Technical analysis is a powerful, yet exceedingly difficult method of analysis to understand and quantify. By leveraging historical data, technical analysis aims to anticipate the future price movement of a financial asset. While many of these strategies may look promising, the reality is that implementation of these strategies can sometimes yield large deviations from the textbook. Moreover, there are a wide array of technical indicators to choose from, but not enough evidence of their long-term performance. There are many resources that promote the use of technical indicators without providing any evidence of its long-term consistency.
At OptionsPlay, we evaluated some of the most popular technical indicators and strategies to see how well they performed. Firstly, we calculated a benchmark return to compare our strategies against. This was calculated by entering a long and short position in completely random stocks on randomly selected dates in the S&P/TSX 60 Index* with a holding period of 30 days. We then tested the same method of buying or selling a stock on a date where a technical indicator generated a buy or sell signal for 30 days. While this does not represent the best possible back-test, it provides a level playing field to gauge all the indicators under equal conditions. The back test was conducted on price data from 2007 to 2020.
The benchmark returns calculations showed that the return on long positions in 2000 randomly selected stocks on random dates, held for 30 days, had an average win ratio of 49.9% with an annualized return of 14.4%.
After the benchmark returns were calculated, we proceeded to test each indicator under the same conditions for both long and short positions, during the same period (2007 – 2020) and the same holding period of 30 days. Analysis of the results provided some surprising observations.
- We were surprised as to how strongly randomly selected stocks on random dates that were held for 30 days performed. This set a very high bar for each technical indicator to beat.
- Sell signals from the technical analysis strategies performed extremely poorly. Only two technical indicators performed better than the benchmark annualized return for short entries. This speaks to the market’s natural upward drift that puts short positions in a statistical disadvantage and proves how difficult it is to consistently call short trades using technical analysis.
- The “RSI20 Cross above 25” indicator proved to yield the best annualized return for buy signals. This strategy provided a standout performance compared to the other indicators.
- Most strategies had a surprising high number of consecutive losses. This is critical for trader’s risk management strategy. With limited amounts of capital to risk for each trade, traders need to understand in a worst-case scenario. The number of consecutive false signals that have historically ever generated by each indicator is important to determine the amount of capital to risk on each trade and avoid account blowups even in a worse case scenario. This is due to the fact that consecutive losing trades are what can cause severe underperformance of a portfolio. An indicator that has a higher number of consecutive false signals, should be traded with a smaller % of the account allocated to each signal. With indicators that have a lower number of false signals, allocations can be larger per trade as a percentage of the total account value.
- The Simple Moving Average (SMA) performed similar to the Exponential Moving Average (EMA) where both the SMA9 and EMA9 yielded similar results, with the EMA9 slightly outperforming the SMA9 as the best moving average indicator. Moving averages are widely used and fiercely debated as to their efficacy and success rates. While the Moving Average period will vary widely dependent on an investor’s time horizon, we have found that the long period moving averages were the worst performing indicators in the entire study for long trades.
The benchmark returns between 2007 and 2020 were calculated by entering a long/short position with a randomly selected stock and holding this position for 30 days. This resulted in the following benchmarks for long and short positions:
Win ratio: 49.9%
Annualized return: 14.4%
Win ratio: 50.5%
Annualized return: -10.6%
All the results of the strategy back testing are compared to the benchmark return to give an idea of how well they perform.
Summary of Results
Long Positions – Benchmark 14.4%
Short Positions – Benchmark -10.6%
Simple Moving Average
The Simple Moving Average (SMA) strategy calculates the average closing price of a stock over a given number of periods. A buy signal is generated when the stock crosses above the moving average, and a sell signal is generated when the stock crosses below the moving average. It is important to note that the lower the period used for the moving average calculation, the closer the moving average line tracks the current price of the stock. This means that shorter moving averages will generate more signals than longer ones – the SMA(9) calculates the average closing price of the previous 9 candles while the SMA(200) calculates the average closing price over the previous 200 candles.
- Similar win rates in long and short trades for all the SMA combinations
- SMA periods close to the holding periods tend to provide the best results.
- The SMA(200) provided the best annualized return for short trades
Exponential Moving Average
The Exponential Moving Average (EMA) strategy follows the same principle as the SMA strategy but the method of calculating the moving average is slightly different. The EMA of a stock places a greater weight on the most recent data points and therefore reacts more significantly to recent price changes instead of the SMA which reacts to all price changes over the given period equally. A buy signal is generated when the stock crosses above the exponential moving average, and a sell signal is generated when the stock crosses below the exponential moving average.
- Higher period EMA’s were the worst performing indicators overall
- The win rate % for EMA’s are similar in short trades
- The 9 period EMA was the 2nd best performing bullish indicator overall
Relative Strength Index
The Relative Strength Index (RSI) is a popular indicator based on momentum and is used to identify overbought and oversold conditions. The indicator is displayed as a line graph that moves between 0 and 100 where readings below 30 are considered oversold and over 70 is considered overbought. For the purposes of this study, we tested the RSI values based on the previous 14 periods and previous 20 periods. A buy signal is generated when the RSI value crosses above 25 or 30, well a sell signal is generated when the RSI value crosses below 70 or 75.
Below is an example of RSI(14) generating buy and sell signals crossing above 30 and below 70:
- Long trades produced a relatively low number of consecutive losses while short trades had a much higher disparity between consecutive winners and consecutive losers.
- For long trades, 3 out of the 4 RSI strategies outperformed the benchmark with only RSI(20) cross above 30 failing to produce better returns than the benchmark. Only the longer 20 period RSI with a 30 buy threshold produced returns that underperformed our benchmarks. This makes RSI one of the best performing indicators in our backtest.
- RSI(20) crossing above 25 had the best performance overall with an annualized return of 32.4% (more than double the benchmark). However, it also produced very few signals.
Originally Posted on April 29, 2021 – Options Trading Insights with our 13 Year Technical Analysis Backtest
Dollars expressed are in CAD
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