August 19th, 2016

Simple Stock Trading II

Can a selection process of tradable stocks work in the future as it did in the past? We are always able to rank stuff, past data that is, since it is part of the information set available to us at the time. The objective is to find in the past data set something to activate decision surrogates to generate marketable trades in the future.

For one, I am looking for tools to help me answer the following graph:

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August 16th, 2016

Simple Stock Trading I

Since I've returned to Quantopian, I've been busy getting reacquainted with their trading software. What follows are my first attempts at participating in their forums, even if I should have waited. But, the occasion presented itself. Anthony Garner, like many others, graciously posted his trading strategy results and code for all to see. It is the first strategy I looked at. I found it the easy way to review Python syntax as in learning by example.

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August 5th, 2016                                                                                                            Modified August 6th, see bottom section

Big Bucks Will Travel

In a previous article, it was argued that it was not enough to generate profits over the long term, but rather, that it was necessary to generate positive alpha. By this, meaning that whatever stock trading strategy you might want to use, it had to ultimately outperform the averages. Otherwise, an index fund would have been a better choice. In fact, it's more like any set of investments that could at least beat market averages over the long term would prove to be a better choice.

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August 3rd, 2016

Back to Quantopian

Over the past few days, I went back to the Quantopian website after some 3 years of absence to find that they had improved a lot, an impressive job, sufficient in fact to warrant not only a second look but enough to want to make it a strategy design platform. Sure, it will require that I re-familiarized myself with Python, its syntax and packages, but I think it will be worth it. I do like what they did, and it shows promise for what I want to do.

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July 28th, 2016

Generate Positive Alpha

A short term stock trader has a choice, and that is to participate, take a position, or not. It is always his/her prerogative. Participating, taking action, is a deliberate act, that it be discretionarily done or delegated to a trading script.

A trading program will do what it is programmed to do, nothing else, and therefore, it is about the same as if its designer had made those same trading decisions except much faster, without hesitation or second guessing. A program, or a discretionary trader for that matter, will issue buy and sell orders according to their respective preset trading rules. Neither will invent a new rule on the fly, both will want some evidence that their prospective rules has the potential to generate profits.

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July 23rd, 2016

A CAGR Debate

An interesting recent article that appeared in MarketWatch had as introduction:

"Consider: The 30-year annualized return for the S&P 500 average was 10.35% through 2015, but the average investor in the U.S. market pocketed just 3.66%, according to an analysis of investors by researcher Dalbar Inc."

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July 11th, 2016

Trading Strategy Survival

Over the weekend I was confronted with the problem of stock trading strategy survivability as I was reading Prado's book on optimization of trading strategies. Since a lot of what you see in the financial literature puts emphasis on that most trading strategies fail, I had to show that at least my preferred strategy was not designed to do so.

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June 22nd, 2016                        Also available in PDF

The Revisited Stop Loss III           (Part 3 of 3),    (for Part I, Part II)

 

Another Stock

Should the picture change that much if I change the stock under the microscope?

I picked FDX from the same 10-stock list I often use in testing trading procedures. If a stock can pass my preliminary tests, then I can go further with the exploratory analysis.

It is when you change the stock under study that you can better view common elements. And from there maybe extract further trading rules designed to help at the portfolio level and not just apply to a single stock.

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June 20th, 2016                         Also available in PDF

 

The Revisited Stop Loss II          (Part 2 of 3)

An Inquisitive Backtest

I opted to test the protective stop loss hypothesis starting with the notion of having a 10% trailing stop loss. The intention is to buy stocks on their way up and sell them later at a higher prices (see intro, part 1). To execute a trailing stop, you first need to buy some shares, so I also put in a 10% trailing buy order from a bottom.

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June 20th, 2016                  Also available in PDF

The Revisited Stop Loss                              (Part 1 of 3)

What is it you want? The money, the entertainment, recognition, or maybe just something to talk about as if you were in the know of worldly events. Just in case it is the money, then you might appreciate what follows since it is all about your long term portfolio protection.

This is a 3-part series that elaborates on the use of stop losses in stock trading strategies. I think you will be able to benefit from my observations. To skip the text, examine the charts for what they have to say.

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Commentary

 June 7th, 2016 

You Don't Win All The Time

Last month, after a week of designing on paper a trading system, I spent another three trying to formalize in code its trading procedures. At its core, I needed a special derivative function as I thought it might enable a different perspective on trading cycles. On paper it showed a huge profit potential.

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 May 3rd, 2016 

On Portfolio Drawdowns

After reading the article: 180 years of market drawdowns, I thought I could add something to it. A different perspective, but nothing contradicting the author's point of view, on the contrary. I found his article interesting.

Portfolio drawdowns are relative, they are relative to the trading strategy used. But one thing is sure, a lot of trades will see some drawdown, more than people think. I opted to use one of my programs to illustrate the point doing a simple test on two stocks I have tested before (see DEVX8 related programs). I just wanted to verify some numbers.

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April 29th, 2016 

A Different Take

In a LinkedIn forum I participate from time to time there was this statement: Why it's not possible to teach most people to be successful (primarily related to automated trading). To see the thread follow the link above.

I somewhat disagreed with the initial appraisal. I did have a different take on it.

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April 20th, 2016

The Value of a Stock Trading Strategy II   (the analysis)

After doing the long term simulation described in my last article. It was time to open the black box and analyze what was inside. What follows is my analysis of the strategy presented in the previous article and I will reference it often. I want to extract what went wrong in that trading script to make it lose when without really trying it should easily have won the day, meaning that it could have ended positive, even if not by much.

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April 13th, 2016

The Value of a Stock Trading Strategy

Finding badly designed stock trading strategies is easy. I have hundreds of those on my machines. Took only a few minutes to locate one to illustrate my point. Didn't look at the code, technically, it was not required. But did perform a 20-year simulation on a small group of stocks. The same 10 stocks I used in recent months to explore a strategy's strengths, weaknesses, and limitations. The main reason for using that group was keeping the ability to compare strategies, and performance levels, while seeking the answer to the question: is strategy A better than strategy B?

