Before you start

Are you browsing portfolios for the first time? Be sure to read our article dedicated to portfolio creation.

Strategies in the SAP S portfolio

1. Triple B

2. RSI Range Rider

3. Turtle Trend Titan

4. Momentum IBS 3xETF

5. Gold Monthly Miner

   
   

Assumptions

• Account size - for basic tests I used a $10k account (MM$), margin is occasionally used (x2).

• Strategies used in the portfolio are tested with MM$ according to the table below, for example, the Triple B strategy has been allocated the same amount of $5000 throughout the entire 30-year period.

• Strategy tests are conducted for the period 01.1994-07.2024, except for instruments that do not have such distant historical data where the maximum available historical period is taken into account.

• The portfolio of strategies using MM$ was created in Strategy Quant.

• Strategies were tested on default instruments (Primary Instrument) indicated in the Recommended Instrument section in the strategy descriptions. For most Stockpicker type strategies, these are S&P500 stocks.

• Transaction costs and slippage have been taken into account in the tests, according to the study for the Alpaca broker (see our detailed study).

Strategies in the portfolio vs. capital distribution

Source: Extract from Portfolio Summary spreadsheet

Max Drawdown as a starting point

The starting point when constructing a portfolio, in addition to selecting good and low-correlated strategies, is the maximum acceptable drawdown for the entire portfolio. In the case of the above settings, it was only 12%.

Historical exposure

When building a portfolio, I also always consider how your broker account will be utilized over time. The exposure over a 30-year period for the above portfolio looks as follows:

Source: Exposure Master

The capital allocated to individual strategies in the backtest is indicated in the table in the MM$ column. The combined settings of the strategies result in a 170% theoretical maximum exposure, but as shown in the chart, the average exposure was $8.15k (81.15% of the account).

For about 20% of the time, this exposure was higher than the $10k capital, utilizing margin, but only for 5% of the time did it exceed a total of 12.3k. The maximum historical exposure of 16.44k never fully utilized the x2 margin level (which, for advanced users, provides the option to further increase exposure % in the Algocloud settings).

Backtest MM$

In this backtest, each strategy has a fixed capital to trade with throughout the entire testing period (in accordance with the table above).

The backtest doesn't factor in the compounding percentage.

The goal of the test is to determine:

1. What was the maximum historical drawdown expressed in $ in the portfolio.

2. What financial outcomes should we anticipate from using this portfolio right from the start, before compounding returns come into play.

3. What are the basic parameters of the analyzed portfolio.

Here is the Equity chart of the portfolio from this test:

The chart also shows the share of individual strategies within the portfolio.

Basic test parameters are marked in green:

Financial results month by month and year by year:

Additional information about the results:

Correlation

The correlation test is conducted on the same period of data for all strategies (01.2010-07.2024), so that all strategies have an equal opportunity to trade. Below are the correlation results for the portfolio:

Correlation Profit/Loss by DAY:

Correlation Profit/Loss by WEEK:

Correlation Profit/Loss by MONTH:

The two strategies Triple B and RSI Range Rider show increased correlation, especially on a monthly interval. However, when comparing only the losses of both strategies, the correlation appears much more favorable:

In trading, it's crucial that losses aren't linked. I'm okay with profits moving in tandem, as long as this doesn't lead to connected losses. These two strategies often resulted in gains at the same times.

You can read more about correlations here.

Pattern Day Trading

A test for the entire portfolio was conducted to check for the occurrence of a Pattern Day Trading. It showed 62 occurrences over 30 years of history and 27'000 transactions. This means that on average, such a pattern could occur about 2 times a year.

Test details:

Source: PDT Finder

Because there are systemic protections on the broker side with Alpaca and proper management of such positions by Algocloud, we believe it is acceptable that in these rare cases, an excess Daytrade may not be closed on the same day but on the next day when the conditions are met. If you have an account below $25k and would like to use this portfolio, it is important to understand how PDT works. You can read more about it here.

Max Trade Value

For smaller accounts, it is important to pay attention to the Max Trade Value column (capital distribution by strategies table above). For example, in the Turtle Trend Titan strategy, the value per trade is only $200. This means that the Stockpicker mechanism in Algocloud will only open positions on stocks valued below $200. In other words, if the unit price of a stock is, for example, $45, Stockpicker will buy max. 4 shares before reaching the limit of $200. If the price were $220, it would not buy any.

