《The Man Who Solved The Market》中文版翻译连载20
研究 间是否有潜在关联性：通过小麦价格能预测 么
The MAn Who Solved The Market (20)
Hullender was startled. Until that moment, Simons’s self-confidence seemed boundless. Now he appEAred to be second-guessing his decision to ditch mathematics to try to beat the market. Still on the couch, as if in a therapist’s office, Simons told Hullender about Lord Jim, which centers on failure and redemption. Simons had been fascinated with Jim, a charACter who had a high option of himself and yearned for glory but failed miserably in a test of courage, condemning himself to a life filled with shame.
胡伦德吓了一跳。在此之前，西蒙斯的自信心看起来无限的。而现在他似乎在反思放弃用 试图打败 的决定。就像躺在在心理咨询办公室的沙发上，西蒙斯跟胡伦德说起了以失败和救赎为中心的吉姆勋爵。西蒙斯看起来对吉姆很着迷，这个角色对自己评价很高，渴望荣誉，却在一次关于勇气的测试中失败了，使后来的生活充满羞愧｡
Simons sat up straight and turned to Hullender.
“He had a really good death, though,” he said. “Jim died nobly.”
Wait, is Simons contemplating suicide?
Hullender worried about his boss — and about his own future. Hullender realized he had no money, was alone on the East Coast, and had a boss on a couch talking about death. Hullender tried reassuring Simons, but the conversation turned awkward.
In the following days, Simons emerged from his funk, more determined than ever to build a high-tech trADIng system guided by algorithms, or step-by-step computer instructions, rather than human judgement. Until then, Simons and Baum had relied on crude trading models, as well as their own instincts, an approach that had left Simons in crisis. He sat down with Howard Morgan, a technology expert he’d hired to invest in stocks, and shared a new goal: building a sophisticated trading system fully dependent on preset algorithms that might even be automated.
在接下来的几天，西蒙斯从恐惧中走出来，前所未有的下决心要开发一个高科技的交易系统，甚至可以自动生成算法｡，它将由算法或分布计算指令引导而非人 判断。在那之前，西蒙斯和鲍姆依靠的都是原始的 模型，以及他们自己的直觉，这种方法让西蒙斯陷入困境。他和公司聘请的 技术专家霍华德·摩根坐下来交流，分享一个新目标：要建立一个完全依赖于预设算法的复杂的
“I don’t want to have to worry about the market every minute. I want models that will make money while I sleep,” Simons said. “A pure system without humans interfering.”
The technology for a fully automated system wasn’t there yet, Simons realized, but he wanted to try some more sophisticated methods. He suspected he’d need reams of historic data, so his computers could search for peRSIstent and repeating price patterns across a large swath of time. Simons bought stacks of books from the World Bank and elsewhere, along with reels of magnetic tape from various commodity exchange, each packed with commodity, bond, and currency prices going back decades, some to before World War II. This was ancient stuff that almost no one cared about, but Simons had a hunch it might prove valuable.
西蒙斯意识到当时还没有全自动化系统的技术，但是他想试试一些更复杂的办法。他察觉自己需要大量历史数据，这样他的电脑系统就可以在很长一段 里搜索持续且重复的价格模式。西蒙斯从世界银行和其他地方买了很多书，还从各种商品交易所买了一卷卷磁带，每卷都是几十年前商品、债券和 价格，有些甚至是二战前的价格。这是几乎没人关注的古老的玩意，但是西蒙斯预感到它们可能是有价值的｡
Hullender’s fIVe-foot-tall, blue-and-white PDP-11/60 computer couldn’t read some of the older data Simons was amassing because its formatting was outdated, so Hullender surreptitiously carried the reels to the nearby headquarters of Grumman Aerospace, where his friend Stan worked. Around midnight, when things slowed down at the defense contractor, Stan let Hullender fire up a supercomputer and spend hours converting the reels so they could be read on Simons’s computer. As the reels spun, the friends caught up over coffee.
To gather additional data, Simons had a staffer travel to lower Manhattan to visit the Federal Reserve office to painstakingly record interest-rate histories and other information not yet available electronically. For more recent pricing data, Simons tasked his former Stony Brook secretary and new office manager, Carole Alberghine, with recording the closing prices of major currencies. Each morning, Alberghine would go through the Wall Street Journal and then climb on sofas and chairs in the firm’s Library room to update various figures on graph paper hanging from the ceiling and taped to the walls. (The arrangement worked until Alberghine toppled from her perch, pinching a nerve and suffering permanent injury, after which Simons enlisted a younger woman to scale the couches and update the numbers.)
Simons recruited his sister-in-law and others to input prices into the database Hullender created to track prices and test various trading strategies based on both mathematical insights and the intuitions of Simons, Baum, and others. Many of the tactics they tried focused on various momentum strategies, but they also looked for potential correlations between commodities. If a currency went down three days in a row, what were the odds of it going down a fourth day? Do gold prices lead silver prices? Might wheat prices predict gold and other commodity prices? Simons even explored whether natural phenomena affected prices. Hullender and the team often came up empty, unable to prove reliable correlations, but Simons pushed them to keep searching.
西蒙斯又招募了他的嫂子和其他人向胡伦德建立的数据库中输入价格，以便追踪并测试各种交易策略是基于数学观点以及西蒙斯、鲍姆等人的直觉。他们尝试的很多策略侧重于动量策略，但他们也在寻找大宗商品之间的潜在相关性。如果一种货币连续三天 ，那么第四天该货币下跌的几率如何？ 价格会引导 价格么？小麦价格能预测黄金和其他商品的价格么？西蒙斯甚至去探索自然现象能否影响价格。胡伦德和他的团队经常一无所获，无法证明可靠的相关性，但是西蒙斯仍鼓励他们继续寻找。，这些
“There’s a pattern here; there has to be a pattern,” Simons insisted.
Eventually, the group developed a system that could dictate trades for various commodity, bond, and currency markets. The office’s single computer wasn’t powerful enough to incorporate all the data, but it could identify a few reliable correlations.
The trading system had live hogs as a component, so Simons named it his “Piggy Basket.” The group built it to digest masses of data and make trading recommendations using the tools of linear algebra. The Piggy Basket produced a row of numbers. The sequence “0.5, 0.3, 0.2,” for example, would signify that the currency portfolio should be 50 percent yen, 30 percent deutsche marks, and 20 percent Swiss francs. After the Piggy Basket churned out its recommendations for about forty different futures contracts, a staffer would get in touch withthe firm’s broker and deliver buy-and-sell instructions based on those proportions. The system produced automated trade recommendations, rather than automated trades, but it was the best Simons could do at the time.
这个交易系统里有一部分是活猪交易，所以西蒙斯又叫它“小猪篮子”。该小组建立它用来消化大量数据，利用线性代数工具提出交易建议｡“小猪篮子”能产生一系列数字。例如数列“0.5,0.3,0.2”，就意味着 组合应该为50%日元，30%德国马克以及20%的瑞士法郎。在“小猪篮子”对大约40种不同的 合同提出大量建议后，一位工作人员将跟该公司经纪人取得联系，并根据比例给出买卖指令。该系统会自动产生交易建议,，而不是自动交易，但这已经是西蒙斯当时能做的最好的结果了。
For a few months, the Piggy Basket scored big profits, trading about $1 million of Monemetrics’ money. The team generally held its positions for a day or so, then sold them. Encouraged by the early results, Simons transferred several million dollars of additional cash from the Limroy account into the model, scoring even larger gains.