市场资讯及洞察

It's well-known that many discretionary traders struggle with discipline, emotional control, and other psychological hurdles that can impact their decision-making process, particularly when it comes to entering and exiting trades. One of the widely recognized benefits of automated trading models, however, is the belief that these psychological barriers are removed or significantly reduced. Automation, after all, is designed to eliminate human emotion from trading decisions.

However, the assumption that psychological challenges vanish with automated trading is far from reality. As you delve into the exciting world of creating and trading “Expert Advisors” (EAs), it is crucial to understand that psychological challenges still exist, albeit in a different form. You must be prepared to face various mindset issues during the EA development and trading process.

This article outlines and aims to inform on nine key potential psychological challenges traders might encounter when working with EAs and offers guidance on how to navigate them effectively. Use this checklist to develop an awareness of potential issues and take meaningful action to enhance your trading performance. 1. Over-Optimization and Curve Fitting One of the most common challenges traders face when developing EAs is the temptation to over-optimize their algorithms.

This refers to tweaking the EA to perform perfectly in historical backtests but at the expense of real-world effectiveness. While an over-optimized EA may show stellar performance on past data, it often falters when faced with live market conditions, leading to frustration and self-doubt. To mitigate this, it is vital to stay focused on each stage of the EA creation process and avoid the trap of endless refinement.

Always keep in mind two fundamental principles: The purpose of an EA is to reliably generate profits. Once this is achieved, the next step is simply scaling the strategy. The purpose of backtesting is not just to validate that the settings work but to justify moving to a forward test. 2.

Fear of Loss Fear and anxiety can emerge when transitioning from testing to live trading, especially when real money is involved. Traders may worry about losing their capital or encountering a significant drawdown that tests their emotional resilience. This fear can act as a barrier, preventing traders from taking their EAs live or increasing trade sizes, even when results suggest it is the right time to scale up.

Developing confidence in your EA through thorough backtesting and forward testing is key to overcoming this fear. 3. Lack of Control Another psychological hurdle is the feeling of losing control when relying on an automated system. With discretionary trading, traders are actively involved in every decision, whereas, with an EA, the algorithm executes trades without human intervention.

This can lead to feelings of helplessness, especially if the EA doesn’t perform as expected. Watching trades unfold on your account without direct involvement can be unnerving, tempting traders to interfere prematurely. Resisting the urge to manually override the EA is crucial.

Trust the system you’ve created, as long as it is backed by solid logic and testing. 4. Confirmation Bias Traders may fall into the trap of confirmation bias, where they only acknowledge the positive aspects of their EA’s performance while overlooking warning signs or evidence of flaws. This bias can be dangerous, as it blinds traders to potential weaknesses that may lead to significant losses over time.

Creating a set of objective performance measures, such as maximum drawdowns and key profit metrics, can help maintain a clear and rational perspective on the EA’s success. Emotional attachment to an EA that has taken considerable effort to build can cloud judgment, so it’s important to remain objective, especially when difficult decisions arise. 5. Overconfidence Success with one or more EAs can lead to overconfidence, which is a major psychological pitfall.

Traders may begin to overlook necessary refinements, substitutions, or additional testing. Early successes might lead them to believe they can expedite the process of moving an EA to live trading and scaling it, without taking the time to gather sufficient data from a large enough sample of trades. Patience is essential when transitioning to live trading.

Ensure that a critical mass of data is available before making decisions about scaling or altering your approach. 6. Impatience Many traders expect immediate results from their EAs, which can lead to impatience. This impatience often results in premature modifications or abandonment of strategies that could have been profitable over a longer time horizon.

There are no shortcuts in trading. Allow time for your EA to demonstrate its potential over a defined period, rather than making snap judgments based on short-term performance. Regularly comparing live results to backtests over a reasonable timeframe can provide the necessary context to assess whether an EA is working as intended. 7.

Adaptability Market conditions change, and EAs that perform well in one environment may struggle in another. The psychological challenge here lies in being open to the necessity of adaptation. Some traders may hesitate to make changes or replace an EA, fearing the effort it took to develop the original model.

Consistent monitoring and having clear criteria for when adjustments are needed are vital to long-term success. Embrace the process of refinement, knowing that adaptability is essential for keeping your EA portfolio profitable in different market conditions. 8. Social Comparison Comparing your EA’s performance to others can lead to feelings of inadequacy, envy, or frustration, especially if you perceive that your system isn’t performing as well as someone else’s.

