Inspirations

This approach allowed for achieving results weaker than the benchmark over a 31-year period, but with only 14% average capital utilization and a much smaller drawdown, while maintaining a low correlation with other strategies.

Key Components

• Time restriction - trading only on Mondays.

• Additional components are 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 from 1995 to 2025, covering the last 31 years.

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).

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 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 $1.10M in the analyzed strategy is slightly lower than the benchmark (S&P 500 Index, represented by the SPY ETF, marked in yellow on the chart), which is $2.44M, translating to a CAGR of 8.33% vs. 11.00%.

Drawdown and Return/Drawdown ratio

The Max Open Drawdown in the analyzed strategy was 23.18% vs 55.19% in the benchmark, resulting in a significantly better Return/Open Drawdown ratio of 12.53 vs 5.77.

Exposure

The average exposure in the analyzed strategy was only 14.32%, compared with 100% in the benchmark. The study was conducted on the underlying instrument: the Nasdaq 100 index.

Please consider that, if applying to trade the S&P 500 index, the exposure would increase to approximately 25% because there will be many 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 66.49%. This means that the vast majority of transactions were profitable, highlighting the strategy's effectiveness in generating positive outcomes and giving the user greater confidence in the frequency of profits.

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 accounted for 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

We tested robustness by conducting all possible stock transactions with a maximum of 40 open positions for the period from 1995 to 2025. This included 14'312 transactions for the S&P 500 index and 18'370 transactions for the Russell 1000, both at %MM.

S&P500 max transactions: 14,312

Russell 1000 max transactions: 18,370

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 2.3% 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 strategy's correlation with others, visit the correlations page.

Summary & Strengths and Weaknesses

Strengths of the strategy:

• Capital is engaged only for a small part of the time, which reduces market exposure while still allowing the strategy to systematically exploit the Monday effect.

• The equity curve remains clearly smoother than the benchmark, with shallower downturns and faster recoveries, which translates into a more comfortable risk profile for the investor.

• The rules have been validated using a long historical sample across different stock indices, confirming the stability of the behaviour across various market environments.

Weaknesses of the strategy:

In the most recent part of the test, the day-of-week effect appears weaker, so the future edge of the strategy should be treated as more moderate than in the earlier years.

Summary

Over a long historical period, it has generated a high share of profitable trades with relatively mild drawdowns, while remaining robust across different indices. At the same time, the weakening of the weekly pattern in recent years suggests that the strategy should be treated as a diversified component of a broader portfolio rather than a stand-alone solution.

What you get in the package for this strategy:

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

• The SQX file is ready to use on platforms Algocloud and StrategyQuant.

• Pseudocode describing all rules in an easy-to-understand manner.