March 1, 2018

Basic Portfolio Math

Some of the stuff in portfolio management is so basic that we often forget how really basic it is. The building block of a portfolio is the position taken in some shares of a listed company as an investment or as a short-term speculative move. In both cases, the objective is to make a profit. We buy an asset and hold on to it, or resell it later on for a profit. Trading is simply doing the latter more often.

The problem is not with the understanding of the game, it comes from asking very basic questions, like: what, when, and how much. An even more basic question is: why initiate that trade, at that time, in the first place?


January 10th, 2018

The Payoff Matrix Challenge

Portfolio management theory has had a lot of books written about it. However, few show how easy it can be to express the outcome of a large portfolio's total trading activity using a single mathematical equation.

I use a payoff matrix for simplicity and convenience. The outcome of a payoff matrix gives the total profit or loss of a stock trading strategy: Σ(H∙ΔP). It is a simple expression and it carries a big punch.


November 9th, 2017 

What Is In Your Stock Trading Strategy?

My last article: Trading a Buy & Hold Strategy. A Game You Can Play might have had a better subtitle as:A Game You Can Win. Was made a demonstration that a well planned long-term stock trading strategy can be designed to survive and thrive for years and years. I had my preferred strategy (DEVX8) do its third walk forward, this time for almost a year. See article for details.


November 3rd, 2017 

Trading a Buy & Hold Strategy.   A Game You Can Play

Imagine, proposing to buy stocks in an upmarket. For over 6 years, my website has had a simple message: accumulate shares for the long-term and trade over the process. Using trading profits as a source of added capital to accumulate more shares. A kind of self-financing proposition. Over a dozen different stock trading strategies have demonstrated how it could be done.


October 18, 2017 

Building Your Retirement Fund

You invest and trade in stocks not only to get richer but also to build up a retirement fund either, for yourself, your children or others, from which at some point in time you would want to extract cash for living expenses or whatever other purposes.

My last article (A Price Tag on Alpha - Part III), of a 3-part series, concluded with the realization that should one have a stock trading strategy that is generating some alpha, then he/she/they might be better off implementing it for themselves. Other benefits could be had. One I would like to address is the building of a retirement fund.


October 12, 2017

A Price Tag on Alpha - Part III of III

This 3-part series: A Price Tag on Alpha is trying to answer the question: What would people pay for performance of over 25% on a yearly basis? Part I covered the basics and Part II left some questions unanswered especially concerning the price one should pay for this 15% alpha.

A 15% alpha starts to be interesting if, and, I would say only if, F0 (the initial capital) is large enough, and that the trading strategy is designed to maintain its CAGR for years. If not, the strategy is not worth as much.


October 8, 2017

A Price Tag on Alpha - Part II of III

In A Price Tag on Alpha - Part I of this series, we barely covered alpha generation. All we did was put on the table an expression for the future value of the most expected portfolio outcome, this taken from the US stock market secular trend over durations of 20 and 30 years. We did provide a formula with the alpha considered but have not shown its long-term impact. Time to remedy that.


October 4, 2017

A Price Tag on Alpha - Part I of III

The other day, someone in a Quantopian forum, probably referring to his stock trading strategy, asked the question: What would people pay for performance of over 25% on a yearly basis?

The answer evidently should be a lot as this might put someone at the very top of the 0.1% of portfolio managers. For instance, to give this some perspective, Mr. Buffett has maintained a 20% CAGR over the years. And, look at what he achieved for his shareholders and himself. Maintaining a 25% CAGR (compounded annual growth rate) would be nothing less than most impressive.


September 1, 2017

Stock Portfolio Alpha Definition

To a question asked by a Quantopian forum member wanting to clarify my use of the word alpha in a stock trading strategy, I replied with:

I use alpha as defined by Jensen in the late 60's. That is, as the premium return above market averages. Often also referred to as some added portfolio management skills.

