# Tag Info

## Hot answers tagged performance

30

Column-oriented storage is faster for reading because of the cache efficiency. Looking at your sample query: select price, time from data where symbol = `AAPL Here I'm concerned with three columns: price, time, and symbol. If all ticks were stored by row, the database would have to read through all rows just to search for the symbols. It would look like ...

19

I have long hungered for the ultimate, super-fast, super-scaleable data storage solution. I have used relational databases, kdb, flatfiles, and binary files. In the end, I used binary files in my research language of choice. My advice is to KISS. The choice of storage is actually not that critical (unless maybe you're working with options tick data). ...

12

Personally I make a distinction between two conflicting goals: (1) storing data incoming in real-time for immediate processing and (2) storing the gathered data for "offline" purposes. Such approach makes things a lot easier if we're talking about a home-grown solution. (1) must be as fast as possible but not necessarily scalable beyond a few dozen millions ...

9

Here are couple references. Especially the first link to Andy Lo's paper contains a list of Sharpe ratios of popular mutual and hedge funds: The Statistics of Sharpe Ratios Dow Jones Credit Suisse Hedge Fund Index Hedge Fund Performance and Generalized Sharpe Ratios I would go with the first paper.

9

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 ...

8

Perform a returns analysis by regressing the returns of your composite strategy on the returns of the component strategies. Constrain the beta coefficients to sum to 100% and bound them from 0 to 1. You will then have the % explained by each component.

8

An index is just an abstract concept and does not hold securities. Hence no source of revenue from lending them. A portfolio mirroring an index holds the securities and can in fact generate revenue by loaning the securities to others wanting to short the stocks. This provides a positive bias. That is often offset by a negative bias when the index ...

7

The answer your are looking for might be the story in "Benchmarking Measures of Investment Performance with Perfect-Foresight and Bankrupt Asset Allocation Strategies", by Grauer (Journal of Portfolio Management). While this work main concerns are the differential ranking of various performance measures and with negative betas for market timing strategies, ...

7

I have been using FastBit for a while now and find it to be quite performant. It's very non-intrusive to your existing binary storage format provided your data is stored in a columnar manner. I have briefly tested Tokyo/KyotoCabinet and didnt find it suitable for my (persistent storage) requirements.

6

In my mind, there are two questions here: 1) How does DB make money given a zero expense ratio? This is covered by Dirk and Lliane. Basically, DB gets cheap funding and stock loan fees in return for paying marketing / index / hedging costs. The ETF investor gets zero expense ratio in return for taking DB credit risk. 2) Why does it look like the etf ...

6

Are there any other mechanisms at play here which might explain this kind of tracking error? Dirk is right, you often lend the titles internally or not, etc. You can also write calls for your index, this is not orthodox, but it's ETF, there is no orthodoxy there... Edit : With the graph and given the outperforming is seasonnal (around May), I think we ...

6

This depends a little bit on your definition of volatility arbitrage but in general what is meant is a strategy that takes advantage of the difference between implied volatility and realized volatility. Normally you receive implied variance and pay realized variance. This strategy is the classical example of picking up nickles in front of a steamroller ...

6

Coming from an HPC background myself, I know too well the feeling of owning a hammer and yet having no nail. Your question is about computational bottlenecks that can be relieved with GPGPU, though I'm afraid to admit that there aren't many in finance. For realtime applications, the network is the bottleneck; for historical applications, the memory is the ...

5

Thanks gappy for your precise response. However the answer to this auto-correlation is much more important than an academic discussion of which portfolio performance ratio is best. Auto-correlation distorts max draw-down calculations raising the question of whether the (positive) auto-correlation will continue in the future producing large draw-downs, or ...

