小市值市场轮动版 5年12倍

策略作者: 水滴

```python
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# 原文网址：https://www.joinquant.com/post/44880
# 标题：5年12倍-小市值
# 作者：道尘

#导入函数库
from jqdata import *
from jqfactor import get_factor_values
from jqlib.technical_analysis import *
import numpy as np
import pandas as pd
import statsmodels.api as sm
import datetime as dt

#初始化函数 
def initialize(context):
    # 设定基准
    set_benchmark('000905.XSHG')
    # 用真实价格交易
    set_option('use_real_price', True)
    # 打开防未来函数
    set_option("avoid_future_data", True)
    # 交易量限制
    set_option('order_volume_ratio', 1)
    # 将滑点设置为0，不同滑点影响可在归因分析中查看
    set_slippage(PriceRelatedSlippage(0.002),type='stock')
    # 设置交易成本万一免五
    set_order_cost(OrderCost(open_tax=0, close_tax=0.001, open_commission=0.0001, close_commission=0.0001, close_today_commission=0, min_commission=0.1),type='fund')
    # 过滤order中低于error级别的日志
    log.set_level('order', 'error')
    #初始化全局变量
    g.stock_num = 9
    g.limit_up_list = [] #记录持仓中涨停的股票
    g.hold_list = [] #当前持仓的全部股票
    g.history_hold_list = [] #过去一段时间内持仓过的股票
    g.not_buy_again_list = [] #最近买过且涨停过的股票一段时间内不再买入
    g.limit_days = 10 #不再买入的时间段天数
    g.target_list = [] #开盘前预操作股票池
    # 设置交易运行时间
    run_daily(prepare_stock_list, time='9:05', reference_security='000300.XSHG')
    run_weekly(weekly_adjustment, weekday=1, time='9:30', reference_security='000300.XSHG')
    run_daily(check_limit_up, time='14:00', reference_security='000300.XSHG') #检查持仓中的涨停股是否需要卖出
    run_daily(print_position_info, time='15:10', reference_security='000300.XSHG')

def after_code_changed(context):
    unschedule_all()
    run_daily(prepare_stock_list, time='9:05', reference_security='000300.XSHG')
    run_weekly(weekly_adjustment, weekday=1, time='9:30', reference_security='000300.XSHG')
    run_daily(check_limit_up, time='14:00', reference_security='000300.XSHG') #检查持仓中的涨停股是否需要卖出
    run_daily(print_position_info, time='15:10', reference_security='000300.XSHG')

#1-1 选股模块
def get_factor_filter_list(context,stock_list,jqfactor,sort,p1,p2):
    yesterday = context.previous_date
    score_list = get_factor_values(stock_list, jqfactor, end_date=yesterday, count=1)[jqfactor].iloc[0].tolist()
    df = pd.DataFrame(columns=['code','score'])
    df['code'] = stock_list
    df['score'] = score_list
    df = df.dropna()
    df.sort_values(by='score', ascending=sort, inplace=True)
    filter_list = list(df.code)[int(p1*len(stock_list)):int(p2*len(stock_list))]
    return filter_list

#1-2 选股模块
def get_stock_list(context):
    yesterday = str(context.previous_date)    
    initial_list = list(set(get_all_securities().index) & set(get_hot_industry_stock(context)))
    # initial_list = get_all_securities().index.tolist()
    initial_list = filter_new_stock(context,initial_list)
    initial_list = filter_kcb_stock(context, initial_list)
    initial_list = filter_st_stock(initial_list)
    #SG 5年营业收入增长率
    sg_list = get_factor_filter_list(context, initial_list, 'sales_growth', False, 0, 0.1)
    q = query(valuation.code,valuation.circulating_market_cap,indicator.eps).filter(valuation.code.in_(sg_list)).order_by(valuation.circulating_market_cap.asc())
    df = get_fundamentals(q, date=yesterday)
    df = df[df['eps']>0]
    sg_list = list(df.code)
    #MS
    factor_values = get_factor_values(initial_list, [
        'operating_revenue_growth_rate', #营业收入增长率
        'total_profit_growth_rate', #利润总额增长率
        'net_profit_growth_rate', #净利润增长率
        'earnings_growth', #5年盈利增长率
        ], end_date=yesterday, count=1)
