For last 40 years, the best day of the week on the US stock market has been Tuesday. The next day with the highest return is Wednesday. We present a strategy that skillfully exploits this market behavior by opening positions only on Mondays and cashing in profits in almost 70% of cases over the following days.
Inspirations
The strategy explores this pattern by trading exclusively on Mondays, with additional conditions to increase its effectiveness. This approach allowed for achieving similar results to the benchmark over a 30-year period but with only 15% capital utilization and much less drawdown, while maintaining a low correlation with other strategies.
Key Components
Time restriction - trading only on Mondays.
Additional components used to create a unique strategy profile.
The Stockpicker mechanism searches and automatically selects stocks that meet the entry criteria.
Backtest 1, Fixed $ 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 February 2025.
The backtest automatically selects stocks that meet the criteria from the Nasdaq 100 index. It's important to note that the list of stocks in the index changed over the years, which is taken into account in the Stockpicker data (survivorship bias).
Invested capital: $100k
Test period in years: 30
Test years: 1994 to February 2025
Test Index: Nasdaq 100
Equity chart for this test:
Basic statistics and results month by month:
Click the button to see the latest backtest:
Backtest 2, % Money Management
In this backtest, we invest 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.
The yellow line is a benchmark chart.
A red line is an Open Drawdown line.
Basic statistics resulting from the test:
Trading Strategy Analysis
Net profit and CAGR
The net profit of about $1.96 million in the analyzed strategy is a bit lower than the benchmark (S&P 500 Index in the form of SPY ETF marked in yellow on the chart), which is $2.12 million, translating to a CAGR of 10.26% vs. 10.53%. This means that the analyzed strategy achieves a slightly lower net profit and a lower average annual return rate.
Drawdown and Return/Drawdown ratio
The Max Open Drawdown in the analyzed strategy was -22.85% vs -55.19% in the benchmark, resulting in a significantly better Return/Open Drawdown ratio of 11.11 vs 5.07. This translates to better risk management compared to the benchmark.
Exposure
The average exposure in the analyzed strategy was only 15% vs 100% in the benchmark. The study was conducted on the underlying instrument, which is the Nasdaq 100 index stocks.
Please consider that in the case of applying to trade the S&P500 index, the exposure would increase to approximately 25% because there will be a lot more signals. Exposure is measured by a dedicated study, which you can read about here. Thanks to low capital usage, it was much less exposed to risk, and the free capital is suitable for use in other strategies.
Winning percent
The Winning percent was 68%. This means that the vast majority of transactions were profitable, highlighting the effectiveness of the strategy in generating positive outcomes, giving the user greater confidence in the frequency of profit generation.
SL & TP
The strategy doesn't use standard stop-loss and take-profit orders. Our tests show that these settings often worsen results for most stock strategies (see why). Instead, the strategy includes one exit signal or an additional safety exit after X bars (a time-based stop-loss). Position diversification within a single strategy and across the strategy portfolio helps protect against the significant 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. You can find more on this topic here.
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
We tested robustness by conducting all possible stock transactions with a maximum of 100 open positions for the period from 1994 to February 2025. This included 23'590 transactions for the S&P 500 index and 34'253 transactions for the Russell 1000, both at %MM. Our strategy successfully passed parameter modification tests. We believe that fewer parameters lead to a more robust strategy. Therefore, we strive to minimize the number of parameters, selecting only those that significantly impact the strategy's effectiveness and align with its character.
S&P500 max transactions: 23'590
Russell 1000 max transactions: 34'253
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
Recommended Instruments
The recommended primary instrument for this strategy in Algocloud Stockpicker is the Nasdaq 100 index, which has shown the best historical results. However, the strategy also yields stable results on S&P 500 stocks.
Primary instrument: Nasdaq 100.
Supplementary instrument: S&P 500.
Pattern Day Trader
The strategy statistically closed 2.22% of transactions on the same day, and there have been no cases of PDT being met in 30 years. This means that it can be used on accounts below $25k. Read more about Pattern Day Trader
Correlation
To check the correlation of the strategy with others, visit the correlations page.
Summary & Strengths and Weaknesses of the strategy
Strengths of the strategy:
Exposure. The strategy utilizes capital for only 15-25% of the time (depending on the index traded). This is very low compared to most strategies of this type.
Profit stability. The CAGR was 10.26%, slightly lower than the Benchmark's 10.53%, but the Drawdown was significantly smaller (22% vs 55%).
Robustness. The strategy was tested on various indices, including S&P 500 and Russell 1000, and its behavior was verified over a 30-year history and more than 50,000 trades.
Low percentage of same-day closed trades. The strategy is suitable for accounts below $25k.
Weaknesses of the strategy:
This weekly edge seems to be lower in recent years.
Summary
A strategy that trades only on Mondays has shown excellent results over 30 years, using just 15% of capital. With 68% of trades being profitable, it offers a high level of comfort. The strategy is robust, tested on various indices, and doesn't require a large capital. Although this weekly edge seems to have decreased in recent years, the strategy can still be an attractive addition for investors of various scales.
What you get in the package for this strategy:
An e-book presenting detailed rules and results of the strategy.
SQX file ready to use on platforms Algocloud and StrategyQuant.
Pseudocode describing all rules in an easy-to-understand manner.
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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