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 31 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 of almost 70%, which supports its ease of use. Based on the tests below, get an idea of 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 1 indicator and 4 filters to trade only during the best periods for itself.

• The strategy uses simple, effective rules for entering and exiting positions.

• The Stockpicker mechanism searches and automatically selects stocks that meet the entry criteria.

Backtest 1 - $ Money Management

We are testing the period from 1995 to 2025, covering the last 31 years. 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.

The Stockpicker automatically selects stocks that meet the criteria from the S&P 500 index. It is important to note that the list of stocks in the index has changed over the years, which is reflected in the historical data used (survivorship bias).

• Invested capital: $100k

• Test period (years): 31

• Tested years: 1995-2025

• Tested Index: S&P500

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 (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.

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 in the analyzed strategy is lower than the benchmark. This translates to a CAGR of 6.6% vs 10.9%. This means that the analyzed strategy yields a lower net profit and a lower average annual return than the benchmark.

Exposure

The average exposure in the analyzed strategy was only 12.38% (!) vs 100% in the benchmark. The study was conducted on the underlying instruments, which are stocks of the S&P500 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. You can read more about this topic here.

Drawdown and Return/Drawdown ratio

The maximum open drawdown in the analyzed strategy was 20.45% vs 55.19% in the benchmark, resulting in a significantly better Return/Open Drawdown ratio of 12.82 vs 5.76. This means the analyzed strategy is less risky and more stable because its maximum capital drawdown is smaller, leading to better risk management than the benchmark.

Winning Percent

A hit rate of nearly 70% indicates high selectivity and accuracy in signal selection.

SL & TP

The strategy doesn't use a typical stop-loss and relies on an exit condition. But you can add an SL if it makes you more comfortable. Instead, diversifying positions within a single strategy and across the whole portfolio helps protect against the significant impact of a potential price change in one stock on the entire portfolio. Visit the stop loss order page.

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.

Strategy Robustness

The robustness was tested by practically executing all possible stock transactions (max open positions 40 from 1995 to 2025. This strategy passed our parameter modification tests.

Strategy is using 4 filters to pick the best time to trade. It is higher than the 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.

• Nasdaq 100 max transactions: 4'016

• Russell 1000 max transactions: 8'795

  
  

Recommended Instruments

The recommended primary instrument for this strategy in Algocloud Stockpicker is the S&P500 index, which has shown the best historical results. However, the strategy also yields stable results with Nasdaq 100 stocks.

Primary Instrument: S&P500

Supplementary Instrument: Nasdaq 100

Pattern Day Trader

The strategy statistically closed approximately 4% of transactions on the same day, and more importantly, over the 31-year test on the S&P 500 index, there were only 2 instances of PDT being met. This means that, in our assessment, the strategy can be used for smaller accounts, assuming an understanding of the PDT mechanisms described here.

Practical note: In a portfolio with other systems, the total activity may already meet the PDT requirements — it's worth keeping this in mind and also checking your whole portfolio using the PDF Finder.

Correlation

To check the correlation of the strategy with others, visit the cChecking correlation helps avoid duplicating risk in a portfolio and better combine systems with different profiles. You can find more about correlation here.

Summary & Strengths and Weaknesses

Strengths of the strategy:

• Profit stability. The equity curve shows a long-term, smooth upward path, even across very different market environments.

• Very low capital utilization. The strategy uses only a small fraction of available capital most of the time, so it can be easily combined with other systems in one portfolio.

• Low drawdown and risk profile. Open drawdowns remain relatively shallow compared to the broad market, which supports the comfort of use and capital protection.

• High quality of signals. A high share of trades ends with profit, which makes day-to-day execution psychologically easier.
  

Weaknesses of the strategy:

Robustness. The strategy uses 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 also successfully tested on the Nasdaq 100 and Russell 1000 indices.

Summary

Over 31 years, the tested strategy had four losing years, which is a very good result for a strategy that is based on a single indicator and simple rules. The number of losing years is small, and the average loss in these years is relatively low, which further increases confidence in the strategy's effectiveness.

What you receive in the package for this strategy:

• The eBook presents detailed rules and results for 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.