22

The lead paper in the January 2011 Journal of Finance (Hendershott, Jones, and Menkveld) addresses algorithmic trading (AT). In short, they find that AT improves liquidity as measured by bid-offer spreads. Taking the econometrics as correct (it is in the Journal of Finance) the next question is if bid-offer spreads are a sufficient statistic for measuring ...


17

Stanford University has a free online course in machine learning with video lectures, problem sets, and even a promise of online help with coursework from Stanford faculty. This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric ...


17

Yes. First, it is much easier to proceed if you standardize the output of your forecast so they are in the same units (returns, for example, or probabilities of an event/condition occurring). After you have done this, there are 3 general approaches: Signal weighting: Then you need to define a weighting scheme for your factors. Richard Grinold has an one ...


16

This is a very interesting question. I believe it is getting a lot of up-votes from people who have wondered the same thing and don't know where to begin, whereas you have at least laid out a reasonable-sounding plan. I commend you for that. However, it is not clear to me what you're trying to learn by posting this question. In my opinion, the plan you ...


13

This answer is my ongoing attempt to consolidate some recent commentary on this hot topic. A good place to start for anyone thinking about this question is the Economists's Buttonwood: Not So Fast, which mentions recent research by Biais and Woolley (2011) and Dichev, Huang, and Zhou (2011). Does Algorithmic Trading Improve Liquidity? This paper claims yes....


13

Interactive Brokers does have a .NET API, albeit a free (as in speach) one written by Karl Schulze, not IB themselves. http://www.dinosaurtech.com/utilities/ It's written in C# (and IMHO well written). I've examined both it and the Java API and find the .NET version more to my liking. That's probably just because I'm more familiar with .NET than I am ...


13

As the others have already mentioned, this is a very broad question. Anyway, as a starting point there are some blogs that come to my mind that have some up to date high quality content on these issues from time to time: http://quantivity.wordpress.com/ http://epchan.blogspot.com/ http://www.automated-trading-system.com/ http://intelligenttradingtech....


12

Windham Capital Management is using hidden markov models for their Risk Regime Strategies. Mark Kritzman, who is also CEO, has published an article about the general outline of the strategy (with source code so you can replicate the results!): Regime Shifts: Implications for Dynamic Strategies (corrected August 2012) by M. Kritzman, S. Page, D. Turkington]...


11

I think R's CRAN Task Views on Machine Learning is an excellent resource for beginners moving to advanced algorithm traders. It is well-structured, broad, up-to-date, and ready-to-use! http://cran.r-project.org/web/views/MachineLearning.html I believe all advanced quantitative traders already know this. But I haven't seen anyone post it here and Flake's ...


11

This answer summarizes some of my comments. HFT is certainly a very hot topic these days, but it's hard to point to any one reason. A large part of it is the mystery and the profits, but also part of it is the relative novelty. Note that there is no lack of papers about medium and low frequency strategies, it's just that they are not labeled as such. Medium ...


10

I found this solid overview of different trading algorithms by Deutsche Bank Research: Trade execution algorithms Designed to minimise the price impact of executing trades of large volumes by ‘shredding’ orders into smaller parcels and slowly releasing these into the market. Strategy implementation algorithms Designed to read real-time market data and ...


9

I don't know if you can really improve, the point of Market Making is that you don't know when you'll be executed. It also depends a lot on the type of product you're trading, it's not the same business Market Making far from the money options (where you will never be executed but just offer a reference price and answer traders phone calls) and MM on Bonds/...


9

In terms of pricing, Zen-Fire seems to be the best "retail" solution. But as you said, you need to be faster, so you can try some faster and more serious options: QuantHouse - CME's Level 1 market data will cost you around 1500 Euros per month. They have points of presence in most local financial centers in Europe (Stockholm, Frankfurt, etc.) so you can ...


9

We cannot give you a relative bid-ask spread that would make sense. The reason for that is that it really depends on several parameters: The type of financial asset you invest in (futures, funds, index, options, ...) The period during which you're trading (I think the liquidity in markets hasn't been the same over time). If you trade intraday, it depends on ...


