Main Sequence Data workflows

Main Sequence Is a Data Driven Platform

Whether the outcome is a new prediction, a risk factor, model signals, a presentation, or a dashboard, every result begins with collecting, transforming, and delivering data—and at Main Sequence, that orchestration isn’t an afterthought, it’s the core of our platform. By mastering, unifying, and scaling data workflows through the Main Sequence Data Engine, we give enterprises the foundation for both efficient financial operations and true artificial intelligence.

architectural principles

Main Sequence Data Engine Principles

Simplicity is at the heart of every data workflow. Main Sequence makes it effortless to connect, build, and maintain data pipelines without complexity or friction.
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Our Main Sequence SDK, a Python-based library, enables seamless integration of any data source within a single, unified framework. Users simply provide code that produces sequential time-series updates — the platform then takes care of scheduling, maintenance, and optimization for all read and write operations automatically.

Simplicity in Integration, Creation, and Maintenance

 Forget about managing the data storage stack. The Main Sequence platform lets users focus entirely on building and orchestrating data workflows through DataNodes, while the platform manages storage, optimization, and scaling behind the scenes — without sacrificing transparency or control.

Think of it as a network of interconnected time-series nodes — each feeding the next, governed by a unified data schema and framework that keeps your entire data ecosystem consistent and efficient.

Intelligent Infrastructure, Invisible Complexity

Platform Overview

How the Main Sequence Data Engine Works

Step 1

Define a Data Node

Define a process by simply creating a class. Each node is uniquely identified by hashing its code and configuration, and it automatically links to the Data Engine for persistence.

class RawTrades(DataNode):
  def update(self): ...
Step 2

Build your Data Graph

Construct complex workflows by simply declaring dependencies. The system automatically builds the graph structure, ensuring inputs flow correctly to outputs in the Compute Engine.

def dependencies(self):
  return ["RawTrades"]
Step 3

Deep Logic & Workflows

Orchestrate deep dependency chains. AlphaSignal recalculates automatically whenever VolumeBasedBars or Volatility updates, maintaining data integrity.

return ["VolumeBasedBars", "Volatility"]
COMPUTE ENGINE Data Engine RawTrades 1 VolumeBasedBars 2 Volatility 2 AlphaSignal 3
Platform Overview

The Power of a Revolutionary Data Layer

One-Shot Data Integrations

Main Sequence abstracts away nearly 80% of the architectural complexity behind data workflows, making integrations fast, simple, and reliable.
Even a simple LLM or code assistant can perform a “one-shot” integration — without losing visibility, control, or falling into hidden processes and cumbersome frameworks.

Data Integrity That Turns Data Into Enterprise Value

Every DataNode in Main Sequence is a code-defined workflow, delivering complete governance, traceability, and transparency across your data lifecycle.Think of it as a network of interconnected time-series nodes — each feeding the next, governed by a unified data schema and framework that keeps your entire data ecosystem consistent and efficient.

Scalability & Maintenance

By unifying the data-generation framework and offloading architectural management to the Main Sequence platform, you can scale seamlessly.
Build longer, more sophisticated workflows with minimal overhead — achieving consistent performance, effortless maintenance, and true enterprise-grade scalability.