虚拟货币(BTC、ETH)炒币量化系统项目。在一版本的基础上加入了趋势判断

Overview

🎉 第二版本 🎉 (现货趋势网格)


介绍

在第一版本的基础上

趋势判断,不在固定点位开单,选择更优的开仓点位

优势: 🎉

  1. 简单易上手
  2. 安全(不用将api_secret告诉他人)

如何启动

  1. 修改app目录下的authorization文件
api_key='你的key'
api_secret='你的secret'

dingding_token = '申请钉钉群助手的token'   # 强烈建议您使用 (若不会申请,请加我个人微信)

如果你还没有币安账号: 注册页面交易返佣40%(系统返佣20%,id私发给我,微信每周返佣20%,长期有效)

免翻墙地址

申请api_key地址: 币安API管理页面

  1. 修改data/data.json配置文件 根据
{
    "runBet": {
        "next_buy_price": 350,      <- 下次开仓价   (你下一仓位买入价)
      
        "grid_sell_price": 375      <- 当前止盈价  (你的当前仓位卖出价)
        "step":0                    <- 当前仓位  (0:仓位为空)
    },
    "config": {
        "profit_ratio": 5,         <- 止盈比率      (卖出价调整比率。如:设置为5,当前买入价为100,那么下次卖出价为105)
        "double_throw_ratio": 5,   <- 补仓比率      (买入价调整比率。如:设置为5,当前买入价为100,那么下次买入价为95)
        "cointype": "ETHUSDT",     <- 交易对        (你要进行交易的交易对,请参考币安现货。如:BTC 填入 BTC/USDT)
        "quantity": [1,2,3]        <- 交易数量       (第一手买入1,第二手买入2...超过第三手以后的仓位均按照最后一位数量(3)买入)
        
    }
}

  1. 安装依赖包 ''' pip install requests json '''
  2. 运行主文件
# python eth-run.py 这是带有钉钉通知的主文件(推荐使用钉钉模式启动👍)

注意事项(一定要看)

  • 由于交易所的api在大陆无法访问(如果没有条件,可以使用api.binance.cc)
    • 您需要选择修改binanceAPI.py文件
# 修改为cc域名
class BinanceAPI(object):
    BASE_URL = "https://www.binance.cc/api/v1"
    FUTURE_URL = "https://fapi.binance.cc"
    BASE_URL_V3 = "https://api.binance.cc/api/v3"
    PUBLIC_URL = "https://www.binance.cc/exchange/public/product"
  • 如果您使用的交易所为币安,那么请保证账户里有足够的bnb

    • 手续费足够低
    • 确保购买的币种完整(如果没有bnb,比如购买1个eth,其中你只会得到0.999。其中0.001作为手续费支付了)
  • 第一版本现货账户保证有足够的U

  • 由于补仓比率是动态的,目前默认最小为5%。如果您认为过大,建议您修改文件夹data下的RunbetData.py文件

    def set_ratio(self,symbol):
        '''修改补仓止盈比率'''
        data_json = self._get_json_data()
        ratio_24hr = binan.get_ticker_24hour(symbol) #
        index = abs(ratio_24hr)

        if abs(ratio_24hr) >  **6** : # 今日24小时波动比率
            if ratio_24hr > 0 : # 单边上涨,补仓比率不变
                data_json['config']['profit_ratio'] =  **7** + self.get_step()/4  #
                data_json['config']['double_throw_ratio'] = **5**
            else: # 单边下跌
                data_json['config']['double_throw_ratio'] =  **7** + self.get_step()/4
                data_json['config']['profit_ratio'] =  **5**

        else: # 系数内震荡行情

            data_json['config']['double_throw_ratio'] = **5** + self.get_step() / 4
            data_json['config']['profit_ratio'] = **5** + self.get_step() / 4
        self._modify_json_data(data_json)

钉钉预警

如果您想使用钉钉通知,那么你需要创建一个钉钉群,然后加入自定义机器人。最后将机器人的token粘贴到authorization文件中的dingding_token 关键词输入:报警

钉钉通知交易截图

钉钉交易信息

25日实战收益

收益图

私人微信:欢迎志同道合的朋友一同探讨,一起进步。

交流群 wechat-QRcode 币圈快讯爬取群 wx号:findpanpan 麻烦备注来自github

钉钉设置教程

钉钉设置教程

免责申明

本项目不构成投资建议,投资者应独立决策并自行承担风险 币圈有风险,入圈须谨慎。

?? 风险提示:防范以“虚拟货币”“区块链”名义进行非法集资的风险。

Owner
幸福村的码农
努力中...
幸福村的码农
Simple Machine Learning Tool Kit

Getting started smltk (Simple Machine Learning Tool Kit) package is implemented for helping your work during data preparation testing your model The g

