1 00:00:03,000 --> 00:00:05,880 Evan: Welcome to Daily Paper Cast. 2 00:00:05,988 --> 00:00:15,068 Evan: Today we're delving into a paper from the Hugging Face daily paper list of September 17, 2026, which gathered 24 upvotes. 3 00:00:15,120 --> 00:00:23,380 Ashley: The title of the paper is EvolveTrade: Experience-Driven Policy Refinement for Self-Evolving LLM Trading Agents. 4 00:00:23,484 --> 00:00:31,804 Evan: It’s authored by Sehee Kim, Yumin Choi, Minki Kang, and Sung Ju Hwang from KAIST and DeepAuto.ai. 5 00:00:31,848 --> 00:00:35,418 Evan: Ashley, let’s dive right into the introduction. 6 00:00:35,418 --> 00:00:38,988 What's the main background and motivation for this paper? 7 00:00:39,048 --> 00:00:49,788 Ashley: The authors point out that large language models, or LLMs, have shown impressive capabilities in reasoning, following instructions, and integrating varied information sources. 8 00:00:49,788 --> 00:00:54,078 Yet, in financial markets, their decision-making has been less reliable. 9 00:00:54,078 --> 00:01:04,168 This is primarily because these agents must repeatedly collect relevant information, verify noisy signals, manage risks, and take action in ever-changing environments. 10 00:01:04,224 --> 00:01:05,454 Evan: That makes sense. 11 00:01:05,454 --> 00:01:09,784 So what's the main issue with current LLM trading agents? 12 00:01:09,840 --> 00:01:17,420 Ashley: Most current LLM trading agents operate using static, hand-written tool-use policies that are set before deployment. 13 00:01:17,420 --> 00:01:21,700 This limits their adaptability in response to changing market conditions. 14 00:01:21,700 --> 00:01:27,800 The fixed nature of these policies means that they don't learn or evolve from their own experiences in the market. 15 00:01:27,864 --> 00:01:32,624 Evan: Why is this static policy such a bottleneck? 16 00:01:32,688 --> 00:01:42,058 Ashley: It's a bottleneck because the procedures for acquiring, validating, and acting on information remain static and unresponsive to what’s actually happening in the market. 17 00:01:42,058 --> 00:01:50,688 Essentially, the agent follows a pre-determined script that doesn't adapt based on the actual trading outcomes, which can significantly hinder its performance. 18 00:01:50,806 --> 00:01:56,176 Evan: So, to address this, what approach do the authors propose with EvolveTrade? 19 00:01:56,232 --> 00:02:04,102 Ashley: They introduce EvolveTrade, a self-evolving framework that treats the prompt of a tool-using trading agent as a text-parameterized policy. 20 00:02:04,102 --> 00:02:09,642 The key idea here is to allow the policy to evolve based on the agent's own trading experience. 21 00:02:09,642 --> 00:02:21,252 Essentially, after each trading interval, a Policy Agent revises this policy using accumulated decision traces and realized portfolio feedback, all while keeping the backbone LLM fixed. 22 00:02:21,372 --> 00:02:22,432 Evan: Interesting. 23 00:02:22,432 --> 00:02:25,932 So, how does this self-evolution mechanism work? 24 00:02:25,992 --> 00:02:33,842 Ashley: In EvolveTrade, after each completed trading interval, the Policy Agent updates the policy for the next batch of trading decisions. 25 00:02:33,842 --> 00:02:41,812 This allows the agent to refine how it gathers information, how it uses its tools, and how it manages its portfolio over time. 26 00:02:41,856 --> 00:02:45,176 Evan: And how did they validate that this approach works? 27 00:02:45,240 --> 00:02:51,480 Ashley: They conducted experiments across multiple market regimes, using two different LLM backbones. 28 00:02:51,480 --> 00:03:00,900 The results showed that EvolveTrade often improved key performance metrics like the Sharpe Ratio and Cumulative Return compared to fixed-policy baselines. 29 00:03:00,960 --> 00:03:07,200 Evan: Did they find any specific behavioral changes in the agent due to the evolving policies? 30 00:03:07,248 --> 00:03:08,398 Ashley: Yes, they did. 31 00:03:08,398 --> 00:03:16,698 The self-evolved policies led to increased code-mediated analysis and activated more relevant computations under different market regimes. 32 00:03:16,698 --> 00:03:27,488 For instance, in certain conditions, the agent was able to use more advanced techniques like value-at-risk calculations or trend-following indicators that the static baseline never invoked. 33 00:03:27,552 --> 00:03:30,642 Evan: That sounds like a significant improvement. 