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Strategies in a Compounding Titan L portfolio

1. Volta

2. Triple B

3. Week Explorer

4. IBS Master

5. R2 Turbo

6. Momentum IBS 3xETF

7. Stock Monthly Mover

8. Gold Monthly Miner

9. Bonds Monthly Mover

10. Emerging Monthly Mover PRO
    

Assumptions

• Account size - for basic tests I used an account of $100k (MM$), margin is periodically used (x2).

• The strategies used in the portfolio are tested with MM$ according to the table below, for example, the Week Explorer strategy has been allocated the same amount of $20'000 throughout the entire 30-year period.

• Strategy tests were 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.

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

• Transaction costs and slippage were taken into account in the tests, according to a study for the Alpaca broker (see our detailed study). The study did not include the cost of borrowing capital on a margin account or conversely any interest earned on free capital (e.g. through daily purchase of BIL or interest received from the broker).
  

Strategy in the portfolio vs. capital allocation

Table 1. Source: Excerpt from Portfolio Summary spreadsheet

Max Drawdown - the starting point

The starting point when constructing a portfolio, in addition to selecting very good and low-correlated strategies, is the maximum acceptable drawdown for the entire portfolio. In the case of the above portfolio settings, it was 26.6%. This Max DD was recorded in 1998.

Interestingly, in later bear markets, the portfolio's declines were much smaller, for example:

2000-2002 - about 22%,

2008 - about 15%,

2020 - about 5%,

2022 - about 12%.

In our estimations, we usually assume the worst-case scenarios based on historical data.

Historical exposure

Another key factor that I take into account when constructing a portfolio is how the account usage will look over time. The exposure over a 30-year period for the above portfolio is as follows:

The capital allocated to individual strategies in the backtest is indicated in Table 1, in the column MM$. The combined settings of the strategies result in a 210% theoretical maximum exposure, but as shown in the chart, the average exposure was $91.47k (91% of the account). Approximately 35% of the time, this exposure utilized margin, and for 5% of the time, it exceeded $151k. The exposure of 6x over 30 years (on average once every 5 years) fully utilized margin x2, which could lead to the need to close excess positions. In this portfolio, the use of margin is relatively high, and the costs of this operation should be taken into account.

Backtest MM$

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

This backtest does not take compounding percentage into account.

The goal of the test is to determine:

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

2. What monetary results can we expect by using such a portfolio from the very beginning (before we feel the effect of compounding percentage).

3. What are the basic parameters of the tested 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

Below are the correlation results for the portfolio in the period tested above.

Profit/Loss correlation by DAY:

Profit/Loss correlation by WEEK:

Profit/Loss correlation by MONTH:

We have several strategies that show increased correlation between profits and losses, especially on a monthly interval. However, for me, the key indicator is comparing the losses themselves on a monthly interval:

The fact that the reversal strategies, dominant in this portfolio, regularly have profits at the same times does not surprise me and is expected. In trading, my main goal is for losses not to be too strongly correlated - high correlation of profits is acceptable. The monthly correlation of losses alone is acceptable to me in this case, but I leave it to your individual assessment in the context of the portfolio's goals.

You can read more about correlations here.

Pattern Day Trading

The portfolio is suitable for use only on accounts above $25k USD. We have 4810 day trades and 1600 PDT occurrences over 30 years.

If you have an account smaller than $25k, check out our SAP series portfolios.

You can read more about Pattern Day Trading here.

Test details:

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 has generated an average annual return of 31%, which is 3 times better than the benchmark (SPY) with a CAGR of approximately 10.4% during the same period. The key feature of the portfolio is that despite being LONG ONLY, in very difficult periods for the stock market (highlighted in yellow), the portfolio also generated an average return of around 20%. Even in the Bear market in 2022, the return generated was around 27%.

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

Compound interest is the essence of investing and trading. It is also an excellent feature and setting that we commonly use when trading live in Algocloud. In backtesting for a single strategy, compound interest is very well displayed in Algocloud or Strategy Quant tests. Unfortunately, at the level of combining strategies in a portfolio, a reliable test using MM% is not currently possible. Fortunately, it is easy to conduct your own estimation of compound interest behavior based on data copied from SQX with MM$ in the Portfolio Summary.

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

And Equity chart for this test:

Investing $100k over the years would have turned into $756M thanks to strategies allowing for stable compounding percentage.

Note! The above estimates used the compounding percentage method in monthly cycles. In actual trading on Algocloud, the percentage is calculated in daily cycles. 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 portfolio results, although it may not be the case in every situation. We hope that future versions of SQX/Algocloud will also allow us to calculate and present such results.

Summary

Strengths

• The aggressive MM settings of this portfolio have a strong impact, with very robust reversal strategies that demonstrate their quality through a 50k+ of transactions in backtests.

• The presented portfolio turned $100'000 into over $756'000'000 in 30 years, achieving an average annual return of 31%, which is significantly better than the benchmark (around 10.5% and $1.95M return).

• The portfolio had 2 years with a small loss and, despite being a Long Only, performed very well even during bear markets (average return of 20%).

• We have a high win rate here - 67%, which should provide a significant psychological comfort in using the portfolio. It should be accepted that the average loss will be slightly higher than the average gain.
  

Weaknesses

• The portfolio aims to reach ambitious targets by focusing on highly effective at the stock market - reversal strategies. However, this approach leads to less diversity in the portfolio and a higher correlation, which are drawbacks of this investment method.

• The maximum drawdown of 27% is above the level considered safe for standard psychological conditions, which is for us around 20%. While the lower maximum drawdown in all bear markets post-2002 may provide some comfort, it should always be assumed that the worst drawdown could occur in the future. Therefore, this portfolio is not recommended for beginners. We can also recommend a gradual increases in capital allocation settings as the account grows.

• The use of margin in this portfolio is relatively high compared to others (we use some form of broker credit about 35% of the time), so the costs of this operation should be taken into account.

• The portfolio is not suitable for accounts below $25k USD.
  

This portfolio serves as an illustration of how the higher risk/return portfolio settings and money management can impact results. It is showing us what the difference of a 20% additional return (vs. benchmark) in the long term means. Such a portfolio with more aggressive money management settings is, in our opinion, intended for more advanced users who are willing to accept higher risks.

How can I get strategies for the portfolio?

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

What you receive with your purchase:

For the portfolio:

• Portfolio Summary Sheet

• 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.