how to transfer funds into their investment account. For example, "Sweep idle cash daily into a money market fund" might be one of the toggleable options.
A cash sweep helps avoid cash drag (having idle funds that aren’t growing). This benefits active traders who might frequently sell assets and keep cash on hand for new opportunities.
Trading apps typically use Plaid, Yodlee, or similar financial data aggregation APIs that securely connect users’ bank accounts. While these services will take care of the security of the data while in transfer, it is the responsibility of the trading app to guarantee security from arrival onwards.
Read more: Trading software development: how-to guide for executives
Customizable search and SRI criteria
More advanced users often have specific individual investment strategies, including the types of stocks they are interested in. The app would need a robust search engine capable of querying based on multiple, sometimes complex, criteria to fit their needs. In stock trading app development, these features present an intuitive UI where sliders, drop-down menus, or input fields allow the user to set parameters. For example, a user might specify they’re looking for tech stocks with a P/E ratio below 20 and a market cap above $1 billion.
Search is carried out through an internal search engine and external APIs that fetch real-time stock data, company financials, news sentiment, and technical indicators.
Going beyond traditional metrics, more users are engaging in socially responsible investing (SRI) to make their trading strategies both profitable and sustainable. This aligns with the views of 76% of financial services executives who believe their organizations have an obligation to address societal issues [5]. There's a clear demand for a search function that includes criteria based on personal values and sustainability. For example, one investor might prioritize clean energy, while another focuses on ethical labor practices. However, incorporating such soft metrics is complex. Currently, only three out of ten top stock trading apps offer this feature, mostly through manually created lists of sustainable stocks. The manual approach isn't scalable and does not give full control over the specific selection criteria to the user.
One solution is to use third-party scoring systems to help users gauge how closely a stock aligns with their criteria. Multiple agencies assign sustainability ratings to companies, and in the EU, large organizations are obligated to publish sustainability reports. An even more promising approach involves using AI and ML algorithms to analyze vast datasets related to ESG factors. These systems could scrape, aggregate, and process information from company reports, news articles, government regulations, and third-party ESG rating agencies. This would enable real-time updates and more comprehensive criteria than manually curated lists.
User portfolio analytics
A feature that delivers immense value to users and can make a stock trading app development project stand out is in-depth portfolio analytics. Data analytics tools take on the role of top-tier financial advisors for investors of any skill level or investment size. They not only display the current situation but suggest areas of improvement, highlight high-exposure areas, and even run "what-if" scenarios to understand how different market changes might affect their investments. Algorithms translate complex data into actionable insights and empower users to make well-informed decisions to optimize and balance their portfolios, manage risk, and work toward their financial goals. This personalized guidance is made possible through technologies like real-time financial analysis algorithms and machine learning models, making data analytics an essential financial advisor within the app.
Advanced stock analytics
Comprehensive data on a given asset’s technical and fundamental factors helps users make more informed decisions, thus improving the app's value. With customizable indicators, users can tailor these metrics to suit their needs, applying different formulas and techniques to get a clearer picture of market trends.
Predictive analytics is another powerful feature, with some apps using advanced algorithms, AI, and machine learning models to forecast stock prices and market trends. Through analyzing vast amounts of data, these tools offer predictions that help users adjust their portfolios and manage risk more effectively. Features like sentiment tracking and backtesting also allow users to refine their strategies, whether they’re swing traders, day traders, or long-term investors.
Custom trading automation
Some platforms allow users to automate their trading by setting custom rules through an interface or a coding console. For instance, users can program the app to automatically buy or sell a stock when it hits a specific price or meets certain conditions like changes in volume or technical indicators. This allows users to trade without constantly monitoring the market, as the app carries out their strategy around the clock, even when they’re not actively engaged.
Even with automation, some platforms might require users to confirm transactions, either through a notification or a secondary approval process, to avoid unintended trades. Depending on the jurisdiction, the app might be required to implement security features such as biometrics or two-factor authentication to carry out trades independently on behalf of the client.
Read more: Biometrics in banking: What you should know before implementing
Third-party research
Integrating articles, reports, and updates from trusted research firms like Morningstar or Reuters can significantly enhance the value of a trading app. In-app research summaries provide users with expert analysis, stock ratings, and market forecasts. Reports can focus on specific stocks, financial instruments, or broader topics like economic trends and industry developments. Depending on user experience, the app can offer full reports, charts, models, or short summaries. Less advanced users can benefit from a simplified buy/sell/hold ratio, which gives them a quick, clear snapshot of expert recommendations on a particular stock.
Providers like Morningstar, Refinitiv, and S&P Capital IQ offer APIs, making it straightforward for developers to implement these features and deliver expert data directly to users.
AI support
Although most top trading apps haven't yet unveiled their AI features, surveys of banking and capital markets executives indicate that AI is becoming increasingly prevalent in stock trading app development [6].
AI can provide users with actionable insights, automation, and decision-making tools that would otherwise require significant expertise or manual effort. For beginner traders, AI can act as an advisor by providing personalized recommendations based on their investment profiles and risk tolerance. This might include suggesting optimal trade times, portfolio adjustments, or alerting users to opportunities aligned with their goals. AI-driven trading bots can execute automated strategies around the clock based on pre-set criteria such as technical indicators or market conditions, allowing for precision trading without constant oversight.
Read more: Five non-trading cutting-edge use cases for AI in capital markets
Wrap up
To succeed in the stock trading app market, your app must offer intuitive features and a seamless user experience. Integrate AI-powered insights, customizable trading tools, and personalization to attract both novice and experienced traders. Start by creating a solid app architecture that can easily scale and adapt to changing market demands. Focus on key best practices, such as ensuring strong data security, smoothly integrating APIs, and optimizing the user experience. These steps will boost engagement and foster long-term user loyalty.
Ready to develop a stock trading app that meets market demands and drives user engagement? Reach out to our team of experts today to start building a solution tailored to your business needs.
References
- Embedded finance: Creating the everywhere, everyday bank, IMB 2023
- Stock Trading App Market Research Report, Trading Platform 2024
- Gen Z and Investing, Finra & CFA Institute 2023
- Consumers expect financial advice, Financial Brand 2022
- The Banking in 2035: Global Banking Survey Report, SAS
- Ibid.
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