高增股池小市值轮动

策略作者: 水滴

```python
# 风险及免责提示：该策略由聚宽用户在聚宽社区分享，仅供学习交流使用。
# 原文一般包含策略说明，如有疑问请到原文和作者交流讨论。
# 原文网址：https://www.joinquant.com/post/47330
# 标题：此消彼长
# 作者：明曦
# 原回测条件：2016-01-01 到 2024-03-28, ￥500000, 每天

#导入函数库
from jqdata import *
from jqfactor import *
import numpy as np
import pandas as pd

#初始化函数 
def initialize(context):
    # 设定基准
    set_benchmark('399303.XSHE')
    # 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)
    # 将滑点设置为0
    set_slippage(FixedSlippage(0))
    # 设置交易成本万分之三，不同滑点影响可在归因分析中查看
    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='stock')
    # 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.small_stock_num=5
    g.big_stock_num=5
    g.hold_list = [] #当前持仓的全部股票    
    g.yesterday_HL_list = [] #记录持仓中昨日涨停的股票
    g.factor_list =['non_recurring_gain_loss', 'non_operating_net_profit_ttm', 'roe_ttm_8y', 'sharpe_ratio_20']
    g.coef_list =[-1.3651516084272432e-13, -3.673549665003535e-14, -0.006872269236387061, -3.922028093095638e-12]
  
  
    
    run_daily(prepare_stock_list, time='9:05', 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')
    run_monthly(monthly_adjustment, 1, 'open')

#1-1 准备股票池
def prepare_stock_list(context):
    #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.yesterday_HL_list = list(df.code)
    else:
        g.yesterday_HL_list = []

#1-2 选股模块
def monthly_adjustment(context):
    yesterday = context.previous_date
    initial_list = get_all_securities().index.tolist()
    initial_list = filter_new_stock(context, initial_list)
    initial_list = filter_kcbj_stock(initial_list)
    initial_list = filter_st_stock(initial_list)
    initial_list = filter_paused_stock(initial_list)
    initial_list = filter_limitup_stock(context, initial_list)
    initial_list = filter_limitdown_stock(context, initial_list)
    #MS
    factor_values = get_factor_values(initial_list, [
        g.factor_list[0],
        g.factor_list[1],
        g.factor_list[2],
        g.factor_list[3],
        ], end_date=yesterday, count=1)
    df = pd.DataFrame(index=initial_list, columns=factor_values.keys())
    df[g.factor_list[0]] = list(factor_values[g.factor_list[0]].T.iloc[:,0])
    df[g.factor_list[1]] = list(factor_values[g.factor_list[1]].T.iloc[:,0])
    df[g.factor_list[2]] = list(factor_values[g.factor_list[2]].T.iloc[:,0])
    df[g.factor_list[3]] = list(factor_values[g.factor_list[3]].T.iloc[:,0])
    df = df.dropna()
    df['total_score'] = g.coef_list[0]*df[g.factor_list[0]] + g.coef_list[1]*df[g.factor_list[1]] + g.coef_list[2]*df[g.factor_list[2]] + g.coef_list[3]*df[g.factor_list[3]]
    df = df.sort_values(by=['total_score'], ascending=False) #分数越高即预测未来收益越高，排序默认降序
    complex_factor_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_factor_list)).order_by(valuation.circulating_market_cap.asc())
    df = get_fundamentals(q)
    df = df[df['eps']>0]
    small_stock_list  = list(df.code)
    small_stock_list = small_stock_list[:min(g.small_stock_num, len(small_stock_list))]
    df1 = get_fundamentals(query(
            valuation.code,
        ).filter(
            valuation.code.in_(initial_list),
            valuation.pe_ratio_lyr.between(0,30),#市盈率
            valuation.ps_ratio.between(0,8),#市销率TTM
            valuation.pcf_ratio<10,#市现率TTM
            indicator.eps>0.3,#每股收益
            indicator.roe>0.1,#净资产收益率
            indicator.net_profit_margin>0.1,#销售净利率
            indicator.gross_profit_margin>0.3,#销售毛利率
            indicator.inc_revenue_year_on_year>0.25,#营业收入同比增长率

).order_by(
        valuation.market_cap.desc()).limit(g.big_stock_num)).set_index('code').index.tolist()
    big_stock_list=df1
    final_list=small_stock_list+big_stock_list
    #调仓卖出
    for stock in g.hold_list:
        if (stock not in final_list) and (stock not in g.yesterday_HL_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(final_list)
    if target_num > position_count:
        value = context.portfolio.cash / (target_num - position_count)
        for stock in final_list:
            if context.portfolio.positions[stock].total_amount == 0:
                if open_position(stock, value):
                    if len(context.portfolio.positions) == target_num:
                        break

#1-4 调整昨日涨停股票
def check_limit_up(context):
    now_time = context.current_dt
    if g.yesterday_HL_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.yesterday_HL_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 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

#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)]

#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

#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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