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
Solve automatic numerical differentiation problems in one or more variables.

numdifftools The numdifftools library is a suite of tools written in _Python to solve automatic numerical differentiation problems in one or more vari

Per A. Brodtkorb 181 Dec 16, 2022
Markov bot - A Writing bot based on Markov Chain for Data Structure Lab

基于马尔可夫链的写作机器人 前端 用html/css完成 Demo展示(已给出文本的相应展示) 用户提供相关的语料库后训练的成果 后端 要完成的几个接口 解析文

DysprosiumDy 9 May 05, 2022
Python library for multilinear algebra and tensor factorizations

scikit-tensor is a Python module for multilinear algebra and tensor factorizations

Maximilian Nickel 394 Dec 09, 2022
customer churn prediction prevention in telecom industry using machine learning and survival analysis

Telco Customer Churn Prediction - Plotly Dash Application Description This dash application allows you to predict telco customer churn using machine l

Benaissa Mohamed Fayçal 3 Nov 20, 2021
A repository of PyBullet utility functions for robotic motion planning, manipulation planning, and task and motion planning

pybullet-planning (previously ss-pybullet) A repository of PyBullet utility functions for robotic motion planning, manipulation planning, and task and

Caelan Garrett 260 Dec 27, 2022
A python library for easy manipulation and forecasting of time series.

Time Series Made Easy in Python darts is a python library for easy manipulation and forecasting of time series. It contains a variety of models, from

Unit8 5.2k Jan 04, 2023
GRaNDPapA: Generator of Rad Names from Decent Paper Acronyms

Generator of Rad Names from Decent Paper Acronyms

264 Nov 08, 2022
Decision Tree Regression algorithm implemented on Python from scratch.

Decision_Tree_Regression I implemented the decision tree regression algorithm on Python. Unlike regular linear regression, this algorithm is used when

1 Dec 22, 2021
Titanic Traveller Survivability Prediction

The aim of the mini project is predict whether or not a passenger survived based on attributes such as their age, sex, passenger class, where they embarked and more.

John Phillip 0 Jan 20, 2022
Uses WiFi signals :signal_strength: and machine learning to predict where you are

Uses WiFi signals and machine learning (sklearn's RandomForest) to predict where you are. Even works for small distances like 2-10 meters.

Pascal van Kooten 5k Jan 09, 2023
The Emergence of Individuality

The Emergence of Individuality

16 Jul 20, 2022
MaD GUI is a basis for graphical annotation and computational analysis of time series data.

MaD GUI Machine Learning and Data Analytics Graphical User Interface MaD GUI is a basis for graphical annotation and computational analysis of time se

Machine Learning and Data Analytics Lab FAU 10 Dec 19, 2022
Convoys is a simple library that fits a few statistical model useful for modeling time-lagged conversions.

Convoys is a simple library that fits a few statistical model useful for modeling time-lagged conversions. There is a lot more info if you head over to the documentation. You can also take a look at

Better 240 Dec 26, 2022
Kats is a toolkit to analyze time series data, a lightweight, easy-to-use, and generalizable framework to perform time series analysis.

Kats, a kit to analyze time series data, a lightweight, easy-to-use, generalizable, and extendable framework to perform time series analysis, from understanding the key statistics and characteristics

Facebook Research 4.1k Dec 29, 2022
Getting Profit and Loss Make Easy From Binance

Getting Profit and Loss Make Easy From Binance I have been in Binance Automated Trading for some time and have generated a lot of transaction records,

17 Dec 21, 2022
Automatically create Faiss knn indices with the most optimal similarity search parameters.

It selects the best indexing parameters to achieve the highest recalls given memory and query speed constraints.

Criteo 419 Jan 01, 2023
Automated Machine Learning Pipeline with Feature Engineering and Hyper-Parameters Tuning

The mljar-supervised is an Automated Machine Learning Python package that works with tabular data. I

MLJAR 2.4k Jan 02, 2023
TensorFlow implementation of an arbitrary order Factorization Machine

This is a TensorFlow implementation of an arbitrary order (=2) Factorization Machine based on paper Factorization Machines with libFM. It supports: d

Mikhail Trofimov 785 Dec 21, 2022
YouTube Spam Detection with python

YouTube Spam Detection This code deletes spam comment on youtube videos based on two characteristics (currently) If the author of the comment has a se

MohamadReza Taalebi 5 Sep 27, 2022
Scikit-Learn useful pre-defined Pipelines Hub

Scikit-Pipes Scikit-Learn useful pre-defined Pipelines Hub Usage: Install scikit-pipes It's advised to install sklearn-genetic using a virtual env, in

Rodrigo Arenas 1 Apr 26, 2022