How to identify algorithmic trading strategies
quantstart.com
Discusses methods to identify profitable algorithmic trading strategies and to understand in detail how to find, evaluate and select such systems.
quantstart.com
http://www.fooledbyrandomness.com/
http://www.amazon.com/Nassim-Nicholas-Taleb/e/B000APVZ7W
Structuring the trades to reduce your exposure to downside risk while increasing your exposure to upside from unanticipated random events is the hard strategy to implement, but it is the sole strategy for avoiding a gambler's ruin.
It sounds more regal when you say "structure the trades to reduce your exposure to downside risk while increasing your exposure to upside from unanticipated random events" though.
Given Taleb's understanding and belief in randomness of the market, I see him as "creating his own lotteries" using rather sophisticated techniques that have paid off very well. The cost of entry is small and the potential rewards are high but he loses regularly and consistently and says he was grateful to be in a position where he could execute such a strategy without being looked down upon by his superiors (from Fooled By Randomness).
That is why Wall Street is #1.
Then you start another Wall Street firm. Ben will chip in the funding for basically free if you are well connected.
(This is also why Wall Street pays out all profit to real people, instead of keeping it within the firm.)
More precisely, it's akin to buying mispriced lottery tickets with a high positive expected payoff that hasn't been priced in yet.
Unfortunately, if you had a passive long investment in VIX futures since March 2004 (the earliest you could trade them) you would steadily have lost money - despite huge gains in the latter half of 2008 (and to a lesser extent in other periods).
The market is well aware that insurance-like products are useful, and so they are priced accordingly. Buying volatility is expensive.
The short vol "picking up pennies in front of a steamroller strategy" comes with the risk that you could be completely wiped out in a crisis. This happened to a lot of desks and funds practicing vol arb in 2008. But the flip side is that if you want to buy vol, you need to be able to absorb the punishing losses that can persist for years before you get to the big wins.
If you want to make money trading on the stock market (with algorithms or otherwise), you're directed to devote time, effort, skill, and a large quantity of start-up funds to the effort. Of course, you could also devote time, effort, skill and capital towards starting your own business (based around algorithms or otherwise) or you could devote time, effort, and skill towards just getting a job (programming algorithms or otherwise). Likewise, as there are big players in the stock market, there are big players in any market, and smaller, more nimble businesses can try and maneuver around them (or get crushed trying).
The stock market: just a part of real life. Neither a mystical land of fantastic riches, nor a freakish unholy pit of dishonest vipers and shattered dreams.
Personally I think it's both of those things :-)
Trading is perhaps the ultimate convex work, to borrow Michael O. Church's term. A few extremely skilled people can pull money out of the market like magic. But the average trader's performance is worse than just buying a major index fund. And those of us below average (I am still in this group, alas) can pretty much count on losing money.
Your point that trading takes effort and capital that could be directed in other ways is well stated.
The main difference is that if you're not interested in raising external capital, then you don't need to do any marketing - all of your focus can be on the product.
I have made it clear in the article that it is NOT easy, nor a get-rich-quick scheme which many seem to think it is. It takes a significant amount of work to generate consistently profitable strategies.
* owns their own private jet/yacht/other signs of opulence
* didn't share it with anyone else
try to ignore it
He realised he was observing the upper tail of a distribution of people who were aggressive risk takers, yet naïve about the risks they were taking – the businessmen one sees in the casinos are the ones successful enough to have enough money to lose.
These same people provide the underlying dynamics of capitalism. They are not rational, they do not understand risk and are therefore prepared to take risks that a rational agent would not take. And it is these people who drive the growth of the economy.
What if they got their private jet by tricking others into following the strategy?
Consider the case of finding a set of strategies governed by a particular set of parameters in a book. For instance, the Moving Average lookback period. You will see authors posting certain strategies, albeit without revealing the market/time series with which they're carrying them out on or which exact parameters they use. This is the critical information, but it is also relatively straightforward to trial/test, assuming you have the available data.
Also - the same strategy, implemented identically, can be both successful AND a failure for two different traders with identical starting capital. Why? Because one may not have the stomach for a 50% drawdown in the equity curve, despite the fact that had they waited, a "big swing" would have been around the corner. It is as much about preferences/tolerances as it is about the actual rule set.
"A quantitative hedge fund only needs two members in order to be successful. A quant trader and a dog. The quant trader is there to feed the dog. The dog is there to make sure the quant trader doesn't touch anything."
Also, the first cited site is Ernie Chan's which provides a similar established perspective
Each experience presented interesting challenges. Quant trading was very mathematical, academically interesting and presented "big data" issues right at the start. Tech startups taught me a lot about management, getting things done (TM) and why you need to have a market BEFORE building a product! Academia taught me how to really analyse a problem to an extreme degree and how to quickly find solutions.
Right now I'm enjoying building quant trading systems. To a certain extent they can be fully automated (although you have to be aware of "alpha decay" - i.e. strategies losing their profitability over time) and thus it is possible to have other interests.
- Own a trading floor
- Become a stockbroker
- Become a market maker
- Sell books on the subject
- Work for a financial institution
Despite common perceptions to the contrary, it is actually quite straightforward to locate profitable trading strategies in the public domain. Never have trading ideas been more readily available than they are today.
What is the input of the "retail" trader then? Especially considering that at this level tech does not make a difference (all have access to somehow high computing power).
By the way, any good backtesting tool in python or R? I started implementing a simple trading algo last week during my freetime (yeah, I have to go out more) and I was wondering how will I test it.
"Most platforms slow down when there is an influx of orders into the market. Some are designed to force events during the process (which allows for action while prices move, but the prices may be stale) and others are designed to process all feed messages before forcing an event (which ensure prices are more up-to-date but doesnt allow you to make a trade earlier) Suppose you are betting that this represents a market rally or collapse (directional). Then, you can make money by figuring out the direction of the move (aggressive processing of the first few messages in a burst) and get involved before every other system catches up in the feed."
I imagine there isn't much money to be made in doing that on the equities or futures markets nowadays.
Running algo trading would get me fired so fast. (I work at a mutual fund company, though not just yet on anything trading related)
HFT is just a legal way to pull almost the same scam.
The exchange colos are not in the same space as the physical trading floor. E.g. for U.S. equities, the NYSE trading floor is in NYC, but the machinery for all the major exchanges (barring CHX, I believe) is in northern New Jersey.
"front run any trades before they actually happen between the party holding the equity and the one who will leave with it for the night."
Front running is illegal, and as a prop trader it's not physically possible. In order to front run an order, you have to be in the path between the order originator and the exchange.