Can Algorithms Beat the Market? A New Benchmark for Prediction Trading

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Author: Denis Avetisyan

Researchers have introduced a comprehensive framework for rigorously testing and comparing trading strategies in prediction markets, paving the way for more robust algorithmic and AI-powered approaches.

PredictionMarketBench offers a standardized, deterministic replay environment for evaluating agent performance with realistic market microstructure and execution models.

Despite the increasing sophistication of algorithmic trading, robust and standardized evaluation remains a challenge in the rapidly evolving landscape of prediction markets. This paper introduces PredictionMarketBench: A SWE-bench-Style Framework for Backtesting Trading Agents on Prediction Markets, a novel benchmark designed to rigorously assess the performance of both traditional and large language model (LLM)-based trading agents. By providing a deterministic, event-driven replay of historical market data-including order books, trades, and realistic fee structures-PredictionMarketBench enables reproducible comparisons of algorithmic strategies and tool-augmented LLM agents. Will this framework facilitate the development of more resilient and profitable trading agents capable of navigating the complexities of prediction market microstructure?

The Illusion of Market Fidelity: Why Backtesting Isn’t Enough

The efficacy of trading agents extends beyond historical data analysis; truly robust evaluation necessitates simulation within a convincingly realistic market environment. Backtesting, while valuable, provides only a limited snapshot, failing to account for the complex interplay of order book dynamics, participant behavior, and unforeseen events that characterize live trading. A meticulously crafted simulation allows researchers to stress-test agents against a broader range of scenarios, including varying volatility, liquidity constraints, and the presence of other intelligent traders. This approach reveals vulnerabilities and performance limitations that would remain hidden in purely historical analysis, ultimately leading to the development of more resilient and profitable algorithmic trading strategies. Without this level of fidelity, assessments remain incomplete, and the transition from promising backtest results to consistent real-world performance becomes significantly more challenging.

Current algorithmic trading benchmarks frequently fall short of replicating the complexities of genuine financial markets. Many simulations operate under simplified assumptions, neglecting crucial elements like maker-taker fees – the costs associated with providing or removing liquidity – and the nuances of order execution. This lack of fidelity can lead to overly optimistic performance evaluations, as strategies that appear profitable in a sterile environment may falter when confronted with the realities of transaction costs and imperfect order fills. Accurate modeling of these dynamics is not merely a technical refinement; it is essential for developing trading agents capable of navigating the intricacies of live markets and achieving consistent, real-world profitability. Without such realism, research risks producing results that are statistically significant in simulation but practically irrelevant in application.

The advancement of algorithmic trading strategies hinges on the capacity to thoroughly and reliably evaluate their performance, yet current methods often fall short of mirroring genuine market conditions. To address this, researchers require a standardized benchmark – a meticulously curated and replayable dataset of market activity. Such a benchmark would allow for consistent evaluation across diverse algorithms, fostering rigorous comparison and accelerating progress in the field. By providing a common foundation, it enables researchers to isolate the impact of specific algorithmic choices, ensuring reproducibility of results and ultimately leading to more robust and effective trading strategies. The ability to consistently replay market scenarios is paramount for identifying vulnerabilities and optimizing performance, moving beyond isolated backtests to a more holistic and reliable assessment of algorithmic capabilities.

How PredictionMarketBench Recreates the Trading Floor (Without the Chaos)

PredictionMarketBench generates trading episodes based on historical data sourced directly from the Kalshi prediction market. This approach ensures the benchmark reflects real-world market dynamics, including order book behavior and price discovery. Raw data consisting of order book updates, executed trades, and settlement outcomes are captured and then used to recreate specific market conditions. The resulting episodes represent discrete periods of trading activity, allowing for controlled and reproducible testing of algorithmic trading strategies against authentic, albeit historical, market scenarios. The utilization of Kalshi data provides a foundation for evaluating agent performance in a realistic, data-driven environment.

The Deterministic Simulator forms the central component of PredictionMarketBench, functioning by replaying historical market data from Kalshi to generate reproducible trading scenarios. This simulator accurately models order execution by distinguishing between maker and taker roles, applying corresponding fees based on order type and resting status. The deterministic nature of the simulator ensures consistent results given the same input data and agent actions, facilitating rigorous evaluation of trading strategies. The system processes recorded orderbook updates, trade executions, and settlement data to recreate precise market conditions, allowing agents to interact with a faithful representation of historical trading opportunities.

PredictionMarketBench incorporates transaction costs reflective of real-world exchanges. Specifically, orders that immediately execute against the best available price – categorized as market orders or limit orders crossing the spread – are assessed a 7% taker fee. Conversely, resting limit orders that are filled by other traders incur a 1.75% maker fee. These fees are applied to each transaction within the simulated environment to provide a more accurate assessment of agent performance and strategy profitability.

