stratosyncpro.com

StratosyncPro — AI-Powered Algorithmic Trading Platform

A next-generation algorithmic trading platform that lets retail and institutional investors build, backtest, and deploy AI-driven portfolio strategies — managing $45M+ in assets with real-time market intelligence.

Project Overview

An ambitious fintech startup wanted to democratize algorithmic trading — bringing the kind of AI-powered strategy tools that hedge funds use to retail investors and independent wealth managers. We built Stratosync from the ground up: a platform where users can create custom AI trading strategies using a no-code builder, backtest them against 15 years of historical data, and deploy them to live markets with real-time execution, risk management, and performance analytics.

Client Background

Our client is a fintech startup founded by former quantitative analysts from two major hedge funds. They saw a massive gap in the market: institutional algorithmic trading tools cost $50K-$500K per year and require Python/R expertise, while retail 'robo-advisors' offer no customization beyond basic risk preferences. They raised $8M in Series A funding to build a platform that bridges this gap — sophisticated AI trading accessible through intuitive interfaces.

The Challenge

1

Real-time trade execution required sub-200ms latency to be competitive, while simultaneously running AI strategy calculations, risk checks, and compliance validation on every order.

2

The no-code strategy builder needed to translate visual drag-and-drop configurations into efficient, production-grade trading algorithms — without sacrificing the sophistication needed for alpha generation.

3

Backtesting against 15 years of minute-level market data for 5,000+ instruments required massive computational resources and careful handling of look-ahead bias, survivorship bias, and transaction cost modeling.

4

Regulatory compliance was complex — the platform needed to support broker-dealer requirements, KYC/AML flows, fiduciary duty documentation, and real-time position reporting across multiple jurisdictions.

5

The onboarding flow had to seamlessly integrate brokerage account linking (via Alpaca and Interactive Brokers APIs), KYC verification, and initial strategy selection — converting curious visitors into funded accounts.

Our Approach

Trading Engine & Data Infrastructure (Weeks 1-5)

We built the core trading engine in Rust for maximum performance, achieving consistent sub-200ms end-to-end latency from signal generation to order execution. The data infrastructure ingests real-time market data via WebSocket feeds from 4 providers, normalizes it into a unified format, and stores 15 years of minute-level historical data in TimescaleDB for efficient time-series backtesting.

AI Strategy Builder & Backtesting (Weeks 4-9)

We designed the no-code strategy builder using React with a visual node graph editor. Users connect modular strategy components — AI signal generators (momentum, mean-reversion, sentiment), risk filters, position sizing rules, and execution conditions. Strategies are compiled into optimized execution plans and can be backtested against the full historical dataset with realistic transaction cost modeling, slippage simulation, and walk-forward optimization.

Brokerage Integration & Compliance (Weeks 7-12)

We integrated with Alpaca and Interactive Brokers APIs for live trade execution, implementing a robust order management system with pre-trade risk checks, position limits, and automated stop-loss enforcement. The KYC flow uses document verification with automated identity scoring. All trade activity is logged with full audit trails for regulatory compliance.

Dashboard, Analytics & Launch (Weeks 10-14)

We built comprehensive performance dashboards using D3.js and Recharts, showing real-time P&L, risk metrics (Sharpe ratio, max drawdown, VaR), strategy heatmaps, and attribution analysis. The server-side rendering with Next.js ensures sub-second dashboard loads even with complex visualizations. We launched with a controlled beta of 200 users before scaling to general availability.

Solution Highlights

Rust-based trading engine with consistent <200ms execution latency

No-code strategy builder translating visual configurations into production algorithms

15-year backtesting with walk-forward optimization and realistic cost modeling

Real-time risk management with automated position limits and stop-loss enforcement

Results & Impact

Platform now manages $45M+ in combined assets across 1,200+ active trading strategies, growing 25% month-over-month.
Trading engine maintains consistent sub-200ms execution latency with 99.97% uptime, processing an average of 8,500 trades per day.
No-code strategy builder enabled 68% of users to create profitable strategies without any coding experience — the platform's key differentiator.
Backtesting accuracy validated at 94% correlation between simulated and live performance, building user trust in pre-deployment testing.
User acquisition cost reduced by 60% after the guided onboarding flow launched, with funded account conversion rate reaching 34%.
Successfully passed regulatory audits across the US and the platform was selected for partnership by two regional broker-dealers.

"We spent years watching sophisticated AI trading remain locked behind six-figure paywalls and PhD requirements. Stratosync changes that equation entirely. Inventiple built a platform where a financial advisor with zero coding experience can deploy strategies that compete with hedge fund algorithms. The Rust-based execution engine is genuinely institutional-grade."

M

Marcus Webb

CEO & Co-Founder