Alternatives and similar repositories for ListFold

Users that are interested in ListFold are comparing it to the libraries listed below

• kieranjwood / x-trend
  X-Trend: Few-Shot Learning Patterns in Financial Time-Series for Trend-Following Strategies
  ☆86Updated last year
• maxlamberti / time-series-momentum
  🚂💨 Deep Momentum Networks for Time Series Strategies
  ☆124Updated 5 years ago
• YuxuanXiu / Financial-Time-Series-Trend-Labeling
  This is a non-official implementation of the trend labeling method proposed in the paper "A Labeling Method for Financial Time Series Pre…
  ☆49Updated 10 months ago
• JDE65 / CustomLoss
  Custom Loss functions for asset return prediction with deep learning regression
  ☆36Updated 3 years ago
• jiangtiantu / FactorMining
  基于基因表达式规划算法的因子挖掘
  ☆34Updated 4 years ago
• yuxiangalvin / DeepLOB-Model-Implementation-Project
  This repo contains some codes and outputs of my implementation of DeepLOB model.
  ☆90Updated 4 years ago
• MingjieWang0606 / Kaggle-Jane-Street-AE-MLP-xgb-TOP1
  1th: Kaggle Jane Street Market Prediction: AE MLP+xgb
  ☆49Updated 3 years ago
• Morgansy / Genetic-Alpha
  A genetic programming algorithm used for generating alpha factors in the multi-factor investment strategy
  ☆66Updated 4 years ago
• LeonardoBerti00 / Axial-LOB-High-Frequency-Trading-with-Axial-Attention
  Pytorch implementation of Axial-LOB from 'Axial-LOB: High-Frequency Trading with Axial Attention'
  ☆59Updated 2 years ago
• Eric991005 / Multitask-Stockformer
  A study on volume-price factor stock selection model based on wavelet transform and multitask self-attention network
  ☆95Updated 6 months ago
• goiter / CoCPC
  ☆54Updated 3 years ago
• rian-dolphin / stock-embeddings
  Code relating to the paper - Stock Embeddings: Learning Distributed Representations for Financial Assets
  ☆80Updated 11 months ago
• zcakhaa / Multi-Horizon-Forecasting-for-Limit-Order-Books
  ☆208Updated 2 years ago
• hudson-and-thames / meta-labeling
  Code base for the meta-labeling papers published with the Journal of Financial Data Science
  ☆92Updated 2 years ago
• RichardS0268 / CNN-for-Trading
  Reimplementation of Paper: (Re-)Imag(in)ing Price Trends
  ☆67Updated 2 months ago
• lorenzolucchese / deepOBs
  The Short-Term Predictability of Returns in Order Book Markets: A Deep Learning Perspective.
  ☆55Updated 2 years ago
• YuminQuant / YuminQuant
  Backtest Framework designed by YuminQuant&Yumin.
  ☆19Updated last year
• EduardoRamosP / MultiTransformer
  Transformer and MultiTransformer layers for stock volatility forecasting purposes
  ☆73Updated 4 years ago
• szy1900 / autoAlpha
  The source code for the paper
  ☆24Updated 2 years ago
• rosie068 / BARRA_risk
  A risk evaluation program that follows BARRA's CNE6 and USE4 risk model to predict the risk and distribution of factors in a portfolio. C…
  ☆71Updated 4 years ago
• joelowj / mtl-tsmom
  Multi Task Learning Time Series Momentum
  ☆24Updated last year
• ytliu74 / FactorVAE
  Reproduce AAAI22-FactorVAE
  ☆68Updated 2 years ago
• matteoprata / LOBCAST
  LOBCAST is a Python-based open-source framework for stock market trend forecasting using Limit Order Book (LOB) data. 🤖📈
  ☆114Updated last year
• WongYatChun / Advances-in-Financial-Machine-Learning
  Notes on Advances in Financial Machine Learning
  ☆82Updated 6 years ago
• NikoLee18 / silly_HOHMM
  A Higher-order HMM with EM algo.
  ☆16Updated 3 years ago
• KurochkinAlexey / SpatioTemporalMomentum
  ☆22Updated last year
• MingjieWang0606 / MTMD-Public
  The official implementation of the paper "MTMD: Multi-Scale Temporal Memory Learning and Efficient Debiasing Framework for Stock Trend Fo…
  ☆33Updated 9 months ago
• AutoAlpha2022 / AutoAlpha
  通过遗传算法、强化学习来自动选择高频因子
  ☆23Updated 2 years ago
• nymath / torchqtm
  TorchQuantum is a backtesting framework that integrates the structure of PyTorch and WorldQuant's Operator for efficient quantitative fin…
  ☆48Updated 2 years ago
• kousik97 / Order-Execution-Strategy
  Literature survey of order execution strategies implemented in python
  ☆43Updated 5 years ago