Momentum Stock Screener Research

Agentic Trading: How to Give an AI Trading Workflow Better Research Candidates

Agentic trading means using an AI-assisted workflow to gather information, evaluate rules, or coordinate research steps around a trading process. The risk starts when the agent receives weak inputs. A better workflow begins with clean candidate lists, documented rules, backtesting, benchmark comparison, and human review before any action.

AI can help organize research. It should not turn a vague watchlist into a trading decision.

What agentic trading means in plain English

Agentic trading is an emerging term for trading workflows where an AI system can take multiple steps toward a goal. That may include reading data, checking conditions, summarizing market context, preparing a research note, or passing information to another tool.

In practice, people use the phrase to describe several different setups:

an LLM that helps summarize research;
a scripted workflow that checks market conditions;
a watchlist builder that feeds an analysis process;
an automated strategy lab;
a broker execution system that places orders.

Those setups do not carry the same risk. Research support and broker execution are different categories. Keep them separate.

This article focuses on the research side: how to give an AI trading workflow better candidates before any separate system evaluates next steps.

The biggest problem is not the AI model

Many agentic trading discussions start with the model: which LLM, which agent framework, which prompt, which automation stack.

The first problem usually sits earlier in the process.

What candidates are you asking the agent to review?

If the candidate list comes from social media, hot tickers, newsletter picks, or a broad scan with unclear rules, the agent inherits that noise. It may summarize the noise in a confident voice. It may rank weak inputs. It may turn a loose hunch into a clean-looking report.

A better workflow starts before the agent touches the list.

Better candidates come from rules, not vibes

A clean candidate list starts with written rules. For momentum research, that may mean rules around trend, relative strength, liquidity, volume, recent highs, or benchmark behavior.

A simple candidate rule might read:

Find liquid stocks trading above their 200-day moving average with positive 3-month relative strength versus SPY.

That rule is not perfect. It is useful because you can review it, test it, change it, and compare it with alternatives.

A weak candidate process sounds more like this:

Find stocks that look ready to run.

That prompt may produce interesting names. It does not create a disciplined research process.

A practical candidate workflow for agentic trading research

Use a staged process before you let an AI workflow summarize or rank candidates.

1. Define the research universe

Decide what the workflow can inspect. Examples:

U.S. large-cap stocks;
ETFs only;
liquid stocks above a minimum dollar volume;
a sector-specific universe;
a manually approved watchlist.

The universe matters because it controls what the agent can see. A broad universe can create noise. A narrow one can miss context.

2. Choose the momentum rules

Pick rules that match the question you want to test.

Common momentum candidate rules include:

price above the 50-day moving average;
price above the 200-day moving average;
3-month or 6-month relative strength;
new 20-day or 52-week highs;
volume above a recent average;
performance versus SPY, QQQ, RSP, or IWM.

Do not add filters until the list looks good. Write the rules first, then review the output.

3. Backtest the rule set

A candidate rule deserves historical context before it becomes an input to automation.

Backtesting helps you ask:

How often did this rule produce events?
Did the rule work only in one market period?
How did the model compare with a benchmark?
Did the results depend on a small number of outliers?
Did the idea fail during specific regimes?

A backtest does not predict the future. It tells you how the rule behaved under historical assumptions.

4. Compare with benchmarks

An agentic workflow should not evaluate candidate rules in isolation. Compare historical behavior with benchmarks such as SPY, QQQ, RSP, or IWM.

Benchmark comparison helps you avoid a common mistake: treating a complicated process as useful when a simple market exposure did the same job or did it better.

5. Hand the agent a research packet, not a trade instruction

Once you have candidates, rules, and historical context, the agent can help prepare a research packet.

A useful packet might include:

the candidate list;
the rule set that produced it;
data-through date;
benchmark comparison;
model event count;
plain-language caveats;
questions for human review.

That packet should support review. It should not tell a person to buy, sell, or hold.

What an AI trading workflow can help with

A carefully bounded AI workflow can help with research tasks such as:

summarizing a candidate's recent context;
checking whether a candidate still matches the rule set;
comparing candidate notes across the same template;
drafting a watchlist review;
flagging missing data or stale assumptions;
preparing questions for a human to review.

Those tasks can save attention. They do not remove risk.

What an AI trading workflow should not do without strict controls

Do not let hype blur categories. A research assistant, a backtesting tool, a trade-instruction service, and a broker execution system are not the same thing.

