AI Trading Agents: How Autonomous and LLM-Based Trading Agents Work

Introduction

An AI trading agent is more than a model that predicts whether a market will go up or down.

It is a system that can remain active inside a trading workflow.

Instead of receiving one input and producing one prediction, an agent can potentially:

• observe market and portfolio information
• retrieve additional data
• use external tools
• reason about an objective
• select a next action
• request human approval
• place an order when authorized
• observe the result
• continue the process as conditions change

That creates a fundamentally different architecture from a conventional trading bot whose behavior is fully determined by predefined rules.

Research on LLM-based financial agents increasingly describes trading agents as closed-loop systems that perceive market information, retrieve context, reason about decisions, produce trading actions, and respond to market feedback. A 2026 survey of 77 studies found rapid experimentation with these architectures, while also identifying major weaknesses in evaluation consistency and reproducibility.

Real brokerage integrations are also making the concept less theoretical. Robinhood’s Agentic Trading product currently allows users to connect third-party AI agents to a dedicated brokerage account through its Trading MCP. Depending on the permissions granted, an agent can access account information and place supported orders.

The important questions are:

What information can the agent see?
What tools can it use?
What decisions can it make?
What actions is it permitted to take?
What controls exist outside the model?
How can its behavior be tested and reproduced?

Those questions define the real architecture of an AI trading agent.

Quick Answer

What Is an AI Trading Agent?

An AI trading agent is a goal-directed trading system in which AI participates in the runtime decision process.

A simple model might answer:

“What is the current RSI?”

A trading assistant might answer:

“Based on the information available, here are several factors you may want to consider.”

An AI trading agent can go further.

It might receive an objective such as:

“Monitor this portfolio for material changes in volatility and risk exposure.”

Then it could potentially:

• retrieve current portfolio information
• inspect market data
• access additional research tools
• evaluate whether conditions changed
• determine whether another action is required
• propose or execute that action within its permissions
• observe the result

The key difference is not that the system uses artificial intelligence.

Many conventional trading systems already use machine learning.

The defining feature is that part of the decision process remains dynamic at runtime.

Not Every AI Trading System Is an Agent

A neural network that predicts tomorrow’s return is not automatically an agent.

A chatbot that explains a chart is not automatically an agent.

A strategy created by an LLM but executed according to fixed rules is not automatically an agent.

An automated trading bot can be sophisticated, adaptive, and machine-learning based without being agentic in the modern LLM-agent sense.

The term becomes more useful when the system can combine multiple steps such as observation, reasoning, tool use, action selection, and feedback.

That distinction prevents AI trading agent from becoming a meaningless label for every piece of trading software that uses AI.

How an AI Trading Agent Works

A useful way to understand a trading agent is as a loop.

FigureThe AI Trading Agent Loop

1. Observe

The agent receives information about its environment.

Depending on the system, this could include:

• market prices
• technical indicators
• portfolio positions
• available buying power
• open orders
• news
• financial statements
• economic data
• previous actions
• user instructions

Observation defines what the agent actually knows.

If relevant information is missing, stale, or incorrect, better reasoning cannot fully repair the input problem.

2. Retrieve Data or Use Tools

The agent may have access to external systems.

These can include:

• market-data APIs
• news or search tools
• portfolio analytics
• financial databases
• scanners
• backtesting tools
• broker or exchange interfaces
• order-management functions

Modern agent architectures often treat tool use as one of the characteristics separating an agent from a standalone language model.

Robinhood provides a concrete financial example. Its Trading MCP can expose account, portfolio, market-analysis, and order-related capabilities to connected third-party agents. The agent itself is separate from the brokerage tool layer.

3. Reason or Plan

The model interprets the available context and determines what should happen next.

Depending on the architecture, this might involve:

• comparing several pieces of evidence
• deciding which data source is still needed
• breaking a task into multiple steps
• checking portfolio constraints
• selecting among possible actions
• deciding that no action is justified

This reasoning does not need to resemble human thought.

What matters operationally is that the next action is not completely predetermined by one fixed rule.

4. Choose an Action

Possible outputs are broader than:

buy

or:

sell

An agent might decide to:

• take no action
• request more information
• generate a recommendation
• reduce exposure
• rebalance positions
• prepare an order for approval
• submit an authorized order
• call another analytical tool

The permitted action set is part of the system design.

