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 .. 32 33 34 35 36 37 < 38 > 39 40 41 42 43 44 .. 82 >> Next


Various market participants define swing trading in different ways. For the sake of clarity and consistency we will define swing trading as trades typically held for more than 1 and less than 10 trading days. The intermediateterm mean reversion systems highlighted in Chapter 4 had an average duration of 8 to 20 trading days. Therefore, swing trades bridge the gap between intermediate-term and day trading systems. Some market participants use the term swing trading to designate nondirectionally biased mean reversion strategies; because I define the term based on this purely time-driven criteria, we will examine trend-following, trend-following mean reversion, and nondirectionally biased mean reversion swing trading systems.
Trend-Following Swing Trading: Channel Breakout
To examine a typical trend-following swing trading system, we will modify the channel breakout system used in Chapter 3 by changing the entry criteria from 20- to 15-day highs or lows and reducing the exit condition to the violation of 8-day highs or lows. This shifts our original system from a stop and reverse to one that allows for neutrality during sideways market action. In addition, to ensure that these trades remain “short term” in duration, we have added a time-driven exit criteria that will be triggered on trades held beyond 7.5 days. (See Chapter 4, “Time-Driven Exit Filters,” for the programming code.)
Because we only trade T-bonds during their day session, whereas the euro/U.S. dollar generates trades 24 hours a day, we need to equalize our trading system’s parameters by converting the number of 2-hour bars per trading day for both assets as illustrated in Table 5.3.
Short-Term Systems 93
TABLE 5.3 Example of equalization of trading days for pit versus 24-hour
Bars per Trading Day Number of Trading Days Number of 2-hour Bars
4 1 5 60
12 15 180
Data source: CQG, Inc.
Notice in Table 5.3 that an equal number of trading days means an unequal number of bars (e.g., 15 days in T-bonds is 60 2-hour bars as opposed to 180 bars for IEURUSD). Although this may seem counterintuitive, an examination of the average duration of trades and percentage of time in the market columns from Table 5.4 proves it is the preferable method of comparing pit session assets to those traded over a 24-hour time frame.
Furthermore, because the euro trades around the clock and we trade T-bonds only during local pit trading hours, the lengths of our data histories are different. (IEURUSD runs only from March 28, 2000, to January 30, 2004, whereas CBOT data for continuation pit session T-bonds goes back to December 30, 1998.) Consequently, the combined portfolio results for the two assets have been omitted. We can, however, generate average annualized results for this system for both assets and then compare these to our longer-term trading systems. Annualizing our total net profits for IEURUSD equates to roughly $5,006.25 per year, whereas T-bonds yielded a more modest $2,490 (although we will still retain the maximum drawdowns shown in Table 5.4).
Notice that both assets compare quite favorably with the majority of component-based results generated throughout Chapter 3. In fact, only the annualized profit to maximum drawdown (P:MD) of the Japanese yen proved consistently comparable to those generated in Table 5.4. This suggests that an improved rate of return at least somewhat compensates for the lack of diversification inherent in implementation of short-term trading systems.
TABLE 5.4 Channel breakout with 15-day entry and 8-day exit plus 7.5-day time exit.
Asset # Profit Trades # Days Max Draw MDD MCL P:MD P:L Ratio %W Time %
US IEURUSD 12450 89 20025 95 7.3 6.9 -12237 323 -7840 399 4 1.02 4 2.55 1.24 1.34 52.81 51.58 62.59 59.86
Note: results include a deduction of $100 per round-turn trade for slippage on daily
time frame and $75 per round-turn for shorter time frames. Data source: CQG, Inc.
Swing Trading with 2-Hour Bars: Mean Reversion Systems
Here we will work with two of the mean reversion systems highlighted in Chapter 4: one trend-following mean reversion system and one nondirec-tionally biased mean reversion system. Both systems will use 120-minute bar charts on the Nasdaq 100 index. The data displayed includes history from November 30, 1998, to January 30, 2004.
Relative Strength Index Extremes with 400-Hour Moving Average Filter
Relative strength index (RSI) extremes with 400-hour moving average filter is the same trend-following mean reversion system that generated the best performance of those used throughout Chapter 4 and uses the same CQG code.
Table 5.5 presents results of this system for the Nasdaq 100 index (day session only).
As expected, because this system exits trades near the mean, our average trade duration and percentage of time in the market have decreased when compared with the trend-following swing trading system results shown in Table 5.4. What is most surprising is the system’s unusually poor win/loss ratio. This is in stark contrast to the results shown in Chapter 4 and is a direct result of this asset’s extraordinarily high volatility.
Previous << 1 .. 32 33 34 35 36 37 < 38 > 39 40 41 42 43 44 .. 82 >> Next