Social comparison is common among traders, but it can lead to unnecessary emotional strain unless checked. It’s important to remember that traders are often more vocal about their successes than their losses. Maintain a focus on your own progress and the unique journey of developing a system that works for you. 9.

Emotional Resilience The ability to stay emotionally resilient during drawdowns or periods of underperformance is critical. Fear, anger, frustration, and impatience can cloud your judgment and negatively impact decision-making, including premature withdrawal of an EA. With any strategy there will be periods of under and over performance.

Accepting this is critical for good long-term decision making. Obviously, time is a great “calmer” in terms of developing not only confidence but also this acceptance. Anecdotally, new automated trades are most at risk until there is a “record of achievement”.

This is one of the key reasons why trading any new EA at minimum volume as you discover how it performs under live market conditions is vital. In Summary Addressing these psychological challenges is essential for success in the world of automated trading. Taking these points on board and stepping back to review where you are as many of these may creep in insidiously over time would seem prudent.

Practical steps you can take may include: Developing a deep understanding of your EA’s logic and parameters, so you trust the system you’ve built. Setting clear performance expectations and avoiding comparisons with others. Developing self-awareness and emotional regulation to stay calm during turbulent times.

Regularly reviewing and updating their trading strategy on which the EA is based, including sighting charts of trades taken and refinement of risk management strategies are always worthwhile. Consistent monitoring is vital. Taking breaks to avoid burnout and maintaining a healthy work-life balance.

Trading EAs do create an interesting set of challenges but as stated previously, awareness that these challenges may exist is the first step to be able to take meaningful action and continue the work on yourself. Whether you ae a discretionary or automated trader, this rule is unquestionable and always the start point of long term improvement in trading decision making. If you are interested in the GO Markets automated trading platform and strategy tester, and the education we can provide relating to this topic, please feel free to connect at [email protected] at any time.

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近期，美股市场显现出分化格局，科技股承压明显，而蓝筹股表现相对稳健。这背后反映了市场情绪的波动与宏观经济的不确定性，以下为具体情况：

截至2025年1月28日，标普500指数收于599.37美元，较前一交易日下跌1.39%；纳斯达克100指数下挫2.93%，收于514.21美元；而道琼斯工业平均指数逆势上涨0.68%，收于447.12美元。科技股的表现尤其疲软，其中英伟达大跌17%，市值损失创纪录的5890亿美元，据福布斯网站显示，该公司首席执行官、最大个人股东黄仁勋净资产在收盘时缩水了208亿美元，从1244亿美元跌至1037亿美元，从福布斯实时亿万富翁排行榜的第10位跌至第17位。

甲骨文(ORCL.N)下跌14%，董事长拉里·埃里森净资产缩水276亿美元，从全球富豪榜第三跌至第五。与此同时，苹果公司逆势上涨3.18%，重新夺回全球市值第一的宝座，展现出强劲的基本面支撑。

市场情绪方面，恐慌指数VIX上涨20.54%，达到17.90，市场避险情绪升温。一方面，美联储货币政策调整、通胀数据波动以及经济增长预期的不确定性，尽管黄金价格短期内保持观望，2025年黄金依旧是投资者关注的焦点。另一方面DeepSeek引发的更广泛的市场下跌，迫使投资者在市场中进行套现。展望未来，美股市场可能持续高波动性。投资者需密切关注关键经济数据，如今晨发布的美联储利率决议，午时将要发布的澳大利亚第四季度CPI。此外，科技股的估值调整是否持续以及蓝筹股的稳定表现，将对市场走向产生重要影响。 当前市场环境下，建议投资者合理分散投资组合，关注基本面稳健的优质资产，同时适当配置避险品种如黄金和债券。复杂的市场环境中，保持理性、稳健决策是实现长期收益的关键。免责声明：GO Markets 分析师或外部发言人提供的信息基于其独立分析或个人经验。所表达的观点或交易风格仅代表其个人；并不代表 GO Markets 的观点或立场。联系方式：墨尔本 03 8658 0603悉尼 02 9188 0418中国地区(中文） 400 120 8537中国地区(英文） +248 4 671 903作者：Sylvia Qin | GO Markets 悉尼中文部

Since writing our Thematic paper for 2025 two of the four major themes have already shook markets in 2025. One has been the inauguration of Donald Trump as president of the United States of America and he nationalistic policies the second is the AI revolution taking a massive left hand turn. With the release of DeepSeek, the AI story may actually become the biggest theme of the year (big call considering it's still January).

