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 such patterns in the price of gold.
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 November 2009 to May 2024 (all available data for GDXJ).
Invested capital: $100,000
Test period in years: 15
Test years: 11.2009 - 05.2024
Test instrument: GDXJ
Equity chart for this test:
Basic statistics and results month by month:
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 looks as follows:
Basic statistics resulting from the test:
Trading strategy analysis
Net Profit and CAGR
The net profit above $935k in the analyzed strategy is significantly higher than the benchmark for the same period (Buy & Hold SPY marked on the chart in yellow), which is above $519k, translating to a CAGR of 18.20% vs 13.92%. This means that the analyzed strategy achieves a higher net profit and a higher average annual return rate, indicating its effectiveness in generating profits during the analyzed period.
Drawdown and Return/Drawdown Ratio
The max drawdown in the analyzed strategy was 20% vs 33.70% in the benchmark, resulting in a better Return/Drawdown ratio of 7.77 vs 3.88, respectively. This indicates that the analyzed strategy was less risky and more stable.
Exposure
The exposure in the analyzed strategy was 15.7% (!) vs 100% in the benchmark. This means that the analyzed strategy uses capital for a very short period, making it significantly less exposed to market risks and allowing for the capital to be used by other strategies.
Winning Percent
The winning percentage in the analyzed strategy was 67.3%. This means that the vast majority of transactions were profitable, highlighting the strategy's effectiveness in generating positive results, providing users with comfort and confidence in the frequency of profit generation.
SL & TP
The strategy does not use typical stop-loss and take-profit orders, although there are no obstacles to implementing them. According to our tests, for most ETF/stock strategies, these settings worsen results (see why). Diversification within the portfolio of various strategies serves as protection against the strong impact of a single stock price change on the entire portfolio.
Market Regime
The strategy has been tested in all basic market regimes and includes filters implemented based on this analysis.
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
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 100 months/transactions. This can not 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
Pattern Day Trader
The strategy statistically ended 0.00% of transactions on the same day, so it does not meet the criteria for a Pattern Day Trader (PDT). This means that a real account is not required for the entire portfolio with a minimum of $25k.
Correlation
To check the correlation of the strategy with others, visit the page dedicated to correlations. 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 of the strategy
Strengths of the strategy:
Profitability: In the analyzed strategy, the Net Profit was above $935'684, while the Benchmark achieved over $519'685. The CAGR was 18.20%, higher than the Benchmark's 13.92%.
Inverse correlation to most stock strategies: This is considered a main advantage of Gold Monthly Mover.
Low capital commitment: The strategy offers very low capital commitment with an Exposure of 15.7% vs Benchmark 100%, making it suitable for use in a portfolio alongside other strategies.
Low Drawdown: The Max Drawdown in the analyzed strategy was 20.00% compared to 33.70% in the Benchmark, indicating that the strategy is less risky and quite stable.
High Winning Percent ratio: 67.3% of transactions ended in profit, highlighting the comfort of using the strategy.
No same-day transactions: This avoids meeting the Pattern Day Trader requirements, making it suitable for use on small accounts as well.
Weaknesses of the strategy:
Robustness: The strategy has significantly fewer transactions than Stockpicker-type strategies, which may be considered a disadvantage in terms of robustness. The strategy needs to be closely monitored and integrated into a portfolio of strategies with greater robustness.
It is worth noting that GDXJ is a very volatile instrument, with regular days where the daily movement is 4-6%. This should be taken into account when allocating capital and incorporating this instrument into a broader portfolio.
Summary
Gold is not an easy instrument in terms of daily behavior. However, the discussed strategy shows a specific monthly pattern, confirmed by nearly 100 monthly transactions with positive results and an almost 70% win rate. Additionally, gold is a low-correlated instrument with stocks, which is the main motivator for introducing this strategy into a portfolio.
Considering the multi-year nature of the pattern, low correlation with stock strategies, and the full scalability of the ETF used, I believe it is worth considering when creating a portfolio of strategies. It will not be the driver of the portfolio for me, but it can balance its performance in the long term. Statistics show that the strategy was a much better choice than buy & hold for gold. From 2009 to 2023, the price of gold futures contracts increased by 134%, with a max drawdown of 46%. The results of the presented strategy, i.e., +860% and max drawdown of 24% (MM%), and using capital for only 15% of the time, look significantly better.
What you receive in the package for this strategy:
An e-book presenting detailed rules and results of the strategy.
SQX file ready to be used on the Algocloud and StrategyQuant platforms.
Pseudocode that describes all the rules in an easy-to-understand way.
Disclaimer
The results obtained from historical data do not guarantee future outcomes. The effectiveness of a strategy can change over time. Backtesting is a tool that allows for the analysis and evaluation of an investment strategy based on historical data. Various factors, such as market changes or economic conditions, can influence the effectiveness of a strategy over time.
Investing always involves risk. This material is not investment advice. We share our experience and algorithms for educational purposes. We make efforts to ensure that our algorithms are error-free, but neither we nor the tools we use guarantee the absence of technical issues. Any decisions to use a particular strategy are made at your own risk and should be preceded by careful understanding and verification. You should always carefully consider your investment goals and risk tolerance before making investment decisions.
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