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Options Trading Insights with our 13 Year Technical Analysis Backtest

Tony Zhang
April 29, 2021
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18 minutes read
Options Trading Insights with our 13 Year Technical Analysis Backtest

Overview

Technical analysis is a powerful, yet exceedingly difficult method of analysis to understand and quantify. By leveraging historical data, technical analysis aims to forecast 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 was 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.

Observations Summary:
  • We were surprised as to how strongly randomly selected stocks on random dates that were held it 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 statistically 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 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.
  • 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.

Benchmark Returns

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:

Long

Win ratio: 49.9%
Annualized return: 14.4%

Short

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%

Summary of Results

Short Positions – Benchmark -10.6%

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.

Results
Long Trades

Short Trades

Notes:

  • 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.

Results
Long Trades

Short Trades

Notes:

  • 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:

 

Results
Long Trades

Short Trades

Notes:

  • 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 for RSI strategies outperformed the benchmark with only RSI(20) cross above 30 failing to produce better returns than the benchmark.
  • 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.

Commodity Channel Index

The Commodity Channel Index (CCI) is a technical indicator that is used to spot long term trend changes in a stock. This indicator uses the SMA of the typical price ((high + low + close)/3) in its calculation. Therefore, lower period CCI’s will generate more signals. For the study, the 20 period SMA was used. The CCI indicator generates a long signal when the CCI value crosses above -100 and 100. A short signal is generated when the CCI value crosses below 100 and -100.

Below is an example of the CCI indicator generating buy signals at 100 and -100, and sell signals at 100 and -100:

Results
Long Trades

Short Trades

Notes:

  • CCI(20) crossing below -100 had 16 consecutive losses for short trades, more than double the max consecutive winners.
  • CCI(20) crossing below 100 was the best performing strategy short trades.
  • For long trades, the CCI(20) above 100 strategy generated more than 5x number of signals as the CCI(20) above -100.

Williams %R

The Williams %R, also known as the Williams Percent Range, is a momentum indicator that measures when a stock is in overbought and oversold conditions. This indicator has a range between 0 to -100. A stock is considered overbought when the indicator crosses above 80 and oversold when the stock crosses below 20. This indicator uses a look back period of a certain number of days (14 for this study) to calculate the range. A long signal is generated when the indicator crosses above 20 and a short signal is generated when the indicator crosses below 80.

Results
Long Trades

Short Trades

Notes:

  • William %R on the bullish signals generated the highest probability trades with the with a win rate of 58.5%.
  • Williams %R generated the highest # of bearish signals over any other technical indicator.
  • Williams %R was the 3rd best indicator for long trades based on annualized return.

Moving Average Convergence Divergence

The Moving Average Convergence Divergence (MACD) indicator is a trend-based indicator that tracks the relationship between two moving averages (12 EMA minus 26 EMA). This calculation produces the MACD line which is used to calculate the signal line – a 9 EMA of the MACD line. The signal line is used to generate signals. A long signal is generated when the MACD crosses above the signal line, and a short signal is generated when the MACD crosses below the signal line.

Results
Long Trades

Short Trades

Notes:

  • MACD produced a very average return of 14.4% matching the benchmark for long positions
  • Large amount of consecutive losses for both long and short trades despite producing an average amount of signals.

Know Sure Thing

The Know Sure Thing (KST) indicator is a momentum oscillator. The KST indicator can be used like the RSI indicator in identifying overbought and oversold conditions. This indicator uses two lines to generate signals

  1. KST line – a combination of SMA’s and Rate of Change (ROC) periods
  2. 9 SMA based on KST values

A long signal is generated when the KST value crosses below 0 and crosses above the signal line. A short signal is generated when the KST value crosses above 0 and crosses below the signal line.

Results
Long Trades

Short Trades

Notes:

  • KST was designed as a less frequent signal generator with stronger performance. We see that KST underperformed for both long and short trades over the testing period.
  • KST did not reduce the max # of consecutive losses by very much.

Stochastic Oscillator

The Stochastic Oscillator indicator is a momentum-based indicator with a range from 0 – 100. This indicator identifies overbought and oversold conditions where a value above 80 indicates that the stock is overbought and a value below 20 indicates that the stock oversold. There are two key components to the stochastic oscillator indicator

  1. %K – The “slow component”. This represents the current value of the stochastic indicator
  2. %D – The “fast component”. A 3-period moving average of %K.

A long signal is generated when the %K line crosses above the %D line. The period usually used for this indicator is 14 periods.

Results
Long Trades

Notes:

  • Stochastics produced very lackluster returns of only 13.9% with a fairly high number of signals.
  • The biggest difference between the two strategies was the Slow %K indicator had a relatively high number of consecutive losses – 13.
  • Slow %K and Slow %D had very similar returns and similar win rates.

 

 

Disclaimer:

The strategies presented in this blog are for information and training purposes only, and should not be interpreted as recommendations to buy or sell any security. As always, you should ensure that you are comfortable with the proposed scenarios and ready to assume all the risks before implementing an option strategy.

 

© 2021 OptionsPlay, LLC. All rights reserved.

This content has been produced by OptionsPlay, LLC. The views, opinions and advice provided in this document reflect those of OptionsPlay, LLC. While Bourse de Montréal Inc. has participated as a sponsor, it has not had any input into the content and neither Bourse de Montréal Inc. nor its affiliates shall be responsible or liable for the same. Before making any investment decision, you should obtain professional investment advice tailored to your needs and taking into account your particular investment goals, financial situation and individual needs. This content does not, nor should it be construed as, providing any trading, legal, accounting, tax, investment, business, financial or other advice, and you should not rely on it for such purposes. This document is provided for informational purposes only and is directed at persons having professional expertise in matters relating to investments. Neither TMX Group Limited nor any of its affiliates represents, warrants or guarantees the completeness or accuracy of the information contained in this document and they are not responsible for any errors or omissions in or your use of, or reliance on, the information. This document or any securities referenced in this document are not endorsed by TMX Group or its affiliated companies, and the information provided in this document is not an invitation to purchase securities listed on Montreal Exchange. TMX and the TMX design are the trademarks of TSX Inc. and are used under license.

* The S&P/TSX 60 Index (the “Index”) is the product of S&P Dow Jones Indices LLC or its affiliates (“SPDJI”) and TSX Inc. (“TSX”). Standard & Poor’s® and S&P® are registered trademarks of Standard & Poor’s Financial Services LLC (“S&P”); Dow Jones® is a registered trademark of Dow Jones Trademark Holdings LLC (“Dow Jones”); and TSX® is a registered trademark of TSX. SPDJI, Dow Jones, S&P, their respective affiliates and TSX do not sponsor, endorse, sell or promote any products based on the Index and none of such parties make any representation regarding the advisability of investing in such product(s) nor do they have any liability for any errors, omissions or interruptions of the Index or any data related thereto.

Tony Zhang
Tony Zhang http://tmx.optionsplay.com

Head of Product Strategy for OptionsPlay

OptionsPlay

Tony Zhang is a specialist in the financial services industry with over a decade of experience spanning product development, research and market strategist roles across equities, foreign exchange and derivatives. As the current Head of Product Strategy for OptionsPlay, Tony leads the research and development of their OptionsPlay Ideas & Portfolio platform. He has leveraged his interest in financial technology and product development to provide innovative, reimagined solutions to clients and the users they seek to serve. Previously he spent 7 years at FOREX.com with a capital markets and research background as a market strategist specializing in equity and FX derivatives markets.

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