February 28, 2017

You often hear academics and traders say: "all trading strategies fail over time". They don't provide proof but will provide examples to make their point. And usually, for the examples they present, I agree, those strategies should fail. It is as if their selected trading strategies were designed to fail in the first place, and therefore, no one should be surprised if eventually, they do fail.

There are exceptions, but I do not see them as such. I view them as better strategy designs. They don't need to be elaborate or complicated. But, they don't need to be simple, either. However, they will have to adhere to common sense and the math of the game.

February 21, 2017

The stock market game is played under uncertainty. You are not totally certain of what the future may bring. However, if you take a long term view of things, you could look for "stuff" that does make sense, and might most assuredly continue in the future.

I do expect, with a very high probability, that tomorrow there will be more people on the planet. I can not be certain on a day to day basis, but I do know I will be right almost every day of the year since it will require a huge disaster for that statement not to hold. And those "events" do not happen every day.

February 12th, 2017

This is part of my post-test analysis of the last three articles I wrote (see list below). All the tests were ran on Quantopian servers using their data under the same conditions as everyone else. I used a slightly modified version of the program found on their site.

The original cloned program used (The SPY who loved WVF) showed a 22.43% portfolio CAGR over its 6-year test. And this, while using 3x leveraged ETFs. If you did the math to convert the thing to a no leverage scenario, the CAGR would drop. There were no leveraging fees in this ETF scenario since leveraging is included by design. But, this still made it a 3x leveraged portfolio.

February 7th, 2017

The **previous article** made the point that you could increase a stock portfolio's performance by slightly increasing a single variable. The given portfolio equation was:

A(t) = A(0) + (1+g)^{t} ∙n∙u∙PT.

Based on this, in the previous test, g was raised by 1.5%. This time, it will be raised by 2.0%. And since g is part of a compounding factor, it should show its impact all over the strategy's timeline.

Once you have your trading strategy, meaning you have a long term positive edge. There will remain one question. How can I do more of that?

February 6th, 2017

My last article showed impressive test results.

Yet, my book states that one could do even better. One could start with a trading strategy having some built-in edge as was presented in Part one. And build from there. The portfolio equation to be used would still be:

A(t) = A(0) + (1+g)^{t} ∙n∙u∙PT.

Raising g will increase the total output. You do not need to push by much since there is a compounding effect in place.

As a demonstration of the phenomenon, I used the same trading strategy as presented in the previous article. Raised its g value by 1.5%. A minor modification, yet, the impact is noteworthy.

February 4th, 2017

The **HTML file** at the end of this article relates to my transformation of a cloned trading strategy as found on the Quantopian website. It was first declared as not worth pursuing. But I like to take such strategies and make them do more. A kind of demonstration of what you can find in **my book** holds.

The premise is simple. If the stuff presented in my book works. Then, almost as a foregone conclusion, based on those principles, I should be able to make such a trading strategy outperform. And the applied trading procedures would have a positive impact on the overall performance level. That is what this HTML file is all about. Making do with what was ordinary stuff, and making it great. You be the judge.

January 27th, 2017 **NEW BOOK OUT**

**My new book is out. It is available on Amazon.**

**Building Your Stock Portfolio** has for sole purpose to help you make more money. It is about you building a long term stock portfolio for whatever reason you might have, and making sure you reach your goals.

Is presented the making of a trading philosophy, a methodology which hopefully could become part of yours. My main objective being that you will not be copying what I do, but doing what will be right for you going forward.

January 20th, 2017

In a previous article was put forward the notion of a trading strategy's signature. It was defined as the output of a long term automated stock trading strategy that traded a lot. The result of a program which executed what it was programmed to do over an extensive period of time.

If a stock trading strategy is designed to generate thousands upon tens of thousands of trades, it will asymptotically approach a kind of law of large numbers. Meaning that the numbers in n∙u∙PT will become more representative of the whole due to the sheer size of n.

January 19th, 2017

In my last article, A Stock Trading Strategy Signature, I presented a model for a trading strategy, an equation. It is derived from the payoff matrix, another expression used to resume a portfolio's entire trading activity over its lifetime. This model has interesting properties.

