Financial Markets: Why You Can't Beat The Big Boys In Short Term Trading
BioComp System's Dakota Swarm Intelligence In Action
I don't usually post articles on financial markets in this blog, since I have a special purpose Blog for it at http://www.technifundamentals.com/ . But the great number of advertisements in the local papers claiming to be able to make you rich quick in Forex, Stocks, Futures, Options and other derivatives prompted this post. Small investors may not know it. But for short-term trading, the odds are against them when competing with the Big Boys in the market. For the individual house-wife, retiree and wannabe traders who think that using technical analysis charting is sufficient, they will find it interesting to have a glimpse of machine-controlled high frequency trading supported by tools far beyond their understanding and financial means. I have been fortunate enough to have a glimpse of such tools although these tools are incompatible with my risk profile, my character and mental make-up. This post shows the reader three examples of such fantasy world tools.
First, some basics on short-term trading: Short term-trading is defined here as the range of trading from 500-1000 trades per second to trading within the trading day. Short-term trading is different from medium and long-term investment in that you will be trading on the market noise and not the fundamentals. Noise has three aspects: Momentum [mathematically termed as continuation of trend], Oversold/Overbought [Reversion to the Mean] and spreads [statistical volatility. Because short-term trading is trading on market noise which has a mathematical and not fundamental basis, it is predictable and/or profitable if patterns can be discerned from complex data, or the sheer brute force of computer power can calculate the optimum buy/sell level from a billion combinations, or being the fastest enables profiting from fractions of a cent by doing thosuands of transactions within a minute. Technical analysis suits Short-term trading because the shorter the trading horizon, the less the probability of it being subjected to external shocks, which are inherently unpredictable. In the last five years the prospect of huge profits has resulted in a virtual arms race for new and exotic technologies to discover the Holy Grail of trading systems. I illustrate my post with three Companies I know of, and their products
First, some basics on short-term trading: Short term-trading is defined here as the range of trading from 500-1000 trades per second to trading within the trading day. Short-term trading is different from medium and long-term investment in that you will be trading on the market noise and not the fundamentals. Noise has three aspects: Momentum [mathematically termed as continuation of trend], Oversold/Overbought [Reversion to the Mean] and spreads [statistical volatility. Because short-term trading is trading on market noise which has a mathematical and not fundamental basis, it is predictable and/or profitable if patterns can be discerned from complex data, or the sheer brute force of computer power can calculate the optimum buy/sell level from a billion combinations, or being the fastest enables profiting from fractions of a cent by doing thosuands of transactions within a minute. Technical analysis suits Short-term trading because the shorter the trading horizon, the less the probability of it being subjected to external shocks, which are inherently unpredictable. In the last five years the prospect of huge profits has resulted in a virtual arms race for new and exotic technologies to discover the Holy Grail of trading systems. I illustrate my post with three Companies I know of, and their products
1. Dr. Richard Olsen, CEO of Olsen Associates, OANDA Forex, and other investment technology Companies
10 years ago I made the acquaintance of Dr. Richard Olsen, a Swiss Economist and mathematician who gave me an autographed copy of a book which he and his partners in Olsen and Associates wrote: “ An Introduction To High Frequency Finance” [Academic Press, 2001]. The book is one of the pioneering books on high frequency trading, and deals with collection and analysis of tick-by-tick data on forex transactions. Dr. Olsen’s main point is that usually when we analyse the markets, we have at most a few hundred data points. But when he is able to collect 100000 data points day on price, volume, it makes statistical analysis much more meaningful, i.e. much more valid. He also talks about how elements of Chaos Theory is seen in such data viz the evidence of fractals (self-similarity on different scales] Dr. Olsen has been very generous in sharing his knowledge with all, and if you go his main web site at Olsens World http://www.olsen.ch/ , there is an article entitled " Why Policy Makers Need To Take Note Of High Frequency Finance" that is worthwhile to read. The link is here:http://www.olsenblog.com/2010/02/why-policy-makers-need-to-take-note-of-high-frequency-finance/ . At Olsen Scale http://www.olsen.ch/what_we_do/olsen_scale/ you can see a live demonstration of how news affects currency exchange rates. The Olsen Scale is so-called because of its similarity to the Richter Scale for earthquakes. You can see the effect of news and sudden changes in liquidity on for example USD vs Yen, vs Euro, GBP, CHF, AUD, NZD and CAD all at once.
