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37

Here are a few risks when using historical data: Data fidelity: Is your data an accurate reflection of history? For stocks, should you use actual closing prices or adjusted prices? For futures, how should you construct a realistic, continuous contract? Simulation realism: Are you making realistic assumptions about trade execution? Are you naively assuming, ...


27

Consider the standard error, and in particular the distance between the upper and lower limits: \begin{equation} \Delta = (\bar{x} + SE \cdot \alpha) - (\bar{x} - SE \cdot \alpha) = 2 \cdot SE \cdot \alpha \end{equation} Using the formula for standard error, we can solve for sample size: \begin{equation} n = \left(\frac{2 \cdot s \cdot ...


21

Strictly speaking, data snooping is not the same as in-sample vs out-of-sample model selection and testing, but has to deal with sequential or multiple tests of hypothesis based on the same data set. To quote Halbert White: Data snooping occurs when a given set of data is used more than once for purposes of inference or model selection. When such ...


18

Building an effective backtest is not significantly different than building any other kind of predictive model. The goal is to have similar behavior out of sample as you have in sample. As such, there are methodologies developed in statistics and machine learning that can be useful: Understand the bias/variance tradeoff. This is covered in many places. ...


15

DTN's IQFeed is really good, if a little expensive. I believe it starts at 80 dollars/month and then you add your exchange fees on top. To get access to the developer API you need to pay 300 dollars for a year's worth of access. Details: Real-Time, TRUE Tick-by-Tick Data on US and Canadian Equities (NYSE, NASDAQ, AMEX, Canadian Stock Exchanges) Delayed ...


15

I unfortunately can't point you to a great book on the exact subject that you're describing. The closest thing for beginners is "Quantitative Trading". It's a reasonable introduction, but I really wouldn't recommend it as a primary source. The author is at best incomplete (if not misleading) on a number of issues. My favorite book at the moment is ...


13

Accuracy The trader must make sure the data is not only right, but that the timestamps are useable. That's why a good data warehouse will be bitemporal or point-in-time. Thus, we know not only when the item was announced, but when we received it and could act on it. Gaps An aggressive safety check on incoming data might inadvertently exclude correct data. ...


12

A few pointers: When I looked into this a few years ago, a good solution at the time was LIM's XMIM, which also has an S-Plus/Matlab interface. Whit Armstrong also provided an R package for this, although I don't know how complete it is. This provides both the data and the software for analysis. On the very high end (and expensive) side of the spectrum, ...


12

For "maximum pessimism" you should calculate thus: for longs - enter at the bar high and exit at the bar low on bar following signal bar for shorts - enter at the bar low and exit at the bar high on bar following signal bar I had previously heard this approach to back testing called the "torture test."


12

I did some digging and found the following papers - most of them offering quite a distinct perspective compared to classical option pricing theory! Stock Options as Lotteries by Brian H. Boyer et al. (2011) The Efficiency of the Buy-Write Strategy: Evidence from Australia by Tafadzwa Mugwagwa et al. (2010) The following is my favorite: You could do some ...


12

You're not really asking how to backtest a strategy. You already have run a backtest to generate simulated trades. What you're asking for is a way to assess the performance of those simulated trades. You can do this with the R package blotter. You'll need to setup your account and portfolio, then loop over each row in your CSV and call addTxn. For ...


11

Knowledge leads to profit BUT NOT the reverse YOU are the biggest risk to the process. All the hopes, wishes and bias you come with get in the way of making good decisions. The more you want something to be true, the more you have to kick the tires. So many people try out a bunch of random stuff, find a pattern that has a notional profit and just get ...


10

I'll not say how most people do it, but rather how I think most people should do it. You should compare the actual strategy with a number of goes of randomly trading through the time period using the same constraints as the strategy. Basically this is a way of not mixing species of fruit and seeing what the distribution of luck is for the particular fruit ...


