January 2nd, 2015

Trading short term or Not? That is the question.

What ever automated trading methods you might have used in the past, use now, or will use in the future, it has for unique purpose to make you money. It's not important that the code you use be well structured and nice or which software you will use. What's important however is the ultimate outcome of the trading strategy. One should understand what it really does and how it behaves under favorable and unfavorable conditions.


August 3rd, 2014

One For All?

What follows is an experiment in strategy design.

Scenario: from one stock, over an 8 month period of the past year; I predetermined trade entry and exit points by date. Therefore, this experiment is entirely fabricated. Nonetheless, there was something to learn from the process.


August 2 2011

Everyone Has a Trading Method

Someone made the comment recently: Guy, “I agree with you "everyone has a trading method" - at least in their own minds.”

I didn't know how to take it. The more I thought about it, the more it had a negative tone. True, everyone wishing to play this market game, not just has, but must have a trading method based on what ever concepts best suited to him/her in temperament, style and execution. It requires having a belief system, some knowledge of the game, conviction in one's self and confidence in the trading methods used what ever they may be.


July 20th, 2014

Test Summary

As a summary, up to now, 4 long term trading strategies have been analyzed. All four started as nonproductive, meaning that they could not even beat the Buy & Hold over the long term (read 20+ years). The trading strategies original versions have been in public view on the legacy Wealth-Lab site from 8 to 12 years. Each strategy was modified to gain a long term perspective with for backdrop the accumulation of shares.


July 26, 2011

Over last weekend, I ran my modified version of the Momentum Trader (version 2, model 0.7 Level 2). My objective was to try to push performance higher. For a solution I modified the trend definition, increased the trade basis and jumped to level 2. The performance metrics can be found under the Simulations menu here.

The original version of this script can produce nice results on certain stocks but on others, it should be considered rather dangerous as it has no stop loss. This script has a positive, although awkward, trend definition which can be summarized as: you have 3 up days in a row, then the price is definitely going up. Designing for the long term, it is not sufficient for a trend definition but then again with a few modifications you can coax the script to believe in its new self.


July 1st, 2014

Trade Automation

You want to automate your trading strategies. Why not! You have out there more than enough available software platforms, enough brokers, enough computer power, enough stocks to chose from and you have studied the markets for what appears like ages. After all, it's just an extension of what you have in mind and for stuff you probably have seen or have been doing for years at one time or other.


July 18, 2011

After the Myst's XDev modified script simulation of a few days ago; I had a few questions that were left unanswered such as: Would the stop loss distribution be the same on another data set? Does this modified script have enough general properties to be extendable to other data sets? Would the performance metrics average about the same?

Questions that could only be answered by doing another simulation on a different data set. And still having the need to compare to previous simulations using other scripts, the 2nd data set was chosen. The performance results are available under the Simulations menu.


Febuary 19th, 2014


Last weekend I had to answer a question: < I have read all your posts but I am little unclear about the following where you said: " it does show the value of accumulating shares of a rising stock and letting the market pay for it. " >.

My answer might be of interest to some. I thought the easiest way to answer this question was to illustrate the point with a few charts.


July 16, 2011

This week, I put out another simulation. I modified the Myst’s XDev script from the old WL site and changed its trading structure to more reflect my own trading procedures with some interesting results. High hit rate, very low losses and impressive performance metrics. It is now available from the Simulations menu.

Also over the past week, I was able to do the Trend Study II which is already available in the Simulations menu. It has a 92% hit rate and good performance results. It has a tendency to hoard cash and it does trade a lot. This is where automated trading takes all its meaning.


August 6th, 2013

Optimization Again

Some don't seem to ask the most basic of questions. One would be: why should one “need” to re-optimize in the first place? High on the list of answers: I would bet that the “automated” trading strategy broke down for some reason or other. This could only mean that the trading strategy has been losing money for some time and still is; and for sure, needs to be stopped, or at least modified in some way, if not replaced entirely.


July 10th 2011

Finally, here is my latest paper. Alpha Power: The Implementation.