A portfolio's expected return can have for expression: E[F(t)] = F(0)∙(1 + rm)t which represents some initial capital compounded over time. In the stock market, rm is given away, almost free.


August 14, 2017

Following the Math of the Stock Trading Game

After publishing my latest book: From Zero-Beta to Alpha Generation, Reshaping a Stock Trading Strategy, a few questioned the presented stock trading strategy as if it might be unrealistic. That we could not reach those kinds of numbers. When all this stock trading strategy did was follow the math of the game.

With everything provided in that book, I think anyone could rebuild something similar or better. The benefit: it would now be their own code. A strategy design they would understand well enough to maybe give them the confidence needed to apply it. Or find in it trading procedures that they could apply elsewhere.


August 1, 2017                                                                                                        NEW BOOK RELEASED

From Zero-Beta to Alpha Generation

Just released my new book: From Zero-Beta to Alpha Generation, Reshaping a Stock Trading Strategy.

It chronicles the remodeling phases of a stock trading strategy found on the web.

From its beginnings where it could not really outperform market averages to making it the most powerful trading strategy to have in a portfolio of strategies. So powerful, in fact, that over the long term, it could carry the day for the entire group.


July 21, 2017

Post-Strategy Portfolio Analysis

Recently, I got interested in zero-beta stock trading strategies after reading on Quantopian's preference for such strategiesI always found them to be less productive profit-wise than other methods that would correlate more closely with the market. I got to dig deeper and had to change my mind.

One of Quantopian's forum members put out a zero-beta trading strategy that I found interesting as having some potential for me to modify and improve. It took a few tests to appreciate the trading logic conveyed by this strategy and see how it behaved over time.


June 13, 2017

Where is the Alpha?

It might be hiding in plain sight. In my last article: No Alpha No Game it was stated it was a sufficient condition to have an upward bias in the price data to win a long term stock market game.

Often times, people want to look at the game as if randomly set, meaning that the probability of going up is about the same as going down. As if playing a heads or tails game. A game known for centuries to be a zero-sum game and unbeatable except by luck; when, in fact, the stock market game might be something quite different.


June 9, 2017

No Alpha. No Game.

My latest book: A Quest for Stock Profits If you want more, you will have to do more...  mostly talked about an automated stock trading strategy that was described as gambling its way to the finish line over its 14.42-year journey.

Playing the stock market game has no rerun buttons. It also has no refunds. As a trader, you win, good, it is yours. You lose, well, you lost, next, please.

So, it would sound more than reasonable to make as sure as possible that over the long run you end up a winner. And, you can do this only with some alpha generation.


June 4, 2017

What Can an Automated Stock Trading Program Teach You?

Obviously, to program until there are no bugs left. The important word: program, is just that, a program. An understanding of what you want to do is nonetheless required.

A software program that trades stocks live is playing with real money. It is as if it was not enough for you to lose money on your own, you had to program a machine to do it for you. A way of saying: there are prerequisites.


May 30, 2017

A Stock Trading Strategy That Is Simply Gambling

My latest book: A Quest for Stock Profits. If you want more, you will have to do more... makes the point that the original stock trading strategy, on which it is based, was simply gambling. And this automated gambling was somewhat camouflaged in code as if trying to persuade people that it was trading based on some fundamental market data.

When in fact, it was just playing market noise.


May 21, 2017                                                                                                            NEW BOOK OUT

A Quest for Stock Profits – The Book

My new book is out and available on Amazon: A Quest for Stock Profits.
if you want more, you will have to do more...

A Quest for Stock Profits describes a methodology that could be used by anyone. The same trading principles can apply going forward after having shown to have been reliable and profitable over an extended period of time.

A Quest for Stock Profits looks at the gaming part of the game, and how one could play with a vision of where they are going. Plan for the long term, and see how their trading strategies would behave under uncertainty. There is some math involved, most of it is rather simple.


May 6, 2016

Is This Algo For Real?

