I Built a Paper Trading and Algo Trading App as a Solo Developer. Here's What I Learned.
About 8 months ago, I started building an app called Algomaya . A paper trading and algorithmic trading simulator for beginners. I built the entire thing solo. Frontend, backend, database, deployment, content, Play Store listing, everything. Today it has 1,500+ users, 50+ trading algorithms, and an AI assistant built in. Here's what the journey looked like and what I learned along the way. The…
About eight months ago, the developer began creating an app called Algomaya - a paper trading and algorithmic trading simulator aimed at beginners. The project was built entirely by the developer, including frontend, backend, database, deployment, content, and Play Store listing. Today, the app boasts over 1,500 users, 50 trading algorithms, and an AI assistant. The development journey and lessons learned are discussed below.
The developer chose to use React Native with Expo SDK 54 and Expo Router for the frontend due to the need for cross-platform compatibility. FastAPI, a Python-based framework, was selected for the backend, offering speed, asynchronous functionality, and automatic OpenAPI documentation generation. PostgreSQL 16 on Oracle Cloud (free tier) served as the database, while Google Gemini provided the AI-powered trading mentor, "Maya."
The app allows users to paper trade 150+ US stocks and crypto using $100K virtual money, and build visual trading strategies using indicators such as RSI, MACD, and Bollinger Bands without coding. The app also offers 50+ structured courses, ranging from beginner to advanced levels.
The developer faced several technical challenges during the development process. The first challenge was obtaining reliable real-time market data under a budget. Yahoo Finance APIs were unreliable, leading to the development of a caching layer that fetches data from multiple sources with a one to two-minute delay. Second, the backtesting engine had to be optimized to handle the performance implications of running a strategy across five years of daily candle data for 150+ stocks.
The developer additionally had to optimize PostgreSQL indexing for quick historical data retrieval. Third, subscription management on Google Play Store was tricky, with the need to adapt to changes in the react-native-iap v15 library and deal with sparse documentation. Finally, the developer worked on crafting a system prompt for Google Gemini to ensure it provided educational advice rather than direct trading recommendations.
Throughout the development process, the developer emphasized the importance of content in growing the app. They wrote over 170 blog posts on various algorithmic trading topics to educate users and establish credibility. Additionally, the developer recognized the importance of App Store Optimization (ASO) to maximize search visibility, finding that titles containing the keyword "paper trading" had four times more search volume than "trading simulator" in India.
To improve retention, the developer increased the free tier's limits, and the app currently enjoys a 4.0-star rating on the Google Play Store with 14 reviews. However, revenue remains minimal, as most users remain on the free tier.
The app has been used by real people and has generated revenue, albeit modest. An upcoming milestone for the developer is the iOS launch, followed by the addition of push notifications for price alerts, more AI-powered strategy suggestions, and community features for users to share trading strategies. Those interested in algo trading or practicing stock trading without risking real money are encouraged to check out Algomaya on the Google Play Store. The developer welcomes feedback and can be reached at contact@algomaya.com.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
