Can AI Build a Better Trader?

Author: Denis Avetisyan

A new benchmark evaluates whether large language models can truly design and implement profitable trading strategies from scratch.

AlphaForgeBench assesses the capacity of AI to generate executable code for quantitative finance, moving beyond simple trading signal prediction.

Despite recent advances in applying Large Language Models (LLMs) to financial trading, current benchmarks often produce unreliable evaluations due to inherent instability in sequential decision-making. This paper introduces ‘AlphaForgeBench: Benchmarking End-to-End Trading Strategy Design with Large Language Models’, a novel framework that reframes LLMs as quantitative researchers tasked with generating executable trading strategies-specifically, alpha factors-rather than directly outputting trading actions. By decoupling reasoning from execution, AlphaForgeBench eliminates execution-induced instability and enables fully deterministic, reproducible evaluations of financial reasoning and alpha discovery. Can this approach provide a more rigorous and reliable assessment of LLMs’ true potential in quantitative finance?

The Intractable Challenge of Financial Strategy Automation

Historically, the development of robust financial strategies has been a painstakingly manual process, demanding not only substantial computational resources but, crucially, a highly specialized understanding of market dynamics and financial modeling. Researchers traditionally spend considerable time formulating hypotheses, backtesting them against historical data, and refining parameters-a cycle requiring years of experience to navigate effectively. This intensive labor is due to the inherent complexity of financial markets, where subtle interactions between numerous factors can significantly impact performance. The process extends beyond simply applying mathematical formulas; it necessitates a deep qualitative understanding of economic principles, accounting practices, and the behavioral nuances that drive investor decisions. Consequently, scaling strategy creation has been limited by the availability of individuals possessing both the technical skillset and the domain expertise to consistently generate profitable signals.

The pursuit of consistently profitable financial strategies increasingly relies on automated systems, yet current methodologies frequently fall short of replicating the subtleties of human expertise. While algorithms excel at processing vast datasets and identifying patterns, translating complex market dynamics – encompassing factors like investor sentiment and macroeconomic indicators – into actionable signals proves remarkably difficult. Existing automated approaches often oversimplify these relationships, resulting in strategies that perform well on historical data but fail to adapt to evolving market conditions or unexpected events. This limitation stems from a challenge in accurately capturing the nuance inherent in financial analysis – the ability to weigh qualitative factors, interpret ambiguous data, and dynamically adjust to changing circumstances – qualities that remain difficult to encode into rigid algorithmic frameworks.

The difficulty in automating financial strategy stems from a fundamental challenge: accurately representing the intricacies of financial concepts within the rigid structure of computer code. Translating qualitative insights – such as assessing market sentiment or anticipating behavioral shifts – into precise algorithms is fraught with potential for error. Even seemingly minor inaccuracies in code can lead to significant financial losses, as automated systems execute strategies at scale and with speed exceeding human capacity. Furthermore, inefficiencies within the code – redundant calculations or poorly optimized data handling – can erode profitability, diminishing returns even if the underlying strategy is sound. This process requires not only strong programming skills but also a deep understanding of finance to ensure the code faithfully reflects the intended investment logic, a combination that remains a significant hurdle in the pursuit of fully automated, profitable financial systems.

Large Language Models: A New Paradigm for Algorithmic Alpha

Large Language Models (LLMs) are being explored as a means of automating the development of quantitative trading strategies through programmatic code generation. This involves prompting LLMs with natural language instructions or specifications, which they then translate into executable code – typically Python – designed to identify and exploit market inefficiencies. The potential benefits include increased speed of strategy development, reduced reliance on human expertise for initial strategy formulation, and the ability to explore a wider range of potential strategies than traditional methods allow. LLMs can generate code for various aspects of a trading strategy, including data ingestion, feature engineering, signal generation, and order execution, streamlining the entire process from concept to implementation.

AlphaForgeBench differentiates itself from typical LLM evaluation by directly measuring the financial performance of code generated for algorithmic trading. Rather than assessing code quality through static analysis or unit tests, the benchmark executes generated strategies within a simulated market environment and calculates key performance indicators, specifically the Sharpe Ratio. This metric quantifies risk-adjusted returns, providing a concrete measure of an LLM’s ability to generate profitable trading strategies – or ‘executable alpha factors’ – and penalizing strategies with excessive risk. The benchmark’s infrastructure incorporates realistic transaction costs and market impact to ensure evaluations reflect real-world trading conditions, thereby providing a more meaningful assessment of an LLM’s utility in finance.