5

Your example shows a fundamental ignorance of how hedge funds operate: Hedge funds cannot advertise and are limited to 499 investors. Given these restrictions plus the capital requirements to hold positions overnight, it is a virtual guarantee that a fund would not take an investment of $10K. Hedge funds are usually LPs, which means that the GP (the asset ... 4 As you mention neural network, in general, you may like to look further into various machine learning techniques. On that side, Quant Guy also mentioned ensemble learning which is the general term to combine different learning models. I'd like to elaborate on this point a bit further: In machine learning, traditional ways to combine models are simple ... 4 I would even stick to the original paper by Sharpe (1966): Mutual Fund Performance. The Journal of Business Vol. 39, No. 1, Part 2 pp.119--138 If you look at the numbers on Page 6 you can see that the funds sharpe ratios roughly are between$0$and$1$. Since the Sharpe ratio already adjusts for the risk-free rate, you cannot really argue about its ... 3 Perhaps check out Poti and Levich (2009), or in a different setting but from one of the same authors, Poti and Wang (2010) "The coskewness puzzle" in JBF. They directly address the issue of what level of SR is plausible. 3 Pardon the lack of an actual link, and the formatting, but in footnote 6 of "Alpha is Volatility times IC times Score", Grinold, Richard C., Journal of Portfolio Management, Summer 1994 v20 n4 p9(8), Grinold suggests that "a truly outstanding manager" might have an information ratio of 1.33: (6) A rough guideline for determining the required IC comes from ... 3 Have you considered a Monte Carlo simulation on your returns? Then you could look at the distribution of Maximum Drawdowns. 3 Whatever method you use, I recommend you test your implementation with Monte Carlo simulations as well as real data (although doing the latter subjects you to data mining bias, it can give a sanity check on your Monte Carlo simulations.) For most instances of multiple algorithms, the returns streams will not be independent, and you should take this into ... 2 Have you considered the HDF5 data model? Edit for Louis : Why using HDF5 ? As stated in the HFDF short description page : HDF5 is a unique technology suite that makes possible the management of extremely large and complex data collections. HDF5 is a suitable solution when dealing with very large datasets and you need performance. Again, as ... 2 I have become a fan of SQLite. It's a very lightweight SQL database, which you can use as an intermediate solution. I agree with Rich C that the best thing to do is probably come up with a custom solution that is optimal for your needs. Using SQLite as persistent storage, and loading the data in memory when you want to do intensive computations on it seems ... 2 In exotics options pricing, there are lots of CPU bottlenecks -- for example the calculation of Fast Fourier Transform or Monte Carlo simulation. When I price a range accrual in Libor Market Model, I don't use a lot of data (carefully optimized, everything should fit in a few MB of L2 cache), but I do a lot of calculations. This is where, I think, a GPU may ... 2 This is a very common and serious problem among academic papers and with some hedge fund marketing material's, I can almost guarantee that the high ratio of 7 was with-out transaction cost's and that when included this 7 will drop down some where between 0 and 1. 2 If you do step 1 and step 2 every day, then you indeed assume that you rebalance the strategy every day. If you want to assume differently, for example monthly, you need to first compound the returns for each asset separately during the whole month and then do a weighted sum of the compounded returns using the weights of each asset at the beginning of the ... 2 We actually managed to come up with the answer to this question ourselves but wanted to share the answer since it might be relevant to others as well. The calculation depends on what method is used to calculate the cost. There is the FIFO, LIFO and the average cost method, see: http://www.accounting-basics-for-students.com/fifo-method.html If FIFO or LIFO ... 1 You actually need to consider a 0 return on the periods with no holdings (during that period volatility is 0 and you have a negative return due to the opportunity cost of not holding risk free debt). From that you can compute your daily sharpe ratio and then multiply by$252^{0.5}\$ as you mention.

1

Practically, the best metric is the one your boss wants you to use. Alternately, you can think of the return of the desk like a leveraged security, as described here. This would suggest that the daily performance would be calculated as the profit divided by the basis. If you want to express the return in terms of the capital requirement, then that's one ...

1

Can we make the assumption that the amount of each dividend is directly correlated to the amount of time between dividends? This would be the case if there were a fixed dividend payout ratio, or whatever the equivalent vocabulary is for this instrument. If so, you could annualize each dividend by the number of days since the previous dividend. The reason ...

Only top voted, non community-wiki answers of a minimum length are eligible