Viewing Option Market as Energy Fields
I am currently researching an extremely interesting niche.
If we consider an event lets say Take Profit (TP) onto an option lattice (Contracts x Timestep), not as a fixed label point, but passing it into a kernel to form signals that linger away, they are essentially echoes from a point. Certain areas of this lattice are major attractors with abundant TP events emit many echoes. At the end of the day we add up these echoes to form another lattice called the Gamma Lattice.
Mean Gamma Lattice across 250 0DTE options
This is how a randomized lattice looks like:
Depending the Kernel you use, this could take n arguments.
With these arguments you should be able to build the entire Lattice at any point which includes all events till the endpoint.
My custom built kernel takes in 3 arguments based on which I could build the entire Gamma Lattice at any point.
My current research point is: With this, I tried simulating the days in this fashion:
At point t, of current timestep, search top-K days with similar market features (I bundled the features based on their nature and ran a cosine similarity algorithm).
After you get the similar days, curate a Probability Density Function of these Kernel Parameters and run a small N-Monte Carlo to build N Gamma Lattices. Average them out and build percentile scores of Gamma scores based on past X expiries (no lookahead).
By reconstructing the lattice, you can predict based on these scores before hand.
I ran a small pilot run (because the computations gonna take hours) using Fable 5 with event being TP = 100% (Trades doubling) after careful audit of the logic:
This is just a small pilot run without any model learning and just Monte Carlo.
While this doesn't demonstrate any real market edge, its definitely interesting atleast for me.
Please let me know from a peer review perspective. Thank you.