# Crude oil trading strategies pdf

Tips are highly welcom. Thanks in advance for your help. It should be possible, someone wrote a minimum variance portfolio re-balancing algorithm a few months ago.

You'd need to use fetcher to get your index weights for your prior, make sure to fetch them "as-of" the date you are at in the back-test. It would be an excellent demonstration and example, perhaps you can get the quantopian folks to code it up! Thank Simon for your comment. I wrote my last thesis about BL so I have the theoretical background. But to be honest with you, I am not quite good in programming.

Nevertheless I will try and let the community know. Thomas Wiecki posted the article first on https: I just copied the link here. If you have comments on the article, I suggest posting them to Thomas' thread. Mebane Faber has a few interesting papers at Cambria Investments' website http: If you search for Mebane you should find them. I don't know if they still work in the backtester. This will, of course, add a strong long-term long-biased mean reversion factor to the system.

We analysed one year of front page banner headlines of three financial newspapers, the Wall Street Journal, Financial Times, and Il Sole24ore to examine the influence of bad news both on stock market volatility and dynamic correlation. Our results show that the press and markets influenced each other in generating market volatility and in particular, that the Wall Street Journal had a crucial effect both on the volatility and correlation between the US and foreign markets. We also found significant differences between newspapers in their interpretation of the crisis, with the Financial Times being significantly pessimistic even in phases of low market volatility.

Our results confirm the reflexive nature of stock markets. When the situation is uncertain and unpredictable, market behaviour may even reflect qualitative, big picture, and subjective information such as streamers in a newspaper, whose economic and informative value is questionable.

The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian. In addition, the material offers no opinion with respect to the suitability of any security or specific investment.

No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of , as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein.

If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website.

The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.

There is a clear and consistent dropoff in return as years progress from toward , and I'm curious to see if this trend has continued in the three years since.

I've noticed that the many cryptocurrency exchanges out there have a significant spread. The spread between Mt. Gox has a surge. Personally I'm fascinated by it.

I found this overview of quant investing by Max Dama http: At page 16 he very briefly explains a possible trading idea through the exploitation of the "first day of the month concept". Its probably the most important trading day of the month, as inflows come in from k plans, IRAs, etc. For instance, one time I was visiting Victors office on the first day of a month and one of his traders showed me a system and said, If you show this to anyone we will have to kill you.

Basically, the system was: If the last half of the last day of the month was negative and the first half of the first day of the next month was negative, buy at 11a.

This is an ATM machine the trader told me. I leave it to the reader to test this system. I tried this using excel and intraday data I got from a russian website giving away free historical prices for the 40 most traded stocks in the US, but obviously quantopia is a much better way of trying this simple strategy.

I didn't calculate the sharpe ratio, but my thinking is that if the sharpe ratio is high and you do this 12 mths a year and use a healthy amount of leverage you can make a nice stat arb payoff. I'm a novice to coding so I haven't made an attempt yet at coding this, so if any of u guys who are fast at this feel free to try it and post a backtest. This one looks particularly easy to implement in Quantopian, since it's basically just technical analysis.

We study whether exchange traded funds ETFs —an asset of increasing importance—impact the volatility of their underlying stocks. Using identification strategies based on the mechanical variation in ETF ownership, we present evidence that stocks owned by ETFs exhibit significantly higher intraday and daily volatility.

The driving channel appears to be arbitrage activity between ETFs and the underlying stocks. Consistent with this view, the effects are stronger for stocks with lower bid-ask spread and lending fees. Finally, the evidence that ETF ownership increases stock turnover suggests that ETF arbitrage adds a new layer of trading to the underlying securities. I found this pretty interesting, seems relevant. Optimal Trading Stops and Algorithmic Trading. I don't know how this page hasn't made up here yet, unless I missed it.

Looks promising, and simple for someone to implement! Man is it ever hard to find this thread every time, searching doesn't work well. Is there anything in this thread that would be particularly interesting to code in Quantopian and backtest?

I would check out quantpapers. Grant, I think that's really a personal question, what sort of trading strategy does someone want to deploy, and how does it fit in with their existing trading strategies? For purely academic interest, I am not sure I would be doing quant trading: Well, let me put the question another way.

