新增了new_user_stategy的model,重构了数据库操作代码，转化为tortoise ORM

src/akshare_data/router.py

@@ -1,51 +1,21 @@
-from fastapi import APIRouter, Query, FastAPI, Body
-from starlette.middleware.cors import CORSMiddleware
+from fastapi import APIRouter, Query,  Body
 import akshare as ak
-import pymysql
 import json
 from pydantic import BaseModel
-from typing import List, Dict
-import math
-router = APIRouter()  # 创建一个 FastAPI 路由器实例
+from src.models.user_strategy import *
+from src.models.new_user_strategy import *
+from src.akshare_data.service import *
 
+router = APIRouter()  # 创建一个 FastAPI 路由器实例
 
 # 数据库测试
 @router.get("/userstrategy")
 async def userstrategy():
-    # 创建数据库连接
-    conn = pymysql.connect(
-        host='cqxqg.tech',  # MySQL服务器地址
-        user='wangche',  # MySQL用户名
-        password='fN7sXX8saiQKXWbG',  # MySQL密码
-        database='wangche',  # 要连接的数据库名
-        port=3308,
-        charset='utf8mb4',  # 字符集，确保支持中文等
-        cursorclass=pymysql.cursors.DictCursor  # 使用字典形式返回结果
-    )
-
-    # 使用 cursor() 方法创建一个游标对象 cursor
-    cursor = conn.cursor()
-
-    # 使用 execute()方法执行 SQL 查询
-    # 通配符 *，意思是查询表里所有内容
-    cursor.execute("select * from user_strategy where user_id = 100096")
-
-    # 使用 fetchone() 方法获取一行数据.
-    # data = cursor.fetchone()
-    data = cursor.fetchall()
-    # print(data)
-
-    # 关闭数据库连接
-    cursor.close()
-    # 将strategy_request字段中的JSON字符串转换为Python字典
-    for i in range(0, len(data)):
-        strategy_request = data[i]['strategy_request']
-        # 将JSON字符串转换为Python字典
-        data_dict = json.loads(strategy_request)
-        data[i]['strategy_request'] = data_dict
+    # 查询 user_id = 100096 的记录
+    query = UserStrategy.filter(user_id=100096)  # 使用 Tortoise ORM 的查询方法
+    data = await query.all()  # 异步获取所有结果
     return data
 
-
 # 定义Pydantic模型
 class InfoItem(BaseModel):
     code: str
@@ -77,263 +47,122 @@ class MyData(BaseModel):
 # 新加的new_user_strategy数据库表
 @router.post("/newuserstrategy")
 async def newuserstrategy(strategy: StrategyRequest = Body(...)):
-    # 创建数据库连接
-    conn = pymysql.connect(
-        host='cqxqg.tech',  # MySQL服务器地址
-        user='wangche',  # MySQL用户名
-        password='fN7sXX8saiQKXWbG',  # MySQL密码
-        database='wangche',  # 要连接的数据库名
-        port=3308,
-        charset='utf8mb4',  # 字符集，确保支持中文等
-        cursorclass=pymysql.cursors.DictCursor  # 使用字典形式返回结果
-    )
-    # 使用 cursor() 方法创建一个游标对象 cursor
-    cursor = conn.cursor()
-    # cursor1 = conn.cursor()
-    # cursor2 = conn.cursor()
-    # --------------------new_user_strategy数据------------------
-    # SQL 查询语句
-    sql1 = "SELECT strategy_name FROM new_user_strategy WHERE id = %s"
-    # 执行查询
-    cursor.execute(sql1, ('100096',))
-    # 获取所有数据
-    result1 = cursor.fetchall()
-    # 提取 strategy_name 列并转换为列表
-    strategy_names1 = [row['strategy_name'] for row in result1]
-    # print(strategy_names1)
-
-    # --------------------user_strategy数据------------------
-    # SQL 查询语句
-    sql2 = "SELECT strategy_name FROM user_strategy WHERE user_id = %s"
-    # 执行查询
-    cursor.execute(sql2, ('100096',))
-    # 获取所有数据
-    result2 = cursor.fetchall()
-    # 提取 strategy_name 列并转换为列表
-    strategy_names2 = [row['strategy_name'] for row in result2]
-    # print(strategy_names2)
-
-
-    # --------------------获取整个请求数据--------------------
+    # 获取请求数据
     request_data = strategy.dict()
-    # print(request_data)
+    # 查询 new_user_strategy 表中是否存在相同的 id 和 strategy_name
+    existing_strategy = await NewUserStrategy.filter(
+        id=request_data['id'], strategy_name=request_data['strategy_name']
+    ).first()
 