We also need to rethink demand and the flow of funds in the wake of DeepSeek R1 disruption. Because if open-source DeepSeek R1 model does deliver performance comparable to OpenAI’s o1 reasoning model at a fraction of the cost (VentureBeat, Jan 20), it raises critical questions for not just AI, but periphery players in the AI chains as well. Here’s why: DeepSeek R1 is reshaping investor perceptions of the AI compute investment cycle.

Reports suggest that the model achieved competitive performance using significantly fewer computing resources and lower inference costs. These efficiency gains have cast doubts on the future scale of AI semiconductor and equipment investments, leading to selloffs across semiconductor stocks. Look at NVIDIA, Meta and closer to home NextDC and the like.

We also know that in China, DeepSeek v3 has already driven AI compute cost deflation. The R1 model leverages advanced techniques like multi-head latent attention (MLA) and mixture of experts (MOE), enabling more efficient training by breaking workloads into smaller parts. This is something o1 has only just begun doing – but at a higher cost load.

While these innovations may not apply universally to all models, they signal a shift that could impact global AI training strategies. Now the caveat the tech boffins point out here is that the greater training efficiency is unlikely to reduce overall compute spending in the near term, as improvements typically fuel more inference demand. But and it is a but, economies of scale always come home to roost in the end.

The consumer will benefit – the investor needs to get picky – who is going to lead and who is going to fall by the wayside? Lets look at the Tech Gorillas like Amazon, Google, and Meta, DeepSeek R1’s efficiencies create a mixed outlook. While these firms develop proprietary models it's important to remember that they also monetise AI services through platforms like Amazon Bedrock and Google Vertex.

Lower training costs could reduce operating and capital expenditures, boosting profitability. Amazon benefits from its partnerships with external developers. Google, meanwhile, leverages its Gemini model for growth.

Meta appears best positioned, as its Llama model generates minimal direct revenue, while its announcement of a 54%-66% year on year increase in capex to $60-$65 billion in 2025 demonstrates its commitment to scaling AI capabilities. This follows news of the Stargate project, which is estimated to add $100 billion in incremental cloud-related capex. These moves underscore that demand for high-performance data centres remains robust, even as cost-efficient AI models emerge.

So what about the hardware developers like NVIDIA (NVDA), Broadcom (AVGO), and Marvell (MRVL) that have been savaged by DeepSeek’s cost efficiencies? While concerns about reduced training investment persist, long-term demand for AI compute remains robust and one player will not be enough to change that. Training requirements, particularly for inference workloads, are expected to drive growth over the next 2-3 years and no model as yet can do that without increased hardware.

The question will be margins – will hardware margins get squeezed from cost efficiencies? Then there is the memory sector, DRAM demand remains steady despite short-term pressures. TSMC, a critical player in AI accelerators, has also faced declines but remains integral to the semiconductor supply chain.

So, no real change here from DeepSeek. Where does this all leave us? The emergence of cost-efficient AI models like DeepSeek R1 introduces both opportunities and risks.

Yes, declining compute costs have mixed implications for the tech sector. On one hand, they alleviate margin pressures for software companies grappling with expensive AI features, potentially accelerating AI integration across product lines. But the risk to this is, lower barriers to entry could intensify competition from agile AI startups, challenging incumbents.

In short these innovations could reshape AI economics, the sustained demand for robust infrastructure, driven by broader AI adoption and multi-cloud trends are likely to overawe the negatives for a positive long-term outlook. Thus investors should focus on companies well-positioned to benefit from these shifts while remaining cautious of near-term volatility.

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澳洲小长假结束，金融市场却变了天。幻方和Deepseek创始人梁文峰昨夜击溃了华尔街资本堆叠的AI，幻方量化陆政哲用一句拗口的话诠释了博大精深：热爱决定相信，专注带来复利。是的，这个低调的团队用不到6百万美金的浇灌和不到三个月的测试创造的Deepseek追上了美国科技巨头们动辄上百亿美元花费数年堆叠起来的ChatGPT，这是一场美国对华芯片出口限制下被迫弯道超车产生的科技变革。倒不是说Deepseek有多完美，而是高效低价算法加上非顶级芯片造就的AI模型竟然可以跟顶流AI模型扳手腕，那么这样的方法应用到顶级芯片和金钱堆叠模型下的效果又会是怎样的呢？AI基础硬件的成本是否会出现断崖式下降，这是人类的福音，确是资本的噩梦。因为英伟达不会再按照剧本中安排的那样每年再赚那么多钱了，OpenAI的估值也会被打骨折。由此，我们从周一的美盘看到了真实的资本反应。