If so, what has been the result? Can't speak for others. Something with a good win loss ratio would be ideal. I would appreciate it. Anyone know if you can import Futures data? Sam, I don't know about getting the data from volatility made simple, but you can use Quandl to import the data, or get it directly from CBOE. I believe they have the historical holdings as well. This link is for VXX, the others are available as well though.

Their concept of "Dual Momemtum" is very intriguing. As well, extending it in the manner which is described here:. Campbell Harvey's website is also a useful site for financial glossaries and papers on risk.

It's not clear if this is a mean-reversion strategy on this cointegrated basket, or whether it's a static investment portfolio somehow optimized for low variance. Simon - have you looked through the "premium" offerings on Quantpedia at all?

Am curious whether they are worth the fee or not. I haven't, no, I was just planning on going through their free stuff to see what anomalies and papers look interesting and suitable. Not sure where else to put this. Useful for HF algo development. Videos and PDFs are available. Statistical Arbitrage and Algorithmic Trading: Matthieu, that looks like a great resource indeed. The link seems to have changed, here is an updated one: Folks, whilst all these seem to be great resources, they need a certain amount of knowledge in Statistics.

What is the base amount of statistical knowledge from where one can kick on? Any books or resources for the uninitiated? Preliminiaries are I think basic single and multivariable analysis maybe some real analysis and intermediate combinatorics , linear algebra then get into basic probability theory and after that statistical inference, stochastic processes and simulation and econometrics and after that look at financial mathematics and optimization theory and stochastic partial differential equations.

Just Google or go to Amazon etc to find books with solutions. You can grab the basics on probabilities and statistics on www. Then you can follow the good introduction machine learning class from Andrew Ng on Coursera. If you want to move to more advanced understanding of learning algorithms you may want to have a look at The Elements of Statistical Learning.

After that and maybe some stochastic calculus and time series analysis you should be able to understand most of the articles you are interest in or at least know what to Google to fill the gap. Portfolio Optimization for strategies using sort information on expected returns. Subbed to this twitter feed a long time ago and rediscovered it today. They post quant papers from SSRN https: Most of the papers have been mentioned by you guys above. A couple of good tutorial style resources I found recently: There are lot of papers and detailed litratures in this link.

I ust came across this 30 stretagy thought it would be good place to post. The paper was a Dow Award Winner. There's already some posts about end of the month stock behavior above, but here's a detailed paper about it:. A form "risk parity" using Differential Evolution to optimize portfolio contributions to risk. Another pair trading algorithm using 2-stage correlation and co-integration based approach on 15 minute OHLC intra-day data on oil sector stocks.

They claim monthly 2. Will be interesting to see if this can be replicated in Quantopian. Claims that acceleration difference of returns has more explanatory power than simple momentum.

Not a 'trading strategy' per se, but an interesting site with some python related code, and some clear thinking. I'm at academic finance conferences quite often these days as part of our academic outreach. I see a lot of interesting papers and would be happy to make some best-of lists the next time I'm at a conference.

Would people be interested in lists like this for potential ideas? I'll start up a forum thread and post live once I'm there. Another statistical arbitrage paper but using step-wise regression and variance ratio tests to identify co-integrated baskets.

Quite old paper though. I've read it Mean reversion after price drops multiple times because I'm testing some Josef Rudy's research for my thesis to see if his findings hold water. Delaney that would be fantastic. I've been working on converting the ideas from this paper into Python code http: Hey, my new thread is up here.

Statistical arbitrage on ETF [ https: Simple idea - buy negative EV stocks and hold for a year! Probably a wicked drawdown though. Keeping it from the announcement date till 14 days later when the actual action is done will result positive retunrs for going long on added stocks and short on deleted ones.

This paper has a collection of strategies that may be helpful. Looking through the list and although some are simple there are several that look interesting We present explicit formulas - that are also computer code - for real-life quantitative trading alphas.

Their average holding period approximately ranges 0. The average pair-wise correlation of these alphas is low, The returns are strongly correlated with volatility, but have no significant dependence on turnover, directly confirming an earlier result by two of us based on a more indirect empirical analysis.