5. Pass Through Risk and Permission Controls

A serious trading-agent architecture should not assume that whatever the model proposes should automatically happen.

The model’s intended action can be checked against external rules.

Examples could include:

• whether the instrument is permitted
• whether sufficient buying power exists
• whether the action requires human approval
• whether the requested order type is allowed
• whether the agent has execution authority

The exact control architecture varies by system.

The important distinction is that model reasoning and execution permission are separate concepts.

6. Recommend or Execute

After the relevant checks, the agent may:

• return a recommendation to the user
• stage an action for approval
• call an execution tool
• place an authorized trade

Robinhood’s current documentation illustrates why this distinction matters. Users can review what an agent is about to do, but if they have instructed the agent to act without approval, trades can also be submitted without confirmation for every individual transaction.

7. Observe the Result

The environment changes after an action.

The agent may then receive updated information:

• Was the order filled?
• Did the portfolio exposure change?
• Did price move?
• Did new information arrive?
• Did the previous action fail?
• Is another step required?

The cycle can then repeat.

The Core Components of an AI Trading Agent

The language model is only one part of the system.

A practical trading agent can contain several layers.

Model

The model may perform interpretation, reasoning, planning, synthesis, or action selection.

In an LLM trading agent, this role is performed partly or primarily by a large language model.

But the model alone is not the agent.

Market and Financial Data

The agent needs an information environment.

That may include price data, news, fundamentals, portfolio information, or other inputs.

The usefulness of the agent is therefore constrained by the quality, timing, and availability of the information it receives.

State and Memory

An agent may preserve information across steps.

State could include:

• current portfolio positions
• previous actions
• unresolved tasks
• retrieved evidence
• recent observations
• user preferences or constraints

Longer-term memory may also be used in some architectures.

Memory can create continuity, but it can also make decisions harder to reproduce because current behavior may depend on information accumulated earlier.

Tools

Tools give the agent capabilities outside the model.

A model may not know the latest portfolio value.

A portfolio tool can retrieve it.

A model cannot place an order through reasoning alone.

An execution interface can make that action possible.

Decision Layer

The decision layer connects observations and reasoning to an action.

In a simple agent, one model may handle most of this process.

In a more complex system, several specialized agents or deterministic components may participate.

Risk and Permission Layer

This layer determines what the agent is actually allowed to do.

It can be conceptually separate from the agent’s own reasoning.

That separation matters because:

Execution Interface

If the agent can trade, it needs a way to communicate with a broker, exchange, or order-management system.

Execution introduces another set of realities:

• accepted order types
• order rejection
• delays
• slippage
• partial fills
• account restrictions

A correct decision can still produce an undesirable execution outcome.

AI Trading Agent vs Trading Bot

The words agent and bot are often used interchangeably in marketing.

Architecturally, they can mean different things.

That bot-versus-agent distinction sits inside a broader question: how agentic trading differs from algorithmic trading when runtime decision authority is considered.

FigureTrading Bot vs AI Trading Agent Architecture

The distinction should not be overstated.

Modern bots can use machine learning.

AI agents can also contain deterministic rules.

An agent may even delegate parts of execution to conventional bots.

So the difference is not:

The more useful distinction is:

This is also why adding an LLM chat interface to a bot does not automatically make the underlying trading architecture agentic.

The question is what the AI is actually allowed to decide.

AI Trading Agent vs AI Assistant

An AI assistant and an AI trading agent can use the same underlying language model while behaving very differently.

An assistant usually works reactively.

The user asks:

“What are the main risks in my portfolio?”

The assistant analyzes the available information and responds.

Then it waits.

An agent can operate across several steps.

The user might instead define an objective:

“Monitor this portfolio for changes in concentration risk and alert me if exposure moves outside the limits I defined.”

The system may then need to:

• retrieve the portfolio
• calculate current exposure
• compare it with the user’s limits
• determine whether intervention is needed
• generate an alert or proposed action
• repeat the process when new information becomes available

The difference is not simply that one system is smarter.

It is the operating structure.

That distinction also explains why adding brokerage connectivity to a chatbot does not automatically create a sophisticated autonomous trading agent.

A system that only calls an order tool when the user explicitly says:

“Buy 10 shares.”

may still behave primarily as an assistant with tool access.

Agentic behavior becomes more meaningful when the system has responsibility for determining some of the intermediate steps or next actions itself.

