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 Tech Stack
I went with:
- Frontend: React Native with Expo SDK 54 and Expo Router for file-based routing
- Backend: FastAPI (Python) on Google Cloud Run
- Database: PostgreSQL 16 on Oracle Cloud (free tier)
- AI: Google Gemini for the in-app trading mentor
I chose React Native because I wanted to ship on Android first and eventually iOS without rewriting everything. Expo Router made navigation dead simple. Just create a file and you have a route.
FastAPI was perfect for the backend. It's fast, async, and the automatic OpenAPI docs saved me hours of debugging API contracts.
What the App Does
Algomaya lets you:
- Paper trade 150+ US stocks and crypto with $100K virtual money
- Build algo strategies visually using RSI, MACD, Bollinger Bands. No coding needed.
- Backtest strategies on years of real historical data
- Deploy trading bots that run automatically
- Learn with 50+ structured courses from beginner to advanced
The idea was simple: most people want to learn algo trading but the tools are either too expensive (Bloomberg Terminal), too complex (QuantConnect), or too risky (real money). I wanted to build something where a complete beginner could open the app and start learning within 2 minutes.
Biggest Technical Challenges
1. Real-Time Market Data on a Budget
Yahoo Finance APIs are unreliable and break constantly. I ended up building a caching layer that fetches from multiple sources and falls back gracefully. The key insight: for a paper trading app, data delayed by 1-2 minutes is totally fine. Users don't care about millisecond accuracy. They care about learning the patterns.
2. Backtesting Engine Performance
Running a strategy across 5 years of daily candles for 150+ stocks sounds simple until you actually try it. I had to optimize the backtesting loop, pre-compute indicator values, and cache results aggressively. PostgreSQL with proper indexing handles the historical data surprisingly well.
3. Subscription Management
Google Play billing is painful. react-native-iap v15 changed field names (subscriptionOfferDetailsAndroid not subscriptionOfferDetails), the documentation is sparse, and testing requires license tester accounts. I spent 3 full days just getting subscriptions to work correctly.
4. The AI Assistant
I integrated Google Gemini as an in-app trading mentor called Maya. You can ask it any trading question and it responds with context about your portfolio. The trick was crafting the system prompt to make it educational rather than giving direct trading advice. Important for compliance.
What I Learned About Growing as a Solo Dev
Content is Everything
I wrote 170+ blog posts on algorithmic trading topics. RSI strategies, MACD crossovers, backtesting guides, the works. This drives organic traffic to the website and establishes credibility. If you're building a niche app, become the content authority in that niche.
Play Store ASO Matters More Than You Think
My initial app title was generic. After researching Google Trends, I found that "paper trading" has 4x more search volume than "trading simulator" in India. Small keyword changes in your title and description can dramatically change your impressions.
Free Tier Needs to Be Generous
I initially set free limits too low (5 watchlist items, 5 AI chats). Users bounced before seeing the value. I doubled them to 10 each and retention improved. The free tier is your marketing. If it feels restrictive, people leave before they ever consider paying.
The Numbers (Real Talk)
- 1,500+ registered users
- 293 installed audience (Play Store)
- 4.0 star rating with 14 reviews
- Revenue: Minimal. Most users are on free tier.
- Cost: ~$10/month (Cloud Run + domain)
It's not a unicorn. But it's a real product used by real people, and I built every piece of it myself.
What's Next
- iOS launch
- Push notifications for price alerts
- More AI-powered strategy suggestions
- Community features for sharing strategies
Try It Out
If you're interested in algo trading or just want to practice stock trading without risking real money, check out Algomaya. It's free on the Google Play Store.
I'd love feedback from the dev community, especially on the technical architecture. Drop a comment or reach out at contact@algomaya.com.
Top comments (1)
1,500 users on about $10 a month, solo, is a great result, and the point about the free tier being your marketing matches what I've seen.
One thing worth thinking about for a product aimed at beginners: a visual strategy builder plus a backtester is the fastest way for a new trader to overfit. They nudge the RSI period and the thresholds until the equity curve looks good, and the final backtest shows the best of maybe 50 tries without saying so. Two cheap additions would set Algomaya apart from most retail tools:
Also worth a note in the backtester: running it on today's 150 stocks over five years quietly excludes the companies that dropped out or went bust, which flatters every strategy a little.
If it's useful, I maintain a free API that computes the deflated Sharpe, probability of backtest overfitting and minimum track record from those inputs, with no signup (canlicapital.com/developers). Happy to help wire it into the results screen if you want to try it.