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Vi er helt uavhengige. Vi selger ikke dine data. Vi sammenligner ikke bare selskaper som betaler oss. Vi sjekker ut hvert selskap vi lister. We er et team av penger eksperter. Handel med Bollinger Bands R. Det er to forhold vi ser etter i en handelsmulighet. Markedet handlet ned til det nedre Bollinger Band i hver av de tilfellene som er notert i boksene.

SMA av 50 dager dager dager W ell, denne algoritmen tar den muligheten for overfitting ut av hendene. I dette innzoomsbildet kan vi se at algoritmen s estimater virker ganske hoppete. Min linkinprofil finner du her. Systemet som er detaljert i boken, har fordeler fra nestet rangering langs aksen X, ta den topp decile, ranger langs aksen Y innenfor toppdekilen i X og ta toppdekilet langs akse Y, i det vesentlige begrenser valget til 1 av universet.

I alle fall, her er linken. I utgangspunktet er del 1 for uinitiert. Derfor tar toppdekilet av topp decile deg med 1 av universet. Min kritikk av boken er imidlertid dette. Jeg tror faktisk at denne boken er veldig omfattende. Med resultatet. Ikke akkurat oppmuntrende, men datatiden er ikke dyrt i disse dager. Det er, uten lookahead-forstyrrelsen, er tilstandsforutsigelsesalgoritmen forferdelig.

Hvorfor er det. Vel, her er statens plot. NOTE 5. Min linkinprofil kan bli funnet her. For de som er interessert i en grundig analyse av intuisjonen av komponentbetinget verdi i fare, refererer jeg dem til papir skrevet av Brian Peterson, Peter Carl og Kris Boudt. Det er ingen strategi her, bare en demonstrasjon av syntaks.

I hvert fall var dette tilfelle over dagens datasett. Det henter dataene for deg vanligvis fra Yahoo, men en stor takk til hr. For eksempel, her er noen proxies. Her er den grunnleggende oppsettkoden. Dette er hva slike transaksjoner ser ut til denne strategien. Det ser ut som dette. Dette er langt fra ubetydelig.

Slik beregner du faktisk omsetning og transaksjonskostnader. I dette tilfellet er transaksjonen cost model was very simple However, given that returns transactions at the individual asset level, one could get as complex as they would like with modeling the transaction costs. Thanks for reading.

This post will outline an easy-to-make mistake in writing vectorized backtests namely in using a signal obtained at the end of a period to enter or exit a position in that same period The difference in results one obtains is massive.

Today, I saw two separate posts from Alpha Architect and Mike Harris both referencing a paper by Valeriy Zakamulin on the fact that some previous trend-following research by Glabadanidis was done with shoddy results, and that Glabadanidis s results were only reproducible through instituting lookahead bias. The following code shows how to reproduce this lookahead bias. First, the setup of a basic moving average strategy on the S P index from as far back as Yahoo data will provide.

And here is how to institute the lookahead bias. These are the results. Of course, this equity curve is of no use, so here s one in log scale. As can be seen, lookahead bias makes a massive difference. Here are the numeric al results. Again, absolutely ridiculous. Note that when using the function in PerformanceAnalytics , that package will automatically give you the next period s return, instead of the current one, for your weights However, for those writing simple backtests that can be quickly done using vectorized operations, an off-by-one error can make all the difference between a backtest in the realm of reasonable, and pure nonsense However, should one wish to test for said nonsense when faced with impossible-to-replicate results, the mechanics demonstrated above are the way to do it.

Now, onto other news I d like to thank Gerald M for staying on top of one of the Logical Invest strategies namely, their simple global market rotation strategy outlined in an article from an earlier blog post. Up until March the date of the blog post , the strategy had performed well However, after said date. It has been a complete disaster, which, in hindsight, was evident when I passed it through the hypothesis-dri ven development framework process I wrote about earlier. So, while there has been a great deal written about not simply throwing away a strategy because of short-term underperformance, and that anomalies such as momentum and value exist because of career risk due to said short-term underperformance, it s never a good thing when a strategy creates historically large losses, particularly after being published in such a humble corner of the quantitative financial world.

In any case, this was a post demonstrating some mechanics, and an update on a strategy I blogged about not too long ago. NOTE I am always interested in hearing about new opportunities which may benefit from my expertise, and am always happy to network You can find my LinkedIn profile here. This post will shed light on the values of R 2s behind two rather simplistic strategies the simple 10 month SMA, and its relative, the 10 month momentum which is simply a difference of SMAs, as Alpha Architect showed in th eir book DIY Financial Advisor.

