If you only ever look for edges within the parameter space others have defined as reasonable, you'll never find the really good ideas. Question what others have defined as impossible or unviable.
More fucking around and finding out. Less parroting established "known knowns".
1. Think of 2-5 stylized facts in your trading market/sport market etc. that everyone accepts as "roughly true".
2. Start from scratch and prove to yourself with data why these things are roughly true - understand why that consensus exists.
...continued 👇
3. Now take inventory of all the necessary assumptions that are required for the particular consensus stylized fact to remain buoyant. These are your potential attack vectors.
4. Start testing them out, looking for places or times that the assumptions might fail.
🫡🤫
There's a guy on twitter (can't remember the handle) with a brilliant tagline: "all models are wrong but you're not even trying"
So to point that back at this thread I'll end with:
Edges are tough to find, but how hard are you really trying?
Have a great Easter weekend.
"...but Andrew, what if I try and it doesn't work?"
That's going to happen. A lot.
With every failure, you'll learn something that makes your ideas slightly better.
The reward isn't defined by the success %, but the payoff of finding something others don't think is viable.
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Parrondo's Paradox refers to the concept that 2 losing games can sometimes be combined to producing a winning result. This strategy might not be a perfect example, but I think you'll see what I mean.
1/n
Backstory: back in 2014, Danrules24 posted on the covers SB forum about a parlay martingale strategy applied to both MLB and NHL. On its surface it appeared to be completely degenerate - but the results over a few seasons were intriguing.
2/n
The strategy takes the two strongest road teams and parlays them together, then sequences bets in a martingale structure for 8 bets. The betting sequence itself comes from a book called "12 Ways to Beat Your Bookie" by Tony Stoffo (basically a book of martingale strats).
3/n
Despite the fact that most sports data is relatively stationary, most professional bettors know there is a certain seasonality to major sports.
As an example, the NBA betting season can be broadly broken down into 4 components...
1/n
1. Early Season - First month to 1.5 months.
Lineups and minutes are being worked out by coaches. Team chemistry + global offense/defense too. Bottom up (player focused) modelling approaches tend to get an early step on the uncertainty.
2/n
2. "Regular Season" - November to Trade Deadline.
Standard operating procedure here. Most models are built on this period (and subsequently suffer during the other periods). Both top-down and bottom-up approaches can work. Teams players roughly playing to expectation
3/n
In sports betting, there are situations where using a known market expectation with a slightly better distributional assumption can locate small edges in various markets. 2 years ago, I thought a similar approach might yield modest results in the options trading space. 1/n
This idea came to mind when initially studying the Black Scholes model and so my first options related idea was trying to use a more realistic distribution to accurately model log returns. While doing some initial analysis I noticed that returns aren’t normally distributed. 2/n
After thinking about the problem and fitting a few test distributions, the Laplace distribution proved a much better empirical fit that also appeared to explain two phenomena observed by traders in real life:
3/n
In law school, we were taught to prepare case briefs/ exam answers using a structure called FIRAC. Facts, Issue, Ratio, Analysis, Conclusion. After spending some time this weekend going over some of @therobotjames trading threads, I sketched out a structure for trading edges.
1/n