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April 8th, 2016

Retail Stock Trading Environment

There are millions of traders, millions of trading methods, but a lot more investors. At the end of the day, all financial assets are accounted for, to the penny, and in someone's hands. In the US, that's about $99 Trillion dollars worth; this includes real estate, stocks and bonds. It's a big number. Some hold some of these assets for a short time, others up to multiple decades.

 

The short term retail trader is part of the minority, doesn't control anything. 

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March 9th, 2016

Randomness in Stock Prices II

From the comments received over my last article on Randomness in Stock Prices, there appears to be some confusion for some in the terms used. I'll try to clarify my point of view.

Usually, the word random implies that you can not predict the next move better than by chance, otherwise it would not be random. You can assign odds, probabilities, to the outcome from observed statistics. For instance, in a random game like heads or tails, you can assign 0.50 as probability of getting head on the next flip of a coin.

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March 4th, 2016

Randomness in Stock Prices

Will a game with 51:49 odds still show some randomness? YES, definitely, and a lot. Even if it has a positive expected value. The same goes for a 52:48 game, there would still remain a lot of randomness. It might not matter much how the data might be distributed, it would still be mostly random-like.

Does the classification of a quasi-random game require a Gaussian distribution? NO, not at all. It could be any other type of distribution with or without fat tails.

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February  8th, 2016

A Stock Trading Strategy Experiment V

My take on my Stock Trading Strategy Experiment.

The whole Strategy Experiment had two surprises. The first one being that the MACDv03 program managed to outperform one of my preferred strategy: DEVX8. The second, how unexpected it was since it was not my primary objective.

My objective was to show that you could take an ordinary trading script and transform it into a portfolio builder. I considered the task a worthwhile experiment, hence the title. It is also why I opted to do it live, posting the results as I went along. A way of forcing me to look for better solutions and be more creative.

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January  30th, 2016

A Stock Trading Strategy Experiment IV

Time for some analysis. It took 2 days to design the first productive version of the program MACDv01. Another 3 to add the improvements that generated Strategy Experiment II (MACDv02). Made some minor improvements overnight which resulted in MACDv03, the one used in Strategy Experiment III, where I ran the program once on the 10-stock portfolio, and then reported the results.

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January  26th, 2016

A Stock Trading Strategy Experiment III

In Strategy Experiment II, I presented a stock trading strategy based on the MACD, a technical indicator often used in developing strategies. In Trading Strategy Experiment I was shown that such a minimalistic based trading strategy would not produce much over the long term. However, in Strategy Experiment II, it was shown that it could be transformed and used to produce interesting results at the portfolio level, and over the long haul.

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January  22nd, 2016

A Stock Trading Strategy Experiment II

In my last article: A Stock Trading Strategy Experiment, I said it was time to do the portfolio level test. That I would take the same trading script, or slightly improved, that generated ABT's results and then use it on the other 9 candidates in the data set. The same stock list as tested in Delayed Gratification. This way I would also be able to make some strategy comparisons.

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January 19th, 2016

A Stock Trading Strategy Experiment

The objective is to design an end of day (EOD) stock trading strategy almost from scratch with for background an old trading script that did no trading at all. It was published in 2000, over 15 years ago, author's handle: Glitch. As given by the author:

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 January 2nd, 2016

Stock Trading Strategy Mechanics

Book released this January 1st, 2016. My first ever made available on Amazon. Never thought I would do this. But there it is.

Stock Trading Strategy Mechanics

It's a major transformation for me. I usually put out stuff that is clearly free, with no strings attached. But then, you observe that because it is given free, people attached no value to it. So, maybe now they will think it is worth something.

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 December 28th, 2015

Stock Trading Randomness

A stock price series is the same for everyone. Everyone trading it wants to profit from it. Anyone wishing to trade it, implying short term, understandably, will have some kind of method to do so. Trading one stock or instrument at a time might not be enough. One has to have some perspective, a long term plan, not only to build up a portfolio, but also on how to manage it over time.

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 October 24th, 2015

Delayed Gratification II

The article Delayed Gratification presented the 20-year test results of running the trading script DEVX6 (last modified June 2014, over 16 months ago) to which was now added 4 lines of code made to insert a conditional one-day time delay before salable shares might be sold. This pushed the 10-stock portfolio performance higher by $226M compared to the previous version of the program, and this on a 12-week walk forward test where the market average declined by -3%.

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October 23rd, 2015

Delayed Gratification

In my previous article: A Case Study, commenting on the DEVX6 strategy, I said: "...those added lines ...could be used in the trading process itself since they were pretty good at isolating most of the trade clusters". It raised questions: why not use them? Can you get something extra using that information? Visually, those lines seem to be doing a decent job.

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October 22nd, 2015 

A Case Study

Over the last week or so, I've had some discussions on my trading methodology. One of which centered around a demonstration of what it could do. In reply to a friend's statement, I said: "my program would have done that too on that stock", which was to buy shares during the last price decline about a month and a half ago or so. I realized afterward that it was very easy to say.

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August 24th, 2015 

Stock Trading Strategy Design II

(see part 1) The evolution of a portfolio is determined by its ongoing inventory composition. It can be written as a time function:

A(t) = A(0)*f(n, q, Δp, I, D, t).

The information set (I) can be independent of everything. It's just one's way of looking at things and reaching trading decisions or not (D).

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August 23rd, 2015

Stock Trading Strategy Design I

Any stock trading strategy should be basic common sense. A stock portfolio does not grow instantaneously; it takes years to build it up and nurture. It is not enough to make a trade here in there without considering the size of the portfolio or the time span under which it will have to grow.