I received the following short and direct question by email: "Is that algo for real? 40,000%?" It was referring to chart #11 in my last article: A Quest for Stock Profits – Part II

My reply was rather direct too:

Yes, and the trading procedures used are perfectly legitimate operations. They all survived within their coded limitations. There were no errors in the code, mathematical, logical or otherwise. No gimmick or deception. Just plain Python programming.


For me, the real question would have been: would you trade like that?


April 9, 2017

A Quest for Stock Profits – Part II

From what was presented in A Quest for Stock Profits – Part I, one might conclude that there was very little there of interest. Most of it almost ordinary. Nothing to make a fuss about. On the other hand, it might have been an appetizer, a part one of a two-part series. There is definitely more to the story.


April 5, 2017

A Quest for Stock Profits – Part I

Over the last two weeks, I did some new tests using another trading strategy found on Quantopian. I only started modifying this strategy after someone made modifications to another version of the original program. The first time I looked at the original, I classified it as a throwaway. It could not even generate a speck of alpha.

A lot of time and work with nothing to show for it profit wise. It ended not even beating the index over its 13-year trading interval. At least, it finished close to it which is a lot better than most. But, nonetheless, not enough.


February 28, 2017

Not All Stock Trading Strategies Fail

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

A Stock Market's Driving Force

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

The Leveraged Leveraged Portfolio

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

A Trading Strategy's Search For Profits - Part 3

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

A Trading Strategy's Search For Profits - Part 2

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

A Trading Strategy's Search For Profits

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

Building Your Stock Portfolio

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

Stock Trading Strategy Math II

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

Stock Trading Strategy Math I

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

A Stock Trading Strategy Signature

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) = A0 + n∙u∙PT, then, A0 + n∙u∙PT is its unique signature. Leaving us with 3 portfolio metrics of consequence.


January 7th, 2017

Playing the Stock Market Game: Time is All

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

Stock Trading Profits: Take Your Share

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

The WOW Factor – Added Notes

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

The WOW Factor

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

Stock Trading: You Think, and The Machine Works

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

Boost Your Stock Trading Performance

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.*ΔP). 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 Buy & Weak Hold

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.


November 16th, 2016

Controlling a Stock Trading Strategy

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

The Deviation X Strategy

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

A Stock Trading System – Part IV

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

A Stock Trading System – Part III of IV

In A Stock Trading SystemPart 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

A Stock Trading System – Part II of IV

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

A Stock Trading System – Part I

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

A Tradable Plan - Part I

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

Portfolio Core Position

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

Extracting Tradable Information

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

Prediction Dilemma

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

Stock Trading Decisions

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

Trade Detection

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

Strategy Enhancers

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

Strategy Design Defects

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.

HTML file

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


September 8th, 2016

The Game Inside

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 Payoff Matrix

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

Simple Stock Trading Strategy III

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

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:


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.


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.


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.


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.


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."


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.


June 27th, 2016

Trade Slicing Stocks

Here is an aspect of trading that I have not seen often discussed in stock trading strategy design. It starts with the concept of line segmentation, or the slicing of stock price time series, and deals with what might be considered stochastic stopping times.

Most aspects of it has been covered before in financial literature, but maybe not in this fashion. Hoping to provide a slightly different perspective.


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


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.


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.



 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.


 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.


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.


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.


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?


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. 


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.


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.


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.


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.


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.


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.


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:


 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.


 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.


 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%.


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.


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.


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).


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.


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.


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.


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.


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.


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.


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?



 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.


 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.


 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.


 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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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

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.


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...


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.


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.


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.


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.


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.


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.



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.


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.


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.


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.


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.


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.


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. 


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.


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.


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.


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:


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.


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.


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.


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.


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).


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.


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.


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.


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.


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.


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.


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.


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:


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)


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:


                        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.


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.


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.


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?


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.


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.


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.


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.


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.


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.


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.


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.


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.


