AI in Algorithmic Trading & Investment Analysis: The Complete 2026 Guide

Updated July 2026  |  Originally published July 2025  |  19 min read

Editor's Update Note — July 2026

This article was first published in July 2025. Since then we have: replaced the widely-repeated but weakly-sourced "89% of global volume" statistic with a transparent range across multiple 2026 research estimates; added a full section on agentic AI trading funds, which did not meaningfully exist a year ago; rebuilt the market-size data with a comparison table showing how much research estimates actually diverge; updated the regulatory section to reflect the SEC's June 2025 withdrawal of its predictive-analytics rule, the EU AI Act's August 2, 2026 high-risk deadline, and the Bank of England's July 2026 Financial Stability Report; and added a systemic-risk section covering algorithmic herding, since this is now a named supervisory priority in the UK and at IOSCO. All statistics below are dated and sourced.

Quick Answer

How much of global trading is AI-driven depends entirely on whose numbers you use. The Bank of England puts UK automated trading above 50% of daily volume in 2026, up from roughly 35% in 2020 (5); other research estimates put global equity volume at 60–75% (4); and a frequently repeated "89% of global volume" figure traces back to a single 2025 marketing source that other outlets have since copied without re-verification (3). The bigger story in 2026 isn't the percentage — it's the shift from rule-based algorithms toward agentic AI: autonomous systems that research, decide, and trade with limited human input. The first fully agentic hedge funds launched in 2026 (9), yet a May 2026 survey of 131 asset managers found only 6% let AI make final investment calls, versus 74% using it for back-office work (10). On regulation: the SEC withdrew its AI-related predictive-analytics rule proposal in June 2025 and now polices AI through existing exam authority rather than a dedicated rulebook (19), while the EU AI Act's high-risk obligations for financial services take full effect on August 2, 2026 (24).

Executive Summary

Algorithmic trading and AI-powered investment analysis have moved from a hedge-fund specialty to the default operating mode of modern financial markets. Machine learning models, natural language processing, and — increasingly — large language model (LLM) agents now sit somewhere in the decision chain of most institutional trades and a growing share of retail ones. What has changed materially since this article's original 2025 publication is not the underlying technology so much as its packaging: 2026 is the year "agentic AI" moved from a research demo to an actual product category, with autonomous trading agents now offered by mainstream retail brokers like eToro and moomoo (34)(35) and by at least two institutional funds built around AI-first decision-making (9)(18).

At the same time, 2026 has produced the clearest evidence yet that adoption and autonomy are two different things. Surveys of institutional managers consistently show heavy use of AI for research, compliance, and operations, but real caution about letting it make unsupervised trading decisions (10). Regulators, meanwhile, have converged on a similar message from different directions: the US has chosen not to write AI-specific trading rules and instead leans on existing exam and enforcement powers (18), the EU has classified many AI-driven financial applications as "high-risk" under a law that becomes fully binding in August 2026 (24), and the Bank of England and IOSCO have both flagged "algorithmic herding" — correlated AI behavior across firms — as a live financial-stability concern rather than a theoretical one (29)(27).

Introduction

Algorithmic trading (algo-trading) refers to systems that automatically execute trades based on predefined rules, statistical models, or AI-driven signals with little or no human intervention at the point of execution. AI-powered investment analysis complements this by processing multi-source data — market prices, filings, earnings calls, news, and alternative data — to forecast trends and manage portfolios. The two have effectively merged: most modern "algo" systems now have a machine learning or LLM component somewhere in the pipeline, whether for signal generation, execution optimization, or risk oversight.

What's genuinely new in 2026 is the emergence of agentic AI as a distinct category. Traditional algo-trading is explicit: if a moving average crosses a threshold, execute a trade. Agentic systems instead use an LLM as a reasoning engine — reading an earnings transcript, weighing it against other signals, and deciding what to do next in natural language before translating that decision into an order (17). This is a genuine architectural shift, not just a rebrand of existing quant tools, and it's why 2026 coverage of this space looks noticeably different from 2025 coverage.

How Big Is the Algorithmic Trading Market, Really?

