ZORO is putting its AI trading-agent platform in front of the financial creator and fintech community at FinCon 2026, demonstrating how professional traders can convert rules-based strategies into automated trading agents. The Los Angeles startup says its Pro App is designed to keep licensed strategies running continuously, while its planned retail product will let followers deploy those strategies through their own connected brokerage accounts.
ZORO is using FinCon 2026 in Palm Springs to publicly demonstrate an AI trading-agent platform designed to turn professional traders’ existing strategies into continuously running automated systems.
The company is livestreaming a demonstration of its Pro App on September 16, the opening day of FinCon 2026. The event runs September 16–18 at the Palm Springs Convention Center and brings together financial content creators, advisors, educators, fintech companies and brands.
The demonstration represents ZORO’s first major public appearance ahead of the planned launch of its retail application later this fall, according to the company. ZORO’s website currently describes the platform as an environment where users can turn trading ideas into automated algorithms and deploy groups of agents to connected brokerage accounts.
The company’s core proposition is different from simply asking a large language model to make trading decisions.
A trader provides setups and rules in natural language and licenses the resulting strategy through ZORO’s Pro App. ZORO says its technology uses AI to assist in building the strategy but relies on algorithms for live execution rather than having a generative AI model independently decide whether to buy or sell in real time.
That distinction is significant for an AI trading platform. Large language models are designed primarily for language and reasoning tasks, while algorithmic trading systems require deterministic execution, predictable latency, market-data handling and integration with brokerage infrastructure.
ZORO says each live agent remains associated with the trader whose strategy it represents. The company’s website describes a model in which traders can deploy agents to their own brokerage accounts and eventually make successful strategies available through the ZORO marketplace.
The platform also creates a record of trading activity. According to ZORO, each trading call is timestamped using the market price at the moment the call is generated. That creates an execution history that can be compared with market data rather than relying exclusively on historical backtests.
For professional traders, the infrastructure around execution may be as important as the strategy-generation interface. ZORO co-founder and CFO James Kim says the company has built brokerage connections and billing infrastructure intended to allow multiple users to operate a trader’s strategy through their individual accounts.
That model forms the foundation for ZORO’s planned retail marketplace.
Instead of moving money into a central ZORO account, followers connect an existing brokerage account and select a trader’s strategy. ZORO says the follower’s funds remain in that brokerage account while the selected strategy operates automatically.
The company characterizes the approach as an evolution of copy trading. Conventional copy-trading systems generally mirror individual trades after a trader has executed them manually. ZORO’s proposed model instead attempts to deploy the underlying rules as an automated strategy capable of operating continuously and responding to multiple qualifying setups.
Whether that distinction produces better outcomes for individual investors remains an open question. Automated execution can remove some human timing constraints, but it does not eliminate the risks associated with the underlying trading strategy, market conditions, leverage or execution.
The technology also arrives as financial institutions and fintech developers continue experimenting with AI agents. Agentic AI is moving from conversational interfaces toward systems that can perform multi-step actions, interact with external tools and operate with greater autonomy.
Financial services presents a particularly demanding environment for that transition because automated systems can directly affect financial assets. Brokerage connectivity, permissions, auditability, risk controls and regulatory requirements therefore become part of the technology architecture rather than secondary product features.
ZORO is attempting to position its platform specifically around professional traders who already have established strategies. That differs from AI products that focus on generating investment ideas, summarizing financial information or creating research assistants.
The company is led by Chris Hnatko, founder of Spartan Trading, while its leadership team includes Head of Product Hunter Isaacson and co-founder and CFO James Kim, according to the company. ZORO says Isaacson previously built consumer applications with more than 400 million combined downloads, while Kim previously worked at Tinder.
The company’s public launch strategy is closely tied to the financial creator economy. FinCon describes its 2026 event as a gathering for creators, coaches, advisors, influencers and educators, with more than 50 booths featuring financial brands and technology platforms.
That makes the event a relevant venue for a product built around both professional traders and followers who discover strategies through financial content.
ZORO’s immediate milestone is the Pro App demonstration. Its larger test will come when the retail application becomes available and users begin connecting real brokerage accounts to automated strategies.
The technology illustrates a broader direction for enterprise and consumer AI: moving from systems that generate information to software agents that perform actions. In trading, however, the transition carries a higher operational threshold because an autonomous agent is not simply answering a question—it can potentially initiate a financial transaction.
Market Landscape
AI in financial markets spans research assistants, algorithmic trading, portfolio analytics, fraud detection and automated customer services. ZORO occupies a narrower category focused on converting trader-defined strategies into continuously operating automated agents.
The platform’s architecture also illustrates an important distinction within AI agents: the language model can assist with strategy creation while conventional algorithms handle live execution. That separation can reduce dependence on a model making unpredictable decisions at market speed.
Competition comes from established algorithmic-trading platforms, broker APIs, quantitative trading software, copy-trading services and newer AI-native fintech companies. The differentiating factors will include execution reliability, brokerage connectivity, transparency, risk controls and how the platform handles real-money automated activity.
Top Insights
- ZORO is demonstrating a platform that converts professional traders’ rules-based strategies into automated agents designed to operate continuously.
- The company says AI assists strategy creation, while algorithms rather than a live language model handle trade execution.
- Followers are expected to connect existing brokerage accounts and run selected strategies without transferring their funds to ZORO.
- Each trading call is timestamped with the market price at execution time, according to ZORO, creating an auditable activity record.
- The retail application is planned for fall 2026, while the Pro App is already being demonstrated publicly at FinCon.
Power Tomorrow’s Intelligence — Build It with TechEdgeAI