We further find empirically that turnover has poor explanatory power for alpha correlations. Could you please tell me what does 'alpha' mean? Alpha is a commonly used metric of how much new information is contained in another signal. It is found by performing a linear regression between the return stream generated by the new signal, and existing factors such as the market. The equation might look like this. By seeing how much of your returns are historically explained by each of the other factors, you can make an estimate for how much of your returns are coming from new information, which is what is left over in the alpha.

For more info on this see lectures 4, 13, and 14 in the Quantopian Lecture Series. Not really a paper but this is an excellent quandl post on the general process to test trading ideas:. Entropy theory of mind.

Numerically derives the link between Entropy in physics and finance. The only paper I've read that models market volume in a somewhat intuitive way.

The link to the PDF is in the first paragraph. A few studies of mine these models actually traded real money for a long time like 20 years, not hypothetically. Built to illustrate the idea of trading standard deviation, here is the link to a simple Crude Oil strategy with a z of 1. Built to illustrate the ideas of trading relationships, fundamentals, yesterday and seasonals, here is the link to a second simple Crude Oil strategy.

This one has a z of 2. Built to illustrate the ideas of trading a seasonal, trading volatility, and trading yesterday, here is the link to a third simple Crude Oil strategy. Built to illustrate the ideas of trading the tails of a candlestick and trading volatility, here is the link to a fourth simple Crude Oil strategy.

This one has a z of 3. Built to illustrate the ideas of portfolios of systems and reusing systems, here is the link to the portfolio of the four previously described Crude Oil strategies. The portfolio has an annual return of Built to show the idea of trading the tail of a candlestick instead of the body when volatility leaves a big tail after the natural gas supply report on Wednesday, here is the link to the first simple Natural Gas strategy.

This strategy has a z of 2. Built to illustrate the ideas of trading other traders and trading a fundamental, this Natural Gas system trades the positioning prior to the Wednesday supply report.

Here is the link. This strategy has a z of 1. A z-score is a statistics term, it measures how many standard deviations a value is from the mean of a set of values. I promise I'm not trying to be snarky, but you can learn that yourself in about 3 seconds by searching "z score" in Google.

That will probably be true for most of the finance and statistics terms you see here. Sorry i have to reduce everything to some oversimplified format. Medellin to be exact. I own a coffee farm called Finca Milena and will put you up if you come down here and get me caught up on quants, algos, thoery etc etc.

I wonder what the z score is for that algo???? Fair point - a guy named Edward Altman didn't really do anyone any favors when he also named his bankruptcy prediction model the "z score". William, Here is a simple example of zscore of an asset, others will comment if its wrong in any way. This third Natural Gas system illustrates the ideas of trading relationships, trading change and trading rate of change between Natural Gas and Crude Oil.

Rules and results are Here. World Quant alphas: We've actually already done a bunch of work implementing the paper you posted, Pravin. Figured linking to it might be useful to some folks. Looking forward to the actual talk, to find out what the method is! Knowing de Prado's stuff, which is very good, he'll be making the point that mean variance analysis doesn't work in practice any more.

It's easy to overfit it to some historical period by naively optimizing, but will have little correlation to out of sample performance. This is similar to Thomas Wiecki's recent paper on how sharpe ratio also has no correlation between in and out of sample performance. Bootstrapping works great for avoiding overfitting, but you end up with pretty average portfolios. I suspect just not using mean-variance and using other more sophisticated portfolio selection techniques.

Correlation reduction filters, sector neutrality filters, etc. The book, Systematic Trading , by Robert Carver, was recommended to me by Simon, and I just finished an entire chapter dedicated to over-fitting.

There is a quantitative discussion of relevant backtest time scales to distinguish one approach from another. And approaches to avoiding fitting to a single historical period. Flipping ahead in the book, bootstrapping is covered, as well. The author seems to be very sober and realistic and is not promoting particular strategies, per se althoug he does distinguish styles of trading.

The focus is on the process and the pitfalls. It is very approachable from a technical standpoint. It might be a good starting point for many Quantopian users who are aspiring quants. Agree with you on that book Grant. Must say there are parts that I have difficulty getting my head around.

His blog is excellent as well. The Chemistry of Asset Allocation. Would you be able to add the z-score code to it and repost here? That the z-score curve stays above 1. In this case, this is a positive table so one would do 0. Applied to any trading strategy, z-scores are a common way to assign a statistical probability value of something occurring, which can act as a "confidence" interval.