Ask five market-research firms how big the algorithmic trading market is and you'll get five different answers — not because anyone is lying, but because "algorithmic trading market" means different things depending on whether a firm counts software licensing revenue only, total trading-desk technology spend, HFT firm revenue, or the broader AI-in-trading category. We think readers deserve to see that spread rather than a single falsely precise number. Here's how five recent 2026 reports compare:

Research Firm	2026 Estimate	Forecast	CAGR
Research and Markets (2)	$25.04B	$44.34B by 2030	15.4%
Mordor Intelligence (1)	$20.23B	$29.54B by 2031	7.87%
Lunefi (AI-in-trading scope) (3)	$27.85B	$45.74B by 2030	13.2%
Coherent Market Insights (7)	$3.59B	$6.68B by 2033	9.3%
Verified Market Reports (8)	$14.2B (2024 base)	$37.2B by 2033	11.5%

Why the ~8x spread between the smallest and largest 2026 estimates? Coherent Market Insights appears to be sizing a narrower software/solutions segment, while Research and Markets and Lunefi count a broader mix of platforms, services, and AI-specific tooling. Rather than pick the most flattering number, our working estimate is that the algorithmic trading market sits somewhere in the $20–28 billion range in 2026, expanding at roughly 13–16% a year through the end of the decade — a range that is consistent across four of the five reports above. Institutional investors accounted for the largest trader-type share in most reports, though retail participation is the fastest-growing segment as broker-level tools (covered below) put quant-style features in front of ordinary investors (1)(7).

A note on the "89% of trading volume" statistic: this figure appears across dozens of 2025–2026 articles, nearly all tracing back to a single source (3) with no disclosed methodology. More conservative, better-sourced estimates put algorithmic and automated trading at 60–75% of major equity market volume (4), with high-frequency trading firms — roughly 2% of trading firms — responsible for an estimated 73% of that volume (4). The Bank of England separately reports that algorithmic and automated systems now execute more than half of UK daily trading volume, up from about 35% in 2020 (5), while some analyses cite the IMF describing algorithmic systems handling over 80% of transactions during peak trading hours specifically (5). We'd treat any single-number claim above roughly 75% for overall global volume with some skepticism.

Key Trends Reshaping Algorithmic Trading in 2026

1. The Rise of Agentic AI Trading

The defining shift of the past year is the emergence of AI trading agents that use LLMs as reasoning engines rather than fixed rule sets. Instead of "if RSI drops below 30, buy," an agentic system reads an earnings transcript or news feed, reasons in natural language about what it means, and only then converts that reasoning into a trade decision (17). Open-source frameworks such as TradingAgents and ai-hedge-fund — the latter deploying agents modeled on named investors including Warren Buffett's approach and Michael Burry's contrarian style — have collectively drawn well over 100,000 GitHub stars, though their creators are explicit that these are backtesting and research tools, not live trading products (16)(15).

At the institutional level, two funds have publicly staked a claim to being "AI-first": Lumenai Investments, whose Lumenai Innovation Fund targeted a June 2026 launch built around autonomous agents that generate, evaluate, and manage investment ideas continuously (with humans retained for governance and risk oversight, not stock-picking) (9); and Standard Signal, a 2026 Y Combinator graduate marketing itself as the first hedge fund where AI does the actual trading (10). Worth noting: "first fully autonomous AI fund" claims have been made before and haven't aged well — a Hong Kong fund made an identical claim in 2016 and a US competitor made the same claim later that year; the latter liquidated in 2018 (10). Larger, established managers are taking a more measured public stance: Man Group's leadership has described agentic AI as contributing to alpha-generating strategies without claiming full autonomy (10).

The clearest data point on where the industry actually stands comes from a Mercer survey of 131 asset managers conducted in February–March 2026: 74% reported using AI for operational tasks, 69% used it as a "co-pilot" for research or analysis, but only 6% said AI was making final investment decisions (10). A separate industry survey found the share of hedge fund managers giving staff access to generative AI tools rose from 86% in February 2024 to 95% by September 2025 (11). Read together, these numbers describe an industry that has adopted AI everywhere except the final decision — for now.

Adoption vs. autonomy, in numbers: 95% of hedge fund staff have GenAI tool access (Sept. 2025) (11) · 91% of hedge funds report using or planning to use AI tools (32) · 74% use AI for operational/back-office work (10) · 69% use AI as a research co-pilot (10) · just 6% let AI make the final investment call (10).

