哪个大神给改一下，菜场大妈策略，能在QMT上自动运行

这个策略是在聚宽里面的代码，想做实盘看看，不懂就问，有没有大神看看怎么整

克隆自聚宽文章：https://www.joinquant.com/post/46544

标题：正黄旗大妈选股资金利用率的极限改进

作者：jql123

克隆自聚宽文章：https://www.joinquant.com/post/40004

标题：删

作者：开心果

克隆自聚宽文章：https://www.joinquant.com/post/40038

标题：正黄旗大妈选股法

作者：GoodThinker

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 # 聚宽的panda版本是 0.23.4 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 g.choice = [] g.just_sold = [] run_daily(prepare_stock_list, time='9:05', reference_security='000300.XSHG') run_daily(check_limit_up, time='14:00') run_monthly(my_Trader, 1 ,time='9:30') run_monthly(go_Trader, 1 ,time='14:55')

def my_Trader(context):

1 all stocks

dt_last = context.previous_date stocks = get_all_securities('stock', dt_last).index.tolist() stocks = filter_kcbj_stock(stocks)

2 股息率

stocks = get_dividend_ratio_filter_list(context, stocks, False, 0, 0.25)

3 peg

stocks = get_peg(context,stocks)

4 各种过滤

choice = filter_st_stock(stocks) choice = filter_paused_stock(choice) choice = filter_limitup_stock(context,choice) choice = filter_limitdown_stock(context,choice)

5 低价股

choice = filter_highprice_stock(context,choice) g.choice = choice[:g.stock_num]

def go_Trader(context): g.just_sold = [] #每月清零一次 g.just_sold 防止其中内容一直膨胀 cdata = get_current_data() choice = g.choice

Sell

for s in context.portfolio.positions: if (s not in choice) : log.info('Sell', s, cdata[s].name) order_target(s, 0) # 如果跌停怎么办？

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) # 这里如果涨停怎么办？ if len(context.portfolio.positions) == g.stock_num: break

def cap(context): current_data = get_current_data() #获取日期 hold_stocks = context.portfolio.positions.keys() for s in hold_stocks: q = query(valuation).filter(valuation.code == s) df = get_fundamentals(q)

log.info(s,current_data[s].name,'流值',df['circulating_market_cap'][0],'亿')

log.info(s,current_data[s].name,'市值',df['market_cap'][0],'亿') log.info(s,current_data[s].name,'股价',current_data[s].last_price,'元')

def get_peg(context,stocks):

获取基本面数据

q = query(valuation.code, valuation.pe_ratio / indicator.inc_net_profit_year_on_year,# PEG indicator.roe / valuation.pb_ratio, # PB-ROE 收益率指标：ROE/PB特别适合于周期类、成长性一般企业的估值分析 indicator.roe, ).filter( valuation.pe_ratio / indicator.inc_net_profit_year_on_year>-3, valuation.pe_ratio / indicator.inc_net_profit_year_on_year<3,

indicator.roe / valuation.pb_ratio > 3.2, #国债收益率

valuation.code.in_(stocks)) df_fundamentals = get_fundamentals(q, date = None) stocks = list(df_fundamentals.code)

fuandamental data

df = getfundamentals(query(valuation.code).filter(valuation.code.in(stocks)).order_by(valuation.market_cap.asc())) choice = list(df.code) return choice

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_XRXD.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_XRXD.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.marketcap).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(p1len(DR)):int(p2len(DR))] return final_list

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: order_target(stock, 0) g.just_sold.append(stock) buy_after_high_limit_sell(context)

def buy_after_high_limit_sell(context):

检查持仓，如果有卖出就再买入

position_count = len(context.portfolio.positions) if g.stock_num > position_count and position_count != 0: # position_count != 0 用于避免第一次运行时代替go_trader 买入 my_Trader(context) # 计算 g.choice cdata = get_current_data() psize = context.portfolio.available_cash/(g.stock_num - position_count) for s in g.choice: if s not in context.portfolio.positions and s not in g.just_sold: log.info('涨停卖出后的买入:', s, cdata[s].name) order_value(s, psize) if len(context.portfolio.positions) == g.stock_num: break

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()

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