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An Empirical Evaluation of Neural Process Meta-Learners for Financial Forecasting
Python 84.14%
Shell 0.66%
Julia 15.20%
thesis-code's Introduction
- 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
- has all the model code (Pytorch models wrapped in PytorchLightning)
{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
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