Inspiration

The strategies from the Monthly Mover series utilize the best periods of the month for trading selected instruments. This time, I present a strategy exploring patterns in the price of gold. Where did this pattern come from? I'm not exactly sure. I can speculate that it results from the rebalancing of portfolios by large players and investors. However, the pattern works, and whether it is interesting, you can judge for yourselves.

Below is an illustration for the strategy discussed above. Two charts show how the behavior of the same instrument (correlated to the price of gold, as we will see in a moment) differs during the utilized period (BIAS) and outside of it.

But let's go step by step.

The pattern being discussed relates to gold, and the strategy being discussed below could be used in approximate rules on gold futures contracts or ETFs like GLD. However, I prefer instruments that are strongly correlated with the price of gold while also offering much stronger daily movements than gold. Additionally, instead of charging storage fees for gold, they pay... dividends.

We're talking about ETFs on gold mining companies. Two popular ETFs of this type are GDX for larger companies and GDXJ for smaller companies, which I will focus on in the conducted tests.

Why gold? Firstly, it must be acknowledged that it is a rather difficult instrument to explore, but it has a very important feature that is worth utilizing - it is lowly correlated with stocks. Therefore, strategies like this can balance a larger portfolio of stock-based strategies.

Key components

• Exploration of the most interesting periods of the month and year for an ETF tracking the price of gold.

• The basis of the strategy is the knowledge of monthly and yearly patterns occurring in the underlying asset, which is gold, and using only those selected periods for investing when it is statistically worth doing so.

  
  

Before I start discussing the results of the strategy, a brief introduction. Why do I believe that GDXJ, an ETF composed of industry companies, is more correlated with gold than, for example, the S&P500 index? After all, we are dealing with an ETF containing shares of companies. As usual, it is not my opinion that matters here, but the hard data that support it.

Here are the charts of gold (GC futures contracts) and GDXJ, along with the correlation indicator of these instruments:

As we can see, during the majority of the time, there is a very strong correlation (above 0.8) or a high correlation (above 0.5). Since 2014, there have been 3 relatively short periods when GDXJ was lowly correlated with the price of gold.

And how does the correlation between GDXJ and the S&P500 index (SPY) look?

As we can see, the correlation follows quite regularly from top to bottom. The average would probably be somewhere in the middle, which is characteristic of instruments with low long-term correlation.

We can therefore assume that GDXJ is strongly correlated with gold. I also want to show you that it offers a much larger daily % move. At the time I am writing this article, the ATR% for the last 500 days for gold was 0.7%, and for GDXJ it was 2.04%. This means that during the same period, GDXJ moves almost 3 times faster than gold - or in other words, we can achieve the same profit with 3 times less capital. I will expand on this topic in the summary of the strategy.

I hope you now understand why I chose GDXJ when looking for a strategy with low correlation to stocks. Let's start with the tests.

Backtest 1 - $ Money Management

In this scenario, we are investing a constant amount of $100k.

We are testing the period of the last 15 years from 2010 to 2025.

• Invested capital: $100,000

• Test period in years: 15

• Test years: 2010–2025

• Test instrument: GDXJ

Equity chart for this test:

Basic statistics and results month by month:

In the table, we highlighted the moment when the strategy was published.

Click the button to see the latest backtest:

Backtest 2 - % Money Management

In this backtest, we are investing in a strategy that constantly uses 100% of the current capital (starting with $100'000). This means that as the capital grows or decreases, the position value changes proportionally. The rest of the parameters remain unchanged.

The equity chart for this test is shown below. It includes a benchmark, represented by a faint yellow line at the bottom.

Basic statistics resulting from the test:

Trading strategy analysis

Net Profit and CAGR

The net profit of over $1,061,619 in the analyzed strategy is significantly better than the benchmark for the same period (Buy & Hold SPY, marked in yellow on the chart), which is over $737,157. This translates to a CAGR of 16.56% compared to 14.20% in this period. This indicates that the analyzed strategy achieved a higher net profit and a higher average annual return, indicating its effectiveness in generating profits during the period under analysis.