It too resumes, in just three numbers, the total outcome of any stock trading strategy:

January 16th, 2017

This is the continuation of: Playing the Stock Market Game: Time is All

Repeatedly applying an automated trading strategy to a bunch of stocks in a backtest will produce the same answer every time. It is the output of a program. A recipe, a set of trading rules, procedures, coded instructions and software routines.

Since the output of a trading strategy can be expressed as a time function: A(t) = A_{0} + n∙u∙PT, then, A_{0} + n∙u∙PT is its unique signature. Leaving us with 3 portfolio metrics of consequence.

January 7th, 2017

People don't see how easy it could be to do more, if only they gave it more time. The ultimate objective is to outperform long term averages, and making sure you do. So, here is a back to the basics.

You give yourself the job to go from point **A** to point **B**. Nobody is forcing you on this, that is to play this game. You already know your point **A**, that is where you are right now; with all your resources, know how, and expertise. You know where you want to go. The only thing left is to determine the path to get there. And here Google Earth or a GPS won't help you.

December 29th, 2016

My previous article (**The WOW Factor**) might appear at first glance as an exaggeration of some kind. For one thing, it is not a hoax or a data manipulation of some kind. It is just an aggressive trading program. It only needed deep pockets. The simulation was part of the development cycle where one tests for up and down limits. A lot of it is doable under more restrained methods. These added methods would have for only purpose to reduce the strategy's volatility and drawdowns. They would still generate high returns, lower than what was shown, but still relatively quite high compared to market averages.

December 18th, 2016

In my previous post, it was said I would not trade in that fashion. For one, I do not have that kind of capital available. And two, I may be too chicken. I prefer a smoother ride. But, that does not mean that this particular trading strategy is wrong, or that we can not extract useful trading procedures from it. Even downplayed the strategy could make quite an impact.

The strategy did give more than an indication of where upper trading limits might reside. And based on the strategy's code, it could do even more. I was exploring to find where these limits were, and even at the presented level, the program had not reached them yet.

December 16th, 2016

I will start with the conclusion since it is intended to raise eyebrows and it can be given in one screenshot. The chart below comes from modifications to the program found in the Quantopian Lecture 43: Example Momentum Algorithm. To achieve such results, I modified parts of the code that dealt with n, u and PT since they are the only portfolio metrics of significance. For more explanations on the portfolio payoff: n*u*PT, please refer to recent articles.

December 7th, 2016

Usually, when changing an automated stock trading strategy, it implies making changes to the trade selection process and trading rules resulting in changes to a portfolio's trading history. But, each time doing this brings changes to trading procedures, and these changes tend more and more to over-fitting the data.

The very process intended to improve a trading strategy might be moving it further and further away from reality. Often, even making it less valuable. Some go as far as actually destroying any chance a strategy might have had of ending with a profit.

November 25th, 2016

My last series of articles started with setting up the mathematical backdrop to a stock trading methodology made to last. Putting a stock portfolio payoff matrix at the center of it all as the bean counter for any trading strategy: A(t) = A(0) + Σ(**H**.*Δ**P**). This time function was then reduced to: A(t) = A(0) + n * u * PT.

Three numbers of interest: number of trades done, trading unit used, and average profit percent for trade. Three portfolio metrics given by any simulated, or live, stock trading portfolio whatever its composition. One could view n * u * PT as a trading strategy's signature.

November 18th, 2016

The previous article: Controlling a Stock Trading Strategy was to show you could control a trading strategy to do more than it had before by using mathematical functions that could impact its 3 most important portfolio metrics: n, u, and PT, namely the number of trades, the trading unit used, and the profit margin.

To show you could do more,

November 16th, 2016

It was said in: The Deviation X Strategy, that it was controllable. When saying something like that, I like to provide some kind of evidence that what was said holds.

The **DEVX8** stock trading strategy has nine controls that can be viewed as sliders or knobs. Each having its purpose. Only six are shown on a chart (see chart #1 below: Control Setting, top left, second line).