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July 26th, 2015

Building a Stock Portfolio

In building a stock portfolio, the account size alone will more likely dictate the trading/investment management style, its constraints, and conditions.

Also, it will depend on other things such as return objectives, acquired market knowledge, acceptable risks, available time, and temperament. I would say: "ultimately, the portfolio manager will be the focal point, the only decision maker whatever approach one might want to use" be it automated or discretionary.

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July 23rd, 2015

Still More DEVX V6

In my previous article: More DEVX V6 was shown a simulation of the program over 10 stocks over durations of 10 and 20 years. The point was to show that this particular stock trading strategy would easily survive not only over its first 10-year trading interval, but also over a 20 year period, and this including one year of walk forward.

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July 3rd, 2015

More DEVX V6

One of the hardest part in managing a stock portfolio is designing a workable and profitable long term trading strategy. It needs to be based on sound principles and provide a foundation as to how it will handle an unknown future. Trading automation presents an added dimension to the problem.

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June 16th, 2015

Connecting the Dots

There is sufficient data to start connecting the dots. What follows are explanations given to tests performed over the last few weeks to answer some questions on a LinkedIn forum. The last two tests have not been presented yet, but they will shortly. The point was to show that the trading method used mattered more that the stock selection that could be made.

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June 1st, 2015

Bringing Perspective to Stock Trading Strategies

A stock price series can be viewed as a stochastic, erratic, chaotic and random-like time function with shocks, gaps and fat tails. Mostly unpredictable. Accepting this has for direct consequence: one can't predict with any significant accuracy the price of any stock, be it today, tomorrow, next week, next year, or 20 years from now for that matter. Saying that a stock might be between $0.00, $10,000 or whatever with a 95% confidence level in some 20 years does not help at all.

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April 27th, 2015

Cheating by Spoofing.

 

Navinder Singh Sarao was caught cheating by spoofing. It took 5 years to finally prosecute him. 5 years during which time he continued to cheat. Could one say: regulatory agencies were sleeping at the wheel? For sure. Could one add that: brokers, exchanges and secondary parties that observed the misconduct were lending a blind eye as they could benefit indirectly by doing so?

 

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 January 30th, 2015

Portfolio Math I

When designing stock trading systems it is a good idea to view the problem, not only with a vision of what a trading program could or should do, but also with an understanding of the environment in which this program will have to operate.

In a software trading program, which we can make it do whatever we want it to do, we only have logical decisions, calculations and statements in code to execute. No feelings, no moods, no hunches and no trader psychology even if these in some way could also be programmed in. But this won't stop anyone from just gambling, be it in a discretionary manner or by using a partially or totally automated solution.

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 November 25th, 2014 

DEVX V6 Revisited

Recently, I made the remark somewhere that if my DEVX V6 random trading strategy back test was done again it would achieve about the same results as the one done on November 2nd. It is always easy to make such a statement. But for me, when I express something like this, I need to show some proof or at least some evidence that it would be so. Expressing it, even if I know the end results before making such a test, it might not be considered sufficient by others.

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 November 11th, 2014 

A Unique Approach

In my last paper: A Donor Within, it is explained how an existing trading strategy was modified to reach a higher performance level. The section: One More Thing, starting on page 30, goes through the process.

First, the desired expectations were put on paper before any testing: increase position size by a factor of 10, and then improve on the compounded annual growth rate (CAGR) for the 30 stock portfolio over the last 25 years. Needed software procedures were determined, then the program modified and debugged on a single stock.

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 November 6th, 2014                                                                    New paper: A Donor Within

A Donor Within

Just sent my pledge to the Bill & Melinda Gates Foundation. I found it to be the best outcome for my years of research. Over the last few years, I've developed a series of better and improved trading strategies. My best strategies should be considered sophisticated, designed for long term appreciation, and should prove to be most profitable.

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August 11th, 2014

Winning by Default II

As a follow-up to Winning by Default, I wanted to show intermediary test results. The objective being to show the evolution of such a trading strategy from day one. There was no need to enhanced its performance level beyond the rudimentary settings as was done in Winning by Default. This is more a what if scenario analyzing a trading strategy's long term behavior and system metrics.

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July 28th, 2014

Winning by Default

In the same vein as in previous articles, I'd like to present the following charts from a portfolio simulation done over last weekend. It's huge and I am still analyzing the details involved with such a big portfolio. Its payoff matrix has for size: 13,000 rows (days) by 985 columns (stocks); that's 12,805,000 data entries for each of the matrices involved.

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July 13th, 2014

Nest Egg on Support

Or How to Build Your Retirement Fund

I would say all people would like to build a nice retirement account if they could. However, the process has always been considered difficult and often required professional help while most of the time they could just do it all on their own. What follows is all about a long term trading strategy that could simply help anyone take the first step in that direction. And if able, do even more.

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June 24th, 2014

Deviation X

Following my previous note, there was only one thing left to do and that was to perform all the mentioned long term tests. First, on the original program version as published on Wealth-Lab in July 2002. Then on one of my modified version of this trading script DEVX (version 3) and leave for the end improvements that could push performance results higher using general trading policies rather than trying to optimize parameters.

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June 18th, 2014

Swinging It

My next trading strategy to be analyzed is kind of another strange trading script, it buys and sells on about every price swing. It sets a no trade zone and will buy below and sell above it. Yet all entries are the result of random functions. It gives the illusion of perfect timing, when in fact, trades are coincidental, meaning not hitting the highs and the lows on purpose, but as a side effect and direct consequence of the methodology used.

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June 8th, 2014

Unorthodox Trading

Still in the process of re-evaluating my old trading strategies. This time the selected strategy is the BBB System (BullPower and BearPower Balance). It was designed in 2003 and its author also published it in Stocks & Commodities magazine (October issue). This means at least 10 years of out of sample (OOS) data. One can do a 10 year walk forward test on this one since it has been literally frozen in time.