34 00:03:30,642 --> 00:03:34,592 So what can we conclude from this approach, according to the authors? 35 00:03:34,656 --> 00:03:47,166 Ashley: The key takeaway is that allowing the procedure governing how trading agents use their tools to be shaped by their own trading experience is crucial for creating more robust and adaptable LLM trading agents. 36 00:03:47,166 --> 00:03:56,056 The results suggest that adapting these reusable procedures can lead to better performance and more relevant analysis aligned with current market conditions. 37 00:03:56,172 --> 00:04:00,282 Evan: And that wraps up the introduction section of the paper on EvolveTrade. 38 00:04:00,282 --> 00:04:07,932 It's clear this approach has the potential to significantly enhance the adaptability and robustness of LLM trading agents. 39 00:04:09,274 --> 00:04:13,574 Evan: Let's move on to the methodology behind EvolveTrade. 40 00:04:13,634 --> 00:04:20,214 Evan: Ashley, could you break down how the authors designed and implemented the EvolveTrade framework? 41 00:04:20,258 --> 00:04:21,198 Ashley: Sure, Evan. 42 00:04:21,198 --> 00:04:25,638 The EvolveTrade framework is centered around the concept of policy self-evolution. 43 00:04:25,638 --> 00:04:33,578 This means the trading agent's system prompt, which acts as a tool-use policy, is updated regularly based on the agent's trading experience. 44 00:04:33,578 --> 00:04:44,618 After each trading interval, a Policy Agent reads the realized trades, the agent’s reasoning for each asset, and the portfolio feedback, then refines the system prompt for future trading intervals. 45 00:04:44,726 --> 00:04:45,776 Evan: Interesting. 46 00:04:45,776 --> 00:04:48,456 So this refinement process is crucial. 47 00:04:48,456 --> 00:04:51,426 How is the actual update of the policy carried out? 48 00:04:51,482 --> 00:04:54,912 Ashley: The update process is driven by a function called `fupdate`. 49 00:04:54,912 --> 00:05:02,202 This function uses the LLM with a fixed update instruction to analyze the feedback and update the policy text accordingly. 50 00:05:02,202 --> 00:05:07,522 Specifically, it looks at batch records which include the realized trades and the agent’s reasoning. 51 00:05:07,522 --> 00:05:16,662 Based on these records, the Policy Agent identifies aspects of the prompt that contributed to the outcomes, and revises the policy to improve future performance. 52 00:05:16,706 --> 00:05:23,226 Evan: So it’s essentially refining the instructions given to the agent based on its past performance, right? 53 00:05:23,282 --> 00:05:24,222 Ashley: Exactly. 54 00:05:24,222 --> 00:05:28,752 The idea is that the system prompt evolves based on quantitative feedback. 55 00:05:28,752 --> 00:05:37,882 For example, if certain tools or metrics consistently lead to better trading decisions, the Policy Agent will emphasize those tools or metrics more. 56 00:05:37,882 --> 00:05:43,942 Conversely, if certain aspects lead to poor performance, they will be de-emphasized or refined. 57 00:05:43,994 --> 00:05:51,514 Evan: What sort of metrics and tools does the system prompt initially include, and how do these change over time? 58 00:05:51,578 --> 00:05:59,858 Ashley: Initially, the system prompt includes tools like `get_price`, `news_searcher`, and `code_interpreter`. 59 00:05:59,858 --> 00:06:06,788 The trading agent retrieves price data, runs computational analysis through the code interpreter, and searches for relevant news. 60 00:06:06,788 --> 00:06:13,598 Metrics like 20-day and 60-day returns, Sharpe ratio, volatility, and maximum drawdown are calculated. 61 00:06:13,598 --> 00:06:24,358 Over time, the Policy Agent may introduce new metrics, like signal normalization and trend-following indicators, or refine how existing metrics are used based on observed performance. 62 00:06:24,410 --> 00:06:30,270 Evan: And what kind of decisions or changes does the Policy Agent focus on during the updates? 63 00:06:30,314 --> 00:06:39,704 Ashley: The Policy Agent’s updates can change how the agent selects tools, formulates queries, interprets signals, verifies evidence, and manages risk. 64 00:06:39,704 --> 00:06:48,654 For example, it might instruct the agent to adjust exposure differently under specific market conditions, or to prioritize certain signals over others. 