Understanding compound returns
The chart "Quant Yeti vs JNUG" displays how our model has performed versus buying and holding JNUG in 2019. It is important to understand how both strategies are impacted by compound returns.
Quant Yeti Strategy: We reinvest all profits each time a new trade signal is generated. This means mistakes are getting compounded... but so are profits.
JNUG: It is designed to return 3x the daily performance of GDXJ, the ETF for junior gold miners. The fact that it rebalances its leverage to 3x every day means that it also follows the laws of compound returns. More about compound returns and the decay of leveraged ETF's in the section below.
Backtest versus reality
Please be aware that the track record for 2019 is hypothetical. In 2020, we are putting the Quant Yeti to work in a live account. We at Quant Yeti believe transparency is very important. That's why we are documenting the process every step of the way.
The starting balance of the live account was $10,482.20 as of December 29, 2019. Every time a new trade signal is generated, the Yeti will move the entire value of the account into JNUG or JDST.
If the quant model does not have an active signal for JNUG or JDST, he might, temporarily, invest in senior gold miners via NUGT or DUST instead. We are confident that the Quant Yeti Strategy we are applying in 2020, will confirm the results of 2019 but please be aware that the reinvesting strategy is very aggressive. It is only wise to apply such strategy when the trading model it is based on is sound.
Compound returns works both ways and it is the reason why all leveraged ETF's decay over time. That's why it is ill-advised to buy and hold JNUG, JDST or any other leveraged ETF. Drawdowns are the kryptonite of leveraged ETFs because each drawdown makes it harder to get back to even.
To illustrate this, let's assume that GDXJ, the underlying ETF that JNUG is derived from, has a current value of $100. If price drops from $100 to $80 on one day, and then goes back to $100 the following day, the investors who simply kept the position break even.
Here is how JNUG would perform during that scenario. Since JNUG returns 3x the daily performance of GDXJ, it would lose 60% the first day (-20% x 3 = -60%).
Assuming JNUG was also trading at $100, it would lose 60% and therefore drop to $40 a share.
When GDXJ then rallies from $80 back to $100, it is gaining 25% which means JNUG would rise by 75% (25% x 3). The problem is, a 75% rally from $40 will only get JNUG back to $70 because it is rallying from a much lower base (75% of $40 = $30).
In other words, while GDXJ is back to even at $100, JNUG only recovered to $70 and is still down by 30%.
Understanding this concept is very important and it makes trading in and out of leveraged ETF's such as JNUG or JDST paramount. Reality is that timing the market is very difficult for human traders because emotions get in the way of rational decision making.
Quant Yeti does not have emotions and makes decisions based on a quantitative analysis of price action. Every decision it is making is based on math.
Below are screenshots of all trade signals the Quant Yeti has generated in 2019. Screenshots of all live entries and exits in 2020 are at the bottom of the page..
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