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June 3rd, 2014

Bizarre Trading Behaviors

About a week or so ago I started doing the inventory of my trading strategies, a project that has been delayed for over a year due mostly to procrastination and lack of time. It's a big project. I'll have to go through over 200 trading strategies of mine and document what each trading procedure does. Then determine their relative importance and the why they contributed to overall performance. Hopefully, this should translate into designing even better trading scripts or at least selecting the best of the crop.

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April 30th, 2014

This Crazy Game

In my previous note, I presented a chart displaying the evolution of the stochastic differential equation SDE based on the length of the trading interval Δt (from Δt → 0, to Δt → T (long term horizon). The SDE is an idealized and acceptable model to depict price action, and has been widely documented in academic papers for over 60 years. It's a simple regression line over the considered data.

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April 22nd, 2014

The Drift

This article starts from the conclusion of a few lines drawn on a piece of paper, a simple representation for what I had in mind. I knew that the two drawn sigmoids were the answer to what I wanted to express. It's not that it was saying anything new, these curves have been out there for ages, it's just that much information could be extracted from those 2 curves.

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March 24th, 2014

Leveraging II

This is a followup to my previous article on leveraging, where additional explanations were required to make my point clear. The formula presented lets one "control" an acceptable leveraging factor without changing much to the long term output (as a matter of fact, less than 1%). And even there it will be to one's advantage.

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Febuary 9th, 2014

Fix Fraction Position Sizing

My new paper deals with an equation designed to help you make more money by adding small improvements to your own trading strategies (should they not be present already). The equation (named red5) is a complete trading strategy in itself. It presets trading rules in order to generate long term positive alpha. I would suggest that your objective is to extract from red5 what you need and incorporate the lessons learned into your own trading methods.

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December 9th, 2013

Designing a Trading Machine VI

In Designing a Trading Machine V, and previous notes in this series, the point was made, hopefully, that accumulating shares over the long term while trading profitably over the process was another way of looking for some kind of trading edge. This edge had for vision: nΔP, with ΔP > T > 0; producing on average, a positive difference ΔP, a trade profit threshold T to be reached, and which was desirable to repeat many times (n), or else go for a larger ΔP on a small number of trades.

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October 22nd, 2013

Trends?

Everyone seems to agree on the notion and existence of a "trend", but no one seems to agree on its definition. Some want a universal definition with no compromise; like in: this is "the" trend, period. Geez, it's evident, see, it starts here and stops there; it can plainly be seen by anyone of age on any chart of past stock price data what ever the selected time frame.

They want a one size fits all definition that a trend spans a microsecond, a week, a month, 100 years, anything in between or longer. Not only that, but this "trend" should apply to all classes of tradable assets at the same time in the same direction. Please...

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October 5th, 2013
 
A Basic View III
 

The prior two sections: A Basic View and A Basic View II were simply a necessary introduction, just as this part is, to the reasoning needed to look at the trading/investing problem from the  perspective of portfolio optimization under long term uncertainty. 

When considering the graphic presented in the previous section, one soon realizes that it is stating the obvious: we know the past to the penny, we know the now for what it is, and the future remains almost a complete unknown. And yet, we can draw for future data almost a mirror image of the past data presented, even though none of those curves (for t > 0) could be predicted in advance.
 

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August 18th, 2013

A Basic View - Part II

Trend Distribution

When looking for a trading strategy, one usually start with a search for methods and indicators that can identify trends, then proceeds to find triggers as decision surrogates to execute entries and exits. Looking at past stock data, trends of all lengths can be found with ease even if one does not have a clear, precise or universal definition of what a trend is.

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August 14th, 2013

A Basic View

How ever you want to look at your trading methods, some very basic math apply. For sure... It is not, and can not be: those math things don't apply to me, I've got "my proprietary trading system" of play that circumvents all that. Sure...

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August 1st, 2013

Optimization Revisited

Optimization of a stock trading script over past data is the process of finding optimum procedures and parameter value sets which can produce the highest possible return over the testing interval. Robustness could be said of a system having wide ranges of values for each of the parameters in the set and still maintain high performance levels.

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June 5, 2013

Old Routines II

As a follow-up to my previous article on the long term simulated IBM performance results, I've opted to provide more details on the origin and make-up of the underlying trading strategy. That article concluded with the results of a prior simulation done almost 2 years ago in July 2011 using about the same trading script.

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June 5, 2013

Old Routines

Last week, while I was looking for some software routines, I started trying out some of my old programs to see if visually it would be easier to locate them. These programs had not been executed for over two years, and out of curiosity I also wondered how they would have done. It would be like a walk forward test and an out-of-sample test at the same time since none of data could possibly have been known to these programs outside their initial testing intervals.

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April 28, 2013

On Randomness

When looking at stock price series, you often hear that such series are random or almost random in nature and if such was the case, then such series would have little predictability if any. However, some interesting observations could be made depending on the model used to mimic random stock prices.

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April 27, 2013

On Cutting Losses

Over the past few weeks I've been posting in a LinkedIn forum on the subject of trends, randomness and designing out-performing trading strategies. It started as an attempt to answer the question: "Cut your losers and let your winners run". What I wanted to show was this type of market wisdom is not necessarily true.

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April 3, 2013

Designing a Trading Machine V

From the observations made in Designing a Trading Machine IV, it was said to find and select some n(ΔP > T) on a daily basis (or any trading interval for that matter) where n was the number of profitable trades exceeding a certain threshold T.

 

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March 30, 2013

Designing a Trading Machine IV

In Designing a Trading Machine III, I was making the point that ΔP > 0 was a sufficient condition to make a profit: P(out) – P(in) > 0. It says nothing about how the profit is made, but it does say that to have one, the relation must hold. This relation could be considered time, and size independent.