2. Retail Brokers Are Shipping Agentic Features Fast

Retail access to AI trading tools accelerated sharply in 2026. eToro began letting users delegate trades to their own AI agents through sub-accounts with defined budgets and risk limits, and opened a developer App Store in April 2026 offering agent skills and a Model Context Protocol server (34). Moomoo followed roughly a month later with "API Skills," which converts plain-English trading intent into structured, executable orders across US, Canadian, Hong Kong, Singapore, and Japanese markets (34). eToro's own research found US retail investors' use of AI tools to build portfolios rose sharply year-over-year through late 2025, with a majority of users specifically wanting to learn more about AI as an investing subject (33). Both eToro and FXCM have since cited agentic AI adoption as a factor in headcount reductions, framing automation as an efficiency lever rather than purely a customer feature (34).

This matters for anyone evaluating retail AI trading tools: explainability and regulatory readiness remain open problems industry-wide, and moomoo's own product materials warn that losses can compound faster with algorithmic and quant-style trading than with manual trading (34). Convenience has clearly outpaced regulatory clarity at the retail level.

3. Compute Infrastructure: GPUs and Quantum-Inspired Optimization

Execution-speed competition has shifted from pure network latency toward compute architecture. Citadel Securities committed $300 million to GPU-accelerated execution algorithms in partnership with Nvidia in November 2025, aiming to cut transaction costs by 15% (1). JPMorgan Chase introduced a quantum-inspired optimization module on its Fusion platform in September 2025, reportedly cutting portfolio-construction runtimes by 20% (1) — a meaningfully more conservative and verifiable claim than the "true quantum computing" narrative some trading-technology marketing still implies. Consolidation also continued: Virtu Financial took a minority stake in AlgoTrader AG to extend white-label algorithmic services to mid-tier funds, and London Stock Exchange Group completed integration of Refinitiv FXall with Tradeweb for multi-asset execution (1).

4. Expansion Across Asset Classes

AI-driven strategies continue to expand beyond equities into forex, commodities, and round-the-clock cryptocurrency markets, where volatility and 24/7 trading hours particularly reward adaptive, always-on systems. Retail brokers are increasingly bundling crypto, tokenized securities, and equities into single AI-assisted workflows rather than treating them as separate product lines (34).

AI-Powered Investment Analysis

Research at Scale

AI agents can now cover far more securities than a human analyst could alone — firms report expanding effective coverage from a few dozen names per analyst to several hundred, with agents standardizing data, flagging anomalies, and surfacing candidate signals for human review (14). Specialized agent roles have become fairly standardized across the industry: sentiment agents parsing news and social data, fundamental agents digesting financial statements, quantitative agents applying statistical models, and risk agents monitoring exposure and drawdown limits, with a portfolio-manager agent typically synthesizing the others' output into a final call (14).

Operational Efficiency Gains

The most measurable AI gains in 2026 aren't in stock-picking but in operations. Funds deploying agentic systems for onboarding, compliance monitoring, and reconciliation report cost savings of up to 50% in those specific functions (12), alongside reported gains such as a 20x increase in data onboarding speed and roughly 1,000x improvements in signal compute speed at some firms (12). Some funds report headcount reductions in the low single digits even as trading volume grew 30–50%, citing AI as a contributing factor (12).

Portfolio Management and Robo-Advisory

AI-driven platforms and robo-advisors continue to offer automated, risk-profile-based asset allocation and rebalancing. This category is now squarely inside the EU's AI Act high-risk perimeter for credit- and investment-related decisions, which is pushing providers toward more documented, auditable decision logic rather than opaque black-box models (25).

Enabling Technologies at a Glance

Technology	What It Does	2026 Example
Deep learning / neural networks	Pattern recognition and price-movement forecasting	JPMorgan's LOXM execution algorithm, which routes orders across venues in real time to reduce slippage (7)
Natural language processing (NLP)	Scans news, filings, and social feeds for market-moving signals	Sentiment-agent roles standard in hedge fund research stacks (14)
Large language models (LLMs)	Summarizes documents, powers research assistants and agent reasoning	Enterprise assistants used as the "horizontal layer" across fund research and drafting (17)
Agentic AI	Autonomous multi-step reasoning, tool use, and (in some cases) trade execution	Lumenai Innovation Fund; eToro/moomoo agent delegation for retail (9)(34)
Quantum-inspired optimization	Classical algorithms mimicking quantum approaches for portfolio/optimization problems	JPMorgan Fusion platform module, ~20% faster portfolio construction (1)

The 2026 Regulatory Landscape

Regulators worldwide have landed on strikingly different approaches to the same underlying concern: AI making financially consequential decisions with limited transparency.

