August 14, 2018
Yesterday, I put the following text in a forum on Quantopian. The subject of discussion was gaming stock trading strategies in a multi-strategy portfolio scenario. It stressed the importance of the few best trading strategies over the long term. It also covered strategy selection.
May 29, 2018
Recently, Quantopian released its white paper: Quantopian Risk Model where they discuss market-neutral and beta-neutral stock trading strategies. The purpose of the paper was to show what is required, at least for them, or more likely, what their contest participants should seek when designing market-neutral trading strategies. That is if they wanted to be part of the small and select group that might see their trading program receive a funding allocation and get a participation in the generated profits.
July 1, 2014
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 stuff you probably have seen or have been doing for years at one time or other.
February 9, 2014
My new paper deals with an equation designed to help you make more money by adding small improvements to your own trading strategies (should they not be present already). The equation (named red5) is a complete trading strategy in itself. It presets trading rules in order to generate long-term positive alpha. I would suggest that your objective is to extract from red5 what you need and incorporate the lessons learned into your own trading methods.
August 6, 2013
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 19, 2013
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 day 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 8, 2013
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.
February 18, 2013
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: the answer is in the game itself, time and CAGR. A definition can be found here.
Personally, I use a mix of my own trading strategies and also have my friends benefit from them. So, technically, I am kind of small hedge fund. But still, because of limited account size, I'm certainly not using my strategies to their fullest potential. Here is why.
January 30, 2013
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 showing 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 21, 2012
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-stock 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.
February 3, 2012
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.
October 5, 2011
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).
August 8, 2011
Recently some asked the questions: Are there non-conventional equities trading algorithms/strategies? What are the algorithmic trading system challenges? Does trend following really work?
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.
August 2, 2011
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 whatever concepts best suited to him/her in temperament, style, and execution.
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 unit and jumped to level 2. The performance metrics can be found HERE.
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 datasets? Would the performance metrics average about the same?
July 16, 2011
This week, I put out another simulation. I modified the Myst’s XDev script from the old WL4 website 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.
July 10, 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 webpage. 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 6, 2011
This paper is an extension of my previous work which has finally arrived at the implementation phase. It presents, almost in chronological order, simulations and their performance metrics performed on 3 different datasets. The purpose is to demonstrate that when the Alpha Power trading methodology is applied to real market data; it does better than theoretical expectations or tests performed on randomly generated data series as developed and described in my previous paper.
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 days) made available on the simulation platform provided on the old Wealth-Lab 4 website. From the first draft in April to the end of the Livermore challenge in June where performance started with about a 47% annual compounded return over the investment period to over 100% by the end of the challenge.
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.
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.
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 dataset, the third dataset, 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 3rd, 2011
The Alpha Power methodology plays mathematical functions; not necessarily market indicators. The formulas are in my papers. You pre-set 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.
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 feeling. 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.
June 2nd, 2011
Yesterday, tried to entice other members of the Wealth-Lab forum to participate in the Livermore Challenge. A kind of competition where a published trading script from the old Wealth-Lab 4 site is taken as a basis to demonstrates one's ability to improve the performance of an otherwise lackluster script.
After posting the Livermore Challenge, I started my own modifications to this trading script. It took about an hour to raise performance to exceed the Buy & Hold strategy. I thought it would take a few weeks to increase performance way above the Buy & Hold, but only an hour or so was sufficient.
June 1st, 2011
Started the Livermore Challenge.
The object is to improve the Livermore Market Key trading script as found on the old Wealth-Lab 4 website. 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.
December 17, 2010
The 10% drift as presented in my first paper was only a $0.02 per day, on average, of upward movement for the total portfolio. This signal was drowned in the noise of random fluctuations (the error term). Taking away the drift part would leave you with totally unpredictable price variations where no tools could help you predict a future outcome. There would be no optimized 39-period moving average that could be applied to any of the data series. No technical indicator that would have any predictive value.
January 4, 2011
One needs to do a lot of tests to convince himself of the methodology just as I did in my own process of trying to understand the dynamics of the underlying equations. My strategy does not use fixed-percentage of equity trades; they start at 2% or less and decrease in time from there. In time each trade becomes a smaller percentage of available equity. Each data series was different within each test and from test to test. The initial price was random – then normalize to 20 – all three Gaussian were randomly set in amplitude and drift for each stock. I could not replicate any data series.