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March 28, 2013

Designing a Trading Machine III

Designing a Trading Machine II ended with the presentation of a simulation test where the objective was to increase the number of profitable trades over the trading interval. The selected script was transform in order to increase its buying procedures and thereby increase its number of trades (some 40-fold over the original) for the 6 years trading interval.
 

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March 26, 2013

Designing a Trading Machine II 

Following the previous article: Designing a Trading Machine, it's time to start designing it. Some considerations or constraints will first be addressed, and from there start to give a structure to a trading strategy that will or should survive over the years.
 

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March 19, 2013

Designing a Trading Machine.

I participate in a LinkedIn forum on automated trading strategies, here are some my observations over the past few days, starting with March 11th.
 
This forum is about automated trading strategies, and yet a lot of talk is on discretionary trading methods which by definition are not automated. In fact, if your trading method is not programmable, it is discretionary; and thereby can not be systematically back tested; otherwise going full circle it would be amenable to code.
 

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January 19, 2013

An Experiment II 

In my previous note: An Experiment, I chronicled the process of modifying the Livermore Market Key trading script after having issued a challenge to anyone wishing to show their system development skills. What follows is the continuation of that article starting with its ending.

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January 10, 2013

An Experiment

In early June 2011, I had this great idea: take a known and publicly available trading strategy; offer a challenge to improve its performance level using what ever enhancements one could bring to the task. I figured it would be a simple way to showcase my own trading philosophy; if it was any good, it would easily show in the test results.
 

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December 5, 2012

Winning the Game 1.1

To make the concepts in my trading methodology clearer and mainly to answer some recent criticism concerning the mathematical expressions used in my notes, short of maybe giving my programs away, I opted to revisit the foundations on which my methods rest and explain them in more detail. 

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October 30, 2012

Winning the Game

How can I win the stock market game? One asks this simple question and is bound to receive a million answers. Almost everyone has a piece of advice on this subject with lots of investment folklore, hot tips and unsubstantiated claims.

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September 30, 2012

A Kind of Review

I have had this web page up for over 15 months now. Its prior version has been on since 2008, and during all this time I have promoted the concept of trading over a stock accumulation process as a methodology that has more than the potential to out-perform most trading methods out there.

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September 8, 2012

Script Transform

I occasionally participate in the Automated Trading Strategies forum on LinkedIn.  And over the past few weeks, I provided some comments which elaborate on the trading methods I use in my strategy design. The following observations are almost in chronological order.

Everyone has a trading method. However, what ever it is, it must do the job, it must deliver. Otherwise why spend so much time and effort to produce under-performers.

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August 19th, 2012

On Compounding

Designing a very profitable trading system is all about compounding. And if there is one thing that any trading method should strive for is to acquire, as much as possible, long term sustainable alpha points. Playing for a 40% return in one year has little value if it is lost the year after ($1.00 x 1.40 x (1 - 0.40) = $0.84).
 
The following graph shows $1,000 at various rates of return over 40 years:
 

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August 6th, 2012

A Changing Game

My latest paper: A Changing Game is out. It summarizes some of my latest articles on random trading over randomly generated stock prices made to mimic real stock prices including rare events or infrequent price gaps. An Excel file is provided for the more venturous. It contains a lot of lessons for those that want to look beyond what is there.

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July 30, 2012

On Doubling Time

The stock market game is a compounding rate of return game. The main objective is to obtain a compounded annual rate of return as high as possible over the longest time interval within portfolio constraints of which the first is not to go bankrupt.

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July 8, 2012

Changing the Game III

After the conclusion of my previous note: Changing the Game II, it was time to put the finishing touches to the Excel file.

This file is a working model designed to showcase some basic trading principles and methodology. It is not an end all, but it does show that accumulating shares and trading over this accumulative process can generate profits even if the entries and exits are taken at random.
 

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July 4, 2012

Changing the Game II

In the previous chapter: Changing the Game, it was presented that even trading randomly over randomly generated stock prices could not only generate a positive outcome to the portfolio payoff matrix, but that this outcome could generate exponential growth.

To some, it is unthinkable that a trading strategy governed by randomly generated trades over randomly generated stock prices (including unpredictable gaps) could have profits on an exponential growth rate or even a positive growth rate for that matter. As was said in prior notes, the expected value of using heads or tails to determine some other heads or tails' bet is zero. Except if one or both coins are slightly biased.

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June 25, 2012

Changing the Game

The Setup

In my last commentary: Randomly Trading, I presented the execution of a randomly generated  trading strategy over randomly generated stock prices. The original intent was to answer someone on a LinkedIn forum on how to build a payoff matrix:  Σ(H.*ΔP). So model 1 (very basic Excel file) was provided to show how to set up all 4 of the needed matrices: P, ΔP, H and H.*ΔP. Each matrix dealing with an aspect of a the payoff matrix used to  simulate a portfolio of 10 stocks over 250 trading days (about a 1 year trading interval).

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June 1, 2012

Leftover Bollinger Band

After dumping the Ichimoku script and starting to transform a Bollinger Band trading system found on the old Wealth-Lab 4 site, it was obvious that this new system had more potential. At least it could kept part of its identity.

It turned out that this “new” 2008 system was a variation of a 2002 Bollinger Band system designed by Mark Brown participating in one of the forums I visit on LinkedIn. At times, the world may be very small.

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May 23, 2012

End of Ichimoku

The Ichimoku Kinko script was improved to such an extent that it has become an enviable script with desirable long term performance results. Over its almost 6 year test, it achieved a 47% CAGR while accumulating shares and cash in its account. It's average hit rate improved from some 35% to over 70%. 40 of the 43 stocks in the portfolio showed higher performance results. Of the 16 stocks showing losses in the original test, only 3 remained with total losses representing less than 1% of the generated portfolio profits. A very small price to pay for the added performance.