-    # 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）
-    sql = "INSERT INTO new_user_strategy (id, strategy_name, message, info) VALUES (%s, %s, %s, %s)"
-    # 要插入的数据（确保数据类型与数据库表列匹配）
-    # 将message和info转换为JSON字符串
-    import json
+    if existing_strategy:
+        # 如果已存在，直接返回提示信息
+        return {"message": "该策略已存在"}
+
+    # 查询 user_strategy 表中是否存在相同的 user_id 和 strategy_name
+    existing_user_strategy = await UserStrategy.filter(
+        user_id=request_data['id'], strategy_name=request_data['strategy_name']
+    ).first()
+
+    if existing_user_strategy:
+        # 如果已存在，直接返回提示信息
+        return {"message": "该策略已在 user_strategy 表中存在"}
+
+    # 将 message 和 info 转换为 JSON 字符串
     message_json = json.dumps(request_data['message'])
     info_json = json.dumps(request_data['info'])
-    values = (request_data['id'], request_data['strategy_name'], message_json, info_json)
 
-    # 是否进行写入数据库表中
-    set_strategy_names1 = set(strategy_names1)
-    set_strategy_names2 = set(strategy_names2)
+    # 创建新的 new_user_strategy 记录
+    new_strategy = await NewUserStrategy.create(
+        id=request_data['id'],
+        strategy_name=request_data['strategy_name'],
+        message=message_json,
+        info=info_json
+    )
 
-    if request_data['strategy_name'] in strategy_names1:
-        # print("信息已存在")
-        conn.close()
-    else:
-        # 执行SQL语句
-        cursor.execute(sql, values)
-        # 提交事务到数据库执行
-        conn.commit()
-        print("数据插入成功")
-        # 关闭数据库连接
-        conn.close()
-        return {"message": "数据插入成功"}
-    # for i in range(0, len(result2)):
-    #     if result2[i] not in strategy_names1:
-    #         # 执行SQL语句
-    #         cursor.execute(sql, values)
-    #         # 提交事务到数据库执行
-    #         conn.commit()
-    #         print("数据插入成功")
-    #         # 关闭数据库连接
-    #         conn.close()
-    #         return {"message": "数据插入成功"}
-
-
-    # conn.close()
+    return {"message": "数据插入成功", "strategy_id": new_strategy.id}
 
 @router.post("/newadd")
 async def newadd(strategy: StrategyRequest = Body(...)):
-    conn = pymysql.connect(
-        host='cqxqg.tech',  # MySQL服务器地址
-        user='wangche',  # MySQL用户名
-        password='fN7sXX8saiQKXWbG',  # MySQL密码
-        database='wangche',  # 要连接的数据库名
-        port=3308,
-        charset='utf8mb4',  # 字符集，确保支持中文等
-        cursorclass=pymysql.cursors.DictCursor  # 使用字典形式返回结果
-    )
-
-    # 使用 cursor() 方法创建一个游标对象 cursor
-    cursor = conn.cursor()
-
-    # SQL 查询语句
-    sql1 = "SELECT strategy_name FROM new_user_strategy WHERE id = %s"
-    # 执行查询
-    cursor.execute(sql1, ('100096',))
-    # 获取所有数据
-    result1 = cursor.fetchall()
-
-    # 获取整个请求数据
+    # 获取请求数据
     request_data = strategy.dict()
-    print(request_data)
+    # 查询 new_user_strategy 表中是否存在相同的 id 和 strategy_name
+    existing_strategy = await NewUserStrategy.filter(
+        id=request_data['id'], strategy_name=request_data['strategy_name']
+    ).first()
 
-    import json
+    if existing_strategy:
+        # 如果已存在，返回提示信息
+        return {
+            "code": 204,
+            "message": "该分组已经存在"
+        }
+
+    # 将 message 和 info 转换为 JSON 字符串
     message_json = json.dumps(request_data['message'])
     info_json = json.dumps(request_data['info'])
 
