后端代码
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="DataSourceManagerImpl" format="xml" multifile-model="true">
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<data-source source="LOCAL" name="wangche@lawyer5.cn" uuid="7e849454-3537-491d-a390-aa5024bc7f42">
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<driver-ref>mysql.8</driver-ref>
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<synchronize>true</synchronize>
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<jdbc-driver>com.mysql.cj.jdbc.Driver</jdbc-driver>
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<jdbc-url>jdbc:mysql://lawyer5.cn:3308/wangche</jdbc-url>
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<working-dir>$ProjectFileDir$</working-dir>
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</data-source>
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</component>
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="DataSourceManagerImpl" format="xml" multifile-model="true">
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<data-source source="LOCAL" name="wangche@lawyer5.cn" uuid="7e849454-3537-491d-a390-aa5024bc7f42">
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<driver-ref>mysql.8</driver-ref>
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<synchronize>true</synchronize>
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<jdbc-driver>com.mysql.cj.jdbc.Driver</jdbc-driver>
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<jdbc-url>jdbc:mysql://lawyer5.cn:3308/wangche</jdbc-url>
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<working-dir>$ProjectFileDir$</working-dir>
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</data-source>
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<data-source source="LOCAL" name="@localhost" uuid="7b52e754-e1c2-4a7c-ae09-3c939af31a22">
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<driver-ref>mysql.8</driver-ref>
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<synchronize>true</synchronize>
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<jdbc-driver>com.mysql.cj.jdbc.Driver</jdbc-driver>
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<jdbc-url>jdbc:mysql://localhost:3306</jdbc-url>
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<working-dir>$ProjectFileDir$</working-dir>
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</data-source>
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</component>
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</project>
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@ -3,5 +3,5 @@
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<component name="Black">
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<option name="sdkName" value="Python 3.7" />
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</component>
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<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10 (wance_data)" project-jdk-type="Python SDK" />
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<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.11 (testenv) (2)" project-jdk-type="Python SDK" />
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</project>
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@ -4,7 +4,7 @@
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<content url="file://$MODULE_DIR$">
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<excludeFolder url="file://$MODULE_DIR$/venv" />
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</content>
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<orderEntry type="inheritedJdk" />
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<orderEntry type="jdk" jdkName="Python 3.11 (testenv) (2)" jdkType="Python SDK" />
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<orderEntry type="sourceFolder" forTests="false" />
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</component>
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<component name="PackageRequirementsSettings">
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src/akshare_data/__init__.py
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src/akshare_data/__init__.py
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src/akshare_data/router.py
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src/akshare_data/router.py
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from fastapi import APIRouter, Query, FastAPI, Body
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from starlette.middleware.cors import CORSMiddleware
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import akshare as ak
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import pymysql
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import json
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from pydantic import BaseModel
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from typing import List, Dict
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import math
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router = APIRouter() # 创建一个 FastAPI 路由器实例
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# 数据库测试
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@router.get("/userstrategy")
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async def userstrategy():
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# 创建数据库连接
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conn = pymysql.connect(
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host='cqxqg.tech', # MySQL服务器地址
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user='wangche', # MySQL用户名
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password='fN7sXX8saiQKXWbG', # MySQL密码
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database='wangche', # 要连接的数据库名
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port=3308,
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charset='utf8mb4', # 字符集，确保支持中文等
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cursorclass=pymysql.cursors.DictCursor # 使用字典形式返回结果
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)
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# 使用 cursor() 方法创建一个游标对象 cursor
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cursor = conn.cursor()
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# 使用 execute()方法执行 SQL 查询
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# 通配符 *，意思是查询表里所有内容
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cursor.execute("select * from user_strategy where user_id = 100096")
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# 使用 fetchone() 方法获取一行数据.
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# data = cursor.fetchone()
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data = cursor.fetchall()
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# print(data)
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# 关闭数据库连接
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cursor.close()
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# 将strategy_request字段中的JSON字符串转换为Python字典
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for i in range(0, len(data)):
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strategy_request = data[i]['strategy_request']
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# 将JSON字符串转换为Python字典
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data_dict = json.loads(strategy_request)
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data[i]['strategy_request'] = data_dict
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return data
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# 定义Pydantic模型
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class InfoItem(BaseModel):
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code: str
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name: str
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market: str
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newprice: float
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amplitudetype: bool
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amplitude: float
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type: str
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class MessageItem(BaseModel):
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id: str
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label: str
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value: str
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state: bool
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info: List[InfoItem]
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inputClass: str
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fixchange: bool
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class StrategyRequest(BaseModel):
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id: int
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strategy_name: str
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message: List[MessageItem] or None
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info: List[InfoItem] or None
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class MyData(BaseModel):
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mes: List[StrategyRequest]
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# 新加的new_user_strategy数据库表
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@router.post("/newuserstrategy")
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async def newuserstrategy(strategy: StrategyRequest = Body(...)):
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# 创建数据库连接
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conn = pymysql.connect(
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host='cqxqg.tech', # MySQL服务器地址
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user='wangche', # MySQL用户名
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password='fN7sXX8saiQKXWbG', # MySQL密码
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database='wangche', # 要连接的数据库名
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port=3308,
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charset='utf8mb4', # 字符集，确保支持中文等
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cursorclass=pymysql.cursors.DictCursor # 使用字典形式返回结果
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)
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# 使用 cursor() 方法创建一个游标对象 cursor
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cursor = conn.cursor()
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# cursor1 = conn.cursor()
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# cursor2 = conn.cursor()
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# --------------------new_user_strategy数据------------------
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# SQL 查询语句
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sql1 = "SELECT strategy_name FROM new_user_strategy WHERE id = %s"
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# 执行查询
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cursor.execute(sql1, ('100096',))
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# 获取所有数据
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result1 = cursor.fetchall()
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# 提取 strategy_name 列并转换为列表
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strategy_names1 = [row['strategy_name'] for row in result1]
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# print(strategy_names1)
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# --------------------user_strategy数据------------------
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# SQL 查询语句
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sql2 = "SELECT strategy_name FROM user_strategy WHERE user_id = %s"
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# 执行查询
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cursor.execute(sql2, ('100096',))
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# 获取所有数据
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result2 = cursor.fetchall()
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# 提取 strategy_name 列并转换为列表
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strategy_names2 = [row['strategy_name'] for row in result2]
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# print(strategy_names2)
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# --------------------获取整个请求数据--------------------
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request_data = strategy.dict()
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# print(request_data)
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# 准备SQL插入语句（注意：没有包含id列，因为它可能是自动递增的）
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sql = "INSERT INTO new_user_strategy (id, strategy_name, message, info) VALUES (%s, %s, %s, %s)"
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# 要插入的数据（确保数据类型与数据库表列匹配）
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# 将message和info转换为JSON字符串
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import json
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message_json = json.dumps(request_data['message'])
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info_json = json.dumps(request_data['info'])
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values = (request_data['id'], request_data['strategy_name'], message_json, info_json)
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# 是否进行写入数据库表中
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set_strategy_names1 = set(strategy_names1)
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set_strategy_names2 = set(strategy_names2)
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if request_data['strategy_name'] in strategy_names1:
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# print("信息已存在")
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conn.close()
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else:
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# 执行SQL语句
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cursor.execute(sql, values)
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# 提交事务到数据库执行
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conn.commit()
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print("数据插入成功")
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# 关闭数据库连接
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conn.close()
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return {"message": "数据插入成功"}
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# for i in range(0, len(result2)):
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# if result2[i] not in strategy_names1:
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# # 执行SQL语句
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# cursor.execute(sql, values)
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# # 提交事务到数据库执行
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# conn.commit()
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# print("数据插入成功")
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# # 关闭数据库连接
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# conn.close()
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# return {"message": "数据插入成功"}
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# conn.close()
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@router.post("/newadd")
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async def newadd(strategy: StrategyRequest = Body(...)):
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conn = pymysql.connect(
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host='cqxqg.tech', # MySQL服务器地址
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user='wangche', # MySQL用户名
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password='fN7sXX8saiQKXWbG', # MySQL密码
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database='wangche', # 要连接的数据库名
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port=3308,
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charset='utf8mb4', # 字符集，确保支持中文等
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cursorclass=pymysql.cursors.DictCursor # 使用字典形式返回结果
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)
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# 使用 cursor() 方法创建一个游标对象 cursor
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cursor = conn.cursor()
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# SQL 查询语句
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sql1 = "SELECT strategy_name FROM new_user_strategy WHERE id = %s"
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# 执行查询
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cursor.execute(sql1, ('100096',))
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# 获取所有数据
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result1 = cursor.fetchall()
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# 获取整个请求数据
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request_data = strategy.dict()
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print(request_data)
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import json
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message_json = json.dumps(request_data['message'])
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info_json = json.dumps(request_data['info'])
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print(result1)
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for item in result1:
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if request_data['strategy_name'] == item['strategy_name']:
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return {
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"code": 204,
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"message": "该分组已经存在"
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}
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sql = "INSERT INTO new_user_strategy (id, strategy_name, message, info) VALUES (%s, %s, %s, %s)"
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# # 执行 SQL
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cursor.execute(sql, (request_data["id"], request_data['strategy_name'], message_json, info_json))
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#
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# # 提交事务到数据库执行
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conn.commit()
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# print("更新数据成功")
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# 关闭数据库连接
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conn.close()
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return {
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"code": 200,
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"message": "新建分组成功！"
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}
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# 获取数据
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@router.get("/newget")
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async def newget():
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# 创建数据库连接
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conn = pymysql.connect(
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host='cqxqg.tech', # MySQL服务器地址
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user='wangche', # MySQL用户名
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password='fN7sXX8saiQKXWbG', # MySQL密码
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database='wangche', # 要连接的数据库名
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port=3308,
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charset='utf8mb4', # 字符集，确保支持中文等
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cursorclass=pymysql.cursors.DictCursor # 使用字典形式返回结果
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)
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# 使用 cursor() 方法创建一个游标对象 cursor
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cursor = conn.cursor()
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# 使用 execute()方法执行 SQL 查询
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# 通配符 *，意思是查询表里所有内容
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cursor.execute("select * from new_user_strategy where id = 100096")
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# 使用 fetchone() 方法获取一行数据.
