“Simplicity is the ultimate sophistication.”
Leonardo Da Vinci
Leonardo Da Vinci
ABSTRACT
This manuscript is an introduction to mechanical day trading. The proposed approach is based on empirical and mathematical evidence. The problems of Trading Edge and Risk Management were revisited. The result is a rational methodology for mechanical day trading providing that a market of interest is liquid. The manuscript should prove useful for small-scale traders interested in developing machine trading systems.
INTRODUCTION
A daily move in Emini futures can easily exceed $1000 while, for example, ThinkOrSwim (TOS) trading platform at the moment requires about $7000 as the initial margin to enter a trade. It is even wilder in the crude oil market where the initial margin on TOS is about $4000, while a $1000 daily move is commonplace. With this in mind, a day trader sees a lot of easy money opportunities. Like a moth to a flame, day traders are attracted by the idea of getting rich fast. Alas, more than 90% of them lose money in their first year of trading. “I can make 10% profit per month” is a typical daydream of a day trader. There's plenty of ego in this game but the statistics completely contrast with what the average trader expects.
To a significant extent, I earn my living as a Statistical Mechanics scholar. As a day trader, however, I followed the ordinary path of Technical Analysis-based trading (TA). The ever-growing complexity of my TA knowledge never resulted in trading with conviction nor any profit. At some point, randomness became the only sure thing in my day trading. Upon reflection, it made sense to employ statistics and to find out if there is a rational approach to day trading at all. With my experience in statistics and research, I managed to build a mathematical model of day trading. The result is presented here in a general form that is applicable to any liquid market. In the proposed model, similar to options pricing models, daily price evolution is described by the probability distribution of the daily return. Within this description, the complexity of day trading is reduced to strikingly simple mechanics known as stochastic binary games. The aforementioned simplicity is related to the ultimate complexity of a large trading ecosystem in a way similar to how the simplicity of the distribution laws in Statistical Mechanics is related to the ultimate complexity of a many-particle system, i. e. it is not necessary to trace the trajectory of all particles to figure out the physical properties of a many-particle system providing that the system is in the thermodynamic equilibrium; it is not necessary to know the position and intention of all market players to figure out the result of a day trading system providing that this market is fairly priced, i. e. in equilibrium, and liquid, i. e. the equilibrium arises from interactions of a large number of market participants. This methodology solves stop loss, take profit and risk management problems.
Statistical Mechanics of Day Trading.
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