Drawdown and Return/Drawdown Ratio

The max open drawdown in the analyzed strategy was 23.76% vs 33.72% in the benchmark, resulting in a better Return/Open Drawdown ratio of 10.73 vs 5.28, respectively. This indicates that the analyzed strategy was less risky and more stable.

Exposure

• Exposure 17.0% – the strategy keeps capital in the market only about one‑sixth of the time, which clearly reduces market risk and leaves funds available for other systems when this one is flat.

• Exposure Adjusted Return 103% – after adjusting for this low exposure, the strategy uses capital extremely efficiently: its results correspond to a system that, with capital invested 100% of the time, would generate about 103% annual return.

 You can read about it here.

Winning Percent

The winning percentage in the analyzed strategy was 67.27%. This indicates that the vast majority of transactions were profitable, highlighting the strategy's effectiveness in generating positive outcomes and providing users with confidence in the frequency of profits.

SL & TP

The strategy does not employ typical stop-loss and take-profit orders, although they can be implemented if desired. Our tests indicate that for most ETF/stock strategies, these settings tend to worsen results (see why). Diversification across a portfolio of various strategies provides protection against the significant impact of a single stock price change on the entire portfolio.

Market Regime

The strategy was tested in all basic market regimes and includes filters implemented based on this. Read more about market regimes.

Trading Costs

Trading costs and slippage were taken into account in the backtests. You can check our last research about trading costs using Alpaca Broker here. 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

It is important to consider that the main issue with this strategy is the relatively low number of confirming transactions for the pattern. In this case, we have around 110 months/transactions. This cannot be compared to strategies like Stockpicker on stocks, where we can execute even 100'000 transactions. Additionally, it does not perform consistently every month. Gold also has seasonal patterns, meaning there are periods in the year when the strategy does not perform well. The strategy includes filters for such weak months. Therefore, the strategy receives a lower score from us for robustness.

Recommended Instruments

The recommended primary instrument for this strategy on Algocloud is GDXJ, which has shown the best historical results. The strategy does not require any additional instruments.

• Primary Instrument: GDXJ

  
  

Correlation

To check the correlation of the strategy with others, visit the correlations page. The strategy generally has a negative correlation with most stock strategies, which is very positive in the composition of the portfolio.

Summary & Strengths and Weaknesses

Strengths of the strategy:

• Strong advantage over a simple index approach – the strategy clearly outperforms passive benchmark holding over the long term, combining attractive profit potential with a controlled level of risk.

• Very good diversification relative to stocks – due to the low correlation with equity strategies, the strategy effectively "weights" the portfolio, improving the risk-return profile and mitigating capital behavior during periods of stock market weakness.

• Low capital commitment with high efficiency – the strategy uses capital only in selected time windows, yet generates results typical of heavily engaged systems. This allows the freed-up capital to be allocated to other strategies without sacrificing an attractive profitability profile.

• High quality signals and psychological comfort – a large portion of transactions end in profit, resulting in a relatively smoother capital curve and less susceptibility to long losing streaks, which are typical for many trend strategies.
  

Weaknesses of the strategy:

• Limited resilience due to the number of transactions and filters – the pattern is confirmed with a significantly smaller number of plays than in stockpicker-type strategies, and additional seasonal filters improve results at the cost of simplicity and resilience. The strategy requires inclusion in a broader portfolio and periodic verification as new data comes in.

• High volatility of the underlying instrument – GDXJ can make very dynamic daily moves, which require careful capital allocation and readiness for rapid changes in position valuation. The strategy works best as a supplement rather than the main "engine" of the portfolio.
  

Summary

The Gold Monthly Miner Strategy combines limited capital commitment with a distinct advantage over a simple index approach, offering an attractive risk-return profile. It complements an equity-based portfolio well, as it behaves differently from classic equity strategies and helps smooth the capital path. Due to the volatility of the underlying instrument and the use of specific seasonal filters, it is best treated as a specialized component of a larger portfolio, regularly monitored as new data come in.

What you receive in the package for this strategy:

• An eBook presenting detailed rules and results of the strategy.

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

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