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is not indicative of future performance or success.
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I want to introduce you to my strategy ‘Don’t Worry‘. Don’t Worry should be core strategy for every portfolio as it exhibits low correlations to traditional markets. The strategy has been institutionally developed and traded for the last two years in an alternative fund.
The strategy uses trend-following methods combined with pattern recognition approaches to generate superior performance. For more details on the rationale behind the strategy and the historical risk and performance numbers as well as the monthly returns (since 2003) please see the following excel spreadsheet:
Please be advised that historical returns do not guarantee future returns. The strategy is designed to be robust, i.e. to work in various market environments.
The strategy can be found here on C2:
For the start I want to offer an early bird discount to subscribers:
After January, subscription price will be USD 99.00. When the six-month track record on C2 is completed, price will go up to USD 199.
About me:
I have been involved in Equity and FX markets since 2003, holding several positions at investment banks where I focused on the development of quantitative models to diversify portfolio risk. During that time, I advised family offices, endowment funds, institutional and high net worth investors on implementing derivative products and trading strategies to achieve desired portfolio risk/return characteristics.
Since 2010, I have worked for two alternative investment funds which are completely quantitative and systematic driven. I have developed various systematic trading strategies in different asset classes and I have worked on various risk management strategies to optimize trading performance.
Please feel free to comment or contact me directly if you have questions regarding the trading strategy.
Thank you for your question. The 10% drawdown occured intraday on the day of the 4th of November. An E-MINI S&P 500 futures position was open at that time and caused the drawdown. The position itself had a drawdown of 9.63% on that day but managed to close in positive territory a couple of days later when the S&P recovered.
You can see position specific drawdowns when you click on ‘Show more details’ on the top right sight of the trading record or when you download the csv file of the trading history.
The system usually trades and invests in 3-4 ETFs per month (these trades happen on month end). The ETFs are selected based on multiple trend-following systems.
A second component of the strategy trades certain equity index futures depending on pattern recognition systems. The trades in futures happen infrequently, depending on high probability setups. Usually these future trades have a probability of >80% of being positive and the system identifies approximately 1-2 trades per month.
Nothing has changed in the strategy so far - and I do not expect anything to change in the near future. The trading strategy is designed to be highly robust. Robust methods are those that are expected to remain valid over the years. Robust techniques are based on very general, successful trading principles and as such are non-optimized and rarely exactly fit to any specific market situation. Hence, there can be some fluctuation in performance when you compare monthly/annual returns. But over a period of 3-4 years, the strategy is generally able to generate above-market returns.
The last year in particular was a not so strong year (compared to previous years) as especially the trend-following element in the strategy did not perform as good as in previous years. The reason for this was that most markets went into a consolidation/sideways period. The pattern recognition part of the strategy was much stronger and helped the strategy to gain overall 17.5%, still outperforming most markets significantly. Historically those weaker periods are followed by period which are far better (see 2004, 2011).
I want to take the opportunity to shed some more light on how the strategy works:
The strategy has three elements to improve the risk-return profile of the strategy.
Stable Core Portfolio - The strategy invests in a core portfolio across various asset classes which are selected based on a trend following approach
Tactical Positions - Usage of observable inefficiencies to create additional, uncorrelated performance
Active Risk-Management – Limiting downside risk while investing in cash through market downturns
I want to start on commenting on the first part, the Core Portfolio.
The core portfolio is able to invest in various asset classes, namely equity, sovereign and corporate fixed income, commodities and real estate. Access to these asset classes is gained exclusively through Exchange Traded Funds (ETFs), which, in turn, invest into 8,500 individual securities across approximately 90 countries.
The Core Portfolio is selected based on a systematic quantitative approach to capture behavioral biases in the market. 160 signals are analyzed per ETF to determine its trend.
The system is self-learning as it constantly uses the historically best signals to assess trends. The system then finds the best combination of asset classes and products that offer a superior risk adjusted return. The portfolio is adjusted monthly by trend and volatility control adjustments to minimize maximum drawdowns.
More to follow on the second and third element of the strategy.