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# data = cursor.fetchone()
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data = cursor.fetchall()
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# print(data)
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# 关闭数据库连接
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cursor.close()
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# 将strategy_request字段中的JSON字符串转换为Python字典
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for i in range(0, len(data)):
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strategy_request1 = data[i]['message']
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strategy_request2 = data[i]['info']
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# 将JSON字符串转换为Python字典
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data_dict1 = json.loads(strategy_request1)
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data_dict2 = json.loads(strategy_request2)
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data[i]['message'] = data_dict1
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data[i]['info'] = data_dict2
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return {
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"code": 200,
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"data": data
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}
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# 新增分组
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@router.post("/newupdata")
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async def newupdata(strategy: StrategyRequest = Body(...)):
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# 创建数据库连接
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conn = pymysql.connect(
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host='cqxqg.tech', # MySQL服务器地址
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user='wangche', # MySQL用户名
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password='fN7sXX8saiQKXWbG', # MySQL密码
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database='wangche', # 要连接的数据库名
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port=3308,
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charset='utf8mb4', # 字符集，确保支持中文等
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cursorclass=pymysql.cursors.DictCursor # 使用字典形式返回结果
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)
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# 使用 cursor() 方法创建一个游标对象 cursor
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cursor = conn.cursor()
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# 获取整个请求数据
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request_data = strategy.dict()
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print(request_data)
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import json
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message_json = json.dumps(request_data['message'])
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info_json = json.dumps(request_data['info'])
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# SQL 语句
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sql = """
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UPDATE new_user_strategy
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SET message = %s, info = %s
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WHERE strategy_name = %s;
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"""
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# 执行 SQL
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cursor.execute(sql, (message_json, info_json, request_data['strategy_name']))
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# 提交事务到数据库执行
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conn.commit()
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print("更新数据成功")
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# 关闭数据库连接
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conn.close()
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return "更新成功"
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class delItem(BaseModel):
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strategy_name: str
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# 删除分组
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@router.post("/newdel")
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async def newdel(delitem: delItem):
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delitem = delitem.strategy_name
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# 创建数据库连接
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conn = pymysql.connect(
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host='cqxqg.tech', # MySQL服务器地址
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user='wangche', # MySQL用户名
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password='fN7sXX8saiQKXWbG', # MySQL密码
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database='wangche', # 要连接的数据库名
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port=3308,
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charset='utf8mb4', # 字符集，确保支持中文等
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cursorclass=pymysql.cursors.DictCursor # 使用字典形式返回结果
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)
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# 使用 cursor() 方法创建一个游标对象 cursor
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cursor = conn.cursor()
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sql1 = "DELETE FROM new_user_strategy WHERE strategy_name = %s"
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sql2 = "DELETE FROM user_strategy WHERE strategy_name = %s"
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cursor.execute(sql1, (delitem,))
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cursor.execute(sql2, (delitem,))
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# 提交事务到数据库执行
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conn.commit()
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print("数据删除成功")
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# 关闭数据库连接
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conn.close()
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return "删除成功"
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@router.get("/newmodify")
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async def newmodify(
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strategy_name: str = Query(..., description="原始值"),
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new_strategy_name: str = Query(..., description="更改值")
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):
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print(strategy_name)
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print(new_strategy_name)
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# return "success"
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# pass
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# 创建数据库连接
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conn = pymysql.connect(
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host='cqxqg.tech', # MySQL服务器地址
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user='wangche', # MySQL用户名
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password='fN7sXX8saiQKXWbG', # MySQL密码
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database='wangche', # 要连接的数据库名
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port=3308,
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charset='utf8mb4', # 字符集，确保支持中文等
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cursorclass=pymysql.cursors.DictCursor # 使用字典形式返回结果
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)
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# 使用 cursor() 方法创建一个游标对象 cursor
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cursor = conn.cursor()
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# 更新 strategy_name
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update_sql = "UPDATE new_user_strategy SET strategy_name = %s WHERE strategy_name = %s"
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cursor.execute(update_sql, (new_strategy_name, strategy_name))
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# 提交事务到数据库执行
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conn.commit()
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print("重命名成功")
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# 关闭数据库连接
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conn.close()
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return "重命名成功"
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# 侧边栏webview数据
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@router.post("/asidestrinfo/")
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async def asidestrinfo():
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pass
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# 股票数据
