Movatterモバイル変換


[0]ホーム

URL:


Skip to content

Navigation Menu

Sign in
Appearance settings

Search code, repositories, users, issues, pull requests...

Provide feedback

We read every piece of feedback, and take your input very seriously.

Saved searches

Use saved searches to filter your results more quickly

Sign up
Appearance settings

Market-Derived Financial Sentiment Analysis: Context-Aware Language Models for Crypto Forecasting

NotificationsYou must be signed in to change notification settings

hamidm21/Revisit_FSA

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

corresponding code to the paper:https://arxiv.org/abs/2502.14897

Easy access

  • Neptune AI results for language model experiments can be foundhere including tables, confusion matrixes and more.
  • the main notebook on Kaggle called tweet-classification can be foundhere
  • the final implementation and optimization of Triple Barrier Labeling can be found in the notebooknext_day_prediction
Triple Barrier Labeling
  • backtesting experiments and results can be foundhere

How to run Experiments

use poetry to install the packages withpoetry install. for more information go to poetrydocs

then run withpython src/run.py [Experiment ID]

Project Folders and structure

Here are the folders and what they contain:

  • raw: unprocessed data
  • dataset: processed data
  • notebook: notebooks
  • src: contains the source code for experiments

Overall Architecture

Overall Scheme

Summary of the Backtesting results

Backtest Table

About

Market-Derived Financial Sentiment Analysis: Context-Aware Language Models for Crypto Forecasting

Topics

Resources

Stars

Watchers

Forks

Contributors5


[8]ページ先頭

©2009-2025 Movatter.jp