Identical trading terminals in the background, with distinct gold data streams feeding a research terminal in the foreground.

NexusTrade · Agentic Trading

I saw agentic trading coming in 2023. Here's what I'm predicting next.

I think we've got about five years of abundant opportunity before trading agents become commonplace. The next edge is alternative data.

Austin Starks ✦ Founder, NexusTrade ✦ October 9, 2026 ✦ 10 min read

I was right from Day 1 about the biggest opportunity in investing.

On September 29, 2026, Robinhood announced Robinhood Agents: research markets, create strategies, and trade through a dedicated account, right inside its app. More than 150,000 customers had already opened accounts through its earlier agentic trading offering. The new embedded Agents and Agent Apps are coming soon to eligible U.S. customers, with manual trade approval on by default.

Robinhood's original announcement showing its September 29, 2026 date and headline — Source: Robinhood Newsroom, “Robinhood Puts the Power of Hedge Funds in Every Trader's Pocket”, September 29, 2026. This screenshot was captured on October 9, 2026.

I was building LLM trading tools in May 2023, more than three years before Robinhood's announcement. My later o1 trading experiment reached 512K views and 227K reads on Medium.

Medium's original lifetime analytics showing 512K views, 227K reads, and the September 15, 2024 publication date — Source: Medium's lifetime analytics for my September 15, 2024 article. This screenshot was captured on October 9, 2026.

Now here's my next prediction: we have about five years of abundant opportunity in agentic trading. As agents become ubiquitous, the advantage will shift to the data they can access.

It started as a chatbot. Now it runs the research.

When I first wrote about AI trading in 2023, the LLM was mostly an interface. Instead of going into the UI and clicking a bunch of buttons, you described your idea. The chatbot configured the trading rules and spit out a backtest.

That was useful. But I still had to come up with the idea, decide what to test, interpret the results, and tell it what to try next.

Today's workflow is much sharper. With the tools and permissions in place, I can start with a vague idea and have an agent research it, build a strategy, backtest it, optimize it, and deploy it. It can investigate financial questions before it even decides which trading rules to create.

I wrote about that research potential in December 2024, in “This technology will BREAK Wall Street.” I showed o1 answering a financial research question, using the answer to develop a trading idea, and turning that idea into a strategy. In April 2025, I tested GPT-4.1 on financial-data queries and argued that asking questions in plain English would change how people worked with data.

My earlier article titled “I just tried OpenAI's updated o1 model. This technology will BREAK Wall Street” — My December 22, 2024 article is available on NexusTrade. This screenshot was captured on October 9, 2026.

Today, the agent can come up with the next question and carry the research forward itself.

Robinhood is playing catch-up

Robinhood's agents can retrieve financial statements, news, SEC filings, and technical indicators; screen stocks; read real-time quotes; and place stock, options, and crypto orders.

The gap is the research loop I've been building: create a strategy with deterministic rules, run those rules through historical markets, optimize them, then execute the same rules on live market data and track what happens.

Robinhood's published native toolset doesn't include a strategy backtesting or optimization engine. Its order-preview tools simulate an individual order. They don't test years of a strategy's behavior.

What Robinhood's agents can do todayRead the company's native tool list, including market data, screening, order previews, and execution.robinhood.com

In NexusTrade, the agent develops the strategy and the trading engine executes its rules. I can inspect exactly what makes it buy or sell, compare its backtest with a benchmark, and follow its performance after deployment.

I expect Robinhood to catch up. A company of that size isn't going to leave such an important part of financial research to smaller builders forever.

But by the time it does, the easiest opportunities may already be crowded.

We're in a golden age of agentic trading

Back then, it took me years of experimenting to find even a cherry-picked backtest that beat the S&P 500 across two or three different market regimes.

I'd sit there for hours driving the conversation, trying different parameters, and doing the research myself. I basically became a mini-quant because I wanted to show my audience what the platform could do and prove that it could work.

For me, that process now starts with a single prompt.

A labeled example research request for Claude Code connected to NexusTrade — This example prompt asks Claude Code to research congressional buying, compare strategy candidates with SPY, and deploy a validated candidate to a paper account.

Give your coding agent a trading research questionConnect Claude Code or Codex to NexusTrade and use its research, strategy, and backtesting tools.nexustrade.io/developers

I've used Claude Fable 5 through Claude Code to create options-strategy candidates, run backtests, compare optimization approaches, and prepare deployment records.

Read the public Fable campaign logFollow the research, historical tests, rejected candidates, and deployment decisions in the open-source runbook.github.com/austin-starks/public-portfolio-challenge

Years of doing this by hand taught me what the agent needed to know: which questions to ask, how to turn an idea into rules, what to compare, and when to go back and try something else. I've distilled that into Aurora, NexusTrade's autonomous research agent.

You give her a question. She researches it, creates strategies, tests them, and works through the results. She can deploy trading bots when you authorize it. You can watch her work in real time, open the tool results, inspect the rules, and follow the bots after deployment. Here's how Aurora works.

A real screen-recorded walkthrough of Aurora's completed SPY research session — This screen-recorded walkthrough shows Aurora's completed SPY research session at 2× speed. She created a moving-average strategy and a buy-and-hold benchmark, then compared their backtests from 2020 to 2025.

I've used this workflow to build algorithmic strategies running across two real-money public portfolio challenges. The accounts total $41,980.42 as of October 9, 2026.

The original challenge started with $25,000. I've used it to take AI-generated strategies from research into live trading, including the options work documented in the Fable campaign. Its account value is $28,158.98.

The Semis + Biotech challenge started with $13,500. Grok Bot supplied the stock ideas, and I used Codex to develop the executable options rules around them. Its account value is $13,821.44.

