Inspiration

I once had the opportunity to work closely with traders from around the world in a trading room for almost a year, engaging in day trading on stocks. I gained many valuable lessons there, which I incorporate into my algorithms. Many of the setups we used during trading revolved around price gaps. Trading on gaps can take many forms, and I have integrated some of them into my automated strategies. However, I take into account that the AlgoCloud platform we use for stock trading is definitely more suited for swing trading than day trading.

Nevertheless, the Gap Jumper strategy, which you will learn about today, opens and closes all positions on the same day.

This strategy will not be the top performer in terms of returns, as it offers a relatively low return rate, but its strength lies in its very low correlation to other strategies and very low capital utilization (intraday leverage can be used for this trading).

Is it worth adding this strategy to your portfolio? Evaluate it yourself based on this article.

Entry rules

The strategy scans all stocks in the market at the beginning for the occurrence of a price gap below the last low that is greater than 0.5 ATR and less than 5 ATR. It simultaneously applies a trend-related filter (C > SMA200) and filters out statistically weaker months of the year.

The entry is made by using a Limit order - 1 ATR below the opening price. We expect that after the opening there will be a further strong price drop caused by day traders using the Gap&Go strategy. The price has a chance to reach our target price, which is 1 ATR away. At some point during the day, however, day traders will start taking profits, meaning closing their short positions, and the price, as statistics show, will start returning towards the gap. In some situations, the gap may even be closed on the same day.

The pending order is removed at the end of the day.

Position Score and max open positions:

TThe applied position score causes us to choose positions that have fallen the most during the day (ROC), filtering the number of open positions to a maximum of 5 at a time. However, such a large number of simultaneous positions rarely occurs in practice on the S&P500 index.

Exit Rules

Exiting positions occurs at the end of each day.

Backtest 1, $ Money Management

In this scenario, we are investing a constant amount of $100k, which is divided by the maximum number of open positions. This results in a capital commitment of up to $20k per position.

We are testing the period of the last 30 years from 1994 to May 2024.

The backtest automatically selects stocks that meet the criteria from the Nasdaq 100 index. It is important to note that the list of stocks included in the index changed over the years, which is taken into account in the Stockpicker data (survival bias).

• Invested capital: $100k

• Test period in years: 30

• Tested years: 1994-05.2024

• Tested Index: Nasdaq 100

Equity chart for this test:

Basic statistics and results month by month:

Backtest 2, % Money Management

In this backtest, we invest in a strategy that constantly uses 100% of the current capital (with an initial capital of $100k). This means that as the capital increases or decreases, the value of the position changes proportionally. The rest of the parameters remain unchanged.

The equity chart for this test looks as follows:

Basic statistics resulting from the test:

During testing, several interesting facts about the strategy are revealed:

Strategy has a very low profits generated compared to other strategies and the benchmark. But since this is an intraday strategy, on margin accounts, there is no need to commit your capital to trading with this strategy (0% exposure). We borrow capital from the broker at a cost of 0%. The strategy has a very low maximum drawdown of 5.7%.

Trading strategy analysis

Net profit and CAGR

The net profit above $82'079 in the analyzed strategy is significantly lower than the Benchmark (S&P 500 Index in the form of the SPY ETF marked on the chart in yellow), which is $1,836,975, translating to a CAGR of 2.00% vs 10.38%.

This means that the analyzed strategy achieves a significantly lower net profit and lower average annual return, indicating its lower effectiveness in generating profits over the long term.

Drawdown and Return/Drawdown ratio

The max drawdown in the analyzed strategy was 4.80% vs 55.19% in the benchmark, resulting in a better Return/Drawdown ratio of 12.00 vs 4.41. This means that the analyzed strategy is less risky and quite stable.

Exposure

The strategy can use margin capital entirely, which will be free for the user (we do not hold positions overnight). The exposure in the analyzed strategy was therefore 0% vs 100% in the benchmark.

