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Dispelling Myths and Defining Terms
Mathematical Technical Analysis: A Building Block for Mechanical Trading System
Contents
- Trend-Following Systems: A Matter
- Mean Reversion Systems
- Short-Term Systems: A Matter of Quick-
- Knowing Oneself: How to Challenge
- System Development and Analysis
- Price Risk Management: Schools of Price Risk Managment and Other
- Improving the Rate of Return: Improving
- Discretion and Systems Trading
- Psychology of Mechanical Trading
Trend-Following Average Reversion Systems 74 Unidirectional Non-Directional Average Reversion Systems 81 The Psychological Profile of a Medium-Term Average Reversion. Average Reversion Systems Using 60-Minute Bars 95 Non-Directional Average Reversion Systems 96 Average Reversion Systems Using 30-Minute Bars 98.
Preface
Finally, the chapter dispels the myth of mechanical trading systems as an easy method of generating profits. Finally, the chapter examines the psychological aspects of price risk management and shows how the use of mechanical trading systems can help promote confidence during withdrawals.
Acknowledgments
Mechanical Trading
Systems
Perhaps the most compelling evidence in terms of market participant irrationality is put forth by behavioral finance proponents. Behavioral finance, in which traders or investors base decisions on emotions, is diametrically opposed to theories of random market behavior and efficient market hypotheses, which assume that all market participants behave rationally.1.
Dispelling Myths and
The theory behind using a moving average is that if the market is in a significant uptrend, prices should not be weak enough to fall below the 200-day moving average. Both types of trigger events can be used to produce successful trading systems because they capitalize on recurring psychological conditions in the market.
Mathematical Technical
The general who wins a battle makes many calculations in his temple before the battle is fought. Many excellent books on technical analysis provide readers with a comprehensive description of various mathematical technical indicators.
Analysis
Although moving average envelopes are traditionally used to filter out false trend-following signals, they can also be used as countertrends. Moving Average Convergence-Divergence The moving average convergence/divergence indicator—better known as the MACD—was developed.
Trend- Following
A cursory review of the examples of trend-following systems in this chapter shows us two qualities necessary for successful trend traders: patience and fortitude. For this reason, I chose the E-mini S&P 500 futures contract instead of the full-size S&P 500 futures contract. All the trading systems discussed here are the simplest imaginable, but still show overall returns.
This is in stark contrast to our comparison of two moving average crossovers and two mov-. Note: Changing the trigger points away from the zero level to reduce vertigo is also useful for all trend-following conditional trading systems, including the two moving average crossovers, MACD, momentum and ROC.). A simple example of this approach would be the addition of a stop loss based on 1 to 5 percent of the asset's value at entry (see the “Cutting Losses” section later in this chapter).
When looking at the portfolio results of these trend-following systems, we see time and time again that specific asset classes generated the lion's share of the trend-following system's profits.
Mean Reversion
How can Eurodollars be volatile enough to work for our trend following systems and not mean return systems. Close examination of the mean reversion charts in Chapter 2 shows that I used either the stock indices or non-U.S. Unfortunately, the average return portfolio does not have the same number of assets as its trend-following counterpart.
Here we take the mean reversion Bollinger band system used earlier and replace the 200-day moving average filter with the ADX. Although this is not always the case, most trend average reversion systems outperform their non-directional one-sided counterparts. Here again we see significant performance degradation when compared to trend-mean reversion systems.
Often, the next stage of our maturation as a mean reversion trader ends with the manifestation of a form of perfect trader syndrome.
Short- Term
Although market liquidity and volatility are constantly changing, as of this writing I feel that generally only the assets shown in Table 5.2 should be considered for swing and day trading time frames and trade types. dollar with MACD over different timeframes. From table 5.2 we can see that only the stock indices showed consistent profitability in shorter time frames. Although this may seem counterintuitive, an examination of the average trade duration and percentage of time in the market columns from Table 5.4 shows that it is the preferred method for comparing pit-session assets to those traded over a 24-hour time frame .
