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

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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

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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

Vehicle detection using machine learning and computer vision techniques for Udacity's Self-Driving Car Engineer Nanodegree.

Code for CPM-2 Pre-Train

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TensorFlow GNN is a library to build Graph Neural Networks on the TensorFlow platform.

pip install python-office

SatelliteNeRF - PyTorch-based Neural Radiance Fields adapted to satellite domain

Aircraft design optimization made fast through modern automatic differentiation

Heat transfer problemas solved using python

PyTorch implementation of DirectCLR from paper Understanding Dimensional Collapse in Contrastive Self-supervised Learning

🌾 PASTIS 🌾 Panoptic Agricultural Satellite TIme Series

Modelisation on galaxy evolution using PEGASE-HR

An efficient PyTorch library for Global Wheat Detection using YOLOv5. The project is based on this Kaggle competition Global Wheat Detection (2021).

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