“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 and will stay 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 not a rare event at all. In hindsight, a day-trader sees a lot of easy money opportunities. Like a moth to a flame, day-traders are attracted by the idea to get rich fast. Alas, more than 90% of them end up losing money in their first year of trading. “I can make 10% profit per month” is a typical day-dream of a day-trader. There's plenty of ego in this game but the statistical is quite different from the psychological expectation.
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). Suffice to say, that the ever-growing complexity of my TA knowledge never resulted in trading with conviction not to mention any profit. At some point, randomness became the only sure thing in my day trading. Upon reflection, naturally came the decision to employ statistics and find out if there is a rational approach to day trading at all. I used historical SP500 data 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 daily price evolution is described by the probability distribution of the daily return. Within this approach, the complexity of day trading is reduced then to the strikingly simple mechanics of a stochastic binary game. Keep in mind, that 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: it is not necessary to trace the trajectory of all particles to figure out the physical properties of the many-particle system providing that the system is in the thermodynamic equilibrium; it is not necessary to trace the position and intention of all market players to figure out the result of a day trading system providing that this market is liquid. The proposed Statistical Mechanics of Day Trading is a general methodology for rational day trading. Within this methodology the stop loss, take profit, and risk management problems were solved based on the statistical properties of a market of interest.
Statistical Mechanics of Day Trading.
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