Not too long ago, a friend of mine named Josh asked me a question regarding R 2s in finance He s finishing up his PhD in statistics at Stanford, so when people like that ask me questions, I d like to answer them His assertion is that in some instances, models that have less than perfect predictive power EG R 2s of 4, for instance , can still deliver very promising predictions, and that if someone were to have a financial model that was able to explain 40 of the variance of returns, they could happily retire with that model making them very wealthy Indeed 4 is a very optimistic outlook to put it lightly , as this post will show.

In order to illustrate this example, I took two staple strategies buy SPY when its closing monthly price is above its ten month simple moving average, and when its ten month momentum basically the difference of a ten month moving average and its lag is positive While these models are simplistic, they are ubiquitously talked about, a nd many momentum strategies are an improvement upon these baseline, out-of-the-box strategies.

Here s the code to do that. And here are the results. In short, the SMA10 and the month momentum aka ROC 10 aka MOM10 both handily outperform the buy and hold, not only in absolute returns, but especially in risk-adjusted returns Sharpe and Calmar ratios Again, simplistic analysis, and many models get much more sophisticated than this, but once again, simple, illustrative example using two strategies that outperform a benchmark over the long term, anyway.

Now, the question is, what was the R 2 of these models To answer this, I took a rolling five-year window that essentially asked how well did these quantities the ratio between the closing price and the moving average 1, or the ten month momentum predict the next month s returns That is, what proportion of the variance is explained through the monthly returns regressed against the previous month s signals in numerical form perhaps not the best framing, as the signal is binary as opposed to continuous which is what is being regressed, but let s set that aside, again, for the sake of illustration.

Here s the code to generate the answer. And the answer, in pictorial form. This review will review the Adaptive Asset Allocation Dynamic Global Portfolios to Profit in Good Times and Bad book by the people at ReSolve Asset Management Overall, this book is a definite must-read for those who have never been exposed to the ideas within it However, when it comes to a solution that can be fully replicated, this book is lacking.

Okay, it s been a while since I reviewed my last book, DIY Financial Advisor from the awesome people at Alpha Architect This book in my opinion, is set up in a similar sort of format. This is the structure of the book, and my reviews along with it. Part 5 some more formal research on topics already covered in the rest of the book namely a section about how many independent bets one can take as the number of assets grow, if I remember it correctly Long story short You easily get the most bang for your buck among disparate asset classes, such as treasuries of various duration, commodities, developed vs emerging equities, and so on, as opposed to trying to pick among stocks in the same asset class though there s some potential for alpha there just a lot less than you imagine So in case the idea of asset class selection, not stock selection wasn t beaten into the reader s head before this point, this part should do the trick The other research paper is something I briefly skimmed over which went into more depth about volatility and retirement portfolios, though I felt that the book covered this topic earlier on to a sufficient degree by building up the intuition using very understandable scenarios.

So that s the review of the book Overall, it s a very solid piece of writing, and as far as establishing the why , it does an absolutely superb job For those that aren t familiar with the concepts in this book, this is definitely a must-read, and ASAP. However, for those familiar with most of the concepts and looking for a detailed how procedure, this book does not deliver as much as I would have liked And I realize that while it s a bad idea to publish secret sauce, I bought this book in the hope of being exposed to a new algorithm presented in the understandable and intuitive language that the rest of the book was written in, and was left wanting.

Still, that by no means diminishes the impact of the rest of the book For those who are more likely to be its target audience, it s a 5 5 For those that wanted some specifics, it still has its gem on universe construction. Overall, I rate it a 4 5.

Happy new year This post will be a quick one covering the relationship between the simple moving average and time series momentum The implication is that one can potentially derive better time series momentum indicators than the classical one applied in so many papers.

Okay, so the main idea for this post is quite simple. I m sure we re all familiar with classical momentum That is, the price now compared to the price however long ago 3 months, 10 months, 12 months, etc E G P now P 10 And I m sure everyone is familiar with the simple moving average indicator, as well E G SMA Well, as it turns out, these two quantities are actually related. It turns out, if instead of expressing momentum as the difference of two numbers, it is expressed as the sum of returns, it can be written for a 10 month momentum as.

MOM10 return of this month return of last month return of 2 months ago return of 9 months ago, for a total of 10 months in our little example. In other words, momentum aka the difference between two prices, can be rewritten as the difference between two cumulative sums of prices And what is a simple moving average Simply a cumulative sum of prices divided by however many prices summed over.

Here s some R code to demonstrate. With the resulting number of times these two signals are equal. In short, every time. Now, what exactly is the punchline of this little example Here s the punchline. The simple moving average is fairly simplistic as far as filters go It works as a pedagogical example, but it has some well known weaknesses regarding lag, windowing effects, and so on. Here s a toy example how one can get a different momentum signal by changing the filter.

With the following results. While the difference of EMA10 strategy didn t do better than the difference of SMA10 aka standard month momentum , that s not the point The point is that the momentum signal is derived from a simple moving average filter, and that by using a different filter, one can still use a momentum type of strategy.