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May 21, 2012

Growth Optimal Portfolio IX

Designing Trading Rules

If you want to implement an “enhanced” trading strategy H+ based on buying and selling functions and/or procedures, your primary objective as before is for your portfolio to exceed the Buy & Hold, performance wise.

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May 14, 2012      Last modified May 20th, 2012  (see bottom of page)

Improving Ichimoku Kinko

After presenting the performance results of the original  Ichimoku Kinko Yho script, it was time to start making some improvements, but first, there is a requirement of understanding what the script is doing exactly, and once this understood, proceed to the next phase.

The  Ichimoku Kinko Yho charts 5 lines over past data resulting in a variation of a moving average cross-over system. It goes on the assumption that a lagging moving average projected in the future might have some forecasting value! The price 26 days ago is just that, the price 26 days ago.

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May 6, 2012

Growth Optimal Portfolio VIII

Developing a Trading Philosophy

Usually, developers start by programming trading strategies based on some particular idea or concept they might have on market or price behavior. The objective is simply to design a better trading strategy. They will backtest over past market data to see if the trading procedures do generate profits or not. And from there will start an iterative process to improve the structural design of their new preferred trading strategy. But maybe they should also look at the trading environment and trading philosophy behind their trading strategies. What are the concepts that govern the methodology used? This research note tries to answer that very question. Not just to design a trading strategy but to design a trading methodology and a supporting long term investment philosophy.

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April 23, 2012

Growth Optimal Portfolio VII

Conquering Compounding Power

Recently doing some tests on some scripts that are still available on the old Wealth-Lab 4 site, I noticed that I could select almost any script and push its performance level higher. And that raised the question: why? You should not be able to do this. All scripts are like trading philosophies, a modus operandi, a kind of recipe trying to print money. These trading strategies are not just some set of random buy and sell orders. They make assumptions on their “tradable” universe and try to profit from the vagaries of the price action. All try to exploit some form of price behavior. Be it dip-buyers, trend-followers, break-out players, mean reversal gamers or contrarian illusionists; all try to make a profit. All the scripts had some form of concept on how prices move and what ever the trading strategy applied, it was to transform this perception into dollars in the bank.

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April 20, 2012

Some observations.

Over the past few months, I've been mostly involved in backtesting various concepts to see if they could enhance my trading methodology. The process is still on-going. But meanwhile, I thought it might be interesting to respond to a post on the LinkedIn group: Automated Treading Strategies, where the question was: why not show your methods live on Covestor or Collective2? This way any one would see your so called “over”-performance results, and if your trading methods have any value.

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March 4, 2012

A Single Equation

All I have written on this web site is dedicated to a single equation which translates a single concept. This equation has evolved over the years but not in what it represents or its governing trading philosophy. The concept remained the same throughout: trade over a long term share accumulation program. And in its latest iteration, serving as an explanation for the process, its pay-off matrix representation looks like this:

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March 22, 2012

Growth Optimal Portfolio VI

After having added a covered call and a naked put program to the trading component applied to the share accumulation routine, we were left with the following pay-off matrix:

  Σ [ hioI(1 + gi + Ti + CCi + NPi) t-1 .* Pio((1 + ri)t  - 1)]

where gi was the reinvestment policy rate, Ti the trading strategy contribution rate, CCi the covered call contribution rate for each of the individual stocks, and NPi the naked put program contribution. All these procedures combined to generate alpha at an exponential rate. (see On Growth Optimal Portfolio V for details)

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January 31, 2012 

On Trade Slicing

Recently, in a LinkedIn forum, I presented my latest research note: Optimal Portfolio V. The object was to show that designing holding functions that can increase exponentially in time had for secondary effect to increase portfolio profitability at an exponential rate as well.  Using a stock accumulative process to which was added a trading component could produce exponential alpha that went way beyond the Buy & Hold strategy.

I thought that the only way to show my point of view was to apply my trading methods on a stock which resulted in the following IBM chart:

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                        New PresentationAlpha Power Trading Methods.

January 22, 2012  

On Growth Optimal Portfolio V

On Growth Optimal Portfolio IV ended with: “By adding a trading component to the above equation, one can push performance to an even higher level”.

A major feature of my Alpha Power Trading methodology resides in the fact that the excess equity buildup is used (instead of letting it go to waste), not to predict future prices, but simply to increase the inventory at a rate as close as possible to the price appreciation rate. This way reaching exponential alpha using a profit reinvestment policy; similar to reinvesting dividends as they are received.

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January 4, 2012

Growth Optimal Portfolio IV

Programming trading strategies is a succession of transforming an idea or ideas into procedures that when applied performs better than the previous iterations.

I am always looking for the best performer. I also know that I can always do better. But there are some basics. First, I know that what ever effort I deploy I must at least beat the long term Buy & Hold strategy. If I can not do that, then why do all the work?

I can not know now what the Buy & Hold strategy will bring 20 years from now. I don't even know which stocks will survive or prosper for that matter. However, I have to decide now on a particular trading strategy and live with it no matter what happens and should that strategy fail, then I better turn around fast, limit the losses, quit or go back to the drawing board.

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November 26, 2011

Latest Simulations

What follows are some of my posts on LinkedIn presented in reverse order, starting with the most recent and going back in time. Therefore it might be preferable to read this from the bottom up by date.

November 26

Here are some of my observations concerning the last 9 charts (AAPL, AGQ, AMZN, DDS, IMAX PNRA, SHS, SINA, ULTA) presented.

All 9 charts used the same script. There are currently 47 competing trading procedures in that script making it a complex structure to manage; each procedure having its own mission at trying to improve on performance if it can. Not knowing the future, a position is taken based on a decision surrogate, and then managed with only two possible outcomes: a profit or a loss.