November 14th, 2016

For those that have followed this series of article over the last two months starting with the Payoff Matrix, it is time to show how all of it can be applied in a trading strategy now that the mathematical background has been provided.

The last time I did a portfolio level simulation using the **DEVX** strategy was last November, not quite a year, but close enough. The one last shown was dated October, using a prior version (DEVX6 dated June 21, 2014) which was more aggressive.

November 11th, 2016

In the previous 3 parts of this series was presented the output of any stock trading strategy using just 3 portfolio metrics: n*u*PT. The number of trades done, the bet size, and the profit margin, as if dealing with an inventory management problem. Only 3 numbers, two of which you can fix yourself, and the other, you can control to some extent.

November 11th, 2016

In **A Stock Trading System** – Part I, and Part II have analyzed some of the workings of the 3 metrics: n*u*PT, which summed up a portfolio's trading history. Part II, ended with a question. It was not: can more be done? But, will you do more?

Each stock trading strategy has its own "signature". It depends on the portfolio's stock composition and how trading is performed over time. In the end, at bean counting time, all you did trading will be explained by these 3 numbers: n*u*PT.

November 8th, 2016

In A Stock Trading System – Part I, was made the case that 3 stock portfolio metrics were sufficient and of major concern when making the analysis of a stock portfolio's end results.

Having only 3 metrics to describe the output of a portfolio management system, it then falls on those three metrics to explain what is going on.

November 6th, 2016

In my last article: A Tradable Plan – Part I, it was expressed that only 3 numbers, three portfolio metrics are sufficient to summarize all the trading activity and trading history of any stock portfolio over any duration.

Those numbers were: **n**, the number of trades, **u**, the trading unit used (bet size), and **PT**, the average percent profit per trade, profit margin, edge, or, whatever you might like to call it.

November 2nd, 2016

*This new HTML file is another step in this series of articles. Refer to preceding articles starting with the Payoff Matrix to gain a better understanding of what is being put forward in this two part installment.*

Any automated stock trading strategy can be resumed by 3 of its performance metrics. Namely, the number of trades, average bet size, and net profit margin per trade (n, u, PT). Everything else is of lesser consequence, part of features, preferences, or descriptive properties.

If those 3 numbers totally explain a strategy's final result, then that is where one should put his/her efforts when designing, or modifying a trading strategy.

October 22nd, 2016

This article shows what I consider the core of a trading strategy. Looks at the trading problem from a different angle than most. Starting from the end results metrics, then going back to design strategies that will affect these metrics over the entire trading interval. As if designing a strategy backwards, but most certainly constructively, allowing for a multi-asset, multi-period view of the stock portfolio management problem.

October 13th, 2016

The HTML file below starts to elaborate on trading methodology infrastructure. It is part of the background information needed to go forward. It uses a MACD trading strategy as example to set mathematical structure to trading procedures. It could have used something else, the whole point is not on the MACD, but trading strategies in general.

October 10th, 2016

The HTML file below tries to elaborate on the predictability, not of stock price movements, but mostly on portfolio performance outcomes. It tries to do this using only two numbers, one of which is just a trade counter.

The objective being to show that those two numbers which characterize a trading strategy can add some understanding of a strategy's long term goals. As if giving the ability to make napkin estimates of where a portfolio might be some 20+ years down the line, thereby providing a reasonable guesstimate.

October 3rd, 2016

The HTML file below deals with the perception of trading decisions within the context of building a long term stock portfolio. It is the continuation of a series of articles dealing with the underlying math behind a stock trading strategy.

Instead of looking for a trading strategy that tries to shift its portfolio weighs from period to period as in a Markowitz or Sharpe rebalancing scenario, the search is for long term repeatable procedures that can affect a portfolio's payoff matrix over its entire multi-period multi-asset trading interval. The main interest is not on a trade here and there, but on the possible thousands and thousands of trades over a portfolio's lifespan. All influenced by the trading functions put on the table.

September 25th, 2016

What I see most often are stock trading strategies that operate on the premise of finding some kind of anomaly or pattern that the developer hopes will repeat in the future. He tries to select the best methods he has to do the job. But, it still is limiting in the sense that one is not looking to increase the number of trades but simply to accept the strategy's generated number of trades. As if looking only at one way to increase end results. It's okay, but one should want more, and could do more.