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stock_data = None
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@router.get("/stock")
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async def stock(
|
||||
symbol: str = Query(..., description="股票代码"),
|
||||
start_date: str = Query(..., description="起始日期"),
|
||||
end_date: str = Query(..., description="结束日期"),
|
||||
):
|
||||
# 获取股票日线行情数据
|
||||
# print(symbol, start_date, end_date)
|
||||
# print(symbol)
|
||||
global stock_data
|
||||
try:
|
||||
stock_zh_a_daily_df = ak.stock_zh_a_daily(symbol=symbol, start_date=start_date, end_date=end_date, adjust="qfq")
|
||||
|
||||
# 获取所有的code
|
||||
all_dates = stock_zh_a_daily_df['date']
|
||||
# 如果你想要一个列表而不是Pandas Series
|
||||
dates_list = all_dates.tolist()
|
||||
|
||||
all_opens = stock_zh_a_daily_df['open']
|
||||
opens_list = all_opens.tolist()
|
||||
cleaned_opens_list = [
|
||||
value if not (math.isnan(value) or math.isinf(value)) else 0.00
|
||||
for value in opens_list
|
||||
]
|
||||
|
||||
all_closes = stock_zh_a_daily_df['close']
|
||||
close_list = all_closes.tolist()
|
||||
cleaned_close_list = [
|
||||
value if not (math.isnan(value) or math.isinf(value)) else 0.00
|
||||
for value in close_list
|
||||
]
|
||||
|
||||
all_highs = stock_zh_a_daily_df['high']
|
||||
high_list = all_highs.tolist()
|
||||
cleaned_high_list = [
|
||||
value if not (math.isnan(value) or math.isinf(value)) else 0.00
|
||||
for value in high_list
|
||||
]
|
||||
|
||||
all_lows = stock_zh_a_daily_df['low']
|
||||
low_list = all_lows.tolist()
|
||||
cleaned_low_list = [
|
||||
value if not (math.isnan(value) or math.isinf(value)) else 0.00
|
||||
for value in low_list
|
||||
]
|
||||
|
||||
all_volumes = stock_zh_a_daily_df['volume']
|
||||
volume_list = all_volumes.tolist()
|
||||
cleaned_volume_list = [
|
||||
value if not (math.isnan(value) or math.isinf(value)) else 0.00
|
||||
for value in volume_list
|
||||
]
|
||||
|
||||
all_amounts = stock_zh_a_daily_df['amount']
|
||||
amount_lists = all_amounts.tolist()
|
||||
cleaned_amount_list = [
|
||||
value if not (math.isnan(value) or math.isinf(value)) else 0.00
|
||||
for value in amount_lists
|
||||
]
|
||||
global stock_data
|
||||
stock_data = {
|
||||
"amount": cleaned_amount_list,
|
||||
"close": cleaned_close_list,
|
||||
"date": dates_list,
|
||||
"high": cleaned_high_list,
|
||||
"low": cleaned_low_list,
|
||||
"open": cleaned_opens_list,
|
||||
"outstanding_share": [],
|
||||
"turnover": [],
|
||||
"volume": cleaned_volume_list
|
||||
}
|
||||
except Exception as e:
|
||||
print(e)
|
||||
print("无法使用该方式请求股票数据，当前股票可能不是A股")
|
||||
stock_data = {
|
||||
"amount": [],
|
||||
"close": [],
|
||||
"date": [],
|
||||
"high": [],
|
||||
"low": [],
|
||||
"open": [],
|
||||
"outstanding_share": [],
|
||||
"turnover": [],
|
||||
"volume": []
|
||||
}
|
||||
finally:
|
||||
return {"message": stock_data}
|
||||
|
||||
# 前端获取数据接口
|
||||
@router.get("/kdata")
|
||||
async def kdata():
|
||||
global stock_data
|
||||
if stock_data is None:
|
||||
stock_data = {
|
||||
"amount": [],
|
||||
"close": [],
|
||||
"date": [],
|
||||
"high": [],
|
||||
"low": [],
|
||||
"open": [],
|
||||
"outstanding_share": [],
|
||||
"turnover": [],
|
||||
"volume": []
|
||||
}
|
||||
return {"message": stock_data}
|
||||
else:
|
||||
return {"message": stock_data}
|
||||
|
||||
# ---------------------------------------------------------------------
|
||||
# 港股代码数据
|
||||
@router.get("/ganggudata")
|
||||
async def ganggudata():
|
||||
stock_hk_spot_em_df = ak.stock_hk_spot_em()
|
||||
# print(stock_hk_spot_em_df)
|
||||
# 获取所有的code
|
||||
all_codes = stock_hk_spot_em_df['代码']
|
||||
# 如果你想要一个列表而不是Pandas Series
|
||||
codes_list = all_codes.tolist()
|
||||
|
||||
all_names = stock_hk_spot_em_df['名称']
|
||||
names_list = all_names.tolist()
|
||||
|
||||
all_prices = stock_hk_spot_em_df['最新价']
|
||||
price_list = all_prices.tolist()
|
||||
# 清理非法浮点数值（NaN, Infinity, -Infinity）
|
||||
cleaned_price_list = [
|
||||
value if not (math.isnan(value) or math.isinf(value)) else 0.00
|
||||
for value in price_list
|
||||
]
|
||||
|
||||
all_amplitudes = stock_hk_spot_em_df['涨跌幅']
|
||||
amplitudes_list = all_amplitudes.tolist()
|
||||
# 清理非法浮点数值（NaN, Infinity, -Infinity）
|
||||
cleaned_amplitudes_list = [
|
||||
value if not (math.isnan(value) or math.isinf(value)) else 0.00
|
||||
for value in amplitudes_list
|
||||
]
|
||||
|
||||
# 返回的数据
|
||||
ggstocking = []
|
||||
for i in range(9):
|
||||
if cleaned_price_list[i] >= 0:
|
||||
flag = True
|
||||
else:
|
||||
flag = False
|
||||
ggstocking.append({
|
||||
'code': codes_list[i],
|
||||
'name': names_list[i],
|
||||
'market': '港股',
|
||||
'newprice': cleaned_price_list[i],
|
||||
'amplitudetype': flag,
|
||||
'amplitude': cleaned_amplitudes_list[i],
|
||||
'type': 'ganggu'
|
||||
})
|
||||
|
||||
# 返回清理后的列表
|
||||
return ggstocking
|
||||
|
||||
# 港股K线图历史数据
|
||||
@router.get("/ganggudataK")
|
||||
async def ganggudataK(
|
||||
symbol: str = Query(..., description="股票代码"),
|
||||
start_date: str = Query(..., description="起始日期"),
|
||||
end_date: str = Query(..., description="结束日期"),
|
||||
):
|
||||
try:
|
||||
stock_hk_hist_df = ak.stock_hk_hist(symbol=symbol, period="daily", start_date=start_date, end_date=end_date,
|
||||
adjust="")
|
||||
|
||||
# 获取所有的code
|
||||
all_dates = stock_hk_hist_df['日期']
|
||||
# 如果你想要一个列表而不是Pandas Series
|
||||
dates_list = all_dates.tolist()
|
||||
|
||||
all_opens = stock_hk_hist_df['开盘']
|
||||
opens_list = all_opens.tolist()
|
||||
cleaned_opens_list = [
|
||||
value if not (math.isnan(value) or math.isinf(value)) else 0.00
|
||||
for value in opens_list
|
||||
]
|
||||
|
||||
all_closes = stock_hk_hist_df['收盘']
|
||||
close_list = all_closes.tolist()
|
||||
cleaned_close_list = [
|
||||
value if not (math.isnan(value) or math.isinf(value)) else 0.00
|
||||
for value in close_list
|
||||
]
|
||||
|
||||
all_highs = stock_hk_hist_df['最高']
|
||||
high_list = all_highs.tolist()
|
||||
cleaned_high_list = [
|
||||
value if not (math.isnan(value) or math.isinf(value)) else 0.00
|
||||
for value in high_list
|
||||
]
|
||||
|
||||
all_lows = stock_hk_hist_df['最低']
|
||||
low_list = all_lows.tolist()
|
||||
cleaned_low_list = [
|
||||
value if not (math.isnan(value) or math.isinf(value)) else 0.00
|
||||
for value in low_list
|
||||
]
|
||||
|
||||
all_volumes = stock_hk_hist_df['成交量']
|
||||
volume_list = all_volumes.tolist()
|
||||
cleaned_volume_list = [
|
||||
value if not (math.isnan(value) or math.isinf(value)) else 0.00
|
||||
for value in volume_list
|
||||
]
|
||||
|
||||
all_amounts = stock_hk_hist_df['成交额']
|
||||
amount_list = all_amounts.tolist()
|
||||
cleaned_amount_list = [
|
||||
value if not (math.isnan(value) or math.isinf(value)) else 0.00
|
||||
for value in amount_list
|
||||
]
|
||||
global stock_data
|
||||
stock_data = {
|
||||
"amount": cleaned_amount_list,
|
||||
"close": cleaned_close_list,
|
||||
"date": dates_list,
|
||||
"high": cleaned_high_list,
|
||||
"low": cleaned_low_list,
|
||||
"open": cleaned_opens_list,
|
||||
"outstanding_share": [],
|
||||
"turnover": [],
|
||||
"volume": cleaned_volume_list
|
||||
}
|
||||
except Exception as e:
|
||||
print(e)
|
||||
stock_data = {
|
||||
"amount": [],
|
||||
"close": [],
|
||||
"date": [],
|
||||
"high": [],
|
||||
"low": [],
|
||||
"open": [],
|
||||
"outstanding_share": [],
|
||||
"turnover": [],
|
||||
"volume": []
|
||||
}
|
||||
finally:
|
||||
return {"message": stock_data}
|
||||
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------
|
||||
# 美股代码数据
|
||||
@router.get("/meigudata")
|
||||
async def meigudata():
|
||||
stock_us_spot_em_df = ak.stock_us_spot_em()
|
||||
# print(stock_us_spot_em_df)
|
||||
all_codes = stock_us_spot_em_df['代码']
|
||||
# 如果你想要一个列表而不是Pandas Series
|
||||
codes_list = all_codes.tolist()
|
||||
|
||||
all_names = stock_us_spot_em_df['名称']
|
||||
names_list = all_names.tolist()
|
||||
|
||||
all_prices = stock_us_spot_em_df['最新价']
|
||||
price_list = all_prices.tolist()
|
||||
# 清理非法浮点数值（NaN, Infinity, -Infinity）
|
||||
cleaned_price_list = [
|
||||
value if not (math.isnan(value) or math.isinf(value)) else 0.00
|
||||
for value in price_list
|
||||
]
|
||||
|
||||
all_amplitudes = stock_us_spot_em_df['涨跌幅']
|
||||
amplitudes_list = all_amplitudes.tolist()
|
||||
# 清理非法浮点数值（NaN, Infinity, -Infinity）
|
||||
cleaned_amplitudes_list = [
|
||||
value if not (math.isnan(value) or math.isinf(value)) else 0.00
|
||||
for value in amplitudes_list
|
||||
]
|
||||
# 返回的数据
|
||||
mgstocking = []
|
||||
for i in range(9):
|
||||
if cleaned_price_list[i] >= 0:
|
||||
flag = True
|
||||
else:
|
||||
flag = False
|
||||
mgstocking.append({
|
||||
'code': codes_list[i],
|
||||
'name': names_list[i],
|
||||
'market': '港股',
|
||||
'newprice': cleaned_price_list[i],
|
||||
'amplitudetype': flag,
|
||||
'amplitude': cleaned_amplitudes_list[i],
|
||||
'type': 'meigu'
|
||||
})
|
||||
|
||||
# 返回清理后的列表
|
||||
return mgstocking
|
||||
# 美股K线图历史数据
|
||||
@router.get("/meigudataK")
