Thesis Code

Thesis Details

Title: An Empirical Evaluation of Neural Process Meta-Learners for Financial Forecasting
Author: Kevin Patel

dump basic preprocessed data: data/Preproc/run.sh
ANP volatiltiy model nowcast hyperparam tuning over train/val: run-exp-000.bash
ANP volatiltiy model forecast hyperparam tuning over train/val: run-exp-001.bash
ANP volatiltiy model forecast (fixed hyperparams) over train/val/test: run-exp-002.bash
simulated trading model over train/val: model/TradingModel/run.sh
simulated trading model over train/val/test: model/TradingModel/run-final.sh
basic smoketests: smoke-*.sh

This branch, master/data-frd-minutely, has all the code submitted for the thesis
The branch data-tr-hourly is for a previous iteration of the project
Unfortunately raw data used to generate the results was purchased from FirstRate Data LLC, so it cannot be shared

has all the raw and preprocessed data
preprocessing is done from Julia scripts/Pluto.jl notebooks in the Preproc package
preprocessed data can be loaded in Python via a PytorchLightning DataModule

{model, np}_util.py contain Pytorch model classes
pl_{generic, np}.py are LightningModule classes that wrap Pytorch models for PytorchLightning
expo.py is the optuna hyperparameter optimizing runner
expm.py is the manual/fixed hyperparameter experiment runner
the model/exp-<proc>-<data> directories contain completed realized volatility trial results
hyperparameter sets are stored on disk in json files

model/TradingModel contains the julia package with the trading model code
Trading model uses features/predictions dumped from the last realized volatiltiy models
model/tm-<proc>-<data> directories contain completed trading model results

common_util.py contains functions, data structures, and classes used throughout the project
Much of the code in here is not used anymore (previous iterations of project)

Most code is arranged in python subpackages, <subpackage>/common.py contains common constants, defaults, and utilities
Subpackage scripts are run by running them as modules (using the -m flag), see the shell scripts at the project root for examples