65 00:06:48,654 --> 00:06:56,734 It might even refine the rules for when and how to use certain tools, ensuring the agent's approach remains aligned with the latest market feedback. 66 00:06:56,846 --> 00:06:59,946 Evan: What about the actual trading decisions? 67 00:06:59,946 --> 00:07:03,086 How are those affected by this evolving policy? 68 00:07:03,176 --> 00:07:04,176 Ashley: Good question. 69 00:07:04,176 --> 00:07:09,436 Each trading decision is essentially an output of the agent's computations guided by the policy. 70 00:07:09,436 --> 00:07:13,596 When the policy evolves, the agent’s decisions tend to become more robust. 71 00:07:13,596 --> 00:07:23,296 For instance, EvolveTrade's agents exhibited improvements in Sharpe Ratio and cumulative returns, indicating more risk-adjusted and profitable decisions post-evolution. 72 00:07:23,296 --> 00:07:29,846 In specific cases, the agent activated previously unused metrics to better respond to market conditions. 73 00:07:29,976 --> 00:07:36,026 Evan: Can you provide a concrete example of how these updates changed the agent's behavior? 74 00:07:36,074 --> 00:07:46,864 Ashley: During a January 2025 drawdown in NVIDIA stock, the Static Tool-Calling Agent maintained a higher exposure based on short-term metrics despite the stock falling sharply. 75 00:07:46,864 --> 00:07:58,534 In contrast, EvolveTrade’s refined policy reduced exposure based on longer-term metrics and volatility checks, resulting in significantly lower losses when the stock continued to decline. 76 00:07:58,586 --> 00:08:00,366 Evan: That's impressive. 77 00:08:00,366 --> 00:08:06,206 It sounds like EvolveTrade allows the agent to make more informed and cautious decisions. 78 00:08:06,266 --> 00:08:07,606 Ashley: Indeed, Evan. 79 00:08:07,606 --> 00:08:19,046 The updates reduce the reliance on static, potentially outdated rules, and instead ensure the agent’s strategy is continuously informed by its own performance and market conditions. 80 00:08:19,186 --> 00:08:22,646 Evan: And how is the effectiveness of this approach measured? 81 00:08:22,706 --> 00:08:30,726 Ashley: The authors use key financial metrics like Sharpe Ratio—which assesses risk-adjusted return—and cumulative return to measure performance. 82 00:08:30,726 --> 00:08:35,176 They also examine portfolio-level analytics such as volatility and drawdown. 83 00:08:35,176 --> 00:08:45,586 During their experiments across different market regimes and two LLM backbones, EvolveTrade demonstrated substantial improvements in these metrics compared to static policy baselines. 84 00:08:45,710 --> 00:08:53,170 Evan: Did they report any challenges or limitations with the self-evolution process? 85 00:08:53,234 --> 00:08:55,454 Ashley: Yes, they did mention a few. 86 00:08:55,454 --> 00:09:01,754 One limitation is the fixed update interval, which may not always be optimal across varying market conditions. 87 00:09:01,754 --> 00:09:09,524 There's also the risk of overfitting if the updates are too frequent, as daily updates showed poorer performance due to noise amplification. 88 00:09:09,524 --> 00:09:14,074 The authors suggest moderate intervals for stable adaptation and performance. 89 00:09:14,138 --> 00:09:21,138 Evan: Right, balancing updates to avoid overfitting while still adapting effectively sounds like a key challenge. 90 00:09:21,194 --> 00:09:22,314 Ashley: Precisely. 91 00:09:22,314 --> 00:09:34,834 They also recognized the importance of maintaining a balance between agility in adapting to new information and stability in decision-making to harness the benefits of self-evolution without succumbing to the drawbacks. 92 00:09:34,898 --> 00:09:38,308 Evan: And that’s a wrap for the methodology section, Ashley. 93 00:09:38,308 --> 00:09:42,538 Thanks for explaining how the EvolveTrade framework works in such detail! 94 00:09:43,803 --> 00:09:50,263 Evan: Alright, let's dive into the experiments and results outlined in the EvolveTrade paper. 95 00:09:50,307 --> 00:09:55,707 Evan: Ashley, how did the authors validate the effectiveness of the EvolveTrade framework? 96 00:09:55,755 --> 00:10:02,225 Ashley: The authors conducted a series of experiments across different market regimes to gauge EvolveTrade’s performance. 97 00:10:02,225 --> 00:10:09,595 They tested the framework using two different LLM backbones: GPT-5-mini and Gemini-2.5-Flash. 98 00:10:09,711 --> 00:10:13,991 Evan: What specific metrics did they use to evaluate the performance? 