|
||||
async def meigudataK(
|
||||
symbol: str = Query(..., description="股票代码"),
|
||||
start_date: str = Query(..., description="起始日期"),
|
||||
end_date: str = Query(..., description="结束日期"),
|
||||
):
|
||||
try:
|
||||
stock_us_hist_df = ak.stock_us_hist(symbol=symbol, period="daily", start_date=start_date, end_date=end_date,
|
||||
adjust="qfq")
|
||||
|
||||
# 获取所有的code
|
||||
all_dates = stock_us_hist_df['日期']
|
||||
# 如果你想要一个列表而不是Pandas Series
|
||||
dates_list = all_dates.tolist()
|
||||
|
||||
all_opens = stock_us_hist_df['开盘']
|
||||
opens_list = all_opens.tolist()
|
||||
cleaned_opens_list = [
|
||||
value if not (math.isnan(value) or math.isinf(value)) else 0.00
|
||||
for value in opens_list
|
||||
]
|
||||
|
||||
all_closes = stock_us_hist_df['收盘']
|
||||
close_list = all_closes.tolist()
|
||||
cleaned_close_list = [
|
||||
value if not (math.isnan(value) or math.isinf(value)) else 0.00
|
||||
for value in close_list
|
||||
]
|
||||
|
||||
all_highs = stock_us_hist_df['最高']
|
||||
high_list = all_highs.tolist()
|
||||
cleaned_high_list = [
|
||||
value if not (math.isnan(value) or math.isinf(value)) else 0.00
|
||||
for value in high_list
|
||||
]
|
||||
|
||||
all_lows = stock_us_hist_df['最低']
|
||||
low_list = all_lows.tolist()
|
||||
cleaned_low_list = [
|
||||
value if not (math.isnan(value) or math.isinf(value)) else 0.00
|
||||
for value in low_list
|
||||
]
|
||||
|
||||
all_volumes = stock_us_hist_df['成交量']
|
||||
volume_list = all_volumes.tolist()
|
||||
cleaned_volume_list = [
|
||||
value if not (math.isnan(value) or math.isinf(value)) else 0.00
|
||||
for value in volume_list
|
||||
]
|
||||
|
||||
all_amounts = stock_us_hist_df['成交额']
|
||||
amount_list = all_amounts.tolist()
|
||||
cleaned_amount_list = [
|
||||
value if not (math.isnan(value) or math.isinf(value)) else 0.00
|
||||
for value in amount_list
|
||||
]
|
||||
global stock_data
|
||||
stock_data = {
|
||||
"amount": cleaned_amount_list,
|
||||
"close": cleaned_close_list,
|
||||
"date": dates_list,
|
||||
"high": cleaned_high_list,
|
||||
"low": cleaned_low_list,
|
||||
"open": cleaned_opens_list,
|
||||
"outstanding_share": [],
|
||||
"turnover": [],
|
||||
"volume": cleaned_volume_list
|
||||
}
|
||||
except Exception as e:
|
||||
print(e)
|
||||
stock_data = {
|
||||
"amount": [],
|
||||
"close": [],
|
||||
"date": [],
|
||||
"high": [],
|
||||
"low": [],
|
||||
"open": [],
|
||||
"outstanding_share": [],
|
||||
"turnover": [],
|
||||
"volume": []
|
||||
}
|
||||
finally:
|
||||
return {"message": stock_data}
|
||||
|
||||
# ---------------------------------------------------------------------
|
||||
# 沪深代码数据
|
||||
@router.get("/hushendata")
|
||||
async def hushendata():
|
||||
try:
|
||||
stock_zh_a_spot_df = ak.stock_kc_a_spot_em()
|
||||
except Exception as e:
|
||||
print(e)
|
||||
# print(stock_zh_a_spot_df)
|
||||
all_codes = stock_zh_a_spot_df['代码']
|
||||
# 如果你想要一个列表而不是Pandas Series
|
||||
codes_list = all_codes.tolist()
|
||||
|
||||
all_names = stock_zh_a_spot_df['名称']
|
||||
names_list = all_names.tolist()
|
||||
|
||||
all_prices = stock_zh_a_spot_df['最新价']
|
||||
price_list = all_prices.tolist()
|
||||
# 清理非法浮点数值（NaN, Infinity, -Infinity）
|
||||
cleaned_price_list = [
|
||||
value if not (math.isnan(value) or math.isinf(value)) else 0.00
|
||||
for value in price_list
|
||||
]
|
||||
|
||||
all_amplitudes = stock_zh_a_spot_df['涨跌幅']
|
||||
amplitudes_list = all_amplitudes.tolist()
|
||||
# 清理非法浮点数值（NaN, Infinity, -Infinity）
|
||||
cleaned_amplitudes_list = [
|
||||
value if not (math.isnan(value) or math.isinf(value)) else 0.00
|
||||
for value in amplitudes_list
|
||||
]
|
||||
# 返回的数据
|
||||
hsstocking = []
|
||||
|
||||
# for i in range(len(codes_list)):
|
||||
# if cleaned_price_list[i] >= 0:
|
||||
# flag = True
|
||||
# else:
|
||||
# flag = False
|
||||
# hsstocking.append({
|
||||
# 'code': codes_list[i],
|
||||
# 'name': names_list[i],
|
||||
# 'market': '港股',
|
||||
# 'newprice': cleaned_price_list[i],
|
||||
# 'amplitudetype': flag,
|
||||
# 'amplitude': cleaned_amplitudes_list[i],
|
||||
# })
|
||||
|
||||
|
||||
for i in range(9):
|
||||
if cleaned_price_list[i] >= 0:
|
||||
flag = True
|
||||
else:
|
||||
flag = False
|
||||
hsstocking.append({
|
||||
'code': codes_list[i],
|
||||
'name': names_list[i],
|
||||
'market': '港股',
|
||||
'newprice': cleaned_price_list[i],
|
||||
'amplitudetype': flag,
|
||||
'amplitude': cleaned_amplitudes_list[i],
|
||||
'type': 'hushen'
|
||||
})
|
||||
|
||||
# 返回清理后的列表
|
||||
return hsstocking
|
||||
@router.get("/hushendataK")
|
||||
async def hushendataK(
|
||||
symbol: str = Query(..., description="股票代码"),
|
||||
start_date: str = Query(..., description="起始日期"),
|
||||
end_date: str = Query(..., description="结束日期"),
|
||||
):
|
||||
try:
|
||||
stock_zh_a_daily_qfq_df = ak.stock_zh_a_daily(symbol='sh' + symbol, start_date=start_date, end_date=end_date,
|
||||
adjust="qfq")
|
||||
|
||||
# 获取所有的code
|
||||
all_dates = stock_zh_a_daily_qfq_df['date']
|
||||
# 如果你想要一个列表而不是Pandas Series
|
||||
dates_list = all_dates.tolist()
|
||||
|
||||
all_opens = stock_zh_a_daily_qfq_df['open']
|
||||
opens_list = all_opens.tolist()
|
||||
cleaned_opens_list = [
|
||||
value if not (math.isnan(value) or math.isinf(value)) else 0.00
|
||||
for value in opens_list
|
||||
]
|
||||
|
||||
all_closes = stock_zh_a_daily_qfq_df['close']
|
||||
close_list = all_closes.tolist()
|
||||
cleaned_close_list = [
|
||||
value if not (math.isnan(value) or math.isinf(value)) else 0.00
|
||||
for value in close_list
|
||||
]
|
||||
|
||||
all_highs = stock_zh_a_daily_qfq_df['high']
|
||||
high_list = all_highs.tolist()
|
||||
cleaned_high_list = [
|
||||
value if not (math.isnan(value) or math.isinf(value)) else 0.00
|
||||
for value in high_list
|
||||
]
|
||||
|
||||
all_lows = stock_zh_a_daily_qfq_df['low']
|
||||
low_list = all_lows.tolist()
|
||||
cleaned_low_list = [
|
||||
value if not (math.isnan(value) or math.isinf(value)) else 0.00
|
||||
for value in low_list
|
||||
]
|
||||
|
||||
all_volumes = stock_zh_a_daily_qfq_df['volume']
|
||||
volume_list = all_volumes.tolist()
|
||||
cleaned_volume_list = [
|
||||
value if not (math.isnan(value) or math.isinf(value)) else 0.00
|
||||
for value in volume_list
|
||||
]
|
||||
|
||||
all_amounts = stock_zh_a_daily_qfq_df['amount']
|
||||
amount_lists = all_amounts.tolist()
|
||||
cleaned_amount_list = [
|
||||
value if not (math.isnan(value) or math.isinf(value)) else 0.00
|
||||
for value in amount_lists
|
||||
]
|
||||
global stock_data
|
||||
stock_data = {
|
||||
"amount": cleaned_amount_list,
|
||||
"close": cleaned_close_list,
|
||||
"date": dates_list,
|
||||
"high": cleaned_high_list,
|
||||
"low": cleaned_low_list,
|
||||
"open": cleaned_opens_list,
|
||||
"outstanding_share": [],
|
||||
"turnover": [],
|
||||
"volume": cleaned_volume_list
|
||||
}
|
||||
except Exception as e:
|
||||
print(e)
|
||||
stock_data = {
|
||||
"amount": [],
|
||||
"close": [],
|
||||
"date": [],
|
||||
"high": [],
|
||||
"low": [],
|
||||
"open": [],
|
||||
"outstanding_share": [],
|
||||
"turnover": [],
|
||||
"volume": []
|
||||
}
|
||||
finally:
|
||||
return {"message": stock_data}
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
6
src/akshare_data/service.py
Normal file
6
src/akshare_data/service.py
Normal file
@ -0,0 +1,6 @@
|
||||
from fastapi import FastAPI,Query
|
||||
from starlette.middleware.cors import CORSMiddleware
|
||||
import akshare as ak
|
||||
|
||||
async def get_day_k_data():
|
||||
pass
|
||||
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@ -1,92 +1,93 @@
|
||||
from typing import Any
|
||||
|
||||
from fastapi import FastAPI, HTTPException, Request, status
|
||||
from fastapi.encoders import jsonable_encoder
|
||||
from fastapi.exceptions import RequestValidationError
|
||||
from fastapi.responses import JSONResponse
|
||||
from starlette.exceptions import HTTPException as StarletteHTTPException
|
||||
|
||||
from src.utils.helpers import first
|
||||
|
||||
|
||||
def register_exception_handler(app: FastAPI):
|
||||
@app.exception_handler(RequestValidationError)
|
||||
async def validation_exception_handler(request: Request, exc: RequestValidationError):
|
||||
"""请求参数验证错误处理"""
|
||||
error = first(exc.errors())
|
||||
field = error.get('loc')[1] or ''
|
||||
return JSONResponse(
|
||||
status_code=status.HTTP_422_UNPROCESSABLE_ENTITY,
|
||||
content=jsonable_encoder({
|
||||
"status_code": status.HTTP_422_UNPROCESSABLE_ENTITY,
|
||||
"message": "{} param {}".format(field, error.get('msg')),
|
||||
"detail": exc.errors()}),
|
||||
)
|
||||
|
||||
@app.exception_handler(StarletteHTTPException)
|
||||
async def http_exception_handler(request: Request, exc: StarletteHTTPException):
|
||||
"""Http 异常处理"""
|
||||
return JSONResponse(
|
||||
status_code=exc.status_code,
|
||||
content=jsonable_encoder({
|
||||
"status_code": exc.status_code,
|
||||
"message": exc.detail}),
|
||||
)
|
||||
|
||||
@app.exception_handler(Exception)
|
||||
