A bestiary of algorithmic trading strategies

Bill Gates is only there because he's standing next to a bunch of hooters models who are obviously there for Warren's ineffable awesomeness. Warren is the most famous fundamentals trader there is, therefore he belongs in the picture, even if he isn't well known as a quant. I don't have any pictures of Richard Grinold next to pretty girls.

Buying a short, selling a long: it's still buying low and selling high if you do it right.

As for why I'm linked here: I haven't got a whit of a clue. My best guess is that someone realized I'm a big Lisp nerd.

-Scott

The basics of devising algorithmic strategies come from the field of Market Microstructure in Finance/Economics.

I took a graduate level course on it (math prof. from Stanford).

I found a decent graduate level course syllabus with all the main literature and links to literature in it:

http://pages.stern.nyu.edu/~jhasbrou/Teaching/2006%20Fall%20...

One of the main models is the Kyle model: Kyle, Albert S., 1985, Continuous auctions and insider trading. Econometrica 53, 1315-1336 (http://www.jstor.org/pss/1913210)

I'm halfway through Harris _Trading and Exchanges_, recommended at the top of this article, and it's just a phenomenally awesome book. I brought it up to the lake for summer reading, it's been that fun to read so far.

Why do algorithmic trading articles pop up frequently on HN?

I understand why people are attracted to it because they think that they can beat the market by implementing their own stat arb strategy using neural networks/genetic programming etc. They think that they'll fare out better than the average day-traders because they have the know-how's to program and the quantitative skills to formulate the models and the technical skills to implement the models.

But what people don't realize is that despite of all that, you still can't beat the street because: speed and volume. All high frequency trading strategy rely on fast execution speed (get in and get out, most prop shops at investment banks and hedge funds hold their positions usually for a couple of seconds, or even less), because due to prevalence of electronic trading, the inefficiency in the market for arbitrage opportunities exists in the time-frame of seconds or micro-seconds. In arbitrage, it's no longer a matter of who can discover the treasure map; it's more like everyone already has the treasure map, whoever can get to the X mark the spot first wins.

Goldman Sachs/SAC/DE Shaw etc. all "colo" with the market exchanges, meaning they have their algorithmic trading servers hooked up directly to the same trading servers of NYSE/Nasdaq/BATS. They might be connected directly to those market servers in the same server room in NYC/Kansas, or they might be connected over a dark fiber network in Jersey City. In the matter of seconds a retail brokerage customer puts out a buy order for GOOG, or even a aspiring trading-hacker sends out a order over Interactive Broker API, these algo machines have already piped out thousands of orders and a good percentage of those orders have already been filled.

Secondly, good algo's require fast real-time information processing of the quote book, general market condition, real-time calculation of the basket of related stocks in the stat arb strategy. On an single active stock, there might be hundreds of bid/ask/trade ticks per minute from a single market center. Can an individual's machine setup handle real-time analysis of a basket of stocks and general market condition from multiple market centers? This issue is so critical and complex that there exists a sub-industry (Complex Event Processing, CEP) and technology companies (StreamBase) that created and profit from dealing with this problem for hedge funds and prop shops.

Finally, usually high frequency algo's are only profitable with high volume and low transaction cost. Typically, arbitrageurs and liquidity providers make pennies on the share per trade. But their daily volume are in the tens or hundreds of millions shares traded; their transaction cost are in the quarters of pennies or sometimes negative (meaning they are compensated by the exchanges for providing liquidity). Compare this to a retail consumer account with $10,000 and trading comission of $5 (ETrade) - $0.01/share (IB).

Still, I know of people who have IB API setup's and profit consistently. But I wanted to let would-be traders know the competitive disadvantage that they are up against the big boys before they pour out their life savings in their neural network futures arbitrage algorithm.

> To make money as a trader, assuming your motivation is to make a profit, you need to buy low and sell high.

Not to pic a nit but most traders also sell short, i.e. reverse the order to make money; (borrow stock) Sell High and Buy Low (return stock). The guy seems pretty smart so he probably left this out to keep his explanation simple.

Side note; Short selling serves an important function in price formation and liquidity and is not the primary cause of a stock price to fall. In fact, for the short seller to make profits there must be an independent cause of the fall apart from the short seller's actions or they lose money.

Bill Gates and Buffet are both bad examples because both of them are essentially managers rather than traders.

What's a good book on algorithmic trading?

To fix all types of http://en.wikipedia.org/wiki/Information_asymmetry issues in stock markets, every Algorithmic and high-frequency trade should be regulated via http://www.sec.gov/answers/form345.htm