The rise and fall of technical trading rule success

The limitation of our study is that we cover only the more popular technical trading strategies with standard parameters. Future studies could extend to include more technical indicators with different parameters. In addition, there was a generally strong uptrend in our sample, future studies may attempt to include other market situations in the samples. In terms of testing instruments, this paper tests technical trading systems only on the market indices. Future studies can extend the coverage to individual sectors or stocks as the performances of technical trading rules may vary across sectors or stock characteristics. Technical trading rules (TTRs) are commonly employed in practise as a method of generating buy/sell signals from past data (Gehrig and Menkhoff, 2006).

In short, profitable strategies produce significant average daily return, but only DMI generates both a significant return and a profit after transaction costs in the Indonesian market. Park and Irwin (2007) provide a review of empirical studies on the issue of trading rule profitability. In their review, modern studies (papers published from 1988 to 2004) indicate that technical trading strategies consistently generate profits at least until the early 1990s. Among a total of 95 studies, 56 studies find profitability of technical trading, 20 studies obtain negative results and 19 studies indicate mixed results. The studies, which find profitability of trading rules, are Sullivan et al. (1999), Lo et al. (2000), and Kavajecz and Odders-White (2004). However, Brock et al. (1992), Bessembinder and Chan (1998), Ready (2002), Marshall et al. (2008) show that transaction costs would eliminate any trading profits.

What’s the Difference Between Fundamental and Technical Analysis?

Market corrections up or down usually retrace a significant portion of the previous trend. You can measure the corrections in an existing trend in simple percentages. A minimum retracement is usually one-third of the prior trend.

The table shows that, like Table 8, there is convincing evidence that there is an association between the returns and trade signals. Overall, the study finds that concerning buy signals, the Bitcoin market possesses a momentum effect across the board as the results are strongly significant at conventional levels. Technical analysts believe that there is a bigger probability that a certain market movement may continue rather than reverse its direction. In other words, technical analysts believe that prices follow trends. What this means is that if trading is highly based on probability, then in order to increase the probability of the success of a trade, traders should try to trade in the direction of the trend. The Average Directional Movement Index (ADX) line helps determine whether a market is in a trending or a trading phase.

To be profitable, the breakeven cost (C) or the average additional return per signal must be greater than a round-trip transaction cost. If we divide the additional return (π) by the numbers of buy and sell signals, this will give us the average additional return per signal or, in other words, the round-trip breakeven cost (C) (Bessembinder and Chan 1998). A negative index number, say −20, reveals that overall a trading strategy generates a loss. However, the actual loss is only 20 % of the maximum possible loss (HOD) during a simulation. The index with a value of “−100” means that a trading strategy incurs the maximum possible loss (HOD). Risk measures include the standard deviation of daily returns and the “Highest Open Drawdown” (HOD), which is the maximum distance the equity line fell below the initial investment during the back-testing simulation.

References to securities trading are made on behalf of the BD Division of SFI and are intended only for an audience of institutional clients as defined by FINRA Rule 4512(c). References to exchange-traded futures and options are made on behalf of the FCM Division of SFI. It’s no small feat to develop a live market technical trading plan. In fact, becoming a competent market technician takes time, effort, and dedication.

Time-varying short-horizon predictability

Therefore, the current study examines the profitability of technical trading methods and whether they are feasible alternatives as an analytical tool to evaluate Bitcoin exchange rate returns. Nevertheless, certain technical strategies like MACD, STOCH-D or more sophisticated strategies may still provide net excess returns. Our results also suggest that traders should optimize parameters of their trading strategies rather than stick with standard textbook parameters. According to the above evidence, the average number of buy and sell signals for BTC/JPY is 69 and 179, respectively (columns 3 and 4).

The Moving Average Convergence Divergence (MACD) indicator (developed by Gerald Appel) combines a moving average crossover system with the overbought/oversold elements of an oscillator. A buy signal occurs when the faster line crosses above the slower and both lines are below zero. A sell signal takes place when the faster line crosses below the slower from above the zero line. An MACD histogram plots the difference between the two lines and gives even earlier warnings of trend changes. It’s called a “histogram” because vertical bars are used to show the difference between the two lines on the chart. Tables 8 and 9 present the results on the momentum effect being tested for the third study hypothesis [H3].

Technical Trading Rule Profitability and Foreign Exchange Intervention

In the Malaysian market, the STOCH-D, MACD, DMI and OBV produce highly significant average daily returns, yet none generate profits after transaction costs. In the Philippine market, only STOCH-D and MACD trading strategies generate highly significant average daily returns. Yet again, both of them do not produce after-transaction cost profits. https://trading-market.org/ Only the OBV trading strategy produces an after-transaction cost profit, but the average daily return is not statistically significant. In the Indonesian market, the STOCH-D, DMI and OBV trading strategies produce highly significant average daily returns. Nevertheless, only DMI trading strategy could generate a profit after transaction costs.

The implication is that even with seemingly profitable technical trading strategies, traders cannot expect to buy at a relatively low price and sell at a relatively high price by just using technical trading rules. An observation of the results in the context of the BBL’s mean difference test shows that TA may be profitably used in the BTC/EUR exchange rate. The evidence for this finding is that out of the 24 tested trading rules, 96% (column 15) of the buy signals are strongly significant at 1% level. In addition, the sell signals are less impactful than buys, but 63% (column 16) of the executed experimental sell trades are significant.