September 15th, 2016

The following article is part of a series. It deals with ways to enhance a stock trading strategy by incrementally increasing the number of trades to be executed over a long term trading interval as well as increasing the average profit per trade. Thereby, giving a higher performance at the portfolio level.

File: Strategy Enhancers

September 13th, 2016

This article examines stock trading strategies with structural defects. Meaning strategies designed to fail, even before they start trading. It is not because someone has designed a stock trading program that it will make money. You need more than that. One thing is sure, might as well learn not to include in your own programs trading procedures that are almost assured to obliterate your long term portfolio performance. But then, anyone can design their trading strategies the way they want.

This is the third part of a series, other two can be accessed at:

September 8th, 2016

Designing trading programs implies mathematical formulas. We all have a vision of what our trading programs should do. Presented in this article, as in the prior one (Payoff Matrix) are building blocks for what I want to do with Quantopian. As if putting on paper, preparing an overall plan on how I want to use its facilities. The process could help others.

September 6th, 2016

The HTML file listed below is full of matrix formulas. You don't need math to understand the message. For me, putting an equal sign on something is a big statement. All one can do after is declare: not equal, and show why. It is not a matter of opinion anymore, it is a matter of proof.

The file looks at the trading problem from a payoff matrix perspective, which in itself can represent any trading strategy whatsoever. It concludes with any trading strategy could also be expressed as: the number of trades times the average profit per trade, leaving only two variables to consider when designing trading strategies.

August 27th, 2016 **Updated**

The chart below is a simplified model of the SMRS where I've idealized market swings based on the setup premises in the trading program. The strategy's source code is available on the Quantopian platform and referenced at the start of: Simple Stock Trading Strategy I. Further test results on some modifications to the program with their explanation can be found in: Simple Stock Trading Strategy II.

August 19th, 2016

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:

August 16th, 2016

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.

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

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.

August 3rd, 2016

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.

July 28th, 2016

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.

July 23rd, 2016

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."**

July 11th, 2016

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.

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.

June 20th, 2016 **Also available in PDF**

**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.

June 20th, 2016 **Also available in PDF**

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.*

*Commentary*

June 7th, 2016

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.

May 3rd, 2016

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.

April 29th, 2016

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.

April 20th, 2016

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.

April 13th, 2016

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?

April 8th, 2016

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.

March 9th, 2016

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.

March 4th, 2016

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.

February 8th, 2016

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.

January 30th, 2016

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.

January 26th, 2016

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.

January 22nd, 2016

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.

January 19th, 2016

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:

January 2nd, 2016

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.

December 28th, 2015

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.

October 24th, 2015

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%.

October 23rd, 2015

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.

October 22nd, 2015

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.

August 24th, 2015

(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).

August 23rd, 2015

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.

July 26th, 2015

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.

July 23rd, 2015

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.

July 3rd, 2015

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.

June 16th, 2015

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.

June 1st, 2015

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.

April 27th, 2015

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?

January 30th, 2015

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.

November 25th, 2014

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 2^{nd}. 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.

November 11th, 2014

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.

November 6th, 2014 New paper: **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.

August 11th, 2014

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.

July 28th, 2014

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.

July 13th, 2014

**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.

June 24th, 2014

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.

June 18th, 2014

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.

June 8th, 2014

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.

June 3rd, 2014

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.

April 30th, 2014

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.

April 22nd, 2014

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.

March 24th, 2014

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.

Febuary 9th, 2014

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.

December 9th, 2013

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.

October 22nd, 2013

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...

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.

August 18th, 2013

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.

August 14th, 2013

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...

August 1st, 2013

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.

June 5, 2013

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.

June 5, 2013

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.

April 28, 2013

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.

April 27, 2013

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.

**April 3, 2013**

From the observations made in **De****signing 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.

March 30, 2013

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.

March 28, 2013

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.

March 26, 2013

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.

March 19, 2013

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.

January 19, 2013

*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.*

January 10, 2013

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.