    df = pd.DataFrame(index=initial_list, columns=factor_values.keys())
    df['operating_revenue_growth_rate'] = list(factor_values['operating_revenue_growth_rate'].T.iloc[:,0])
    df['total_profit_growth_rate'] = list(factor_values['total_profit_growth_rate'].T.iloc[:,0])
    df['net_profit_growth_rate'] = list(factor_values['net_profit_growth_rate'].T.iloc[:,0])
    df['earnings_growth'] = list(factor_values['earnings_growth'].T.iloc[:,0])
    df['total_score'] = 0.1*df['operating_revenue_growth_rate'] + 0.35*df['total_profit_growth_rate'] + 0.15*df['net_profit_growth_rate'] + 0.4*df['earnings_growth']
    df = df.sort_values(by=['total_score'], ascending=False)
    complex_growth_list = list(df.index)[:int(0.1*len(list(df.index)))]
    q = query(valuation.code,valuation.circulating_market_cap,indicator.eps).filter(valuation.code.in_(complex_growth_list)).order_by(valuation.circulating_market_cap.asc())
    df = get_fundamentals(q)
    df = df[df['eps']>0]
    ms_list = list(df.code)
    #PEG
    peg_list = get_factor_filter_list(context, initial_list, 'PEG', True, 0, 0.2)
    turnover_list = get_factor_filter_list(context, peg_list, 'turnover_volatility', True, 0, 0.5)
    q = query(valuation.code,valuation.circulating_market_cap,indicator.eps).filter(valuation.code.in_(turnover_list)).order_by(valuation.circulating_market_cap.asc())
    df = get_fundamentals(q, date=yesterday)
    peg_list = list(df.code)
    
    final_list = [sg_list, ms_list, peg_list]
    return final_list

#1-3 准备股票池
def prepare_stock_list(context):
    yesterday = context.previous_date
    #获取已持有列表
    g.hold_list= []
    for position in list(context.portfolio.positions.values()):
        stock = position.security
        g.hold_list.append(stock)
    #获取最近一段时间持有过的股票列表
    g.history_hold_list.append(g.hold_list)
    if len(g.history_hold_list) >= g.limit_days:
        g.history_hold_list = g.history_hold_list[-g.limit_days:]
    temp_set = set()
    for hold_list in g.history_hold_list:
        for stock in hold_list:
            temp_set.add(stock)
    g.not_buy_again_list = list(temp_set)
    #获取昨日涨停列表
    if g.hold_list != []:
        df = get_price(g.hold_list, end_date=context.previous_date, frequency='daily', fields=['close','high_limit'], count=1, panel=False, fill_paused=False)
        df = df[df['close'] == df['high_limit']]
        g.high_limit_list = list(df.code)
    else:
        g.high_limit_list = []
    if len(g.high_limit_list):
        log.info("昨日涨停:[%s]" % (g.high_limit_list))
        
    '''
    # 每日潜池变化提醒
    get_stock_prepare_list = get_stock_list(context)
    sg_list = get_stock_prepare_list[0][:5]
    ms_list = get_stock_prepare_list[1][:5]
    peg_list = get_stock_prepare_list[2][:5]
    union_list = list(set(sg_list).union(set(ms_list)).union(set(peg_list)))
    q = query(valuation.code,valuation.circulating_market_cap).filter(valuation.code.in_(union_list)).order_by(valuation.circulating_market_cap.asc())
    df = get_fundamentals(q)
    get_prepare_list = list(df.code)
    for stock in get_prepare_list:
        stock_market_cap = get_factor_values(stock,'circulating_market_cap', end_date=yesterday, count=1)['circulating_market_cap'].iloc[0]
        stock_name = get_security_info(stock, date=yesterday).display_name
        log.info("潜力股:%s,代码:%s,流通市值:%f" ,stock_name, stock, stock_market_cap/100_000_000)
'''

#1-5 整体调整持仓
def weekly_adjustment(context):
    #1 #获取应买入列表 
    all_list = get_stock_list(context)
    sg_list = all_list[0][:5]
    ms_list = all_list[1][:5]