10 years ago I made the acquaintance of Dr. Richard Olsen, a Swiss Economist and mathematician who gave me an autographed copy of a book which he and his partners in Olsen and Associates wrote: “ An Introduction To High Frequency Finance” [Academic Press, 2001]. The book is one of the pioneering books on high frequency trading, and deals with collection and analysis of tick-by-tick data on forex transactions. Dr. Olsen’s main point is that usually when we analyse the markets, we have at most a few hundred data points. But when he is able to collect 100000 data points day on price, volume, it makes statistical analysis much more meaningful, i.e. much more valid. He also talks about how elements of Chaos Theory is seen in such data viz the evidence of fractals (self-similarity on different scales] Dr. Olsen has been very generous in sharing his knowledge with all, and if you go his main web site at Olsens World http://www.olsen.ch/ , there is an article entitled " Why Policy Makers Need To Take Note Of High Frequency Finance" that is worthwhile to read. The link is here:http://www.olsenblog.com/2010/02/why-policy-makers-need-to-take-note-of-high-frequency-finance/ . At Olsen Scale http://www.olsen.ch/what_we_do/olsen_scale/ you can see a live demonstration of how news affects currency exchange rates. The Olsen Scale is so-called because of its similarity to the Richter Scale for earthquakes. You can see the effect of news and sudden changes in liquidity on for example USD vs Yen, vs Euro, GBP, CHF, AUD, NZD and CAD all at once.
2. Carl Cook of BioComp Systems. Carl has been developing pattern recognition trading software since the early '90s. A Chemical Engineer by training, he has a thriving business in the Oil & Gas Industry, utilising his knowledge of advanced technologies for optimization of refinery operations as well as stimulation and extraction of the output of old wells. He is a consultant to Petronas and makes frequent trips to Miri, Sarawak. Carl's latest invention is a software called Dakota, that makes use of Swarm Intelligence to get a consensus on forecasts. Swarm Intelligene is based on the observation that swarms of bees, schools of fish, flocks of birds, colonies of ants are each a super-organism with distributed processes i.e. decentrakized decision-making. Wikipedia expresses it well: Swarm intelligence (SI) describes the collective behaviour of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems.[1]
SI systems are typically made up of a population of simple agents or boids interacting locally with one another and with their environment. The agents follow very simple rules, and although there is no centralized control structure dictating how individual agents should behave, local, and to a certain degree random, interactions between such agents lead to the emergence of "intelligent" global behavior, unknown to the individual agents. Natural examples of SI include ant colonies, bird flocking, animal herding, bacterial growth, and fish schooling.I have a copy of Dakota, but have not had the time to fully explore it. My initial impression is that you need the real-time version and some consultancy service from Carl if you are going to trade intra-day with it. It is difficult to get even a 1-day ahead forecast with the end-of-day version of Dakota.
SI systems are typically made up of a population of simple agents or boids interacting locally with one another and with their environment. The agents follow very simple rules, and although there is no centralized control structure dictating how individual agents should behave, local, and to a certain degree random, interactions between such agents lead to the emergence of "intelligent" global behavior, unknown to the individual agents. Natural examples of SI include ant colonies, bird flocking, animal herding, bacterial growth, and fish schooling.I have a copy of Dakota, but have not had the time to fully explore it. My initial impression is that you need the real-time version and some consultancy service from Carl if you are going to trade intra-day with it. It is difficult to get even a 1-day ahead forecast with the end-of-day version of Dakota.
3. RML Technologies and its Genetic Programming technology This unique Company's technology is licensed to http://www.tradingsystemlab.com/ and the software licences start at a cool US$60000 a month for the professional version. I can imagine only the likes of Goldman Sachs or a hedge fund like Renaissance Technologies can afford to use systems like that. RML uses Genetic Programming in machine code to 'evolve' a trading system for you. You just have to specify your objectives. Genetic Programing, like Swarm Intelligence, is derived from biological systems. The language of GP will sound weird to the uninitiated. An initial random population is seeded. 'Individuals' in this population compete to be the fittest acccording to a specified fitness function. There are tournaments and 'winner take all' algorithms. There are 'parents' and 'children' and the ' elite'. The population evolves by cross-overs of genes, and by mutations. Only fit individuals are allowed to carry over to the next generation. At the end of it all, comes an optimally robust solution after billions of runs and thousands of generations in computer time. Being programmed in machine code, RML is able to run 200 times faster than its competitors, and able to run through a billion combinations to find the optimal strategy in a few minutes.
These examples are meant to illustrate that at the most, the individual investor can ride on the coat-tails of super volume super-speed traders using super technologies and hopefully get off before they stop the music. Logically speaking in this type of trading environment, it is better to use trend-following systems than overbought/oversold oscillators. You are going to have a harder time picking the bottom and top than riding on the momentum of a trend. You will also have Volume to assist in confirmation of the trend's sustainability.
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