Trading Strategies Revealed – “Stochastic Divergence” review

Trend reversal trading strategy “Stochastic Divergence” is a popular Stochastic strategy that uses divergences between Stochastic Oscillator and price to predict a change (reversal) in market trend. It is designed mainly for Forex and can be used on all currency pairs on any time frame. Stochastic divergence trading strategy has appeared to be a profitable Forex trading strategy.

How to trade Stochastic Divergence?

A trader should open a buy trade once the following pattern is detected on the chart: the price makes lower lows, while Stochastic Oscillator makes higher lows. This usually means that the trend has started to exhaust and a reversal may happen shortly.

The opposite pattern signals about selling possibility – price makes higher highs, while Stochastic Oscillator makes lower highs. An example of divergence and a sell trade is shown on the screenshot below:

Stochastic divergence meaning

Stochastic divergence means that price momentum has started to slow down and signals about soon trend change (either a consolidation or a trend reversal).

For example, when the price still moves up after a long bullish run making higher highs while Stochastic Oscillator starts making lower highs – this means that the price momentum slows down and signals about possible trend reversal.

Stochastic settings for divergence

To identify a Stochastic Divergence you should use a divergence indicator that is developed specifically for such task. Its complexity depends on many factors and on which settings (parameters) you want to apply to detect a divergence.

Particularly, the following questions should be asked and answered while configuring your Stochastic divergence settings:

• What is the maximum and minimum time range for a divergence that you want to detect
• Which type of divergence do you want to identify – hidden or classic
• Whether or not you want to detect divergences in which price and Stochastic vertex are shifted from each other
• Which type of price and Stochastic swing may be considered as a valid vertex. Specifically how many higher highs/lower lows should be there between a price/Stochastic swing to consider it as a valid vertex for divergence calculation
• Whether or not it is allowed for a price/Stochastic to break a divergence line between 2 divergence vertexes used for the divergence calculation
• Whether or not a divergence is allowed to be calculated between 2 vertexes that are not next to each other (means they have other price/Stochastic vertexes in-between)
• etc.

Initial back-test

We have run a back-test trading both classical and hidden divergences using a most basic Stochastic divergence indicator. For our test as a trade exit rule we have used a Trailing Stop of 30 pips which is launched after a trade has started and is modified each new 1 pip of profit. From our point of view, such approach allows to maximize profit and minimize drawdown.

We have run the test for 2010.01.01-2019.09.11 using Control Points modelling on EURUSD-H1, using leverage 1:10, without reinvestment, assuming spread equals 10 ticks. These are the main parameters of Stochastic divergence trading strategy performance at its non-optimized state:

ROI	# of trades	Winning ratio	Max. drawdown
28.18%	5430	37.35%	51.77%

Trading data analysis

After running the initial test of a simple non-filtered strategy we perform a trading data analysis that allows to identify possible filters to use to make the strategy more profitable reducing the drawdown simultaneously.

The following charts may give some possible insights on which filters to apply (time sessions, day of week limitation, trend strength threshold, overbought/oversold conditions, volatility range) to turn this strategy profitable should you decide to use this strategy in your investment portfolio:

Optimization

After analysing trading data we have found the following insights which have helped us to increase the profitability of Stochastic divergence trading strategy in 2.5 times reducing drawdown more than twice:

1. There were more profitable trades at a lower value of ADX while trading “Stochastic Divergence” trading strategy during 2010 – 2019. Since “Stochastic Divergence” is a trend reversal strategy and ADX shows the power of a trend it is more reasonable to take trades at the beginning of a trend when ADX is low.
2. Buy trades have had a bigger profit ratio at Stochastic value lower than 40, while sell trades – at Stochastic more than 60 when trading “Stochastic Divergence” trading strategy during 2010 – 2019. Since Stochastic shows overbought and oversold zones it is more reasonable to take sell trades at a higher value of Stochastic and buy trades at a lower value.
3. “Stochastic Divergence” trading strategy has produced 28.02% annualized ROI during 2010–2019 years using 1:10 leverage. Maximum drawdown was 51.96%.
4. We have analysed data received from a test of “Stochastic Divergence” trading strategy during 2010 – 2019 years and applied some filters such as RVI, ADX, Bollinger Bands and Gator. As a result, the profitability of the strategy has increased from 28.18% up to 94.53% and its drawdown has reduced from 51.77% to 9.79% using leverage 1:10.

Post-optimization back-test

Reducing the drawdown more than 5 times has allowed us to increase the leverage that can be used while trading this strategy up to 1:30, which in turn, has resulted in annualized ROI increase up to 262.88%!

ROI	# of trades	Winning ratio	Max. drawdown
262%	2241	41.10%	11.35%

Analyze your trading strategy!

Nordman Algorithms’ trading software developers have more than 10 years of experience in MT4/MT5 Expert Advisors programming and NinjaTrader 7/8 Automated Trading Strategies development.
Make a request on your custom trading strategy automation of any complexity and we will send you an individual offer!

Disclaimer: Hypothetical or Simulated performance results have certain limitations, unlike an actual performance record, simulated results do not represent actual trading. Also, since the trades have not been executed, the results may have under-or-over compensated for the impact, if any, of certain market factors, such as lack of liquidity.

Simulated trading programs in general are also subject to the fact that they are designed with the benefit of hindsight. No representation is being made that any account will or is likely to achieve profit or losses similar to those shown.

Past performance is not necessarily indicative of future results. The customer is responsible for using the product at his or her own risk and “Nordman Algorithms” is not responsible for any possible losses caused by use of the product, including but not limited to losses.