I’ll be 41 in a few weeks. I’ve been trading nearly my whole life — since I was 10. Most of my peers didn’t start dabbling in markets until they were out of college. Relying on intuitive decisions has always been my hallmark, a gift that helped me finance my decadent lifestyle since I decided to enter into the business.
However, as some of you might’ve noticed, I am transitioning away from that dynamic strategy, which is wrought with error and dependent on the caprices of human emotion, in exchange for a more quantitative model that can be sterilized using the algorithms and tools already abundant in Exodus.
In layman’s terms, I have been racking my brain for months now developing trading models that are automated and can beat the S&P 500. While human intuition is important, the very essence and art of money management, I do believe there is a better way. Starting on Friday, I will begin to unveil some of the strategies that I’ve been working on inside Exodus — which are scalable and open for discussion.
When I’m picking stocks out of the sky, there isn’t much of a method or ‘secret sauce’ that I can scale or open up to the public for improvement. In my opinion, this is a major issue for both self directed investors and advisors. How can you enter the arena with confidence without a sound plan or strategy? On the same token, how can advisors convince sophisticated prospects or clients to send them money — based upon a whim that cannot be proven or properly examined? We’ll be getting into this important subject in greater detail in the months to come.
In the meantime, let’s do a top down analysis — examining what sectors and industries are absorbing money flow and outperforming. Then let’s discuss the process of putting together a strategy using this data.
(1 wk median return)
Basic Resources +3%
Oil & Gas Exploration +7.5%
Gold +4.3%
Copper +4%
Consumer Goods +0.39%
Household & Accessories +7.7%
LED Lighting +4.2%
Textile-Apparel +3.2%
Financial -0.28%
REIT- healthcare +2.2%
Foreign Regional Banks +1.6%
Property Management +1.2%
Healthcare +0.87%
Drug Delivery +2.9%
Hospitals +2.8%
Long term Care +2.6%
Industrials +0.10%
Residential Construction +2.2%
Machine Tools +1.4%
Industrial Electric Equip +1%
Services +1.02%
Apparel Stores +4.4%
Shipping +3.4%
Sporting Goods +3.2%
Technology +1.35%
3-D Printing +9%
Solar +7.45%
Multimedia & Graphics +6.17%
Utilities -0.05%
Nuclear +7.2%
Gas Utilities +0.28%
Foreign Utilities +0.02%
The strategy I am looking to pursue with this post is Alpha Sector Diversified, Equal Weighted, which means I am interested in high returns, zero hedges, diversified amongst the top performing industries in all of the market sectors, with money spread out evenly across each investment.
Makes sense?
Before getting into risk management, the goal here is to kick ass and try to capture momentum. I haven’t applied any fundamental inputs into this model, so I could be barreling into piles of shit — and that’s ok — providing I know and adhere to the rules.
I won’t map the entire process out — because it’ll take too long. But let’s go into the top industry in the top sector this week, Oil & Gas Exploration -> Basic Materials. How will I choose my stocks? Lucky for me, I have a quant at my disposal that grades them by fundamentals and technicals. There is also what’s called a ‘Hybrid score’ applied to each stock — which is a rank based on the combination of the two.
I am going to apply a minimum volume of 500k to this to negate liquidity risk. Here are the top two stocks.
WLL, ESV
Let’s imagine I selected two stocks from each industry from all 8 sectors — compiling a portfolio of 48 stocks. Allocations will be mapped according to the current market weightings across sectors, which is 12% basic materials, 11% consumer goods, 19% financials, 10% healthcare, 7% industrial goods, 13.6% services, 25% tech and 2.25% utilities. I can further filter my selections to mirror the market cap weighting of the respective sectors and market as a whole, or I can base my selections purely on the top hybrid scores — which adds the comforting feature of including some basic fundamental analysis.
My selection process and allocations are set, now for the trigger. I can trigger purchases immediately, or I can wait for a tipping point — such as an overall Exodus oversold signal, a significant market drawdown or even for esoteric reasons, such as Friday at 3:30pm. Let’s assume I buy immediately. Do I leave some cash for a rainy day? And if so, do I have a contingency plan to execute another strategy with it?
I do, as a matter of fact. Twenty percent of my money will be in cash waiting for an overall Hybrid Oversold signal, which is a mean-reversion strategy. Cash will be allocated into a macro ETF, like QQQ, and sold after 5 trading days upon purchasing it.
My model is almost complete. Now for risk management.
For this, I am going to forgo using stop losses or applying option strategies in favor of a reassessment of the overall strategy on a bi-weekly basis. Since I am buying 48 stocks, I want to keep commissions to a minimum, as I do not have an account with the fuckers from Robinhood yet. So at the end of the second week, this entire process will be repeated, ensuring that I’m chasing alpha and not dicking around in some underperforming assets.
Again, this is an aggressive strategy and not for everyone. If I wanted to tame it down, I could narrow the quintile of stocks to choose from based on a sundry of fundamental criteria and juxtapose that against market factors. Furthermore, I could assess the model on a weekly basis and implement triggers that would remove money from equities and place them in ‘risk off’ assets, like treasuries, munis or gold. If I did that, I’d need to figure out how much money and where to place it amongst those ‘risk off’ asset classes. By doing this, I’d greatly reduce my systematic risk, as the market meanders into underperformance.
Naturally, all of these strategies should be backtested and optimized over time.
Aside from menacing my readership with polyprop blogs, this is what I’ve been doing in my spare time — trying to crack the code to unparalleled growth and success in money management, using a quantitative approach.
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