It is all about my quest for alpha points. After all the research, last winter was finally the time for my implementation phase using real market data. A lot of this continued search has been documented; almost real time on my web page. For those that followed this journey over the last few years and wondered how the alpha power method would do with real market data, please note that performance results exceeded theoretical settings.


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.


June 18th 2011

Just putting the finishing touch to my new paper: Alpha Power: The Implementation. It should be released shortly.

It covers simulations done on real market data over the 5.83 years of data (1500 bars) made available on the simulation platform provided on the old Wealth-Lab 4 site. From the first draft in April to the end of the Livermore challenge in June where performance started with about 47% annual compounded return over the investment period to over 100% with the end of the challenge.


June 8, 2013

The Notion of Drawdowns

It is often said that the worst drawdown is the one to come. However, no one seems to quantify the notion. You can't see if they are expressing these drawdowns on a percentage basis or on an amount basis. In most cases, these two views have major differences when looked at from a single trade perspective, at a portfolio level, or over a long term trading interval like 20 years or more.


June 14th, 2011

Over the last few days, I've been busy writing a new paper to describe the implementation phase of the Alpha Power trading methods. It is not an easy task. But based on the Livermore challenge results, one is forced to reconsider the benefits of trading over an accumulative process.

This new paper should take a few weeks to complete and provide an overview of my trading methods. My goal is to simply show how controlling the inventory level over the investment period can greatly improve portfolio performance.


February 18, 2013

The Question

I occasionally participate in a LinkedIn forum on automated trading systems. One poster raised the question: < if your returns are so good, why aren't you a hedge fund? >  It is a very legitimate question to which I answered:


June 6, 2011

I started my Alpha Power implementation phase around mid-March. It took a long time to get there. It seemed that I was always sidetracked by something or other. I first wanted to prove to myself mathematically that the concept worked. After all, it worked in my randomly generated stock price series. I would at times hit a mathematical wall so to speak; not being able to express in mathematical form what I had in mind. For those that have read my papers, you simply don’t get up in the morning saying: what you need is a function matrix of stochastic differential equations.


January 30, 2013

On Forward Testing

Recently, I was asked about showing some forward tests on systems based on my alpha research. Having opted to go live instead of waiting for further testing, and that most likely the request would have required to show some of my programs, I declined. Nonetheless, forward testing could be considered as having been done in parallel to being live. Here was my reply:


June 5, 2011

After the Livermore Challenge’s 2nd act, which made its point quite clear, there was only one question left opened and that was what about the other data set, the third data set, presented way back in the series. Again, only one way to know and that is to run the test using the same script.

You still don’t know what the future will bring. You still don’t know which stocks will outperform. You still don’t know how much profit any of the stocks will bring.


June 21, 2012

Randomly Trading

Over the last few days, I commented in a LinkedIn forum on randomly generated trading. To answer a question, I built an Excel file to illustrate the construction of a payoff matrix Σ(H.*ΔP): a 10 stocks by 250 trading days (which can be extendable to any size). The trading procedures were randomly generated as well as the stock prices. You simply pressed F9 to generate a totally new scenario; same as picking 10 stocks at random from an infinite normalized stock universe. Naturally, the expectancy of that system is zero.


June 3rd, 2011

The Alpha Power methodology plays mathematical functions; not necessarily market indicators. The formulas are in my papers. You preset your trading behavior based on these mathematical functions and then wait for the market to hit all the triggers generating the trades. If the market does not move in a way to trigger the buy, sell or stop loss orders, you simply wait for it to come to your terms of engagement.


May 28, 2012

After Dumping Ichimoku

After dumping the Ichimoku Kinko script, I decided to improve another old free WL4 script. This one based on a Bollinger Band system. The intention was to study the strengths and weaknesses of the trading procedures implemented in the original design and then try to improve the trading strategy to reach better performance levels.


June 3rd, 2011

After ending the Livermore Challenge the same day it started I was left with a why did you start that in the first place felling. I thought it would have taken at least a couple of weeks to push performance levels that high. But then again, that was just the first draft of the program. I could design improvements to the script with ease and raise performance even higher.