What Does Autonomous AI Trading Actually Mean?

The word autonomous is often used too loosely.

It should not mean:

“The AI can do anything it wants.”

A useful definition is narrower:

Autonomy is therefore a matter of degree.

Robinhood’s current Agentic Trading architecture provides a practical example. A connected third-party agent can access portfolio and market information and, when authorized, place supported orders in a dedicated Agentic account. Users can review actions before execution, but Robinhood also states that an agent can place trades without confirmation if the user has instructed it to act that way.

That is real trading autonomy.

It is not unlimited autonomy.

The agent is still constrained by:

• the dedicated account
• available funds
• supported instruments
• available order functionality
• the tools exposed to it
• user instructions
• platform permissions

The Trading Agent Autonomy Spectrum

Rather than dividing systems into “manual” and “fully autonomous,” it is more useful to think in levels.

Level 0: Research Assistant

The system can analyze information and answer questions.

It cannot initiate actions.

AI role: research and explanation

Human role: all decisions and execution

Level 1: Recommendation Agent

The system can monitor information and generate proposed actions.

For example:

“Portfolio concentration exceeded the threshold you defined. Consider reducing exposure to sector X.”

The user still decides whether anything happens.

AI role: observe, interpret, recommend

Human role: approve and execute

Level 2: Supervised Execution Agent

The agent can prepare a concrete action or order, but the user must approve it before execution.

AI role: analyze, select action, prepare execution

Human role: approve material action

Level 3: Delegated Trading Agent

The user grants the agent authority to execute certain actions without approval for each transaction.

For example, the agent may be allowed to rebalance a dedicated portfolio or execute a predefined trading mandate.

Robinhood currently supports this type of architecture when a user instructs a connected agent to take action without asking for approval on every trade.

Level 4: Higher-Autonomy Adaptive Agent

A more complex agent may dynamically:

• select tools
• gather additional evidence
• modify a plan
• respond to previous outcomes
• choose among multiple actions

IBM’s description of agent planning emphasizes that more capable agents may combine perception, reasoning, planning, memory, action, and feedback, and may revise plans when conditions change. IBM also notes that not every agent has planning capabilities.

This spectrum matters because two products can both call themselves AI trading agents while assigning very different responsibilities to AI.

What Are LLM Trading Agents?

An LLM trading agent uses a large language model as an important part of its decision or coordination layer.

The LLM may help with:

• interpreting natural-language objectives
• analyzing news
• summarizing financial documents
• combining qualitative and quantitative information
• choosing tools
• generating intermediate reasoning
• coordinating specialized agents
• translating market context into possible actions

This creates capabilities that conventional rule-based systems do not naturally provide.

For example, an LLM can process:

earnings transcript + news article + portfolio exposure + user instruction

inside one workflow.

That does not mean the LLM itself possesses reliable trading skill.

It means it provides a flexible interface for combining heterogeneous information.

The LLM Is One Component

A complete LLM trading agent may still depend on:

The distinction is important because failures can come from any layer.

The language model might reason from stale data.

The data API might return an error.

Memory might preserve an incorrect assumption.

The execution tool might receive the wrong parameters.

The strategy might simply have no edge.

Calling the whole system “an LLM” hides these differences.

Why LLMs Are Attractive for Trading Agents

Financial decision workflows include large amounts of unstructured information.

Examples include:

• earnings calls
• financial reports
• news
• analyst commentary
• economic statements
• user-defined objectives

LLMs are naturally suited to interpreting and combining text.

They also provide a natural-language interface for defining tasks.

This interface overlaps with natural language trading, but an agent becomes architecturally different when AI remains responsible for runtime decisions, tool use, or action selection.

Instead of programming every workflow branch directly, a user might describe an objective such as:

“Review new earnings information for companies in this portfolio, identify material changes to the investment thesis, and flag positions that need deeper review.”

An agent can then use tools to retrieve relevant information and perform several intermediate steps.

This flexibility is one reason LLM-based financial-agent research has expanded rapidly.

It is also why these systems can be difficult to evaluate.

The decision process may depend on changing text, retrieval results, prompts, model versions, and state rather than one fixed mathematical rule.

Single-Agent Trading Systems

In the simplest architecture, one agent handles most of the workflow.

It may:

The advantages are architectural simplicity and easier coordination.

