# 克隆自聚宽文章：https://www.joinquant.com/post/40004
# 标题：菜场大妈选股法
# 作者：开心果

import pandas as pd
from jqdata import *

def initialize(context):
    # setting
    log.set_level('order', 'error')
    set_option('use_real_price', True)
    set_option('avoid_future_data', True)
    set_benchmark('000905.XSHG')
    # 设置滑点为理想情况，纯为了跑分好看，实际使用注释掉为好
    set_slippage(PriceRelatedSlippage(0.000))
    # 设置交易成本
    set_order_cost(OrderCost(open_tax=0, close_tax=0.001, open_commission=0.0003, close_commission=0.0003, close_today_commission=0, min_commission=5),type='fund')
    # strategy
    g.stock_num = 10
    run_daily(prepare_stock_list, time='9:05', reference_security='000300.XSHG')
    run_monthly(my_Trader, 1 ,time='9:30')
    run_daily(check_limit_up, time='14:00')


#1-1 根据最近一年分红除以当前总市值计算股息率并筛选    
def get_dividend_ratio_filter_list(context, stock_list, sort, p1, p2):
    time1 = context.previous_date
    time0 = time1 - datetime.timedelta(days=365)
    #获取分红数据，由于finance.run_query最多返回4000行，以防未来数据超限，最好把stock_list拆分后查询再组合
    interval = 1000 #某只股票可能一年内多次分红，导致其所占行数大于1，所以interval不要取满4000
    list_len = len(stock_list)
    #截取不超过interval的列表并查询
    q = query(finance.STK_XR_XD.code, finance.STK_XR_XD.a_registration_date, finance.STK_XR_XD.bonus_amount_rmb
    ).filter(
        finance.STK_XR_XD.a_registration_date >= time0,
        finance.STK_XR_XD.a_registration_date <= time1,
        finance.STK_XR_XD.code.in_(stock_list[:min(list_len, interval)]))
    df = finance.run_query(q)
    #对interval的部分分别查询并拼接
    if list_len > interval:
        df_num = list_len // interval
        for i in range(df_num):
            q = query(finance.STK_XR_XD.code, finance.STK_XR_XD.a_registration_date, finance.STK_XR_XD.bonus_amount_rmb
            ).filter(
                finance.STK_XR_XD.a_registration_date >= time0,
                finance.STK_XR_XD.a_registration_date <= time1,
                finance.STK_XR_XD.code.in_(stock_list[interval*(i+1):min(list_len,interval*(i+2))]))
            temp_df = finance.run_query(q)
            df = df.append(temp_df)
    dividend = df.fillna(0)
    dividend = dividend.set_index('code')
    dividend = dividend.groupby('code').sum()
    temp_list = list(dividend.index) #query查询不到无分红信息的股票，所以temp_list长度会小于stock_list
    #获取市值相关数据
    q = query(valuation.code,valuation.market_cap).filter(valuation.code.in_(temp_list))
    cap = get_fundamentals(q, date=time1)
    cap = cap.set_index('code')
    #计算股息率
    DR = pd.concat([dividend, cap] ,axis=1, sort=False)
    DR['dividend_ratio'] = (DR['bonus_amount_rmb']/10000) / DR['market_cap']
    #排序并筛选
    DR = DR.sort_values(by=['dividend_ratio'], ascending=sort)
    final_list = list(DR.index)[int(p1*len(DR)):int(p2*len(DR))]
    return final_list

def my_Trader(context):
    # all stocks
    dt_last = context.previous_date
    stocks = get_all_securities('stock', dt_last).index.tolist()
    stocks = filter_kcbj_stock(stocks)
     #高股息(全市场最大25%)
    stocks = get_dividend_ratio_filter_list(context, stocks, False, 0, 0.25)
    # fuandamental data
    df = get_fundamentals(query(valuation.code).filter(valuation.code.in_(stocks)).order_by(valuation.market_cap.asc()))
    
    choice = list(df.code)
    choice = filter_st_stock(choice)
    choice = filter_paused_stock(choice)
    choice = filter_limitup_stock(context,choice)
    choice = filter_limitdown_stock(context,choice)
    choice = filter_highprice_stock(context,choice)
    choice = choice[:g.stock_num]
    cdata = get_current_data()
    # Sell
    for s in context.portfolio.positions:
        if (s  not in choice) :
            log.info('Sell', s, cdata[s].name)
            order_target(s, 0)
    # buy
    position_count = len(context.portfolio.positions)
    if g.stock_num > position_count:
        psize = context.portfolio.available_cash/(g.stock_num - position_count)
        for s in choice:
            if s not in context.portfolio.positions:
                log.info('buy', s, cdata[s].name)
                order_value(s, psize)
                if len(context.portfolio.positions) == g.stock_num:
                    break
            

# 准备股票池
def prepare_stock_list(context):
    #获取已持有列表
    g.high_limit_list = []
    hold_list = list(context.portfolio.positions)
    if hold_list:
        df = get_price(hold_list, end_date=context.previous_date, frequency='daily',
                       fields=['close', 'high_limit'],
                       count=1, panel=False)
        g.high_limit_list = df[df['close'] == df['high_limit']]['code'].tolist()
        
#  调整昨日涨停股票
def check_limit_up(context):
     # 获取持仓的昨日涨停列表
    current_data = get_current_data()
    if g.high_limit_list:
        for stock in g.high_limit_list:
            if current_data[stock].last_price < current_data[stock].high_limit:
                log.info("[%s]涨停打开，卖出" % stock)
                order_target(stock, 0)
            else:
                log.info("[%s]涨停，继续持有" % stock)
 
 
# 过滤科创北交股票
def filter_kcbj_stock(stock_list):
    for stock in stock_list[:]:
        if stock[0] == '4' or stock[0] == '8' or stock[:2] == '68':
            stock_list.remove(stock)
    return stock_list

# 过滤停牌股票
def filter_paused_stock(stock_list):
	current_data = get_current_data()
	return [stock for stock in stock_list if not current_data[stock].paused]


# 过滤ST及其他具有退市标签的股票
def filter_st_stock(stock_list):
	current_data = get_current_data()
	return [stock for stock in stock_list
			if not current_data[stock].is_st
			and 'ST' not in current_data[stock].name
			and '*' not in current_data[stock].name
			and '退' not in current_data[stock].name]


# 过滤涨停的股票
def filter_limitup_stock(context, stock_list):
	last_prices = history(1, unit='1m', field='close', security_list=stock_list)
	current_data = get_current_data()
	
	# 已存在于持仓的股票即使涨停也不过滤，避免此股票再次可买，但因被过滤而导致选择别的股票
	return [stock for stock in stock_list if stock in context.portfolio.positions.keys()
			or last_prices[stock][-1] < current_data[stock].high_limit]

# 过滤跌停的股票
def filter_limitdown_stock(context, stock_list):
	last_prices = history(1, unit='1m', field='close', security_list=stock_list)
	current_data = get_current_data()
	
	return [stock for stock in stock_list if stock in context.portfolio.positions.keys()
			or last_prices[stock][-1] > current_data[stock].low_limit]

#2-4 过滤股价高于9元的股票	
def filter_highprice_stock(context,stock_list):
	last_prices = history(1, unit='1m', field='close', security_list=stock_list)
	return [stock for stock in stock_list if stock in context.portfolio.positions.keys()
			or last_prices[stock][-1] < 9]
						
# end