Technical Trading Rules SpringerLink

These rules assume that recent trends in stock prices are likely to continue in the future. Trading signals are generated once a stock shows a trend (i.e., positive or negative cumulative price changes of a certain magnitude) during a specific period of time. We repeatedly recommend trading small position sizes until you have gained confidence and knowledge. Learning to trade takes time, many years, and you need confidence to pull the trigger when you get a trading signal. It’s not easy to follow trading rules when you have no detachment from money or low confidence.

We then divide the sample periods into subsamples of 7 years in length to examine the evolution of predictive ability over time. We find a significant time trend in the predictive power of technical rules, which is highest during the first subperiods and declines sharply thereafter. Almost no developed market is predictable with the considered technical trading rules during the last two subperiods starting in 2002. In contrast, we still find that half of the emerging markets are predictable, with at least one rule during the period between 2002 and 2008.

Performance measurement

The levels most commonly scrutinized by active traders are 38.2 percent and 61.8 percent. Moving averages are great tools for a trader to use, but they are best used along with an overbought/oversold oscillator like the RSI. This maximizes exit profitability on extensions from a moving average.

Trading rules are based on value ratios such as price to book (P/B), price to earnings (P/E), and financial ratios like leverage. The assumption is that cheaper stocks eventually reflect the real value, while overpriced stocks do the opposite and perform poorly. These are trading rules that can be quantified and backtested easily.

The index data are retrieved from Datastream and cover the maximum available sample periods ending in May 2016. Markets for which less than 14 years of data are available are not included to ensure appropriately long time series and to have at least two subsamples of 7 years each in the subperiod analysis. The classification of countries as either “developed” or “emerging” is based on the categorization of the International Monetary Fund (IMF 2016), which applied at the end of the sample period. The 23 developed countries are considered as “advanced economies” by the IMF. An early and simple approach to account for multiple hypotheses in statistical tests is provided by the popular Bonferroni correction.

1 Performance of technical trading rules

The fundamentals of John’s approach to technical analysis illustrate that it is more important to determine where a market is going (up or down) rather than the reason behind its direction. John’s famous ten (plus one) rules that everyone should know about charting and technical analysis. In sum, if enough people use the same signals, they could cause the movement foretold by the signal.

• Given the general disagreement in the academic literature on the predictive ability of technical trading rules, our work aims to shed light on this ongoing debate.
• In fact, becoming a competent market technician takes time, effort, and dedication.
• We then divide the sample periods into subsamples of 7 years in length to examine the evolution of predictive ability over time.
• We recommend optimizing so you get a better grasp of what is driving the returns.

Follow that Average

The results presented in Table 11 in the appendix confirm our baseline findings obtained with the Sharpe ratio criterion. A significant part of this paper is devoted to an out-of-sample analysis on the applicability of technical heuristics in a way that aims to mimics the trading behavior of a real trader. Most of the previous literature on technical analysis examines only the ex-post performance of technical heuristics and neglects whether these heuristics actually exhibit out-of-sample persistent performance in the future.

• An early and simple approach to account for multiple hypotheses in statistical tests is provided by the popular Bonferroni correction.
• Because of this, the max drawdown is only 14%, compared to buy-and-hold, which had 55%.
• These findings suggest that the performance of best rules is not persistent and technical trading signals can be considered as noise.
• A combination chart of two moving averages is the most popular way of finding trading signals.
• The ease with which these naive technical trading rules can be copied suggests that initially predictive rules may become unfavorable relatively quickly.

Map the Trends

The ease with which these naive technical trading rules can be copied suggests that initially predictive rules may become unfavorable relatively quickly. While at least some of the trading rules have performed significantly better than buy-and-hold strategies under a zero-transaction cost scheme in early subperiods, they all appear to be useless in current markets. In summary, the results presented in this section demonstrate that past superior performance of technical trading rules does not persist in the near future. This seriously calls into question whether the studied rules could have been traded at any profit.

A rising ADX line favors moving averages; a falling ADX favors oscillators. By plotting the direction of the ADX line, the trader is able to determine which trading style and which set of indicators are most suitable for the current market environment. A trend is a directional move in price, typically identified via a set technical chart. Typically, traders qualify trends as being a series of periodic higher highs (bullish) or lower lows (bearish). It’s a cliché, but the old saying technical trading rules “the trend is your friend” is among the most popular basic trading rules in existence for a reason. The Average Directional Movement Index (ADX) line helps determine whether a market is in a trending or trading phase.

You should revise your trading rules at certain intervals. However, you must decide the intervals BEFORE you start trading the rules. The original RSI level performs pretty well based on the table.

The results strongly suggest that the investigated trading signals are noise and that a trading strategy which follows these signals ultimately underperforms the market due to an accumulation of transaction costs. Our in-sample results show that technical trading rules have predictive power in some markets during relatively early periods when transaction costs are ignored. However, in recent years, the investigated technical rules do not have predictive power anymore. To evaluate the impact of transaction costs, we run a stepwise algorithm to determine the number of outperforming trading rules for different transaction cost levels. This analysis reveals a high sensitivity of trading performance to moderate single-trip transaction costs. Moreover, an out-of-sample analysis suggests that the performance of the best technical heuristics is generally not persistent over shorter time periods in the future.

After the market’s condition is established, you may use intraday charts (hour, minute, tick) to fine-tune entry and exit points. Hundreds of patterns and signals have been developed by researchers to support technical analysis trading. Technical analysts have also developed numerous types of trading systems to help them forecast and trade on price movements. Professional analysts often use technical analysis in conjunction with other forms of research. Retail traders may make decisions based solely on the price charts of a security and similar statistics. But practicing equity analysts rarely limit their research to fundamental or technical analysis alone.

Limitations of Technical Analysis

In trading, emotions and egos are expensive collaborators. Our goal as traders is to capture price moves inside our time frame, while limiting our drawdowns in capital. Another criticism of technical analysis is that history does not repeat itself exactly, so price pattern study is of dubious importance and can be ignored. For some analysts and academic researchers, the EMH demonstrates why no actionable information is contained in historical price and volume data. However, by the same reasoning, nor should business fundamentals provide actionable information.