Tuesday, May 5, 2015

Fundamentals of Short-Term Trading: Part Two

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 riskprudent.
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).
N = ff * E/R
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 riskprudent.
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 fixedpoint
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.

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