Or, put differently, the main general takeaway here is that momentum is the slope of a filter, and one can compute momentum in an infinite number of ways depending on the filter used, and can come up with a myriad of different momentum strategies. Post navigation. Category Archives Trading. This post will introduce John Ehlers s Autocorrelation Periodogram mechanism a mechanism designed to dynamically find a lookback period That is, the most common parameter optimized in backtests is the lookback period.

Before beginning this post, I must give credit where it s due, to one Mr Fabrizio Maccallini the head of structured derivatives at Nordea Markets in London You can find the rest of the repository he did for Dr John Ehlers s Cycle Analytics for Traders on his github I am grateful and honored that such intelligent and experienced individuals are helping to bring some of Dr Ehlers s methods into R.

The point of the Ehlers Autocorrelation Periodogram is to dynamically set a period between a minimum and a maximum period length While I leave the exact explanation of the mechanic to Dr Ehlers s book, for all practical intents and purposes, in my opinion, the punchline of this method is to attempt to remove a massive source of overfitting from trading system creation namely specifying a lookback period. SMA of 50 days days days Well, this algorithm takes that possibility of overfitting out of your hands Simply, specify an upper and lower bound for your lookback, and it does the rest How well it does it is a topic of discussion for those well-versed in the methodologies of electrical engineering I m not , so feel free to leave comments that discuss how well the algorithm does its job, and feel free to blog about it as well.

In any case, here s the orig inal algorithm code, courtesy of Mr Maccallini. One thing I do notice is that this code uses a loop that says for i in 1 length filt , which is an O data points loop, which I view as the plague in R While I ve used Rcpp before, it s been for only the most basic of loops, so this is definitely a place where the algorithm can stand to be improved with Rcpp due to R s inherent poor looping. Those interested in the exact logic of the algorithm will, once again, find it in John Ehlers s Cycle Analytics For Traders book see link earlier in the post.

Of course, the first thing to do is to test how well the algorithm does what it purports to do, which is to dictate the lookback period of an algorithm. Let s run it on some data. Now, what does the algorithm-set lookback period look like.

Let s zoom in on through , when the markets went through some upheaval. In this zoomed-in image, we can see that the algorithm s estimates seem fairly jumpy. Here s some code to feed the algorithm s estimates of n into an indicator to compute an indicator with a dynamic lookback period as set by Ehlers s autocorrelation periodogram.

And here is the function applied with an SMA, to tune between and days. As seen, this algorithm is less consistent than I would like, at least when it comes to using a simple moving average. For now, I m going to leave this code here, and let people experiment with it I hope that someone will find that this indicator is helpful to them.

NOTES I am always interested in networking meet-ups in the northeast Philadelphia NYC Furthermore, if you believe your firm will benefit from my skills, please do not hesitate to reach out to me My linkedin profile can be found here. Lastly, I am volunteering to curate the R section for books on quantocracy If you have a book about R that can apply to finance, be sure to let me know about it, so that I can review it and possibly recommend it Thakn you.

This post will be about attempting to use the Depmix pack age for online state prediction While the depmix package performs admirably when it comes to describing the states of the past, when used for one-step-ahead prediction, under the assumption that tomorrow s state will be identical to today s, the hidden markov model process found within the package does not perform to expectations.

So, to start off, this post was motivated by Michael Halls-Moore, who recently posted some R code about using the depmixS4 library to use hidden markov models Generally, I am loath to create posts on topics I don t feel I have an absolutely front-to-back understanding of, but I m doing this in the hope of learning from others on how to appropriately do online state-space prediction, or regime switching detection, as it may be called in more financial parlance.

While I ve seen the usual theory of hidden markov models that is, it can rain or it can be sunny, but you can only infer the weather judging by the clothes you see people wearing outside your window when you wake up , and have worked with toy examples in MOOCs Udacity s self-driving car course deals with them, if I recall correctly or maybe it was the AI course , at the end of the day, theory is only as good as how well an implementation can work on real data.

For this experiment, I decided to take SPY data since inception, and do a full in-sample backtest on the data That is, given that the HMM algorithm from depmix sees the whole history of returns, with this god s eye view of the data, does the algorithm correctly classify the regimes, if the backtest results are any indication. Here s the code to do so, inspired by Dr Halls-Moore s.

Essentially, while I did select three states, I noted that anything with an intercept above zero is a bull state, and below zero is a bear state, so essentially, it reduces to two states. With the result. So, not particularly terrible The algorithm works, kind of, sort of, right. Well, let s try online prediction now. So what I did here was I took an expanding window, starting from days since SPY s inception, and kept increasing it, by one day at a time My prediction, was, trivially enough, the most recent day, using a 1 for a bull state, and a -1 for a bear state I ran this process in parallel on a linux cluster, because windows s doParallel library seems to not even know that certain packages are loaded, and it s more messy , and the first big issue is that this process took about three hours on seven cores for about 23 years of data Not exactly encouraging, but computing time isn t expensive these days.

