matteoprata / LOBCAST
LOBCAST is a Python-based open-source framework for stock market trend forecasting using Limit Order Book (LOB) data. π€π
β89Updated 6 months ago
Related projects β
Alternatives and complementary repositories for LOBCAST
- A new formulaic alpha mining framework for quantitative investmentβ80Updated 2 months ago
- This repo contains some codes and outputs of my implementation of DeepLOB model.β78Updated 3 years ago
- This repository is for the demonstration of our work, "Market Making with Deep Reinforcement Learning from Limit Order Books"β50Updated last year
- ππ¨ Deep Momentum Networks for Time Series Strategiesβ106Updated 4 years ago
- β80Updated 4 months ago
- Implementation of various deep learning models for limit order book. DeepLOB (Zhang et al., 2018), TransLOB (Wallbridge, 2020), DeepFolioβ¦β90Updated last year
- Feature engineering of a Limit Order Book. Extraction of features from a LOB in order to analyse the behaviour of trade market.β191Updated 2 years ago
- β24Updated last year
- β70Updated 4 months ago
- TorchQuantum is a backtesting framework that integrates the structure of PyTorch and WorldQuant's Operator for efficient quantitative finβ¦β33Updated last year
- pseudocode and algorithms for the paper "Alpha$^2$: Discovering Logical Formulaic Alphas using Deep Reinforcement Learning"β114Updated 4 months ago
- β186Updated last year
- Fintech literature, including journal, conference, book and useful linksβ88Updated 2 years ago
- A genetic programming algorithm used for generating alpha factors in the multi-factor investment strategyβ56Updated 3 years ago
- X-Trend: Few-Shot Learning Patterns in Financial Time-Series for Trend-Following Strategiesβ61Updated 8 months ago
- High-frequency statistical arbitrageβ149Updated last year
- β27Updated 6 months ago
- Using tabular and deep reinforcement learning methods to infer optimal market making strategiesβ167Updated last year
- Pytorch implementation of Axial-LOB from 'Axial-LOB: High-Frequency Trading with Axial Attention'β48Updated last year
- We release `LOBFrame', a novel, open-source code base which presents a renewed way to process large-scale Limit Order Book (LOB) data.β125Updated 5 months ago
- Our codebase trials provide an implementation of the Select and Trade paper, which proposes a new paradigm for pair trading using hierarβ¦β112Updated last year
- CS7641 Team projectβ87Updated 4 years ago
- The Short-Term Predictability of Returns in Order Book Markets: A Deep Learning Perspective.β37Updated last year
- This repository contains the main code used in the paper "Deep Reinforcement Learning for Market Making Under a Hawkes Process-Based Limiβ¦β51Updated last year
- Notes on Advances in Financial Machine Learningβ76Updated 5 years ago
- High Frequency Analysis Based On Level-2 DataοΌLimit Order Book& Transaction Data)β85Updated 6 months ago
- We introduce the first end-to-end Deep Reinforcement Learning based framework for active high frequency trading.β53Updated 11 months ago
- This forked repo additionally includes our DoubleAdapt (KDD'23) and MASTER (AAAI'24) for re-experiment.β103Updated 7 months ago
- Pytorch implementation of TransLOB from Transformer for limit order booksβ18Updated last year
- Stock factor mining with CNN and GRU.β41Updated last year