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README
Apache-2.0

BaoAI 小宝人工智能和量化系统

人工智能和量化从这开始



小宝人工智能和量化平台是简洁、直观、强大的前端和后端SPA开发框架,支持国际化,以模块为基础,让WEB应用、人工智能和量化系统开发更迅速、更简单。平台包含多个模块,主要包括基于角色的权限管理基础平台(用户、角色、权限、日志、附件、配置参数、分类管理)、通知模块、自动代码产生模块、任务系统模块、内容管理系统模块、网站模块、电子手册模块、人工智能模块、图像识别模块,人脸识别模块,金融数据采集模块,大数据模块,量化交易模块等。

功能特点:

  • 超10万行代码
  • 平台模块化,易于开发扩展
  • 前端兼容多种浏览器
  • 兼容性好,跨平台,响应式设计
  • 平台二次开发学习曲线低,易上手
  • 国际化
  • 前后端代码分离
  • 基于H5的单页面应用(SPA)
  • 自动代码产生器
  • 自动产生API文档及测试界面
  • 支持多数据库和数据迁移
  • 强大的富文本编辑
  • 人工智能
  • 大数据网络爬虫
  • 金融数据采集模块
  • 量化分析
  • 完善的开发和部署工具和方案

下载源码

BaoAI前后端分离框构,包含有前端项目和后端项目

文档

前端和后端开发工具

Visual Studio Code

安装插件:

Chinese (Simplified) Language Pack for Visual Studio Code

jshint

Python

Git history

项目后端 BaoAIBack 安装步骤

需要 Python 3.6

安装依赖库国内常用镜像

https://pypi.tuna.tsinghua.edu.cn/simple/   # 清华大学
https://mirrors.aliyun.com/pypi/simple/     # 阿里云
https://pypi.douban.com/simple/             # 豆瓣
https://pypi.mirrors.ustc.edu.cn/simple/    # 中国科学技术大学
https://pypi.hustunique.com/                # 华中科技大学

安装步骤:

# 1. 创建虚拟环境
# windows, 假设项目根路径:d:/baoai/BaoaiBack/
cd d:/baoai/BaoaiBack
mkdir venv
cd venv
python -m venv .

# 运行虚拟环境
d:/baoai/BaoaiBack/venv/Scripts/activate.bat
cd d:/baoai/BaoaiBack

# linux, 假设项目根路径:/baoai/BaoaiBack/
cd /baoai/BaoaiBack
mkdir venv
cd venv
python -m venv .

# 运行虚拟环境
source /baoai/BaoaiBack/venv/bin/activate
cd /baoai/BaoaiBack

# 2. 安装依赖库(必须处于虚拟环境)
# windows 安装依赖库
python -m pip install --upgrade pip
pip install -r requirements.txt
# 如果下载速度慢可以采用国内镜像(推荐)
python -m pip install --upgrade pip -i https://pypi.tuna.tsinghua.edu.cn/simple
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple -r requirements.txt

# linux 安装依赖库
python -m pip3 install --upgrade pip
pip3 install -r requirements.txt
# 如果下载速度慢可以采用国内镜像
pip3 install -i https://pypi.tuna.tsinghua.edu.cn/simple -r requirements.txt

# 3. 运行 Restful 服务
# windows
# 默认访问:http://localhost:5000/api
run_baoai.bat

# linux
# 默认使用gunicorn做为wsgi
chmod +x run_baoai.sh
./run_baoai.sh

# 4. 运行 www 服务(Jinja模块)
# windows
run_www.bat

# linux
chmod +x run_www.sh
./run_www.sh

# 常用功能
# 清空缓存
python manage.py clean

项目后端数据库

本项目支持绝大部门流行的关系数据库,包括:SQLite、MySQL、Postgres、Oracle、MS-SQL、SQLServer 和 Firebird。

已提供Sqlite数据库,和MySQL数据脚本文件。MySQL支持5.5及以上版本。

数据库转换无需修改代码,仅修改config.py中的SQLALCHEMY_DATABASE_URI即可。

默认使用sqlite数据库,优点是无需安装专门数据库软件,方便测试开发,生产部署请使用mysql或其它数据库软件。

sqlite数据保存在 db/baoai.db,直接使用。

mysql数据库脚本保存在 db/baoai.mysql.sql,需要新建数据库如baoai,然后导入脚本。

如果使用其他数据库,可以使用Navicat Premium工具菜单中的数据传输,进行不同数据库之前的数据迁移。

数据库相关操作:

# 数据迁移服务
# 初始化
python manage.py db init

# 模型迁移
python manage.py db migrate

# 数据库脚本更新(操作数据)
python manage.py db upgrade

项目代码自动产生模块

使用自动代码产生模块,可以使字段、模型、生成数据库、前端代码、后端代码和权限配置一并可视化完成,一般项目可以零代码实现。 该部份主要包括三个扩展模块: 数据迁移模块、自动代码模型模块和自动代码产生模块

BaoAI 小宝人工智能和量化平台系统架构

logo

BaoAI 小宝人工智能和量化平台知识体系

可用于各行业的前端和后端系统软件开发、CMS、人工智能、图像识别、人脸识别、大数据和量化投资领域等。前后端分离SPA架构,使用AngularJS/Bootstrap等前端框架实现响应式和SPA程序设计,后端主要使用Python语言,主要包括如下框架:flask提供web服务,Jinja2提供模板服务,Numpy、Pandas、Scikit-Learn、Tensorflow和Keras等实现人工智能服务,celery实现任务调度,scrapy提供网络爬虫,基于Backtrader的金融量化服务等。

logo

基于BaoAI设计案例:

内容管理网站:

logo

管理系统后台:

logo

人工智能:

logo

量化系统:

logo

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版权说明

Apache2.0

版权说明

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简介

小宝人工智能和量化平台BaoAI后端项目。BaoAI采用前后端分离框架,前端项目baoaifront,后端项目baoaiback。BaoAI是简洁、直观、强大的前端和后端SPA开发框架,支持国际化,以模块为基础,让WEB应用、人工智能和量化系统开发更迅速、更简单。平台包含多个模块,主要包括基于角色的权限管理基础平台(用户、角色、权限、日志、附件、配置参数、分类管理)、通知模块、自动代码产生模块、任务系统模块、内容管理系统模块、网站模块、电子手册模块、人工智能模块、图像识别模块,人脸识别模块,金融数据采集模块,大数据模块,量化交易模块等。 展开 收起
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