Research-to-trading workflow
The platform combines research, backtesting, and live execution in one workflow, so users can move from idea testing to broker-connected trading without switching tools.
Overview
Scalar Field is an AI agentic trading desk built for quantitative research and execution. It combines market data access, backtesting, strategy automation, and brokerage-connected trading in a single Python-first platform.
The site positions the product around a research-to-live-trading workflow: users can pull datasets, test strategies, and deploy agents across equities, options, prediction markets, and tokenized assets. Documentation examples show direct use of `scalarlib` functions such as `getOHLCV()` and `getOptionsQuotes()` to retrieve market data for analysis and trading workflows.
The platform’s pricing page also shows plan-based access with Pro, Ultra, and Enterprise tiers. Those tiers add higher usage limits, automation features, premium support, custom integrations, and deployment or security options for larger teams.
Core capabilities
Python data access
Documentation exposes Python functions such as `getOHLCV()` for equity and ETF bars and `getOptionsQuotes()` for options quotes, showing a programmatic way to request market data.
Multi-asset market data
The pricing page lists a broad set of datasets, including equities, options, earnings, insider and congressional trades, institutional holdings, analyst ratings, prediction markets, and FRED series.
Brokerage and venue connectivity
Trading integrations include Alpaca, Robinhood, Polymarket, and Jupiter DEX, with additional venues listed as coming soon and custom brokerage integrations available on Enterprise.
Tiered limits and controls
Pricing tiers add larger context windows, longer compute time, automation tools, premium support, and, on Enterprise, private workspaces, SSO/SAML/RBAC, and private cloud or VPC deployment.
Historical and intraday coverage
The docs describe live, intraday, and historical data modes, with examples for daily, minute, and hourly requests, which supports both long-horizon analysis and shorter-term signal work.
Common use cases
Strategy research and backtesting
Use the platform to pull historical daily, hourly, or minute bars for equities and ETFs, then test signal ideas or portfolio rules before connecting them to live execution.
Options analysis
Query options quotes with bid/ask/mid pricing, contract metadata, Greeks, and implied volatility to study options surfaces or build derivatives models.
Broker-connected automated trading
Connect brokerage accounts or supported venues to automate order placement for strategies that have already been researched and validated.
Cross-venue market monitoring
Monitor prediction markets, live quote snapshots, and market data updates to track short-term opportunities across different venues and asset types.
Team deployment and governance
Use Enterprise features when a team needs custom brokerage integrations, private workspaces, security controls, or private cloud deployment.
Pros and Cons
Pros
- Covers research, backtesting, and live execution in one platform.
- Provides Python-accessible market data and reference datasets.
- Supports both historical analysis and near-real-time data for multiple asset classes.
- Includes brokerage and venue connections for automated trading workflows.
- Offers higher-tier controls for teams that need custom integrations, security, or private deployment.
Cons
- The public docs shown here are stronger on data access than on end-to-end workflow details, so some operational behavior remains unclear from the source pages alone.
- Several integrations and plan features are listed as coming soon or custom, which means availability is not uniform across every venue or workflow.
FAQ
What is Scalar Field used for?
Scalar Field is a quantitative research platform that supports research, backtesting, and live trading through a Python library called `scalarlib`. The documentation also shows market data access and brokerage-connected trading workflows.
How do users interact with the platform?
The docs present Python examples for accessing datasets through functions such as `getOHLCV()` and `getOptionsQuotes()`. That indicates a code-first workflow rather than a no-code trading interface.
Does Scalar Field offer different plans?
The pricing page lists a Pro plan, an Ultra plan, and an Enterprise plan. Ultra adds higher usage limits and premium support, while Enterprise adds custom integrations, security options, and private deployment capabilities.
Which brokerages or venues are supported?
The site shows trading connectivity for Alpaca, Robinhood, Polymarket, and Jupiter DEX, with Public.com and Webull listed as coming soon. The Enterprise plan also mentions custom brokerage integrations such as Interactive Brokers, Ameritrade, and Schwab.
What kinds of data does Scalar Field provide?
The docs page says the platform provides compute, data, and infrastructure for live trading through a simple Python library, and the market-data pages show datasets for equities, options, and other reference data. The exact breadth of access depends on the dataset or plan.
Quick Facts
- Category
- AI trading / quantitative research platform
- Platform
- Web documentation plus Python library (`scalarlib`)
- Primary users
- Quantitative researchers and trading teams
- Source domain
- scalarfield.io
- Notable workflows
- Market research, backtesting, automated strategy execution, and broker-connected trading
- Pricing
- Paid plans shown: Pro, Ultra, and Enterprise
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