There are fewer communication layers and fewer agent interactions to debug.

But one agent may also be asked to perform very different tasks:

• technical analysis
• fundamental research
• sentiment interpretation
• portfolio reasoning
• risk evaluation
• execution planning

That can produce a broad but overloaded system.

Multi-Agent Trading Systems

A multi-agent architecture divides responsibilities.

One agent might analyze fundamentals.

Another may focus on technical information.

Another could evaluate sentiment.

A separate risk agent might challenge the proposed action.

A portfolio manager agent could then synthesize the outputs.

A simplified multi-agent workflow might look like:

Fundamental Agent
+
Technical Agent
+
Sentiment Agent

↓

↓

↓

↓

This resembles the division of labor found in an investment organization.

It also introduces new problems.

Coordination Overhead

Agents need to exchange information correctly.

More components create more interfaces where information can be lost, misunderstood, or repeated.

Conflicting Conclusions

Different agents may produce different recommendations.

The system needs a rule for resolving disagreement.

Error Propagation

If one research agent produces incorrect information, later agents may treat it as valid input.

Cost and Latency

More agent calls, retrievals, and tool interactions may increase computational cost and decision time.

Harder Evaluation

When a final action results from several conversations and intermediate agents, attributing success or failure becomes more difficult.

So multi-agent architecture should not automatically be treated as superior.

Whether that trade is useful depends on the task.

How AI Trading Agents Use Tools

Tool use is one of the most important parts of modern agent architecture.

A language model can interpret text.

It cannot independently access a brokerage account, retrieve a live portfolio, or send an exchange order unless the surrounding system gives it access to those capabilities.

Possible financial tools include:

• live market data
• historical data
• financial statements
• news search
• portfolio analytics
• screening tools
• backtesting engines
• risk calculators
• broker APIs
• exchange APIs
• order-management systems

The agent decides when or how to use these tools according to the architecture.

IBM describes tool calling and external-system interaction as important capabilities in agentic workflows, particularly when an agent needs current information or must perform actions beyond language generation.

What Is MCP in AI Trading?

Model Context Protocol, or MCP, is an open standard for connecting AI systems to external applications and services.

In a trading context, an MCP server can expose structured tools that an agent can call.

For example:

get portfolio

retrieve buying power

analyze market data

place order

Robinhood currently uses this model in its Agentic Trading product. Its Trading MCP lets supported third-party agents access account and portfolio information and use permitted trading functionality.

This is an important architectural distinction.

The LLM does not directly become the brokerage system.

Instead:

Agent → Tool Interface → Brokerage Function

That separation can make permissions and capabilities more explicit.

It does not automatically make the system safe.

If the agent misunderstands the task or calls an allowed tool incorrectly, the tool connection can turn a reasoning error into a real financial action.

That is why tool access and authority need to be evaluated separately from model intelligence.

Tool Access Changes the Meaning of AI Error

Consider two systems.

The first agent incorrectly concludes that a portfolio should reduce one position.

It has no execution permissions.

The result is a bad recommendation.

The second agent reaches the same incorrect conclusion but has permission to trade.

The result may become an executed order.

The model error is identical.

The operational consequence is not.

This is the central reason permissions matter so much in financial agents:

How Financial Trading Agents Are Tested

Testing an AI trading agent is harder than testing a conventional rule-based strategy.

With a fixed algorithm, researchers can usually specify:

If the data and implementation remain unchanged, the same rules should generally produce the same decisions.

An LLM trading agent may depend on a much larger state:

The resulting action may therefore depend on more than historical prices.

A useful evaluation process needs to answer several different questions.

Did the Agent Have Access to Future Information?

All data supplied to the agent should reflect what would actually have been available at the decision time.

Using information that was unavailable at that point introduces look-ahead bias, even when the leakage comes through news, retrieved documents, or model context rather than price data.

This applies not only to prices, but also to:

• news
• financial statements
• economic releases
• analyst information
• retrieved documents
• portfolio state

The problem becomes more complicated with pretrained language models because the model itself may already contain historical information from training.

Did the Agent Actually Produce Tradable Actions?

A system that summarizes market news is not necessarily a trading agent evaluation.

A stronger test requires the agent’s outputs to affect a simulated or real portfolio.

