System R AI
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System R AI

Agentic Trading System

Built for active traders deploying meaningful capital in the market for risk-adjusted returns.

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Workflow

How the Agentic Trading System Works

System R follows the trader’s real workflow: personalize the Agentic Trading System, organize work by project, scan markets, research setups, log trades, review performance, and improve as context builds.

  1. Personalize

    Set markets traded, trading style, strategy notes, risk defaults, timeframe, asset focus, preferred output, and profile files so the agent starts with trader-specific context.

  2. Organize

    Create projects for assets, themes, strategies, or market ideas so chats, files, notes, and research stay connected across sessions.

  3. Scan

    Use market scanner filters and presets to find candidates by asset class, direction, momentum, breakout, pullback, volatility, volume, relative strength, and quality gates.

  4. Research and analyze

    Use chat, market data, news, filings, sources, uploaded files, and decision visuals to understand whether a setup deserves action.

  5. Log

    Save trades, rationale, notes, screenshots, imported records, outcomes, and asset-specific details so the decision has a record.

  6. Review

    Review performance, R multiples, P&L, drawdown, risk behavior, mistakes, strengths, patterns, and process quality.

  7. Improve

    As profile, projects, chats, files, logs, and reviews build up, System R learns more about the trader’s process and can support the next decision with better context.

Product surfaces

Core Agentic Trading System Modules

These are the product surfaces traders use to move through the workflow.

  1. Profile

    Personalize the Agentic Trading System with markets traded, trading style, strategy notes, default timeframe, risk per trade, account context, asset focus, output style, memory, and profile files.

  2. Projects

    Organize related chats, assets, themes, details, and project files so longer research threads can stay connected across sessions.

  3. Market Scanner

    Scan equities, options, futures, crypto, forex, commodities, ETFs, and funds with presets such as momentum, breakout, pullback, volatility, high volume, and relative strength.

  4. Research & Analysis

    Ask market questions and build a structured read with market data, news, filings, catalysts, sector context, cited sources, uploaded files, and decision visuals beside the answer.

  5. Trade Log

    Save or import trade records with symbol, asset class, direction, prices, size, risk, fees, strategy, setup, timeframe, notes, tags, outcome, and asset-specific fields.

  6. Trade Metrics

    Review closed trade performance through win rate, R multiples, P&L, expectancy, profit factor, drawdown, distribution, concentration, data quality, and review signals.

Capabilities

Agentic Trading System Capabilities

These capabilities make the Agentic Trading System more useful than a standalone chat, scanner, journal, or dashboard.

  1. Market data access

    Bring quotes, price structure, regime, volatility, liquidity, scanner evidence, and asset-specific context into the workflow.

  2. Research sources

    Use web research, news, filings, earnings context, macro context, and cited evidence where available.

  3. Decision visuals

    Inspect market maps, scenario views, risk distance, charts, and structured visual blocks beside the analysis.

  4. File processing

    Upload profile files, project files, trade imports, screenshots, notes, and supported documents for analysis in context.

  5. Voice chat

    Use voice input when it is easier to talk through a market question, trade review, or planning thought.

  6. Memory

    Carry useful context from profile, projects, prior chats, logs, notes, and review history into future analysis.

  7. Agentic workflow

    System R uses profile, project context, files, market data, research tools, and prior records to move each question toward a structured trading decision.

SYSTEM R AI is software for trading support. It is not financial advice, not a broker, not a signal service, and not a guarantee of results. AI outputs can be wrong. You remain responsible for your own trading and investing actions.

// FAQ

Frequently Asked Questions

Product capabilities, connected context, current autonomy, and risk boundaries.

What is an Agentic Trading System?

An Agentic Trading System uses machine learning, Large Language Models, trading-domain expertise, and connected tools to understand a user's goal, reason through the problem, and complete multi-step trading tasks with user consent where required. System R AI is being released in stages: the current open beta is trader-directed, and the autonomous system is scheduled to launch on or before September 7, 2026.

What is available in System R AI today?

Today, the open beta provides trader-directed market research, analysis, and trade planning. You initiate the work, review the evidence and output, and decide what action to take. The autonomous system and remaining workflow features are scheduled to launch on or before September 7, 2026.

How is System R AI different from a general AI chat?

A general AI chat is built to answer many kinds of questions. System R AI is built specifically for trading and investing, so it frames research and analysis around market conditions, evidence, scenarios, risk, invalidation, and a structured trade plan. The current beta remains trader-directed rather than autonomous.

How does System R AI preserve trading context?

System R AI uses the trading context relevant to the task, such as the market or instrument, timeframe, objective, strategy, risk constraints, available evidence, and the user's instructions. During the open beta, this process is trader-directed. Broader continuity across the agentic workflow will expand with the remaining features scheduled for launch on or before September 7, 2026.

What markets and asset classes does System R AI support?

System R AI is designed to support research and analysis across equities, ETFs, indices, options, futures, forex, commodities, crypto, and funds. The exact instruments, market coverage, data depth, and feature availability can vary during the open beta.

What data and research sources can System R AI use?

Depending on the market and available coverage, System R AI can use market data, price structure, news, company filings, earnings information, macroeconomic context, and cited web research. Sources should be identified where available, and important missing, delayed, or uncertain information should be stated clearly.

Does System R AI execute trades autonomously?

No. In the current open beta, users initiate the research, analysis, and planning process and remain responsible for every approval and action. The autonomous Agentic Trading System is scheduled to launch on or before September 7, 2026, with user consent required where applicable.

What are the risk boundaries?

System R AI is trading-support software, not a broker, investment adviser, signal service, or guarantee of results. Market data may be delayed, research sources may be incomplete, and AI-generated analysis or plans may be wrong. Users must verify critical information, define their own risk limits, and remain responsible for every trading and investment action.