There are hundreds of trading platforms. Most of them solve the same problem: get your order to the exchange. Trade OS solves a different problem entirely—how do you know the order is worth placing in the first place?
Two Backtesting Engines, One Strategy Definition
Most platforms offer backtesting as a feature. We offer it as a workflow with two complementary engines.
The first is a vectorized engine—blazing fast, designed for screening and rapid iteration. You can test a strategy across years of data in seconds. It's how you find ideas worth exploring.
The second is an event-driven engine that simulates real market mechanics: order-by-order execution, realistic fill models, latency, queue positioning, and margin requirements. It's how you validate whether an idea survives contact with reality.
Key insight
Both engines accept the same strategy definition. Write your rules once, screen them fast, then promote the winners to realistic simulation without rewriting anything.
AI-Driven Research, Not Just AI-Driven Alerts
Every platform is rushing to add an "AI assistant" that tells you when RSI is oversold. That's a push notification, not intelligence.
Trade OS uses AI differently. Our research layer is built on semantic search across a knowledge base that includes news, earnings transcripts, macro data, strategy research, and your own uploaded documents. When you ask a question, the system retrieves relevant context, weighs recency and source reliability, and synthesizes an answer grounded in evidence.
More importantly, AI participates in the backtesting process itself. Machine learning models generate trading signals that feed directly into the strategy engine. The system trains on rolling windows, validates out-of-sample, and tracks feature importance so you understand why the model is making its predictions.
Backtesting with the Full Picture
A typical backtest runs price data through a set of rules and produces an equity curve. That's a start, but it misses the context that real traders operate in. Trade OS backtests can incorporate:
- ▸Sentiment data — How was the market feeling when this signal fired? A buy signal during extreme fear means something different than the same signal during euphoria.
- ▸Macro factors — Yield curve shape, volatility regimes, sector rotation signals, and economic indicators.
- ▸Alternative data — Institutional ownership changes, corporate actions, earnings surprises, and factor exposures.
- ▸Market regime detection — Automatic classification of trending vs. mean-reverting markets, with strategy parameters that adapt to the current regime.
Walk-Forward Validation as a First-Class Feature
The #1 reason trading strategies fail in production is overfitting. Trade OS treats walk-forward validation as a core feature. Every strategy can be automatically tested using expanding or rolling windows that separate training data from validation data. The system calculates an overfit ratio so you can see at a glance whether your edge is real or an artifact of curve fitting.
110+ Strategies, Not 5 Templates
Most platforms ship with a handful of example strategies. Trade OS includes over 110 production-ready strategy templates spanning trend-following, mean-reversion, volatility, momentum, volume-based, multi-indicator, and event-driven approaches. Each comes with tested default parameters and support for both backtesting engines.
Composite Workflows, Not Isolated Tools
Where Trade OS really shines is in how everything connects. Instead of running separate tools manually, you can trigger composite workflows that chain the full research pipeline:
- ▸Strategy Pipeline — Scan candidates, compare performance, optimize parameters, walk-forward validate, run diagnostics, calculate position sizing, and save the result. One command.
- ▸Stress Test Suite — Walk-forward analysis, Monte Carlo equity curves, bootstrap confidence intervals, and regime-specific stress tests. All in one pass.
- ▸Research Report — Full technical analysis, regime classification, strategy comparison, volatility profiling, risk assessment, and fundamental overlay. Generated automatically.
Built for AI Agents, Not Just Humans
Trade OS exposes its entire capability set as a tool ecosystem that AI agents can use autonomously. An AI research agent can discover strategies, validate them through walk-forward testing, stress-test the results, and surface only the opportunities that pass every validation gate—all without human intervention.
This isn't a chatbot skin on a trading app. It's a platform where AI and human researchers operate with the same tools, the same validation standards, and the same risk controls.
See It in Action
Trade OS is opening to early adopters soon. Get in touch to see how the platform can transform your research workflow.
Request AccessTrade OS is currently in beta. Features described here represent our current platform capabilities. Trading involves risk—past performance is not indicative of future results.