Excel data analysis 6.0 (Buy Low/Sell High simlation)

If we bought a unit of an index at the end of each down day, and sold on an up day, how would it do?
In this post we look at the raw EV (statistically expected value) of this idea, with excel, in a theoretical trade where we toss all risk management principles out the window.

Buy Low/Sell High -> short gamma

This is an implicitly short gamma trade, since position accumulate with each unfavorable move, increasing risk. Therefore the payoff is concave, and requires significant effort in risk management to implement.

Historical data

We will look at IWM (iShares Russell 2000 Index) daily prices for the period 5/26/00 - 12/21/12. So after sorting the adjusted closing prices, we work out the close to close log returns.

Sample stats:
















Trade signals
We can create the next column for buy/sell signals with respect to the latest dIWM. Here I used a threshold of 0.09%, median dIWM of the past 12 years; i.e. if the latest dIWM is above 0.09%, we sell a unit of IWM, and vice versa, where buy signal = 1, sell signal = -1.

Buy/Sell price simulations
This is pretty straight forward, if the signal = 1,  "Buys" column gets the latest price, if not, blank. Same for the "Sells" column.

From the average prices, we can see that this concept had derived an average of $0.72/share with each round trip, before transaction costs. OK, so it has been +EV.

What about the risks of position accumulation?

Position simulation

I created a column "Position", and it's simply an accumulation of the signals.

We can graph it against the date:

So from this we can see that some trades require more work around risk management to implement in a practical manner.

Adding to Winning Directional Bets

Having seen many documentaries of professional (directional) traders talking about adding to winning positions, I had a little brainstorm of why this helps with profitability of trades. Here’re some reasons I’ve come up. 

So the basic idea is to only add to a winning position, and reduce position size as it moves negatively. We can already see how this replicates option delta, where the trade becomes essentially long Gamma, without risks around Vega, Theta, and other Greek uncertainties.

 

1)      It gives a convex payoff; it creates monster winning trades with respect to average losing trade size. (Convexity vs. Concavity)

2)      Having a convex payoff requires much lower win-rate for a trading strategy to be net profitable i.e. maintain a positive EV.  

3)      The trade would have a significantly reduced probability of taking relatively large losses. We’ve all heard stories of people blowing out from short Gamma trades, such as adding to losing positions, selling naked options.

4)     Counter-Intuitive, and we know going against the crowd is usually a GOOD thing in this industry.