-    print(result1)
-    for item in result1:
-        if request_data['strategy_name'] == item['strategy_name']:
-            return {
-                "code": 204,
-                "message": "该分组已经存在"
-            }
-
-    sql = "INSERT INTO new_user_strategy (id, strategy_name, message, info) VALUES (%s, %s, %s, %s)"
-    # # 执行 SQL
-    cursor.execute(sql, (request_data["id"], request_data['strategy_name'], message_json, info_json))
-    #
-    # # 提交事务到数据库执行
-    conn.commit()
-    # print("更新数据成功")
-    # 关闭数据库连接
-    conn.close()
+    # 创建新的 new_user_strategy 记录
+    await NewUserStrategy.create(
+        id=request_data['id'],
+        strategy_name=request_data['strategy_name'],
+        message=message_json,
+        info=info_json
+    )
     return {
         "code": 200,
         "message": "新建分组成功！"
     }
 
-
-
 # 获取数据
 @router.get("/newget")
 async def newget():
-    # 创建数据库连接
-    conn = pymysql.connect(
-        host='cqxqg.tech',  # MySQL服务器地址
-        user='wangche',  # MySQL用户名
-        password='fN7sXX8saiQKXWbG',  # MySQL密码
-        database='wangche',  # 要连接的数据库名
-        port=3308,
-        charset='utf8mb4',  # 字符集，确保支持中文等
-        cursorclass=pymysql.cursors.DictCursor  # 使用字典形式返回结果
-    )
-
-    # 使用 cursor() 方法创建一个游标对象 cursor
-    cursor = conn.cursor()
-
-    # 使用 execute()方法执行 SQL 查询
-    # 通配符 *，意思是查询表里所有内容
-    cursor.execute("select * from new_user_strategy where id = 100096")
-
-    # 使用 fetchone() 方法获取一行数据.
-    # data = cursor.fetchone()
-    data = cursor.fetchall()
-    # print(data)
-
-    # 关闭数据库连接
-    cursor.close()
-    # 将strategy_request字段中的JSON字符串转换为Python字典
-    for i in range(0, len(data)):
-        strategy_request1 = data[i]['message']
-        strategy_request2 = data[i]['info']
-        # 将JSON字符串转换为Python字典
-        data_dict1 = json.loads(strategy_request1)
-        data_dict2 = json.loads(strategy_request2)
-        data[i]['message'] = data_dict1
-        data[i]['info'] = data_dict2
+    # 查询 new_user_strategy 表中 id = 100096 的所有记录
+    query = NewUserStrategy.filter(id=100096)
+    data = await query.all()
     return {
         "code": 200,
         "data": data
     }
 
-
-
 # 新增分组
 @router.post("/newupdata")
 async def newupdata(strategy: StrategyRequest = Body(...)):
-    # 创建数据库连接
-    conn = pymysql.connect(
-        host='cqxqg.tech',  # MySQL服务器地址
-        user='wangche',  # MySQL用户名
-        password='fN7sXX8saiQKXWbG',  # MySQL密码
-        database='wangche',  # 要连接的数据库名
-        port=3308,
-        charset='utf8mb4',  # 字符集，确保支持中文等
-        cursorclass=pymysql.cursors.DictCursor  # 使用字典形式返回结果
-    )
-
-    # 使用 cursor() 方法创建一个游标对象 cursor
-    cursor = conn.cursor()
-
-    # 获取整个请求数据
+    # 获取请求数据
     request_data = strategy.dict()
-    print(request_data)
 
-    import json
+    # 将 message 和 info 转换为 JSON 字符串
     message_json = json.dumps(request_data['message'])
     info_json = json.dumps(request_data['info'])
 
+    # 查询是否存在对应的 strategy_name
+    existing_strategy = await NewUserStrategy.filter(strategy_name=request_data['strategy_name']).first()
+    if not existing_strategy:
+        return {"message": "不存在"}
 