Jurisdiction	Key 2025–2026 Development	What It Means
United States (SEC)	2023 predictive data analytics rule proposal formally withdrawn, June 2025 (19)	No AI-specific trading rule exists or is imminent; the SEC oversees AI through existing fiduciary, conflict-of-interest, and exam authority (18)
United States (Reg S-P)	Amended Regulation S-P reached final compliance date June 3, 2026 (20)	Incident response and oversight of AI vendors are now enforceable obligations for every SEC-registered adviser
United States (Colorado)	Colorado AI Act effective June 30, 2026 (17)	State-level disclosure and impact-assessment duties for high-risk AI affecting financial services
European Union	EU AI Act high-risk obligations fully apply August 2, 2026 (legacy systems: February 2027) (24)(25)	Credit scoring and robo-advisory tools face conformity assessments, documentation, and human-oversight duties; stacks on top of MiFID II algorithmic-trading controls (26)
United Kingdom	Bank of England/FPC developing AI-specific stress tests for "herding" behavior (30)	Supervisors are treating correlated AI trading behavior across firms as a systemic-risk category in its own right
Global (IOSCO)	May 2026 supervisory toolkit for AI in capital markets (27)	Non-binding framework helping regulators worldwide classify AI-system risk and proportion supervisory response

The practical upshot for firms operating across borders: there is no single global standard, and the compliance burden is heaviest where AI touches individual investor outcomes directly (credit decisions, robo-advice, digital engagement practices) rather than pure execution technology, which mostly remains governed by pre-existing market-structure rules like MiFID II (26).

Benefits

• Speed and consistency: Execution in milliseconds removes emotion-driven decisions and improves fill precision, particularly in high-frequency and 24/7 markets.
• Research coverage: AI agents extend effective analyst coverage from dozens to hundreds of securities (14).
• Operational cost reduction: Documented savings of up to 50% in specific back-office functions like reconciliation and compliance monitoring (12).
• Democratized access: Retail platforms now offer no-code strategy builders, copy-trading, and agent delegation that were institutional-only tools a few years ago (34)(35).

Risks and Challenges

Algorithmic Herding and Systemic Risk

When large numbers of firms train models on similar data and deploy similar architectures, their outputs can converge — producing correlated buy or sell decisions across the market simultaneously. Academic and regulatory analyses point to real historical examples of this dynamic amplifying shocks: the August 2024 single-day 12.4% decline in Japan's Nikkei index, triggered by a comparatively modest interest-rate move but amplified by correlated algorithmic unwinding, and isolated incidents like a sudden multi-percent after-hours price spike traced to a data error rather than genuine news (32). The Bank of England's Financial Policy Committee is now developing AI-specific stress tests explicitly aimed at this "herding" risk, and its 2026 systemic risk survey found cyberattack was named among the top five risks to the UK financial system by 82% of respondents (30)(31).

Cyber and Model-Risk Exposure

The Bank of England's July 2026 Financial Stability Report specifically flags that rapid progress in frontier AI models increases the risk of more sophisticated cyberattacks on banks and market infrastructure, alongside elevated equity valuations concentrated in a narrow set of AI-related companies and rising leverage in equity markets more broadly (28)(29).

Explainability and Accountability

Advanced AI models can produce trading decisions that are difficult for humans to audit or explain after the fact, complicating oversight when something goes wrong and raising open questions about who bears responsibility for AI-driven losses (23). Regulators in the US have specifically flagged "AI washing" — overstating a fund's AI capabilities in marketing — as an active enforcement concern (17).

Track-Record Risk with New Agentic Funds

By definition, brand-new agentic AI funds have no meaningful multi-cycle track record. Industry commentary on this space is consistent on one point: allocators should demand audited performance history, governance transparency, and regulatory compliance evidence before treating "AI-managed" as a selling point on its own (10).

Real-World Examples to Watch

• JPMorgan LOXM: A long-standing AI execution algorithm that intelligently routes orders across exchanges to minimize market impact and slippage (7).
• Citadel Securities × Nvidia: $300 million commitment to GPU-accelerated execution, announced November 2025 (1).
• Lumenai Innovation Fund: Targeted a June 2026 launch as an institutional fund built around agentic architecture from the ground up, developed with quantitative research firm ETS Asset Management Factory (9).
• Standard Signal: A 2026 Y Combinator-backed startup marketing itself as an AI-driven hedge fund; unproven at scale as of this writing (10).
• eToro and moomoo: Rolled out retail agent-delegation features within a month of each other in spring 2026, both citing efficiency gains alongside customer-facing features (34).

Should Retail Investors Use AI Trading Tools?