    peg_list = all_list[2][:5]
    union_list = list(set(sg_list).union(set(ms_list)).union(set(peg_list)))
    q = query(valuation.code,valuation.circulating_market_cap).filter(valuation.code.in_(union_list)).order_by(valuation.circulating_market_cap.asc())
    df = get_fundamentals(q)
    g.target_list = list(df.code) #...2
    g.target_list = filter_paused_stock(g.target_list)
    g.target_list = filter_limitup_stock(context, g.target_list)
    g.target_list = filter_limitdown_stock(context, g.target_list)
    #过滤最近买过且涨停过的股票
    recent_limit_up_list = get_recent_limit_up_stock(context, g.target_list, g.limit_days)
    black_list = list(set(g.not_buy_again_list).intersection(set(recent_limit_up_list)))
    g.target_list = [stock for stock in g.target_list if stock not in black_list]
    #截取不超过最大持仓数的股票量
    g.target_list = g.target_list[:min(g.stock_num, len(g.target_list))]
    #调仓卖出
    for stock in g.hold_list:
        if (stock not in g.target_list) and (stock not in g.high_limit_list):
            log.info("卖出[%s]" % (stock))
            position = context.portfolio.positions[stock]
            close_position(position)
        else:
            log.info("已持有[%s]" % (stock))
    #调仓买入
    position_count = len(context.portfolio.positions)
    target_num = len(g.target_list)
    if target_num > position_count:
        value = context.portfolio.cash / (target_num - position_count)
        for stock in g.target_list:
            if context.portfolio.positions[stock].total_amount == 0:
                if open_position(stock, value):
                    if len(context.portfolio.positions) == target_num:
                        break

#1-6 调整昨日涨停股票
def check_limit_up(context):
    now_time = context.current_dt
    if g.high_limit_list != []:
        #对昨日涨停股票观察到尾盘如不涨停则提前卖出，如果涨停即使不在应买入列表仍暂时持有
        for stock in g.high_limit_list:
            current_data = get_price(stock, end_date=now_time, frequency='1m', fields=['close','high_limit'], skip_paused=False, fq='pre', count=1, panel=False, fill_paused=True)
            if current_data.iloc[0,0] < current_data.iloc[0,1]:
                log.info("[%s]涨停打开，卖出" % (stock))
                position = context.portfolio.positions[stock]
                close_position(position)
            else:
                log.info("[%s]涨停，继续持有" % (stock))

#2-1 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]

#2-2 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]

#2-3 获取最近N个交易日内有涨停的股票
def get_recent_limit_up_stock(context, stock_list, recent_days):
    stat_date = context.previous_date
    new_list = []
    for stock in stock_list:
        df = get_price(stock, end_date=stat_date, frequency='daily', fields=['close','high_limit'], count=recent_days, panel=False, fill_paused=False)
        df = df[df['close'] == df['high_limit']]
        if len(df) > 0:
            new_list.append(stock)
    return new_list

#2-4 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]

#2-5 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]

#2-6 过滤科创板
def filter_kcb_stock(context, stock_list):
    return [stock for stock in stock_list  if stock[0:3] != '688']

#2-7 过滤次新股
def filter_new_stock(context,stock_list):
    yesterday = context.previous_date
    return [stock for stock in stock_list if not yesterday - get_security_info(stock).start_date < datetime.timedelta(days=375)]

# 2-8 筛选热门行业成分股
def get_hot_industry_stock(context, count=10, number=24):
    end_date = context.previous_date
    by_date = get_trade_days(end_date=end_date,count=count+20)[0]