Please keep it to concrete strategy ideas, the more explicit the better, and preferably those that could be implemented in Quantopian! Quite a few papers in Turnkey's alpha DB: And do you mean the Academic Alpha section? Yes I did, my mistake. It's a free sign-in.

They are one half of the guys who wrote Quantitative Value the other half is Empiritrage , and they regularly write little papers about the sort of exploitable opportunities people here might be interested in. I am not affiliated with them. This blog post on Limited Attention and the Earnings Announcement looks interesting login not required. I also have some code that I can share.

What are the top 3 you'd recommend reading through carefully, that could be coded in Quantopian without a heroic effort? Hi Grant, I think good old trend following is always fun. In case you haven't checked it out, I noticed that Claus Herther has a great starting point. I'd like to add in measurement of the slope of a trend, momentum, and williams to help add some "trend anticipation" into a standard trend following system.

Just stumbled upon this goldmine of hundreds of papers, most with pdf links, on a variety of topics: Note he doesn't actually give the formula for this indicator, so one would have to do some work to try and figure out what he's talking about Hi everyone, is ist possible to program the black litterman approach with Quantopian? Tips are highly welcom. Thanks in advance for your help. It should be possible, someone wrote a minimum variance portfolio re-balancing algorithm a few months ago.

You'd need to use fetcher to get your index weights for your prior, make sure to fetch them "as-of" the date you are at in the back-test.

It would be an excellent demonstration and example, perhaps you can get the quantopian folks to code it up! Thank Simon for your comment. I wrote my last thesis about BL so I have the theoretical background. But to be honest with you, I am not quite good in programming. Nevertheless I will try and let the community know. Thomas Wiecki posted the article first on https: I just copied the link here. If you have comments on the article, I suggest posting them to Thomas' thread.

Mebane Faber has a few interesting papers at Cambria Investments' website http: If you search for Mebane you should find them. I don't know if they still work in the backtester. This will, of course, add a strong long-term long-biased mean reversion factor to the system. We analysed one year of front page banner headlines of three financial newspapers, the Wall Street Journal, Financial Times, and Il Sole24ore to examine the influence of bad news both on stock market volatility and dynamic correlation.

Our results show that the press and markets influenced each other in generating market volatility and in particular, that the Wall Street Journal had a crucial effect both on the volatility and correlation between the US and foreign markets. We also found significant differences between newspapers in their interpretation of the crisis, with the Financial Times being significantly pessimistic even in phases of low market volatility.

Our results confirm the reflexive nature of stock markets. When the situation is uncertain and unpredictable, market behaviour may even reflect qualitative, big picture, and subjective information such as streamers in a newspaper, whose economic and informative value is questionable. The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian.

In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of , as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein.

If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.

There is a clear and consistent dropoff in return as years progress from toward , and I'm curious to see if this trend has continued in the three years since. I've noticed that the many cryptocurrency exchanges out there have a significant spread. The spread between Mt. Gox has a surge. Personally I'm fascinated by it. I found this overview of quant investing by Max Dama http: At page 16 he very briefly explains a possible trading idea through the exploitation of the "first day of the month concept".

Its probably the most important trading day of the month, as inflows come in from k plans, IRAs, etc. For instance, one time I was visiting Victors office on the first day of a month and one of his traders showed me a system and said, If you show this to anyone we will have to kill you.

Basically, the system was: If the last half of the last day of the month was negative and the first half of the first day of the next month was negative, buy at 11a. This is an ATM machine the trader told me. I leave it to the reader to test this system. I tried this using excel and intraday data I got from a russian website giving away free historical prices for the 40 most traded stocks in the US, but obviously quantopia is a much better way of trying this simple strategy.

I didn't calculate the sharpe ratio, but my thinking is that if the sharpe ratio is high and you do this 12 mths a year and use a healthy amount of leverage you can make a nice stat arb payoff. I'm a novice to coding so I haven't made an attempt yet at coding this, so if any of u guys who are fast at this feel free to try it and post a backtest. This one looks particularly easy to implement in Quantopian, since it's basically just technical analysis.