The 2026 Agentic Trading survey reviewed 77 studies, but only 19 entered its primary empirical subset because they satisfied the authors’ minimum criteria of producing action outputs and using closed-loop evaluation.

Were Trading Costs and Execution Defined?

A recommendation is not the same as an executable trade.

Evaluation should establish:

• when the decision becomes available
• when an order could be submitted
• which price is used
• which costs are included
• what happens when execution is imperfect

This remains a major weakness in the research literature.

Within the survey’s 19-study primary subset, only 1 of 19 reported an explicit transaction-cost model, while 11 of 19 reported execution timing or execution semantics.

Was the Test Truly Out of Sample?

The agent should face information that did not determine its design or evaluation choices.

This is especially difficult when researchers repeatedly modify prompts, tools, memory systems, agent roles, or orchestration after inspecting historical outcomes.

Agentic systems can overfit even when no traditional indicator parameter is being tuned.

The optimization target may simply move into the prompt and architecture.

Why LLM Trading-Agent Backtesting Is Difficult

One unusual problem with LLM trading agents is historical memory contamination.

Suppose an agent is backtested on a famous stock event from several years ago.

A frontier language model may already have encountered:

• the company name
• the date
• the news event
• the resulting market move
• commentary written after the event

during pretraining.

The backtest may appear chronological while the model itself contains information from the future relative to the simulated decision.

A 2026 benchmark called KTD-Fin was designed specifically around this problem. The researchers anonymized identifiers and calendar information to reduce the ability of LLM agents to rely on memorized historical narratives. They also decomposed portfolio results to distinguish market and style exposure from stock-selection alpha.

Their results illustrate a broader lesson:

Returns can come from:

• broad market exposure
• factor exposure
• favorable regimes
• historical leakage
• chance
• genuine security selection

Those explanations should not be treated as equivalent.

Reproducibility Is Another Problem

The same agent may behave differently if any of these change:

• model version
• prompt wording
• sampling settings
• available tools
• retrieved documents
• memory contents
• API responses
• previous decisions

The Agentic Trading survey found that only 2 of 19 studies in its main empirical subset reported extractable time-consistent split protocols, and none reached the survey’s highest reproducibility category.

That does not prove AI trading agents cannot work.

It shows that the evidence base is still difficult to compare.

The immediate research bottleneck is not simply inventing more sophisticated agent architectures.

It is producing evaluation procedures strong enough to distinguish architecture demos from reliable trading evidence.

Main Risks and Failure Modes

AI trading agents combine ordinary trading risk with additional AI and systems risk.

Incorrect or Incomplete Information

An agent may reason coherently from bad input.

Market data can be stale.

News can be incomplete.

A retrieval system can surface the wrong document.

Portfolio state can change between observation and execution.

The quality of the reasoning does not repair missing facts.

Hallucination and Unsupported Inference

An LLM can produce explanations that sound well supported while introducing details that were not present in the evidence.

In trading, that can affect:

• event interpretation
• company facts
• causal explanations
• confidence assessments
• tool parameters

A long rationale should not be confused with a verified premise.

Instruction Misinterpretation

Natural language is flexible.

That is useful when defining objectives, but it also creates ambiguity.

A phrase such as:

“Reduce risk if volatility gets too high.”

leaves several questions unresolved:

What measure of volatility?

How high?

Reduce which positions?

By how much?

For how long?

Robinhood explicitly warns that connected AI agents can make errors, misinterpret instructions, act on incomplete or outdated information, and behave unexpectedly.

Tool Misuse

The model can reach a reasonable objective but use the wrong tool, wrong parameters, or wrong order structure.

Tool access converts language generation into operational capability.

That increases the consequence of certain errors.

Non-Determinism

Agent decisions can vary even when conditions appear similar.

This complicates debugging.

A deterministic strategy can often be traced to:

Rule X triggered because condition Y was true.

An LLM agent may require reconstruction of:

• retrieved context
• conversation history
• model output
• intermediate tool calls
• memory state

before the action can be understood.

Memory Errors

Memory can help an agent retain context.

It can also preserve mistakes.

An incorrect earlier conclusion may influence later decisions.

This is why:

Permission Risk

A bad recommendation is one type of failure.

A bad recommendation with execution authority is another.

Robinhood’s current Agentic Trading documentation states that agents can place trades without confirmation when the user has instructed them to act without transaction-by-transaction approval.