Excursion – Why trend following works
One of the most important underlying concepts that contribute to the success of Trend Following is the fact that the strategy is based on the non-normality of market returns. Trend followers position themselves to profit from and capture the “fat tails” exhibited in market returns distribution. In a fat-tail distribution (Levy or Mandelbrot), extreme occurrences occur with a probability greater than normal. As Dave Harding of Winton Capital puts it:
“If you put in stops and run your profits and trade randomly you make money; and if you put in targets and no stops, and you trade randomly you lose money. So the old saw about cutting losses and running profits has some truth to it.”
The basics of trend following is to ride the trend until the end and to protect yourself on the downside by cutting your losses. This ensures that the location of trades in the returns distribution will:
Never venture on the left fat-tail (i.e. no extreme negative return)
Not be bounded on the right-hand side of the distribution (i.e. allow for extreme positive returns)
As the markets are mostly random, most of the trades will end up in the center of the distribution curve either side of the horizontal axis – and their return should cancel each other out. Trend Following’s alpha (the actual strategy return) is generated by extreme movements: By letting trades run on the right-hand side fat-tail and stopping them from “wandering” on the left-hand side one, an overall positive return is generated. This outlines the fact that Trend Following relies on rare extreme returns (outliers) whereas the bulk of trades cancel each other out.
The correlation to the S&P at over .60 seems on the high side to me, which I suppose is not surprising considering the long positions in the S&P Minis…
That is correct. In periods where the equity markets are going up, the correlation is usually between 0.5 and 0.7 as the trend following part of the strategy is exposed to the uptrending markets. As we currently see strong equity markets, some ETF holdings are long equity positions, giving the strategy a positive correlation at the moment.
However, in downturns, i.e. when markets are going down, the correlation is negativ as the strategy takes short positions or is holding cash/cash equivalents. Hence, over a longer period, the correlation is expected to be slightly above zero.
In my previous post, I mentioned that the strategy hast three elements to improve the risk-return profile of the strategy. These are:
Stable Core Portfolio - The strategy invests in a core portfolio across various asset classes which are selected based on a trend following approach
Tactical Positions - Usage of observable inefficiencies to create additional, uncorrelated performance
Active Risk-Management – Limiting downside risk while investing in cash through market downturns
In this post, I want to comment on the second part, the Tactical Positions.
The sub-strategy takes advantage of irrational behavior and decision making of market participants which lead to deviations of prices. These deviations can be statistically measured and traded. The strategy measures those patterns with its own proprietary variables based on observed market data. The trading system takes long and short positions to take advantage of mispriced markets. An integrated risk-management system adjusts the positions and orders based on current market behavior and volatility. Through computer driven systems the sub strategy invests in highly liquid futures markets: currently capturing inefficiencies in S&P 500 E-Mini futures.
More to follow on the third element of the strategy.
Substantial research has revealed that investors do not act rationally when making decisions. This irrational behavior leads to several exploitable market phenomena, one of which is mean reversion. Specifically, mean reversion is the byproduct of the availability bias, the aversion to losses, and the affinity for lower prices.
Academic research illustrates that humans bias their choices towards information that is easily recallable from memory. The consequence of this availability bias is that new information is given too much weight when making decisions because of the cognitive ease with which it can be recalled. Academic research also explains why stock prices are predisposed to overreaction; investors overweight new data, such as news events, while ignoring other pertinent information that is less cognitively available. After the initial overreaction to a negative news event occurs investors who still hold the stock become loss averse, unwilling to sell their position. Conversely, other investors become enticed by the lower price. As all investors finish digesting the new information, prices revert from the extremes causing a previously underperforming stock to outperform. Overreaction to new information is accentuated when the decision-making environment is complex and many variables have to be analyzed. Therefore, investors are given many opportunities to overweight new information because of the multitude of complex factors that drive stock prices.
Another factor that contributes to mean reversion in stocks is the attractiveness of lower prices. Psychologically, it is satisfying to purchase an item at a discount and it is painful to purchase an item at a premium. This is one reason why portfolio managers periodically increase their exposure to underperforming positions and trim their exposure to outperforming positions. These psychological biases naturally inflate buying pressure for underperforming stocks and selling pressure for outperforming stocks, leading to a cycle of mean reversion in equities that has been evident for at least the last 86 years.
while I don’t see any problems with your system. I think the majority of people on here want to see a more active system. Most people just don’t want to pay $100 or more for a system that makes 1-2 trades a month. It just takes too long to see if you even make enough to cover the cost of the system. I think if your system was more active more people would definitely sign up.
