High Frequency Trading Models

High Frequency Trading Models from the Trading Floor TSE

Trading computers, Trading Floor TSE—tenaciousme (Flickr.com)

High Frequency Trading Models are used by traders and institutions as a way to both train their traders and automate their trading activities. They are often sophisticated software tools employed in markets that trade anything from forex, futures, stocks, bonds, and more. They are highly quantitative computerized algorithms that analyze market data to either find profitable trading opportunities, or opportunities to help facilitate trades and liquidity required by the firm employing them.

High Frequency Trading Models are usually built around the idea that they can be more effective than day traders, and in most cases are only active during times of high liquidity (usually during market hours). This means that they will sometimes seek opportunities that may only be available from a fraction of a second up to hours at a time.

In some cases, these models may be a simple macro program that a daytrader develops to execute trades and orders faster, where at other times they may be a complex algorithm designed for black box robotic trading. They are often used as part of an investment strategy that includes, market making, inter-market spreading, arbitrage, or pure speculation.

High Frequency Trading Firms account for 73% of US Equity trading volume, and it has been measured that as of 2006 33% of all European Union and USA stock trades are driven by automatic programs and algorithms. At the LSE alone (London Stock Exchange), over 40% of all orders were entered by algo traders during the year, with an expected increase to 80%+ after 2008.

The game of high frequency trading models is essentially a round robin war between the chicken and the egg and is unlikely ever to be won completely by a single side. Humans write algorithms to get around human inefficiencies (like having a narrower focus and slower reaction time). In turn, humans adapt and seek to exploit weaknesses in computerized trading algorithms by identifying patterns in them, the same way they would in human market participants. When the algo is made unprofitable, it is then either tweaked, or rewritten again to adapt to exploit the weaknesses of the traders once more. The traders then attempt to adapt again, and this is the cycle we are currently in.

It is said that when one door closes another door opens. This is true in algorithmic trading too, as every time a computer takes an opportunity away from a human, it also opens up a new opportunity for the human to take an advantage away from the computer.

In the world of high frequency trading models, the motto is: “Adapt, or die”

HFT Trading Models Aren't for Kids

Trading computers, Online Investing—Monkey Mash Button (Flickr.com)

Common Strategies for High Frequency Trading Models:

Trend following algorithms attempt to take a advantage of securities that are making consistent new highs or new lows within a given time frame. The algorithms may be set to buy and sell shares on both sides, but take more liquidity in one direction.

For example on an up trend (the stock is rising), the algo may be set to bid 100 shares and offer 1000 shares, giving the impression to the market that the algorithm is “selling”. However, the bid of 100 shares could be a refreshing bid, (meaning that once those 100 shares get bought by the computer, it puts another 100 on the bid to replace it) This means that the computer may be showing its interested in buy 100 shares, when really it wants 1000. As well, the 1000 shares on the offer may be a FOK offer, in that once it gets hit by an order (say for 100 shares) it disappears. This means that although the computer is offering 1000, it may only be doing so to give the impression of selling so that it can in fact “Buy” at a lower price.

If a human trader is able to see this, they could potentially take advantage of the situation and buy the entire 1000 shares and continue to buy in front of the computer. It is also completely possible that a trader is buying the stock for the exact same reasons as the computer, but possibly in a different way. When this happens it becomes not a battle about who is right about where the stock is going, but who is right about how the stock gets there.

High frequency trading computers, Wall Street Trading Error

High frequency trading computers, Wall Street Trading Error—Mike Licht NotionsCapital.com (Flickr.com)

Delta Neutral Strategies and High Frequency Trading Models

Delta refers to the relationship of change between an option and its underlying. Delta hedging is the act of keeping the delta of a portfolio as close to zero as possible. This helps firms lower their risk exposure to fluctuations. However, the cost of completing the transactions required for a true delta neutral position is often more costly than the risk exposure itself. Algorithms help lessen the burden on human traders by completing these transactions by themselves.

High Frequency Trading Models for Pairs Trading and Arbitrage.

Arbitrage is the practice of taking advantage of a price difference between two or more markets by capitalizing on the imbalance. Theoretically, it involves no risk, since there is no negative cash flow. Arbitrage traders help to create a fairer and more stable market by bringing prices towards a single true uniform price. In this way, non market professionals can enter transactions and be assured that the price they received was fair at the time of their order. One example of arbitrage occurs when a company’s stock is traded in two different currencies on two different exchanges. Sometimes large buyers or sellers on one market can make the price significantly more expensive than if they purchased the same stock on the other market. When this happens, arbitrageurs attempt to sell the expensive stock and buy the cheaper one in order to realize a profit.

Similarly Pairs traders attempt to take advantage of historical relationships in the market. Because individual currencies, futures, and equities all move in their own unique way, from time to time, the spread or “relationship” between them widens or shrinks to a ratio that is so far beyond normal that traders will attempt to profit from it in the hopes that it returns back to it original ratio or “spread”

For example, if 100 shares of Gold Stock ABC always trades at the price of gold and one day gold goes up 20 percent and ABC only goes up 10 percent, traders who recognize this opportunity will attempt to buy up ABC’s stock until it matches the price of gold.

Nowadays, more an more computers are entering this type of trading, and because they are able to look a more securities with faster calculations, pairs trading algorithms are actually able to take advantage of opportunities that weren’t even available to human traders in the past, giving them a supreme advantage in this area.

Although high frequency trading software does have advantages over humans, its important to remember that it still must be designed by humans. As well, it is never as adaptable as a human is. If a human sees a terrorist attack, they may very well decide to exit the market, whereas a computer would just keep trading. Computers also have the disadvantage of being tricked by false data. If computer sees a trade that looks like easy money it will take it immediately with no delay. If the trade is actually a losing trade, the computer will continue to take it until the information is corrected. This means that the computers speed can allow it to lose an enormous amount of money in a short span if it acts on faulty information.

When dealing with high frequency trading models you want to make sure you get the best of both worlds. This means you want a human to tell the robot to do, not the other way around.