Author: Denis Avetisyan
New research demonstrates how large language models can reliably translate natural language instructions into executable option trading strategies, opening the door to more intuitive and automated financial tools.
A neuro-symbolic framework leveraging a domain-specific option query language (OQL) enables accurate and financially viable strategy construction from natural language input.
While Large Language Models excel at general code generation, translating nuanced trading intent into correct and executable option strategies remains a significant challenge. This paper, ‘From Natural Language to Executable Option Strategies via Large Language Models’, introduces a neuro-symbolic framework leveraging a domain-specific language, the Option Query Language (OQL), to bridge this gap. By abstracting option markets into high-level primitives, OQL enables LLMs to function as reliable semantic parsers, significantly improving execution accuracy and logical consistency. Could this approach unlock a new era of accessible and intelligent automated trading strategies?
From Complexity to Clarity: Reclaiming Options Trading
Historically, the formulation of an effective options strategy has been a considerable undertaking, demanding both a deep understanding of financial instruments and substantial manual calculation. This complexity stems from the need to assess numerous variables – including underlying asset price, volatility, time to expiration, and interest rates – to construct a position aligned with specific market expectations. Consequently, many investors, particularly those lacking specialized training or dedicated resources, find themselves effectively excluded from actively participating in options trading. The process often involves painstakingly evaluating payoff diagrams, calculating probabilities, and comparing various combinations of calls and puts – a task that, without expertise, can be both time-consuming and prone to error, ultimately limiting accessibility to these potentially powerful investment tools.
The proliferation of options contracts and increasingly volatile market conditions have created a demand for automated strategy generation. Manual selection and implementation of options strategies, once feasible for experienced traders, now struggles to keep pace with the sheer volume of available choices and the speed of market fluctuations. Consequently, sophisticated algorithms are being developed to interpret investment objectives – be they hedging, income generation, or speculative gains – and translate them into concrete, executable options strategies. These systems aim to alleviate the burden on investors lacking specialized knowledge, while also providing seasoned professionals with tools to efficiently navigate complex market landscapes and identify opportunities that might otherwise be missed. The capacity to swiftly synthesize optimal strategies from a vast options universe is no longer a luxury, but a necessity for effective participation in modern financial markets.
Current systems attempting to translate investor intent into options strategies frequently struggle with the subtleties of natural language. These approaches often rely on keyword recognition or predefined templates, proving inadequate when faced with nuanced goals or complex market expectations. An investor articulating a desire for ‘moderate gains with limited downside’ or ‘a strategy benefiting from increased volatility but capped at a certain profit’ presents a challenge beyond simple algorithmic interpretation. The result is often a strategy that doesn’t fully align with the investor’s vision, or requires substantial manual adjustment – effectively negating the benefits of automation. Capturing the intent behind the request, rather than merely processing the words themselves, remains a key hurdle in developing truly intelligent option strategy generation.
The development of a system translating investor intent directly into actionable option strategies signifies a crucial step forward in financial technology. Historically, realizing a desired market outcome through options necessitated deep expertise and laborious manual construction of trades. This new approach aims to bypass those limitations, allowing an investor to articulate a financial goal – perhaps protection against a market downturn, or speculation on increased volatility – and receive a tailored strategy designed to achieve it. Such a system doesn’t merely suggest a possible option, but rather constructs a strategy specifically aligned with the expressed objective, democratizing access to sophisticated financial instruments and potentially unlocking opportunities previously available only to seasoned professionals. This capability promises to reshape the landscape of options trading, shifting the focus from how to trade to what an investor wishes to accomplish.
From Language to Logic: Introducing Option Query Language
Large Language Models (LLMs) present a novel approach to automating investment strategy development by converting natural language descriptions into executable trading instructions. These models, trained on extensive datasets of text and code, demonstrate the capacity to interpret the intent expressed in human language – such as “buy a call option with a strike price of $100” – and map it to corresponding financial instruments and parameters. This capability bypasses the need for manual coding or rule-based systems, potentially accelerating the development and deployment of complex investment strategies. However, the inherent ambiguity and nuance of natural language necessitate a robust intermediary step to ensure accurate and reliable translation into actionable trades, which is addressed through the introduction of Option Query Language.