-    # SQL 语句
-    sql = """
-    UPDATE new_user_strategy
-    SET message = %s, info = %s
-    WHERE strategy_name = %s;
-    """
-
-    # 执行 SQL
-    cursor.execute(sql, (message_json, info_json, request_data['strategy_name']))
-
-
-    # 提交事务到数据库执行
-    conn.commit()
-    print("更新数据成功")
-    # 关闭数据库连接
-    conn.close()
-    return "更新成功"
-
-
+    # 更新记录
+    await NewUserStrategy.filter(strategy_name=request_data['strategy_name']).update(
+        message=message_json,
+        info=info_json
+    )
+    return {"message": "更新成功"}
 
 class delItem(BaseModel):
     strategy_name: str
 # 删除分组
 @router.post("/newdel")
 async def newdel(delitem: delItem):
-    delitem = delitem.strategy_name
-    # 创建数据库连接
-    conn = pymysql.connect(
-        host='cqxqg.tech',  # MySQL服务器地址
-        user='wangche',  # MySQL用户名
-        password='fN7sXX8saiQKXWbG',  # MySQL密码
-        database='wangche',  # 要连接的数据库名
-        port=3308,
-        charset='utf8mb4',  # 字符集，确保支持中文等
-        cursorclass=pymysql.cursors.DictCursor  # 使用字典形式返回结果
-    )
+    strategy_name = delitem.strategy_name
 
-    # 使用 cursor() 方法创建一个游标对象 cursor
-    cursor = conn.cursor()
+    # 删除 new_user_strategy 表中的记录
+    delete_count_new = await NewUserStrategy.filter(strategy_name=strategy_name).delete()
 
-    sql1 = "DELETE FROM new_user_strategy WHERE strategy_name = %s"
-
-    sql2 = "DELETE FROM user_strategy WHERE strategy_name = %s"
-
-    cursor.execute(sql1, (delitem,))
-    cursor.execute(sql2, (delitem,))
-    # 提交事务到数据库执行
-    conn.commit()
-    print("数据删除成功")
-    # 关闭数据库连接
-    conn.close()
-    return "删除成功"
+    # 删除 user_strategy 表中的记录
+    delete_count_user = await UserStrategy.filter(strategy_name=strategy_name).delete()
 
+    # 检查是否删除成功
+    if delete_count_new == 0 and delete_count_user == 0:
+        return {"message": "未找到指定的策略名称"}
+    return {"message": "删除成功"}
 
 
 @router.get("/newmodify")
@@ -341,42 +170,18 @@ async def newmodify(
     strategy_name: str = Query(..., description="原始值"),
     new_strategy_name: str = Query(..., description="更改值")
 ):
-    print(strategy_name)
-    print(new_strategy_name)
-    # return "success"
-    # pass
-    # 创建数据库连接
-    conn = pymysql.connect(
-        host='cqxqg.tech',  # MySQL服务器地址
-        user='wangche',  # MySQL用户名
-        password='fN7sXX8saiQKXWbG',  # MySQL密码
-        database='wangche',  # 要连接的数据库名
-        port=3308,
-        charset='utf8mb4',  # 字符集，确保支持中文等
-        cursorclass=pymysql.cursors.DictCursor  # 使用字典形式返回结果
-    )
-
-    # 使用 cursor() 方法创建一个游标对象 cursor
-    cursor = conn.cursor()
-
+    # 检查是否存在原始 strategy_name
+    existing_strategy = await NewUserStrategy.filter(strategy_name=strategy_name).first()
+    if not existing_strategy:
+        return {"message": "原始策略名称不存在"}
+    # 检查新策略名称是否已存在
+    existing_new_strategy = await NewUserStrategy.filter(strategy_name=new_strategy_name).first()
+    if existing_new_strategy:
+        return {"message": "新策略名称已存在"}
     # 更新 strategy_name
-    update_sql = "UPDATE new_user_strategy SET strategy_name = %s WHERE strategy_name = %s"
-    cursor.execute(update_sql, (new_strategy_name, strategy_name))
-    # 提交事务到数据库执行
-    conn.commit()
-    print("重命名成功")
-    # 关闭数据库连接
-    conn.close()
-    return "重命名成功"
-
-
-
-
-# 侧边栏webview数据
-@router.post("/asidestrinfo/")
-async def asidestrinfo():
-    pass
+    await NewUserStrategy.filter(strategy_name=strategy_name).update(strategy_name=new_strategy_name)
 