99 00:10:14,043 --> 00:10:34,403 Ashley: The evaluation was based on several key financial metrics: Sharpe Ratio, which measures risk-adjusted return; cumulative return, which assesses total portfolio growth; maximum drawdown, which indicates the largest percentage drop from a peak; win rate, which is the fraction of days with positive portfolio returns; and daily volatility. 100 00:10:34,507 --> 00:10:39,047 Evan: And how did EvolveTrade perform compared to the static policy baselines? 101 00:10:39,099 --> 00:10:43,549 Ashley: EvolveTrade outperformed the static policy baselines in many settings. 102 00:10:43,549 --> 00:10:53,419 For instance, using GPT-5-mini, EvolveTrade achieved the best Sharpe Ratio and cumulative return in the January and September 2025 testing periods. 103 00:10:53,419 --> 00:11:07,499 It recorded a Sharpe Ratio of 5.12 and a cumulative return of 5.10% in January, outperforming other LLM-based methods and rule-based baselines like Buy and Hold or moving-average-based strategies. 104 00:11:07,563 --> 00:11:08,953 Evan: That's impressive. 105 00:11:08,953 --> 00:11:13,083 Were there similar results with the Gemini-2.5-Flash backbone? 106 00:11:13,131 --> 00:11:14,831 Ashley: Yes, absolutely. 107 00:11:14,831 --> 00:11:29,361 EvolveTrade showed the best performance in November 2025 with Gemini-2.5-Flash, achieving a Sharpe Ratio of -1.03 and a cumulative return of -1.07%, despite the generally bearish market. 108 00:11:29,361 --> 00:11:40,011 The framework also performed strongly in February 2026, yielding substantial improvements over both static LLM-based configurations and traditional rule-based strategies. 109 00:11:40,129 --> 00:11:45,739 Evan: How did policy evolution affect the trading behavior of the agents? 110 00:11:45,795 --> 00:11:52,275 Ashley: The evolving policies led to significant changes in the agent's tool-use behavior and analytical metrics. 111 00:11:52,275 --> 00:12:03,395 For example, the refined policies activated previously unused metrics like value-at-risk, signal normalization, and trend-following indicators such as EMA and RSI. 112 00:12:03,395 --> 00:12:10,815 These additions were especially relevant in specific market conditions, such as April's drawdown and September's upward trend. 113 00:12:10,875 --> 00:12:15,735 Evan: Were there any notable examples demonstrating this change? 114 00:12:15,795 --> 00:12:30,895 Ashley: Yes, during the January 2025 drawdown in NVIDIA stock, the Static Tool-Calling Agent maintained a 10.7% exposure based on short-term metrics, leading to significant losses when the stock fell by 17%. 115 00:12:30,895 --> 00:12:42,035 Conversely, EvolveTrade reduced NVIDIA's exposure to 2.9% by relying on longer-term returns and volatility checks, resulting in notably lower losses. 116 00:12:42,159 --> 00:12:47,599 Evan: It sounds like EvolveTrade allowed for more cautious and informed decision-making. 117 00:12:47,643 --> 00:12:48,623 Ashley: Exactly. 118 00:12:48,623 --> 00:12:57,503 These careful adjustments ensure that the agent's exposure to volatile assets is managed more prudently, leading to better overall portfolio performance. 119 00:12:57,503 --> 00:13:05,983 EvolveTrade also showed reduced turnover compared to static agents, implying more stable and thoughtful rebalancing based on refined policies. 120 00:13:06,027 --> 00:13:10,847 Evan: Did the authors address any challenges with implementing EvolveTrade? 121 00:13:10,899 --> 00:13:15,549 Ashley: Yes, they noted that fixed update intervals might not always be optimal. 122 00:13:15,549 --> 00:13:22,169 Short intervals, such as daily updates, can amplify noise and lead to poorer performance due to overfitting. 123 00:13:22,169 --> 00:13:27,739 Moderating the frequency of updates was suggested as a way to balance adaptation with stability. 124 00:13:27,795 --> 00:13:29,035 Evan: That makes sense. 125 00:13:29,035 --> 00:13:32,475 So, what's the overall takeaway from these results? 126 00:13:32,523 --> 00:13:41,063 Ashley: The main takeaway is that allowing trading policies to evolve based on actual market performance leads to more robust and adaptive trading agents. 127 00:13:41,063 --> 00:13:52,343 EvolveTrade significantly improves risk-adjusted returns and helps agents activate relevant computations aligned with current market conditions, all while maintaining operational stability. 