Why I Built This
I've been fascinated by markets for years. Not the "I read about stocks on Reddit" kind of fascinated — the "I want to understand the mechanics of price discovery at the tick level" kind.
I started building trading algorithms because I was tired of the emotional rollercoaster. I'd analyze a setup, take the trade, watch it go against me, panic, close it, then watch it reverse and hit my original target. Every discretionary trader knows this cycle. The analysis is sound. The execution is where humans fail.
So I started automating. First for myself. Then I realized other traders had the same problem, and the existing solutions were either wildly expensive (institutional platforms), incomplete (webhook-to-broker services where you still build your own strategy), or outright scams (fake signal groups on Telegram).
Quanntick was built to fill that gap: a fully automated trading platform where the algorithms, the execution infrastructure, and the risk management are all handled — and you keep full custody of your funds in your own brokerage account.
The Tech Stack
I built Quanntick as a solo developer. Every line of code, every database schema, every API integration. Here's what powers it:
Backend: Python running on Heroku with four separate processes — a web server for the dashboard, a dedicated worker for the TrendFollower algorithm, a dedicated process for the DayTrader algorithm, and a scheduler for background tasks like referral credits, email automation, and daily reconciliation.
Database: MongoDB handles everything — user accounts, trade history, position tracking, algorithm state, and real-time signal data. The schema evolved significantly over months as the platform grew from a simple signal dashboard to a full copy-trading system.
Real-time Data: Databento provides streaming CME market data. The TrendFollower processes 4-minute bars around the clock. The DayTrader processes 1-minute bars during the cash session. Both algorithms make decisions on every new bar.
Broker Integration: TradeStation and TastyTrade APIs handle order placement and account management. Each subscriber's broker credentials are used to place trades directly in their account. The master-follower architecture means one algorithm runs the strategy, and the follower system replicates trades across all subscriber accounts simultaneously.
Frontend: A Next.js dashboard where subscribers can see real-time signals, track performance, manage their bots, and monitor their positions. Plus the public marketing site with live proof of performance.
Execution Infrastructure: This is the part that took the most engineering. Parallel follower execution using thread pools so all subscriber orders fire simultaneously. OCO (One-Cancels-Other) brackets placed within milliseconds of every entry. Continuous position reconciliation. Double-confirmation systems to prevent false signals from triggering unwanted actions.
The Hard Problems
Building a trading algorithm is the easy part. Building reliable infrastructure around it is where the real engineering challenge lives.
Problem 1: Race conditions. When the master algorithm enters a trade, the follower system has to place orders for potentially dozens of accounts. If any of those orders fail, partially fill, or time out, the system needs to handle it gracefully without blocking other subscribers or slowing down the master. This required careful async architecture and parallel execution.
Problem 2: Broker API reliability. Broker APIs are not as reliable as you might assume. Rate limits, timeouts, authentication token expiry, and inconsistent response formats across different brokers are all real issues. Building a system that works across TradeStation and TastyTrade — with different API designs, different order formats, and different position reporting — required extensive abstraction and error handling.
Problem 3: Position reconciliation. What happens when your system thinks a subscriber is flat but the broker shows they have a position? Or vice versa? These discrepancies can happen due to network issues, partial fills, or manual intervention. The reconciliation engine runs continuously, comparing intended positions against actual positions and flagging any mismatches.
Problem 4: Contract rollovers. Futures contracts expire quarterly. The platform needs to automatically detect upcoming expirations, warn users, block new trades near expiry, and transition to the new contract — all without human intervention. Getting this wrong means placing orders on expired contracts or missing trades during the rollover window.
Problem 5: Protecting subscribers' capital. This is the non-negotiable constraint that drives every architectural decision. Every trade must have a protective bracket. If the bracket fails, the system must detect it and retry. If the system goes offline, the brackets are already at the exchange, protecting the position. There is zero tolerance for unprotected positions in subscriber accounts.
The Launch
The DayTrader algorithm launched live on March 3, 2026. The first week was intense — multiple production incidents that required real-time diagnosis and fixes. An OCO bracket failure that needed immediate patching. A position reconciliation issue that caused false exits. API rate limiting cascades from overly aggressive position checking.
Each incident led to a specific architectural improvement. The OCO bracket system was redesigned with order ID polling for faster detection. The reconciliation engine got a grace period and double-confirmation logic. Rate limiting was addressed with per-account throttling and smart skip logic.
This is the reality of running live trading infrastructure: the first week is a controlled chaos where you discover every edge case that testing didn't catch. What matters is how fast you find, fix, and prevent each issue.
By the end of the second week, the system was stable. By the third week, it was smooth enough that I could step back and let it run without constant monitoring.
What I'd Tell Other Solo Founders
If you're thinking about building a trading platform — or any fintech product — as a solo developer, here's what I've learned:
Start with the risk management, not the strategy. The algorithm is 20% of the work. The other 80% is ensuring that your system can't hurt people. Build the protective layers first, then drop the strategy logic in.
Deploy incrementally. I learned this the hard way after a full file replacement caused an outage. Small, surgical changes. Always compile-check before deploying. Never rewrite an entire file when you can change three lines.
Your first users will find every bug. That's okay. What matters is how fast you respond and how transparent you are about what happened and what you fixed.
Automation is a trust product. People are giving you permission to place trades in their brokerage accounts. That's an enormous amount of trust. Every engineering decision should be made with the awareness that you are handling other people's capital.
Where We Are Now
Quanntick runs two algorithms — the TrendFollower (backtested over 20 years, live since November 2025) and the DayTrader (live since March 2026, 23-2 record). The platform supports TradeStation and TastyTrade, with both signal-only and auto-execution options.
We're CME Group licensed, fully transparent in our trade history, and growing through word of mouth and organic reach.
I built this because I believed automated futures trading should be accessible to regular traders, not just hedge funds with million-dollar infrastructure budgets. If that resonates with you, come watch the algorithms trade. Paper trading is free. You can decide for yourself whether it's worth your trust.