+    return {"message": "重命名成功"}
 
 
 # 股票数据
@@ -388,69 +193,29 @@ async def stock(
         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()
+        dates_list = stock_zh_a_daily_df['date'].tolist()
+        opens_list = clean_data(stock_zh_a_daily_df['open'].tolist())
+        closes_list = clean_data(stock_zh_a_daily_df['close'].tolist())
+        highs_list = clean_data(stock_zh_a_daily_df['high'].tolist())
+        lows_list = clean_data(stock_zh_a_daily_df['low'].tolist())
+        volumes_list = clean_data(stock_zh_a_daily_df['volume'].tolist())
+        amounts_list = clean_data(stock_zh_a_daily_df['amount'].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,
+            "amount": amounts_list,
+            "close": closes_list,
             "date": dates_list,
-            "high": cleaned_high_list,
-            "low": cleaned_low_list,
-            "open": cleaned_opens_list,
+            "high": highs_list,
+            "low": lows_list,
+            "open": opens_list,
             "outstanding_share": [],
             "turnover": [],
-            "volume": cleaned_volume_list
+            "volume": volumes_list
         }
     except Exception as e:
         print(e)
@@ -494,35 +259,14 @@ async def kdata():
 @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
-    ]
-
+    codes_list = stock_hk_spot_em_df['代码'].tolist()
+    names_list = stock_hk_spot_em_df['名称'].tolist()
+    price_list = clean_data(stock_hk_spot_em_df['最新价'].tolist())
+    amplitudes_list = clean_data(stock_hk_spot_em_df['涨跌幅'].tolist())
     # 返回的数据
     ggstocking = []
     for i in range(9):
-        if cleaned_price_list[i] >= 0:
+        if price_list[i] >= 0:
             flag = True
         else:
             flag = False
@@ -530,9 +274,9 @@ async def ganggudata():
             'code': codes_list[i],
             'name': names_list[i],
             'market': '港股',
-            'newprice': cleaned_price_list[i],
+            'newprice': price_list[i],
             'amplitudetype': flag,
-            'amplitude': cleaned_amplitudes_list[i],
+            'amplitude': amplitudes_list[i],
             'type': 'ganggu'
         })
 
@@ -549,64 +293,24 @@ async def ganggudataK(
     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
-        ]
+        dates_list = stock_hk_hist_df['日期'].tolist()
+        opens_list = clean_data(stock_hk_hist_df['开盘'].tolist())
+        close_list = clean_data(stock_hk_hist_df['收盘'].tolist())
+        high_list = clean_data(stock_hk_hist_df['最高'].tolist())
+        low_list = clean_data(stock_hk_hist_df['最低'].tolist())
+        volume_list = clean_data(stock_hk_hist_df['成交量'].tolist())
+        amount_list = clean_data(stock_hk_hist_df['成交额'].tolist())
         global stock_data
         stock_data = {
-            "amount": cleaned_amount_list,
-            "close": cleaned_close_list,
+            "amount": amount_list,
+            "close": close_list,
             "date": dates_list,
-            "high": cleaned_high_list,
-            "low": cleaned_low_list,
-            "open": cleaned_opens_list,
+            "high": high_list,
+            "low": low_list,
+            "open": opens_list,
             "outstanding_share": [],
             "turnover": [],
-            "volume": cleaned_volume_list
+            "volume": volume_list
         }
     except Exception as e:
         print(e)
@@ -624,55 +328,38 @@ async def ganggudataK(
     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()
+    codes_list = stock_us_spot_em_df['代码'].tolist()
+    names_list = stock_us_spot_em_df['名称'].tolist()
+    price_list = clean_data(stock_us_spot_em_df['最新价'].tolist())
+    amplitudes_list = clean_data(stock_us_spot_em_df['涨跌幅'].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:
+        if 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],
+            'market': '美股',
+            'newprice': price_list[i],
             'amplitudetype': flag,
-            'amplitude': cleaned_amplitudes_list[i],
+            'amplitude': 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(
@@ -683,64 +370,25 @@ async def meigudataK(
     try:
         stock_us_hist_df = ak.stock_us_hist(symbol=symbol, period="daily", start_date=start_date, end_date=end_date,
                                             adjust="qfq")
+        dates_list = stock_us_hist_df['日期'].tolist()
+        opens_list = stock_us_hist_df['开盘'].tolist()
+        close_list = clean_data(stock_us_hist_df['收盘'].tolist())
+        high_list = clean_data(stock_us_hist_df['最高'].tolist())
+        low_list = clean_data(stock_us_hist_df['最低'].tolist())
+        volume_list = clean_data(stock_us_hist_df['成交量'].tolist())
+        amount_list = clean_data(stock_us_hist_df['成交额'].tolist())
 