128 00:13:52,395 --> 00:13:55,735 Evan: That's the end of our coverage of the Experiment section. 129 00:13:55,735 --> 00:14:04,235 It's fascinating to see how EvolveTrade can refine LLM trading agents to perform better under varying market conditions. 130 00:14:05,561 --> 00:14:12,641 Evan: Next, let's delve into the Related Work section to understand the broader context of this research. 131 00:14:12,701 --> 00:14:19,641 Evan: Ashley, how does EvolveTrade relate to existing work in the field of LLM agents and financial trading? 132 00:14:19,685 --> 00:14:32,345 Ashley: The paper situates EvolveTrade within two main branches of research: the use of large language models as financial trading agents and the self-evolution of LLM agents from their own trading performance and feedback. 133 00:14:32,345 --> 00:14:43,065 Recent work has explored the capabilities of LLMs in financial trading by enhancing them with memory, tools, multimodal signals, and multi-agent deliberation. 134 00:14:43,109 --> 00:14:49,629 Evan: Can you give us more details on how LLMs are being used in financial trading? 135 00:14:49,685 --> 00:14:50,655 Ashley: Yes, Evan. 136 00:14:50,655 --> 00:14:55,205 Several approaches have been developed to leverage LLMs for financial trading. 137 00:14:55,205 --> 00:15:01,485 For instance, FinMem introduces layered memory to retain market observations across trading sessions. 138 00:15:01,485 --> 00:15:13,065 TradingAgents, StockAgent, QuantAgent, and TradExpert use specialized agent roles, simulated trading environments, and fixed analysis pipelines to organize their decision-making processes. 139 00:15:13,109 --> 00:15:22,989 Evan: So these systems use LLMs along with various tools and strategies to make more informed trading decisions? 140 00:15:23,045 --> 00:15:24,165 Ashley: Exactly. 141 00:15:24,165 --> 00:15:35,935 Other platforms like FinAgent and related financial agent frameworks incorporate multimodal inputs, real-time data, domain-specific tools, and reflective decision-making processes. 142 00:15:35,935 --> 00:15:45,865 Recent benchmarks like AI-Trader, LiveTradeBench, and FinAgentBench evaluate LLM agents under realistic financial information and market conditions. 143 00:15:45,977 --> 00:15:50,837 Evan: And how reliable are these LLM trading systems? 144 00:15:50,885 --> 00:15:56,525 Ashley: Despite their potential, several studies highlight reliability issues in LLM trading. 145 00:15:56,525 --> 00:16:06,125 Challenges include noisy-source trust, spurious ticker memorization, and temporal leakage, which can affect the accuracy and robustness of trading decisions. 146 00:16:06,173 --> 00:16:08,153 Evan: That sounds quite challenging. 147 00:16:08,153 --> 00:16:11,153 So how does EvolveTrade address these issues? 148 00:16:11,213 --> 00:16:19,713 Ashley: EvolveTrade diverges from existing methods by focusing on adaptive policy refinement rather than static or hard-coded decision procedures. 149 00:16:19,713 --> 00:16:30,853 It converts successes and failures into reflective policies that can evolve from actual trading outcomes, mitigating some of the issues found in previous systems like noisy data and overfitting. 150 00:16:30,967 --> 00:16:37,457 Evan: What are some examples of other self-evolving LLM agents in the financial domain? 151 00:16:37,517 --> 00:16:41,987 Ashley: Within this area, we have promising directions such as ATLAS and SHARP. 152 00:16:41,987 --> 00:16:46,357 These systems adapt prompts or structured policies from market feedback. 153 00:16:46,357 --> 00:16:57,157 Similarly, AlphaQuanter and FLAG-Trader demonstrate that learned tool orchestration and policy optimization can improve trading outcomes compared to static multi-agent baselines. 154 00:16:57,221 --> 00:17:05,681 Evan: So these methods also focus on refining the agent's decision-making processes based on their trading performance? 155 00:17:05,741 --> 00:17:06,531 Ashley: Indeed. 156 00:17:06,531 --> 00:17:15,041 However, most of these methods either optimize reasoning instructions, maintain experience memories, or learn tool-use policies offline. 157 00:17:15,041 --> 00:17:24,521 What sets EvolveTrade apart is its online, continuous refinement of the operational procedure through which the agent gathers, validates, and acts on information. 158 00:17:24,641 --> 00:17:30,341 Evan: How does EvolveTrade achieve this online policy refinement specifically? 