最新gp.py

import pandas as pd
import numpy as np
import baostock as bs
import requests
from datetime import datetime, timedelta
import time
# import talib as ta 
#使用quant()函数替换

class GP:
    def __init__(self,bs=True):
        if bs:
            self.login()
    def login(self):
        bs.login()
    def logout(self):
        bs.logout()
    def get_history(self,gp_type,code,end_date=False,start_date=False,days=14):
        if not end_date:
            end_date=datetime.now().strftime("%Y-%m-%d")
        if not start_date:
            start_date=(datetime.strptime(end_date, "%Y-%m-%d") - timedelta(days)).strftime("%Y-%m-%d")
        rs = bs.query_history_k_data_plus(
            code=gp_type+"."+str(code),         
            fields="code,date,open,high,low,close,preclose,volume,pctChg,turn,isST,adjustflag",  # 要获取的字段（手动指定，Akshare 自动返回全字段）
            start_date=start_date,   # 日期格式：必须带横杠（Akshare 可无）
            end_date=end_date,
            frequency="d",             # 周期：d=日线（和 Akshare 的 period="daily" 对应）
            adjustflag="2"             # 复权：2=前复权（对应 Akshare 的 adjust="qfq"）
        )
        # 3. 转为DataFrame（核心步骤）
        df = rs.get_data()
        df = df.replace("", 0)  # 把所有空字符串变成 0
        if df['preclose'].iloc[-1]=="":
            df.drop(df.index[-1],inplace=True)
        # 4. 数据类型转换（避免数值以字符串显示）
        df = df.astype({
            "code":str,
            "open": float, 
            "high": float, 
            "low": float,
            "close": float, 
            "preclose": float, 
            "volume": int,
            "pctChg":float,
            "turn":float,
            "isST":int,
            "adjustflag": int  # 复权标识转整数
        })
        return df
    #股票代码数据
    def gp_code(self,code):
        str_code=str(code)
        if str_code[0] in ['0','1','2','3']:
            gp_type='sz'
        elif str_code[0] in ['5','6']:
            gp_type='sh'
        elif str_code[0] in ['4','8','9']:
            gp_type='bj'
        else:
            return False
        return [gp_type,str_code]
    #当日股票
    def get_today(self,gp_type,code):
        str_code=str(code)
        url="https://qt.gtimg.cn/q="
        url=url+gp_type+str_code
        headers = {
            'User-Agent': "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/133.0.0.0 Safari/537.36"
        }
        res=requests.get(url,headers=headers)
        if res.status_code==200:
            if len(res.content)>30:
                reb = res.content.decode("gbk")
                reb = reb.replace("~~~", "~").replace("~~", "~")
                vsx = reb.split('~')
                return [
                            {
                                "code":gp_type+"."+str_code,
                                "date":datetime.now().strftime("%Y-%m-%d"),
                                "open": float(vsx[5]), 
                                "high": float(vsx[32]), 
                                "low": float(vsx[33]),
                                "close": float(vsx[3]), 
                                "preclose": float(vsx[4]), 
                                "volume": float(vsx[6])*100,
                                "pctChg":float(vsx[31]),
                                "turn":float(vsx[37]),
                                "isST":0, #默认不返回ST
                                "adjustflag": 2  # 复权标识转整数 统一baostock
                            },
                            {
                                "name":vsx[1],
                                'money':vsx[42]
                            }
                        ]
        return False
    def gupiao(self,gp_type,gp_code,days=180):
        #获得最近180天的交易 包括今天 解决baostock 没有今日数据
        if "9:30"<=datetime.now().strftime('%H:%M')<"17:30" and datetime.now().weekday() in [0,1,2,3,4]:
            ma=self.get_today(gp_type,gp_code)
            if ma:
                if int(ma[0]['volume'])  != 0:
                    df=self.get_history(gp_type,gp_code,days=days)
                    df = pd.concat([df, pd.DataFrame([ma[0]])], ignore_index=True)
                    return df
            return False
        else:
            print("user 17:30",gp_type,gp_code)
            return self.get_history(gp_type,gp_code,days=days)
    def gp_all(self,xstr_code):
        # str_code=sh600000,sz000301,sz300010
        url="https://qt.gtimg.cn/q="
        url=url+xstr_code
        headers = {
            'User-Agent': "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/133.0.0.0 Safari/537.36"
        }
        res=requests.get(url,headers=headers)
        if res.status_code==200:
            if len(res.content)>30:
                res_c=res.content.decode("gbk")
                re_vax=res_c.split(';')
                re_a=[]
                for oneg in re_vax:
                    if len(oneg)>30:
                        oneg = oneg.replace("~~~", "~").replace("~~", "~")
                        vsx = oneg.split('~')
                        re_a.append([
                                    {
                                        "code":vsx[0],
                                        "date":datetime.now().strftime("%Y-%m-%d"),
                                        "open": float(vsx[5]), 
                                        "high": float(vsx[32]), 
                                        "low": float(vsx[33]),
                                        "close": float(vsx[3]), 
                                        "preclose": float(vsx[4]), 
                                        "volume": float(vsx[6])*100,
                                        "pctChg":float(vsx[31]),
                                        "turn":float(vsx[37]),
                                        "isST":0, #默认不返回ST
                                        "adjustflag": 2  # 复权标识转整数 统一baostock
                                    },
                                    {
                                        "name":vsx[1],
                                        'money':vsx[42]
                                    }
                                ])
                return re_a
        return False