The operational risk therefore depends not only on what the model thinks, but also on what the system allows it to do.

Ordinary Trading Risk Still Exists

None of these AI-specific problems replace traditional trading risks.

Position sizing, drawdown limits, exposure controls, and other forms of algorithmic trading risk management still matter even when the decision layer is agentic.

An agent can interpret the information correctly and still lose money.

Markets can behave differently from historical patterns.

Liquidity can disappear.

Execution can deteriorate.

A strategy can stop working.

The fact that an action resulted from reasoning does not create an edge.

Human Approval and Permissions

“Human in the loop” is not a single architecture.

There are several possible control points.

Strategy-Level Approval

The human defines or approves the overall mandate.

The agent may then act independently inside that mandate.

Action-Level Approval

The agent proposes individual trades, but a person must approve each material action.

Exception-Based Approval

Routine actions may proceed automatically, while unusual actions require escalation.

Fully Delegated Actions Within Defined Permissions

The agent can execute without transaction-by-transaction confirmation, but only through the tools and account access it has been granted.

The important design question is therefore not simply:

Is there a human in the loop?

It is:

Risk management guidance for generative AI more broadly also emphasizes that AI risk should be managed across the system lifecycle rather than treated solely as a model-quality problem. NIST’s Generative AI Profile frames risk management around how AI systems are designed, evaluated, used, and monitored in their actual context.

For financial agents, permissions are part of that context.

AI Trading Agents vs Agentic Trading

The two terms overlap, but they are not identical.

An AI trading agent is the system or software component.

Agentic trading is the broader trading architecture or approach in which agents retain some responsibility for runtime reasoning, tool use, decisions, or actions.

A platform could therefore use several AI trading agents inside one agentic trading system.

Likewise, discussing an AI trading agent does not tell us its level of autonomy.

It might only recommend actions.

It might require approval.

Or it might execute within delegated permissions.

The more useful question is not whether a product uses the word agent.

It is:

Where Algorier Fits

Algorier should not currently be described as an unrestricted autonomous LLM trading agent.

Its architecture is different.

According to the current Algorier Platform Whitepaper, AlgoBuild allows a trader to describe a trading idea in plain English. According to the Algorier Platform Whitepaper v1.1, AlgoBuild lets a trader describe a trading idea in plain English, converts that description into explicit algorithmic logic, and centrally backtests the resulting strategy. A creator can additionally enable a live Forward Test to build an out-of-sample record.

That puts Algorier closer to:

than to:

The distinction matters.

In AlgoBuild, AI helps bridge the gap between human trading intent and structured algorithmic logic.

Once the strategy has been formalized, the objective is to evaluate explicit rules rather than leave every trade open to fresh LLM interpretation.

That does not make one architecture universally superior.

They solve different problems.

An AI trading agent emphasizes flexible runtime decision-making.

AlgoBuild emphasizes turning a trading idea into a testable algorithm.

Final Verdict

AI trading agents represent a meaningful shift in trading-system architecture.

The change is not simply that newer systems use better AI.

The deeper change is that AI can remain inside the decision loop after deployment.

An agent may:

That flexibility can support workflows that fixed trading bots were not designed to handle, particularly when the task involves natural language, unstructured financial information, tool selection, or multi-step decisions.

It also makes evaluation harder.

The more freedom the agent has to interpret context, use tools, remember prior information, and take action, the more the system must be evaluated as a complete decision process rather than as a single predictive model.

Current research reflects that tension.

Architectural experimentation is moving quickly, but comparable testing, transaction-cost modeling, leakage control, execution semantics, and reproducibility remain major gaps.

The central question is therefore not:

It is:

Frequently Asked Questions

Risk Disclaimer

Trading involves substantial risk, including the possibility of losing some or all invested capital.

AI trading agents can make incorrect decisions, misunderstand instructions, act on incomplete information, misuse available tools, or behave differently from historical tests. Granting an agent execution permissions can turn a reasoning error into a financial action.

Backtests, simulations, benchmarks, paper trading, forward testing, and historical agent performance do not guarantee future results.

This article is provided for educational and informational purposes only and does not constitute investment, financial, or trading advice.

About the Author

Written by: Algorier Research Team
Last Updated: September 2026

The Algorier Research Team covers algorithmic trading, trading strategy development, backtesting, systematic risk, strategy evaluation, and trading automation.