So let s see if this process actually works. First, let s test if the algorithm does what it s actually supposed to do and use one day of look-ahead bias that is, the algorithm tells us the state at the end of the day how correct is it even for that day. So, allegedly, the algorithm seems to do what it was designed to do, which is to classify a state for a given data set Now, the most pertinent question how well do these predictions do even one day ahead You d think that state space predictions would be parsimonious from day to day, given the long history, correct.

That is, without the lookahead bias, the state space prediction algorithm is atrocious Why is that. Well, here s the plot of the states. In short, the online hmm algorithm in the depmix package seems to change its mind very easily, with obvious negative implications for actual trading strategies. So, that wraps it up for this post Essentially, the main message here is this there s a vast difference between loading doing descriptive analysis AKA where have you been, why did things happen vs predictive analysis that is, if I correctly predict the future, I get a positive payoff In my opinion, while descriptive statistics have their purpose in terms of explaining why a strategy may have performed how it did, ultimately, we re always looking for better prediction tools In this case, depmix, at least in this out-of-the-box demonstrati on does not seem to be the tool for that.

If anyone has had success with using depmix or other regime-switching algorithm in R for prediction, I would love to see work that details the procedure taken, as it s an area I m looking to expand my toolbox into, but don t have any particular good leads Essentially, I d like to think of this post as me describing my own experiences with the package.

NOTE My current analytics contract is up for review at the end of the year, so I am officially looking for other offers as well If you have a full-time role which may benefit from the skills you see on my blog, please get in touch with me My linkedin profile can be found here. This post will demonstrate how to take into account turnover when dealing with returns-based data using PerformanceAnalytics and the function in R It will demonstrate this on a basic strategy on the nine sector SPDRs.

So, first off, this is in response to a question posed by one Robert Wages on the R-SIG-Finance mailing list While there are many individuals out there with a plethora of questions many of which can be found to be demonstrated on this blog already , occasionally, there will be an industry veteran, a PhD statistics student from Stanford, or other very intelligent individual that will ask a question on a topic that I haven t yet touched on this blog, which will prompt a post to demonstrate another technical aspect found in R This is one of those times.

So, this demonstration will be about computing turnover in returns space using the PerformanceAnalytics package Simply, outside of the PortfolioAnalytics package, PerformanceAnalytics with its function is the go-to R package for portfolio management simulations, as it can take a set of weights, a set of returns, and generate a set of portfolio returns for analysis with the rest of PerformanceAnalytics s fun ctions.

Again, the strategy is this take the 9 three-letter sector SPDRs since there are four-letter ETFs now , and at the end of every month, if the adjusted price is above its day moving average, invest into it Normalize across all invested sectors that is, 1 9th if invested into all 9, into 1 if only 1 invested into, cash, denoted with a zero return vector, if no sectors are invested into It s a simple, toy strategy, as the strategy isn t the point of the demonstration. Here s the basic setup code.

So, get the SPDRs, put them together, compute their returns, generate the signal, and create the zero vector, since treats weights less than 1 as a withdrawal, and weights above 1 as the addition of more capital big FYI here. Now, here s how to compute turnover. The way that turnover is computed is simply the difference between how the day s return moves the allocated portfolio from its previous ending point to where that portfolio actually stands at the beginning of next period That is, the end of period weight is the beginning of period drift after taking into account the day s drift return for that asset The new beginning of period weight is the end of period weight plus any transacting that would have been done Thus, in order to find the actual transactions or turnover , one subtracts the previous end of period weight from the beginning of period weight.

This is what such transactions look like for this strategy. Something we can do with such data is take a one-year rolling turnover, accomplished with the following code. It looks like this. This essentially means that one year s worth of two-way turnover that is, if selling an entirely invested portfolio is turnover, and buying an entirely new set of assets is another , then two-way turnover is is around at maxim um That may be pretty high for some people.

Now, here s the application when you penalize transaction costs at 20 basis points per percentage point traded that is, it costs 20 cents to transact So, at 20 basis points on transaction costs, that takes about one percent in returns per year out of this admittedly, terrible strategy This is far from negligible. So, that is how you actually compute turnover and transaction costs In this case, the transaction cost model was very simple However, given that returns transactions at the individual asset level, one could get as complex as they would like with modeling the transaction costs.

T oday, I saw two separate posts from Alpha Architect and Mike Harris both referencing a paper by Valeriy Zakamulin on the fact that some previous trend-following research by Glabadanidis was done with shoddy results, and that Glabadanidis s results were only reproducible through instituting lookahead bias.

