zcakhaa / DeepLOB-Deep-Convolutional-Neural-Networks-for-Limit-Order-BooksLinks
This jupyter notebook is used to demonstrate our recent work, "DeepLOB: Deep Convolutional Neural Networks for Limit Order Books", published in IEEE Transactions on Singal Processing. We use FI-2010 dataset and present how model architecture is constructed here. The FI-2010 is publicly avilable and interested readers can check out their paper.
☆494Updated 4 years ago
Alternatives and similar repositories for DeepLOB-Deep-Convolutional-Neural-Networks-for-Limit-Order-Books
Users that are interested in DeepLOB-Deep-Convolutional-Neural-Networks-for-Limit-Order-Books are comparing it to the libraries listed below
Sorting:
- This code accompanies the the paper Trading with the Momentum Transformer: An Intelligent and Interpretable Architecture (https://arxiv.o…☆558Updated last year
- This code accompanies the the paper Slow Momentum with Fast Reversion: A Trading Strategy Using Deep Learning and Changepoint Detection (…☆255Updated 2 years ago
- ☆203Updated 2 years ago
- Deep Learning Statistical Arbitrage☆240Updated 2 years ago
- Feature engineering of a Limit Order Book. Extraction of features from a LOB in order to analyse the behaviour of trade market.☆233Updated 3 years ago
- experiments with pair trading☆318Updated 9 months ago
- Code implementation of the Quantigic 101 Formulaic Alphas☆578Updated 6 years ago
- ☆416Updated 4 years ago
- Benchmark Dataset of Limit Order Book in China Markets☆210Updated 4 years ago
- GPU-accelerated Factors analysis library and Backtester☆739Updated 5 months ago
- High frequency trading (HFT) framework built for futures using machine learning and deep learning techniques☆499Updated 2 years ago
- We release `LOBFrame', a novel, open-source code base which presents a renewed way to process large-scale Limit Order Book (LOB) data.☆189Updated last year
- Collection of algorithms for online portfolio selection☆829Updated this week
- Market Making via Reinforcement Learning☆335Updated 5 years ago
- Using tabular and deep reinforcement learning methods to infer optimal market making strategies☆218Updated 2 years ago
- An workflow in factor-based equity trading, including factor analysis and factor modeling. For well-established factor models, I implemen…☆372Updated 7 years ago
- Intelligently optimizes technical indicators and optionally selects the least intercorrelated for use in machine learning models☆451Updated last year
- Implementation of various deep learning models for limit order book. DeepLOB (Zhang et al., 2018), TransLOB (Wallbridge, 2020), DeepFolio…☆121Updated 2 years ago
- ☆337Updated 2 years ago
- Limit Order Book data analysis and modeling using LSTM network☆138Updated 6 years ago
- Implementation of Reinforcement Learning in Pair Trading☆10Updated 2 months ago
- All the answers for exercises from Advances in Financial Machine Learning by Dr Marco Lopez de Parodo.☆745Updated last year
- Avellaneda-Stoikov HFT market making algorithm implementation☆583Updated 2 years ago
- Experimental code supporting the results presented in the scientific research paper entitled "An Application of Deep Reinforcement Learni…☆207Updated 3 years ago
- Performance analysis of predictive (alpha) stock factors☆472Updated last week
- Mostly experiments based on "Advances in financial machine learning" book☆556Updated 4 years ago
- GARCH and Multivariate LSTM forecasting models for Bitcoin realized volatility with potential applications in crypto options trading, hed…☆279Updated 4 years ago
- PyTorch-based framework for Deep Hedging☆316Updated last year
- High Frequency Market Making☆580Updated last year
- 🚂💨 Deep Momentum Networks for Time Series Strategies☆124Updated 5 years ago