AlgoLab automated trading software is now connected to and live-trading for just under 100 clients. Over 132,000 futures trades have been executed, algorithmically and automatically. The longest-running account, AlgoLabHouse as been live for almost 28 months. Most importantly, live trading data has been consistent with the Performance Viewer modeling estimates thereby reaffirming that AlgoLab software has and will continue to perform as designed.

Based on the results to date, it becomes increasingly apparent that AlgoLab has the potential to play a bigger role for those with larger investment capital. AlgoLab, combined with the new “fixed number of contracts risk setting” allows a client to easily select a risk profile that matches or exceeds the performance results of equity, bond and GIC markets or a managed portfolio using a combination of these.

In the following article, I have identified in more detail how an “AlgoLab all-in” strategy could achieve better investment results as measured by a risk/reward metric than conventional investment classes and do so for lower overall fees.

THE CASE FOR ALGOLAB ALL-IN

Historically, the average return from an investment in the stock market is 9.8%, but you would have to withstand an average annual drawdown (loss) of 12% to earn that return.

https://www.calamos.com/Volatility-Opportunity-Guide/significant_intra_year_drawdowns_are_common

I wanted to see how much better AlgoLab could do with an “apples-to-apples”

comparison - so as a simulation, I set up a hypothetical $500,000 AlgoLab investment and specified an AlgoLab leverage level (risk level) such that the average annual drawdown equaled that of a stock market investment which is 12%. My AlgoLab return? It was over 7 greater than the average stock market return at 72%!!!

That’s right - for the same risk, the same “downside”, AlgoLab returns over 7 times more profit per year.

(For our existing AlgoLab customers reading this, we have recently switched from the old variable risk method to a fixed risk system and renamed it to “Leverage”. I have provided an explanation of both at the end of this post).

Many of our customers believe that AlgoLab is a higher risk investment that can boost the performance of a traditional investment portfolio consisting of equities and bonds. In fact, we even include a slide in our seminar that shows how only 25% of your capital invested into AlgoLab can boost the overall return of your entire portfolio by almost double by year 5, and nearly triple by year 10. And that includes paying tax on your AlgoLab profits.

This sounds great, but it actually isn’t the best strategy. The best strategy is to put 100% of your capital into an AlgoLab controlled trading account. If this sounds too risky to you, it’s actually the LEAST risky investment strategy according to a study that I’ve just completed.

What is the best way to measure and compare different investing strategies / different asset classes, etc.?

IT IS NOT ABOUT THE RETURN

Judging an investment strategy should not be solely based on how much money it can earn for you. Here’s why: I once designed a trading system that returned 95% winning trades, and generated a consistent 50% average monthly return. Sound good? It was a HORRIBLE strategy because those 5% losses were absolutely massive. In fact, so big that at least once every couple of months, your losses - rare as they are - would amount to over 100% of your trading capital. So… not a good system, and evaluating an investing methodology or asset class solely by its returns is a bad idea.

The best way to measure and compare different investing strategies is by comparing how many dollars it earns for each dollar risked. In the case above, this strategy earns 50%, but its average loss is 100%, so the ratio of reward to risk is 50/100, or 50 cents earned for every $1.00 lost. It’s actually worse because once you reach 100% loss, you have to stop trading because your money is gone.

A version of this risk/reward metric is called the Sharpe ratio - specifically, that’s your average annual return divided by your average maximum annual drawdown. Using the Sharpe ratio, let’s compare some different investing strategies including AlgoLab:

—————————

STOCKS (S&P 500 index)

Average annual return = 9.8%

Management fees = 1%

Inflation = 2.5%

Net return = 6.3%

Average annual drawdown = 12%

Sharpe ratio = .525

—————————

MANAGED INVESTMENTS

Average annual return = 7%

Management fees = 1.2%

Inflation = 2.5%

Net return = 3.3%

Average annual drawdown = 7%

Sharpe ratio = .47

—————————

GIC

Average annual return = 3%

Management fees = .5%

Inflation = 2.5%

Net return = 0

Average annual drawdown = 0

Sharpe ratio = 0

—————————

30 YEAR GOV BONDS

Average annual return = 2.5%

Management fees = .5%

Inflation = 2.5%

Net return = -.5%

Average annual drawdown = 0

Sharpe ratio = / by 0 error

—————————

ALGOLAB

Leverage (risk) set to average 12% drawdown which is the same drawdown as stocks

Average annual return = 72%

Fees = 2%

Inflation = 2.5%

Net return = 67.5%

Average annual drawdown = 12%

Sharpe ratio = 5.625

So basically, according to the Sharpe ratio metric, assuming you have enough capital, you can set your AlgoLab trading system up such that it is equally as risky as a stock market investment (12% average drawdowns), but, your returns will be a whopping FIVE TIMES HIGHER!

Here’s a couple of examples as to how you might structure an AlgoLab all-in strategy:

Capital = $250,000

AlgoLab leverage setting = 2.5

Ave return = 90%

Ave drawdown = 15%

Result = almost the same risk of a stock market investment with almost 9 times the return!!

Capital = $1,000,000

AlgoLab leverage setting = 4

Ave return = 36%

Ave drawdown = 6%

Result = HALF the average risk of a stock market investment with almost 4 times the return!!

Capital = $1,000,000

AlgoLab leverage setting = 2

Ave return = 18%

Ave drawdown = 3%

Result = 1/4 the average risk of a stock market investment with double the return.

You can run your own leverage / capital scenarios using our new calculator at:

https://www.theAlgoLab.com/home1.html

Note about “Risk” and “Leverage”: the old method of determining the number of contracts to trade was a variable method that used your trading capital. It is called “Risk”, and the values ranged from .05 (1 contract) to .9 (multiple contracts depending on how much trading capital you have). The new method is called “Leverage” and is a fixed # of contracts and is independent of your trading capital. A leverage value of “2” will trade 2 contracts for each symbol for each trade regardless of the amount of capital you have in your IB account. At smaller capital levels ($20,000 to $150,000), risk levels of .05 are the same as leverage values of “1” because they both trade 1 contract. The benefit to using Leverage over the old risk values is for larger capital accounts - the gain/pain improves considerably. Try it yourself using the Performance Viewer. Switch between the old risk method and the new leverage method using the contact calculation drop-down menu in settings.