Here are the numerical results. It has been a complete disaster, which, in hindsight, was evident when I passed it through the hypothesis-driven development framework process I wrote about earlier. So, while there has been a great deal written about not simply throwing away a strategy because of short-term underperformance, and that anomalies such as momentum and value exist because of career risk due to said short-term underperformanc e, it s never a good thing when a strategy creates historically large losses, particularly after being published in such a humble corner of the quantitative financial world.

I m sure we re all familiar with classical momentum That is, the price now compared to the price however long ago 3 months, 10 months, 12 months, etc E G P now P 10 And I m sure everyone is familia r with the simple moving average indicator, as well E G SMA This post will outline a first failed attempt at applying the ensemble filter methodology to try and come up with a weighting process on SPY that should theoretically be a gradual process to shift from conviction between a bull market, a bear market, and anywhere in between This is a follow-up post to this blog post.

So, my thinking went like this in a bull market, as one transitions from responsiveness to smoothness, responsive filters should be higher than smooth filters, and vice versa, as there s generally a trade-off between the two In fact, in my particular formulation, the quantity of the square root of the EMA of squared returns punishes any deviation from a flat line altogether although inspired by Basel s measure of volatility, which is the square root of the day EMA of squared returns , while the responsiveness quantity punishes any deviation from the time series of the real ized prices Whether these are the two best measures of smoothness and responsiveness is a topic I d certainly appreciate feedback on.

In any case, an idea I had on the top of my head was that in addition to having a way of weighing multiple filters by their responsiveness deviation from price action and smoothness deviation from a flat line , that by taking the sums of the sign of the difference between one filter and its neighbor on the responsiveness to smoothness spectrum, provided enough ensemble filters say, , so there are differences , one would obtain a way to move from full conviction of a bull market, to a bear market, to anything in between, and have this be a smooth process that doesn t have schizophrenic swings of conviction.

Here s the code to do this on SPY from inception to And here s the very underwhelming result. Essentially, while I expected to see changes in conviction of maybe 20 at most, instead, my indicator of sum of sign differences did exactly as I had hoped it wouldn t, which is to be a very binary sort of mechanic My intuition was that between an obvious bull market and an obvious bear market that some differences would be positive, some negative, and that they d net each other out, and the conviction would be zero Furthermore, that while any individual crossover is binary, all one hundred signs being either positive or negative would be a more gradual process Apparently, this was not the case To continue this train of thought later, one thing to try would be an all-pairs sign difference Certainly, I don t feel like giving up on this idea at this point, and, as usual, feedback would always be appreciated.

NOTE I am currently consulting in an analytics capacity in downtown Chicago However, I am also looking for collaborators that wish to pursue interesting trading ideas If you feel my skills may be of help to you, let s talk You can email me at or find me on my LinkedIn here. This review will be about Inovance Tech s TRAIDE system It is an application geared towards letting retail investors apply proprietary machine learning algorithms to assist them in creating systematic trading strategies Currently, my one-line review is that while I hope the company founders mean well, the application is still in an early stage, and so, should be checked out by potential users venture capitalists as something with proof of potential, rather than a finished product ready for mass market While this acts as a review, it s also my thoughts as to how Inovance Tech can improve its product.

A bit of background I have spoken several times to some of the company s founders, who sound like individuals at about my age level so, fellow millennials Ultimately, the selling point is this. Systematic trading is cool Machine learning is cool Therefore, applying machine learning to systematic trading is awesome And a surefire way to make profits, as Renaissance Technologies has shown.

While this may sound a bit snarky, it s also , in some ways, true Machine learning has become the talk of the town, from IBM s Watson RenTec itself hired a bunch of speech recognition experts from IBM a couple of decades back , to Stanford s self-driving car invented by Sebastian Thrun, who now heads Udacity , to the Netflix prize, to god knows what Andrew Ng is doing with deep learning at Baidu Considering how well machine learning has done at much more complex tasks than create a half-decent systematic trading algorithm , it shouldn t be too much to ask this powerful field at the intersection of computer science and statistics to help the retail investor glued to watching charts generate a lot more return on his or her investments than through discretionary chart-watching and noise trading To my understanding from conversations with Inovance Tech s founders, this is explicitly their mission.

Here s how it works. Users select one asset at a time, and select a date range data going back to Dec 31, Assets are currently limited to highly liquid currency pairs, and can take the following settings 1 hour, 2 hour, 4 hour, 6 hour, or daily bar time frames. Furthermore, regarding indicator selections, users also specify one parameter setting for each indicator per strategy E G if I had an EMA crossover, I d have to create a new strategy for a 20 crossover, a 21 crossover, rather than specifying something like this.

