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> For the rest I do financial modeling (which has a fair overlap with the cryptanalysis from a signal processing perspective anyway) What kind of modeling are

> For the rest I do financial modeling (which has a fair overlap with the cryptanalysis from a signal processing perspective anyway) What kind of modeling are you doing with a beast of a setup like that?

dsacco 10y ago
The approach has a few tiers: 1. Use one stock that I believe I have unique and actionable data on as the input barometer. Take it as a premise that this stock is currently mispriced by the market, and I have an idea of what it should be. We'll call this the primary stock. 2. Find all stocks that have a consistent positive or negative correlation with the input stock, (do they share volatility?), at different significance levels. (This is not particularly hard, it's just getting accurate data with real granularity that is the challenge.) These are the corollary stocks. If there is a calendar event upcoming, I restrict the backtest to the values of other stocks on similar days. 3. For each correlated stock found, calculate an options model with a new terminal price distribution (as opposed to what is currently priced by market consensus). 4. Simulate different options trading strategies and their outcomes across different projected prices for each stock and present the ones with the highest probability of success versus return. At a minimum, the primary and corollary underlyings should have a large enough misprice that trading them can be profitable across the bid/ask spread. So for example, a $1 increase or decrease will not generally be meaningful enough to make the trade, especially if there is a lot of volatility priced in (such as around earnings releases). You'll notice that there is nothing truly sophisticated about any of this. It's just a bunch of computation used to extrapolate the best leverage available using derivatives. The core thesis involves having material data that the rest of the market has not yet priced in, and I don't often have that (or rather, enough of a directional conclusion based on it). When I do, I like to do this because 1) I don't trust most profitability calculators which assume constant volatility and 2) it maximizes potential results by not just looking at the primary stock I know about. tl;dr: Assuming one stock is mispriced, do a bunch of derivative re-pricing and choose an optimal one.

dsacco 10y ago

The approach has a few tiers: 1. Use one stock that I believe I have unique and actionable data on as the input barometer. Take it as a premise that this stock is currently mispriced by the market, and I have an idea of what it should be. We'll call this the primary stock. 2. Find all stocks that have a consistent positive or negative correlation with the input stock, (do they share volatility?), at different significance levels. (This is not particularly hard, it's just getting accurate data with real granularity that is the challenge.) These are the corollary stocks. If there is a calendar event upcoming, I restrict the backtest to the values of other stocks on similar days. 3. For each correlated stock found, calculate an options model with a new terminal price distribution (as opposed to what is currently priced by market consensus). 4. Simulate different options trading strategies and their outcomes across different projected prices for each stock and present the ones with the highest probability of success versus return. At a minimum, the primary and corollary underlyings should have a large enough misprice that trading them can be profitable across the bid/ask spread. So for example, a $1 increase or decrease will not generally be meaningful enough to make the trade, especially if there is a lot of volatility priced in (such as around earnings releases). You'll notice that there is nothing truly sophisticated about any of this. It's just a bunch of computation used to extrapolate the best leverage available using derivatives. The core thesis involves having material data that the rest of the market has not yet priced in, and I don't often have that (or rather, enough of a directional conclusion based on it). When I do, I like to do this because 1) I don't trust most profitability calculators which assume constant volatility and 2) it maximizes potential results by not just looking at the primary stock I know about. tl;dr: Assuming one stock is mispriced, do a bunch of derivative re-pricing and choose an optimal one.