    def quant(self,df):
        #处理量化指标
        #VOL MA
        df['vol_ma5']=df["volume"].rolling(window=5).mean()
        df['vol_ma10']=df["volume"].rolling(window=10).mean()
        df['vol_ma20']=df["volume"].rolling(window=20).mean()
        df['vol_ma60']=df["volume"].rolling(window=60).mean()
        #MA
        df['ma5']=df["close"].rolling(window=5).mean()
        df['ma10']=df["close"].rolling(window=10).mean()
        df['ma20']=df["close"].rolling(window=20).mean()
        df['ma60']=df["close"].rolling(window=60).mean()
        #ATR
        df['hl']=df['high']-df['low']
        df['hc']=abs(df['high']-df['close'].shift(1))
        df['lc']=abs(df['low']-df['close'].shift(1))
        df['tr']=df[['hl','hc','lc']].abs().max(axis=1) #axis=1行计算
        df['atr'] = df['tr'].ewm(span=14, adjust=False).mean()
        #RSI 14 6
        df['delta']=df['close'].diff()
        df['gain']=df['delta'].where(df['delta']>0,0)
        df['loss']=-df['delta'].where(df['delta']<0,0)
        df['avg_gain(14)']=df['gain'].rolling(14).mean()
        df['avg_loss(14)']=df['loss'].rolling(14).mean()
        df['avg_loss(14)'] = df['avg_loss(14)'].replace(0, 0.01)
        df['rs_14']=df['avg_gain(14)']/df['avg_loss(14)']
        df['rs_14'] = df['rs_14'].clip(0, 100)    # 限制在0-100
        df['rsi_14']=100-(100/(1+df['rs_14']))
        df['avg_gain(6)']=df['gain'].rolling(6).mean()
        df['avg_loss(6)']=df['loss'].rolling(6).mean()
        df['avg_loss(6)'] = df['avg_loss(6)'].replace(0, 0.01)
        df['rs_6']=df['avg_gain(6)']/df['avg_loss(6)']
        df['rs_6'] = df['rs_6'].clip(0, 100)    # 限制在0-100
        df['rsi_6']=100-(100/(1+df['rs_6']))
        #成交 MA5
        df['money_ma5']=df['ma5']*df['vol_ma5']
        #阳 1 阴 0
        df['status']=np.where((df['close']-df['open'])>0,1,0)
        #价pp
        df['pp']=df['close']-df['close'].shift(1)
        #量kk
        df['kk']=df['volume']/df['vol_ma5']
        # ==================== 【新增】MACD 标准公式 ====================
        # 短期EMA(12)、长期EMA(26)、DIF、DEA(9)、MACD柱
        df['ema12'] = df['close'].ewm(span=12, adjust=False).mean()
        df['ema26'] = df['close'].ewm(span=26, adjust=False).mean()
        df['dif'] = df['ema12'] - df['ema26']  # 快线
        df['dea'] = df['dif'].ewm(span=9, adjust=False).mean()  # 慢线
        df['macd'] = 2 * (df['dif'] - df['dea'])  # MACD柱