Winning percent

56.0% of trades were profitable, which is a much weaker result compared to average reversal strategies. However, the strategy provides an additional edge because the Avg Win ($506) is greater than Avg Loss ($427).

SL & TP

The strategy does not use typical stop-loss and take-profit orders. According to our tests, for most stock strategies, these settings worsen results (see why).

However, the strategy closes all trades at the end of the day, and protection against the strong impact of a potential price change of a single stock on the entire portfolio is achieved by diversifying positions within one strategy and across the portfolio of strategies.

Market regime

The strategy has been tested in all basic market regimes and includes filters implemented based on this.

Trading costs

Trading costs and slippage were taken into account in the backtests, which occurred in real account tests for the Alpaca broker (detailed study). With a diversified portfolio of stocks and strategies, transaction costs can determine your profit or loss, so take the time to thoroughly test and choose a broker.

Robustness

The robustness was tested by conducting practically all possible transactions on stocks (max open positions 100) for the period from 1994-05.2024 for the S&P500 index (4'932 transactions) and Russel1000 index (8'099 transactions) at %MM.

S&P500 max transactions: 4'932

Russell1000 max transactions: 8'099

The results are as follows:

Large transaction volumes on several indices confirm the general robustness, however, the strategy includes 3 filters for the worst months, which according to our criteria gives it lower score in this category.

Recommended Instruments

The recommended primary instrument for this strategy in Algocloud Stockpicker is the Nasdaq 100 index companies, which have shown the best historical results. However, the strategy also shows stable results with S&P 500 stocks.

Primary instrument: Nasdaq 100

Supplementary instrument: S&P 500

Pattern Day Trader

The strategy statistically closed 100.00% of trades on the same day, thus meeting the Pattern Day Trader (PDT) criteria. This means a real account with a minimum of $25k is required for the entire portfolio.

Correlation

To check the correlation of the strategy with others, visit the correlation page.

Summary & Strengths and weaknesses of the strategy

Hmm, what do you think? Mixed feelings? So here's a brief summary from my side.

Over the course of 30 years, the analyzed strategy had 9 losing years, which is a much weaker result compared to most of our Stockpicker strategies. However, the Max Drawdown was only 4.8%, which is a very low level in the context of long-term investment strategies.

Strengths of the strategy:

No Base Capital Burden: The strategy cleverly applied practically does not burden the base capital (we use free intraday margin).

Low Drawdown: The Max Drawdown in the analyzed strategy was 4.80% compared to 55.19% in the Benchmark. This shows that the strategy is low-risk and quite stable, allowing for more aggressive use of leverage.

Correlation: Virtually zero correlation with other typical strategies is a significant advantage when considering its use.

Weaknesses of the strategy:

Profit: Very low absolute income from $100k invested capital compared to other strategies. In the analyzed strategy, the Net Profit was above $82'079, while the Benchmark achieved over $1'836'975. The CAGR was 2.00%, significantly lower than the Benchmark's 10.38%.

PDT Compliance: Requires a real account of $25k+.

Summary

And now I will tell you why I like and use the Gap Jumper strategy on a daily basis. It is one of the few strategies that gives me "instant gratification," meaning I can observe and see results every day. Of course, sometimes I make a profit, sometimes a loss (win rate 56%), but this strategy provides a nice break from strategies where trades last for several days.

Since it practically does not burden my capital at all and has a low historical drawdown, I can use higher leverage here. However, this requires experience. It is not a strategy that I rely on to drive my annual results, but one that I can observe with curiosity every day.

I will also reveal to you that I use not just one strategy, but the entire Portfolio Gap Jumper 123, which improves the results of the individual Gap Jumper and you can also use it 🙂

Download is free - login required

What do you receive in the package for this strategy:

• An SQX file ready to be used on the Algocloud and StrategyQuant platforms.

• Pseudocode that describes all the rules in an easy-to-understand way.
  

If you need the code for this strategy in formats such as MultiCharts, MT4, or MT5 (MQL), please contact us on this topic.