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.


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.



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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?

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Backtesting Market Volatility

If you want to trade volatility, you can place a bet on the option market. Just buy an at the money put and call, and at expiry day you will either win or lose, depending on the actual market move since you bought the straddle and the price you paid for the straddle. To put it simple, if the market moves more than you paid for the two options you will win, otherwise you will lose. This article is about a back test of volatility. Continue reading

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Demystifying the 200 day average

The 200 day average is considered as a key indicator in everyday technical analysis. It tells us if markets are bullish or bearish. But can this claim be proved statistically, or is it just an urban legend handed down from one generation of technical analysts to the next? Let’s find out and demystify the 200 day moving average. Continue reading

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KVOL Volatility part 2

How to calculate volatility based on the expected return of a straddle strategy has been shown in part 1 of fair bet volatility KVOL.

Using and Displaying K-Volatility:

KVOL uses the given amount of historic returns to calculate an expected value of an at the money put and call option. The sum of these prices are the historic fair value for implied volatility. It can be used to compare current market implied volatility to historic fair values.

Beside calculating KVOL for a specific return period it can also be used to show it as a projection indicator on the chart.

The example on the chart gives such an expectation channel for the s&P500 at the beginning of each month. The 250 days before are used to calculate KVOL. The line underneath the chart is running KVOL for 13 trading days.

Simplified trading:

to win, with higher volatility expected: you would have bought a straddle at the beginning of the month, expiring at the end of the month. You should not have paid more than a KVOL for 25 bars (working days to expiry) would have suggested. You win if the chart is outside of the projection at the end of the month.

The shown example uses the 250 daily bars before  the beginning of the month to calculate the returns and the price of KVOL. The projected lines represent the winning boundaries of the straddle at expiry.

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Statistics of VIX

The CBOE volatility index VIX  measures the market’s expectation of future volatility. This article will show you some key statistics of VIX and help you to decide if it is better to buy or to sell volatility.

Statistics of VIX

The spikes to the top and the long phases of relatively low volatility are reflected in a left-leaning distribution diagram and a long tail towards the higher levels. The median value is 17%, meaning 50% of the prices are above (below) this level.

The next chart shows the distribution of returns over 25 trading days. The median price movement being slightly shifted to the negative area shows the mean reverting characteristics of volatility.

Buy or sell volatility?

Analysing the level of VIX and the returns afterwards yields an even more interesting picture:

The green line gives the 25 bar percentage returns of VIX, with VIX noting above 25, the red line gives the returns with VIX below 15. Observe the median of the two lines:

The median 25 bar return with VIX above 25 (green) is around -15%, only 20% of the returns are positive when VIX is currently above 25. Sell volatility.

The median returns with VIX currently below 15 (red) is above 0% and with a fat tail to positive returns. Buy volatility. (data from 2004-2018)

Adverse movement of VIX

The above chart suggests that going short on volatility, if VIX is above 25, seems to be a good idea. But it is not without risk. The chart below shows what can go wrong during the next 25 days. The distribution diagram gives the maximum adverse movement of the VIX, with VIX currently trading above 25.

The green line, VIX currently above 25, shows a +10% median maximum up movement over the next 25 days. So do not expect a short vola position to be without risk. Some adverse movement has to be expected.

On the other side, the distribution of the maximum loss of the VIX during a 25 day period shows a median of below -20%. This represents the profit potential of a short volatility position.

Conclusion of VIX statistics:

If you plan to short volatility wait until VIX is trading above 25. If you want to buy volatility, do so if VIX is trading below 15.


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