周一美盘被科技板块拖累，标普跌超1.4%；道指强势上涨0.65%展示美国经济强势；而高科技代表纳指收跌3.07%，盘前期货跌幅一度超过5%。英伟达跌近17%创造美股市值蒸发单日记录，也让出了市值排名第一的王座。资本涌入AI ASIC，美系AI和思维定势上与大陆对立的台系AI相关股暴跌，台积电跌超13%，芯片定制大佬博通也受到波及跌超17%，“小英伟达”ALAB跌超28%。年初提到的AI应用层部分股票逆市上涨，Adobe小涨0.74%，CRM涨近4%，SNOW也收下小涨。2025年AI的应用将全面超越AI基础层的回报，这个判断昨晚表现得尤为明显。另外遭罪的无疑是暴涨数日的核电板块了，由于AI算力对电力需求而暴涨的核电股，在Deepseek带来的极高效运作下被解读为短期电力需求被预判过度了，核技术股均跌超20%，美铀均跌超10%，今天澳铀也将遭遇血洗。上周中本人一波止盈离场既是巧合也是对本周财报的悲观预判，没想到周一竟然能躲过一劫，正好等待回调后的抄底。美元指数小幅走低，金价跌超1%回到2740平台，油价继续大跌，美油回到了73平台，依然处于去年宽幅震荡区间。恐慌指数因标普500大跌而暴力拉升，盘间一度涨近50%，期货收盘收涨近7%。外汇方面日元走势较强，美元保持平稳，但盘间经历了暴涨暴跌，美元人民币变动不大。免责声明：GO Markets 分析师或外部发言人提供的信息基于其独立分析或个人经验。所表达的观点或交易风格仅代表其个人；并不代表 GO Markets 的观点或立场。联系方式：墨尔本 03 8658 0603悉尼 02 9188 0418中国地区(中文） 400 120 8537中国地区(英文） +248 4 671 903作者：Xavier Zhang | GO Markets 高级分析师

Achieving long-term success in trading requires more than just knowledge and technical skills. It depends on building a foundation of mindset, behaviours, and self-awareness. This foundation is built on three critical drivers: Trading Confidence and Reliability, Trading Self-Relevance, and Trading Locus of Control.

These drivers work together to create a framework for sustainable growth and success in the market. However, failing to implement these drivers can lead to frustration, inconsistency, stagnation, and trading outcomes that fall short of what may be possible for you. In this article we explore these drivers in detail, enriched with definitions, examples, and insights into the consequences of neglecting them. 1.

Trading Confidence and Discipline Definition: Confidence is the belief in your ability to succeed and overcome challenges, while reliability is about creating consistent, dependable outcomes through your actions and systems. Confidence is the psychological pillar that allows traders to operate with clarity and conviction, even in volatile markets. This IS the KEY ISSUE in trading discipline.

Confident traders invariably are disciplined traders. This attribute needs work, being cultivated through deliberate practice and the accumulation of small wins over time. Core Concepts: Confidence in Your Ability to Take Action: What it means: This is about trusting in your capability to take the necessary steps, no matter how small or challenging, to achieve positive outcomes.

It requires the ability to see yourself as an active participant in your success, rather than a passive observer. This confidence grows through persistence and a willingness to learn from setbacks. You need to believe that even if you don’t have all the answers today, you are equipped to figure things out over time.

Example: A trader analyses their losses to identify mistakes and refine their approach, developing resilience to re-enter the market with improved strategies. Consequences of Neglect: Without confidence, traders may hesitate to take action or abandon trades prematurely, missing out on potential gains and learning opportunities. Confidence in the Importance of Taking Action and then Testing: What it means: Recognizing the value of consistent effort and the power of experimentation is essential in trading.

Small, deliberate actions, such as testing new strategies or refining old ones, provide insights that build trust in your systems. Testing allows you to bridge the gap between theory and application, proving to yourself that what you do matters and can lead to improved results. Example: A trader refines a new risk management rule on a demo account, building trust in its reliability.

Consequences of Neglect: Neglecting testing can lead to impulsive decisions based on unverified strategies, increasing the likelihood of inconsistent or poor outcomes. Confidence in Your Trading Systems: What it means: Believing in your system means trusting the process you’ve developed, knowing it has been built on solid foundations, and understanding that, over time, it is capable of delivering reliable results. This confidence doesn’t mean blind faith—it’s about the discipline to stick to your system because you’ve put in the work to validate it.

