5 ms·
If you want to read useful academic papers about trading there is one author in particular who is actually not bad - Zura Kakushadze. Most of his stuff is appli
by traderthrow454 6y ago
If you want to read useful academic papers about trading there is one author in particular who is actually not bad - Zura Kakushadze. Most of his stuff is applicable to mid-frequency trading, not HFT. He worked at WorldQuant (reputable trading firm) and the founder of WQ, Igor Tulchinsky, is a coauthor on one of his papers.
Example of a pretty interesting and accessible one - is "101 Formulaic Alphas" [0].
[0] - https://arxiv.org/pdf/1601.00991.pdf https://arxiv.org/pdf/1601.00991.pdf
- huac 6y agopicking a random one out of the pile: > Alpha#90: ((rank((close - ts_max(close, 4.66719)))^Ts_Rank(correlation(IndNeutralize(adv40, IndClass.subindustry), low, 5.38375), 3.21856)) * -1) I wonder how these magic numbers get picked (4.66719, 5.38375, etc) -- I guess there is some optimization solver which attempts to find the most profitable variables for a given alpha formulation, but isn't this approach also very vulnerable to overfit?
- traK6Dcm 6y agoYup, it's probably just the output of an optimizer and then tested on held-out future data. Not overfitting is the key here and what's really hard. You need to be careful about the number of parameters and the amount of validation data you have. These alphas will likely be only profitable for a short time period as long as the market data distribution (i.e. strategies of other market participants) doesn't change. So you would need to continually optimize and update them. The way I think about it is that you are essentially finding the right parameters to "exploit" the combination of algorithms of all other participants, where algorithm could also be a human looking at charts and following certain rules, with a lot of random noise from retail traders thrown in.
- laxatives 6y agoSeems kind of rudimentary. Namely > (sign(delta(volume, 1)) * (-1 * delta(close, 1))) That's crazy. Would be interesting to see WTF a "mega-alpha" actually does using these strategies.
- smdz 6y agoI believe they may have used something on the lines of genetic-programming to create this equation - not sure about the high precision constants. The search space is compute intensive. Many years back, I used that technique to generate a profitable strategy. These things work and are different depending on the timeframe/sampling, stock, trend and money management.
- young_rutabaga 6y agoThis paper is a hilarious dump of WQ's randomly generated formulas that (hopefully) happen to pass in-sample test. Alpha#33: rank((-1 * ((1 - (open / close))^1))) This formula trivially reduces to rank(open/close - 1) which is an example of a mean-reversion strategy. But: 1) nobody bothered to simplify this formula, 2) as any mean reversion, it is extremely difficult to trade.
- astrofinch 6y agoWhy is mean reversion difficult to trade?
- young_rutabaga 6y agoHigh turnover, high costs. You flip your position too often
- stainforth 6y agoIf the market flips against me, I just double my bet on the next play.