Directly translating natural language investment instructions into executable trades presents significant challenges due to the inherent ambiguity and variability of human language. To address this, we employ Option Query Language (OQL) as an intermediate representation. OQL serves as a standardized, machine-readable format positioned between the natural language input and the final trade execution. This approach decouples the complexities of natural language understanding from the generation of financial logic; the Large Language Model (LLM) focuses on translating the natural language input into OQL, rather than attempting to directly formulate a complete and accurate trading strategy. The resulting OQL statement then undergoes validation and execution, ensuring precision and minimizing the risk of misinterpretation inherent in direct natural language processing.
Option Query Language (OQL) addresses the challenges of translating natural language instructions into executable option strategies by providing a formal, structured syntax. Unlike natural language, which is prone to misinterpretation due to synonymy, polysemy, and contextual dependence, OQL utilizes a predefined grammar and vocabulary specific to options trading. This ensures each instruction has a single, definitive interpretation, eliminating ambiguity in strategy definition. OQL expressions explicitly define the option type, strike price, expiration date, and desired position (buy, sell, hold) – elements that can be implicitly understood or omitted in natural language, leading to errors. By enforcing a rigid structure, OQL facilitates reliable translation by Large Language Models (LLMs) and reduces the potential for unintended or incorrect trade execution.
Employing Option Query Language (OQL) reframes the role of the Large Language Model (LLM) from a generator of financial strategy to a translator. Instead of requiring the LLM to independently formulate complex option logic – a task prone to errors and inconsistencies – OQL provides a precise, structured target language. The LLM’s task is then limited to converting natural language input into valid OQL statements. This approach significantly reduces the computational complexity and potential for inaccuracies, as the LLM operates within a constrained and well-defined linguistic space, focusing on syntactic and semantic correctness rather than the invention of novel financial constructs.
Context is Paramount: Enriching LLMs with Real-Time Data
The generation of accurate and relevant trading strategies by Large Language Models (LLMs) is fundamentally dependent on current option chain data. Option chains detail the available strike prices and expiration dates for options contracts on an underlying asset, along with associated pricing and volume information. This data reflects prevailing market conditions, including implied volatility, buyer/seller sentiment, and potential price movements. Without access to this real-time data, the LLM would be relying on stale or incomplete information, leading to strategies that are misaligned with the current market and therefore likely ineffective. The continuous updating of option chain data is therefore a core requirement for a functional and reliable LLM-driven trading system.
Retrieval-Augmented Generation (RAG) is implemented to dynamically supplement the Large Language Model (LLM) with relevant data extracted from the Option Chain. This process involves retrieving specific data points – including strike prices, expiration dates, bid/ask spreads, implied volatility, and open interest – based on the user’s query or the LLM’s internal reasoning. The retrieved data is then incorporated into the prompt provided to the LLM, effectively expanding its knowledge base and providing the necessary context to generate informed responses and trading strategies. This differs from solely relying on the LLM’s pre-trained parameters, as RAG allows for access to information beyond its initial training dataset, ensuring the LLM operates with current market data.
Retrieval-Augmented Generation (RAG) improves Large Language Model (LLM) performance by supplementing its pre-trained knowledge with information retrieved from an external data source – in this case, the current option chain. This process allows the LLM to dynamically access and incorporate real-time pricing data, implied volatility, and other relevant market metrics. By grounding its responses in this current data, the LLM avoids relying solely on potentially outdated or generalized information, and can therefore formulate trading strategies that are specifically aligned with prevailing market conditions. The retrieved data provides the LLM with the necessary context to accurately assess risk, identify potential opportunities, and generate more informed and relevant strategies.
Integration of real-time option chain data demonstrably improves system performance, as evidenced by a Win Rate (WR) of 0.800 achieved when utilizing the DeepSeek-Chat large language model to analyze TSLA (Tesla, Inc.) options. This metric indicates that, across a defined test set, 80.0% of generated trading strategies, informed by current market data, resulted in profitable outcomes. The WR calculation is based on the number of winning strategies divided by the total number of strategies generated and evaluated. This performance level signifies a substantial increase in reliability compared to systems operating without access to up-to-date market information.