The Episode Construction Pipeline within PredictionMarketBench processes raw market data – orderbook updates, executed trades (trade prints), and final settlement outcomes – to generate discrete, self-contained benchmark instances. This pipeline normalizes and organizes the data, converting the continuous stream of events into a series of snapshots representing specific points in time. Each instance encapsulates the complete state of the market at that moment, including orderbook depth, open positions, and relevant historical data, enabling repeatable and isolated testing of trading agents. The resulting episodes are designed to be statistically representative of real-world market behavior while providing a controlled environment for performance evaluation.

PredictionMarketBench incorporates a position concentration limit of 20% to reflect realistic risk management constraints. This restriction prevents agents from allocating more than 20% of their total equity to any single position within the simulated market. The limit is enforced throughout each trading episode, and any order that would cause an agent to exceed this threshold is rejected. This constraint is designed to evaluate agent performance under conditions that mirror the portfolio diversification requirements often found in professional trading environments and discourages excessively risky strategies.

The PredictionMarketBench environment is accessed through a standardized Agent Interface, enabling interaction with the deterministic simulator. This interface allows external trading agents – algorithmic strategies or human traders – to submit orders and receive real-time market data, including order book updates and trade executions. Agents connect to the simulation via a defined API, receiving information regarding order fills, position updates, and account balances. The interface is designed to be flexible, accommodating various programming languages and trading frameworks, and facilitates the evaluation of agent performance across a range of simulated market conditions.

Proof is in the Pudding: Validating the Benchmark with Diverse Agents

PredictionMarketBench facilitates the comparative analysis of diverse agent strategies within a simulated prediction market environment. This includes implementations ranging from basic random agents, which execute trades randomly, to complex agents leveraging Large Language Models (LLMs) for decision-making. The platform’s design enables consistent evaluation of these agents across identical market conditions and historical data, allowing for quantifiable performance comparisons. This range of agent complexity allows researchers to benchmark the effectiveness of increasingly sophisticated approaches against simple baselines and identify the benefits – or drawbacks – of utilizing LLMs in prediction market scenarios. The ability to evaluate both simple and complex strategies is central to understanding the potential of AI-driven approaches in this domain.

Reproducibility of the Tool-Calling LLM Agent was achieved through deterministic decoding during the evaluation process. Specifically, the agent’s responses were generated using a fixed random seed and a temperature of 0.0, eliminating stochasticity in the token selection process. This ensures that, given the same market conditions and agent state, the agent will consistently produce the same actions. This deterministic behavior is critical for reliable benchmarking and allows for consistent performance comparisons across different runs and configurations, facilitating debugging and iterative improvement of the agent’s strategy.

A Random Agent was implemented to establish a baseline performance metric within the PredictionMarketBench benchmark. This agent executes trades randomly, providing a quantitative lower bound against which the performance of more complex strategies, including those leveraging Large Language Models, can be directly compared. The Random Agent’s trading behavior is characterized by uniform probability across all available actions, ensuring an unbiased, albeit unsophisticated, approach to market participation. Performance data from the Random Agent, specifically its Profit and Loss (P&L) and settlement losses, serves as a critical reference point for evaluating the efficacy and cost-efficiency of alternative agent designs and trading algorithms.

PredictionMarketBench incorporates a fee model to simulate real-world trading costs, impacting agent profitability and providing a more accurate performance assessment. This model applies a standardized fee structure to all agents, accounting for both exchange fees and potential slippage costs incurred during trade execution. Specifically, a 0.1% exchange fee is applied to each trade, and slippage is modeled as 0.05% of the trade value, reflecting the difference between the expected price and the actual execution price. By including these costs, the benchmark ensures that agent evaluations are not artificially inflated by neglecting realistic transaction expenses, allowing for a more meaningful comparison of strategy performance under practical conditions.

Evaluation of the Bollinger Bands trading strategy within the PredictionMarketBench framework yielded a positive overall Profit and Loss (P&L). Performance analysis indicates that the majority of profits generated by this strategy were concentrated during the Bitcoin threshold episode, which exhibited heightened market volatility. This suggests the Bollinger Bands strategy is particularly effective in capitalizing on price swings during periods of increased market fluctuation, though overall performance requires consideration across diverse market conditions.

Evaluation of LLM-based agents within PredictionMarketBench revealed substantial settlement losses, primarily attributed to high-frequency trading behavior. These agents executed a greater volume of trades compared to the baseline Random Agent, increasing exposure to unfavorable price movements during settlement. Conversely, the Random Agent, characterized by significantly lower trading intensity, incurred comparatively less loss despite achieving lower overall profit. This difference highlights a trade-off between trading frequency and settlement risk within the benchmark’s simulated market environment, suggesting that minimizing trading intensity can be a viable strategy for mitigating losses, even if it limits potential gains.

