import talib as ta
import numpy as np
import pandas as pd
import jqdata as jq
import re
import requests

'''定义公共变量和函数'''

# 获取交易日数据
today = datetime.date.today()
trade_days = jq.get_trade_days(end_date=today, count=21)
today = trade_days[-1] # 当天
yesterday = trade_days[-2] # 上一个交易日
previous_day_20 = trade_days[-21] # 前20个交易日

# 获取涨跌幅函数
def get_stock_rise(stock_list, start_date, end_date):
    # 获取当日未停牌股票
    panel = get_price(stock_list, start_date=end_date, end_date=end_date, fields=['paused'])
    df = panel['paused'].T
    df = df[df[end_date] == 0]
    stock_list = list(df.index)

    # 获取收盘价数据
    panel = get_price(stock_list, start_date=start_date, end_date=end_date, fields=['close'])
    df = panel['close'].T

    # 计算涨跌幅
    df['rise'] = (df[end_date] - df[start_date]) / df[start_date]
    df = df.sort_index(by='rise', ascending=False)
    
    return df

# 匹配行业和概念名称
def match_text(industry_name):
    r  = requests.get(r'https://www.joinquant.com/data/dict/plateData')
    tmp = r.content.decode('utf-8')
    matches = re.findall(r"<h[\d]{1,} id=\"" + industry_name + "\">.*?" + industry_name + ".*?</h[\d]{1,}>.*?<table>(.*?)</table>", tmp, re.S)
    content = matches[0]
    tr_matches = re.findall(r"<tr>.*?<td>(.*?)</td>.*?<td>(.*?)</td>.*?<td>(.*?)</td>.*?</tr>", content, re.S)
    for code, name, start_date in tr_matches:
        yield code, name, start_date

# 获取板块和概念数据
def get_block_list(textlist):
    df = map(lambda x:pd.DataFrame([[code, name, start_date] for code, name, start_date in match_text(x)], columns=['code','name','start_date']), textlist)
    df_block = pd.concat(df,axis=0).reset_index(drop=1)
    
    # 增加涨跌幅列
    df_block['rise'] = 0
    
    return df_block

'''统计盘面数据'''

# 获取涨跌幅数据
stock_list = list(get_all_securities(['stock']).index)
df = get_stock_rise(stock_list, yesterday, today)

# 涨跌幅统计
df_positive = df[df['rise'] > 0]
df_negative = df[df['rise'] < 0]
df_zero = df[df['rise'] == 0]
df_positive_5 = df[df['rise'] >= 0.05]
df_negative_5 = df[df['rise'] <= -0.05]
rise_median = df['rise'].median()

# 获取首日上市新股
# 因首日上市新股涨幅统计为nan，需在涨幅统计时加上新股的数量
df_new = np.isnan(df.rise)
df_new = df_new[df_new == True]

# 获取两市成交额
index_sh = '000001.XSHG' #上证综指
index_sz = '399106.XSHE' #深证综指
panel = get_price([index_sh,index_sz], start_date=yesterday, end_date=today, fields=['money'])
df = panel['money'].T

# 成交额统计
money_today = df[today].sum() / 1e+8
money_rise = (df[today].sum() - df[yesterday].sum()) / 1e+8

# 复盘总结
review = '（%s）\n复盘：\n' % today
review += '今日两市上涨%s家、下跌%s家、平盘%s家，涨幅在5%%以上%s家、跌幅在5%%以上%s家，涨幅中位数%.2f%%。\n' % (
           (len(df_positive) + len(df_new)), 
           len(df_negative),
           len(df_zero), 
           (len(df_positive_5) + len(df_new)), 
           len(df_negative_5), 
           rise_median*100)

review += '两市成交额%.2f亿，较上一交易日%s%.2f亿。\n' % (
           money_today, 
           ('增加' if money_rise > 0 else '减少'), 
           abs(money_rise))

print(review)
write_file('复盘统计/review.txt', review)

'''统计SAR指标'''