While such superior performance is undeniable, I wouldn't throw away the 2 hour time frame. TABLE 5.6 Slow stochastic extremes with CCI filter and 30-hour time output. In general, as shown in Table 5.1, the performance of mechanical trading systems deteriorates with shorter time frames. Note how the same target profit in Table 5.8 and a safe stop loss resulted in a higher percentage of wins and a lower number of largest consecutive losses than in Table 5.9.
Comparing the performance in Table 5.14 with that in Table 5.13, there were two notable improvements when the time frames of our bars were shortened: the maximum number of consecutive losses and the win/loss ratio of the system.
Knowing Oneself
As with long-term trend-following systems, participation in this time frame usually means an inability to take advantage of obvious short-term event-driven countertrend opportunities. As with all trend-following systems, it requires the ability to buy recent highs, sell recent lows, and give up a significant portion of unrealized profits. Since these systems do not have a directional bias, they generate a greater number of trading opportunities than systems with trend-following filters.
Unlike many traditional trend-following strategies, these systems (like most mean reversion techniques) quantify both risk and reward before entering the trade. Eliminating the trend-following filter often results in less confidence in the trading system and a higher likelihood of stoppages during withdrawals. The use of the trend-following filter allows participation in the trend over the short to medium term, while still allowing you to take advantage of sideways market behavior over the medium to longer term.
Trading in the direction of the short- to medium-term trend leads to greater confidence when recent highs or lows fade.
System
Development and Analysis
Avoidance of data curve fitting in the system development process can be achieved by strict adherence to an objective data history criterion for backtesting the trading system. But suppose system developers were unhappy with the poor win/loss ratio of the two moving average crossovers. Examining the tables, note how rarely the top performer from the 10-year “in-sample” period was the top performer in our “out-of-sample”.
The out-of-sample or step-forward study is probably one of the most important aspects of the system development process. The most essential aspect of the out-of-sample data window is its integrity. Consequently, the 15 percent rule seems a sensible alternative to the real-time failure of the trading system.17.
A case study based on one of the trading systems from Chapter 3 can be instructive. Both performances were atypical based on almost all of the studies in the sample shown in Chapter 3. Violation of this peak-to-trough withdrawal rate will result in liquidation of the fund (or trading account).
Price Risk
While price risk management will not turn a losing trading strategy into a winner, it is perhaps the most important topic in this book as it can prevent the failure of an overall profitable trading system. Imagine a system that produces an astonishing 90 percent win/loss ratio, and pair this system with a trader who spends all accumulated winnings – along with the original bet – on the next trade. According to the laws of probability, such a trader would go bust on average if he exits the tenth position.
The other reason why I believe price risk management is such an important tool in the trader's arsenal is that we can never control the markets, only how much risk we take in them. Because we can never know for sure whether any particular trade will result in a profit or loss, it is the height of folly to focus our attention and capital on those aspects of trading over which we have no control, while neglecting price risk management. the one essential aspect of trading over which we exercise absolute control. Of course, we also have control over the entry point we choose for any particular trade.
However, the selection of precise entry levels probably represents the least significant aspect of a successful trading system.
Management
Some, such as liquidity risk, are discussed in this chapter because of their critical and universal impact on price risk management issues. Moreover, only by adapting the advantages of both approaches can a reliable solution for price risk management be achieved. Volumetric price risk management based on the size of positions taken in the markets or how many volumetric units (eg contracts, shares, etc.) will be traded.
Stop loss price risk management, which determines the size of the risk taken per traded position or how much capital will be risked per volume unit traded. Perhaps one of the most effective and fundamental aspects of price risk management is the placement of stop loss orders on a per trade basis. One of the most common reasons retail and institutional traders fail is the lack of adherence to comprehensive and systematized price risk methodologies.
Mechanical trading systems can be invaluable in implementing the various price risk management tools discussed in this chapter.
Improving the Rate