Please bear in mind that if your account is below $10k, the values in the table will decrease proportionally, so please conduct your own calculations in this range.

This is a normal behavior and it's already factored into the backtest. To manage this, you can lower the Max Open Position value in the Position Score tab from 15 to 10. But remember to run the necessary tests after making this adjustment.

Heads Up! The Turtle Trend Titan strategy in this portfolio now has a Max Open Positions limit of 15, down from the original 20. This change helps manage smaller accounts effectively. Make sure to review this setting before you go live with the strategy.

In-depth analysis of results

The following estimations were made in a dedicated Excel spreadsheet (Portfolio Summary). Monthly results from SQX were copied into this spreadsheet as a basis for further calculations.

The strategy portfolio generated an average annual return of 22%, significantly outperforming the benchmark (SPY) which had a CAGR of approximately 10.4% during this period. A key feature of the portfolio is that during very difficult periods for the stock market (highlighted in yellow), the portfolio also generated an average return of about 19%. Even in the last bear market in 2022, the return generated was around 21% (!).

Here are the returns in percentage, month by month and year by year:

Compound interest

The use of compound interest magic is the essence of investing and trading. It is also an excellent feature and setting that we use by default when trading in Algocloud.

In backtesting for a single strategy, compound interest is very well displayed in Strategy Quant. Unfortunately, at the level of combining strategies in a portfolio, a reliable test directly in Strategy Quant using MM% is not currently possible.

Fortunately, you can easily estimate the behavior of compound interest based on data copied from SQX or Algocloud with MM$ in the portfolio summary spreadsheet provided by us.

Here are the results of compound interest for the discussed portfolio:

And Equity chart for this test:

Investing $10k over the years has turned into $6.6M, all thanks to strategies that allowed for stable compound percentage growth.

Note: The estimates provided were calculated using the compound percentage method on a monthly basis. In actual trading on Algocloud, the percentage is calculated in daily basis. Each strategy opens new positions every day based on the current account value, i.e. the total capital generated by all strategies. According to our estimates, the daily method should additionally have a positive impact on the long-term results of the portfolio, although it may not be the case in every situation. We hope that future versions of SQX will also allow us to calculate and present such results.

Summary

Strengths

• The portfolio is based on two robust reversal strategies, which have proven their quality through a large number of transactions in backtests. The portfolio is also balanced by 3 strategies with different characteristics.

• The presented portfolio turned $10'000 into over $6'400'000 over 30 years, achieving an average annual return of 22%, which is significantly better than the benchmark (around 10.5%).

• The portfolio had a maximum drawdown of only 12% compared to 55% in the benchmark, which is an excellent result (of course, there is no guarantee that the drawdown in the future will not be greater).

• The portfolio did not have a single losing year and despite being Long Only, it performed only slightly worse during bear markets.

• There is a high win rate (66.5%), which is the driving force of the portfolio and compensates for the fact that the average loss is slightly larger than the average gain.

• The portfolio, according to the original assumption, is suitable for accounts with smaller capital, not meeting the PDT requirements.

Weaknesses

• The weakest point of the portfolio is that it consists of only five strategies. This results in lower diversification than it should ideally have. This is a compromise that can be accepted at the beginning with a smaller account, but in the long term, we suggest expanding your portfolio by adding more strategies.

• An obvious concern might be the higher correlation between the Triple B and RSI Range Rider strategies. Yet, these strategies have distinct characteristics: Triple B is quicker, while RSI Range Rider catches longer moves. This actually works to their advantage. When you look at how they correlate by losses, the picture is more positive, even monthly. That's why we've decided to include both in the portfolio.

The strategies included in the portfolio are consistent with the long-term characteristics of the stock market. Reversal strategies dominate, balanced by strategies utilizing Momentum, Trend, and Bias.

A maximum drawdown of 12% is quite impressive compared to the profits made, especially since the market dropped by more than 55% at the same time.

The strategy portfolio's 27,000 transactions over the past 30 years showcase its strength in various market conditions.

How much do the strategies from this portfolio cost?

PRO strategies on Algohub are available for purchase individually or in the form of sets/portfolios.

What you receive with your purchase:

For the portfolio:

• Portfolio Summary Spreadsheet

• Portfolio SQX file

For each strategy included in the portfolio:

• E-book detailing the specific rules and results of the strategy.

• SQX file ready to use on platforms such as Algocloud and StrategyQuant.

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