The strategy is based on a monthly pattern that has been occurring in stocks for several decades. A great advantage of it is the low capital commitment (on average around 13% of real exposure), which allows for simultaneous use of capital in other strategies.
Inspiration
The Stock Monthly Mover strategy, like the other strategies in this series, focuses on trading during the best times of the month for a given instrument. Over the past 30 years, it has provided a unique level of return in relation to the committed capital. It only trades during a short period of the month, with a win rate above 71%, which supports its ease of use. Get an idea based on the tests below whether it is worth adding to your portfolio.
Key components
Entry and exit timing - the strategy trades only on specific days of the month.
The strategy uses one indicator and 4 filters that allow it to trade only during the best periods for itself.
The strategy uses effective and simple rules for entering and exiting positions.
The Stockpicker mechanism searches and automatically selects stocks that meet the entry criteria.
Backtest 1, $ Money Management
In this variant, we invest a constant amount of $100k, which is divided by the maximum number of open positions (10). This results in a capital commitment of up to $10k per position.
We are testing the period of the last 30 years from 1994 to May 2024.
The Stockpicker automatically selects stocks that meet the criteria from the Nasdaq 100 index. It is important to note that the list of stocks in the index has changed over the years, which is taken into account in the historical data used (survival bias).
Invested capital: $100k
Test period (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 are investing in a strategy that constantly uses 100% of the current capital (with an initial capital of $100k). 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 $980k in the analyzed strategy is lower than the benchmark (S&P500 Index in the form of SPY ETF marked in yellow on the chart), which is above $1,8 milion. This translates to a CAGR of 8.26% vs 10.30%. This means that the analyzed strategy achieves lower net profit and a lower average annual return compared to the benchmark.
Exposure
The average exposure in the analyzed strategy was 13% (!) vs 100% in the benchmark. The study was conducted on the underlying instrument, which are stocks of the Nasdaq 100 index. Exposure is measured by a dedicated study, which you can read about here. The analyzed strategy used on average half the capital, making it much less exposed to market risk, and the remaining capital can be utilized in other strategies.
Max Drawdown and Return/Drawdown Ratio
The max drawdown in the analyzed strategy was 12.54% vs 55.19% in the benchmark, resulting in a significantly better Return/Drawdown ratio of 16.50 vs 4.4. This means that the analyzed strategy is less risky and more stable because the maximum capital drawdown is smaller, leading to better risk management compared to the benchmark.
Winning Percent
The winning percent in the analyzed strategy was 71.4%. This means that 71% of the transactions were profitable, highlighting the effectiveness of the strategy in generating positive results and giving the user greater confidence in the frequency of profit generation.
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. Instead, the strategy has one exit signal or additional safety exit after X bars (time-based stop-loss). Diversification of positions within one strategy and across the portfolio serves as protection against the strong impact of a price change in one stock on the entire portfolio. Click for more about stop loss order.
Market Regime
The strategy was 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. With a diversified portfolio of stocks and strategies, transaction costs can significantly impact your profit or loss, so take the time to thoroughly test and choose a broker.
Strategy Robustness
The robustness was tested by practically executing all possible stock transactions (max open positions 100) from 1994 to May 2024 for the S&P500 (17'694 transactions) and Russell1000 (20'971 transactions) indexes at %MM. This strategy passed our parameter modification tests.
Strategy is using 4 fliters to pick the best time to trade. It is higher than an average in our strategies, this is one point to deduct from the general note.
We adhere to the principle that the fewer parameters, the greater the strategy's robustness. Therefore, we make an effort for our strategies to have as few parameters as possible and to select only those parameters that have a significant impact on the strategy's effectiveness while aligning with its character.
S&P500 max transactions: 17'694
Russell1000 max transactions: 20'971
Recommended Instruments
The strategy has shown very good results on the Nasdaq 100 over the long term. However, recently it had even better results on the S&P 500. We leave the decision to you on which index to trade. Please note that with the S&P 500, there will be greater exposure. The final decision requires your individual tests.
Primary Instrument: S&P 500
Supplementary Instrument: Nasdaq 100
Pattern Day Trader
The strategy statistically closed approximately 4% of transactions on the same day, and more importantly, over the 30-year test on the S&P500 index, there were only 2 instances of PDT being met. This means that in our assessment, the strategy can be used on smaller accounts, assuming an understanding of the mechanisms related to PDT described here.
Correlation
To check the correlation of this strategy with others, visit the dedicated correlation page.
Summary & Strengths and weaknesses of the strategy
Strengths of the strategy:
Profit Stability: We have a long-term stable growth in Equity.
Very low capital utilization: This is one of the main strengths of the strategy. It can be successfully incorporated into many other portfolios, adding their profits while possibly not summing up losses.
Low correlation: It has a low correlation to other stockpicker strategies, even on monthly basis.
Low Drawdown: The Max Drawdown in the tested strategy was 12.54% compared to 55.19% in the Benchmark.
High Winning Percent ratio: 71% of trades ended in profit, emphasizing the effectiveness and convenience of using the strategy.
Robustness: The strategy was successfully tested on the S&P500 and Russel1000 indices, achieving a maximum of 17'694 and 20'971 trades respectively.
Weekness of the strategy:
Robustness: The strategy is using 4 filters to pick the best time to trade. It is higher than average in our strategies; this is one point to deduct from the general note. But on the other hand, the strategy was successfully tested on the S&P500 and Russell 1000 indices, achieving a maximum of 17'694 and 20'971 trades respectively.
Summary
Over 30 years, the tested strategy had four losing years, which is a moderate result for a Stockpicker-type strategy (not counting the open year 2024). The Max Drawdown was 12.5%, which is a very low level compared to the benchmark.
The strategy trades during the best periods in months, staying on the sidelines of the market most of the time. As a result, it achieved a lower net profit than the benchmark, but this was done with significantly less exposure, indicating a great candidate for a portfolio supplement.
What you receive in the package for this strategy:
The ebook presents detailed rules and results for 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.
If you need the strategy code in formats such as MultiCharts, MT4, or MT5 (MQL), please contact us regarding this matter.
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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