Trading Strategies Revealed -“Intraday Gaps Trading” review

“Intraday Gaps” is a popular trading strategy that uses gaps to identify trade entries.

What is a Gap?

A gap in price is essentially a zone where little or no trade has taken place after the close of the previous candle. Therefore a gap appears between the close of the previous candle and the start of the current candle and the asset’s price chart shows a definite gap in standard price pattern.

Types of Gaps

  • An Up Full Gap Up occurs when the opening price is greater than previous candle high price.
  • A Down Full Gap Down occurs when the opening price is less than previous candle low.
  • An Up Partial Gap Up occurs when opening price is higher than previous close, but not higher than previous high.
  • An Up Partial Gap Up occurs when opening price is higher than previous close, but not higher than previous high.

How to trade using intra-day Gaps?

A trader should open a Buy trade when Full Gap Down occurs and a Sell trade when Full Gap Up happens.

Initial back-test

To run a back-test we have coded a complete Intraday Gaps trading strategy as a MetaTrader 4 Expert Advisor. During preliminary analysis we have identified that the best time frame for Intraday Gaps trading strategy is 1 hour (H1). We have run a back-test of Intraday Gaps strategy. 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 2009.01.01-2019.12.11 using Every Tick modelling on EURUSD-H1, using 1:10 leverage, without reinvestment, assuming spread equals 10 ticks. These are the main parameters of Intraday Gaps trading strategy performance at its non-optimized state:

ROI# of tradesWinning ratioMax. drawdown
184.22%136586.04%37.29%

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

Intraday Gaps trading strategy can be used with other indicators to filter out losing trades and make entry signals more accurate. After analysing trading data we have found the following insights which have helped us to increase the profitability of Intraday Gaps trading strategy up tp 40% reducing it’s drawdown in 2.5 times:

  1. Buy trades that were opened at a too high value of Stochastic and sell trades that were opened at a too low value were losing in most of the cases when trading “Intraday Gaps” trading strategy during 2009 – 2019. Stochastic shows overbought and oversold zones and it is not recommended to buy at overbought and to sell at oversold.
    (ROI increase 18% -> 36%, Drawdown reduction 86% -> 64%)
  2. Most trades that were opened at too high and too low values of ADX were losing when trading “Intraday Gaps” trading strategy during 2009 – 2019. ADX shows the power of a trend: at a higher value of ADX the probability that the trend will reverse is higher while at a lower value the trend is not yet established.
    (ROI increase 18% -> 27%, Drawdown reduction 86% -> 49%)
  3. Sell trades that were opened at a positive values of Bears indicator were losing when trading “Intraday Gaps” trading strategy during 2009 – 2019. This indicator shows the power of Bears and its positive values indicate that the current trend is bullish.
    (ROI increase 18% -> 21%, Drawdown reduction 86% -> 63%)

Optimization results

We have analysed data received from a test of Intraday Gaps trading strategy during 2009 — 2019 years and applied some filters such as Stochastic, ADX and Bears Power. As a result, the profitability of the strategy has increased from 18.42% up to 40.90% and it’s drawdown has reduced from 86.04% to 35.83% using leverage 1:10.

Post-optimization back-test

Reducing the drawdown more than 2 times has allowed us to increase the leverage that can be used while trading this strategy up to 1:20, which in turn, has resulted in annualized ROI increase up to 82.52%!

Reducing the drawdown has also allowed us to use risk based lot calculation. Below you can see the back-test results using $10,000 initial balance and 5% risk per trade:

ROI# of tradesWinning ratioMax. drawdown
26651%63022.4741.75%

Analyze your trading strategy!

If you have a trading strategy that you want to analyse, optimize and increase its profitability (or even turn it from losing into a profitable Forex trading strategy) – feel free to contact us! Our trading data analysis team will respond to you within 24 hours clarifying all the details needed.

Our team of experienced MQL4/MQL5 developers and NinjaScript programmers guarantees the quality of your trading strategy automation in accordance with your individual requirements.
Send us the description of your project and we will provide you a free consultation regarding your trading system.

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.