AI Agents in Automated Trading (Q3 2025 Report)

AI‐driven trading agents – autonomous software that senses markets, makes decisions, and executes trades – are rapidly reshaping both equity and crypto markets. Modern agents combine advanced machine learning models (deep reinforcement learning, transformer-based forecasting, and large language models) to analyze market data, news, and on-chain signals in real time. Unlike fixed-rule bots, these AI agents adapt and learn: for example, hybrid frameworks like FLAG-Trader fine-tune a pre-trained LLM as a trading policy network and optimize it with gradient-based reinforcement learning on trading rewardsaclanthology.org. Similarly, multi-agent systems (e.g. HedgeAgents) deploy many specialist agents that coordinate hedging strategies; one study even reported simulated 70% annualized returns over three years using LLM-powered agentsarxiv.org. These developments exemplify how technical advances (LLMs, transformers, RL) are being harnessed to generate trading “alpha” and optimize portfolios more dynamically than ever before.

Image: AI-powered trading analytics on multi-monitor setup (source: Pixabay)

Technical Approaches: Reinforcement Learning, LLMs, Transformers

AI trading agents rely on several key techniques. Reinforcement Learning (RL) is used to train agents by trial and error in a simulated market environment. An RL agent observes market states (prices, volumes, indicators) and learns to maximize cumulative reward (e.g. portfolio Sharpe ratio). For instance, research platforms implement Markov Decision Processes where states include prices and news sentiment, and actions include buy/hold/sell; the RL agent updates its policy to maximize trading performancearxiv.org. RL allows strategies to adapt to regime changes, learn from past mistakes, and even simulate competition among multiple RL agents to study market impactimf.orgimf.org.

In parallel, Large Language Models (LLMs) are being used as decision-making brains. Unlike simple rule-based systems, LLM-powered agents parse unstructured data (news, reports, social media) and generate trading insights or orders in natural language. Cutting-edge architectures like FLAG-Trader integrate an LLM as the policy network: a pre-trained LLM is partially fine-tuned on financial data, and then reinforcement learning (policy gradients on PnL rewards) further optimizes it for trading tasksaclanthology.org. This leverages the LLM’s deep reasoning (e.g. for sentiment or event analysis) while also giving it goal-oriented learning. Other projects (FINMEM, FINAGENT, FINROBOT, etc.) similarly propose hierarchical and memory-augmented LLM agents for high-frequency trading and portfolio allocation, showing that text-processing models can be extended into sequential decision domainsarxiv.orgarxiv.org.

Transformer-based deep learning models (e.g. BERT, GPT, Vision-Language Models) also underpin prediction engines. These models capture complex temporal patterns in price series and incorporate diverse signals (technical indicators, seasonality, on-chain data). For example, practitioners combine neural networks with classical forecasting (like NeuralProphet) to improve trend prediction and anomaly detection. In practice, many AI trading systems fuse multiple models: an LLM may generate natural-language reports or trade suggestions, which a separate RL model then tests in simulation. Overall, the convergence of LLM “thinking” and RL “acting” is a major trend, as evidenced by FLAG-Trader’s success in financial tasks through policy-gradient optimization of a fine-tuned LLMaclanthology.org.

AI Agents in Traditional Equity Markets

In equity markets, AI agents are used by quant funds, hedge funds, and even retail platforms to select stocks, time trades, and rebalance portfolios. These systems can ingest vast structured and unstructured data – from price feeds to earnings transcripts – far beyond human capacity. For example, AI-driven ETFs (exchange-traded funds) use proprietary models to pick or weight stocks. Amplify’s AI-Powered Equity ETF (ticker AIEQ) uses IBM Watson NLP and ML to choose mid-cap stocks; by mid-2025 it had delivered a ≈+8.2% year-to-date return versus +3.8% for its benchmark categoryschwab.wallst.comschwab.wallst.com. More broadly, a June 2025 review found that hedge funds incorporating generative AI enjoy roughly 3–5% higher annualized returns than peerspapers.ssrn.com. This lift is largely in equity strategies, where AI helps identify subtle value patterns and news-driven alpha.

Portfolio optimization is another area where AI shines. Agents continuously adjust asset weights to maximize long-run Sharpe ratio, considering transaction costs and risk limits. Here reinforcement learning can out-perform static Markowitz allocations by adapting to changing volatility regimes. In practice, sophisticated portfolio managers blend classic mean-variance techniques with ML-driven forecasts: e.g. using RL to fine-tune rebalancing thresholds or employing neural networks to predict next-day returns. Even traditional asset managers are deploying AI for risk management and order execution: portfolio managers now feed AI agents with their risk budgets and let them execute optimal trades across multiple venues. The net effect is more automated, faster decision-making.