Example: A trader follows a trend-following system backed by thorough back testing and evidence in live markets of positive outcomes. Consequences of Neglect: Without trust in your system, you may second-guess trades, frequently change strategies, or fail to commit to a plan, resulting in erratic performance. The link between this and the ability to be disciplined is undeniable.

Believing in the Impact of Learning and Action: What it means: Understanding that your effort to grow and take deliberate action is the engine that drives success. This belief empowers you to view setbacks as opportunities for growth, rather than roadblocks. It shifts your focus from outcomes solely to recognising the important processes, enabling you to learn and improve continually.

Example: A trader uses mindfulness techniques to reduce emotional errors, significantly improving decision-making. Consequences of Neglect: Failing to learn from mistakes or take deliberate action can result in repeated errors and a lack of meaningful progress. Key Takeaway: Confidence and, subsequently, discipline are essential for building consistency.

Without them, traders are likely to operate reactively, undermining their potential for long-term success. 2. Trading Self-Relevance Definition: Trading self-relevance is the alignment of your trading activities with your values, goals, and purpose. It ensures that trading is not just an activity, but a meaningful pursuit tied to your identity and aspirations.

Core Concepts: Purpose: What it means: Having a clear “why” behind your trading journey is about understanding the deeper motivation that drives your actions. Purpose provides the emotional anchor that keeps you steady, even when the market becomes unpredictable. It transforms trading from a task into a mission, connecting it to something personally significant.

Example: A trader pursuing financial independence views trading as a means to an end, which keeps them motivated. Consequences of Neglect: Without a strong purpose, trading can feel aimless, leading to a lack of discipline, motivation, and ultimately, poor results. Level of Importance: What it means: Treating trading as a priority requires committing the time, energy, and focus necessary for improvement.

It involves recognizing the importance of consistent effort and giving trading the same respect as any other profession or life goal. Example: A trader allocates specific hours for market analysis, reflecting their commitment. Consequences of Neglect: Treating trading as a low priority can lead to inconsistent effort, incomplete preparation, and missed opportunities.

Developmental Evidence: What it means: Monitoring your progress and recognizing improvement is key to maintaining motivation. Evidence of growth reinforces that your actions are effective, encouraging you to stay the course. It creates a feedback loop where success builds confidence and confidence drives further effort.

Example: A trader reviews their journal weekly to identify profitable patterns. Consequences of Neglect: Without tracking progress, traders may lose confidence, fail to learn from their experiences, and struggle to refine their approach. Key Takeaway: Self-relevance connects your trading to your identity and goals.

Neglecting this alignment can lead to a lack of direction and reduced motivation to improve. 3. Trading Locus of Control Definition: Locus of control refers to your belief about whether outcomes are determined by your own actions (internal) or by external factors (external). Core Concepts: Internal Locus of Control (ILOC): What it means: Believing that your outcomes are shaped by your decisions, behaviours, and preparation.

This mindset puts you in the driver’s seat, enabling you to take responsibility for your actions and their consequences. It empowers you to adapt, improve, and proactively address challenges. Example: A trader reviews losses to identify mistakes and improve, rather than blaming external factors.

Consequences of Neglect: Without an ILOC, traders may externalize blame, failing to take responsibility for their growth and repeating the same mistakes. External Locus of Control (ELOC): What it means: Attributing outcomes to luck, market conditions, or other external influences. This mindset often leads to feelings of helplessness, as you perceive success as being outside of your control.

Example: A trader blames sudden news events for losses without analysing their own decisions. Consequences of Neglect: An ELOC mindset often results in a lack of accountability, leaving traders feeling powerless and unmotivated. Take charge of what you can control!

Here are the actionable aspects within your control to make sure that your locus of control remains primarily internal: What You Learn: Continuously improving knowledge through deliberate effort. Your Systems: Refining strategies with evidence and adapting to market changes. Your Trading Time: Managing when and how much you trade.

Performance Measurement: Evaluating progress using clear metrics. Execution: Maintaining discipline in trade management. Permission Not to Trade: Knowing when to step back.

Consequences of Neglect: Failing to focus on what you can control leads to frustration, emotional decisions, and a reactive mindset. Key Takeaway: An internal locus of control empowers you to take responsibility for your outcomes, fostering resilience and proactive growth. Summary - Bringing It All Together Ultimately, these three drivers— Trading Confidence and Reliability, Trading Self-Relevance, and Trading Locus of Control —must work in harmony to achieve lasting success.