        # ==================== 【新增】KDJ 标准公式 ====================
        # ==================== 【修复 1】KDJ 分母为 0 问题 ====================
        low_list = df['low'].rolling(9, min_periods=9).min()
        high_list = df['high'].rolling(9, min_periods=9).max()
        
        # 安全除法：分母为0时直接=0，永不爆炸
        df['rsv'] = np.where(
            high_list == low_list, 
            50, 
            (df['close'] - low_list) / (high_list - low_list) * 100
        )

        df['k'] = df['rsv'].ewm(com=2, adjust=False).mean()
        df['d'] = df['k'].ewm(com=2, adjust=False).mean()
        df['j'] = 3 * df['k'] - 2 * df['d']

        # ==================== 【修复 3】清理所有异常值 ====================
        df = df.replace([np.inf, -np.inf], np.nan)
        df = df.fillna(0)                 # J=3K-2D

        
        drop_cols = [
            'hl', 'hc', 'lc', 'delta', 'gain', 'loss',
            'avg_gain(14)', 'avg_loss(14)', 'avg_gain(6)', 'avg_loss(6)'
        ]
        df = df.drop(columns=drop_cols, errors='ignore')
        return df







#1 10收盘价接近10日最高点  10
# df.iloc[-1]['close']>(8*(df['high'].iloc[-10:].max()-df['high'].iloc[-10:].min())/10+df['high'].iloc[-10:].min())
# #2 收盘价大于MA5 10
# df.iloc[-1]['close']>df.iloc[-1]['ma5']
# #3 今日MA5>昨日MA5 10
# df.iloc[-1]['ma5']>df.iloc[-2]['ma5']
# #4 今日ATR在最近20个交易日内ATR排序 10
# atrr=(df['atr'].iloc[-20:]<df['atr'].iloc[-1]).sum()/20
# atrr>0.2 and atrr<0.8
# #5 平均成交额大于2亿 10
# df.iloc[-1]['money_ma5']>200000000
# #6 RSI 6小于70 10
# df.iloc[-1]['rsi(6)']<70
# df.iloc[-1]['rsi(6)']>30
# #7 收盘价大于MA20 10
# df.iloc[-1]['close']>df.iloc[-1]['ma20']
# #8 今日MA20>昨日MA20 10
# df.iloc[-1]['ma20']>df.iloc[-2]['ma20']
# #9 20日新高点 10
# df.iloc[-1]['high'] > df['high'].iloc[-20:-1].max() 
# #10 量价指标 -100 -10 5 15
# p=df.iloc[-1]['close']-df.iloc[-2]['close']
# v=df.iloc[-1]['volume']/df['volume'].iloc[-20:].mean()

#p>0 2>v>1.5 +15
#p>0 v>1 +5
#p>0 v<1  -10 缩量
#p<0 v>2 -100 恐慌抛售
#p<=0 v<1  0 横盘


def score_def(df):
    score=0
    score_list=[]
    atrr=(df['atr'].iloc[-20:]<df['atr'].iloc[-1]).sum()/20
    p=df.iloc[-1]['close']-df.iloc[-2]['close']
    v=df.iloc[-1]['volume']/df['volume'].iloc[-20:].mean()
    if df.iloc[-1]['close']>(8*(df['high'].iloc[-10:].max()-df['high'].iloc[-10:].min())/10+df['high'].iloc[-10:].min()):
        score=score+10
        score_list.append("收盘价接近10日最高点 +10")
    if df.iloc[-1]['close']>df.iloc[-1]['ma5']:
        score=score+10
        score_list.append("收盘价大于MA5 +10")
    if df.iloc[-1]['ma5']>df.iloc[-2]['ma5']:
        score=score+10
        score_list.append("今日MA5>昨日MA5 +10")
    if atrr>0.2 and atrr<0.8:
        score=score+10
        score_list.append("ATR在20日区间位置位于中部 +10")
    if df.iloc[-1]['money_ma5']>200000000:
        score=score+10
        score_list.append("平均成交额大于2亿 +10")
    if df.iloc[-1]['rsi(6)']<70 and df.iloc[-1]['rsi(6)']>30:
        score=score+10
        score_list.append("RSI6小于70大于30 +10")
    if df.iloc[-1]['close']>df.iloc[-1]['ma20']:
        score=score+10
        score_list.append("收盘价大于MA20 +10")
    if df.iloc[-1]['ma20']>df.iloc[-2]['ma20']:
        score=score+10
        score_list.append("今日MA20>昨日MA20 +10")
    if df.iloc[-1]['high'] > df['high'].iloc[-20:-1].max():
        score=score+10
        score_list.append("20日新高点 +10")
    if p > 0:  # 上涨
        if v > 1.5:
            score += 15
            score_list.append("健康上涨 +15")
        elif v >= 1:      # 1 <= v <= 1.5
            score += 5
            score_list.append("温和放量 +5")
        else:             # v < 1
            score -= 10
            score_list.append("缩量背离 -10")
    else:  # p <= 0 下跌或平盘
        if v >= 2:
            score -= 20
            score_list.append("恐慌下跌 -20")
        elif v >= 1:      # 1 <= v < 2
            score -= 10
            score_list.append("放量下跌 -10")
        else:             # v < 1
            score += 0
            score_list.append("缩量整理 0")
    return [score,score_list,df.iloc[-1].to_dict(),df.iloc[-5:].to_dict('records')]