Before any workflow touches real money, the risk changes. That requires controls beyond content research, including compliance review, account permissions, execution limits, audit logs, and human approval.

This article does not cover broker execution. It covers candidate research before execution enters the picture.

Common mistakes in agentic trading research

Mistake	Why it matters
Starting with hot tickers	The agent can summarize popularity instead of quality.
Prompting for recommendations	You push the workflow toward advice instead of research.
Skipping benchmark comparison	A complex workflow can hide weak model context.
Ignoring data-through dates	The agent may reason from stale or mismatched data.
Treating a backtest as proof	Historical behavior does not guarantee future results.
Removing human review	The workflow can miss context, constraints, or risk.

The fix is not a better prompt alone. The fix is a better research pipeline.

Where Perray fits

Perray's Momentum Lab can help produce momentum-focused candidate lists and historical model context that a human can review before any separate agentic trading workflow is considered.

Perray is for educational research and historical model comparison. It does not provide investment advice, ticker recommendations, broker execution, or buy/sell/hold instructions.

Use Perray when you want to ask questions like:

Which candidates matched this momentum rule?
How did the model behave across historical periods?
How did the model compare with SPY, QQQ, RSP, or IWM?
Did this rule produce enough events to review?
Is this candidate list clean enough for a separate research workflow?

Perray is not the agent. Perray is not a trading bot. Perray does not execute trades. It can support candidate discovery and historical model comparison before a human decides what deserves further research.

A safer prompt pattern for AI-assisted research

If you use an AI assistant in a trading research workflow, keep the prompt away from instructions to trade.

A safer research prompt might look like this:

Review this candidate research packet. Summarize the rule set, benchmark comparison, missing assumptions, and risks a human should inspect. Do not recommend whether to buy, sell, or hold any security.

That prompt keeps the agent in a review role. It also forces the workflow to start with a packet, not a vague ticker request.

FAQ

What is agentic trading?

Agentic trading describes AI-assisted workflows that can take multiple research or analysis steps around a trading process. The term can refer to research support, strategy testing, or automation. Research workflows and broker execution systems should be treated as separate risk categories.

Can AI choose stocks to buy?

An AI system can summarize data, rank candidates, or follow rules, but that does not make the output investment advice. A human should review the rules, assumptions, data quality, risks, and compliance boundaries before any decision.

Why do AI trading workflows need better candidate lists?

An AI workflow inherits the quality of its inputs. If the candidate list comes from hype, vague prompts, or unclear filters, the agent may produce a polished summary of weak research. Rules-based candidate lists make the workflow easier to review.

How can a momentum stock screener support agentic trading research?

A momentum stock screener can create a cleaner list of candidates based on written rules such as trend, relative strength, volume, liquidity, and benchmark behavior. That list can become an input for human-reviewed research, not a trading instruction.

Can backtesting make agentic trading safe?

No. Backtesting gives historical context under specific assumptions. It does not guarantee future results or remove execution, compliance, or market risk.

Does Perray run an agentic trading bot?

No. Perray supports educational research, momentum candidate discovery, historical model comparison, and benchmark comparison. It does not execute trades, connect to a broker, provide ticker recommendations, or issue buy/sell/hold instructions.

CTA

Want to give your research workflow cleaner candidates? Run a Free Test in Perray and compare a rules-based momentum model against historical benchmarks before any separate automation enters the process.

Educational research only. Perray does not provide investment advice, ticker recommendations, broker execution, or buy/sell/hold instructions.

Schema notes

Recommended structured data for publication:

BlogPosting for the article;
FAQPage for the FAQ section;
BreadcrumbList for the article path.

Measurement plan

Track:

organic sessions to /blog/agentic-trading-research-candidates/;
Google Search Console queries containing agentic trading, AI trading workflow, AI trading bot for stocks, and agentic trading strategy;
clicks to the momentum stock screener article;
clicks to the stock backtesting software article;
clicks on Run a Free Test in Perray;
clicks to the self-hosted interest list when available;
assisted conversions from AI-trading and automation-related search queries.

References for final publication review

Use authoritative references during final site publication review:

SEC Investor.gov investor education resources: https://www.investor.gov/
FINRA investor education resources: https://www.finra.org/investors
NIST AI Risk Management Framework: https://www.nist.gov/itl/ai-risk-management-framework
Jegadeesh, N., and Titman, S. Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. Journal of Finance, 1993.

Next step: Use these ideas as research prompts, then compare candidates and backtests inside the screener. This is educational research, not financial advice.

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