是啊，成功的交易员对市场有相同的或类似的认识，做着相同的或类似的正确事情；不成功的交易员对市场也有相同的或类似的认识，做着相同的或类似的错误事情。当然成功与不成功中间有各种层级，包含正确与错误的各种不同混合。
继续这个帖子，全自动人工智能的Hedge Fund 据说是华尔街绝无仅有的完全无人操作介入的第一家。 看到这个新闻在这个楼主要说一句，这个楼的方向肯定是未来，但是肯定很难，业内做过十几年的人也没不比别人有更高的成功机会，因此希望有兴趣的人不要放弃，尤其不要听“过来人”的“劝导”，坚持探索。 The Rise of the Artificially Intelligent Hedge Fund “If we all die,” says Goertzel, a longtime AI guru and the company’s chief scientist, “it would keep trading.” “I firmly believe that money grows in the dark and a lot of people who are doing this (AI investing) are keeping their mouths shut as they don’t want people to find out what they are doing and how they are doing it,” he said.
https://www.chiphell.com/thread-1526971-1-1.html 将机器自我学习的大脑真正激活！ 「我们现在已经找到了一种更好的无人自驾驶解决办法，它完美地跟深度学习结合起来。」其他的无人自驾驶汽车编程代码是几十万行，但是Hotz的软件编程代码只有2000行。 之所以有这么大的差别，是因为一般的无人自驾驶汽车的编程都是根据不同的路况而预设定程序。一些代码保证了车子能够在高速路上跟其他车子保持相对安全的车距，但是还有一些代码得保证鹿如果冲上了马路汽车得及时刹车。Hotz并没有在车里内置这样的规则，机器是真的在学习司机是如何应付复杂的路况，然后学着模仿，并且将这些行为通过算法进行改善提升。如果讴歌这个车子旁边有个骑自行车的人，那么它会自然而然给这次骑车人多留一点空间，因为Hotz就是这么做的。 谁能解释下吗，他的方法能借鉴到交易编程吗
这和我掌握的情况一样，关于在交易中使用人工智能的研究已进行了多年，如果没记错的话，我读过最早的论文还是上世纪发表的。这么多年下来，给人的感觉就是成功者很少，我觉的有两个原因， 1. 难度太高，成功者本来就不多。2.相互保密，没人发表真正有价值的研究成果，需要重复造轮子, 导致进展缓慢。 最近深度学习大热，感觉奇点到了似的，我想强调一下，切毋神话深度学习，它只是一种技术，不是万灵药，其它机器学习方法也一样。
离奇点还很远，但是这是另外一回事。 股市上人工智能难以使用还是在寻找规律这个问题上。只要认定某些事有规律，使用这些工具才可以帮助找到规律。但是股市上的规律本身有没有就是个问题，就是找到也是暂时的，变化的规律也不叫规律了，所以股市上使用人工智能难度比较高。比如拿人脸识别的方式去使用在股市上，显然这个思路是走不通的，连试都不用试。人脸有基本特征，股市呢？谁要是知道答案，也不用探讨这个工具问题了。 现在的人工智能是在人可以做的事情上“仿生”，什么无人驾驶汽车了。但是股市人自己都做不来，谈何仿生。所以，思路要变一变。人脑本身就是靠大数据堆出来的，眼观六路，耳听八方，这就是人天天面对的大数据。所以现在人工智能也要依靠大数据去训练。这个办法对付有一定固定规则，显著规律的事情可行，人就是这个道理，人的偏见与习惯都是这么被大数据训练出来的。但是人还有智谋，会推理，这个能力就很难用大数据去堆积了。 比如一个企业会被另一个企业并购，因此其中一个股票被打压或提升，这种判断或猜测人工智能是无法去完成的，但是人如果是某个领域的行家并且对商业运作熟悉，是可能在不知道暗箱操作的情况下做出一些判断的，并主动挖掘佐证。于是你可以以此为逻辑训练你的人工智能模型去判断搜索，但是这只是千百个影像股价的因素一部分，汇率石油等等都可能会对短期价格走向发生影响，如何在各种因素中综合判断走势以达到你对时间点与价格的预测目的呢，这基本上远远超出一个人的能力之外。 一般基金的做法（高频除外）都是用规模战胜行情，甚至直接坐庄。靠内部消息操作的本质与高频是一样的，都是依靠比别人信息多或快的优势。如果你没有这个信息优势，或者说你的优势只能在与别人获得的信息相同情况依靠超过别人的分析能力，去解读出别人看不到的信息内涵，也就是从data->intel，信息到知识的转化，这是只能依靠人工智能这个工具的地方，而且不太可能只依靠数据量的过人去实现。 深度学习获得发展是恰好结合了计算机技术在速度与数量的长足发展上得以实现的，因此在语言识别图像识别上突破与人眼观六路耳听八方的道理一致。但是在推理逻辑上没有突破。比如人类婴儿看两遍就可以学会的事情，看到猫狗的卡通就知道是猫狗的能力，单靠大数据深度学习是不成的，缺乏的就是人这种外延想象的能力。
Will AI-Powered Hedge Funds Outsmart the Market? Every day computers make many millions of electronic trades by performing delicate calculations aimed at eking out a tiny edge in terms of speed or efficiency. Increasingly, however, more significant trading decisions are being made by smarter, more autonomous algorithms. Both established trading firms and a handful of startups are exploring whether such trading techniques, borrowed from the field of artificial intelligence, could help them outfox other traders. And anyone with money invested might well be curious to know if the trend could alter the dynamics of markets. Quantitative hedge funds, including Bridgewater Associates, Renaissance Technologies, D.E. Shaw, and Two Sigma, have, of course, been using advanced algorithmic approaches for some years. Many of the methods employed by these businesses are found in areas of artificial intelligence research. But the past couple of years have also seen a tremendous resurgence of interest in artificial intelligence, thanks to new machine-learning techniques—especially deep learning (involving training a large virtual neural network to recognize patterns in data)— that have made computers capable of human-level perception of images, text, and audio (see “10 Breakthrough Technologies 2013: Deep Learning”). Now the question is whether AI can do the same for financial data. It’s clear that this recent progress has caught the attention of engineers working in finance. At an important academic event for AI researchers, the Neural Information Processing Systems (NIPS), held in Montreal last December, several thousand academic and industry researchers gathered to discuss progress in developing new machine-learning algorithms. In an area reserved for poster presentations by graduate students, big tech companies, including Google, Facebook, Apple, Microsoft, Amazon, and IBM, had paid to set up recruitment tables, hoping to lure the hottest new talent to come work for them. But almost half of the companies recruiting at NIPS were not tech companies at all but hedge funds and financial firms. One of the companies there was the large British investment firm MAN AHL, which for years has been focused on using statistical approaches to devise investment