This is a decision that depends on individual circumstances, risk tolerance, and financial goals, and nothing here should be read as a specific recommendation. A few factual considerations worth weighing:

• AI-assisted tools can genuinely lower the technical barrier to strategies that once required coding skill — no-code builders, natural-language order translation, and copy-trading are now widely available (34).
• Lower barriers don't remove risk. Even platform providers themselves caution that losses can compound faster with algorithmic and quant-style approaches than with manual trading (34).
• Institutional adoption data is a useful sanity check: if only 6% of professional asset managers currently trust AI with final investment decisions (10), that's a meaningful signal about where the technology's genuine reliability currently sits.
• Regulatory protections specific to AI-driven advice are still developing in most jurisdictions, meaning existing suitability, fiduciary, and disclosure rules — not new AI-specific safeguards — are what currently apply.

Frequently Asked Questions

What percentage of trading is done by AI and algorithms in 2026?

It depends on the market and methodology used. The Bank of England reports UK automated trading above 50% of daily volume, up from about 35% in 2020 (5). Other estimates put global equity market volume at 60–75% (4). A commonly cited "89% of global volume" figure traces to a single, loosely sourced 2025 article and should be treated with caution (3).

What is the difference between algorithmic trading and agentic AI trading?

Traditional algorithmic trading executes explicit, predefined rules ("if X, then sell"). Agentic AI trading uses a large language model as a reasoning engine that interprets unstructured information — like an earnings call — in natural language and then decides on an action, often coordinating multiple specialized sub-agents (research, risk, execution) before a trade is placed (17).

How big is the algorithmic trading market in 2026?

Estimates range from roughly $20 billion to $28 billion in 2026 depending on the research firm and how narrowly "algorithmic trading" is defined, with most reports projecting 13–16% annual growth through 2030 (1)(2)(3).

Is AI trading regulated? What does the SEC require in 2026?

The SEC has no AI-specific trading rule. It withdrew its 2023 predictive data analytics rule proposal in June 2025 and instead applies existing fiduciary, suitability, and conflict-of-interest standards, with AI use a specific focus of its FY2026 examination priorities (18)(19). Amended Regulation S-P, with a final compliance date of June 3, 2026, does impose enforceable incident-response and AI-vendor-oversight obligations on registered advisers (20).

How is the EU regulating AI in trading and investment management?

The EU AI Act classifies AI systems used in credit scoring and certain investment-related decisions as "high-risk." Those obligations — technical documentation, human oversight, conformity assessment — become fully binding for new systems on August 2, 2026, with legacy systems given until February 2027 (24)(25). This stacks on top of existing MiFID II algorithmic-trading controls rather than replacing them (26).

What are the biggest risks of AI-driven trading?

Regulators point to algorithmic herding (correlated AI behavior across firms amplifying market moves), rising cyber-risk as frontier AI models become more capable, model opacity and explainability gaps, and — for newer agentic funds specifically — the absence of any meaningful audited track record (29)(30)(32).

Can retail investors access AI trading tools?

Yes. As of 2026, brokers including eToro and moomoo let retail users delegate trading decisions to AI agents within defined budgets and risk limits, alongside no-code strategy builders and copy-trading features (34)(35). These tools lower technical barriers but do not reduce financial risk.

Will AI replace human traders and portfolio managers?

Not based on current data. A May 2026 survey of 131 asset managers found just 6% let AI make final investment decisions, versus 74% using it for operational tasks and 69% as a research co-pilot (10). The clearer trend is AI expanding what each human analyst or trader can cover, not eliminating the role outright.

The Bottom Line

AI has moved from augmenting trading and investment analysis to, in a small but growing number of cases, actually conducting it — but the honest 2026 picture is one of uneven adoption rather than wholesale automation. Institutions are pouring resources into AI for research, compliance, and execution while remaining cautious about full decision-making autonomy, and retail platforms have moved faster than most people realize in giving ordinary investors access to genuinely institutional-grade tools. Regulators, for their part, have chosen to extend and adapt existing rulebooks rather than start from scratch — which means the compliance and disclosure standards that already govern financial advice and trading are, for now, doing double duty as AI governance too. Anyone evaluating a claim about AI trading — whether it's a market-size statistic, a fund's track record, or a broker's new feature — is well served by asking for the underlying source and date, exactly as this article has tried to do.

