Skip to main content

​
Agentic Trading

Agentic trading uses AI agents to help research, structure, test, monitor, and support execution of trading strategies. Instead of only placing trades manually or relying on fixed algorithms, users can describe a thesis and use agents to help convert it into a strategy workflow.
Algorithmic trading usually follows predefined rules written in code. Agentic trading is more flexible: users can describe objectives in plain English, and AI agents can help research data, structure logic, test assumptions, monitor conditions, and support execution workflows under user-defined rules.
Scalar Field is better understood as an AI agentic trading desk, not a blind trading bot. It helps users build, backtest, refine, and monitor strategies. Any execution workflow remains user-directed, with trades initiated at the user’s discretion.
A trading bot usually follows fixed rules or signals. An AI trading agent can help with a broader workflow: understanding a market thesis, building strategy logic, testing assumptions, monitoring changing conditions, and supporting user-directed action.

​
Strategy Building

No. Scalar Field is designed so users can build strategies using plain-English prompts. You can describe what you want to test, and the platform helps translate that idea into structured strategy logic.
Yes. Users can start with ready-made strategy templates or create custom strategies from scratch based on their own market thesis.
You can build strategies around stocks, options, earnings, macro events, news cycles, filings, disclosures, insider activity, institutional ownership, prediction markets, and broader market themes.
Scalar Field can help users explore and structure trading strategy ideas based on a market hypothesis. The goal is not to provide blind stock picks, but to help users convert ideas into testable strategy logic.
Yes. Users can build strategies around themes such as AI infrastructure, rate cuts, energy shocks, defense spending, earnings cycles, or other market narratives. Scalar Field helps translate the theme into a basket, rules, filters, and backtestable logic.
Yes. Scalar Field can help users define strategies with multiple legs, hedges, triggers, rebalance rules, event filters, and risk conditions.

​
Backtesting

Yes. Scalar Field helps users backtest trading strategies across historical market data, events, and user-defined conditions.
Yes. Users can describe a strategy in plain English and use Scalar Field’s AI-assisted workflow to help structure and test it.
Yes. Users can build and test strategies around events such as earnings, news cycles, macro releases, disclosures, filings, and other market catalysts.
Yes. Scalar Field supports options strategy backtesting, including strategies across individual stocks, not just popular ETFs like SPY and QQQ.

​
Options

Yes. Users can create and test multi-leg options strategies involving different strikes, expiries, legs, triggers, and conditions.
Yes. Scalar Field allows users to backtest options strategies across individual stock options, helping traders go beyond only the most common ETF options.
Scalar Field can help users create AI-assisted options strategy workflows that can monitor conditions, evaluate triggers, and support user-directed action based on defined strategy logic.

​
Research & Data

Scalar Field helps users build strategies using connected market data and research context, including prices, options data, earnings, filings, disclosures, macro events, insider activity, institutional ownership, and other relevant datasets where available.
Yes. Scalar Field can be used as an AI stock research platform where users ask market questions, explore data, and convert research into testable strategy logic.

​
Execution & Brokers

Scalar Field supports broker-connected workflows where available. Broker connectivity helps users move from tested strategy signals toward user-directed execution infrastructure.
Scalar Field provides AI-assisted trade execution technology infrastructure. All trade executions are initiated at the user’s discretion.
User-directed execution means the user remains responsible for initiating trade execution decisions. Scalar Field can support strategy workflows and execution infrastructure, but trades are initiated at the user’s discretion.
Yes. Users can create AI-assisted agents that monitor strategy conditions, market events, signals, and triggers based on user-defined logic.