Bianace Prediction Pytorch Model

Last update: Jul 20, 2022

Overview

Bianace Prediction Pytorch Model

Main Results

ETHUSDT from 2021-01-01 00:00:00 to 2021-12-01 00:00:00

Time interval	ROI
1d (Human)	2.74%
1d (Model)	125.05%
4h (Human)	36.86%
4h (Model)	300.37%
1h (Human)	37.55%
1h (Model)	393.66%

BTCUSDT from 2021-01-01 00:00:00 to 2021-12-01 00:00:00

Time interval	ROI
1d (Human)	3.11%
1d (Model)	30.08%
4h (Human)	18.30%
4h (Model)	30.67%
1h (Human)	19.79%
1h (Model)	32.07%

Getting started

Environment

Test OS: Ubuntu 16.04 LTS
Python version: 3.8

Preparation

Create folders.

mkdir images
mkdir checkpoints

Please run pip install –r requirements.txt to install the needed libraries.

Dataset

Binance Public Data

Clone the repo.
Follow the instruction to download required data.

# ETHUSDT
python download-kline.py -s ETHUSDT -startDate 2017-08-01 -endDate 2021-12-01

# BTCUSDT
python download-kline.py -s BTCUSDT -startDate 2017-08-01 -endDate 2021-12-01

It will download the required data as below. Unzip the zip files under the 1h, 4h and 1d directories.

binance_prediction_pytorch
    `-- binance-public-data
        `-- data
            `-- data
                `-- spot
                    |-- daily
                    `-- monthly
                        `-- klines
                            |-- ETHUSDT
                            `-- BTCUSDT

Then soft link the data directory to the repo root as below.

binance_prediction_pytorch
    |-- binance-public-data
    `-- data
        `-- spot
            |-- daily
            `-- monthly
                `-- klines
                    |-- ETHUSDT
                    `-- BTCUSDT

Experiments

Training

Run training and evaluation on ETHUSDT. It will store the checkpoints under checkpoints with ticker name and time interval if don't specify the checkpoint path with --ckpt.

# 1d
./run.sh ETHUSDT 1d

# 4h
./run.sh ETHUSDT 4h --sell_rate 0.03

# 1h
./run.sh ETHUSDT 1h --sell_rate 0.03

Run training and evaluation on BTCUSDT

# 1d
./run.sh BTCUSDT 1d

# 4h
./run.sh BTCUSDT 4h --sell_rate 0.03

# 1h
./run.sh BTCUSDT 1h --sell_rate 0.03

Inference

Specify the checkpoint path with eval mode to only do the inference.

./run.sh ETHUSDT 1h --sell_rate 0.03 --ckpt ${YOUR_CHECKPOINT_PATH} --eval

Bianace Prediction Pytorch Model

Related tags

Overview

Bianace Prediction Pytorch Model

Main Results

ETHUSDT from 2021-01-01 00:00:00 to 2021-12-01 00:00:00

BTCUSDT from 2021-01-01 00:00:00 to 2021-12-01 00:00:00

Getting started

Environment

Preparation

Dataset

Binance Public Data

Experiments

Training

Inference

Owner

RoyYang

Leveraging OpenAI's Codex to solve cornerstone problems in Music

Pytorch Implementation of Neural Analysis and Synthesis: Reconstructing Speech from Self-Supervised Representations

Code repository of the paper Neural circuit policies enabling auditable autonomy published in Nature Machine Intelligence

Rendering color and depth images for ShapeNet models.

This tutorial aims to learn the basics of deep learning by hands, and master the basics through combination of lectures and exercises

Parris, the automated infrastructure setup tool for machine learning algorithms.

Keyhole Imaging: Non-Line-of-Sight Imaging and Tracking of Moving Objects Along a Single Optical Path

Project looking into use of autoencoder for semi-supervised learning and comparing data requirements compared to supervised learning.

ZSL-KG is a general-purpose zero-shot learning framework with a novel transformer graph convolutional network (TrGCN) to learn class representation from common sense knowledge graphs.

PINN(s): Physics-Informed Neural Network(s) for von Karman vortex street

Rest API Written In Python To Classify NSFW Images.

YOLTv5 rapidly detects objects in arbitrarily large aerial or satellite images that far exceed the ~600×600 pixel size typically ingested by deep learning object detection frameworks

AoT is a system for automatically generating off-target test harness by using build information.

Simple Baselines for Human Pose Estimation and Tracking

Official implementation of "Synthetic Temporal Anomaly Guided End-to-End Video Anomaly Detection" (ICCV Workshops 2021: RSL-CV).

Accelerated NLP pipelines for fast inference on CPU and GPU. Built with Transformers, Optimum and ONNX Runtime.

Augmented Traffic Control: A tool to simulate network conditions

KITTI-360 Annotation Tool is a framework that developed based on python(cherrypy + jinja2 + sqlite3) as the server end and javascript + WebGL as the front end.

Forecasting Nonverbal Social Signals during Dyadic Interactions with Generative Adversarial Neural Networks

SubOmiEmbed: Self-supervised Representation Learning of Multi-omics Data for Cancer Type Classification