# Tag Info

26

As mentioned elsewhere on this site, Lo, Mamaysky, and Wang (2000) do exactly what you're talking about, namely algorithmic detection of head and shoulders patterns. Their definition: Head-and-shoulders (HS) and inverted head-and-shoulders (IHS) patterns are characterized by a sequence of five consecutive local extrema $E_1,...,E_5$ such that  HS \...

17

There's a lot of people here talking about how GAs are empirical, don't have theoretical foundations, are black-boxes, and the like. I beg to differ! There's a whole branch of economics devoted to looking at markets in terms of evolutionary metaphors: Evolutionary Economics! I highly recommend the Dopfer book, The Evolutionary Foundations of Economics, ...

16

There are tons of languages used in this field. As for Java-based trading platforms, Marketcetera is popular with customers. To justify switching languages, you'd need to show that there is a bottleneck preventing your team from collecting more P&L. Have you run a profiler and compared the results with tcpdump? You must show that your existing platform ...

16

I would recommend that you read "Evidence-Based Technical Analysis" by David Aronson. Firstly, I am mentioning it because it is a highly worthwhile book. Secondly, on pp151--161 he attempts to "objectify subjective TA", using the head-and-shoulders pattern as an example.

14

Dynamic Time Warping, recursive, time-delayed feedforward neural networks, wavelets, empirical mode decomposition, ..., there's plenty of it. BUT If you want my advice, don't go this way, I wasted too much time doing things like that. Neither big nor small players (profitably and consistently) trade this way and for a good reason. Technical analysis is a ...

13

Oracle hosted a Trading Applications Developer Workshop in New York on March 15th, 2011. The slides from each of the presentations are here. One of them covers java for Trading Applications, and it seemed to me that the biggest issue raised by the audience was garbage collection. The presentation talks about some configuration parameters that can limit ...

11

In a typical HFT scenario (process incoming UDP quotes and send TCP order entry responses) well written Java can compete with C++ for pure speed. If you need more speed, look to improve the following: Your code (setup a good benchmark and then profile and tune) Your networking environment (low-latency switches, DMA NICs) Your architecture (are you doing ...

11

After having done a lot of research on the topic I found the following excellent research piece on ETF.com: Wealthfront modifies historic asset-class returns with current market implied expected returns (Black-Litterman) as well as with the in-house views of Chief Investment Officer Burton Malkiel’s team. In addition, Wealthfront sets minimum and ...

9

I have these posts favorited from stackoverflow. They might help you. High Frequency Trading What programming language(s) is algorithmic trading software written in? Why does a derivative trading position always require C++ knowledge? Jane Street Capital, a high-frequency market making firm, uses OCaml. Here are videos from the head programmer where he ...

9

For years, I performed this brute-force search daily on my universe of tradable stocks and futures. It is a waste of time. If your computer discovers that hog futures and MSFT are cointegrated, for example, do you really care? I would never trade that pair. There is no economic connection between hogs and Microsoft, so I must assume that the reported, small ...

8

Theoretically, the answer to the question is yes, a correlation matrix for potential pairs trades can be computed in $O\left((n^2t)^{(\omega+\epsilon)/3}\right)$ time, for any $\epsilon > 0$, where $\omega < 2.38$ is the so-called exponent of matrix multiplication. However, these algorithms have a reputation for having a very large constant factor ...

8

You can find an exact algorithm with a step-by-step explanation here: https://www.dropbox.com/s/t4fq067kzx26mhw/project_paper.pdf As you can see from the URL it is an archived document because the original site is unfortunately long gone and the tool referenced in the paper with it :-( But it should be helpful anyway to understand what is going on. Notice ...

8

The following link has a good summary of a typical pair trading strategy: https://www.quantstart.com/articles/Backtesting-An-Intraday-Mean-Reversion-Pairs-Strategy-Between-SPY-And-IWM It actually has full python code as well. It doesn't include a cointegration check though. Edit: if X and Y are cointegrated: calculate Beta between X and Y ...

7

I have worked with C++, java and C# implementing a google like search engine for DOD in along with many other software that require high performance (using low level memory mapping and named pipes/tcp). In my experience, you cannot match speed of C++ with the managed code. In managed code, every call to new, garbage collection, etc. requires multiple ...