Sources and References

1. Mordor Intelligence, "Algorithmic Trading Market Size & Share Trends" (2026) — mordorintelligence.com
2. Research and Markets, "Algorithmic Trading Market Global Report 2026" — researchandmarkets.com
3. Lunefi, "AI Trading Strategies 2026: Trends, Stats & Tips" — lunefi.com
4. The Paper Trading Journal, "Algorithmic Trading Statistics (2026)" — papertradingjournal.com
5. Business Research Insights, "Algorithmic Trading Market Size & Share Trends, 2035" — businessresearchinsights.com
6. Technavio, "Algorithmic Trading Market Growth Analysis 2026-2030" — technavio.com
7. Coherent Market Insights, "Algorithmic Trading Market Size & YoY Growth Rate, 2026-2033" — coherentmarketinsights.com
8. Verified Market Reports via OpenPR, "Algorithmic Trading Market Size Accelerating at 11.5% CAGR" — openpr.com
9. Hedgeweek, "Lumenai plans launch of fully agentic AI hedge fund" — hedgeweek.com
10. Founderland, "The Race to Build Fully Autonomous AI Hedge Funds" — founderland.ai
11. Sify, "The Dawn of Hedge Agents: How Agentic AI Is Transforming Hedge Fund Operations" — sify.com
12. AlphaSense, "AI in Hedge Funds: Use Cases, Risks, and Best Practices" — alpha-sense.com
13. Digiqt, "AI Agents in Hedge Funds: Use Cases for Alpha & Risk (2026)" — digiqt.com
14. Reelfinancial, "Agentic AI Transforms Hedge Fund Operations with Advanced Market Analysis in 2026" — reelfinancial.com
15. Pinggy, "Best AI Trading Agents in 2026: Do They Actually Make Money?" — pinggy.io
16. GitHub, "virattt/ai-hedge-fund" — github.com
17. Tommaso Maria Ricci, "AI for Hedge Funds: 2026 Costs, Tools and Alpha Playbook" — tommasomariaricci.com
18. InnReg, "SEC Guidance on AI: Rules, Alerts, and Enforcement Signals" — innreg.com
19. MEXC News, "US Wealth Management Technology in 2026" — mexc.com
20. Leigh Coney, "The RIA's AI Governance Playbook" (SSRN, 2026) — papers.ssrn.com
21. U.S. SEC, Press Release 2023-140 — sec.gov
22. WealthManagement.com, "SEC Withdraws AI and ESG Rules for Investment Advisors" — wealthmanagement.com
23. Congressional Research Service, "AI adoption" (IF13103) — congress.gov
24. European Commission, "AI Act | Shaping Europe's digital future" — digital-strategy.ec.europa.eu
25. Alice Labs, "EU AI Act for Financial Services: What Banks & Insurers Must Do" — alicelabs.ai
26. Modulos, "Operationalise EU AI Act in Financial Services" — modulos.ai
27. IOSCO, "Supervisory Toolkit for AI Use in Capital Markets," FR/02/2026 — iosco.org
28. Yahoo Finance UK, "Rapid AI advances increasing financial stability risks, Bank of England warns" — uk.finance.yahoo.com
29. Bank of England, "Financial Stability Report," July 2026 — bankofengland.co.uk
30. UK Parliament Treasury Committee, "Bank of England and FCA commit to action on AI following warnings from MPs" — committees.parliament.uk
31. ExchangeRates.org.uk, "Bank Of England Flags AI, Debt And Market Risks" — exchangerates.org.uk
32. Meng, S. & Chen, X., "Artificial Intelligence and Systemic Risk" (arXiv, 2026) — arxiv.org
33. eToro, "US Retail Investors Flock to AI Tools, With Usage Surging 75% in One Year" — etoro.com
34. TradingView News / FinanceMagnates, "Moomoo Joins the Agentic Investing Club, a Month Behind eToro" — tradingview.com
35. TradingView News / FinanceMagnates, "Retail Traders Gain Quant-Level Tools as eToro Launches Public API and AI Assistant Tori" — tradingview.com
36. OneDayAdvisor.com, "AI Trading and Investment Guide: Using Claude to Personalize Your Trading and Investment," July 2026 - onedayadvisor.com

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Not Financial Advice: This article is for general informational and educational purposes only and does not constitute investment, financial, legal, or trading advice, nor a recommendation to buy, sell, or hold any security or use any specific platform. Algorithmic and AI-driven trading, including retail agentic tools, carries substantial risk of loss and past performance is not indicative of future results. Consult a licensed financial advisor before making investment decisions.