Equity-focused AI agents also use LLMs for analysis. Products like AlphaSense and BloombergGPT (for internal research) rapidly summarize earnings calls and filings. Traders use ChatGPT-like agents to screen news or simulate earnings scenarios. While these LLM assistants are not trading autonomously (yet), they form part of an “AI+human” workflow. Nonetheless, fully automated equity agents exist too. For instance, the Tickeron platform issues a crypto token and plans (in Q3 2025) to offer token-holders exclusive AI trading agents with 5- and 15-minute time-frame modelstickeron.comtickeron.com. Tickeron’s roadmap specifically mentions “AI trading systems” and “Financial Learning Models” combining technical analysis with AI to detect patternstickeron.com.

Image: Automated stock trading algorithms execute based on AI-generated forecasts (source: Pixabay)

AI Agents in Cryptocurrency and DeFi Markets

Crypto markets are especially fertile ground for AI agents. Price volatility, 24/7 trading, and transparent data encourage automated strategies. The term DeFAI has emerged to describe AI-augmented DeFi: blockchain protocols powered by autonomous agents. Unlike simple arbitrage bots, crypto AI agents are often designed as self-learning on-chain programs. For example, Yellow.com reports that crypto AI agents – autonomous programs with wallet permissions – reached a combined market cap of $15+ billion in early 2025, with projections of $150B by year-endyellow.com. These agents use LLMs to parse market context and on-chain indicators, then trigger trades via smart contractsyellow.comyellow.com.

Key crypto use-cases include automated trading, yield farming, and liquidity management. Onekey’s August 2025 report highlights platforms like Flower and Shinkai, which enable both retail and professional traders to deploy custom AI strategies on DEX aggregators with minimal codingonekey.so. These tools compete with traditional bots by incorporating real-time oracle data and machine-learning signals. For instance, Shinkai v1.0 (released July 2025) lets local AI agents charge stablecoin payments and operate privately while interacting on-chaininvesting.com. Shinkai agents are already used to analyze market trends and execute Solana-based arbitrage, coordinating across decentralized networksinvesting.com.

Yield optimization is another rapidly growing area. Advanced AI agents continuously scan lending and liquidity pools, shifting funds to the best APRs. One onekey example is the Virtuals Protocol: its AI-driven strategies allegedly delivered “record-breaking” token performance in 2024onekey.so. More broadly, AI models are embedded in DeFi asset management platforms to rebalance user portfolios, migrate liquidity between AMMs, and maximize farm yields based on predictive market analysis. Similarly, AI agents are being used for fraud/security: firms like Chainalysis now use ML agents to flag anomalous transactions in real time, protecting DeFi platforms and investors.

Image: Crypto trading bot analyzes blockchain data (source: Pixabay)

Platforms, Funds, and Protocols (Q3 2025 Examples)

Many specific projects illustrate these trends. In traditional markets, AI-driven funds and products are proliferating. Besides AIEQ above, firms like QuantConnect and Numerai empower hedge-fund-style quants to run ML models on historical data. Smaller equity trading platforms (e.g. TradingHero, a community project) even integrate RL or Prophet-based models to offer forecasting and reports (its roadmap lists RL integration planned for 2025). Meanwhile, in crypto, a surge of startups are building AI agents:

• Shinkai (Crypto/DeFi) – Open-source LLM-agent framework (deployed July 2025) supporting USDC micropayments and Coinbase X402 walletsinvesting.com. Thousands of users now run Shinkai agents locally to trade on Solana or perform DeFi tasks.

• Cherry AI (AIBOT) – A Telegram/web trading platform rebranded in 2025 to focus on AI utilities. Its roadmap targets Q3 2025 for a full web platform launch with AI sentiment analysis and trade-suggestion bots to rival tools like Axiomcoinmarketcap.com. Cherry’s token ecosystem uses fee-based staking to fund these AI features.

• Tickeron ($TICKERON Token) – A hybrid equity/crypto trading site that released a Solana-based token. By Q3 2025 it plans to introduce an “exclusive AI trading system” for token holders, including short-interval ML agentstickeron.comtickeron.com. Its “Financial Learning Models” use AI to detect market patterns across various assets.

• Virtuals Protocol (DeFi) – A tokenized fund using AI strategies. It has promoted itself as achieving “record-breaking” returns via AI-managed allocationsonekey.so. Such protocols typically allow community validators to deploy strategy bots in exchange for fees or governance tokens.

• HedgeAgents (Research) – While not a commercial fund, this multi-agent RL system (submitted to WWW 2025) demonstrates the potential of LLM-driven trading – claiming huge backtested returnsarxiv.org. It underscores how academia is actively exploring AI agents’ impact on markets.

