Blackarbs Retirement Strategy Algorithm Debut (Part 1)
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Get access to the strategy that has returned 48% in live trading since this article was written [here] [updated: 2024-Mar-02]
Mission Recap
Blackarbs current mission is to create automated strategies with the goal of beating the market with superior risk adjusted returns. Originally, I wanted to illuminate some of the more hidden aspects of markets and investing that I found interesting and of value. Over time, that goal crystallized into creating a strategy or strategies that made (potential) superior performance accessible to investors of all types and demographics. To this end I believe we have finally created a flagship algorithm.
Algorithm Requirements
For this algorithm to deliver value in alignment with the core mission statement it needed to address the following:
It needs to utilize a small number of very liquid assets that everyone could access.
It needs to be able to be traded in a 401k or retirement account.
It needs to beat a simple buy and hold S&P 500 strategy in total return and risk adjusted returns.
It needs to be robust to major market regime shifts.
It needs to be robust against overfitting, factoring in reasonable transaction costs, slippage, small changes in implementation, etc.
Implementation needed to be simple enough to allow investors to manually input orders if preferred.
I will explain the criteria in more detail below:
1) It needs to utilize a small number of very liquid assets that everyone could access.
This is crucial for several reasons. If the edge is made up of very small capacity, illiquid, strictly regulated assets, or even requires the investor to manage a portfolio of many assets, then that presents several problems: the edge can be arbitraged out easily after a certain capital threshold is allocated towards it, investors won’t be able to trade the asset without incurring destructive transaction costs, the average investor won’t have access to the investment security, or the strategy requires too much capital and resources to manage appropriately.
2) It needs to be able to be traded in a 401k or retirement account.
This is important for two reasons. First, there are tax implications to buying and selling securities in the same year that reduces the total return of the portfolio but is not often considered or discussed in the algorithmic trading space. Thus for an average investor, a strategy that trades frequently would be better suited in a tax advantaged account. The second reason is that for most people, their largest investable sum of money is found in their retirement/401k accounts. Thus strategies that are not allowed to be traded in those accounts make the strategy less accessible.
3) It needs to beat a simple buy and hold S&P 500 strategy in total return and risk adjusted returns.
Obviously if you can’t beat the simplest buy and hold strategy available to all market participants, few investors (outside of those looking for diversification strategies) would be interested in investing in the strategy.
4) It needs to be robust to major market regime shifts
Market regimes change frequently and across contexts. From a price action or technical analysis perspective markets shift from trending to mean reverting, bullish to bearish, volatile to stagnant. From an economic perspective markets shift from inflationary to deflationary, rising interest rates to falling interest rates, job and wage growth to increasing employment. A flagship algorithm (or fund) has to be robust against these changing combinations of market regimes otherwise a shift could cause the strategy performance to collapse.
5) It needs to be robust against overfitting, factoring in reasonable transaction costs, slippage, small changes in implementation, etc.
Overfitting is one of the cardinal sins in strategy development. Given the power of computing, automated machine learning and ease of backtesting, it is quite straightforward to develop a strategy that looks good on one realization of past data but will fail (and usually quickly) once exposed to out of sample market conditions. Thus it is crucial that the strategy be developed using best practices, with a logical explanation why the strategy should work. Furthermore in backtesting, modeling realistic market conditions is also important and the strategy should be robust to commissions, slippage, and or small changes in implementation. For example, if you change your broker settings to account for different commissions the strategy should not break down, if your strategy is a daily strategy, changing the rebalance time from 10:00 am to 10:30 am the strategy should not break down.
The Algorithm
After years of work, I believe Blackarbs has an algorithm that meets all of the aforementioned requirements. It was designed with the average investor in mind to utilize in a tax advantaged account, and most importantly it is robust against different combinations of market and economic regimes. It is based on simple, thoroughly researched concepts that have withstood the test of time combined in a unique way that limits drawdown during periods of market volatility.
The strategy is a trend following long only leveraged ETF strategy that capitalizes on the research I’ve done and shared on this website over the years. However, I can already hear people asking, but there are several potentially fatal flaws investing in leveraged ETFs.
Leveraged gains also create leveraged losses.
Volatility reduces the buy and hold return of leveraged ETFs to less than stated leverage.
These concerns are valid and in order to address these issues a couple of tradeoffs had to be made.
To limit the impact of leveraged losses the strategy uses a unique and proprietary market volatility filter. When triggered it exits the market until the increased level of risk subsides.
To combat the the volatility drag on returns the strategy is rebalanced daily when active and weights change more than a specified threshold. To limit the impact of transaction costs and taxes this type of strategy is recommended for tax advantaged accounts using brokers that give free ETF trades. For example, Fidelity, Interactive Brokers and Charles Schwab offer zero-cost ETF trading.
The tradeoff is that the strategy sacrifices absolute return (aka 2x or 3x the long term average of the market) for increased risk adjusted returns.
Performance
Below I will highlight some key performance metrics from the backtest. Keep in mind there are limitations to backtesting which will be highlighted in the disclaimer and appendix. Note that this backtest was performed using Quantconnect and the tearsheet was generated using Quantstats. In another blog post I will detail the strategy creation and robustness testing that was performed.
SIMULATED OR Historical performance displayed is for illustrative purposes only and does not guarantee future results
Note that on cumulative return and sharpe ratios meet the goal of outperforming the benchmark SPY. What I find impressive is we're able to reduce the max drawdown and longest drawdown days by almost half. Also note that the equity curve shows that during periods of downside volatility the strategy tends to exit the market before the worst hits. In exchange for missing some upside this is a reasonable tradeoff in my opinion.
SIMULATED OR Historical performance displayed is for illustrative purposes only and does not guarantee future results
Here again we see the ability of the strategy to outperform during periods when the broader market (SPY) is in drawdown such as 2018 and 2022. While 2022 the strategy also experienced negative returns, they were much less than the overall market. In exchange for this again we see in some years the strategy underperforms the broader market, such as 2013, 2017, 2018 and 2021, although in all 4 years the strategy finished the year with net positive gains.
Get access to the strategy that has returned 48% in live trading since this article was written [here] [updated: 2024-Mar-02]
To access the html backtest tearsheet for more detailed performance analysis click here [backtest link].
With that said I reiterate more details will be forthcoming including how to access the strategy. For now I’m offering access to the daily signals for subscribers through the Blackarbs Research Group Discord. Click the link to join for free and become a part of the Blackarbs trading community [discord link].
