The real question facing institutional desks is no longer “Should we use AI?” but:

“Which type of AI should power our trading, and which AI agents should support our research and operations?”

To answer that, it helps to separate two layers:

1. Core trading models – Machine Learning (ML), Deep Learning (DL), Reinforcement Learning (RL)
2. AI Agents / LLMs – ChatGPT, Claude, Gemini, Grok, DeepSeek, Llama, Mistral, Qwen, Nova, Ernie, Phi, and others

They serve different roles: the first layer is where alpha, execution and risk live; the second layer accelerates research, coding, analysis and documentation.

Part I – Core AI Models for Trading

1. Machine Learning (ML): The Institutional Workhorse

Best for:
Return forecasting, feature engineering, regime detection, macro–factor modelling, risk signals.

Traditional ML (e.g., XGBoost, Random Forests, SVMs) remains the backbone of many systematic desks. It offers:

• Relatively transparent decision rules
• Strong performance on noisy, tabular financial data
• Ease of validation for model risk and regulators

Strengths

• Works with limited, noisy historical data
• Easier to explain to investment committees
• Straightforward cross-validation and stress testing

Limitations

• Relies heavily on human feature engineering
• Often short-horizon and local to the regime it was trained on
• Can degrade under sudden structural breaks

Takeaway:
If you want robust, auditable, repeatable models, ML is usually the first stop.

2. Deep Learning (DL): High-Dimensional Alpha Hunting

Best for:
Market microstructure, order-book modelling, cross-asset dependencies, text & audio (NLP), options surfaces.

Deep learning shines when the dimensionality is high and the patterns are subtle. Architectures like LSTMs, Temporal CNNs and Transformers can digest:

• Full order-book snapshots
• Multi-asset price/volume tensors
• News, filings, call transcripts and central-bank speeches (via NLP)
• Complex volatility or correlation surfaces

Strengths

• Excels at high-dimensional, non-linear pattern recognition
• Very effective for NLP on financial text using Transformer models
• Can replace much manual feature engineering

Limitations

• More opaque – “black box” concerns for risk and compliance
• Requires substantial compute and MLOps
• Vulnerable to data drift if not continuously monitored

Takeaway:
DL is a powerful alpha engine, but best suited to shops with serious data, compute and governance.

3. Reinforcement Learning (RL): Execution, Hedging & Market Making

Best for:
Execution algorithms, dynamic hedging, inventory management, market making, portfolio rebalancing.

RL doesn’t predict returns; it learns policies: when and how to act to maximise a reward (P&L, execution quality, risk-adjusted return).

Strengths

• Learns adaptive execution strategies to minimise market impact
• Designs dynamic hedging policies instead of static rules
• Optimises spreads & inventory for market makers

Risks

• Reward mis-specification can lead to undesirable behaviour
• Harder to explain than traditional models
• Requires tight sandboxing, simulation, and kill-switch governance

Takeaway:
RL is the natural fit for execution and dynamic risk rather than pure alpha prediction.

Part II – AI Agents & LLMs: Who’s Actually on the Desk?

LLMs and AI agents don’t replace your trading models. Instead, they:

• Write and debug backtest code
• Clean and transform datasets
• Summarise macro events, filings and research
• Draft investor letters, policy docs and internal memos

Below is a pragmatic, trading-desk-oriented map of the major LLMs and agents as of late 2025. It’s not every model on the planet (there are dozens), but it covers the ones most relevant to institutional use.

1. OpenAI – ChatGPT, GPT-5, o-series, GPT-OSS

• Strengths:
  • Excellent coding ability and multi-step reasoning
  • Strong ecosystem (plugins, tools, integrations)
  • New “o-series” and “thinking” models oriented to reasoning and tool use
  • Open-weight GPT-OSS models for more custom, agentic workflows
• Best for: Rapid strategy prototyping, backtest scripting, research assistance, report drafting.

2. Anthropic – Claude (3.x, 4.x, Sonnet/Opus/Haiku)

• Strengths:
  • Very strong on long-context reasoning and compliance-friendly tone
  • Popular for internal governance, documentation and policy writing
• Best for: Model documentation, risk/compliance language, long-form research notes.

3. Google DeepMind – Gemini & Gemma

• Strengths:
  • Multimodal (text, images, sometimes video), large context windows
  • Good at ingesting PDFs, tables, and complex documents
• Best for: Parsing central-bank speeches, macro reports, ESG documents, and integrating with Google’s data tools.

4. xAI – Grok

Grok is xAI’s family of models, now including Grok-4.x, optimised for real-time data from X (Twitter) and high-performance agentic tool-calling.