Quantstrat itself has this functionality, and while I don t recall covering parameter robustness checks optimization in other words, testing multiple parameter sets whether one uses them for optimization or robustness is up to the user, not the functionality in quantstrat on this blog specifically, this information very much exists in what I deem the official quantstrat manual , found here In my opinion, the option of cov ering a range of values is mandatory so as to demonstrate that any given parameter setting is not a random fluke Outside of quantstrat, I have demonstrated this methodology in my Hypothesis Driven Development posts, and in coming up for parameter selection for volatility trading.

After the user selects both a long and a short rule by simply filtering on indicator ranges that TRAIDE s machine learning algorithms have said are good , TRAIDE turns that into a backtest with a long equity curve, short equity curve, total equity curve, and trade statistics for aggregate, long, and short trades For instance, in quantstrat, one only receives aggregate trade statistics Whether long or short, all that matters to quantstrat is whether or not the trade made or lost money For sophisticated users, it s trivial enough to turn one set of rules on or off, but TRAIDE does more to hold the user s hand in that regard.

And that s the process. In my opinion, while what Inovance Tech has set out to do with TRAIDE is interesting, I wouldn t recommend it in its current state For sophisticated individuals that know how to go through a proper research process , TRAIDE is too stringent in terms of parameter settings one at a time , pre-coded indicators its target audience probably can t program too well , and asset classes again, one at a time However, for retail investors, my issue with TRAIDE is this.

There is a whole assortment of undocumented indicators, which then move to black-box machine learning algorithms The result is that the user has very little understanding of what the underlying algorithms actually do, and why the logic he or she is presented with is the output While TRAIDE makes it trivially easy to generate any one given trading system, as multiple individuals have stated in slightly different ways before, writing a strategy is the easy part Doing the work to understand if that strategy actually has an edge is much harder Namely, checking its robustness, its predictive power, its sensitivity to various regimes, and so on Given TRAIDE s rather short data history onwards , and coupled with the opaqueness that the user oper ates under, my analogy would be this.

It s like giving an inexperienced driver the keys to a sports car in a thick fog on a winding road Nobody disputes that a sports car is awesome However, the true burden of the work lies in making sure that the user doesn t wind up smashing into a tree. Overall, I like the TRAIDE application s mission, and I think it may have potential as something for the retail investors that don t intend to learn the ins-and-outs of coding a trading system in R despite me demonstrating many times over how to put such systems together I just think that there needs to be more work put into making sure that the results a user sees are indicative of an edge, rather than open the possibility of highly-flexible machine learning algorithms chasing ghosts in one of the noisiest and most dynamic data sets one can possibly find.

My recommendations are these. If all of these things are accounted for and automated, the product will hopefully accomplish its mission of bringing systematic trading and machine learning to more people I think TRAIDE has potential, and I m hoping that its staff will realize that potential. NOTE I am currently contracting in downtown Chicago, and am always interested in networking with professionals in the systematic trading and systematic asset management allocation spaces Find my LinkedIn here.

This post will demonstrate a method to create an ensemble filter based on a trade-off between smoothness and responsiveness, two properties looked for in a filter An ideal filter would both be responsive to price action so as to not hold incorrect positions, while also be smooth, so as to not incur false signals and unnecessary transaction costs.

So, ever since my volatility trading strategy, using three very naive filters all SMAs completely missed a 27 month in XIV I ve decided to try and improve ways to create better indicators in trend following Now, under the realization that there can potentially be tons of complex filters in existence, I decided instead to focus on a way to create ensemble filters, by using an analogy from statistics machine learning. In static data analysis, for a regression or classification task, there is a trade-off betwe en bias and variance In a nutshell, variance is bad because of the possibility of overfitting on a few irregular observations, and bias is bad because of the possibility of underfitting legitimate data Similarly, with filtering time series, there are similar concerns, except bias is called lag, and variance can be thought of as a whipsawing indicator Essentially, an ideal indicator would move quickly with the data, while at the same time, not possess a myriad of small bumps-and-reverses along the way, which may send false signals to a trading strategy.

So, here s how my simple algorithm works. The inputs to the function are the following. A The time series of the data you re trying to filter B A collection of candidate filters C A period over which to measure smoothness and responsiveness, defined as the square root of the n-day EMA 2 n 1 convention of the following a Responsiveness the squared quantity of price filter 1 b Smoothness the squared quantity of filter t filter t-1 1 aka R s f unction D A conviction factor, to which power the errors will be raised This should probably be between 5 and 3 E A vector that defines the emphasis on smoothness vs emphasis on responsiveness , which should range from 0 to 1.

Here s the code. This gets SPY data, and creates two utility functions xtsApply, which is simply a column-based apply that replaces the original index that using a column-wise apply discards, and sumIsNa, which I use later for counting the numbers of NAs in a given row It also creates my candidate filters, which, to keep things simple, are just SMAs Here s the actual code of the function, with comments in the code itself to better explain the process from a technical level for those still unfamiliar with R, look for the hashtags.