8

There is a paper of mine answering To this question: Dealing with the Inventory Risk. A solution to the market making problem by Olivier Guéant, Charles-Albert Lehalle, Joaquin Fernandez Tapia.


8

First, we are few quants and academics to use the full toolkit of machine learning: stochastic algorithms, to optimal trading. Here are at least two papers: Optimal split of orders across liquidity pools: a stochastic algorithm approach, Sophie Laruelle (PMA), Charles-Albert Lehalle, Gilles Pagès (PMA) Optimal posting distance of limit orders: a stochastic ...


8

Ha, interesting, so many responses with "negative" expectations. There are plenty of people that have successfully gone down this road and are producing pretty nice returns, so obviously it is possible. A trader with a smaller capital has better chances of producing good ROC with very reasonable risk parameters, simply because he's would not be constrained ...


7

For starters, I am not even sure why you need to ask this question. There is literally years of free tick data available for FX, just check out quant.SE's data wiki. Having said that, a Gaussian is a very poor fit to high-frequency data, particularly FX. Your strategy for simulating data depends on the idea behind the simulation. If you wish to actually ...


7

DSpace@MIT - High frequency trading system design and process management (non-printable) This thesis provides a detailed study composed of high frequency trading system design, system modeling and principles, and processes management for system development. Particular emphasis is given to backtesting and optimization, which are considered the most ...


7

Measuring expected shortfall (also known as conditional value-at-risk) answers the simpler question of "what is my average expected loss at the i-th quantile?" given the empirical distribution of returns. A variation is value-at-risk which measures the loss at the i-th quantile. Arguably you could leave at this this and you have your answer. You probably ...


7

From this site's perspective, I think nothing would be better than a ML.SE. Finally, we got one awhile ago. UPDATE: Unfortunately, Machine Learning is merging into Cross Validated. To learn more detail, click here." I have no idea why SE admin was rush to merge ML into CrossValidated. Not a fan of it (Orz). I personally prefer a separate site. FYI, http:...


7

Personally, I am very skeptical of the claims in "Twitter mood predicts the stock market". There are several other papers with similar claims, but not so much good quality research is available. Arguably, the sweet bits of these approaches are not public. A sounder approach is to dig at the relationship between social media activity and relate it to the ...


7

As a starting point to my answer, I would say that reading a book is not sufficient to start doing automated trading on your own as chrisaycock suggests in his comment. I would answer your questions in 3 different ways. First, building your "AI bot" which I would rather call a systematic algorithm not only requires programming skills, it also means having ...


7

The most commonly-known approach to this is described in Inferring trade direction from intraday data (1991) by Lee and Ready. You will find that the non-trivial part has to do with classifying trades that are reported inside the spread. I believe you will find that the Lee-Ready algorithm will outperform the naive midpoint reference approach suggested by @...


7

If I was in your position I would start to research how I can create a web server is C++ and expose calls to create a REST service. In other words, can you make your code status output to HTTP? From there, the rest should be easy. You would just need to create a GUI that can access REST services, which virtually all modern languages can. You could focus on ...


6

In addition to Chan's Quantitative Trading, I have also found the description of trading systems in Rishi Narang's Inside the Black Box to be informative and interesting. There are a few chapters there that give some details on system development, but they are very broad overviews.


6

JunoTrade claims to have a streaming .NET API -- http://www.junotrade.com/index.php/junotradeapi Pinncle Trading - http://www.pcmtrading.com/technology/api.html (supports C# according to the last item). TD Ameritrade @ codeplex (unoffical)


6

The most basic strategy is beta-based quantiles. That is to say, you first control for losses on your individual stock versus overall market performance. (Your trading strategy may or may not wish to hedge away the market factor using, say, SPX futures). Then you choose a quantile, call it the 5th percentile, beyond which you consider a move to be ...


6

In 2010 Informs and Kaggle organized time series prediction contest. The methods used by competitors are described here.


6

Jordan (@jordan.baucke) in his answer suggests that most latency arbitrages are actually market making strategies, as opposed to classical price arbitrage. While I generally agree, I can think of two exceptions: Equity price arbitrage in fragmented markets (See the fragulator for more on this). In this environment, negative spreads can arise and the ...


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