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November 11, 2011

Growth Optimal Portfolio III

In my previous notes, I tried to make the case that by adopting administrative procedures, a portfolio manager could achieve an exponential Jensen ratio. All he/she had to do was to re-invest the portfolio's profits as they came in. Not that difficult a task! About the same as re-investing the dividends as they came in. Nothing fancy, I would even say boring, but a task that needs to be done nonetheless.

How come simply re-investing the accumulating profits can increase portfolio performance to such an extent that it would propel the Jensen ratio to an exponential function?  That is the question. Why all the academic financial literature I have read (some 450 thesis), not one, and I mean not one, has ever suggested re-investing the accumulating profits and thereby out-perform the Buy & Hold? Not only out-perform, but achieve an exponential Jensen ratio. Was the concept to hard to grasp? Wasn't it before their eyes all the time?

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October 26, 2011

Growth Optimal Portfolio II

I tried to shown in my previous article that by adopting a profit re-investment policy it was not only possible, but a simple way, to achieve an exponential Jensen ratio. The conclusion was that skill matters, and it grows with age. It's part of the very nature of an exponential time function. So the question becomes: why aren't most investors adopting such a winning strategy? Or better yet: are there ways to improve on this exponential Jensen ratio?

Just opening the door to exponential alpha may represent a major shift in modern portfolio theory.  It says you are no longer bound by the efficient market frontier. That there is more out there and that simple procedures can give you access to higher returns. Not only higher returns, but returns increasing exponentially with time. This is almost heresy. Nobody has challenge the efficient market frontier for the past 60 years; no one has dared. All Modern Portfolio Theory is based on some form of the assumption of the efficient market hypothesis. And if markets are efficient, even under the weak form, then there is no long term alpha generation; and certainly no such thing as exponential alpha.

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October 22, 2011

Growth Optimal Portfolio

Academic financial literature is fond of the notion of No Free Lunch (NFL); which is the same as saying you can not do better than us. If there was a free lunch, we would have eaten it already; there might be some crumbs left but then again...

Long term return expectancies, from what ever trading method you intend to use is simply what the market has to offer which is the average market return. And if you ever do better than average, it most certainly will be due to sheer luck. Skill has very little to do with it, we have found very little evidence of the existence of alpha. And even if we temporarily found some, long term, it would tend to zero. You would be like the exception that confirms the rule (someone has to be at the distribution's extreme, but we don't know who); and we have to confirm that the odds are stacked, really stacked against your over-performance. If we can't do it, be assured you can't either. We have tried hard enough.

Well, not necessarily.

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October 18, 2011

Trend or No Trend

Since last April, I have done numerous trading simulations looking for which of several trading methods I would like to adopt. Its a search not only for the best performer but also for the one I'll be most comfortable with; and which would become my trading platform for the coming years. That is a strange statement, as if when developing trading strategies there was something else than profits that mattered. For instance, even with its numbers, I would not choose the modified Turtles V3.2 as I found it mostly a horrible script, first for its metrics and then for its stressful environment generated. There are much better scripts around.

I am a research team of one. Everything takes time. If I was looking to improve on usual trading methods, my search would not take so long; and I certainly would not even consider writing about it.

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ITRADE Formula

This article is dedicated to Ian who asked very pertinent questions concerning my trading methods on LinkedIn's forum: Automated Trading Strategies. But I did not have simple answers. I was aiming for a short reply, but it morphed almost in a thesis. I am sure he will recognize from the maze below answers to his questions.

On Performance Metrics

Some of the usual performance metrics became irrelevant if not useless due to the nature of the trading strategy used. For instance, because you are holding on to a major portion of your trades, they tend to show high hit rates when in fact, you are just holding the bag on many still opened positions. So you get hit rates in the 80 and 90% with corresponding high profit per trade and comparatively small losses on losers; not because you accurately forecasted future prices but because you held on a long time on your highest winners and a much shorter time on your losers.

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October 7, 2011

On Back-Testing II

My back-testing methods usually start the same. I locate a script for one reason or other, try it on a few stocks, then look at the script to explain what I saw on the generated charts and analyze the trading decision making.

In this case, I started yesterday with the: “One Minute Bollinger Band System” chartscript listed on the old Wealth-Lab 4 site. This script is presented as Technique 16 in the book: “Trade Like a Hedge Fund”.

You do not need to test that many stocks to see if a chartscript is worthwhile. The total profit generated by the five charts below amounts to: $ 2,968 after having taken 553 positions of which 551 were closed ($11,000 in commissions). A total of $500,000 was invested over the 5.8 years test. Roughly, the total profit, on average, was about the equivalent of $100 per stock per year per $100,000 invested.

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September  28, 2011

On Seeking Alpha.  Part III

Trading over Accumulation Procedures

First, a Side Note:

All my writing is to show that by slightly changing one’s point of view, one can design automated trading systems that not only outperform the Buy & Hold but will leave it far behind performance wise. The first point being made is probably that the Buy & Hold and short to mid term trading are not mutually exclusive; combined they can do wonders.

Probably the second most important point would be a critic of the Buy & Hold trading philosophy where the hold is interpreted as doing nothing, just waiting it out long term. This turns out to be a waste of resources.

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September 14, 2011

On Seeking Alpha.  Part II

Trading on What?

For the short term trader, trading on what basis becomes a major concern. Fundamental data is of little help analyzing short term market activity. Technical data is good at saying what was, but has little short term predictive powers.  Statistics seems to become the only source of filtered data that can bring an edge, but there again, all you will find will describe past price action.

The shorter the trading interval, the more a trader is confronted with the quasi random nature of price movements and therefore the closer his trading should be considered a subset of a random data series. And as a consequence, the more a short term trader will become a momentum, volatility or noise trader. He does not have much choice.

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On Back-testing

Not everyone needs to do back-testing and therefore to some it is not a worthwhile endeavor. There are thousands of ways to play the stock market game and a lot of them do not require back-testing at all.