The vast majority of the computational time takes place in the two xtsApply calls On different simple moving averages, the process takes about 30 seconds. Here s the output, using a conviction factor of 2. And here is an example, lo oking at SPY from through In this case, I chose to go from blue to green, orange, brown, maroon, purple, and finally red for smoothness emphasis of 0, 5 , 25 , 50 , 75 , 95 , and 1, respectively. Notice that the blue line is very wiggly, while the red line sometimes barely moves, such as during the drop-off.

One thing that I noticed in the course of putting this process together is something that eluded me earlier namely, that naive trend-following strategies which are either fully long or fully short based on a crossover signal can lose money quickly in sideways markets.

However, theoretically, by finely varying the jumps between 0 to emphasis on smoothness, whether in steps of 1 or finer, one can have a sort of continuous conviction, by simply adding up the signs of differences between various ensemble filters In an uptrend , the difference as one moves from the most responsive to most smooth filter should constantly be positive, and vice versa.

In the interest of brev ity, this post doesn t even have a trading strategy attached to it However, an implied trading strategy can be to be long or short the SPY depending on the sum of signs of the differences in filters as you move from responsiveness to smoothness Of course, as the candidate filters are all SMAs, it probably wouldn t be particularly spectacular However, for those out there who use more complex filters, this may be a way to create ensembles out of various candidate filters, and create even better filters Furthermore, I hope that given enough candidate filters and an objective way of selecting them, it would be possible to reduce the chances of creating an overfit trading system However, anything with parameters can potentially be overfit, so that may be wishful thinking.

All in all, this is still a new idea for me For instance, the filter to compute the error terms can probably be improved The inspiration for an EMA 20 essentially came from how Basel computes volatility if I recall, correct ly, it uses the square root of an 18 day EMA of squared returns , and the very fact that I use an EMA can itself be improved upon why an EMA instead of some other, more complex filter In fact, I m always open to how I can improve this concept and others from readers.

NOTE I am currently contracting in Chicago in an analytics capacity If anyone would like to meet up, let me know You can email me at or contact me through my LinkedIn here. This post will deal with a quick, finger in the air way of seeing how well a strategy scales namely, how sensitive it is to latency between signal and execution, using a simple volatility trading strategy as an example The signal will be the VIX VXV ratio trading VXX and XIV, an idea I got from Volatility Made Simple s amazing blog particularly this post The three signals compared will be the magical thinking signal observe the close, buy the close, named from the ruleOrderProc setting in quantstrat , buy on next-day open, and buy on ne xt-day close.

Let s get started. So here s the run-through In addition to the magical thinking strategy observe the close, buy that same close , I tested three other variants a variant which transacts the next open, a variant which transacts the next close, and the average of those two Effectively, I feel these three could give a sense of a strategy s performance under more realistic conditions that is, how well does the strategy perform if transacted throughout the day, assuming you re managing a sum of money too large to just plow into the market in the closing minutes and if you hope to get rich off of trading, you will have a larger sum of money than the amount you can apply magical thinking to Ideally, I d use VWAP pricing, but as that s not available for free anywhere I know of, that means that readers can t replicate it even if I had such data.

In any case, here are the results. Log scale for Mr Tony Cooper and others. My reaction The execute on next day s close performance being vas tly lower than the other configurations and that deterioration occurring in the most recent years essentially means that the fills will have to come pretty quickly at the beginning of the day While the strategy seems somewhat scalable through the lens of this finger-in-the-air technique, in my opinion, if the first full day of possible execution after signal reception will tank a strategy from a 1 44 Calmar to a 92, that s a massive drop-off, after holding everything else constant In my opinion, I think this is quite a valid question to ask anyone who simply sells signals, as opposed to manages assets Namely, how sensitive are the signals to execution on the next day After all, unless those signals come at 3 55 PM, one is most likely going to be getting filled the next day.

Now, while this strategy is a bit of a tomato can in terms of how good volatility trading strategies can get they can get a lot better in my opinion , I think it made for a simple little demonstration of this techniq ue Again, a huge thank you to Mr Helmuth Vollmeier for so kindly keeping up his dropbox all this time for the volatility data.

NOTE I am currently contracting in a data science capacity in Chicago You can email me at or find me on my LinkedIn here I m always open to beers after work if you re in the Chicago area. This post deals with an impossible-to-implement statistical arbitrage strategy using VXX and XIV The strategy is simple if the average daily return of VXX and XIV was positive, short both of them at the close This strategy makes two assumptions of varying dubiousness that one can observe the close and act on the close , and that one can short VXX and XIV.

So, recently, I decided to play around with everyone s two favorite instruments on this blog VXX and XIV, with the idea that hey, these two instruments are diametrically opposed, so shouldn t there be a stat-arb trade here. So, in order to do a lick-finger-in-the-air visualization, I implemented Mike Harris s momersion indicator. And then I ran the spread through it. In other words, this spread is certainly mean-reverting at just about all times. Here are the equity curves.