# gp=GP()
# co=gp.gp_code('601515')
# df=gp.gupiao(co[0],co[1],days=60)
# gp.logout()

# df=gp.quant(df)
# for i in range(10):
#     print(score_def(df))
#     df=df.drop(df.index[-1])

腾讯接口

import pymysql
from pymysql.err import OperationalError, ProgrammingError
from gp import GP
import pandas as pd
import time


# 数据库配置
config = {
    "host": "localhost",
    "port": 3306,
    "user": "root",
    "password": "123456",
    "database": "db",
    "charset": "utf8mb4"
}

conn = pymysql.connect(**config)
cursor = conn.cursor()


# ===================== ✅ 极速插入：INSERT IGNORE =====================
def insert_datan(data_list):
    sql = """
        INSERT IGNORE INTO `cmf_quantn` 
        ( `mcode`, `code`, `date`, `open`, `high`, `low`, `close`, `preclose`, `volume`, `pctChg`
        , `turn`, `isST`, `adjustflag`,`is_now`
        ) 
        VALUES 
        (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s
        ,%s,%s,%s,%s
        ) """
    
    # 直接批量插入，重复自动跳过！
    cursor.executemany(sql, data_list)
    conn.commit()

    # affected rows 就是插入成功的数量
    insert_count = cursor.rowcount
    skip_count = len(data_list) - insert_count
    return insert_count, skip_count



def do_coden(exchange, code):
    start = time.time()
    dlx = gp.get_today(exchange, code)
    # print(dlx)
    if dlx:
        row=dlx[0]
        dx=[
            row['code'][3:], row['code'], row['date'], row['open'], row['high'], row['low'], row['close'], row['preclose'], row['volume'], row['pctChg'],
            row['turn'], row['isST'], row['adjustflag'],int(time.time())
        ]
        # print(dx)
        insert_count, skip_count = insert_datan([dx])
        
        print(f"股票 {code} → 插入:{insert_count}条 | 跳过:{skip_count}条 | 耗时:{time.time()-start:.2f}s")

def insert_all(info,code_str):
    start=time.time()
    if info:
        dx=[]
        for m in info:
            row=m[0]
            dx.append([
                row['code'].strip()[4:10], row['code'].strip()[2:4]+"."+row['code'].strip()[4:10], row['date'], row['open'], row['high'], row['low'], row['close'], row['preclose'], row['volume'], row['pctChg'],
                row['turn'], row['isST'], row['adjustflag'],int(time.time())
            ])
        insert_count, skip_count = insert_datan(dx)
        print(f"股票 {code_str} → 插入:{insert_count}条 | 跳过:{skip_count}条 | 耗时:{time.time()-start:.2f}s")

gp=GP(False)

# # info=gp.get_today("sh", "600000")
# # print(info)
# do_coden("sh", "600000")

df = pd.read_csv('all.csv', dtype={'code': str})
i=0
code_str=''
for index, row in df.iterrows():
    c=time.time()
    if i<25:
        code_str=row['exchange']+row['code']+","+code_str
        i=i+1
    else:
        print(code_str)
        ccc=gp.gp_all(code_str)
        i=0
        code_str=''
        insert_all(ccc,code_str)
        x=4-(time.time()-c)
        if x>0:
            time.sleep(x)
ccc=gp.gp_all(code_str)
insert_all(ccc,code_str)