diff --git a/.idea/dataSources.xml b/.idea/dataSources.xml index 1e41358..08aa186 100644 --- a/.idea/dataSources.xml +++ b/.idea/dataSources.xml @@ -1,12 +1,19 @@ - - - - - mysql.8 - true - com.mysql.cj.jdbc.Driver - jdbc:mysql://lawyer5.cn:3308/wangche - $ProjectFileDir$ - - + + + + + mysql.8 + true + com.mysql.cj.jdbc.Driver + jdbc:mysql://lawyer5.cn:3308/wangche + $ProjectFileDir$ + + + mysql.8 + true + com.mysql.cj.jdbc.Driver + jdbc:mysql://localhost:3306 + $ProjectFileDir$ + + \ No newline at end of file diff --git a/.idea/misc.xml b/.idea/misc.xml index 74ef8a9..2e7df41 100644 --- a/.idea/misc.xml +++ b/.idea/misc.xml @@ -3,5 +3,5 @@ - + \ No newline at end of file diff --git a/.idea/wance_data.iml b/.idea/wance_data.iml index 5ef22bc..669283b 100644 --- a/.idea/wance_data.iml +++ b/.idea/wance_data.iml @@ -4,7 +4,7 @@ - + diff --git a/src/__pycache__/__init__.cpython-311.pyc b/src/__pycache__/__init__.cpython-311.pyc index 7d636cc..c71b727 100644 Binary files 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+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 + + +# 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 + return data + + +# 定义Pydantic模型 +class InfoItem(BaseModel): + code: str + name: str + market: str + newprice: float + amplitudetype: bool + amplitude: float + type: str + +class MessageItem(BaseModel): + id: str + label: str + value: str + state: bool + info: List[InfoItem] + inputClass: str + fixchange: bool + +class StrategyRequest(BaseModel): + id: int + strategy_name: str + message: List[MessageItem] or None + info: List[InfoItem] or None + +class MyData(BaseModel): + mes: List[StrategyRequest] + +# 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-------------------- + request_data = strategy.dict() + # print(request_data) + + # 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 + 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) + + 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() + +@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 + request_data = strategy.dict() + print(request_data) + + import json + 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() + 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 + 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 + request_data = strategy.dict() + print(request_data) + + import json + message_json = json.dumps(request_data['message']) + info_json = json.dumps(request_data['info']) + + + # 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" + + + +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 + ) + + # 使用 cursor() 方法创建一个游标对象 cursor + cursor = conn.cursor() + + 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" + + + +@router.get("/newmodify") +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() + + # 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 + + + +# 股票数据 +stock_data = None +@router.get("/stock") +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} + + + + + + diff --git a/src/akshare_data/service.py b/src/akshare_data/service.py new file mode 100644 index 0000000..a1e8c38 --- /dev/null +++ b/src/akshare_data/service.py @@ -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 diff --git a/src/backtest/__pycache__/bollinger_bands.cpython-311.pyc b/src/backtest/__pycache__/bollinger_bands.cpython-311.pyc index df0bd0a..c79f5d1 100644 Binary files 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b/src/backtest/__pycache__/router.cpython-311.pyc differ diff --git a/src/backtest/__pycache__/rsi_strategy.cpython-311.pyc b/src/backtest/__pycache__/rsi_strategy.cpython-311.pyc index 8e81093..551a16f 100644 Binary files a/src/backtest/__pycache__/rsi_strategy.cpython-311.pyc and b/src/backtest/__pycache__/rsi_strategy.cpython-311.pyc differ diff --git a/src/backtest/__pycache__/service.cpython-311.pyc b/src/backtest/__pycache__/service.cpython-311.pyc index ce8746d..ef785be 100644 Binary files a/src/backtest/__pycache__/service.cpython-311.pyc and b/src/backtest/__pycache__/service.cpython-311.pyc differ diff --git a/src/exceptions.py b/src/exceptions.py index 0e45d3f..34ca461 100644 --- a/src/exceptions.py +++ b/src/exceptions.py @@ -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"}) diff --git a/src/financial_reports/router.py b/src/financial_reports/router.py index 98be58c..ede4d03 100644 --- a/src/financial_reports/router.py +++ b/src/financial_reports/router.py @@ -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 """ diff --git a/src/klinedata/__init__.py b/src/klinedata/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/main.py b/src/main.py index 8c9f684..2e84300 100644 --- a/src/main.py +++ b/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) diff --git a/src/models/__pycache__/__init__.cpython-311.pyc b/src/models/__pycache__/__init__.cpython-311.pyc index c0f0082..f6aee2d 100644 Binary files a/src/models/__pycache__/__init__.cpython-311.pyc and b/src/models/__pycache__/__init__.cpython-311.pyc differ diff --git a/src/models/__pycache__/back_observed_data.cpython-311.pyc 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- -# 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