    stock_list = get_all_securities(date=by_date).index.tolist()  # count+20个交易日之前就已经上市的
    df_close = get_price(stock_list,end_date=end_date, count=count+20, fields='close', panel=False).pivot(index='time', values='close', columns='code')
    df_bias = df_close.iloc[20:] > df_close.rolling(20).mean().iloc[20:]  # C > MA20
    df_industries = get_industries('sw_l1', date=end_date)
    df_industries['code'] = list(df_industries.index)
    df=pd.DataFrame()
    columns = set(df_bias.columns)
    for idx, row in df_industries.iterrows():
        ind_stocks = set(get_industry_stocks(idx, date=end_date))  # 行业成分股
        ind_avail_stocks = list(columns & ind_stocks) # 成分股 在df_bias表中存在的
        if ind_avail_stocks:
            # 计算该行业成分股C>MA20的百分比，技巧：df_bias[ind_avail_stocks]
            df[row['code']] = (100*(df_bias[ind_avail_stocks].sum(axis=1))/len(ind_avail_stocks)).astype(int)
    df.sort_index(ascending=False, inplace=True)
    sr = df.iloc[0:count,:].mean().sort_values(ascending=False, inplace=False)
    sr = list(sr[0:number].index)
    stocks_set = set()
    for s in sr:
        ind_stocks = set(get_industry_stocks(s, date=end_date))
        stocks_set.update(ind_stocks)
        # get_industry_stocks(sr, date=end_date)
    return list(stocks_set)

#3-1 交易模块-自定义下单
def order_target_value_(security, value):
	if value == 0:
		log.debug("Selling out %s" % (security))
	else:
		log.debug("Order %s to value %f" % (security, value))
	return order_target_value(security, value)

#3-2 交易模块-开仓
def open_position(security, value):
	order = order_target_value_(security, value)
	if order != None and order.filled > 0:
		return True
	return False

#3-3 交易模块-平仓
def close_position(position):
	security = position.security
	order = order_target_value_(security, 0)  # 可能会因停牌失败
	if order != None:
		if order.status == OrderStatus.held and order.filled == order.amount:
			return True
	return False

#3-4 交易模块-调仓
def adjust_position(context, buy_stocks, stock_num):
	for stock in context.portfolio.positions:
		if stock not in buy_stocks:
			log.info("[%s]不在应买入列表中" % (stock))
			position = context.portfolio.positions[stock]
			close_position(position)
		else:
			log.info("[%s]已经持有无需重复买入" % (stock))

position_count = len(context.portfolio.positions)
	if stock_num > position_count:
		value = context.portfolio.cash / (stock_num - position_count)
		for stock in buy_stocks:
			if context.portfolio.positions[stock].total_amount == 0:
				if open_position(stock, value):
					if len(context.portfolio.positions) == g.stock_num:
						break

#4-1 打印每日持仓信息
def print_position_info(context):
    #打印当天成交记录
    trades = get_trades()
    for _trade in trades.values():
        print('成交记录：'+str(_trade))
    #打印账户信息
    for position in list(context.portfolio.positions.values()):
        securities=position.security
        cost=position.avg_cost
        price=position.price
        ret=100*(price/cost-1)
        value=position.value
        amount=position.total_amount    
        print('代码:{}'.format(securities))
        print('成本价:{}'.format(format(cost,'.2f')))
        print('现价:{}'.format(price))
        print('收益率:{}%'.format(format(ret,'.2f')))
        print('持仓(股):{}'.format(amount))
        print('市值:{}'.format(format(value,'.2f')))
        print('———————————————————————————————————')
    print('———————————————————————————————————————分割线————————————————————————————————————————')
```

发布时间: 2025-02-26 20:58:18

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水滴

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