dshaver

dshaver's Stars

• quickfix/quickfix

  QuickFIX C++ Fix Engine Library

  Language:C++1.6k763

• enewhuis/liquibook

  Modern C++ order matching engine

  Language:C++1.2k387

• CompSquad/hawkes-clustering

  Clustering with hawks processes.

  Language:Jupyter Notebook31

• fiquant/marketsimulator

  The project simulates a generic agent based market model. The aim is to explore intimately, by simulation, the process of price formation and the market microstructure.

  Language:Python6327

• treverson/opentrade-1

  An open source OEMS, and algorithmic trading platform in modern C++

  Language:C++5147

• stmorse/hawkes

  Python class for generation and parameter estimation of multivariate Hawkes processes

  Language:Python16448

• JackBenny39/pyziabm

  Zero Intelligence Agent-Based Model of Modern Limit Order Book

  Language:Python5121

• rlabbe/Kalman-and-Bayesian-Filters-in-Python

  Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.

  Language:Jupyter Notebook17k4.2k

• balzer82/Kalman

  Some Python Implementations of the Kalman Filter

  Language:Jupyter Notebook1.1k366

• alanfu/GammaScalping

  Gamma Scalping Trading Strategies

  Language:C++185

• jasonstrimpel/volatility-trading

  A complete set of volatility estimators based on Euan Sinclair's Volatility Trading

  Language:Python1.6k378

• Jackmzw/Price_Prediction_LOB

  Deep learning for price movement prediction using high frequency limit order data

  Language:Python3719

• fffaraz/awesome-cpp

  A curated list of awesome C++ (or C) frameworks, libraries, resources, and shiny things. Inspired by awesome-... stuff.

  60.8k7.9k

• morganstanley/Xpedite

  A non-sampling profiler purpose built to measure and optimize performance of C++ low latency/real time systems

  Language:Python15842

• comeh/LOB_AlgoTrading

  Some codes used for the numerical examples proposed in https://hal.archives-ouvertes.fr/hal-01514987v2 and https://arxiv.org/abs/1705.01446

  Language:C++2011

• ajtulloch/quantcup-orderbook

  Fast C++ adaptation of the QuantCup (http://www.quantcup.org/) limit order book.

  Language:C16572

• rakshitha123/Localised_Ensembles

  This repository contains the experiments of our paper titled, "Ensembles of Localised Models for Time Series Forecasting" which is online available at: https://doi.org/10.1016/j.knosys.2021.107518. In this work, we study how ensembleing techniques can be used to solve the localisation issues of global forecasting models.

  Language:Python141

• Zymrael/wattnet-fx-trading

  WATTNet: Learning to Trade FX with Hierarchical Spatio-Temporal Representations of Highly Multivariate Time Series

  Language:Jupyter Notebook7212

• shimonanarang/pair-trading

  Statistical arbitrage of cointegrating currencies with pair trading where the signal for the next day is predicted using LSTM

  Language:Python5013

• sidorof/Thymus-timeseries

  An intuitive library tracking dates and timeseries in common using numpy arrays. -- https://sidorof.github.io/Thymus-timeseries/

  Language:Python181

• xhshenxin/Micro_Price

  Language:Jupyter Notebook4621

• bashtage/arch

  ARCH models in Python

  Language:Python1.3k253

• cod3licious/autofeat

  Linear Prediction Model with Automated Feature Engineering and Selection Capabilities

  Language:Python50662

• cyrilou242/mockseries

  Easy and intuitive generation of synthetic timeseries for Python.

  Language:Jupyter Notebook354

• tr8dr/tseries-patterns

  trend / momentum and other patterns in financial timeseries

  Language:Jupyter Notebook24571

• facebookresearch/Kats

  Kats, a kit to analyze time series data, a lightweight, easy-to-use, generalizable, and extendable framework to perform time series analysis, from understanding the key statistics and characteristics, detecting change points and anomalies, to forecasting future trends.

  Language:Python5.2k549

• stfbnc/fathon

  python package for DFA (Detrended Fluctuation Analysis) and related algorithms

  Language:Cython12022

• jessgess/deep-learning-for-order-book-price-and-movement-predictions

  Limit Order Book data analysis and modeling using LSTM network

  Language:Jupyter Notebook13046

• imhgchoi/ARIMA-LSTM-hybrid-corrcoef-predict

  Applied an ARIMA-LSTM hybrid model to predict future price correlation coefficients of two assets

  Language:Jupyter Notebook405128

• cholg2003/myBooks-1

  25