Research with point-in-time data
Use market data such as OHLCV bars, options quotes and Greeks, SEC filings, insider and congressional trades, earnings, economic releases, FRED series, and Polymarket data.
Scalar Field for AI agentic trading
What is AI agentic trading?
AI agentic trading uses intelligent agents to move from market research to strategy logic, scheduled execution, and live position management. On Scalar Field, a strategy agent has its own cash allocation, isolated position book, target-based order execution, reconciliation, and NAV tracking.
Turn logic into a strategy agent
A strategy runs scheduled code, has its own cash allocation, maintains an isolated position book, and tracks NAV separately from other strategies.
Execute by target position
Strategies specify the desired position size. Scalar Field computes the buy or sell delta, checks buying power, routes orders, and reconciles fills with the broker or venue.
How Scalar Field makes trading agents practical
The core distinction is that a Scalar Field strategy is not just a chat response. It is an automated trading agent connected to a venue, running scheduled code, managing a defined capital allocation, and staying reconciled with the live brokerage state.
Isolated position books
Multiple strategies can share the same brokerage account, but each strategy only sees and manages its own cash, holdings, and trade history.
Brokerage-aware reconciliation
Scalar Field checks pending orders, applies fills, compares aggregate strategy positions with broker holdings, and can freeze a strategy when a real mismatch persists.
NAV and performance tracking
Strategy NAV is computed from cash plus live position values, giving each automated agent its own performance history, charts, win rate, and P&L summary.
Venue-specific execution
Scalar Field is built around a venue registry spanning brokerages and exchanges such as Alpaca, Public.com, Robinhood, Webull, E*TRADE, Polymarket, and Jupiter DEX.
Built from research data to broker-aware execution.
Scalar Field connects natural-language research with documented market datasets and trading venues. Use it to screen opportunities, monitor catalysts, build rules, trade through chat, or promote a strategy into an automated agent with its own ledger and lifecycle.
Read the docs behind the workflow
This page summarizes the trading and market-data documentation. These references explain how strategies, venues, reconciliation, and data sources work in detail.
Strategy agents
Read how automated strategies use isolated books, target-based execution, and NAV tracking.
Supported brokerages
See active venues, asset-class groups, and chat trading functions.
Reconciliation
Understand pending order tracking, aggregate verification, and freeze/unfreeze behavior.
Market data catalog
Browse datasets for equities, options, tokenized assets, prediction markets, filings, and macro data.
AI agentic trading FAQ
What is AI agentic trading?
AI agentic trading uses autonomous or semi-autonomous agents to monitor markets, analyze data, manage a strategy position book, and execute trading workflows according to user-defined logic.
How does Scalar Field support automated strategy agents?
Scalar Field strategy agents run on a schedule, manage their own cash allocation and isolated positions, execute target position sizes, and track NAV over time for performance reporting.
Which markets can Scalar Field agents work with?
Scalar Field supports workflows across US equities, options, prediction markets, tokenized assets on Jupiter DEX, macro data, SEC filings, earnings, insider trades, and other market datasets.
How does Scalar Field keep strategy positions in sync?
Scalar Field automatically reconciles pending orders, strategy ledgers, aggregate positions, and broker holdings. If a mismatch persists beyond the settlement grace period, the affected strategy can be frozen until reviewed.