June 21, 2006

ST vs LT trading

Filed under: Uncategorized — by TraderMade @ 10:16 am
Tags: , , , ,

One member of the tradingblox forum, Mr. Cyphrograph (from Poland), brought this topic to the forum

Short term VS Long term trading.

Hello everyone. I guess I’m 3rd member from Poland on this forum, together with TK and steady_jake. We had some hot discussion on a polish futures message board, and now I want to continue it at “Trader’s Roundtable” as I believe it is more suitable for that kind of discussion. Here’s the hypothesis: Short term trading can reach the level of robustness (or performance), which can not be achieved by long term methods or long term trend-following systems or – let’s be straight – Original Turtle System. The question I want to ask you is: can we verify the above hypothesis using historical results (hypothetical from backtesing or actual trading figures)? IMHO, Yes we can do it. Since our abilities to predict future are weak, what else do we have beside history? Well-known method used for predicting possible outcomes, namely Monte Carlo Simulation is based on historical figures also.

I want to present a little research I’ve done on this subject. Let’s compare actual trading performance. Turtles vs Active Traders battle. We take 3 famous Turtles on one side (B. Dunn, J.W. Henry, W. Eckhardt) and 3 quants who employ short-term trading methods on the other (T. Crabel, Denali, C-View Limited). Let’s take 2 ratios for measuring robustness / risk-adjusted return / performance quality (name it like you want):

1. Compounded Annual Return / Worst Drawdown (CAR/WDD, monthly basis) – before management and incentive fees,
2. Annualized Sharpe.

Turtles camp:
DUNN Capital Management-DUNN WMA (Nov 84 – Sep 03)
CAR/WDD 0.52, Sharpe 0.64

John W Henry & Company-Financial and Metals (Oct 84 – Aug 03)
CAR/WDD 0.91, Sharpe 0.83

Eckhardt Trading Standard (Jan 87 – Sep 03)
CAR/WDD 1.38, Sharpe 0.75

Active Traders camp:
Crabel Cap. Mgmt-Diversified 1XL (Jan 92 – Sep 03)
CAR/WDD 3.77, Sharpe 1.38

Denali Asset Management-Ascent (May 99 – Sep 03)
CAR/WDD 10.26, Sharpe 2.75

C-View Limited 3XL (Oct 96 – Aug 03)
CAR/WDD 5.62, Sharpe 1.66

Disclaimer: I’m not connected with any managers mentioned above.

Well, numbers speak for themselves Smile As you may suspect, figures for short term systems backtested and optimized against the past data are much, much better – especially, when you set worst drawdown figure to around 40% by position size management rules.

Turtles camp has one advantage over active traders: they have longer track records. However, I don’t want to wait 15 years in order to have comparable periods. That is the zillion dollar question: will these excellent CAR/WDD & Sharpe figures sustain in the future?

Where are the grounds for differences between short and long term trading performance? IMO, they’re located in 3 main areas:

1. Math
Higher frequency of trades enables increasing positions size faster in a given period of time (compounding) during run-ups, but also enables decreasing positions size faster during drawdowns.

2. Source of profits
Long term trend-following / Turtle System captures profits from existence of trends (trends often connected with macro-economic cycles). As we all know, markets tend to move in trends but also experience long “choppy” periods, without any substantial move in one direction. Hence diversification is used to reduce negative impacts of non-trending periods in a single markets. Unfortunately, most liquid markets are highly correlated. As a result, these strategies performance depend on the magnitude of trends.

Short term trading captures profits from market inefficiencies, more precisely – from market’s over-reactions. If we’ll sum up all inefficiencies in the short-term level, they’ll add up to a greater amount than in the long-term level. Of course in order to play on short-term basis we must have low commissions and high liquidity – two things which have dramatically improved since ’90s or ’80s.

3. Predictability
Volatility in the coming short-term periods is more predictable than in the long-term periods. Try to estimate the average daily range for the next 5 days. Now try to predict the average yearly range for the next 5 years – error will be much bigger.


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