in black and white
Main menu
Home About us Share a book
Biology Business Chemistry Computers Culture Economics Fiction Games Guide History Management Mathematical Medicine Mental Fitnes Physics Psychology Scince Sport Technics

Mechanical trading systems - Weissman R.L.

Weissman R.L. Mechanical trading systems - Wiley publishing , 2005 . - 240 p.
ISBN 0-471-65435-3
Download (direct link): mechanicaltradingsystems2005.pdf
Previous << 1 .. 14 15 16 17 18 19 < 20 > 21 22 23 24 25 26 .. 82 >> Next

For example, let us assume that our equalized continuation chart for Nymex natural gas futures shows a long entry price of $1.001 during August 1991 and an exit price of $1.356 for a profit of $3,450.00 per contract (trade profit was $3,550.00 minus $100.00 for slippage and commissions). Although the absolute price difference between entry and exit levels is correct, if we consider this difference in percentage terms based on August 1991 valuations, we can determine that the actual contract was trading at $1.50 and that a price move of $0.355 represents a 23.67% profit. Now compare this same price move based on October 2003 natural gas prices of $6.00 and our 23.67% profit shrinks to a mere 5.92%.
Thomas Stridsman’s book on trading systems addresses these issues in great detail and offers solutions regarding this flaw in equalized continuation data histories. Readers who feel that their backtested results will be affected by such limitations are encouraged to adopt his solutions. In other words, if data are based solely on trading a market with a historical trend similar to the natural gas example, then use percentage instead of price changes.2 However, in pursuing this methodology, remember that the exchange can change the point value of its contracts. Blindly applying a percentage change without consideration of this fact (and of how the software vendor handles such changes) can skew results as dramatically as sticking with the originally flawed price change calculations.3
Examples of other instances in which application of percentage as opposed to price changes would be questionable are the foreign exchange and fixed income markets. Because foreign exchange price increases or decreases are totally dependent on the base currency chosen for valuation, the application of percentage changes are subjective and misleading. This is illustrated by the International Money Market (IMM) Japanese yen contract, which was trading around .003400 in December 1976 and .009200 in
November 2003. Based on these price comparisons, we might erroneously assume that greater weighting should be given to trades executed in 1976 since equal price moves would represent a greater percentage change. This is obviously not the case since the IMM valuation is in Japanese yen-U.S. dollar and use of the interbank market valuations of 298 in 1976 and 109 in 2003 (which are expressed in U.S. dollar-Japanese yen terms) would suggest the exact opposite percentage weightings.
Applying percentage as opposed to price changes to the fixed income market implies a less severe but equally flawed assumption regarding the data. This is due to the inverse relationship between price and yield.4 If an assumption is to be made regarding the application of percentage changes to the fixed income markets, it should be that as prices increase, they may represent lower volatility and therefore would entail a reduced percentage weighting vis-à-vis today’s data.
Despite the flaws just detailed, in light of the nature and historical trends of the assets contained with my model portfolio, I remain reasonably comfortable with using equalized continuation charts and have chosen to set the rollover date to 20 days prior to expiration of the contract. Nevertheless, in some instances, where the liquidity was adequate and the correlations between the spot and futures market for a specific asset were significantly high enough, I have decided to use the spot market’s data history.
Backtested Portfolio Results Another practical limitation in the presentation of historically backtested results on any significant sampling (for intermediate to long-term systems, 10 to 30 years of historical data are considered a statistically significant data sampling) is the problem of estimating worst peak-to-valley equity drawdowns. To accurately calculate the worst peak-to-valley drawdown on a daily basis, we would need to track daily mark to markets on all assets within the portfolio for the entire data history in question. At the time of this writing, most data vendors with system development and backtesting capabilities do not offer backtested results for a portfolio of assets. Consequently, all worst drawdown and maximum consecutive loss numbers shown in the portfolio totals columns in this and the next chapters are derived from profit/loss and win/loss as of trade exit dates.
Explanation of the Portfolio Results Tables
For the asset symbol definitions, refer back to Table 3.1. Although I could have chosen to employ all 24 of the fields used in CQG’s backtested performance results, I have chosen to highlight 10 fields that I feel are most essential in evaluation of a system’s robustness:
Trend-Following Systems
1. Total net profit examines profitability irrespective of risk taken to achieve these results. Because of this limitation, other measures included in our backtested results are superior analytical tools. However, this number is useful because it allows us to quickly add and compare various portfolio component results for numerous systems without additional calculations.
Previous << 1 .. 14 15 16 17 18 19 < 20 > 21 22 23 24 25 26 .. 82 >> Next