10

I find this one very helpful: Re-Examining the Hidden Costs of the Stop-Loss by Wilson Ma, Guy Morita, Kira Detko Abstract: In this paper, we present general implications of the impact of stop-losses to future returns. The use of stop-losses change return distributions, but not in the way that one would typically expect. We find that while ...


9

I think the biggest risk is trusting your model too much. I would summarize modelling like that: Model for the best but risk-manage for the worst! As an example for modelling a portfolio approach with derivatives that could e.g. mean: use black scholes for option pricing (model) but manage your risk by assuming a power law distribution and vary your alpha ...


8

I have seen Hansen's SPA ('Superior Predictive Ability') test and stepwise variants used for this purpose. Hansen's test is a Studentized version of White's Reality Check. The stepwise variants allow one to accept or reject the null of no predictive ability on a subset of some tested strategies while maintaining a familywise error rate. In his book, ...


8

Some LinkedIn groups are particularily adequate to post these questions. Your question has already been asked in "Automated Trading Strategies" at this URL: Seeking input on QuantFactory, Deltix and 4thStory: Professional end-to-end Automated Trading Solutions. Feel free to let us know the state of your research.


8

This is an evergreen. I've been discussing this with many people - without any clear-cut conclusion. The answer and the preferred solution depend on your trading style (e.g. frequency), your skills, the size of the team, and many other factors. For simplicity, I call "Research" the Matlab/R/etc. environments, whereas "Live" refers to the re-programmed ...


8

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 ...


8

You can find everything you want to know about this here (and in a very readable and easily reproducible form): How Students Can Backtest Madoff’s Claims by Michael J. Stutzer (2009) From the abstract: Markopolos’ writings neither described nor included any specific backtests of the strike conversion strategy. Fortunately, a backtest is relatively ...


7

The website www.infochimps.com has a lot of unusual datasets, many of which are free.


7

This blog post points to a presentation about backtesting and data snooping: http://www.portfolioprobe.com/2010/11/05/backtesting-almost-wordless/ I think the only non-datasnooping method there is is to trade live. But the problem of data snooping can be reduced by seeing how significant the backtest result is compared to what would have happened if the ...


7

The output of your model will be a realization of your assumptions. Shane's given you a great answer. Besides doing out of sample testing (i.e., calibrating on period X then testing in period Y only using info available at the time of each trade), I would add that you should test it in sub-periods. If you have a big chunk of data, break it up and see how it ...


7

You can download data from 32 forex pairs from the Dukascopy's JForex platform, tick by tick since 2003. I think it is very accurate relative to its price (free). You can download their different formats by starting here: (no registration required).


7

Slippage is multi-facetted, however, I think the main element to slippage is going to depend on the sophistication of your execution approach. Also, in your case there are 2 types of slippage: execution slippage (i.e. cost above mid to get your fill) tracking slippage (how much price difference between actual close and your fill price) Execution ...


7

Glad people are reading. Simple with more history in terms of time and indexes is better in my book. I have spent 13 years reading over 200 research papers, incorporating complicated and advanced techniques, and studying very reputable buy side research with no improvement in results. Readers are on their own to extend to lots of markets including Nikkei ...


7

Interestingly enough there is no scientific theory that suggests what fraction of the data should be assigned to training and testing and results can be very sensitive to these choices. From Quantitative Trading by Ernest Chan (p. 53-54): Out-of-Sample Testing Divide your historical data into two parts. Save the second (more recent) part of the data ...


7

There is a huge difference between R (and Matlab, SAS, or other statistical languages) and relatively low-level languages such as C/C++/C#/Java in exactly this regard. The latter category is used more often for stable end-products, where speed and performance can be crucial, whereas the former category is used more often for model testing and prototyping. ...


7

High VIX arguably leads to less predictability of the market factor (i.e. market timing), but high volatility does lead to greater predictability of the cross-section of returns. Indeed, linear risk factor models have higher explanatory power during bear markets. However, your goal is to build a better market timing model where the forecasts (and perhaps ...


6

http://ratedata.gaincapital.com/ has tick by tick historical data for Forex if that helps.



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