async def exception_callback(request: Request, exc: Exception):
|
||||
|
||||
"""其他异常处理，遇到其他问题再自定义异常"""
|
||||
return JSONResponse(
|
||||
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
|
||||
content=jsonable_encoder({
|
||||
"status_code": status.HTTP_500_INTERNAL_SERVER_ERROR,
|
||||
"message": "Internal Server Error",
|
||||
# "detail": ''.join(exc.args)
|
||||
}),
|
||||
)
|
||||
|
||||
|
||||
class CommonHttpException(HTTPException):
|
||||
def __init__(self, detail, status_code, **kwargs: dict[str, Any]) -> None:
|
||||
super().__init__(status_code=status_code, detail=detail, **kwargs)
|
||||
|
||||
|
||||
class DetailedHTTPException(HTTPException):
|
||||
STATUS_CODE = status.HTTP_500_INTERNAL_SERVER_ERROR
|
||||
DETAIL = "Server error"
|
||||
|
||||
def __init__(self, **kwargs: dict[str, Any]) -> None:
|
||||
super().__init__(status_code=self.STATUS_CODE, detail=self.DETAIL, **kwargs)
|
||||
|
||||
|
||||
class PermissionDenied(DetailedHTTPException):
|
||||
STATUS_CODE = status.HTTP_403_FORBIDDEN
|
||||
DETAIL = "Permission denied"
|
||||
|
||||
|
||||
class NotFound(DetailedHTTPException):
|
||||
STATUS_CODE = status.HTTP_404_NOT_FOUND
|
||||
|
||||
|
||||
class BadRequest(DetailedHTTPException):
|
||||
STATUS_CODE = status.HTTP_400_BAD_REQUEST
|
||||
DETAIL = "Bad Request"
|
||||
|
||||
|
||||
class UnprocessableEntity(DetailedHTTPException):
|
||||
STATUS_CODE = status.HTTP_422_UNPROCESSABLE_ENTITY
|
||||
DETAIL = "Unprocessable entity"
|
||||
|
||||
|
||||
class NotAuthenticated(DetailedHTTPException):
|
||||
STATUS_CODE = status.HTTP_401_UNAUTHORIZED
|
||||
DETAIL = "User not authenticated"
|
||||
|
||||
|
||||
class WxResponseError(DetailedHTTPException):
|
||||
STATUS_CODE = status.HTTP_400_BAD_REQUEST
|
||||
DETAIL = "请求微信异常"
|
||||
|
||||
def __init__(self) -> None:
|
||||
super().__init__(headers={"WWW-Authenticate": "Bearer"})
|
||||
3
|
||||
from typing import Any
|
||||
|
||||
from fastapi import FastAPI, HTTPException, Request, status
|
||||
from fastapi.encoders import jsonable_encoder
|
||||
from fastapi.exceptions import RequestValidationError
|
||||
from fastapi.responses import JSONResponse
|
||||
from starlette.exceptions import HTTPException as StarletteHTTPException
|
||||
|
||||
from src.utils.helpers import first
|
||||
|
||||
|
||||
def register_exception_handler(app: FastAPI):
|
||||
@app.exception_handler(RequestValidationError)
|
||||
async def validation_exception_handler(request: Request, exc: RequestValidationError):
|
||||
"""请求参数验证错误处理"""
|
||||
error = first(exc.errors())
|
||||
field = error.get('loc')[1] or ''
|
||||
return JSONResponse(
|
||||
status_code=status.HTTP_422_UNPROCESSABLE_ENTITY,
|
||||
content=jsonable_encoder({
|
||||
"status_code": status.HTTP_422_UNPROCESSABLE_ENTITY,
|
||||
"message": "{} param {}".format(field, error.get('msg')),
|
||||
"detail": exc.errors()}),
|
||||
)
|
||||
|
||||
@app.exception_handler(StarletteHTTPException)
|
||||
async def http_exception_handler(request: Request, exc: StarletteHTTPException):
|
||||
"""Http 异常处理"""
|
||||
return JSONResponse(
|
||||
status_code=exc.status_code,
|
||||
content=jsonable_encoder({
|
||||
"status_code": exc.status_code,
|
||||
"message": exc.detail}),
|
||||
)
|
||||
|
||||
@app.exception_handler(Exception)
|
||||
async def exception_callback(request: Request, exc: Exception):
|
||||
|
||||
"""其他异常处理，遇到其他问题再自定义异常"""
|
||||
return JSONResponse(
|
||||
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
|
||||
content=jsonable_encoder({
|
||||
"status_code": status.HTTP_500_INTERNAL_SERVER_ERROR,
|
||||
"message": "Internal Server Error",
|
||||
# "detail": ''.join(exc.args)
|
||||
}),
|
||||
)
|
||||
|
||||
|
||||
class CommonHttpException(HTTPException):
|
||||
def __init__(self, detail, status_code, **kwargs: dict[str, Any]) -> None:
|
||||
super().__init__(status_code=status_code, detail=detail, **kwargs)
|
||||
|
||||
|
||||
class DetailedHTTPException(HTTPException):
|
||||
STATUS_CODE = status.HTTP_500_INTERNAL_SERVER_ERROR
|
||||
DETAIL = "Server error"
|
||||
|
||||
def __init__(self, **kwargs: dict[str, Any]) -> None:
|
||||
super().__init__(status_code=self.STATUS_CODE, detail=self.DETAIL, **kwargs)
|
||||
|
||||
|
||||
class PermissionDenied(DetailedHTTPException):
|
||||
STATUS_CODE = status.HTTP_403_FORBIDDEN
|
||||
DETAIL = "Permission denied"
|
||||
|
||||
|
||||
class NotFound(DetailedHTTPException):
|
||||
STATUS_CODE = status.HTTP_404_NOT_FOUND
|
||||
|
||||
|
||||
class BadRequest(DetailedHTTPException):
|
||||
STATUS_CODE = status.HTTP_400_BAD_REQUEST
|
||||
DETAIL = "Bad Request"
|
||||
|
||||
|
||||
class UnprocessableEntity(DetailedHTTPException):
|
||||
STATUS_CODE = status.HTTP_422_UNPROCESSABLE_ENTITY
|
||||
DETAIL = "Unprocessable entity"
|
||||
|
||||
|
||||
class NotAuthenticated(DetailedHTTPException):
|
||||
STATUS_CODE = status.HTTP_401_UNAUTHORIZED
|
||||
DETAIL = "User not authenticated"
|
||||
|
||||
|
||||
class WxResponseError(DetailedHTTPException):
|
||||
STATUS_CODE = status.HTTP_400_BAD_REQUEST
|
||||
DETAIL = "请求微信异常"
|
||||
|
||||
def __init__(self) -> None:
|
||||
super().__init__(headers={"WWW-Authenticate": "Bearer"})
|
||||
|
||||
@ -8,7 +8,7 @@ from src.responses import response_list_response
|
||||
financial_reports_router = APIRouter()
|
||||
|
||||
@financial_reports_router.post("/query")
|
||||
async def financial_repoets_query(request: FinancialReportQuery )-> JSONResponse:
|
||||
async def financial_repoets_query(request: FinancialReportQuery) -> JSONResponse:
|
||||
"""
|
||||
搜索接口
|
||||
"""
|
||||
|
||||
0
src/klinedata/__init__.py
Normal file
0
src/klinedata/__init__.py
Normal file
36
src/main.py
36
src/main.py
@ -11,7 +11,6 @@ import asyncio
|
||||
from fastapi import FastAPI
|
||||
from datetime import datetime
|
||||
|
||||
|
||||
from starlette.middleware.cors import CORSMiddleware
|
||||
|
||||
from src.exceptions import register_exception_handler
|
||||
@ -19,17 +18,19 @@ from src.tortoises import register_tortoise_orm
|
||||
from src.xtdata.router import router as xtdata_router
|
||||
from src.backtest.router import router as backtest_router
|
||||
from src.combination.router import router as combine_router
|
||||
from src.models.financial_reports import FinancialReport
|
||||
from src.models.financial_reports import FinancialReport
|
||||
from src.utils.update_financial_reports_spider import combined_search_and_list
|
||||
from src.financial_reports.router import financial_reports_router
|
||||
from src.utils.generate_pinyin_abbreviation import generate_pinyin_abbreviation
|
||||
from src.composite.router import composite_router
|
||||
|
||||
|
||||
from src.akshare_data.router import router as akshare_data_router
|
||||
|
||||
from xtquant import xtdata
|
||||
from src.settings.config import app_configs, settings
|
||||
|
||||
import adata
|
||||
import akshare as ak
|
||||
# import adata
|
||||
# import akshare as ak
|
||||
|
||||
|
||||
app = FastAPI(**app_configs)
|
||||
@ -43,6 +44,7 @@ app.include_router(backtest_router, prefix="/backtest", tags=["回测接口"])
|
||||
app.include_router(combine_router, prefix="/combine", tags=["组合接口"])
|
||||
app.include_router(financial_reports_router, prefix="/financial-reports", tags=["财报接口"])
|
||||
app.include_router(composite_router, prefix="/composite", tags=["vacode组合接口"])
|
||||
app.include_router(akshare_data_router, prefix="/akshare", tags=["数据接口"])
|
||||
|
||||
if settings.ENVIRONMENT.is_deployed:
|
||||
sentry_sdk.init(
|
||||
@ -52,11 +54,14 @@ if settings.ENVIRONMENT.is_deployed:
|
||||
|
||||
app.add_middleware(
|
||||
CORSMiddleware,
|
||||
allow_origins=settings.CORS_ORIGINS,
|
||||
allow_origin_regex=settings.CORS_ORIGINS_REGEX,
|
||||
# allow_origins=settings.CORS_ORIGINS,
|
||||
# allow_origin_regex=settings.CORS_ORIGINS_REGEX,
|
||||
allow_origins=["*"],
|
||||
allow_origin_regex=None,
|
||||
allow_credentials=True,
|
||||
allow_methods=("GET", "POST", "PUT", "PATCH", "DELETE", "OPTIONS"),
|
||||
allow_headers=settings.CORS_HEADERS,
|
||||
# allow_headers=settings.CORS_HEADERS,
|
||||
allow_headers=["*"], # 允许所有请求头
|
||||
)
|
||||
|
||||
|
||||
@ -64,12 +69,13 @@ app.add_middleware(
|
||||
async def root():
|
||||
return {"message": "Hello, FastAPI!"}
|
||||
|
||||
|
||||
# 定时检查和数据抓取函数
|
||||
async def run_data_fetcher():
|
||||
print("财报抓取启动")
|
||||
while True:
|
||||
# 获取数据库中最新记录的时间
|
||||
latest_record = await FinancialReport .all().order_by("-created_at").first()
|
||||
latest_record = await FinancialReport.all().order_by("-created_at").first()
|
||||
latest_db_date = latest_record.created_at if latest_record else pd.Timestamp("1970-01-01")
|
||||
|
||||
# 将最新数据库日期设为无时区，以便比较
|
||||
@ -78,8 +84,8 @@ async def run_data_fetcher():
|
||||
# 当前时间
|
||||
current_time = pd.Timestamp(datetime.now()).replace(tzinfo=None)
|
||||
|
||||
# 检查当前时间是否超过数据库最新记录时间 12 个小时
|
||||
if (current_time - latest_db_date).total_seconds() > 43200:
|
||||
# 检查当前时间是否超过数据库最新记录时间 12 个小时
|
||||
if (current_time - latest_db_date).total_seconds() > 43200:
|
||||
print("启动财报数据抓取...")