They create a foundation for continuous growth, adaptability, and resilience. Neglecting these principles often results in frustration, stagnation, and missed opportunities. By adopting these drivers, you align your trading journey with a mindset built for success.

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2025年1月20日，特朗普重返白宫，其主张的“美国优先”政策不仅引发了全球资本市场震荡，更可能重塑美国金融监管格局。其中，银行业的核心监管框架——巴塞尔协议III的“终局”规则（Basel III Endgame）成为焦点，特朗普政策与巴塞尔协议实施之间的复杂博弈也就此展开。巴塞尔协议III自2008年金融危机后推出，旨在通过提高资本充足率、优化风险权重计算等方式增强银行业抗风险能力。2023年，美联储提出“终局”规则草案，要求大型银行额外增加资本金。当时，这一提案遭到银行业强烈反对，认为其过度严苛且会削弱美国银行的国际竞争力。按原提案，全球系统重要性银行（G-SIBs）的普通股一级资本需增加9%，而资产规模超过2500亿美元的银行面临更严格资本要求。而美国银行业普遍已经持有超额资本，根据德勤2025年最新报告，截至2024年第二季度，区域性银行的商业地产贷款占风险资本比例高达199%，远超大型银行的54%，凸显其资本压力。

特朗普政府历来主张放松金融监管。其政策团队已表态支持修订巴塞尔协议III规则，降低资本要求。2024年9月，美联储副主席Michael Barr宣布新提案，取消部分“镀金”标准（即严于国际规则的要求），并保留分级监管模式。这些调整直接回应了银行业的诉求，例如：1.住宅地产和零售业务风险权重下调，减轻中小银行负担；2.税收抵免权益融资风险权重降低，鼓励绿色能源等政策支持领域；3.操作风险资本计算简化，按净收入而非总收入计量。但是，特朗普的政策纲领与巴塞尔协议的实施方向还是存在多重冲突，主要体现在以下三方面：1. 贸易保护主义 vs 全球监管协调特朗普主张对进口商品征收10%基准关税，对个别国家征收更高关税，并推动制造业回流。施罗德报告指出，这类政策可能推高企业融资成本，间接影响银行信贷质量。与此同时，巴塞尔协议要求各国监管标准趋同，但美国若单方面放宽规则，可能引发“逐底竞争”。例如，欧盟已推迟实施巴塞尔3.1至2026年，英国则推迟到2027年1月1日，英格兰银行审慎监管局（PRA）修订后的规则对资本要求影响低于1%，并强调“公平竞争环境”，暗示可能跟随美国调整。

2. 利率政策干预 vs 银行业净息差压力特朗普曾批评美联储加息政策，主张更“宽松的货币政策”。市场预计2025年美国联邦基金利率或降至3.5%-3.75%，净息差（NIM）预计从2024年的3.15%下滑至3%。利率下行虽可能刺激抵押贷款需求，但存款成本高企（2025年计息存款成本预计达2.03%）将挤压银行利润。若特朗普施压美联储进一步降息，银行业需在贷款定价与存款争夺间寻找新平衡。3. 国内优先战略 vs 小银行生存困境特朗普强调“本土经济优先”，利好以国内业务为主的小型银行。美国小盘股（如罗素2500指数成分股）76%收入来自本土，而标普500公司仅59%。区域性银行因商业地产风险敞口集中，2025年净核销率或升至0.66%，创十年新高。若特朗普政府推动减税（如延长2017年税改）并放松社区银行监管，或为小银行注入喘息空间。特朗普的回归，标志着美国金融监管从“风险防范”转向“增长优先”，美国对巴塞尔协议的调整可能加剧国际监管分化。巴塞尔协议III的“终局”规则修订，既是政治博弈的结果，也是银行业自救的契机。然而，放松监管的代价可能是长期风险的积累——若经济衰退与信贷质量恶化叠加，2008年的危机阴影或将重现。对于全球银行业而言，如何在合规与盈利间找到动态平衡，将是未来十年的终极命题。免责声明：GO Markets 分析师或外部发言人提供的信息基于其独立分析或个人经验。所表达的观点或交易风格仅代表其个人；并不代表 GO Markets 的观点或立场。联系方式：墨尔本 03 8658 0603悉尼 02 9188 0418中国地区(中文） 400 120 8537中国地区(英文） +248 4 671 903作者：Christine Li | GO Markets 墨尔本中文部