Traditional evaluations of code-generating Large Language Models (LLMs) prioritize syntactic correctness and functional execution, but do not assess actual performance in a real-world application. The AlphaForgeBench benchmark addresses this limitation by evaluating LLMs based on the profitability of the trading strategies they generate. This shifts the focus to generating executable alpha factors – defined as strategies that demonstrably outperform the market. Performance is quantified using the Sharpe Ratio, a measure of risk-adjusted return; the gemini-3-pro-preview model achieved a Sharpe Ratio of 0.628 on the benchmark, indicating a statistically significant positive return relative to its risk.

Deterministic Evaluation: Isolating Robustness from Stochastic Noise

Run-to-run variance represents a significant challenge in the deployment of strategies generated by Large Language Models (LLMs) due to inconsistencies observed when the same strategy is executed across multiple, independent simulations. This variance manifests as differing performance metrics – such as Sharpe Ratio or total return – despite identical inputs and strategy definitions. The problem arises because even minor stochastic elements within the simulation environment or the LLM’s action selection process can accumulate across multiple time steps, leading to substantial divergence in outcomes. Consequently, a strategy exhibiting strong performance in one simulation run may yield significantly weaker or even negative results in another, hindering reliable backtesting and live deployment.

Run-to-run variance in LLM-generated trading strategies is frequently attributable to the characteristics of the underlying model architecture and the discretization process employed. Stateless autoregressive models, common in LLM applications, lack inherent memory and can produce differing outputs even with identical inputs due to stochastic sampling. Furthermore, financial markets generate continuous signals; converting these into discrete actions-such as buy, sell, or hold-introduces approximation errors and amplifies variability. These combined factors contribute to inconsistent strategy performance across repeated simulations, necessitating deterministic evaluation benchmarks to assess reliability and identify truly robust approaches.

AlphaForgeBench prioritizes deterministic evaluation of trading strategies to assess reliability and consistency. This approach requires strategies to yield predictably similar performance across multiple simulation runs, minimizing the impact of stochasticity. At the Level 1 difficulty setting, this rigorous evaluation methodology results in a narrow inter-model Sharpe Ratio spread of only 0.029. This low spread indicates a high degree of consistency in strategy performance and demonstrates the effectiveness of deterministic evaluation in differentiating robust strategies from those prone to unpredictable behavior.

From Backtesting to Live Validation: The Path to Practical Alpha

A rigorous evaluation of any trading strategy begins with backtesting, a process wherein the strategy is applied to historical data to simulate its performance over time. This isn’t merely about identifying potential profits; it’s a critical stress test for robustness. By subjecting the strategy to varied market conditions – bull markets, bear markets, periods of high volatility, and prolonged stagnation – researchers can uncover hidden weaknesses and biases. Backtesting reveals how a strategy might have fared during significant events, such as economic recessions or geopolitical crises, and helps determine if observed success is due to genuine predictive power or simply a result of favorable historical circumstances. The process allows for iterative refinement, enabling adjustments to parameters and rules to improve performance and reduce the likelihood of unexpected losses when deployed in live markets.

Robust risk management is paramount in any trading strategy, functioning as a crucial buffer against unforeseen market volatility and potential financial losses. Techniques extend beyond simple stop-loss orders, encompassing position sizing-calculating appropriate trade volumes based on account equity and risk tolerance-and diversification across asset classes to minimize exposure to single-point failures. Sophisticated approaches incorporate metrics like Value at Risk (VaR) and Maximum Drawdown to quantify potential downside, enabling traders to proactively adjust portfolio allocations and maintain stability. The consistent application of these principles not only preserves capital during adverse conditions but also fosters long-term, sustainable growth by preventing catastrophic events that could derail even the most promising strategies.

The true measure of any algorithmic trading strategy lies not within the confines of backtesting, but in its performance within live market conditions. While historical data provides valuable insight, it cannot fully replicate the unpredictable dynamics of real-time trading, including slippage, latency, and unexpected events. Recent evaluations demonstrate this principle; strategies developed using the claude-sonnet-4.5 model have undergone live market validation, achieving a noteworthy Calmar Ratio of 1.650. This metric, representing risk-adjusted return, suggests a compelling balance between profitability and drawdown, indicating the strategies’ potential for sustained performance even amidst market volatility. This real-world validation moves beyond theoretical robustness, offering evidence of practical efficacy and bolstering confidence in the model’s predictive capabilities.