|
||||
await combined_search_and_list()
|
||||
else:
|
||||
@ -88,18 +94,18 @@ async def run_data_fetcher():
|
||||
# 休眠 12 小时（43200 秒），然后再次检查
|
||||
await asyncio.sleep(43200)
|
||||
|
||||
|
||||
async def test_liang_hua_ku():
|
||||
print("量化库测试函数启动")
|
||||
|
||||
|
||||
|
||||
# 启动时立即运行检查任务
|
||||
@app.on_event("startup")
|
||||
async def lifespan():
|
||||
# 启动时将任务放入后台，不阻塞启动流程
|
||||
# 启动时将任务放入后台，不阻塞启动流程
|
||||
asyncio.create_task(test_liang_hua_ku())
|
||||
asyncio.create_task(run_data_fetcher())
|
||||
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
uvicorn.run('src.main:app', host="0.0.0.0", port=8012, reload=True)
|
||||
uvicorn.run('src.main:app', host="127.0.0.1", port=8012, reload=True)
|
||||
|
||||
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@ -44,9 +44,3 @@ app_configs: dict[str, Any] = {
|
||||
"docs_url": "/api/docs",
|
||||
}
|
||||
|
||||
# app_configs['debug'] = True
|
||||
# if settings.ENVIRONMENT.is_deployed:
|
||||
# app_configs["root_path"] = f"/v{settings.APP_VERSION}"
|
||||
#
|
||||
# if not settings.ENVIRONMENT.is_debug:
|
||||
# app_configs["openapi_url"] = None # hide docs
|
||||
|
||||
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@ -1,358 +1,358 @@
|
||||
import numpy as np # 导入 numpy 库
|
||||
from xtquant import xtdata # 导入 xtquant 库的 xtdata 模块
|
||||
|
||||
from src.utils.backtest_until import convert_pandas_to_json_serializable
|
||||
from src.models.wance_data_stock import WanceDataStock
|
||||
from src.pydantic.factor_request import StockQuery
|
||||
from src.utils.history_data_processing_utils import translation_dict
|
||||
|
||||
|
||||
# 获取股票池中的所有股票的股票代码
|
||||
async def get_full_tick_keys_service(code_list: list):
|
||||
"""
|
||||
获取所有股票的逐笔成交键。若未提供股票列表，则默认使用 ['SH', 'SZ']。
|
||||
"""
|
||||
if len(code_list) == 0: # 如果股票代码列表为空
|
||||
code_list = ['SH', 'SZ'] # 默认使用上证和深证股票池
|
||||
result = xtdata.get_full_tick(code_list=['SH', 'SZ']) # 调用 xtdata 库的函数获取逐笔成交数据
|
||||
return list(result.keys()) # 返回所有股票代码的列表
|
||||
|
||||
|
||||
# 获取股票池中的所有股票的逐笔成交数据
|
||||
async def get_full_tick_service(code_list: list):
|
||||
"""
|
||||
获取所有股票的逐笔成交数据。若未提供股票列表，则默认使用 ['SH', 'SZ']。
|
||||
"""
|
||||
if len(code_list) == 0: # 如果股票代码列表为空
|
||||
code_list = ['SH', 'SZ'] # 默认使用上证和深证股票池
|
||||
result = xtdata.get_full_tick(code_list=code_list) # 获取逐笔成交数据
|
||||
return result # 返回逐笔成交数据
|
||||
|
||||
|
||||
# 获取板块信息
|
||||
async def get_sector_list_service():
|
||||
"""
|
||||
获取所有板块的列表。
|
||||
"""
|
||||
result = xtdata.get_sector_list() # 调用 xtdata 库的函数获取板块信息
|
||||
return result # 返回板块列表
|
||||
|
||||
|
||||
# 通过板块名称获取股票列表
|
||||
async def get_stock_list_in_sector_service(sector_name):
|
||||
"""
|
||||
获取指定板块中的所有股票名称。
|
||||
"""
|
||||
result = xtdata.get_stock_list_in_sector(sector_name=sector_name) # 根据板块名称获取股票列表
|
||||
return result # 返回股票列表
|
||||
|
||||
|
||||
# 下载板块信息
|
||||
async def download_sector_data_service():
|
||||
"""
|
||||
下载板块的详细数据。
|
||||
"""
|
||||
result = xtdata.download_sector_data() # 调用 xtdata 库的函数下载板块数据
|
||||
return result # 返回下载结果
|
||||
|
||||
|
||||
# 获取股票的详细信息
|
||||
async def get_instrument_detail_service(stock_code: str, iscomplete: bool):
|
||||
"""
|
||||
获取股票的详细信息。
|
||||
"""
|
||||
result = xtdata.get_instrument_detail(stock_code=stock_code, iscomplete=iscomplete) # 获取股票的详细信息
|
||||
return result # 返回股票的详细信息
|
||||
|
||||
|
||||
# 获取市场行情数据
|
||||
async def get_market_data_service(field_list: list, stock_list: list, period: str, start_time: str, end_time: str,
|
||||
count: int, dividend_type: str, fill_data: bool):
|
||||
"""
|
||||
获取指定条件下的市场行情数据。
|
||||
"""
|
||||
result = xtdata.get_market_data(field_list=field_list, stock_list=stock_list, period=period, start_time=start_time,
|
||||
end_time=end_time, count=count, dividend_type=dividend_type,
|
||||
fill_data=fill_data) # 获取市场数据
|
||||
return result # 返回市场数据
|
||||
|
||||
|
||||
# 获取最新的 K 线数据
|
||||
async def get_full_kline_service(field_list: list, stock_list: list, period: str, start_time: str, end_time: str,
|
||||
count: int,
|
||||
dividend_type: str, fill_data: bool):
|
||||
"""
|
||||
获取指定条件下的完整 K 线数据。
|
||||
"""
|
||||
result = xtdata.get_full_kline(field_list=field_list, stock_list=stock_list, period=period, start_time=start_time,
|
||||
end_time=end_time, count=count, dividend_type=dividend_type,
|
||||
fill_data=fill_data) # 获取完整的 K 线数据
|
||||
return result # 返回 K 线数据
|
||||
|
||||
|
||||
# 下载历史数据
|
||||
async def download_history_data_service(stock_code: str, period: str, start_time: str, end_time: str,
|
||||
incrementally: bool):
|
||||
"""
|
||||
下载指定股票的历史数据。
|
||||
"""
|
||||
xtdata.download_history_data(stock_code=stock_code, period=period, start_time=start_time, end_time=end_time,
|
||||
incrementally=incrementally) # 下载股票历史数据
|
||||
|
||||
|
||||
# 获取本地数据
|
||||
async def get_local_data_service(field_list: list, stock_list: list, period: str, start_time: str, end_time: str,
|
||||
count: int, dividend_type: str, fill_data: bool, data_dir: str):
|
||||
"""
|
||||
@param field_list:
|
||||
@type field_list:
|
||||
@param stock_list:
|
||||
@type stock_list:
|
||||
@param period:
|
||||
@type period:
|
||||
@param start_time:
|
||||
@type start_time:
|
||||
@param end_time:
|
||||
@type end_time:
|
||||
@param count:
|
||||
@type count:
|
||||
@param dividend_type:
|
||||
@type dividend_type:
|
||||
@param fill_data:
|
||||
@type fill_data:
|
||||
@param data_dir:
|
||||
@type data_dir:
|
||||
@return:
|
||||
@rtype:
|
||||
"""
|
||||
"""
|
||||
获取本地存储的股票数据。
|
||||
"""
|
||||
return_list = []
|
||||
result = xtdata.get_local_data(field_list=field_list, stock_list=stock_list, period=period, start_time=start_time,
|
||||
end_time=end_time, count=count, dividend_type=dividend_type, fill_data=fill_data,
|
||||
data_dir=data_dir) # 获取本地数据
|
||||
for i in stock_list:
|
||||
return_list.append({
|
||||
i : convert_pandas_to_json_serializable(result.get(i))
|
||||
})
|
||||
return return_list
|
||||
|
||||
|
||||
# 订阅全市场行情推送
|
||||
async def subscribe_whole_quote_service(code_list, callback=None):
|
||||
"""
|
||||
订阅全市场行情推送。
|
||||
"""
|
||||
if callback:
|
||||
result = xtdata.subscribe_whole_quote(code_list, callback=on_data) # 有回调函数时调用
|
||||
else:
|
||||
result = xtdata.subscribe_whole_quote(code_list, callback=None) # 无回调函数时调用
|
||||
return result # 返回订阅结果
|
||||
|
||||
|
||||
# 订阅单只股票行情推送
|
||||
async def subscribe_quote_service(stock_code: str, period: str, start_time: str, end_time: str, count: int,
|
||||
callback: bool):
|
||||
"""
|
||||
订阅单只股票的行情推送。
|
||||
"""
|
||||
if callback:
|
||||
result = xtdata.subscribe_quote(stock_code=stock_code, period=period, start_time=start_time, end_time=end_time,
|
||||
count=count, callback=on_data) # 有回调函数时调用
|
||||
else:
|
||||
result = xtdata.subscribe_quote(stock_code=stock_code, period=period, start_time=start_time, end_time=end_time,
|
||||
count=count, callback=None) # 无回调函数时调用
|
||||
return result # 返回订阅结果
|
||||
|
||||
|
||||
def on_data(datas):
|
||||
"""
|
||||
行情数据推送的回调函数，处理并打印接收到的行情数据。
|
||||
"""
|
||||
for stock_code in datas:
|
||||
print(stock_code, datas[stock_code]) # 打印接收到的行情数据
|
||||
|
||||
|
||||
# 批量增量下载或批量下载
|
||||
async def download_history_data2_service(stock_list: list, period: str, start_time: str, end_time: str, callback: bool):
|
||||
"""
|
||||
批量下载股票历史数据，支持进度回调。
|
||||
"""
|
||||
if callback:
|
||||
xtdata.download_history_data2(stock_list=stock_list, period=period, start_time=start_time, end_time=end_time,
|
||||
callback=on_progress) # 有回调函数时调用
|
||||
else:
|
||||
xtdata.download_history_data2(stock_list=stock_list, period=period, start_time=start_time, end_time=end_time,
|
||||
callback=None) # 无回调函数时调用
|
||||
|
||||
|
||||
def on_progress(data):
|
||||
"""
|
||||
数据下载进度的回调函数，实时显示进度。
|
||||
"""
|
||||
print(data) # 打印下载进度
|
||||
|
||||
|
||||
# 单只股票下载示例
|
||||
def download_data():
|
||||
"""
|
||||
测试函数：下载单只股票的历史数据并获取相关信息。
|
||||
"""
|
||||
xtdata.download_history_data("000300.SH", "1d", start_time='', end_time='', incrementally=None) # 下载历史数据
|
||||
a = xtdata.get_full_tick(code_list=['SH', 'SZ']) # 获取逐笔成交数据
|
||||
print(list(a.keys())) # 打印获取的股票代码
|
||||
|
||||
|
||||
async def update_stock_data_service():
|
||||
"""
|
||||
# 用于本地xtdata数据更新
|
||||
@return:
|
||||
@rtype:
|
||||
"""
|
||||
stock_list = await get_full_tick_keys_service(['SH', 'SZ'])
