What Does The Average Trader Make? Bad Question.

I'm working through Mike Bellafiore's One Good Trade, so far pretty entertaining/informative, and he made the point that those who come with "What does the average trader make when they begin?" ought to get thrown out of interviews. The results of others have no effect on any individual trader. Like any highly competitive industry, there’s always uncertainty with future performance, regardless of how promising the present appears.

Good Trades are Difficult to Automate
Mentioned in Ramsey Theory and Curve Fitting, patterns can be found in any set of numbers. Therefore a purely quantitative and easily automate-able trading strategy isn’t sufficient for consistently profitable trading. A ton of additional research around economic, corporate conditions, sentiment numbers, and etc. remains necessary.

The resulting trades therefore require human decision making, where discipline, memory, decisiveness, and etc. can be assets, or liabilities.  
 
The Seinfeld Anecdote
George and Jerry are waiting to hear from NBC about how much the network will offer for their pilot for a show called “Jerry”. When NBC eventually offers Jerry and George $13,000 for both of them. 

George yells, “That’s insulting! Ted Danson makes $800,000 an episode.”
Jerry: “Oh, would you stop with the Ted Danson.”
George: “Well, he does… I can’t live knowing that Ted Danson makes that much more than me. Who’s he?”
Jerry: “He’s somebody.”
George: “What about me?”
Jerry: “You’re nobody.”
George: “Why him and not me?”
Jerry: “He’s good. You’re not.”
George: “I’m better than him.”
Jerry: “You’re worse. Much, much worse.”
(Bellafiore, 66)


Reference
 Bellafiore, M. (2010). One Good Trade. Hoboken, New Jersey: John Wiley & Sons, Inc.

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.

Floored: Into The Pit (Trading Documentary)

This documentary looks at the emotional, practical sides of traders at Chicago Mercantile Exchange; how decades ago some of the guys made money easily, the smart guys retired or moved on while others have struggled desperately against the transition from Open Outcry to Electronic Trading. Reality is nothing like the movies, "successful" floor traders can go broke, too; it only takes a slip of discipline, risk management, etc.

Some thing I've learned: Always think about the future, and contingency plans.

double click video area to make it full screen

Intraday Trading Options Instead of Underlying Products

I have grown to prefer intraday trading options instead of underlying stocks/futures because of the theoretically superior risk management; this allow us to circumvent need for Stop Loss Orders . While the math for options valuations remain somewhat complex, I believe options markets are close enough to “efficient” that a robust edge purely in direction (1 of many Option Greek uncertainties) would be good enough to make money. 

Trading Strategy

Long only ATM (At The Money)/OTM (Out of The Money) options, this gives the trades a limited downside while leaving the trader open for potentially much higher reward. Pretty simple right? Well there’s a bit more to it as risk management still depends on the core logic of the trade. Viability still depends on several risk-related areas. 

Risk Management- Theta

Off actual trading experience, I’ve noticed that theta does not kick in like clockwork as academic theory implies; in the short span within the trading hours option prices are very much dependent on supply/demand within the exchanges, i.e. Market Microstructure. So what does this mean for practical trading?

Trade logic of shorter average position would have a greater advantage. Don’t worry about it in the short run. On some days time value would go in your favor, and some days it wouldn’t. In the long run, the near-efficiency would equal to a pretty small negatively Statistically Expected Value (EV) of roughly the expected Theta * Average Position Time (hours) / 24.

Therefore, the directional edge must overcome Theta + other transaction costs. A theoretical edge that takes your attention is likely to have a much higher EV than the expected damage off Theta.  If not, move on and develop another trade.

Risk Management- Liquidity

If the options are relatively illiquid (such as the ASX200 Index Options), bid/offer spreads tend to be significantly wide, and if the underlying volatility remains relatively low, the inherent transaction cost could sky rocket and make the trade negative EV . Trade the Limit Order Books, blindly putting in Market Orders puts the trader in additional, undesirable risks. 

Risk Management- Time Value

Of course we want to buy the cheapest options possible and work the gamma. We can simply buy the ATM/OTM option(s) at the lowest points off the Volatility Smile.

EV for Hardcore Gamers

EV (statistical Expected Value) is very much practical for hardcore video gaming. I've always been a big video game buff, and lately it's been Dungeons & Dragons Online. With every battle, the player deals out damage, and takes some too. Of course to win battles on average, a +EV damage dealt/received must be achieved; and maximizing these values is where the math/fun lies.



Examples:
 
A Bastard Sword has a base damage die of 1d10 (10 sided die), and has a 1/20 chance of landing a critical attack for 2x damage. Assuming the dies are fair, the expected base damage is then:

[5.5 * 19 + 11 * 1] / 20 = 5.775/ swing of Bastard Sword

A Khopesh has a base damage die of 1d8 (8 sided die), and has a 1/20 chance of landing a critical attack for 3x damage. Assuming the dies are fair, the expected base damage is then: 

[4.5 * 19 + 4.5 * 3 * 1] / 20 = 4.95 / swing of Khopesh

So based on the minimum damage, the average player would assume that the Bastard is superior than the Khopesh over all. That is not so, the Khopesh is the most powerful single handed melee weapon in the game.

Due to the various damage bonuses, the seasoned player could significantly increase the Khopesh DPS (Damage Per Second) as its critical damage grows at a significantly faster rate than the Bastard Sword. So let's find the damage threshold where the Khopesh becomes superior than the Bastard Sword.

let x = damage bonus per swing of weapon


(5.5 + x)19 + (5.5 + x)2 = (4.5 + x)19 + (4.5 + x)3
     104.5 + 19x + 11 + 2x = 85.5 + 19x + 13.5 + 3x
     16.5 = x

The above is relatively simple to achieve in the game. So there's math for gaming.

Theory of Relativity concisely explained

Pretty cool video explaining Albert Einstein's most famous work. Particularly if you're wondering what the heck does E = MC^2 mean?