Fundamentals of Short-Term Trading: Part II
By Brett N. Steenbarger, Ph.D.

The first article in this series looked at intraday patterns of volume and implications for trading.  A major conclusion was that the distribution of price changes through the day is nonstationary, making it hazardous to employ the same buying and selling parameters through the day.  By analyzing markets horizontally as well as vertically—comparing action at one time of day to action at the same time during previous days—we can generally gauge whether or not a particular movement is significant.

How one employs this information will depend upon his or her time frame of trading, which in turn reflects one’s risk tolerance, which is closely related to personality traits.  Longer holding periods yield more variable results—including drawdowns.  Adjusting the mix of holding period and position size is essential in ensuring that one is taking a level of risk that will produce adequate rewards, but that will not court ruin during a losing streak.  The management of risk is an oft-neglected facet of trading psychology.

Risk, Size, and Holding Period
Let us say, for instance, that we are going to risk 2% of our trading capital on a trade.  If we are trading tick-by-tick, we could trade dozens of contracts and still remain risk-prudent.  If, however, we are holding positions overnight, where the odds of a multipoint move are now greatly increased, the same 2% parameter would yield a position size of only a few contracts.  Even on an intraday basis, a scalping trade placed early in morning has a greater risk of a multi-tick adverse move than the same trade placed nearer to midday.  Keeping size constant during periods of nonstationarity—or worse yet, increasing size when you see volatility ramping up—courts the scenario in which a single losing trade undoes several previous winners.

A fixed-fractional trading strategy defines the number of contracts you can trade for a defined level of risk.  Michael Bryant, in his article “Position Sizing With Monte Carlo Simulation” (Technical Analysis of Stocks and Commodities; Feb. 2001), shows how simulations of trading outcomes with particular strategies can help one define the fraction of trade capital to place in a trade while keeping the risk of severe drawdown under 5%.  Simulations using his MiniMax swing trading system, for example, show that trading 2% of capital produces a maximum peak to valley drawdown of 24% on the ES futures with 95% confidence.  If one wanted to reduce that drawdown to 12% of capital with the same level of confidence, one would risk only 1% of capital.

The fixed-fractional strategy described by Bryant is drawn from the following equation, where N = the number of contracts traded; ff = the percentage of trading capital allocated to the trade; E = total trading equity prior to placing the trade; and R = the risk of the next trade in dollars (which is your stop).

Thus, if I am willing to risk 2% of my $100,000 trading account on a trade where my stop is set at 4 points ($200 per contract), I could trade 10 contracts and still remain risk-prudent.  If I am a scalper and my stop is much smaller, I can trade a larger number of contracts with equivalent risk.  If I am a swing trader willing to set a double-digit point stop, I will trade smaller size.

Adjusting Risk and Reward
This brings us back to the topic of stationarity.  In the above example, I have set my stop at 4 points.  The odds of a four-point setback, however, are not the same early in morning trading as in midday or late in the day.  If I am an intraday trader and rely on a fixed-point stop, I no longer am managing risk consistently.  I may be taking too much risk at one time of day and too little at others.  I need Monte Carlo simulations on a horizontal basis to tell me the 95% probability of a defined market drawdown for morning trades, afternoon trades, etc.  Just as I would not trade similar size on an intraday vs. swing basis, I would not trade identical size at various times of day.

It is difficult to square this position with the reality that very successful traders tend to increase their size in direct proportion to their confidence in a trade.  A consistent theme among “Wizard” traders is that, once they identify a move, they exploit it for all its worth.  The less-successful trader is apt to become risk-averse in the face of a profitable position and exit early.  Since volatility is commonly increasing as a trade is working out, adding to positions is significantly adding to risk.  A reversal at the end of a move, when size is greatest, could eliminate all profits, even if one has been correct in anticipating the direction of the move.

Scaling into positions over time can address this challenge.  In a forthcoming book on Trend Following by Michael Covel, he quotes Ed Seyoka’s approach to pyramiding.  The instructions for pyramiding, Seykota explains, are depicted on every dollar bill:  add smaller and smaller units, while keeping your eye open at the top.  The advantage of scaling into one’s maximum position is that it keeps risk lowest early in the trade, when its outcome is most in question.  As the trade works out, adding to the position allows the trader to maximize profits.  The successful trader is thus thinking like a Bayesian, watching the unfolding of a trade to see if the market is gaining or losing strength, and adjusting the position accordingly.

Conclusion
Short-term trading, like any trading, boils down to mathematics.  If you have a roughly equal number of winning and losing trades, the average size of the winners will have to meaningfully exceed the average size of the losers in order to assure profitability.  When traders do not properly adjust trading size and holding period, they can have a good trading methodology, but a red P&L.  The average size of their losers will swamp the winners. 

A good self-assessment is to measure the amount of time and energy that you spend defining market entries, gauging exits, determining trade size, and managing trades by scaling in and out.  Most traders place great emphasis on entries, are too impulsive on exits, and give little thought to the definition and adjustment of trade size.  Money management, and not simply “Buy when the RSI hits 30”, separates successful traders from less profitable ones.  Very often, a trader’s emotionality during a trade stands in the way of good trade management.

Brett N. Steenbarger, Ph.D. is a clinical psychologist and active trader, writer, and researcher for the past 20 years, Brett is the author of The Psychology of Trading (Wiley; 2003) and numerous articles on trading psychology for print and online financial publications.  Click here for full bio >>