7

I know (by word of mouth from persons directly involved, so cannot back it up with a reference) that Goldman Sachs uses Java a lot in algo trading.

7

I just made a Genetic Algorithms calculator you can try at http://www.gregthatcher.com/Stocks/GeneticAlgorithmCalculator.aspx I'm not a "quant expert" like all of you (I'm just a programmer), but here is what I've found. 1.) If you set the constraints up correctly, the results are amazing. e.g. you can get portfolios that have very high return and low risk....

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

I think you might find this answer in The future language of quant programming? useful. People get this problem wrong because they always end up discussing the theoretical advantages of these languages rather than the practical uses of these languages. Theoretically speaking: Haskell is elegant and has many of the theoretical advantages (language ...

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 ...

5

All things being equal, stocks with the highest bid-ask spread present the greatest opportunity for the market maker The size of the opportunity (i.e. revenue expectation) can be represented as Volume * Bid-Ask Spread. Your algorithm should rank-order that revenue expectation Stocks with high current market values will tend to have narrower spreads and be ...

5

One idea is Dynamic time warping (DTW). There is an R package for that: dtw Here is the vignette:Computing and Visualizing Dynamic Time Warping Alignments in R: The dtw Packageby Toni Giorgino And here is an example from Systematic Investor with full code: Time Series Matching with Dynamic Time Warping

5

I won't give you the answer delivered on a silver platter but hopefully the following will get your started: a) you need to define exactly over which look-back period you aim to derive the maximum drawdown. b) you need to keep track of the max price while you iterate the look-back window. c) you need to keep track of the min price SUBSEQUENT to any NEW ...

5

Hello people. This is quite a complex problem if you want to solve this in a computationally efficient way for a rolling window. I have gone ahead and written a solution to this in C#. I want to share this as the effort required to replicate this work is quite high. First, here are the results: here we take a simple drawdown implementation and re-...

5

SOLUTION: Let $r_t$ be the log-return at time $t$, and $\hat{r}_t$ be the predicted log-return from the regression model. Initialize $loglik(0:T)=0$,$\epsilon_1=0$, $\sigma_1 = 0$, $\mu=U(0,1)*0.0001,\phi=U(0,1)*0.01$, $\alpha_0=U(0,1)*0.00002,\alpha_1=U(0,1)*0.01,\beta_1=0.9 + U(0,1)* 0.01$, $B=10,000$ For $b$ = 1 to $B$ $\quad$ For $t$ = 2 to $T$: \$ \...

5

I would create categories, and work on risk parity among the categories. Otherwise, variance is not really a good measure of downside risk: Change your risk measure, use a rolling window historical VaR or Expected Shortfall at some horizon that matches your investment style. downside semi-variance could do the trick too if don't want to change your algo ...

5

Unfortunately, there is no correct answer for this question, it's like what car you should drive on your weekend. C++ is a popular language in quantitative finance, but it's usually (but not always!) only used to build the application backbone, such as derivative pricing. Why C++? C++ is a good choice because C++ is platform independent, we can natively ...

4

You question is quite strange: so you do not want to use methods inspired by bioinfo and genetics (neural networks, GA, geometry of folding, etc) but methods that are used in these fields? In terms of modeling, the problematics in bioinfo and genetics are mainly: tree or graph matching (to build metrics in the space of molecules), like in SIGMA: a Set-...

4

I am not sure they are comparable as they serve for slightly different purposes. In robotics (specifically vision), Hough Transform is used for objects (or shape) detection. This can subsequently be used for objects tracking, but Hough Transform has no prediction phase. On the other hand, Kalman Filter is a two phase algorithm; measure and predict. With ...

4

Perhaps construct a Brownian Bridge between the day's open and close, then scale it according to the day's high and low.

4

You might want to check out the book Evidence Based Technical Analysis by David Aronson. In it he applies statistical techniques to determine whether certain time series patterns have any predictive power. It's an interesting read and should equip you with some ideas on how to differentiate between folklore and statistical rigor. It also gives you ample ...

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