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The performance of the HST model is amazing (30-50% per year on average), but it shows significant drawdowns (-35/50% in some cases).
In bad times of the markets investors could panic and get out of the model, hurting themselves: seeing our capital cut in half would destroy the believes of many.

Another way to reduce volatility and risk: I have to choose a trading strategy which is more adequate to my risk profile.


Here's how to manage this very important issue

The HST model gives signals. Using with signals, investors shift among 3 positions

  1. Aggressive Long
  2. Moderate Long
  3. Cash/Short

 The original version of the model (actual variant 2) suggests the investors to go

  1. 100% $TQQQ
  2. 100% $UPRO
  3. 100% Cash

but we can apply less risky ETFs (with less or no leverage) and reduce drawdowns consequently.
In some cases, we can apply an inverse ETF (like $QLD) instead of going Cash to take advantage of the bear markets too.

These options  build up a set of strategies, each suitable for different risk profiles.
(variant 1 is the most risky/volatile, variant 5 the less risky/volatile)

[Note: when we choose to apply the HST model with leveraged ETFs, we apply it only for a part of our invested capital: this is money we do not need for our regular life. We are making a risky bet to see our capital grow, but you never go all-in with that bet, just invest a small part of capital]

5 Variants of the HST model

         popular!
higher risk
   

popular!
moderate risk

Status of the market and the Economy Variants / Positions Variant 1 Variant 2 Variant 3 Variant 4 Variant 5
Running bull market or Strong momentum Aggressive Long (AL) 100% $TQQQ 100% $TQQQ 100% $QLD 100% $QLD 100% $QQQ
Weak Momentum in a bull market, or deteriorating macro indicators Moderate Long (ML) 100% $UPRO 100% $UPRO 100% SSO 100% $SSO 100% $SPY
Weak momentum and possible bear market ongoing Cash / Short (C/S) 100% $PSQ 100% Cash 100% PSQ 100% Cash 100% Cash

 




      

 

 

 

Who is Hari Seldon

(from Wikipedia)

Hari Seldon is a fictional character in Isaac Asimov's Foundation series.

In his capacity as mathematics professor on the planet Trantor, Seldon develops psychohistory, an algorithmic science that allows him to predict the future in probabilistic terms. On the basis of his psychohistory he is able to predict the eventual fall of the Galactic Empire and to develop a means to shorten the millennia of chaos to follow. The significance of his discoveries lies behind his nickname "Raven" Seldon.

Our team decided to dedicate this work and this model to this amazing character, creating the even-more-fictional character of "Hari Seldon Trader".