strategies. Anthony Ledford, chief scientist of MAN AHL, explains that the company is exploring whether techniques like deep learning might lend themselves to finance. “It’s at an early stage,” Ledford says. “We have set aside a pot of money for test trading. With deep learning, if all goes well, it will go into test trading, as other machine-learning approaches have.” Trading might seem like an obvious place to apply deep learning, but actually it isn’t clear how comparable the challenge of finding subtle patterns in real-time trading data is to, say, spotting faces in digital photographs. “It’s a very different problem,” Ledford admits. Academic experts also sound a note of caution. Stephen Roberts, a professor of machine learning at Oxford University, says deep learning could be good “for extracting hidden trends, information, and relationships,” but adds that it “is still too brittle with regard to handling of high uncertainty and noise, which are prevalent in finance.” Roberts also notes that deep learning can be a relatively slow process, and cannot offer the kinds of guaranteed behavior that other statistical approaches offer. In general, he says, there is a certain amount of hype around the idea of AI in finance. “AI is a very broad subject,” he says. “And many standard statistical techniques used are being rebranded as AI and machine learning.” That said, new financial firms that advertise themselves as AI-focused may be on to something. These include Sentient, based in San Francisco, Rebellion Research in New York, and a Hong Kong–based investment company called Aidyia. One of the most promising uses of relatively new AI techniques may be processing unstructured natural language data in the form of news articles, company reports, and social media posts, in an effort to glean insights into the future performance of companies, currencies, commodities, or financial instruments. Aidyia was founded by a well-known artificial intelligence researcher, Ben Goertzel, who is also the founder of Hanson Robotics and the chairman of an open-source AI project called OpenCog. Aidyia began trading last year, and Goertzel says his company’s approach is far more ambitious than the techniques used by most hedge funds today, taking inspiration from evolutionary programming, probabilistic logic, and chaotic dynamics. “Our system ingests a variety of inputs, including price and volume from exchanges around the world, news from various sources in multiple languages, macroeconomic and company accounting data, and more,” Goertzel told MIT Technology Review. “It then studies how these various factors have interrelated historically, and learns an ensemble of tens of thousands of predictive models that appear to have predictive value, based on its study of historical data,” which help guide the company’s investments. There is certainly a trend toward increasing automation among financial firms. Preqin, a company that provides financial industry data, reports that 40 percent of hedge funds created last year were “systematic,” meaning they rely on computer models for their decisions. Not everyone is convinced that an AI revolution in finance is imminent, however. David Harding, the billionaire founder and CEO of another British trading company, Winton Capital Management, is generally skeptical of hype over machine learning and AI. “If I squinted a little and looked at Winton, I’d say that’s more or less what we’ve been doing for the past 30 years,” he says. Harding also remembers that a similar boom in interest in neural networks resulted in many startups during the early 1990s. “People started saying, ‘There’s an amazing new computing technique that’s going to blow away everything that’s gone before.’ There was also a fashion for genetic algorithms,” he recalls. “Well, I can tell you none of those companies exist today—not a sausage of them.” Ledford, of Man AHL, also has a few words of caution for anyone who thinks the latest machine-learning techniques could offer a shortcut to riches. “It’s important to remember how humbling the market can be,” he says. “I’d say don’t pat yourself on the back too much, but equally don’t get too disheartened.”
市场结构很难改变的，能改变的一般只是参与者自己在市场中的位置。我以前天真的以为经过08年金融危机，人们会吸取教训，富人财富缩水，减小贫富差距，但事实是，富人比穷人拥有更多的资源，能比老百姓更快的实现转型，而且之后这几年看下来，丝毫没有吸取任何教训的意思。
各位大神，新人报到。 终于可以看久仰大名的海洋论坛了，觉悟晚了。 市场是什么？ 是人性的反射再叠加各种自然规律的汇总。人性和自然规律两者在多维时空不停的运动，进行位置、方向、动量、速度、自旋、互环纠缠。人性和自然规律的变化非常缓慢，以千年计，对当下来说可以认为它们不变。