159 00:17:30,389 --> 00:17:38,819 Ashley: EvolveTrade treats the system prompt as a text-parameterized tool-use policy and updates it from the agent’s own trade records and feedback. 160 00:17:38,819 --> 00:17:49,549 This approach allows for direct incorporation of trading experience into actionable policy revisions between trading intervals, making the agent more adaptable and resilient over time. 161 00:17:49,613 --> 00:17:51,373 Evan: That's a clear distinction. 162 00:17:51,373 --> 00:17:57,373 So adapting the policy continuously online from the agent's performance is key, right? 163 00:17:57,437 --> 00:17:58,387 Ashley: Exactly. 164 00:17:58,387 --> 00:18:13,557 Treating the policy text as an editable variable updated from feedback ensures that the agent's approach remains responsive to market changes and its own performance metrics, highlighting an important direction for developing robust and adaptable LLM trading agents. 165 00:18:13,613 --> 00:18:16,443 Evan: And that sums up the Related Work section. 166 00:18:16,443 --> 00:18:27,113 It's clear that EvolveTrade builds on and advances many previous methods in LLM-based financial trading, offering an innovative approach to policy refinement. 167 00:18:28,434 --> 00:18:36,434 Evan: Alright, to wrap up our discussion on the EvolveTrade paper, let's summarize the key contributions and takeaways. 168 00:18:36,546 --> 00:18:40,466 Evan: Ashley, can you kick us off with the main contributions? 169 00:18:40,518 --> 00:18:41,598 Ashley: Of course. 170 00:18:41,598 --> 00:18:47,838 One of the primary contributions of EvolveTrade is its self-evolving framework for LLM trading agents. 171 00:18:47,838 --> 00:19:01,338 By treating the system prompt as a text-parameterized policy that evolves based on trading outcomes, it allows agents to adapt their tool-use and decision-making processes over time without retraining the underlying language model. 172 00:19:01,398 --> 00:19:09,718 Evan: Right, and this approach specifically addresses the limitations of static hand-written policies used by current LLM agents. 173 00:19:09,774 --> 00:19:10,844 Ashley: Exactly. 174 00:19:10,844 --> 00:19:21,134 The fixed policies of traditional agents don't adapt to market changes, but EvolveTrade's dynamic updates enable continuous improvement based on actual trading performance. 175 00:19:21,258 --> 00:19:26,058 Evan: And in terms of effectiveness, what did the experiments reveal? 176 00:19:26,118 --> 00:19:34,528 Ashley: The experiments showed significant improvements in key financial metrics such as the Sharpe Ratio and cumulative return across different market regimes. 177 00:19:34,528 --> 00:19:43,198 EvolveTrade's ability to activate more relevant computations and refine decision-making processes led to more robust trading performance. 178 00:19:43,254 --> 00:19:55,254 Evan: Indeed, and the examples provided, like reduced exposure to volatile stocks during downturns, illustrate how these policy refinements translate into tangible benefits. 179 00:19:55,302 --> 00:20:07,582 Ashley: Yes, they also highlighted the importance of balancing update intervals to avoid overfitting, underscoring how crucial it is to moderate the frequency of policy updates for stable adaptation and performance. 180 00:20:07,638 --> 00:20:18,298 Evan: So the main takeaway is clear: allowing trading agents to update their tool-use policies based on real-time performance leads to more adaptive and effective trading strategies. 181 00:20:18,342 --> 00:20:28,502 Ashley: EvolveTrade's approach shows that integrating continuous self-evolution into trading agents can enhance their robustness and alignment with current market conditions significantly. 182 00:20:28,626 --> 00:20:34,136 Evan: And with that, we conclude today's episode of Daily Paper Cast. 183 00:20:34,136 --> 00:20:42,366 Thank you for joining us in exploring how EvolveTrade pushes the boundaries of LLM-based trading agents. 184 00:20:42,414 --> 00:20:46,314 Ashley: We're excited to see where this research leads in the future. 185 00:20:46,314 --> 00:20:52,094 Be sure to tune in again for more insightful discussions on the latest in AI research. 186 00:20:52,238 --> 00:20:54,678 Evan: Until next time, I'm Evan. 187 00:20:54,726 --> 00:20:56,206 Ashley: And I'm Ashley. 188 00:20:56,206 --> 00:20:57,906 Thanks for listening!