|
||||
for stock in stock_list:
|
||||
await download_history_data_service(stock_code=stock,
|
||||
period='1d',
|
||||
start_time='',
|
||||
end_time='',
|
||||
incrementally=True)
|
||||
|
||||
|
||||
async def get_stock_factor_service(query_params: StockQuery):
|
||||
"""
|
||||
动态查询方法，根据传入的字段和值进行条件构造
|
||||
:param kwargs: 查询条件，键为字段名，值为查询值
|
||||
:return: 符合条件的记录列表
|
||||
"""
|
||||
"""
|
||||
使用示例：
|
||||
{
|
||||
"financial_asset_value": 10.7169,
|
||||
"financial_cash_flow": -0.42982,
|
||||
"gt": {
|
||||
"profit_gross_rate": 4.0,
|
||||
"valuation_PB_TTM": 0.8
|
||||
},
|
||||
"lt": {
|
||||
"market_indicator": -0.6
|
||||
},
|
||||
"gte": {
|
||||
"valuation_market_percentile":5.38793
|
||||
},
|
||||
"lte": {
|
||||
"valuation_PEGTTM_ratio": 15.2773
|
||||
},
|
||||
"between": {
|
||||
"financial_dividend": {
|
||||
"min": 1.0,
|
||||
"max": 2.0
|
||||
},
|
||||
"growth_Income_rate": {
|
||||
"min": -0.6,
|
||||
"max": 1.0
|
||||
}
|
||||
},
|
||||
"stock_code": "600051.SH",
|
||||
"stock_name": "宁波联合",
|
||||
"stock_sector": ["上证A股", "沪深A股"],
|
||||
"stock_type": ["stock"],
|
||||
"time_start": "0",
|
||||
"time_end": "20240911",
|
||||
"valuation_PEG_percentile": 15.2222,
|
||||
"valuation_PTS_TTM": 2.3077
|
||||
}
|
||||
|
||||
|
||||
financial_asset_value 和 financial_cash_flow 字段为普通等于条件。
|
||||
gt 字段用于指定字段大于某个值的条件，例如 profit_gross_rate 大于 15.0。
|
||||
lt 字段用于指定字段小于某个值的条件，例如 market_indicator 小于 30.0。
|
||||
gte 字段用于指定字段大于等于某个值的条件，例如 valuation_market_percentile 大于等于 0.5。
|
||||
lte 字段用于指定字段小于等于某个值的条件，例如 valuation_PEGTTM_ratio 小于等于 2.0。
|
||||
between 字段用于指定字段在两个值之间的范围，例如 financial_dividend 在 1.0 和 5.0 之间，growth_Income_rate 在 10.0 和 25.0 之间。
|
||||
stock_code 和 stock_name 字段为普通等于条件。
|
||||
stock_sector 和 stock_type 字段为列表类型的条件，可能会进行包含匹配。
|
||||
time_start 和 time_end 字段为普通等于条件。
|
||||
valuation_PEG_percentile 和 valuation_PTS_TTM 字段为普通等于条件。
|
||||
|
||||
比较大小字段说明：
|
||||
'gt' 对应 __gt（大于）
|
||||
'lt' 对应 __lt（小于）
|
||||
'gte' 对应 __gte（大于等于）
|
||||
'lte' 对应 __lte（小于等于）
|
||||
'between' 对应在某个值之间的条件，使用 __gt 和 __lt 组合实现
|
||||
|
||||
时间字段说明：
|
||||
time_start={'recent_years': 2} 近2年
|
||||
time_start={'recent_months': 2} 近2月
|
||||
"""
|
||||
|
||||
filters = {}
|
||||
|
||||
# 处理大于、小于等操作
|
||||
if query_params.gt:
|
||||
for field, value in query_params.gt.items():
|
||||
filters[f"{field}__gt"] = value
|
||||
if query_params.lt:
|
||||
for field, value in query_params.lt.items():
|
||||
filters[f"{field}__lt"] = value
|
||||
if query_params.gte:
|
||||
for field, value in query_params.gte.items():
|
||||
filters[f"{field}__gte"] = value
|
||||
if query_params.lte:
|
||||
for field, value in query_params.lte.items():
|
||||
filters[f"{field}__lte"] = value
|
||||
|
||||
# 处理范围查询
|
||||
if query_params.between:
|
||||
for field, range_values in query_params.between.items():
|
||||
if 'min' in range_values and 'max' in range_values:
|
||||
filters[f"{field}__gte"] = range_values['min']
|
||||
filters[f"{field}__lte"] = range_values['max']
|
||||
|
||||
# 处理普通字段
|
||||
for field, value in query_params.model_dump(exclude_unset=True).items():
|
||||
if field not in ['gt', 'lt', 'gte', 'lte', 'between']:
|
||||
if isinstance(value, float):
|
||||
filters[f"{field}__gte"] = value
|
||||
elif isinstance(value, str):
|
||||
filters[f"{field}__icontains"] = value
|
||||
elif isinstance(value, list):
|
||||
if field == "stock_sector":
|
||||
# 转换 `stock_sector` 中的中文板块名到英文
|
||||
translated_sectors = [translation_dict.get(sector, sector) for sector in value]
|
||||
filters[f"{field}__contains"] = translated_sectors
|
||||
else:
|
||||
filters[f"{field}__contains"] = value
|
||||
|
||||
try:
|
||||
stocks = await WanceDataStock.filter(**filters).all()
|
||||
return stocks
|
||||
except Exception as e:
|
||||
print(f"Error occurred when querying stocks: {e}")
|
||||
return []
|
||||
|
||||
|
||||
# 将 numpy 类型转换为原生 Python 类型
|
||||
async def convert_numpy_to_native(obj):
|
||||
"""
|
||||
递归地将 numpy 类型转换为原生 Python 类型。
|
||||
"""
|
||||
if isinstance(obj, np.generic): # 如果是 numpy 类型
|
||||
return obj.item() # 转换为 Python 原生类型
|
||||
elif isinstance(obj, dict): # 如果是字典类型
|
||||
return {k: convert_numpy_to_native(v) for k, v in obj.items()} # 递归转换字典中的值
|
||||
elif isinstance(obj, list): # 如果是列表类型
|
||||
return [convert_numpy_to_native(i) for i in obj] # 递归转换列表中的值
|
||||
elif isinstance(obj, tuple): # 如果是元组类型
|
||||
return tuple(convert_numpy_to_native(i) for i in obj) # 递归转换元组中的值
|
||||
else:
|
||||
return obj # 如果是其他类型，直接返回
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
# result = asyncio.run(get_instrument_detail_service("000300.SH",False))
|
||||
# print(result)
|
||||
# resulta = asyncio.run(get_sector_list_service())
|
||||
# print(resulta)
|
||||
pass
|
||||
import numpy as np # 导入 numpy 库
|
||||
from xtquant import xtdata # 导入 xtquant 库的 xtdata 模块
|
||||
|
||||
from src.utils.backtest_until import convert_pandas_to_json_serializable
|
||||
from src.models.wance_data_stock import WanceDataStock
|
||||
from src.pydantic.factor_request import StockQuery
|
||||
from src.utils.history_data_processing_utils import translation_dict
|
||||
|
||||
|
||||
# 获取股票池中的所有股票的股票代码
|
||||
async def get_full_tick_keys_service(code_list: list):
|
||||
"""
|
||||
获取所有股票的逐笔成交键。若未提供股票列表，则默认使用 ['SH', 'SZ']。
|
||||
"""
|
||||
if len(code_list) == 0: # 如果股票代码列表为空
|
||||
code_list = ['SH', 'SZ'] # 默认使用上证和深证股票池
|
||||
result = xtdata.get_full_tick(code_list=['SH', 'SZ']) # 调用 xtdata 库的函数获取逐笔成交数据
|
||||
return list(result.keys()) # 返回所有股票代码的列表
|
||||
|
||||
|
||||
# 获取股票池中的所有股票的逐笔成交数据
|
||||
async def get_full_tick_service(code_list: list):
|
||||
"""
|
||||
获取所有股票的逐笔成交数据。若未提供股票列表，则默认使用 ['SH', 'SZ']。
|
||||
"""
|
||||
if len(code_list) == 0: # 如果股票代码列表为空
|
||||
code_list = ['SH', 'SZ'] # 默认使用上证和深证股票池
|
||||
result = xtdata.get_full_tick(code_list=code_list) # 获取逐笔成交数据
|
||||
return result # 返回逐笔成交数据
|
||||
|
||||
|
||||
# 获取板块信息
|
||||
async def get_sector_list_service():
|
||||
"""
|
||||
获取所有板块的列表。
|
||||
"""
|
||||
result = xtdata.get_sector_list() # 调用 xtdata 库的函数获取板块信息
|
||||
return result # 返回板块列表
|
||||
|
||||
|
||||
# 通过板块名称获取股票列表
|
||||
async def get_stock_list_in_sector_service(sector_name):
|
||||
"""
|
||||
获取指定板块中的所有股票名称。
|
||||
"""
|
||||
result = xtdata.get_stock_list_in_sector(sector_name=sector_name) # 根据板块名称获取股票列表
|
||||
return result # 返回股票列表
|
||||
|
||||
|
||||
# 下载板块信息
|
||||
async def download_sector_data_service():
|
||||
"""
|
||||
下载板块的详细数据。
|
||||
"""
|
||||
result = xtdata.download_sector_data() # 调用 xtdata 库的函数下载板块数据
|
||||
return result # 返回下载结果
|
||||
|
||||
|
||||
# 获取股票的详细信息
|
||||
async def get_instrument_detail_service(stock_code: str, iscomplete: bool):
|
||||
"""
|
||||
获取股票的详细信息。
|
||||
"""
|
||||
result = xtdata.get_instrument_detail(stock_code=stock_code, iscomplete=iscomplete) # 获取股票的详细信息
|
||||
return result # 返回股票的详细信息
|
||||
|
||||
|
||||
# 获取市场行情数据
|
||||
async def get_market_data_service(field_list: list, stock_list: list, period: str, start_time: str, end_time: str,
|
||||
count: int, dividend_type: str, fill_data: bool):
|
||||
"""
|
||||
获取指定条件下的市场行情数据。
|
||||
"""
|
||||
result = xtdata.get_market_data(field_list=field_list, stock_list=stock_list, period=period, start_time=start_time,
|
||||
end_time=end_time, count=count, dividend_type=dividend_type,
|
||||
fill_data=fill_data) # 获取市场数据
|
||||
return result # 返回市场数据
|
||||
|
||||
|
||||
# 获取最新的 K 线数据
|
||||
async def get_full_kline_service(field_list: list, stock_list: list, period: str, start_time: str, end_time: str,
|
||||
count: int,
|
||||
dividend_type: str, fill_data: bool):
|
||||
"""
|
||||
获取指定条件下的完整 K 线数据。
|
||||
"""
|
||||
result = xtdata.get_full_kline(field_list=field_list, stock_list=stock_list, period=period, start_time=start_time,
|
||||
end_time=end_time, count=count, dividend_type=dividend_type,
|
||||
fill_data=fill_data) # 获取完整的 K 线数据
|
||||
return result # 返回 K 线数据
|
||||
|
||||
|
||||
# 下载历史数据
|
||||
async def download_history_data_service(stock_code: str, period: str, start_time: str, end_time: str,
|
||||
incrementally: bool):
|
||||
"""
|
||||
下载指定股票的历史数据。
|
||||
"""
|
||||
xtdata.download_history_data(stock_code=stock_code, period=period, start_time=start_time, end_time=end_time,
|
||||
incrementally=incrementally) # 下载股票历史数据
|
||||
|
||||
|
||||
# 获取本地数据
|
||||