We study whether exchange traded funds ETFs —an asset of increasing importance—impact the volatility of their underlying stocks. Using identification strategies based on the mechanical variation in ETF ownership, we present evidence that stocks owned by ETFs exhibit significantly higher intraday and daily volatility. The driving channel appears to be arbitrage activity between ETFs and the underlying stocks. Consistent with this view, the effects are stronger for stocks with lower bid-ask spread and lending fees.

Finally, the evidence that ETF ownership increases stock turnover suggests that ETF arbitrage adds a new layer of trading to the underlying securities. I found this pretty interesting, seems relevant.

Optimal Trading Stops and Algorithmic Trading. I don't know how this page hasn't made up here yet, unless I missed it. Looks promising, and simple for someone to implement! Man is it ever hard to find this thread every time, searching doesn't work well. Is there anything in this thread that would be particularly interesting to code in Quantopian and backtest? I would check out quantpapers.

Grant, I think that's really a personal question, what sort of trading strategy does someone want to deploy, and how does it fit in with their existing trading strategies? For purely academic interest, I am not sure I would be doing quant trading: Well, let me put the question another way.

If so, what has been the result? Can't speak for others. Something with a good win loss ratio would be ideal. I would appreciate it. Anyone know if you can import Futures data? Sam, I don't know about getting the data from volatility made simple, but you can use Quandl to import the data, or get it directly from CBOE.

I believe they have the historical holdings as well. This link is for VXX, the others are available as well though. Their concept of "Dual Momemtum" is very intriguing. As well, extending it in the manner which is described here:. Campbell Harvey's website is also a useful site for financial glossaries and papers on risk.

It's not clear if this is a mean-reversion strategy on this cointegrated basket, or whether it's a static investment portfolio somehow optimized for low variance. Simon - have you looked through the "premium" offerings on Quantpedia at all? Am curious whether they are worth the fee or not.

I haven't, no, I was just planning on going through their free stuff to see what anomalies and papers look interesting and suitable. Not sure where else to put this. Useful for HF algo development. Videos and PDFs are available. Statistical Arbitrage and Algorithmic Trading: Matthieu, that looks like a great resource indeed. The link seems to have changed, here is an updated one: Folks, whilst all these seem to be great resources, they need a certain amount of knowledge in Statistics. What is the base amount of statistical knowledge from where one can kick on?

Any books or resources for the uninitiated? Preliminiaries are I think basic single and multivariable analysis maybe some real analysis and intermediate combinatorics , linear algebra then get into basic probability theory and after that statistical inference, stochastic processes and simulation and econometrics and after that look at financial mathematics and optimization theory and stochastic partial differential equations.

Just Google or go to Amazon etc to find books with solutions. You can grab the basics on probabilities and statistics on www. Then you can follow the good introduction machine learning class from Andrew Ng on Coursera. If you want to move to more advanced understanding of learning algorithms you may want to have a look at The Elements of Statistical Learning.

After that and maybe some stochastic calculus and time series analysis you should be able to understand most of the articles you are interest in or at least know what to Google to fill the gap. Portfolio Optimization for strategies using sort information on expected returns. Subbed to this twitter feed a long time ago and rediscovered it today. They post quant papers from SSRN https: Most of the papers have been mentioned by you guys above.

A couple of good tutorial style resources I found recently: There are lot of papers and detailed litratures in this link. I ust came across this 30 stretagy thought it would be good place to post. The paper was a Dow Award Winner. There's already some posts about end of the month stock behavior above, but here's a detailed paper about it:. A form "risk parity" using Differential Evolution to optimize portfolio contributions to risk. Another pair trading algorithm using 2-stage correlation and co-integration based approach on 15 minute OHLC intra-day data on oil sector stocks.

They claim monthly 2. Will be interesting to see if this can be replicated in Quantopian. Claims that acceleration difference of returns has more explanatory power than simple momentum.

Not a 'trading strategy' per se, but an interesting site with some python related code, and some clear thinking. I'm at academic finance conferences quite often these days as part of our academic outreach. I see a lot of interesting papers and would be happy to make some best-of lists the next time I'm at a conference.

Would people be interested in lists like this for potential ideas? I'll start up a forum thread and post live once I'm there. Another statistical arbitrage paper but using step-wise regression and variance ratio tests to identify co-integrated baskets. Quite old paper though.