Building on Solid Foundations: Extending the State-of-the-Art

PredictionMarketBench directly builds upon the innovative “harness-first” evaluation methodology established by SWE-Bench, a technique that prioritizes robust and standardized testing infrastructure. While traditional algorithmic trading research often relies on ad-hoc datasets and evaluation procedures, this benchmark adopts a more systematic approach, creating a controlled environment for assessing trading strategies. This emphasis on a well-defined harness-the core framework for running and analyzing experiments-ensures reproducibility and facilitates fair comparison of different algorithms. By extending this principle to the complexities of prediction markets, PredictionMarketBench enables researchers to isolate the performance of a strategy from confounding factors, ultimately accelerating progress in the field and promoting the development of more reliable trading agents.

PredictionMarketBench accelerates algorithmic trading research through a commitment to standardized environments and reproducible experiments. Previously, comparing different trading strategies proved challenging due to variations in data handling, market simulations, and evaluation metrics; this benchmark addresses those inconsistencies by providing a consistent playing field. Researchers can now rigorously test and refine their algorithms, knowing that performance improvements are genuine and not artifacts of the experimental setup. This focus on reproducibility not only streamlines the research process but also fosters greater trust in the results, allowing the field to build upon established findings and advance at an increased pace. Ultimately, PredictionMarketBench aims to diminish the time required to translate innovative trading ideas into demonstrably effective agents.

PredictionMarketBench establishes a dedicated arena for rigorously testing and contrasting novel algorithmic trading strategies. This benchmark isn’t merely a dataset; it’s a controlled experimental ground where researchers can objectively measure performance, identify strengths and weaknesses, and refine their approaches. By providing standardized metrics and reproducible results, the platform accelerates the pace of innovation in a historically opaque field. The ability to directly compare different strategies – from reinforcement learning agents to statistical arbitrage models – fosters healthy competition and encourages the development of increasingly sophisticated and effective trading agents, ultimately pushing the boundaries of what’s possible in automated financial markets.

PredictionMarketBench is designed to function as a central hub for both academic researchers and industry professionals seeking to refine algorithmic trading strategies. The platform’s standardized environments and rigorous evaluation metrics allow for direct comparison of agent performance, facilitating quicker identification of promising approaches and accelerating innovation in the field. By providing a shared, reproducible foundation, the benchmark reduces the barriers to entry for new researchers and allows practitioners to confidently test and deploy novel trading agents, ultimately driving the development of more robust and effective automated trading systems. This collaborative ecosystem is intended to foster a cycle of continuous improvement, benefiting the entire community involved in algorithmic trading.

The pursuit of a perfect benchmark, as demonstrated by PredictionMarketBench, feels… familiar. The framework strives for deterministic replay and execution realism, attempting to capture the chaos of actual markets. It’s a noble effort, meticulously recreating market microstructure. However, one suspects that even the most rigorously tested agent will eventually encounter an edge case, a black swan event, or simply a production deployment that reveals unforeseen vulnerabilities. As Ralph Waldo Emerson observed, ‘The only way to have the last laugh over adversity is to make fun of it.’ This benchmark, while valuable, will inevitably become another layer of abstraction, a simplified model that doesn’t quite capture the infinite messiness of real-world trading. It’s less a solution and more a well-defined problem, waiting for the next iteration of complexity.

What’s Next?

PredictionMarketBench, in its attempt to impose order on the chaos of agent interactions, merely formalizes a new set of constraints. The benchmark will undoubtedly reveal which algorithms fail most predictably under stress. The pursuit of ‘execution realism’ is, itself, a fiction. Any model that faithfully replicates market microstructure will quickly become a sprawling, unmaintainable monster, riddled with edge cases no one understands. It will be a testament to how much can go wrong, rather than how things should be built.

The inevitable arrival of increasingly complex LLM-based agents only accelerates the problem. These systems will excel at exploiting superficial patterns in the benchmark data, delivering impressive short-term results. But these same agents will likely be brittle and opaque, susceptible to unforeseen shifts in market dynamics. The real challenge isn’t creating clever algorithms; it’s building tools to diagnose why they fail. Documentation, of course, remains a myth invented by managers.

One suspects the ultimate utility of PredictionMarketBench will not be in discovering novel trading strategies, but in generating a comprehensive catalog of failure modes. A precisely defined taxonomy of loss, meticulously indexed and readily available for future generations of algorithm designers. The benchmark will be a monument to the inescapable truth: anything that promises to simplify life adds another layer of abstraction. And abstraction is simply deferred complexity.

Original article: https://arxiv.org/pdf/2602.00133.pdf

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

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2026-02-03 14:06