This initial strategy was developed specifically for larger pools and is based on taking a moving average and deriving Bollinger Bands to create a projected active liquidity range.

Last update: Dec 02, 2022

Related tags

Overview

Gamma's Strategy One

This initial strategy was developed specifically for larger pools and is based on taking a moving average and deriving Bollinger Bands to create a projected active liquidity range.

View the simulation folder to view the historic behavior of high fee pools on Uniswap.

View the API-endpoint folder to see how we are collecting and deriving Bollinger Bands for the first strategy. This endpoint uses Uniswap v3 subgraph to analize current and historic pricing at a set interval. With the endpoint, the query returns all pools for a given asset and then pricing data with Bollinger Bands for any pair.

Here is a rendering of the Bollinger Bands for the USDT-ETH pair:

Owner

Gamma Strategies

An organization dedicated to funding ‘Active LP’ strategies and research. Applications now accepted.

GitHub Repository

Code for "Diversity can be Transferred: Output Diversification for White- and Black-box Attacks"

Output Diversified Sampling (ODS) This is the github repository for the NeurIPS 2020 paper "Diversity can be Transferred: Output Diversification for W

50 Dec 11, 2022

3.8% and 18.3% on CIFAR-10 and CIFAR-100

Wide Residual Networks This code was used for experiments with Wide Residual Networks (BMVC 2016) http://arxiv.org/abs/1605.07146 by Sergey Zagoruyko

1.2k Dec 29, 2022

PyTorch implementation for Partially View-aligned Representation Learning with Noise-robust Contrastive Loss (CVPR 2021)

2021-CVPR-MvCLN This repo contains the code and data of the following paper accepted by CVPR 2021 Partially View-aligned Representation Learning with

33 Nov 01, 2022

GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models

GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Model This repository is the official PyTorch implementation of GraphRNN, a graph gene

568 Dec 29, 2022

Toward Realistic Single-View 3D Object Reconstruction with Unsupervised Learning from Multiple Images (ICCV 2021)

Table of Content Introduction Getting Started Datasets Installation Experiments Training & Testing Pretrained models Texture fine-tuning Demo Toward R

42 Dec 05, 2022

PyTorch implementation of ShapeConv: Shape-aware Convolutional Layer for RGB-D Indoor Semantic Segmentation.

Shape-aware Convolutional Layer (ShapeConv) PyTorch implementation of ShapeConv: Shape-aware Convolutional Layer for RGB-D Indoor Semantic Segmentatio

82 Dec 29, 2022

PyExplainer: A Local Rule-Based Model-Agnostic Technique (Explainable AI)

PyExplainer PyExplainer is a local rule-based model-agnostic technique for generating explanations (i.e., why a commit is predicted as defective) of J

14 Nov 13, 2022

Physics-Aware Training (PAT) is a method to train real physical systems with backpropagation.

Physics-Aware Training (PAT) is a method to train real physical systems with backpropagation. It was introduced in Wright, Logan G. & Onodera, Tatsuhiro et al. (2021)1 to train Physical Neural Networ

230 Jan 05, 2023

Efficient Online Bayesian Inference for Neural Bandits

Efficient Online Bayesian Inference for Neural Bandits By Gerardo Durán-Martín, Aleyna Kara, and Kevin Murphy AISTATS 2022.

49 Dec 27, 2022

Official Implementation of DDOD (Disentangle your Dense Object Detector), ACM MM2021

Disentangle Your Dense Object Detector This repo contains the supported code and configuration files to reproduce object detection results of Disentan

51 Jan 07, 2023

Fedlearn支持前沿算法研发的Python工具库 | Fedlearn algorithm toolkit for researchers

FedLearn-algo Installation Development Environment Checklist python3 (3.6 or 3.7) is required. To configure and check the development environment is c

89 Nov 14, 2022

Model that predicts the probability of a Twitter user being anti-vaccination.

stylebody {text-align: justify}/style AVAXTAR: Anti-VAXx Tweet AnalyzeR AVAXTAR is a python package to identify anti-vaccine users on twitter. The

10 Sep 27, 2022

Code for "SRHEN: Stepwise-Refining Homography Estimation Network via Parsing Geometric Correspondences in Deep Latent Space"

SRHEN This is a better and simpler implementation for "SRHEN: Stepwise-Refining Homography Estimation Network via Parsing Geometric Correspondences in

1 Oct 28, 2022

This initial strategy was developed specifically for larger pools and is based on taking a moving average and deriving Bollinger Bands to create a projected active liquidity range.

Related tags

Overview

Gamma's Strategy One

Here is a rendering of the Bollinger Bands for the USDT-ETH pair:

Owner

Gamma Strategies

Code for "Diversity can be Transferred: Output Diversification for White- and Black-box Attacks"

3.8% and 18.3% on CIFAR-10 and CIFAR-100

PyTorch implementation for Partially View-aligned Representation Learning with Noise-robust Contrastive Loss (CVPR 2021)

GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models

Toward Realistic Single-View 3D Object Reconstruction with Unsupervised Learning from Multiple Images (ICCV 2021)

PyTorch implementation of ShapeConv: Shape-aware Convolutional Layer for RGB-D Indoor Semantic Segmentation.

PyExplainer: A Local Rule-Based Model-Agnostic Technique (Explainable AI)

Physics-Aware Training (PAT) is a method to train real physical systems with backpropagation.

Efficient Online Bayesian Inference for Neural Bandits

Official Implementation of DDOD (Disentangle your Dense Object Detector), ACM MM2021

Fedlearn支持前沿算法研发的Python工具库 | Fedlearn algorithm toolkit for researchers

Model that predicts the probability of a Twitter user being anti-vaccination.

Code for "SRHEN: Stepwise-Refining Homography Estimation Network via Parsing Geometric Correspondences in Deep Latent Space"

Utilities to bridge Canvas-generated course rosters with GitLab's API.

FB-tCNN for SSVEP Recognition

Official repo for QHack—the quantum machine learning hackathon

NLU Dataset Diagnostics

Official PyTorch implementation of the paper "TEMOS: Generating diverse human motions from textual descriptions"

Guiding evolutionary strategies by (inaccurate) differentiable robot simulators @ NeurIPS, 4th Robot Learning Workshop

Freecodecamp Scientific Computing with Python Certification; Solution for Challenge 2: Time Calculator