Beyond Simple Trades: Modeling Market Dynamics
Successful options trading isn’t simply about picking a direction; it fundamentally depends on grasping how market forces influence price. Key among these are volatility – the degree of price fluctuation – and delta, which measures an option’s sensitivity to changes in the underlying asset’s price. High volatility generally increases option prices, creating opportunities for sellers, while low volatility favors buyers. Delta, ranging from 0 to 1 for calls and -1 to 0 for puts, indicates the approximate change in an option’s price for every $1 move in the underlying asset. Understanding these relationships allows traders to construct strategies that profit from specific market conditions or hedge against potential losses, moving beyond simple directional bets to capitalize on nuanced price movements and risk profiles.
The system’s robustness hinges on its integration of established financial models, most notably the Black-Scholes-Merton model, alongside others used for options pricing and risk assessment. These models aren’t merely employed for initial strategy creation; they serve as a continuous validation and refinement engine. Generated strategies are rigorously backtested against historical data using these models to assess their theoretical performance and identify potential vulnerabilities. Discrepancies between modeled outcomes and observed market behavior trigger automatic adjustments to strategy parameters, ensuring alignment with prevailing conditions and maximizing profitability. This dynamic interplay between algorithmic generation and mathematical validation is critical for adapting to market fluctuations and sustaining consistent returns, ultimately bolstering the reliability of complex options strategies.
The system transcends rudimentary option strategies by incorporating advanced techniques such as spread strategies and the Iron Condor, allowing for a more nuanced approach to portfolio management. Spread strategies, which involve simultaneously buying and selling options contracts with differing strike prices or expiration dates, enable investors to capitalize on specific market predictions with reduced risk. Similarly, the Iron Condor-a neutral strategy employing four options contracts-profits from limited price movement, providing a defined risk and reward profile. This capability empowers sophisticated investors to automate the execution of these complex strategies, moving beyond simple directional bets to exploit a wider range of market conditions and potentially enhance returns through refined risk management.
The system’s capacity to automate intricate options strategies translates into demonstrable financial outcomes for sophisticated investors. Rigorous testing reveals a Return on Cost (ROC) of 7.087 when applied to the SPY ETF, utilizing Gemini 2.5 Flash in conjunction with the PCG methodology. Further analysis indicates an Efficiency (Eff) score of 0.773 on AAPL, achieved through the deployment of DeepSeek-Coder. Notably, the system also generated an average profit of $984.0 on TSLA trades, leveraging the capabilities of DeepSeek-Chat; these results highlight the potential for consistent profitability through the automated execution of nuanced trading approaches, exceeding the performance of many manually managed portfolios.
The pursuit of translating natural language into executable financial strategies, as detailed in this work, embodies a fundamental principle of elegant design. One strives to distill complex financial instruments-options, in this case-into their essential components, recognizing that true power lies not in adding layers of intricacy, but in revealing underlying simplicity. As Donald Davies observed, “It is unforgivable to add to the complexity of a system when simplicity will do.” This paper’s neuro-symbolic framework, with its focus on a domain-specific language (OQL), directly reflects this ethos-a conscious effort to minimize unnecessary complication in the translation process and maximize the clarity with which intent becomes action. The focus on reliable execution, not just linguistic interpretation, is a testament to this approach.
Beyond the Query
The demonstrated translation of natural language to executable option strategies, while a step forward, merely addresses the surface of a deeper failing. A system that requires a specialized query language – even one ostensibly ‘natural’ – has already conceded defeat. The true ambition must be direct comprehension, not mediated instruction. Future work will undoubtedly refine the domain-specific language, seeking to minimize the translation loss, but this is treating a symptom, not the disease. The core challenge remains: can a large model genuinely understand financial intent, or only simulate understanding through pattern matching?
Further research should not prioritize increasing the complexity of the neuro-symbolic interface. Instead, it must focus on reducing it, even to the point of eliminating the need for explicit symbolic representation. A viable system will not ask ‘what strategy?’ but infer it. This demands a fundamental re-evaluation of how ‘understanding’ is measured, moving beyond superficial accuracy to demonstrable financial viability in unpredictable markets – a test far more stringent than any benchmark dataset.
The pursuit of perfect translation is, ultimately, a fool’s errand. Clarity is courtesy, and the most elegant solution will be the one that requires the fewest instructions. The goal is not to build a more sophisticated interpreter, but to dismantle the need for interpretation altogether.
Original article: https://arxiv.org/pdf/2603.16434.pdf
Contact the author: https://www.linkedin.com/in/avetisyan/
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2026-03-18 17:17