Pinned Repositories
Akumuli
Time-series database
goanda
A Golang wrapper for OANDAs v20 API
oandafeeder
Oanda Data Feeder for marketstore
ray_custom_loss
ray_tests
TrajectoryTracking
TripleBarrier
Zorro triple barrier test case
mg64ve's Repositories
mg64ve/oandafeeder
Oanda Data Feeder for marketstore
mg64ve/TripleBarrier
Zorro triple barrier test case
mg64ve/Akumuli
Time-series database
mg64ve/goanda
A Golang wrapper for OANDAs v20 API
mg64ve/ray_custom_loss
mg64ve/ray_tests
mg64ve/StatelessCartPoleMD
MultiDiscrete
mg64ve/TrajectoryTracking
mg64ve/alphasim
Minimalist backtester designed to integrate with the quant workflow.
mg64ve/Arch-Linux-Arm-M1
mg64ve/Bitwarden_Self_Host
Automatically setup and host a Bitwarden instance on a Raspberry Pi or other Linux Server
mg64ve/DarwinexLabs
Datasets, tools and more from Darwinex Labs - Prop Investing Arm & Quant Team @ Darwinex
mg64ve/dislocker
FUSE driver to read/write Windows' BitLocker-ed volumes under Linux / Mac OSX
mg64ve/django-docs
a simple example of document upload
mg64ve/dockeroandafeeder
Docker Oanda DATA Fedder
mg64ve/dwx-zeromq-connector
Wrapper library for algorithmic trading in Python 3, providing DMA/STP access to Darwinex liquidity via a ZeroMQ-enabled MetaTrader Bridge EA.
mg64ve/exchange-api-go
mg64ve/fecon235
Notebooks for financial economics. Keywords: Jupyter notebook pandas Federal Reserve FRED Ferbus GDP CPI PCE inflation unemployment wage income debt Case-Shiller housing asset portfolio equities SPX bonds TIPS rates currency FX euro EUR USD JPY yen XAU gold Brent WTI oil Holt-Winters time-series forecasting statistics econometrics
mg64ve/research
Contains all the Jupyter Notebooks used in our research
mg64ve/risk-premia-app
Simple Risk Premia Strategy
mg64ve/slow-momentum-fast-reversion
This code accompanies the the paper Slow Momentum with Fast Reversion: A Trading Strategy Using Deep Learning and Changepoint Detection (https://arxiv.org/pdf/2105.13727.pdf).
mg64ve/Stacked-Autoencoders-Financial-Trading
Deep Learning via Neural Network featured with Stacked Auto Encoders for Financial Trading Application in R
mg64ve/stockpredictionai
In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.
mg64ve/t6converter
Converter for Zorro
mg64ve/TMwR
Code and content for "Tidy Modeling with R"
mg64ve/trading-momentum-transformer
This code accompanies the the paper Trading with the Momentum Transformer: An Intelligent and Interpretable Architecture (https://arxiv.org/pdf/2112.08534.pdf).
mg64ve/wsae-lstm
implementation of WSAE-LSTM model as defined by Bao, Yue, Rao (2017)
mg64ve/ZorroRandomStrategyBenchmark
Repository to develop a benchmark to test a strategy against several random strategies