January 20, 2011
Since in the beginning, you start by buying less than the Buy & Hold; you suffer less in drawdowns at the portfolio level. You could even forego your initial positions and see a lot less drawdown. Any price decline is the same for all. What matters is the relative quantity on hand at the time of comparison. As the trading strategy evolves, your inventory in rising stocks will increase to the point of exceeding the Buy & Hold strategy, and sometimes, many times over.
January 19, 2011
The zero-drift scenario was requested by a university professor on this site who knew quite well, as I do, that you can not profit from random data series and therefore this test should have blown me away just like a house of cards. But it wasn’t the case. The test itself was a long process. The first spreadsheet had some 400,000 cells filled mostly with elaborate conditional inter-related formulas and some 150,000 calls to the random function to set price variations.
January 24, 2011
We often hear that over 80% of traders are on the losing side of the stock market game. Therefore, the first question is why so many traders fail. I believe it is in the way they play the game.
A few years back a professor (forgot his name, sorry) made up a test for his bright graduating students (most in management and economics). The game was simple and described as follows:
January 26, 2011
“So, if you buy a basket of stocks with a percentage of your assets...”
There is a selection process to be made. You intend to build a portfolio for the long term, and therefore, your “basket” of stocks should be composed of your best candidates for long-term appreciation. Say you want to start with 50 stocks for your first cruising level; you assign initial weights to the 50 stocks in the order of your long-term estimates.
February 7, 2011
This has been discussed many times. It is a recurring theme. It is also the one subject which if not treated with respect can be the main cause of one’s future dismal performance.
I will try to give it a singular perspective.
First, there is nothing wrong with optimizing or over-optimizing for that matter. Optimization should be used to search for trading ideas and concepts; not hard numbers like top performance.
April 26, 2011
I started the Alpha Power project some 4 years ago. Always sidetracked by, you also need this or that. I had to prove to myself that the method was worthwhile by setting the mathematical framework where it would have to survive. All the academic papers I read at the time were saying the same thing: If there is some alpha, long term, it will tend to zero and the optimum portfolio over time will tend to the market average. End of discussion. They are still saying the same thing today.
April 27, 2011
All my portfolio simulations to date were done on the old WL4 website where all you can supply is your trading script and the stocks you want to simulate on. All the price data, simulation program, and testing conditions are on the WL4 website.
The test results shown in the tables are a simple copy and paste into an Excel spreadsheet. Whatever the result was.
April 5, 2011
What about underwater symbols like AA or BAC?
This is a seemingly simple, but relevant, question. Here, it generated the debate of what should be included in the selection process? We backtest on lists of stocks for which we know the past. From the question, without testing, just by seeing the charts, I can say that AA would be nicely positive while BAC would still be underwater being some 80% below its 6-year high. But then, I realized that in the three spreadsheet tables presented, there were no banks. And this generated another question: why not?
May 22nd, 2011
Just finished Alpha Power Overview. It puts into perspective the evolution of the proposed trading methodology. It starts with a 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.
May 17th, 2011
Progress is being made on the implementation phase. At each implementation level - by adding holding functions - performance is improving. The last chart in the Control Settings article shows six levels of controls. The intention was to pre-set control parameters to extract from the price movement more profits as you turn up the pressure on the objective holding functions.
May 22, 2009
After receiving a few questions related to the methods described in my first two papers, I thought it might be appropriate to make a kind of question and answer page.
What is the trading method exactly?
The method has two components: the main one accumulates shares for the long term while the other accepts short-term trading (long and short). And since you are accumulating shares to hold for the long term, might as well write options on those shares. Idle cash can bear interest.
May 16, 2011
The Trading Game is my latest research paper. It is for the few that have followed this thread and were wondering where it all led to.
It is a continuation of the preceding papers. It maintains and re-emphasizes what was presented and leads to part one of my conclusions. The more I researched the subject, the more the equations I used expressed simple trading methods which could all be resumed in: trade mostly like Mr. Buffett does.
May 16, 2011
The Enigma of Financial Expertise
Here is an interesting paper. The subject is financial expertise defined as a combination of skill, experience and market knowledge: another way of saying alpha and how hard it is to get. The document might be narrative with no mathematical formulas, but nevertheless, the points covered are worth noting. It can be downloaded from HERE.