United Healthcare is one of the largest companies in the medical industry worldwide. Like many companies in this sector, they benefit from the business opportunities during the flu season when most of us use more healthcare services. The strategy explores the annual autumn pattern observed in UNH stocks.
Inspiration
This strategy explores the autumn pattern observed in UNH stocks. United Healthcare is one of the largest pharmaceutical companies in the world, employing over 400k employees. It offers a wide range of medical services primarily in the USA. Like many companies in this industry, it benefits from the seasonal increase in illnesses, during which most of us rely more heavily on healthcare.
Key Components
Exploration of the autumn illness season, increased profits for the company during this period, and the corresponding investor behavior regarding the company's stocks.
The strategy is based solely on specific dates. We completely ignore trends, news, the company's valuation, and the overall market situation.
Entry rules
The entry is made on the first working day after October 20th.
 |
 |
Exit rules
We leave just before New Year, no later than after 60 working days.
 |
Backtest 1, $ Money Management
In this variant of backtest, we invest a constant amount of $100k.
We are testing the last 30 years*:
Equity Chart for this test:
Test results:
The average annual result was 8.11%, which, when capital is utilized for only 18% of the time in a year, gives an annualized return of approximately 45%.
*The data for UNH before 1994 in Algoocloud contains an error due to a stock split so we can start testing here since 1994. But we tested this pattern also in the years 1984-1994 with an 80% win rate and 10.45% average return.
Backtest 2, % Money Management
In this backtest, we invest in a strategy that constantly allocates 100% of the current capital (with an initial capital of $100k). This means that as the capital increases or decreases, the value of the position changes proportionally. The rest of the parameters remain unchanged.
The Equity Chart for this test looks as follows:
Basic statistics resulting from the test:
The maximum open drawdown is shown on the chart and was close to 40% during the 2008 bear market. However, maintaining that position according to the rules would have closed the trade with a small profit. If a stop loss (SL) of, for example, 25% was applied, the loss this year would have been 25% instead of the actual profit. This is a classic example of how SL often does not improve the strategy, but it is still important to protect against price fluctuations by diversifying the portfolio with different strategies.
Applying risk management (MM%) and reinvesting all profits in the next trade results in nearly a 3x increase in strategy income over the years. The total profit from the $100k investment was $714k, with capital exposure for about 18% of the time in a year.
Additional information about the strategy
SL & TP
The strategy does not use typical stop loss and take profit levels, although they can potentially be implemented. According to our tests, SL settings worsen the results for most ETF and stock strategies (see why). The diversification within the strategy portfolio serves as protection against the strong impact of any price changes in individual stocks on the entire portfolio.
Market regime
The strategy has been tested in all major market regimes, and despite its simplicity, it performed well in different periods.
Trading costs
The backtests take into account the typical trading costs and slippage for the strategy, which were observed in real tests with the Alpaca broker (detailed study). With a diversified portfolio of stocks and strategies, transaction costs can significantly affect your profit or loss, so take the time to thoroughly test and choose a broker.
Robustness
The strategy was not generated automatically or optimized automatically to fit the historical data (read why).
The main consideration for a seasonality-type strategy is the low number of trades. We cannot directly compare it to the number of trades obtained in backtests of stockpicker-type strategies. Seasonal patterns, by their nature, will never have a large number of trades. Their strength lies in the long-term and logical arrangement of the pattern.
Recommended instruments
The primary instrument is UNH.
Pattern Day Trader
The strategy is also suitable for smaller accounts. The average holding period is 47 business days, which does not meet the PDT requirement.
Correlation with other strategies
The strategy aligns with trends related to the autumn season. It will be correlated with other healthcare strategies that trading during this period. UNH is a wide economic moat company with strong fundamentals. This pattern is supported by strong logic and business reasons, which enhance its effectiveness. Visit the correlation page.
Summary & Strengths and weaknesses of the strategy
The UNH Autumn Rally strategy is another good example of how seasonality works not only in nature but also in financial markets. The results speak for themselves.
The strategy is very easy to implement, even manually. If you value your time, we suggest entrusting this work to simple and effective algorithms, so you don't have to worry about monitoring deadlines, especially since you can use multiple systems simultaneously by using capital precisely for only part of the year.
The main concern one may have with seasonality strategies is the number of transactions in history. Seasonal patterns, by their nature, will never have a large number of transactions.
What matters more in them are:
The pattern's alignment with nature, business patterns, or consumer behavior patterns.
A representative number of years - we consider a minimum of 15-20 years.
A longer pattern duration. We use patterns that last at least a few weeks, preferably months.
There have been many interesting scientific studies on seasonal patterns in financial markets. They confirm that seasonality can often have more power than trends. We enjoy studying and exploring them for you. However, we acknowledge that patterns can evolve. Therefore, we review and make any necessary adjustments to our seasonal strategies once a year. The latest published versions will be available to you on this website.
Download is free - login required
What you get in the package for this strategy:
An 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.
BEST STRATEGIES
R2 Turbo Strategy
The R2 Turbo strategy draws inspiration from Larry Connors' experiences. While it is based on the Relative Strength Index (RSI) indicator, it includes a specific way of using this indicator and filters that enhance its effectiveness. It is a trend reversal strategy that waits for a specific pullback in an uptrend.
Week Explorer Strategy
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.
●
KO Christmas Rally Strategy
The seasonal holiday pattern on Coca-Cola is one fantastic example of how seasons affect stocks. The pattern has a logical justification, which is the association of the brand with holidays built over decades. This consequently influenced consumer and investor behavior before this period.