-        # 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,
+            "amount": amount_list,
+            "close": close_list,
             "date": dates_list,
-            "high": cleaned_high_list,
-            "low": cleaned_low_list,
-            "open": cleaned_opens_list,
+            "high": high_list,
+            "low": low_list,
+            "open": opens_list,
             "outstanding_share": [],
             "turnover": [],
-            "volume": cleaned_volume_list
+            "volume": volume_list
         }
     except Exception as e:
         print(e)
@@ -766,59 +414,26 @@ async def hushendata():
         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()
+    codes_list = stock_zh_a_spot_df['代码'].tolist()
+    names_list = stock_zh_a_spot_df['名称'].tolist()
+    price_list = clean_data(stock_zh_a_spot_df['最新价'].tolist())
+    amplitudes_list = clean_data(stock_zh_a_spot_df['涨跌幅'].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:
+        if 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],
+            'market': '沪深',
+            'newprice': price_list[i],
             'amplitudetype': flag,
-            'amplitude': cleaned_amplitudes_list[i],
+            'amplitude': amplitudes_list[i],
             'type': 'hushen'
         })
 
@@ -834,63 +449,26 @@ async def hushendataK(
         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()
+        dates_list = stock_zh_a_daily_qfq_df['date'].tolist()
+        opens_list = clean_data(stock_zh_a_daily_qfq_df['open'].tolist())
+        close_list = clean_data(stock_zh_a_daily_qfq_df['close'].tolist())
+        high_list = clean_data(stock_zh_a_daily_qfq_df['high'].tolist())
+        low_list = clean_data(stock_zh_a_daily_qfq_df['low'].tolist())
+        volume_list = clean_data(stock_zh_a_daily_qfq_df['volume'].tolist())
+        amount_list = clean_data(stock_zh_a_daily_qfq_df['amount'].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,
+            "amount": amount_list,
+            "close": close_list,
             "date": dates_list,
-            "high": cleaned_high_list,
-            "low": cleaned_low_list,
-            "open": cleaned_opens_list,
+            "high": high_list,
+            "low": low_list,
+            "open": opens_list,
             "outstanding_share": [],
             "turnover": [],
-            "volume": cleaned_volume_list
+            "volume": volume_list
         }
     except Exception as e:
         print(e)
@@ -908,8 +486,3 @@ async def hushendataK(
     finally:
         return {"message": stock_data}
 
-
-
-
-
-

src/akshare_data/service.py

@@ -1,6 +1,5 @@
-from fastapi import FastAPI,Query
-from starlette.middleware.cors import CORSMiddleware
-import akshare as ak
 
-async def get_day_k_data():
-    pass
+from typing import Dict, List
+import math
+def clean_data(data: List[float]) -> List[float]:
+    return [value if not (math.isnan(value) or math.isinf(value)) else 0.00 for value in data]

src/models/new_user_strategy.py

@@ -0,0 +1,12 @@
+from tortoise import fields
+from tortoise.models import Model
+
+class NewUserStrategy(Model):
+    id = fields.IntField(pk=True)
+    strategy_name = fields.CharField(max_length=25, null=False)
+    message = fields.JSONField(null=True)
+    info = fields.JSONField(null=True)
+
+    class Meta:
+        table = "new_user_strategy"
+        default_connection = "default"

src/tortoises.py

@@ -73,7 +73,8 @@ models = [
     "src.models.stock_zhong_zheng_500",
     "src.models.stock_guo_zheng_2000",
     "src.models.stock_hu_shen_jing_a",
-    "src.models.user_strategy"
+    "src.models.user_strategy",
+    "src.models.new_user_strategy"
 
     
 ]