For instance, I am sure Mr. Buffett does not do any kind of back-testing nor does his staff has any time for it. Mr. Buffett's investing methodology is based on his great understanding of the fundamentals of the markets (reads some 200 annual reports a year), his wide experience and the fact that he views the market in a long term uptrend that has lasted for decades and he is ready to bet, and has bet, that this secular trend will continue for some time to come. For other reasons, there is no need to back-test for momentum and discretionary traders following their hard earned experience on how to play and win.

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Implementation: Still On.

I have been in the implementation phase for over 6 months now. A lot of back testing has been done; always improving on the trading model and pushing performance higher and higher. From the first implementation where the compounded annual growth rate (CAGR) was around 50% to the latest iterations where the CAGR exceeds 100%, it has been a long journey.

All back tests were done for the sole purpose of proving that the concepts presented in my papers had a real foundation in reality. And the simulation results on real market data demonstrate that the underlying trading methodology is not only valid but also gives credence to the whole mathematical framework presented in my papers.

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This site is dedicated to a single concept and that is: it is not only possible to beat the Buy & Hold strategy; it can be done easily and by a wide margin. Doing so requires changing slightly the point of view of the stock trading process and is summarized in the graphic below:

   Boosting the Buy & Hold Strategy

Boosting Buy & Hold

Alpha Power Equation.  See Livermore Simulations Tests

 

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Recent Topics

Welcome

Trading Mechanics    (Book available on Amazon)

Trade Slicing Stocks  (Book available on Amazon)

Simple Strategy II       (Aug. 19th, 2016)

Simple Strategy I        (Aug. 16th, 2016)

Big Bucks Will Travel    (Aug. 5th, 2016) 

Back to Quantopian      (Aug. 3rd, 2016)

Generate Positive Alpha  (July 28th, 2016)

A CAGR Debate           (July 23rd, 2016)

Strategy Survival               (July 11th, 2016)

Stop Loss Revisited III  (June 22nd, 2016)

Stop Loss Revisited II   (June 21th, 2016)

Stop Loss Revisited     (June 20th, 2016)

You Don't Always Win (June 7th, 2016)

Portfolio Drawdowns      (Pub. May 3rd, 2016)

A Different Take             (Pub. April 29th, 2016)

Stock Trading Strategy Value II  (Pub. April 20th, 2016)

Trading Strategy Value  (Pub. April 13th, 2016)

Trading Environment  (Pub. April 8th, 2016)

Randomness Stock Prices II  (Pub. March 9th, 2016)

Randomness in Stock Prices   (Pub. March 4th, 2016)

Strategy Experiment V    (Published Feb. 8th, 2016)

Strategy Experiment IV    (Published Jan. 30th, 2016)

Strategy Experiment III     (Published Jan. 26th, 2016)

Strategy Experiment II     (Published Jan. 22nd, 2016)

Strategy Experiment      (Published Jan. 19th, 2016)

Trading Randomness    (Published Dec. 28th, 2015)

Delayed Gratification II    (Published Oct. 24th, 2015)

Delayed Gratification       (Published Oct. 23rd, 2015)

A Case Study          (Published Oct. 22nd, 2015)

Strategy Design II      (Published Aug. 24th, 2015)

Strategy Design I     (Published Aug. 23rd, 2015)

Portfolio Building  (Published July 26th, 2015)

Still More DEVX       (Published July 23rd, 2015)

More DEVX V6               (Published July 3rd, 2015)

Connecting Dots           (Published June 16th, 2015)

Trading Perspectives    (Published June 1st, 2015)

Cheating by Spoofing   (Published April 27th, 2015)

Portfolio Math I       (Published January 30th, 2015)

Trading Short Term?   (Published January 2nd, 2015)

DEVX V6 Revisited   (Published Novermber 25th, 2014)

A Unique Approach   (Published Novermber 11th, 2014)

A Donor Within      (Published Novermber 6th, 2014)

Winning by Default II (Published August 11th, 2014)

One For All?                     (Published August 3rd, 2014)

Winning by Default   (Published July 28th, 2014)

Test Summary               (Published July 20th, 2014)

Nest Egg on Support       (Published July 13th, 2014)

Trade Automation       (Published July 1st, 2014)

Deviation X                 (Published June 18th, 2014)

Swinging It                 (Published June 18th, 2014)

 Unorthodox Trading   (Published June 9th, 2014)

 Bizarre Behaviors   (Published June 3rd, 2014)

This Crazy Game      (Published April 30th, 2014)

The Drift                        (Published April 22nd, 2014)

Leveraging II             (Published:March 24th, 2014)

Leveraging                 (Published: Feb. 19th, 2014)

Fix Fraction                (Published: Feb. 9th, 2014)

A Trading Machine VI  (Published: Dec 9th, 2013)

Trends?                     (Published: Oct 22nd, 2013)

A Basic View III 

August 2013

A Basic View II  

A Basic View 

Optimization Again

Optimization Revisited

June-July 2013

At Last

Old Routines II

On Drawdowns

Old Routines

April-March 2013

On Randomness

On Cutting Losses

A Trading Machine V

A Trading Machine IV

A Trading Machine III

A Trading Machine II

 A Trading Machine

January-February 2013

The Question

On Forward Testing

An Experiment II

An Experiment

October-December 2012

Winning the Game 1.1 

Winning the Game

August-September 2012

A Kind of Review

Script Transform

On Compounding

A Changing Game

June-July 2012

On Doubling Time

Changing the Game III

Changing the Game II 

Changing the Game 

Randomly Trading 

Leftover Bollinger Band 

May  2012

After Dumping Ichimoku 

End of Ichimoku 

Optimal Portfolio IX 

Improving Ichimoku  

Ichimoku Kinko Test 

Optimal Portfolio VIII 

(Published Aug. 24th, 2015)

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