The Edge of Technical Indicators

Classical technical indicators like RSI and Stochastic are commonly used to build algorithmic trading strategies.  But do these indicators really give you an edge in your market? Are they able to define the times when you want to be invested? This article will show you a way to quantify and compare the edge of technical indicators. Knowing the edge of the indicator makes it an easy task to select the right indicator for your market.

The edge of an indicator

Any technical indicator, let it be RSI, moving averages or jobless claims, has got a primary goal. It should signal if it is a wise idea to be invested or not. If this indicator signal has any value, on the next day the market should have a higher return than it has on average. Otherwise  the usage of no indicator and a buy and hold investing approach would be the best solution.

The edge of an indicator in investing consists of two legs.

  1. the quality of the signal
  2. the number of occurrences

The quality of the signal can simply be described as the average close-to-close market move after the signal occurred.

Reading an Indicator

To quantify the prognosis quality of a technical indicator we first have to have a short look on how to set up the test. Therefore I introduce a standardised set of rules on how to interpret the indicator. This will it make possible to compare different indicators based on the same rules, thus only testing the quality of the indicator and not the quality of the rules.

Slow Stochastic

As an example, the slow stochastic indicator (and any other indicator) can be described using 2 rules:

  1. the absolute level of the indicator
  2. the direction of the indicator

Using these 2 rules to describe the indicator, a possible test could be: If slow stochastic is between 0 and 20 (and rising/falling), should I be invested on the day after?

Annualised Indicator Edge

As mentioned above the edge of an indicator is defined by the quality of the signal and the number of occurrences. Let’s first concentrate on the quality of the signal only.

I like to use annualised readings, so I easily can compare the market returns to the quality of my indicator signals. Therefore the average percentage daily market move after a signal has occurred is multiplied by the number of trading days. This I will call the annualised indicator return.

Stochastic and S&P 500

Using the layout given above we can run a test of the edge of the 9-day slow Stochastic indicator (as an example indicator) using various settings:

Stochastic level edge test

Stochastic level edge test

The chart above gives you the annualised indicator edge depending on the absolute level of the indicator. The direction of the indicator was not taken into account and could be the basis of another test.

Comparing these results to the average buy&hold return it is clearly visible which levels of stochastic offer an edge in the S&P.

0-20 annualised Stochastic edge vs. average market returns

If the 9-day slow stochastic is between 0 and 20, the market has an annualised return of more than 30% on the next day (close to close). As the average annualised market return on any given day is around 10%, an investment in S&P in oversold areas seems to give excess returns.

Performance=Edge*No. of Occurrences

Beside the edge of the signal the number of occurrences is important to measure the quality of an indicator. If your 100% confidence signal only occurred every few years, you most probably will die as a poor man.

Stochastic level test sum profit

Stochastic level test sum profit

Summing up the percentage returns after indicator signals shows a clear correlation between the quality of the signal and the quality of the summed up returns. The setting with the highest edge (0-20) also has got the most favourable return curve. The chart above shows the total % return of the market within the given indicator settings.

Compare technical indicators and markets

Using the calculations given above I ran a short cross market test over 2 indicators: The 9-day RSI and the 9-day Stochastic. Both are tested for the signal quality in the oversold area from 0 to 30. Data from 2000-2019 has been used.

indicator-market test

First have a look at the annualised indicator return, defining the quality of the signal. Nasdaq seems to respond best to both indicators, showing the highest annualised return for RSI.

Also the spread between the annualised indicator return and the average market return is important. As it can be seen, all four markets had about the same average yearly buy&hold return since 2000 (5-6%). The spread between the  annualised indicator return and the average market return is the key number in this scan. It defines the absolute edge the indicator offers in a specific market for a long only investor. The scan also shows RSI with Nasdaq or S&P as the best combination for buying in the oversold area.

Takeaway

Separating the quality of the indicator signal from the number of occurrences makes it easy to compare technical indicators and settings. Showing the spread between average market returns and the returns after an indicator signal has occurred enables you to find the best indicator-market combinations.

 

 

If you like it please link it
error

Daily Extremes – Significance of time

Analysing at which time daily market extremes are established shows the significance of the first and last hours of market action. See how different markets show different behaviour and see what can be learned from this analysis.

Probability of Extremes

A day of trading usually starts with a lot of fantasies for the future, then we try to survive the day and end it with a lot of hope for tomorrow. This psychological pattern can also be shown when analysing intraday market data. A high level of fantasies usually leads to a strong market movement, and thus market extremes can often be seen near the beginning or the end of the trading session. Continue reading

If you like it please link it
error

Noisy Data strategy testing

Algorithmic trading adds noise to the markets we have known. So why not add some noise to your historic market data? This way you can check if your algorithmic trading strategies are fit for the future. Learn how to generate noisy data and how to test your strategies for stability in a noisy market.

Synthetic market data?

Continue reading

If you like it please link it
error

Factor investing in portfolio management

Factor investing has been around in portfolio management for some years. Based on algorithmic rules it became the big thing in trading and the ETF industry. But is there still some money to be made? Is small beta still smart or just beta? This article will give you a Tradesignal framework to test the factor investing ideas by your own. Continue reading

If you like it please link it
error