Other notable mentions include AI Connect’s “AI Agent” (SGA Q3 2025) which promises broad finance use-cases from crypto trading to portfolio managementglobenewswire.com. We also see incumbent quant platforms (e.g. BloombergGPT, Aladdin) gradually embedding AI agents under the hood. In sum, Q3 2025 features a blend of decentralized protocols (Shinkai, Virtuals, Cherry) and centralized products (AIEQ, Tickeron) actively deploying or integrating AI agents.

Impact on Performance, Alpha, and Optimization

Real-world results are mixed but promising. AI-driven strategies often demonstrate outperformance: as noted, AI-equipped hedge funds see ~3–5% extra annual returnspapers.ssrn.com, and AI-managed ETFs like AIEQ have beaten peers (e.g. +8.2% vs +3.8% YTD)schwab.wallst.comschwab.wallst.com. In DeFi, protocols claim higher yields, though extreme figures (like HedgeAgents’ 70%/yr) come from simulations and should be viewed cautiously. What is clear is that AI agents help portfolio optimization by reacting faster to new information and by exploring strategies continuously. Agents can adjust allocations intra-day or migrate liquidity to emerging opportunities – tasks too complex for static models.

From a risk standpoint, AI agents also bring benefits: they enable better risk management via anomaly detection and can diversify strategies (e.g. via multiple AI agents with different signals). However, there are concerns about model opacity and market stability. The IMF and regulators warn that proliferating algorithmic AI trading could amplify flash events if many agents react similarlyimf.org. Transparency is therefore key: many new AI trading platforms include explainability (model audit reports, visual trade histories) and impose safeguards (stop-loss constraints) on their agents. In practice, professional shops still backtest extensively and monitor AI agents with human oversight.

On portfolio construction, AI agents improve efficiency. They can simultaneously optimize for multiple goals (alpha, volatility, ESG scores, etc.), something rigid quant models struggle with. Using advanced architectures, agents solve multi-objective optimization via reinforcement learning or genetic algorithms. For instance, an agent might dynamically trade a basket of stocks and futures to maintain a target beta or volatility – effectively automating what a team of quant traders used to do. Case studies show that portfolios managed by AI agents adjust faster to regime shifts: e.g., in volatile Q2 2025 markets, some AI-driven funds reportedly reduced drawdowns by increasing cash or hedges more quickly than benchmarks (anecdotally supported by industry sources, though data is proprietary).

Trends and Emerging Topics

Looking ahead, several trends stand out:

• DeFAI Proliferation: AI + DeFi is hot. We see new “AI agent tokens” and decentralized agent marketplaces emerging. Federated learning (e.g. Flower framework) and on-chain compute will advance agent capabilities while preserving privacy.

• Hybrid Models: Firms are combining transformers with RL and evolutionary strategies. “LLM coaches” are being built to suggest novel trading rules that smaller RL agents then optimize.

• Autonomy & Marketplaces: Platforms like Shinkai illustrate a future where AI agents can buy and sell services (analysis, data) on a marketplace, earning tokens. This could lead to an “AI economy” within trading ecosystems.

• Regulatory Focus: Regulators (SEC, EU) are drafting rules for AI in trading (auditability, data governance). Expect more scrutiny on how AI agents make decisions, and new standards for testing AI trading algorithms.

• Hardware and Infrastructure: Custom AI chips (as noted by NVIDIA/AMD stock rallies) and cloud offerings are scaling to meet the compute demands of real-time trading agents. We are also seeing specialized ML-as-a-Service for finance.

• Human-AI Collaboration: Rather than full automation, many “agents” today assist human traders. Tools like ChatGPT integrated with trading platforms help with research and risk analysis. However, the line is blurring as agents take on execution.

Overall, Q3 2025 sees AI trading agents moving from theory to practice. The leading-edge usage involves fully autonomous bots on blockchains and hybrid AI/HFT desks in finance. Investors and allocators should watch for: rigorous vetting of AI models, emerging AI-native trading funds, and integrations between traditional finance infrastructure and open-source AI ecosystems. In the near term, expect further performance data from these AI systems (particularly in crypto yields) and heightened dialogue on AI’s role in market liquidity and fairness.

Sources: We cite industry reports, SEC filings, and recent research (see references). Notable sources include a June 2025 SSRN review finding 3–5% alpha uplift for AI hedge fundspapers.ssrn.com, Amplify’s AIEQ performance dataschwab.wallst.comschwab.wallst.com, and specialist crypto AI analysesonekey.soonekey.so. Key product roadmaps and news releases (Tickeron, Shinkai, Cherry AI, AI Connect) provide concrete Q3 2025 timelinestickeron.cominvesting.comcoinmarketcap.comglobenewswire.com. All information is drawn from up-to-date industry sources and reflects the cutting-edge of AI in trading as of Q3 2025.