ALGOLAB VS STOCKS FROM MARCH 2017 TO MAY 2020
HOW THIS ANALYSIS WAS PREPARED
Stocks - This is the value of $1000 invested in Dow Jones Industrial Average index stocks from March 7, 2017 to May 28, 2020
AlgoLab - This is the value of $1000 invested in AlgoLab from March 7, 2017 to May 28, 2020. Results from March 7, 2017 to November 10, 2019 are based on a client account ("client1") that was funded with $50,000 and used notional funds of $50,000 to equal $100,000 account value. This account was traded on a flat nominal trading level of $100,000 since inception. Results from November, 2019 to May 28, 2020, are based on a representative managed account. Performance of all individual managed accounts is available here. Profits are not added back into the trading capital (non compounded). The rates of return are adjusted to reflect a monthly management fee of 2% per year of the flat nominal trading level, plus a 20% performance incentive fee charged quarterly, plus all brokerage fees and commissions (in other words, these results include all fees subtracted).
AlgoLab+Stocks - This is $2 invested in AlgoLab for every $1 invested in Dow Jones Industrial Average index stocks.
RISK VS REWARD ANALYSIS
The investment resulting in the most profit over the period of the study is AlgoLab at $1,099 of profit based on a $1000 invested which is 109% return. The investment with the lowest maximum drawdown is AlgoLab+Stocks which lost only $188 (-12.8%) compared to $300 (-27%) for AlgoLab and $623 (-43%) for Stocks. The investment with the most favorable risk reward ratio is AlgoLab+Stocks which earned $4.11 for every $1 lost in a drawdown. Since AlgoLab and equities are not correlated, there is a benefit from the diversification effect which results from a combination of uncorrelated investments. Over the period studied, the largest drawdown for stocks occurred recently from Feb 13 to March 23 which was a profitable period for the AlgoLab investment thereby reducing the total drawdown for the combination. The largest drawdown for AlgoLab investment was from Dec 2, 2018 to March 21, 2019 which was only partially a drawdown for stocks resulting in the combined investment losing less than either of the individual investments during this period.
Stocks: $0.20 profit for every $1 lost in drawdown
$125 ending profit / $623 maximum drawdown from Feb 13, 2020 to March 23, 2020 (-43%)
AlgoLab: $3.66 profit for every $1 lost in drawdown
$1099 ending profit / $300 maximum drawdown from Dec 2, 2018 to March 21, 2019 (-27%)
AlgoLab+Stocks: $4.11 profit for every $1 lost in drawdown
$775 ending profit / $188 maximum drawdown from Dec 3, 2018 to March 22, 2019 (-12.8%)
TEST BEFORE YOU INVEST.
We want to make sure investing in an AlgoLab managed account is right for you. The Test Drive demo can be an effective way to learn more about the typical returns and drawdowns from this type of investment prior to risking your capital. Your AlgoLab test drive performance results will be based on an actual funded account.
REGISTER YOUR FREE DEMO ACCOUNT HERE which includes your own AlgoLab dashboard where you can follow your simulated trading results in real-time.
12 month return
Sum of monthly returns from May 2019 to April 2020
using min and max individual client returns. Ave is (max+min)/2
Minimum investment $75,000 USD
Largest monthly drawdown -26% (Jan, 2019)
Largest peak to valley drawdown -27%
(Dec, 2018 to March, 2019)
LOW CAPITAL OPTION:
If you are interested in a $40,000 minimum investment version of AlphaEngine, please contact us
Past results are not necessarily indicative of future results. Trading futures involves substantial risk of loss and is not suitable for all investors. An investor must read and understand the CTA’s current Disclosure Document before investing.
DIFFERENCES BETWEEN ACCOUNTS DISCLOSURE: To reduce trading costs, AlphaEngine uses proprietary trade entry and exit algorithms. To maximize utilization of available capital, AlphaEngine uses a proprietary trade volume algorithm. These can result in slightly different profit and loss comparisons between accounts. Every effort has been made to ensure that trading is fair between accounts.