async def get_local_data_service(field_list: list, stock_list: list, period: str, start_time: str, end_time: str,
|
||||
count: int, dividend_type: str, fill_data: bool, data_dir: str):
|
||||
"""
|
||||
@param field_list:
|
||||
@type field_list:
|
||||
@param stock_list:
|
||||
@type stock_list:
|
||||
@param period:
|
||||
@type period:
|
||||
@param start_time:
|
||||
@type start_time:
|
||||
@param end_time:
|
||||
@type end_time:
|
||||
@param count:
|
||||
@type count:
|
||||
@param dividend_type:
|
||||
@type dividend_type:
|
||||
@param fill_data:
|
||||
@type fill_data:
|
||||
@param data_dir:
|
||||
@type data_dir:
|
||||
@return:
|
||||
@rtype:
|
||||
"""
|
||||
"""
|
||||
获取本地存储的股票数据。
|
||||
"""
|
||||
return_list = []
|
||||
result = xtdata.get_local_data(field_list=field_list, stock_list=stock_list, period=period, start_time=start_time,
|
||||
end_time=end_time, count=count, dividend_type=dividend_type, fill_data=fill_data,
|
||||
data_dir=data_dir) # 获取本地数据
|
||||
for i in stock_list:
|
||||
return_list.append({
|
||||
i : convert_pandas_to_json_serializable(result.get(i))
|
||||
})
|
||||
return return_list
|
||||
|
||||
|
||||
# 订阅全市场行情推送
|
||||
async def subscribe_whole_quote_service(code_list, callback=None):
|
||||
"""
|
||||
订阅全市场行情推送。
|
||||
"""
|
||||
if callback:
|
||||
result = xtdata.subscribe_whole_quote(code_list, callback=on_data) # 有回调函数时调用
|
||||
else:
|
||||
result = xtdata.subscribe_whole_quote(code_list, callback=None) # 无回调函数时调用
|
||||
return result # 返回订阅结果
|
||||
|
||||
|
||||
# 订阅单只股票行情推送
|
||||
async def subscribe_quote_service(stock_code: str, period: str, start_time: str, end_time: str, count: int,
|
||||
callback: bool):
|
||||
"""
|
||||
订阅单只股票的行情推送。
|
||||
"""
|
||||
if callback:
|
||||
result = xtdata.subscribe_quote(stock_code=stock_code, period=period, start_time=start_time, end_time=end_time,
|
||||
count=count, callback=on_data) # 有回调函数时调用
|
||||
else:
|
||||
result = xtdata.subscribe_quote(stock_code=stock_code, period=period, start_time=start_time, end_time=end_time,
|
||||
count=count, callback=None) # 无回调函数时调用
|
||||
return result # 返回订阅结果
|
||||
|
||||
|
||||
def on_data(datas):
|
||||
"""
|
||||
行情数据推送的回调函数，处理并打印接收到的行情数据。
|
||||
"""
|
||||
for stock_code in datas:
|
||||
print(stock_code, datas[stock_code]) # 打印接收到的行情数据
|
||||
|
||||
|
||||
# 批量增量下载或批量下载
|
||||
async def download_history_data2_service(stock_list: list, period: str, start_time: str, end_time: str, callback: bool):
|
||||
"""
|
||||
批量下载股票历史数据，支持进度回调。
|
||||
"""
|
||||
if callback:
|
||||
xtdata.download_history_data2(stock_list=stock_list, period=period, start_time=start_time, end_time=end_time,
|
||||
callback=on_progress) # 有回调函数时调用
|
||||
else:
|
||||
xtdata.download_history_data2(stock_list=stock_list, period=period, start_time=start_time, end_time=end_time,
|
||||
callback=None) # 无回调函数时调用
|
||||
|
||||
|
||||
def on_progress(data):
|
||||
"""
|
||||
数据下载进度的回调函数，实时显示进度。
|
||||
"""
|
||||
print(data) # 打印下载进度
|
||||
|
||||
|
||||
# 单只股票下载示例
|
||||
def download_data():
|
||||
"""
|
||||
测试函数：下载单只股票的历史数据并获取相关信息。
|
||||
"""
|
||||
xtdata.download_history_data("000300.SH", "1d", start_time='', end_time='', incrementally=None) # 下载历史数据
|
||||
a = xtdata.get_full_tick(code_list=['SH', 'SZ']) # 获取逐笔成交数据
|
||||
print(list(a.keys())) # 打印获取的股票代码
|
||||
|
||||
|
||||
async def update_stock_data_service():
|
||||
"""
|
||||
# 用于本地xtdata数据更新
|
||||
@return:
|
||||
@rtype:
|
||||
"""
|
||||
stock_list = await get_full_tick_keys_service(['SH', 'SZ'])
|
||||
for stock in stock_list:
|
||||
await download_history_data_service(stock_code=stock,
|
||||
period='1d',
|
||||
start_time='',
|
||||
end_time='',
|
||||
incrementally=True)
|
||||
|
||||
|
||||
async def get_stock_factor_service(query_params: StockQuery):
|
||||
"""
|
||||
动态查询方法，根据传入的字段和值进行条件构造
|
||||
:param kwargs: 查询条件，键为字段名，值为查询值
|
||||
:return: 符合条件的记录列表
|
||||
"""
|
||||
"""
|
||||
使用示例：
|
||||
{
|
||||
"financial_asset_value": 10.7169,
|
||||
"financial_cash_flow": -0.42982,
|
||||
"gt": {
|
||||
"profit_gross_rate": 4.0,
|
||||
"valuation_PB_TTM": 0.8
|
||||
},
|
||||
"lt": {
|
||||
"market_indicator": -0.6
|
||||
},
|
||||
"gte": {
|
||||
"valuation_market_percentile":5.38793
|
||||
},
|
||||
"lte": {
|
||||
"valuation_PEGTTM_ratio": 15.2773
|
||||
},
|
||||
"between": {
|
||||
"financial_dividend": {
|
||||
"min": 1.0,
|
||||
"max": 2.0
|
||||
},
|
||||
"growth_Income_rate": {
|
||||
"min": -0.6,
|
||||
"max": 1.0
|
||||
}
|
||||
},
|
||||
"stock_code": "600051.SH",
|
||||
"stock_name": "宁波联合",
|
||||
"stock_sector": ["上证A股", "沪深A股"],
|
||||
"stock_type": ["stock"],
|
||||
"time_start": "0",
|
||||
"time_end": "20240911",
|
||||
"valuation_PEG_percentile": 15.2222,
|
||||
"valuation_PTS_TTM": 2.3077
|
||||
}
|
||||
|
||||
|
||||
financial_asset_value 和 financial_cash_flow 字段为普通等于条件。
|
||||
gt 字段用于指定字段大于某个值的条件，例如 profit_gross_rate 大于 15.0。
|
||||
lt 字段用于指定字段小于某个值的条件，例如 market_indicator 小于 30.0。
|
||||
gte 字段用于指定字段大于等于某个值的条件，例如 valuation_market_percentile 大于等于 0.5。
|
||||
lte 字段用于指定字段小于等于某个值的条件，例如 valuation_PEGTTM_ratio 小于等于 2.0。
|
||||
between 字段用于指定字段在两个值之间的范围，例如 financial_dividend 在 1.0 和 5.0 之间，growth_Income_rate 在 10.0 和 25.0 之间。
|
||||
stock_code 和 stock_name 字段为普通等于条件。
|
||||
stock_sector 和 stock_type 字段为列表类型的条件，可能会进行包含匹配。
|
||||
time_start 和 time_end 字段为普通等于条件。
|
||||
valuation_PEG_percentile 和 valuation_PTS_TTM 字段为普通等于条件。
|
||||
|
||||
比较大小字段说明：
|
||||
'gt' 对应 __gt（大于）
|
||||
'lt' 对应 __lt（小于）
|
||||
'gte' 对应 __gte（大于等于）
|
||||
'lte' 对应 __lte（小于等于）
|
||||
'between' 对应在某个值之间的条件，使用 __gt 和 __lt 组合实现
|
||||
|
||||
时间字段说明：
|
||||
time_start={'recent_years': 2} 近2年
|
||||
time_start={'recent_months': 2} 近2月
|
||||
"""
|
||||
|
||||
filters = {}
|
||||
|
||||
# 处理大于、小于等操作
|
||||
if query_params.gt:
|
||||
for field, value in query_params.gt.items():
|
||||
filters[f"{field}__gt"] = value
|
||||
if query_params.lt:
|
||||
for field, value in query_params.lt.items():
|
||||
filters[f"{field}__lt"] = value
|
||||
if query_params.gte:
|
||||
for field, value in query_params.gte.items():
|
||||
filters[f"{field}__gte"] = value
|
||||
if query_params.lte:
|
||||
for field, value in query_params.lte.items():
|
||||
filters[f"{field}__lte"] = value
|
||||
|
||||
# 处理范围查询
|
||||
if query_params.between:
|
||||
for field, range_values in query_params.between.items():
|
||||
if 'min' in range_values and 'max' in range_values:
|
||||
filters[f"{field}__gte"] = range_values['min']
|
||||
filters[f"{field}__lte"] = range_values['max']
|
||||
|
||||
# 处理普通字段
|
||||
for field, value in query_params.model_dump(exclude_unset=True).items():
|
||||
if field not in ['gt', 'lt', 'gte', 'lte', 'between']:
|
||||
if isinstance(value, float):
|
||||
filters[f"{field}__gte"] = value
|
||||
elif isinstance(value, str):
|
||||
filters[f"{field}__icontains"] = value
|
||||
elif isinstance(value, list):
|
||||
if field == "stock_sector":
|
||||
# 转换 `stock_sector` 中的中文板块名到英文
|
||||
translated_sectors = [translation_dict.get(sector, sector) for sector in value]
|
||||
filters[f"{field}__contains"] = translated_sectors
|
||||
else:
|
||||
filters[f"{field}__contains"] = value
|
||||
|
||||
try:
|
||||
stocks = await WanceDataStock.filter(**filters).all()
|
||||
return stocks
|
||||
except Exception as e:
|
||||
print(f"Error occurred when querying stocks: {e}")
|
||||
return []
|
||||
|
||||
|
||||
# 将 numpy 类型转换为原生 Python 类型
|
||||
async def convert_numpy_to_native(obj):
|
||||
"""
|
||||
递归地将 numpy 类型转换为原生 Python 类型。
|
||||
"""
|
||||
if isinstance(obj, np.generic): # 如果是 numpy 类型
|
||||
return obj.item() # 转换为 Python 原生类型
|
||||
elif isinstance(obj, dict): # 如果是字典类型
|
||||
return {k: convert_numpy_to_native(v) for k, v in obj.items()} # 递归转换字典中的值
|
||||
elif isinstance(obj, list): # 如果是列表类型
|
||||
return [convert_numpy_to_native(i) for i in obj] # 递归转换列表中的值
|
||||
elif isinstance(obj, tuple): # 如果是元组类型
|
||||
return tuple(convert_numpy_to_native(i) for i in obj) # 递归转换元组中的值
|
||||
else:
|
||||
return obj # 如果是其他类型，直接返回
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
# result = asyncio.run(get_instrument_detail_service("000300.SH",False))
|
||||
# print(result)
|
||||
# resulta = asyncio.run(get_sector_list_service())
|
||||
# print(resulta)
|
||||
pass
|
||||
|
||||
Loading…
Reference in New Issue
Block a user