It took about 5 minutes of coding like add this and this and overall performance will rise.


May 14, 2012

Ichimoku Kinko Hyo Test

The idea behind this research note was that it might be interesting to show the progression one might take to improve the performance of a particular script. But then, would that not be like over-optimizing or over-fitting an existing trading strategy?

I think it would be over-fitting only if the results improved just a little. But what if the results were greatly improved?


June 2nd, 2011

Yesterday tried to entice other members of the Wealth-Lab board to participate in the Livermore Challenge. A kind of competition where an old script from the old Wealth-Lab 4 site is taken as a basis to demonstrates one's ability to improve performance of an otherwise lackluster script.


February 3, 2012

Interesting Properties

Lately, I've expanded my research in trying to better understand what's going on under the hood. Trying to refine and/or design more sophisticated procedures with what I simply call “interesting” properties.

As you design new procedures, you are bound to get some with what becomes desirable side effects. For instance, I like the trade clustering in my latest design (Trade Slicing) with its ability to position entries near the low of price swings not by predicting lows but as a byproduct of its scaling-in functions. You hit some lows to a tee, not by seeking them, but as a result of sprinkling some random trades over that region of the chart. You see lows being hit as a coincidence and a consequence to the trading procedures being used.


June 1st, 2011

Started the Livermore Challenge. The object is to improve the Livermore Market Key script as found on the old Wealth-Lab 4 site. It took less than an hour to improve performance above the Buy & Hold strategy! But I am looking for a lot more…

Follow this link to a description of the challenge.

October 28, 2011

Random Entries

Over the past two days, in trying to answer a question on random entries, I tried to design a trading strategy that would use an excessively high number of random entries.

Only two stocks were tested and I posted elsewhere the results as follow:


May 26th, 2011

Working on a new article which aims to explain how and why the Alpha Power trading methodology works. It should be interesting and probably ready in a few days.

Updated, May 29th.

The new article is now out. It can be viewed HERE.


October 5, 2011

Trade Acceleration

Recently, I posted the following AAPL chart in the LinkedIn Automated Trading forum.

The chart speaks for itself. It has very impressive numbers. My response was to see if the script used could be improved upon and mostly would it apply to other stocks. The main purpose was to find ways to increase the number of trades over the trading interval as this seemed to be the conclusion of my most recent article (On Seeking Alpha Part III).


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.

August 27, 2011

On Seeking Alpha.

The most concise mathematical formulation for profits generated from trading stocks that I have encountered is provided by Schachermayer's pay-off matrix:

Profits = ∑ (H . * ∆S)           Schachermayer's Pay-Off Matrix

where H is the holding matrix (the quantity of shares held in each stock over time) and ∆S the matrix of price differentials from period to period. Multiplying element-wise (• .* •) and summing will result in the pay-off or sum of profits generated by the holding function H. These calculations can easily be done using any spreadsheet where columns are stock price variations and rows are the inventory held in each stock by date.


May 17th, 2011

Progress is being made on the implementation phase. At each implementation level - adding holding functions - performance is improving. The last chart in the Control Settings article shows six levels of controls. The intention was to preset control parameters to extract from the price movement more profits as you turn up the pressure on the objective holding functions.

The method is not trying to predict future price movements. It presets the trading behavior. In the Jensen Modified Sharpe paper; trading behavior is set using equations governing profit generation: an initial bet followed by a controlled trade sequence. These equations trigger the trading behavior; the stock accumulation program. Should the price movement behave in such a way as to trigger the trades, then the equations will provide the answer to the accumulated profits for the level reached. No movement in price, no trade meaning no profits.


August 8, 2011

High Frequency Trading Challenges

Recently some asked the questions: Are there non-conventional equities trading algorithms/strategies? What are the algorithmic trading system challenges? Does trend following really works?

I think my best answer to these questions is with an even bigger question. I opted to set up a relatively high frequency trading strategy and then find reasonable solutions under the constraints hoping to address indirectly the challenges being faced.


Alpha Project