The Simmons Grand Prize Fund is a Wall Street hedge fund myth that, for 20 consecutive years, averaged a 35% annual return, giving it an annual return of more than 60% if the fund's 5% management fee and 40% withdrawal are taken into account. This is far more than Buffett and Soros.
Simmons' strategy was to use powerful mathematical models and computer software to make high-frequency trades in different products in the global market, earning a steady, sustained return from small fluctuations. This was a market-neutral strategy, less affected by bull markets and bear markets. Simmons' Grand Prize Fund was able to earn 80% returns in the event of the global market crash of the 2008 financial crisis.
In general, high-frequency trading mainly involves the following strategies: liquidity rebate trading, predatory algorithmic trading, and automated market makers trading.
To clarify the above high-frequency trading strategy, here is a case that is very similar to the actual transaction. A buyer institutional investor decides to buy 10,000 shares of XYZ stock at a price of about $30 and, like most buyer institutional investors such as mutual funds, pension funds, etc., the purchase is first entered into their algorithmic trading system. In order to minimize the impact on the market price, the investor's algorithmic trading system generally processes the large order in two stages: first, it is broken down into dozens or even hundreds of small purchases (a small purchase is usually between 100 and 500 shares), and then these small purchases are placed on the market in some set order.
Liquidity drawdowns
To attract more trading orders, all U.S. stock exchanges offer a certain amount of trading fee rebate, usually 0.25 cents/share, to brokers who create liquidity. Whether a buy or sell order, the exchange pays a rebate to the original provider of that liquidity as long as the transaction is successful, while charging a higher fee to brokers who use that liquidity to trade.
In this case, assume that the institutional investor's psychological transaction price is between $30 and $30.05; if the first payment in the trading system (e.g. 100 shares) is successful, the pairing is done at a price of $30; thus, the second payment in the trading system (e.g. 500 shares) will pop out; again, assume that the purchase is also successful, and the pairing is done at a price of $30; according to the above transaction information, the computer system of the high-frequency trader specializing in liquidity rebate strategies may detect the presence of other subsequent $30 payments from the institutional investor, so the rebate trader's computer takes action and reports a purchase price of $30.01 for 100 shares.
Upon successful completion of the transaction, the rebate trader immediately adjusts the direction of the transaction and sells the 100 shares just purchased for $30.01 at the same price, $30.01 in a call option. The call option is likely to be accepted by institutional investors since the $30 share price is no longer in place.
In this way, although the rebate trader does not make a profit throughout the transaction, he receives a rebate commission of 0.25 cents per share offered by the exchange because the second active bid provides liquidity to the market. Needless to say, the rebate trader's profit of 0.25 cents per share is at the expense of 1.0 cents more paid by institutional investors.
Algorithmic trading of prey
In the United States, more than half of institutional investors' algorithmic quotes follow the SEC's National Best Bid or Offer (NBBO) principle, where the broker must guarantee the best available market price when the customer buys the security; and the same when the customer sells the security. According to this principle, when a quotation has a higher priority in order to outperform another quotation, the second quotation is often adjusted to match the previous one. In fact, the algorithmic quotation of a single stock often tracks each other at a very fast rate, causing the stock price to show a trend from high to low and from high to low dynamic stages.
A prey algorithm is a trading strategy based on the study of the historical laws of stock price movements. Generally speaking, the strategy is to induce institutional investors to raise the buy price or lower the sell price by creating artificial prices to lock in trading profits.
In this case, it is assumed that the institutional investor follows the NBBO and the psychological transaction price is between $30.01 and $30.05; as in the previous case, the predator algorithm trader uses very similar procedures and techniques to search for potential sequential algorithmic orders from other investors. After the computer confirms the existence of the $30 algorithm quote, the predator algorithm trader launches an attack: it reports a buy-in price of $30.01, forcing the institutional investor to quickly raise the price of the subsequent buy-in to $30.01; and then the predator algorithm trader pushes the price further up to $30.02, enticing the institutional investor to pursue it further.
In this way, the predator algorithm trader instantly pushes the price to the upper price acceptable to the institutional investor at $30.05 and sells the stock to the institutional investor at that price. The predator algorithm trader knows that the human price of $30.05 is generally difficult to maintain, so it replenishes the stock to make a profit when the price drops.
Automated marketing strategies
It is well known that the main function of market makers is to provide liquidity to the trading platform. Like normal market makers, automated market makers increase liquidity by offering buy or sell orders to the market. Unlike automated market makers, they usually operate backwards with investors. Automated market makers have high-speed computer systems that have the ability to detect other investors' investment intentions by issuing super-fast orders.
In this case, suppose that an institutional investor sends a series of bids to its algorithmic trading system at a price between $30.01 and $30.03, which is unknown to the outside world. To detect the presence of potential orders, the high-speed computer system of the automated market maker high-frequency trader starts by issuing a bid of 100 shares at a price of $30.05. Because the price is higher than the investor's price cap, it did not cause any response, so the bid was quickly withdrawn. The computer tried again at $30.04, or the result did not cause any response, so the bid was also quickly withdrawn. The computer continued to explore again at $30.03, and the transaction was successful.
These three are the mainstream high-frequency trading strategies, which are so demanding on the performance of computers and networks that some trading institutions have placed their server farms close to the exchange's computers to shorten the distance that trading instructions travel at the speed of light over fiber optic cables.
In fact, the impact of high-frequency trading on the market has long been a hotly debated topic among banks. A report by the Federal Reserve Bank of Chicago notes that while high-frequency trading is beneficial to the market and can increase liquidity in the stock market, it can also have a disastrous effect on market trends if the process goes wrong or is neglected.
Another problem is that high-frequency trading is suspected of being a market fairness issue, and the equipment and computing power required for high-frequency trading is an insurmountable barrier for small and medium-sized investors, who can create market inequities for institutions that profit from high-frequency trading.
在国内市场，目前基本上没有高频交易的土壤，股票市场是T+1，股指期货市场的持仓、交易频率都有很大的限制。商品期货市场可以做一些日内的短线交易，但是离高频交易尚且有很大的距离。从监管层的态度以及国内市场的发展来看，高频交易在国内短期内无法成为一个主要的交易方式。
JackySGGod of Quantity, Simmons one, Zero two