Those account values include cash and marked positions. You can open both public portfolios and follow their trades.

This embed shows the current original challenge. Open the original portfolio or inspect the Semis + Biotech portfolio. The figures above were captured on October 9, 2026.

I have a high risk tolerance, access to the latest coding agents, and years of infrastructure for them to work with. For me, the cost and effort of trying an idea have fallen dramatically. I spend more time deciding what deserves testing while the agent does the work.

That's why I think we're in a golden age of agentic trading. Alpha here means a repeatable return advantage beyond the chosen benchmark after trading costs. I think that opportunity is still bountiful, and I predict this window will last roughly another five years.

There are ideas people never tested because collecting the data, writing the software, and maintaining the system took too much work. Agents have made that backlog much easier to explore.

By around 2031, I expect these workflows to be commonplace. Brokerages will catch up. People will copy what works. Having an agent build and monitor a strategy could feel as ordinary as setting a price alert.

The opportunity is to use that capability before everybody else does, and build for what happens afterward.

The strategies that work now won't stay special

In September 2026, I published an analysis of 222,010 backtests. That research gives me a better starting point for deciding which ideas to test. Agents can use those findings to build and compare strategies much faster than I could by hand.

But other people's agents can investigate the same ideas using the same market data. I expect the advantage to shrink as more of them discover and trade the same opportunities.

Here's an actual example. In February, I asked Aurora: “Create the best momentum-based strategy for fundamentally strong stocks.” After testing competing ideas, I published three of the resulting strategies.

The original February backtest screenshot showing the quality strategy returning 36.94% versus SPY's 15.97%, alongside two other candidates — This is the original screenshot from my February 4 experiment. The left strategy returned **36.94% versus SPY's 15.97%** in the held-out 12-month backtest, with modeled slippage of 0.3% per trade.

That strategy became The “All-Weather” Winner. It selects 15 S&P 500 stocks with strong quality ratings and the highest 30-day momentum, then rebalances monthly.

Open the All-Weather Winner's public paper portfolioFollow the strategy's forward paper performance after the February historical test.nexustrade.io/shared-portfolio

Those inputs are public fundamentals and price history. Another agent can test the same idea. Once millions of agents are doing that, I expect the trades to get crowded.

Published strategies have already shown this kind of decay. McLean and Pontiff studied 97 stock-return predictors and estimated a 32% reduction in predictor-portfolio returns attributable to trading informed by their publication. Read the Journal of Finance study.

I expect agents to speed that process up. By 2031, I think many obvious fundamental and technical strategies that show an edge today will be noise. Earnings and cash flow will still matter to companies. Making money after costs from familiar rules around that public information will get much harder.

While Robinhood plays catch up getting their agents to execute backtests, the next revolution in finance is alternative data.

The next edge is the data

Once everybody can ask an agent to build, backtest, and deploy a strategy, the competition moves to what those agents know. If they're all working from the same prices, earnings reports, and indicators, I expect them to keep discovering the same trades.

The advantage shifts to information other people's agents don't have, or haven't thought to use. That could be satellite observations, weather, specialized industry records, or your own dataset that reveals customer demand before an earnings report. Even connecting public datasets in a way nobody else is testing could reveal an opportunity.

I also expect that competition for information to take uglier forms. Some participants will seek insider information. Others will try to manufacture the data that trading agents consume.

Imagine 100,000 AI agents on Reddit gradually manufacturing a narrative about a stock. Other trading agents gather those posts as social-media data, interpret the narrative as genuine investor sentiment, and trade on it. The operators have created the signal themselves, then used other agents' reactions to it. That's the market manipulation I expect to see more of as agents compete for an information advantage.

My bet is on collecting overlooked data and turning it into indicators agents can actually test.

I'm putting that data to work

I've been building custom indicators into NexusTrade so people can use their own datasets in a strategy.

You bring the data, turn it into a numeric indicator, and combine it with trading conditions. An agent can help find or transform the source, and you can test the resulting rules alongside price, fundamentals, and other inputs. Here's how the custom-data workflow works.

I've also published historical datasets for three kinds of disclosures:

Congressional trades: House and Senate trading disclosures going back to 2012. Download the congressional dataset.
Corporate insider trades: SEC Form 4 disclosures from executives, directors, and other reporting insiders, going back to 2006.
Institutional holdings: SEC Form 13F disclosures going back to 2013. Download the insider and institutional datasets.

The dates matter. Someone can make a trade before you learn about it. A quarterly holdings report tells you about a past position. Your backtest has to use the information you could actually have known on that day.

My next big bet takes this further: a custom-built trading model on custom-built data.

Through NexusTrade, I've spent years collecting trading strategies and their backtest results. The corpus behind my September research contains 222,010 backtests. That research history is itself an alternative dataset.

Price history tells a model what the market did. My backtesting data records which strategies were tested against that market and how they performed: the rules, the parameters, and the variations people tried.

I intend to train my own large language model on the data I've collected building NexusTrade. It will learn from agent conversations, my backtesting data corpus, and, of course, reinforcement learning.

I want it to learn which research decisions led to useful strategies, which experiments failed, and what to test next. The goal is a trading agent trained on years of actual strategy-development work, with a research process that keeps learning from its next experiment.

That's the alternative-data approach I'm pursuing. Other agents can download the same stock prices. They won't have the years of conversations and experiments I've collected while building NexusTrade.

Try NexusTrade

Give Aurora a trading idea, have her build and backtest it, and try it in a paper portfolio. If you prefer working through Claude Code or Codex, connect your coding agent to NexusTrade.

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