With the following statistics. In other words, the short side is absolutely amazing as a trade except for the one small fact of having it be impossible to actually execute, or at least as far as I m aware Anyhow, this was simply a for-fun post, but hopefully it served some purpose. Financial Mathematics and Modeling II FINC is a graduate level class that is current ly offered at Loyola University in Chicago during the winter quarter FINC explores topics in quantitative finance, mathematics and programming The class is practical in nature and is comprised of both a lecture and a lab component The labs utilize the R programming language and students are required to submit their individual assignments at the end of each class The end goal of FINC is to provide students with practical tools that they can use to create, model and analyze simple trading strategies.

Some useful R links. About the Instructor. The folks at Rstudio have done some amazing work with the shiny package From the shiny homepage, Shiny makes it super simple for R users like you to turn analyses into interactive web applications that anyone can use Developing web applications has always appealed to me, but hosting, learning javascript, html, etc made me put this pretty low on my priority list With shiny, one can write web applications in R. This example uses the managers dataset with calls to and from the PerformanceAnalytics package to display a plot and table in the shiny application.

Below is a screenshot of the application. You need to have shiny and Performance Analytics packages installed to run the application Once those are installed, open your R prompt and run. There is a great shiny tutorial from Rstudio as well as examples from SystematicInvestor for those interested in learning more.

The past few posts on momentum with R focused on a relatively simple way to backtest momentum strategies In part 4, I use the quantstrat framework to backtest a momentum strategy Using quantstrat open s the door to several features and options as well as an order book to check the trades at the completion of the backtest. I introduce a few new functions that are used to prep the data and compute the ranks I won t go through them in detail, these functions are available in my github repo in the rank-functions folder.

This first chunk of code just loads the necessary libraries, data, and applies the ave3ROC function to rank the assets based on averaging the 2, 4, and 6 month returns Note that you will need to load the functions in Rank R and monthly-fun R.

The next chunk of code is a critical step in preparing the data to be used in quantstrat With the ranks computed, the next step is to bind the ranks to the actual market data to be used with quantstrat It is also important to change the column names to e g because that will be used as the trade signal column when quantstrat is used.

Now the backtest can be run The function qstratRank is just a convenience function that hides the quantst rat implementation for my Rank strategy. For this first backtest, I am trading the top 2 assets with a position size of units. Changing the argument to gives the flexibility of scaling in a trade In this example, say asset ABC is ranked 1 in the first month I buy units In month 2, asset ABC is still ranked 1 I buy another units. In the previous post, I demonstrated simple backtests for trading a number of assets ranked based on their 3, 6, 9, or 12 i e lookback periods month simple returns While it was not an exhaustive backtest, the results showed that when trading the top 8 ranked assets, the ranking based 3, 6, 9, and 12 month returns resulted in similar performance.

If the results were similar for the different lookback periods, which lookback period should I choose for my strategy My answer is to include multiple lookback periods in the ranking method. This can be accomplished by taking the average of the 6, 9, and 12 month returns, or any other n-month returns This gives us the benefit of diversifying across multiple lookback periods If I believe that the lookback period of 9 month returns is better than that of the 6 and 12 month, I can use a weighted average to give the 9 month return a higher weight so that it has more influence on determining the rank This can be implemented easily with what I am calling the WeightAve3ROC function shown below.

The function is pretty self explanatory, but feel free to ask if you have any questions. Now to the test results The graph below shows the results from using 6, 9, and 12 month returns as well as an average of 6, 9, and 12 month returns and weighted average of 6, 9, and 12 month returns. Case 1 simple momentum test based on 6 month ROC to rank. Case 2 simple momentum test based on 9 month ROC to rank. Case 3 simple momentum test based on 12 month ROC to rank.

And QSVN failed. So Subcommander is, I think, the only option. SmartSVN is a java based svn client. Its working great. There is a free "Foundation version" and a commercial "Professional version". I did not see any reason till now to think about the Pro version. All i need is included in the free version. AND, the question you will have. It runs from USB as long as java is available.

With a small setting in the "smartsvn. In most of the other SVN clients i have tried you have to "install" additional software to enable the SSL tunnel functionality! But not here SmartSVN is doing all of that without adding extra applications or additional files!

I have tried SmartSVN though, and although I feel that it's very ugly, and the time I used it a while ago it crashed in short order, if they have a working version now it'd be a good solution. Skip to main content. Log in or register to post comments. Last post. January 16, - pm. Last seen: 4 years 3 months ago. Joined: I have seen requests for a SVN client here before.

I'm not sure if anyone has found one. I found this on Qt-Apps. The latest version of QSvn is Release 0.

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