Alessandra Bilardi 1 Dec 30, 2021
Intel(R) Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application

Intel(R) Extension for Scikit-learn* Installation | Documentation | Examples | Support | FAQ With Intel(R) Extension for Scikit-learn you can accelera

Intel Corporation 858 Dec 25, 2022
ML-powered Loan-Marketer Customer Filtering Engine

In Loan-Marketing business employees are required to call the user's to buy loans of several fields and in several magnitudes. If employees are calling everybody in the network it is also very length

Sagnik Roy 13 Jul 02, 2022
Hierarchical Time Series Forecasting using Prophet

htsprophet Hierarchical Time Series Forecasting using Prophet Credit to Rob J. Hyndman and research partners as much of the code was developed with th

Collin Rooney 131 Dec 02, 2022
Classification based on Fuzzy Logic(C-Means).

CMeans_fuzzy Classification based on Fuzzy Logic(C-Means). Table of Contents About The Project Fuzzy CMeans Algorithm Built With Getting Started Insta

Armin Zolfaghari Daryani 3 Feb 08, 2022
Test symmetries with sklearn decision tree models

Test symmetries with sklearn decision tree models Setup Begin from an environment with a recent version of python 3. source setup.sh Leave the enviro

Rupert Tombs 2 Jul 19, 2022
Tools for mathematical optimization region

Tools for mathematical optimization region

林景 15 Nov 30, 2022
Diabetes Prediction with Logistic Regression

Diabetes Prediction with Logistic Regression Exploratory Data Analysis Data Preprocessing Model & Prediction Model Evaluation Model Validation: Holdou

AZİZE SULTAN PALALI 2 Oct 23, 2021
A chain of stores, 10 different stores and 50 different requests a 3-month demand forecast for its product.

Demand-Forecasting Business Problem A chain of stores, 10 different stores and 50 different requests a 3-month demand forecast for its product.

Ayşe Nur Türkaslan 3 Mar 06, 2022
💀mummify: a version control tool for machine learning

mummify is a version control tool for machine learning. It's simple, fast, and designed for model prototyping.

Max Humber 43 Jul 09, 2022
Unofficial pytorch implementation of the paper "Context Reasoning Attention Network for Image Super-Resolution (ICCV 2021)"

CRAN Unofficial pytorch implementation of the paper "Context Reasoning Attention Network for Image Super-Resolution (ICCV 2021)" This code doesn't exa

4 Nov 11, 2021
My capstone project for Udacity's Machine Learning Nanodegree

MLND-Capstone My capstone project for Udacity's Machine Learning Nanodegree Lane Detection with Deep Learning In this project, I use a deep learning-b

Michael Virgo 407 Dec 12, 2022
BigDL: Distributed Deep Learning Framework for Apache Spark

BigDL: Distributed Deep Learning on Apache Spark What is BigDL? BigDL is a distributed deep learning library for Apache Spark; with BigDL, users can w

4.1k Jan 09, 2023
The Emergence of Individuality

The Emergence of Individuality

16 Jul 20, 2022
ml4ir: Machine Learning for Information Retrieval

ml4ir: Machine Learning for Information Retrieval | changelog Quickstart → ml4ir Read the Docs | ml4ir pypi | python ReadMe ml4ir is an open source li

Salesforce 77 Jan 06, 2023
Basic Docker Compose for Machine Learning Purposes

Docker-compose for Machine Learning How to use: cd docker-ml-jupyterlab

Chris Chen 1 Oct 29, 2021
Implemented four supervised learning Machine Learning algorithms

Implemented four supervised learning Machine Learning algorithms from an algorithmic family called Classification and Regression Trees (CARTs), details see README_Report.

Teng (Elijah) Xue 0 Jan 31, 2022
Automated Machine Learning Pipeline for tabular data. Designed for predictive maintenance applications, failure identification, failure prediction, condition monitoring, etc.

Automated Machine Learning Pipeline for tabular data. Designed for predictive maintenance applications, failure identification, failure prediction, condition monitoring, etc.

Amplo 10 May 15, 2022
An AutoML survey focusing on practical systems.

This project is a community effort in constructing and maintaining an up-to-date beginner-friendly introduction to AutoML, focusing on practical systems. AutoML is a big field, and continues to grow

AutoGOAL 16 Aug 14, 2022
#30DaysOfStreamlit is a 30-day social challenge for you to build and deploy Streamlit apps.

30 Days Of Streamlit 🎈 This is the official repo of #30DaysOfStreamlit — a 30-day social challenge for you to learn, build and deploy Streamlit apps.

Streamlit 53 Jan 02, 2023