Fundamental analysis is a method of evaluating securities by attempting to measure the intrinsic value of a stock. The core assumption of technical analysis, on the other hand, is that all known fundamentals are factored into price; thus, there is no need to pay close attention to them. Technical analysts do not attempt to measure a security’s intrinsic value, but instead, use stock charts to identify patterns and trends that might suggest what the security will do in the future. Technical analysis attempts to forecast the price movement of virtually any tradable instrument that is generally subject to forces of supply and demand, including stocks, bonds, futures, and currency pairs. In fact, some view technical analysis as simply the study of supply and demand forces as reflected in the market price movements of a security.

The MACD, DMI and OBV trading strategies are profitable even after transaction costs. In summary, we find that in general long-only strategies performed better than similar short-only strategies. This partly reflects general uptrends during the sample period.

They are based on questions and comments that he has received over the years after speaking to various audiences. If you are confused about how to use Technical Analysis at a practical day-to-day level, these suggestions should help. Daniels Trading is division of StoneX Financial Inc. located in the heart of Chicago’s financial district. Established by renowned commodity trader Andy Daniels in 1995, Daniels Trading was built on a culture of trust committed to a mission of Independence, Objectivity and Reliability. Trend lines are the easiest way to measure trends by connecting higher highs or lower lows, and they must always go from left to right. Moving away from the perils of opinions and predictions has improved my mental well-being, and my bottom line.

Filter Rules and Stock-Market Trading

In developing a trading system of your own, you must begin with the big picture. A “Trade Efficiency for long only strategy” is calculated in the following way. This index compares the amount of “Net Profit” (Trade Profit − Trade Loss) to the amount of winning or losing trades. Mathematically, the stochastic oscillator (%K) is calculated by the following formula.

A “Performance” number is a percentage measure of how much net profit or loss the trading rule generated based on initial equity at the end of the simulation. An “Annualized Performance” calculates a performance over a year. It equals to a performance multiplied by 365 and divided by the number of days in the simulation. The first part discusses measures of risk that we use to evaluate each trading system. The second part explains logics and interpretations of each performance measure. The last part discusses the optimization of technical trading rule parameters.

Furthermore, an overview of the rest of the table reveals other interesting insights. There are 180 sell signals compared to 63 buy signals, yet the buy trades outperform the sell (columns 3 and 4). For instance, a glance at columns 8 and 9 shows that there are significantly more returns that are greater than zero for the buy signals (averaging 66%) compared to the sell signals with an average of only 12%. Another example, TTR No. 6 (5,20,1) stands out in that it produces the greatest number of sell signals (of 240 trades) and it yields only three buy signals. The average daily mean returns for buy, buy–hold and sell trades are 0.3%, −0.5% and −0.7%, respectively (columns 10, 11 and 12).

Interestingly, unprofitable trading strategies such as RSI and STOCH sometime are riskier or about as risky as a BH. They are also always riskier than the above profitable strategies. Interestingly, even for profitable trading strategies such as STOCH-D or MACD long-only trading strategies, the percent of profitable trades over total number of trades is still usually less than 50 %.

As a result, an asset can be overpriced or underpriced even more by noise traders at least in the short run. Shleifer and Summers (1990) even suggest that technical trading based on noises can make profits even in the long run. In their herding model, Froot et al. (1992) demonstrate that herding behavior of short-horizon traders can lead to informational inefficiency.

Fundamental analysts study everything from the overall economy and industry conditions to the financial condition and management of companies. Earnings, expenses, assets, and liabilities are all important characteristics to fundamental analysts. Adam Hayes, Ph.D., CFA, is a financial writer with 15+ years Wall Street experience as a derivatives trader. Besides his extensive derivative trading expertise, Adam is an expert in economics and behavioral finance. Adam received his master’s in economics from The New School for Social Research and his Ph.D. from the University of Wisconsin-Madison in sociology.

The RSI ranges from 0 to 100, with high and low levels marked at 70 and 30, respectively. Traditionally, RSI readings greater than the 70 level are considered to be in an overbought territory (Bearish signal), whereas RSI readings lower than the 30 level are considered to be in an oversold territory (Bullish signal). In between the 30 and 70 level is considered neutral, with the 50 level a sign of no trend.

• The findings of moving average rules based on conditional CAPM for the BTC/ZAR exchange rate are displayed in Table 5 and are discussed next.
• Once the basics are understood, from there you can use the same types of materials but those that focus specifically on technical analysis.
• Most traders use 14 days or weeks for Stochastics and either 9 or 14 days or weeks for RSI.
• The repetitive nature of price movements is often attributed to market psychology, which tends to be very predictable based on emotions like fear or excitement.
• For profitable strategies like STOCH-D, MACD and OBV, optimized values would drastically increase investment returns.

This test equation was first applied by Kho (1996) in TA profitability investigation. For this reason, we label Eqn (8) as Kho’s mean-spread test for identification and simplicity. This equation is similar to Eqn (7), but it has advantages of validating the regression analysis with robust standard errors (Newey and West, 1987). Empirically, if the spread equals zero, it will mean that the buy and sell signals are equal. Based on the framework of Eqn (8), then if the spread is zero, the coefficient, α1 will be insignificant.