I've read it Mean reversion after price drops multiple times because I'm testing some Josef Rudy's research for my thesis to see if his findings hold water. Delaney that would be fantastic. I've been working on converting the ideas from this paper into Python code http: Hey, my new thread is up here. Statistical arbitrage on ETF [ https: Simple idea - buy negative EV stocks and hold for a year!

Probably a wicked drawdown though. Keeping it from the announcement date till 14 days later when the actual action is done will result positive retunrs for going long on added stocks and short on deleted ones. This paper has a collection of strategies that may be helpful.

Looking through the list and although some are simple there are several that look interesting We present explicit formulas - that are also computer code - for real-life quantitative trading alphas. Their average holding period approximately ranges 0. The average pair-wise correlation of these alphas is low, The returns are strongly correlated with volatility, but have no significant dependence on turnover, directly confirming an earlier result by two of us based on a more indirect empirical analysis.

We further find empirically that turnover has poor explanatory power for alpha correlations. Could you please tell me what does 'alpha' mean? Alpha is a commonly used metric of how much new information is contained in another signal. It is found by performing a linear regression between the return stream generated by the new signal, and existing factors such as the market. The equation might look like this. By seeing how much of your returns are historically explained by each of the other factors, you can make an estimate for how much of your returns are coming from new information, which is what is left over in the alpha.

For more info on this see lectures 4, 13, and 14 in the Quantopian Lecture Series. Not really a paper but this is an excellent quandl post on the general process to test trading ideas:. Entropy theory of mind. Numerically derives the link between Entropy in physics and finance. The only paper I've read that models market volume in a somewhat intuitive way. The link to the PDF is in the first paragraph.

A few studies of mine these models actually traded real money for a long time like 20 years, not hypothetically. Built to illustrate the idea of trading standard deviation, here is the link to a simple Crude Oil strategy with a z of 1. Built to illustrate the ideas of trading relationships, fundamentals, yesterday and seasonals, here is the link to a second simple Crude Oil strategy. This one has a z of 2.

Built to illustrate the ideas of trading a seasonal, trading volatility, and trading yesterday, here is the link to a third simple Crude Oil strategy. Built to illustrate the ideas of trading the tails of a candlestick and trading volatility, here is the link to a fourth simple Crude Oil strategy. This one has a z of 3. Built to illustrate the ideas of portfolios of systems and reusing systems, here is the link to the portfolio of the four previously described Crude Oil strategies.

The portfolio has an annual return of Built to show the idea of trading the tail of a candlestick instead of the body when volatility leaves a big tail after the natural gas supply report on Wednesday, here is the link to the first simple Natural Gas strategy.

This strategy has a z of 2. Built to illustrate the ideas of trading other traders and trading a fundamental, this Natural Gas system trades the positioning prior to the Wednesday supply report. Here is the link. This strategy has a z of 1. A z-score is a statistics term, it measures how many standard deviations a value is from the mean of a set of values.

I promise I'm not trying to be snarky, but you can learn that yourself in about 3 seconds by searching "z score" in Google. That will probably be true for most of the finance and statistics terms you see here. Sorry i have to reduce everything to some oversimplified format. Medellin to be exact. I own a coffee farm called Finca Milena and will put you up if you come down here and get me caught up on quants, algos, thoery etc etc. I wonder what the z score is for that algo???? Fair point - a guy named Edward Altman didn't really do anyone any favors when he also named his bankruptcy prediction model the "z score".

William, Here is a simple example of zscore of an asset, others will comment if its wrong in any way. This third Natural Gas system illustrates the ideas of trading relationships, trading change and trading rate of change between Natural Gas and Crude Oil. Rules and results are Here.

World Quant alphas: We've actually already done a bunch of work implementing the paper you posted, Pravin. Figured linking to it might be useful to some folks. Looking forward to the actual talk, to find out what the method is! Knowing de Prado's stuff, which is very good, he'll be making the point that mean variance analysis doesn't work in practice any more.

It's easy to overfit it to some historical period by naively optimizing, but will have little correlation to out of sample performance. This is similar to Thomas Wiecki's recent paper on how sharpe ratio also has no correlation between in and out of sample performance. Bootstrapping works great for avoiding overfitting, but you end up with pretty average portfolios. I suspect just not using mean-variance and using other more sophisticated portfolio selection techniques.

Correlation reduction filters, sector neutrality filters, etc.