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



buy sell hold

What is Alpha Power?  Alpha Power is a trading methodology developed and refined over the years to become a total portfolio management solution. It was designed to meet several key objectives: 1- to greatly outperform the Buy & Hold strategy, 2- to accumulate shares over time while doing so, 3- to trade market swings over its accumulative functions, 4- to accept other features that can boost performance.

Its outstanding feature is that it is based on predefined trading procedures; mathematical functions that trigger entry and exit points. This is not a system responding to usual market indicators. It makes no price predictions and yet it’s a portfolio level trading system. 

It's a trading methodology and a trading philosophy backed by a mathematical model. My current working model looks like this: 


I am always looking for reasonable explanations for my trading scripts; what makes them work, what are the principles at play and what is the main reason for their high or low performance. Are the improvements really real and operate at the portfolio level or are they just curve fitting on a single stock? These are all legitimate questions and if I can’t provide a reasonable answer, a common sense answer, then it should be back to the drawing board. I need to know where are the strengths and if I can get more of them. I also need to know where are the weaknesses and if I can get less of those. Naturally, all I do must fit within my global vision of the game or/and until such time as I find something better.

In the Alpha Power Overview article I presented my latest Alpha wealth generation formula. It is repeated here for convenience.


Implementation Phase. At Last!  Over three and a half years in the making; always sidetracked by the need to prove to myself that the methods that were used were worthwhile by setting the mathematical framework where they would have to survive. Had I not achieved to demonstrate mathematically that it was feasible to achieve some alpha by trading stocks then I would have had to stop searching. There would have been no reason to pursue a quest where all others said it was akin to utopia. All the academic papers I read during this time were saying the same thing: if there is some alpha, long term it will tend to zero and the expected optimum portfolio performance over time will tend to the market average. End of discussion. They are still saying the same thing today as they did some fifty years ago, or for that matter even 111 years ago (see Bachelier (1900)).


My lastest trading formula. Over the past few weeks, I have been working on ways to mathematically express some of the performance behaviour of my underlying trading philosophy in an attempt to better understand the whole process. My trading methodology uses partial excess equity build-up to acquire more shares on the way up and consequently slightly leverages the portfolio. See my Jensen Modified Sharpe paper for a more elaborate exposé.

This is kind of a follow-up to the previous page and an attempt to provide more clues as to what to look for even if it is out of beaten paths.

Here is my latest equation:


I wanted  to set one of the controls described in my Jensen Modified Sharpe paper: setting the desired profit level. From the paper, it is said that you can preset the sum of profits generated. It is not said that you will reach them; the governing equation is dependant on the size and nature of the price fluctuations and that can not be guarantied.

However, controls can be implemented in the “should prices move in such and such a way sense”, then the holding function can be scaled to reach profit levels. This is a remarkable attribute of the Alpha Power methodology. You want more profits; you preset more pressure on the holding function controls.


This second paper, the Jensen Modified Sharpe Ratio (2008) is providing the mathematical framework that explains why and how the Alpha Power trading methodology works. A higher mathematical level is used to describe the system and methodology.

The paper is available here: Jensen Modified Sharpe


My first research paper: Alpha Power: Adding More Alpha to Portfolio Return (2007), was my attempt to describe and explain what my research in random stock data generation was doing. Even with stock price series randomly generated, you could extract alpha points by developing trading strategies using methods that would increase the ongoing inventory as time evolved.

The paper describes the theoretical backdrop for the proposed methodology. It attempts to show why a controled portfolio environment can lead to higher profits based on an inventory and risk management program.

July 19, 2013

At Last

During last weekend I noticed that one user on the old Wealth-Lab 4 site was experimenting with trading strategies that behaved like some of my earlier ones. At first, I thought he had reached level 3 of 5; it was hard to detect what he was doing since all you can see is a window of the last 220 days of his 1,500 trading days chart as produced by his trading strategies. Knowing quite well what the old Wealth-Lab 4 simulator can do, I felt well qualified to analyze what his charts were saying.