• Strengths:
  • Real-time access to social feeds via X (subject to product settings)
  • Fast, agent-oriented models for tool use
• Best for: Idea generation around sentiment, flows and narratives, especially in crypto or “headline-driven” markets—as an input, not a trade engine.

5. DeepSeek – DeepSeek-V3, DeepSeek-R1, etc.

DeepSeek provides powerful, cost-effective models (notably MoE architectures like DeepSeek-V3) with a strong presence in English and Chinese.

• Strengths:
  • Competitive performance versus frontier models at lower cost
  • Popular in Asia and open-weight / “open” ecosystems
• Important caveat:
  • Some jurisdictions (e.g. Czechia and several US states) have issued cybersecurity warnings or bans regarding DeepSeek services, citing potential data-sharing obligations under Chinese law.
• Best for: Cost-sensitive internal tools where regulatory and data-sovereignty concerns are fully assessed.

6. Meta – Llama 3 / 4 (Open-Weight)

• Strengths:
  • Open-weight models widely used as a base for in-house, private deployments
  • Strong ecosystem in the open-source / self-hosted community
• Best for: Firms wanting full control, on-prem deployment, and integration into proprietary agent stacks without sending data to external APIs.

7. Mistral – Mixtral & Mistral Large

• Strengths:
  • Mixture-of-Experts architectures (e.g., Mixtral 8x7B, 8x22B) that punch above their parameter count
  • Open-weight and enterprise offerings
• Best for: Latency-sensitive, cost-efficient internal assistants and coding copilots.

8. Alibaba – Qwen (Qwen2.5, Qwen3)

• Strengths:
  • Strong multilingual capability, including Chinese; multiple model sizes
• Best for: Asia-focused desks, cross-border workflows, and cost-efficient in-house tools.

9. Amazon – Nova

• Strengths:
  • Integrated with AWS, attractive for shops already deep in the AWS stack
• Best for: Firms standardising on AWS infra and wanting tight IAM/control.

10. Baidu – Ernie

• Strengths:
  • Strong Chinese-language performance and integration in mainland China
• Best for: China-focused research and investor communications.

11. Microsoft – Phi (Phi-3, Phi-4)

• Strengths:
  • “Small language models” optimised for efficiency, especially code and reasoning tasks
• Best for: Embedded tools, low-latency coding assistants and on-device or tightly controlled environments.

How These Agents Fit a Trading Desk

Here’s a simplified view of who does what in a professional trading context:

Use Case	Strong Candidates
Strategy prototyping, backtest code, indicators	ChatGPT, Claude, Mistral, Llama-based in-house
Parsing macro reports, central bank speeches, filings	Gemini, Claude, ChatGPT
Social & sentiment-driven idea generation	Grok, ChatGPT, DeepSeek (subject to policy)
Private, on-prem research copilots	Llama, Mistral, Qwen, Phi, GPT-OSS, DeepSeek (self-hosted)
Policy docs, risk frameworks, investor letters	Claude, ChatGPT
APAC / China-centric workflows	DeepSeek, Qwen, Ernie, local deployments (subject to local rules)

Critically, none of these agents should be treated as a trade signal generator. They are infrastructure for research, coding, interpretation, and communication—the “front office co-pilot,” not the PM.

Putting It All Together

• Use ML and DL to build explicit, testable trading models.
• Use RL where actions (execution, hedging, market making) matter more than predictions.
• Use LLMs and agents (ChatGPT, Claude, Gemini, Grok, DeepSeek, Llama, Mistral, Qwen, Nova, Ernie, Phi) to:
  • Short-circuit grunt work in research and coding
  • Accelerate data preparation and documentation
  • Improve communication with investors and regulators

There is no single “best AI” for trading.

The real edge comes from choosing the right model for the trading problem, and the right agents to support the humans around it—all under a governance framework your CIO, CRO and regulator can live with.

Sources

1. “Top 9 Large Language Models as of November 2025” – Shakudo blog.
2. “WARNING … regarding certain ‘DeepSeek’ products” – Czech National Cyber & Information Security Agency (NÚKIB) PDF.
3. “DeepSeek a threat to national security, warns Czech cyber agency” – The Record.
4. “The best large language models (LLMs) in 2026” – Zapier blog.
5. “A List of Large Language Models” – IBM Think article.
6. “27 of the best large language models in 2025” – TechTarget article.
7. “The Czech Republic bans DeepSeek usage in public administration” – Reuters.
8. “Czechia warns that DeepSeek can share all user information with the Chinese government” – Tom’s Hardware