1. The idea is hard to backtest 2. The data is hard to acquire 3. A known input can be transformed into something novel or hard to compute. 5. The idea is known, but has only been applied to one specific area or time horizon.
1/n
6. The idea is known, but only applied in a different field 7. The idea attacks a market with low liquidity, which dissuades larger market participants from competing. 8. The idea attacks an excess of liquidity. 9. The idea attacks a behavioural glitch/ logical fallacy.
2/n
10. The idea uses information from highly liquid markets to attack smaller liquidity markets. 11. The idea takes advantage of more or less granularity that the aggregate market. 12. The idea identifies areas where unsophisticated market participants are particularly active.
/fin
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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