May 22nd, 2011

Just finished the Alpha Power Overview article. It puts in perspective the evolution of the proposed trading methodology. It starts with the simple Buy & Hold appreciation formula to which is added boosters, enhancers and accelerators to end with the improved Alpha Power formula.

The overview also provides the trading philosophy behind the method as well as why it will perform better, may I say, much better than the Buy & Hold.

Recent Topics


Zero-Beta to Alpha    (Book Available on Amazon)

Quest Stock Profits   (Book Available on Amazon)

Building Portfolio     (Book Available on Amazon)

Trading Mechanics    (Book available on Amazon)

Trade Slicing Stocks  (Book available on Amazon) 

Basic Portfolio Math    (March 1, 2018)

Payoff Matrix               (Jan. 10, 2018)

In Your Strategy?          (Nov. 9, 2017)

Trading Buy & Hold      (Nov. 3, 2017)

Retirement Fund             (Oct. 18, 2017)

Price Tag on Alpha III   (Oct. 12, 2017)

Price Tag On Alpha II   (Oct. 8, 2017)

Price Tag on Alpha     (Oct. 4, 2017)

Alpha Definition           (Sept. 1, 2017)

Follow the Math          (Aug. 14, 2017)

Zero-Beta to Alpha     (Aug. 1, 2017)

High Alpha Analysis   (July 21, 2017)

Where is the Alpha      (June 13, 2017)

No Alpha No Game     (June 9, 2017)

Trading Program          (June 4, 2017)

Simply Gambling          (May 30, 2017)

Is Algo for Real?          (May 5, 2017)

Quest for Profits - II        (April 9, 2017)

Quest for Profits - I        (April. 5 2017)

Not All Strategies Fail     (Feb. 28 2017)

Market's Driving Force   (Feb. 21 2017)

A Leveraged Portfolio      (Feb. 12th 2017)

A Search For Profits III    (Feb. 7th 2017)

A Search For Profits II   (Feb. 6th 2017) 

A Search For Profits     (Feb. 4th 2017)

Strategy Math II            (Jan. 21 2017)

Strategy Math I            (Jan. 19th 2017)

Strategy Signature       (Jan. 16th 2017)

Time Is All                       (Jan. 7th 2017)

Take Your Share             (Dec. 29th 2016)

WOW Factor - Notes      (Dec. 18th 2016)

The WOW Factor            (Dec. 16th 2016)

The Machine Works         (Dec. 7th 2016)

Boosting Performance       (Nov. 25th 2016)

Buy & Weak Hold             (Nov 18th 2016)

Control Stock Trading Strategy (Nov. 16th 2016)

Deviation X Strategy         (Nov. 14th 2016)

Trading System Part IV   (Nov. 11th 2016)

Trading System Part III   (Nov. 11th 2016) 

Trading System Part II     (Nov. 8th 2016)

Trading System Part I   (Nov. 6th 2016)

Tradable Plan - Part I      (Nov. 2nd 2016)

Portfolio Core Position  (Oct. 22nd 2016)

Extracting Information  (Oct. 13th 2016)

Prediction Dilemma       (Oct. 10th 2016)

Trading Decisions            (Oct. 3rd 2016)

Trade Detection             (Sept. 25th. 2016)

Strategy Enhancers     (Sept. 15th. 2016)

Strategy Defects        (Sept. 13th. 2016)

The Game Inside        (Sept. 10th, 2016)

Payoff Matrix               (Sept. 6th, 2016)

Simple Strategy III       (Aug. 27th, 2016  Updated)

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)

Special Links

Kh Tang : A friend's web site. 

TED : Ideas worth spreading. Riveting talks by remarkable people, free to the world.

MIT OpenCourseWare :Free lecture notes, exams, and videos from MIT. No registration required.

KhanAcademy : Learn almost anything for free.

Bill & Melinda Gates Foundation : All lives have equal value.

Alpha Project