# 初始化参数
n = 10
index_list = ('000001.XSHG', #上证指数
              '399001.XSHE', #深证成数
              '399005.XSHE', #中小板指
              '399006.XSHE', #创业板指
              '000016.XSHG', #上证50
              '000300.XSHG', #沪深300
              '000905.XSHG', #中证500
              '399678.XSHE', #深次新股
             )
df_sar = pd.DataFrame(index=index_list, 
                      columns=('name','signal','critical_value','critical_rise','previous_20_rise','annual_rise'))
review = '\nSAR指标：\n'

# 统计yes/no信号
for index in index_list:
    # 获取指数名称
    df_sar.name[index] = get_security_info(index).display_name
    
    # 计算SAR值
    df = get_price(index, end_date=today, fields=('open','high','low','close'), count=n)
    sar = ta.SAR(df.high.values, df.low.values, acceleration=0.02, maximum=0.2)    
    
    # 判断yes/no信号
    df_sar.signal[index] = 'yes' if df.close[-1] > sar[-1] else 'no'
    
    # 临界值
    df_sar.critical_value[index] = sar[-1]
    
    # 临界幅度，达到临界值需要的涨跌幅度
    df_sar.critical_rise[index] = (sar[-1] - df.close[-1]) / df.close[-1]
    
    # 计算20天涨幅
    df_20 = get_price(index, end_date=previous_day_20, fields='close', count=1)
    df_sar.previous_20_rise[index] = (df.close[-1] - df_20.close[-1]) / df_20.close[-1]
    
    # 计算年度涨幅
    last_year = datetime.date.today().year - 1
    last_end_date = datetime.date(last_year, 12, 31)
    df_last = get_price(index, end_date=last_end_date, fields='close', count=1)
    df_sar.annual_rise[index] = (df.close[-1] - df_last.close[-1]) / df_last.close[-1]
    
    # 输出
    review += '%s：%s，临界值 %.2f，临界幅度 %.2f%%，20天涨幅 %.2f%%，年度涨幅 %.2f%%\n' % (
               df_sar.name[index], 
               df_sar.signal[index], 
               df_sar.critical_value[index], 
               df_sar.critical_rise[index]*100,
               df_sar.previous_20_rise[index]*100,
               df_sar.annual_rise[index]*100)

print(review)
write_file('复盘统计/review.txt', review, append=True)

'''统计行业板块涨跌幅'''

#获取行业数据
textlist = ["申万一级行业"]
df_block = get_block_list(textlist)
df_block['rise_20'] = 0

# 获取板块涨跌幅数据
for index in df_block.index:
    # 获取板块成份股
    stock_list = list(get_industry_stocks(df_block.code[index], today))
    
    # 获取板块今日涨跌幅数据
    df = get_stock_rise(stock_list, yesterday, today)
    df_block.loc[index,'rise'] = df.rise.mean()
    
    # 获取板块20天涨跌幅数据
    df_20 = get_stock_rise(stock_list, previous_day_20, today)
    df_block.loc[index,'rise_20'] = df_20.rise.mean()

print('申万一级行业涨跌幅')
df_block.sort(["rise_20"], ascending=False)

'''统计概念板块涨跌幅'''

#获取行业数据
textlist = ["概念板块"]
df_block = get_block_list(textlist)
df_block['rise_20'] = 0

# 获取板块涨跌幅数据
for index in df_block.index:
    # 获取板块成份股
    stock_list = list(get_concept_stocks(df_block.code[index], today))
    
    # 获取板块今日涨跌幅数据
    df = get_stock_rise(stock_list, yesterday, today)
    df_block.loc[index,'rise'] = df.rise.mean()
    
    # 获取板块20天涨跌幅数据
    df_20 = get_stock_rise(stock_list, previous_day_20, today)
    df_block.loc[index,'rise_20'] = df_20.rise.mean()

print('概念板块涨跌幅')
df_block.sort(["rise_20"], ascending=False)