Systematic Control: A Framework for Scalable and Reliable Alpha Generation

A novel Level-Grade Difficulty Taxonomy offers a structured method for modulating the complexity of tasks assigned to Large Language Models when generating financial strategies. This taxonomy moves beyond simple prompting by explicitly defining levels of difficulty based on constraints and requirements – from basic criteria like asset classes and time horizons at lower grades, to increasingly intricate conditions involving multiple assets, risk parameters, and transaction costs at higher grades. By systematically varying these parameters, researchers and practitioners can precisely control the challenge presented to the LLM, enabling a more granular understanding of model capabilities and limitations. This controlled approach allows for targeted benchmarking, identifying optimal model configurations for specific strategy complexities, and ultimately unlocking the potential for automated financial strategy creation with quantifiable risk and reward profiles. The taxonomy provides a foundation for repeatable experimentation and a clearer path toward building robust and reliable LLM-driven investment tools.

Systematic control over strategy complexity is crucial for effectively evaluating and optimizing large language models in financial applications. Research demonstrates that as strategy difficulty increases-specifically at Level 3 of the established taxonomy-the performance disparity between different models becomes dramatically pronounced, with the spread in $Sharpe \, Ratio$ reaching a factor of 14. This substantial divergence underscores the importance of controlled benchmarking; without it, assessing true model capabilities and identifying optimal configurations becomes exceedingly difficult. The ability to isolate performance differences based on task complexity, rather than random variation, facilitates targeted improvements and allows for a more nuanced understanding of each model’s strengths and weaknesses in automated financial strategy creation.

The confluence of systematically controlled complexity and rigorous evaluation presents a pathway to fully realize the capabilities of Large Language Models in automated financial strategy development. Research indicates that even with variations in temperature τ, a parameter influencing the randomness of LLM outputs, the impact on strategy performance, as measured by the Sharpe Ratio, remains remarkably stable – differing by less than 0.008 between τ = 0 and τ = 0.7. This low variance suggests that LLMs can consistently generate robust strategies across a range of settings, provided the underlying complexity of the task is carefully managed and performance is consistently assessed using standardized benchmarks. Consequently, a framework built on systematic control and rigorous evaluation promises to unlock a new era of algorithmic financial innovation, enabling the creation of consistently high-performing strategies with increased reliability and predictability.

The pursuit of robust financial modeling, as demonstrated by AlphaForgeBench, echoes a timeless principle. As Francis Bacon observed, “Knowledge is power,” and in this context, power derives from verifiable, executable code. The benchmark’s emphasis on generating code for trading strategies, rather than simply predicting actions, underscores the need for demonstrable truth. This aligns with a mathematical worldview where the correctness of an algorithm-its provability-is paramount. AlphaForgeBench doesn’t merely test if a model appears to function, but rigorously assesses if it embodies a logically sound, replicable strategy-a testament to the enduring power of structured, provable knowledge in a field often reliant on heuristics.

What’s Next?

The pursuit of algorithmic finance via Large Language Models inevitably exposes the chasm between syntactic correctness and semantic validity. AlphaForgeBench rightly shifts evaluation from the ephemeral realm of direct action prediction to the more rigorous domain of executable code. However, this merely addresses one layer of abstraction. The benchmark assesses whether a strategy can be generated, not whether that strategy embodies any logical consistency beyond basic compilation. True progress demands a focus on provable properties-risk bounds, convergence criteria, even demonstrable robustness against adversarial market conditions-not simply backtesting performance on historical data.

The current emphasis on code generation, while pragmatic, risks conflating engineering with insight. A beautifully formatted, flawlessly compiling algorithm remains fundamentally flawed if its underlying assumptions are unsound. Future benchmarks should incorporate formal verification techniques, demanding mathematical justifications for generated strategies. The field must move beyond empirical observation-‘it worked on the test set’ is not a scientific argument-and embrace the elegance of provable correctness.

Ultimately, the limitations are not those of the models themselves, but of the evaluation criteria. Alpha generation is not merely about finding patterns; it is about building consistent, justifiable, and demonstrably robust systems. The next generation of benchmarks should reflect this, prioritizing mathematical purity over superficial performance.

Original article: https://arxiv.org/pdf/2602.18481.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/