基于分红率、波动率和负债率的量化策略

该策略基于最近一年分红除以当前总市值计算股息率并筛选，并结合波动率和负债率因子筛选股票。策略每周进行一次调整，并对昨日涨停股票进行观察。

1. 策略核心逻辑

获取高股息率股票: 根据最近一年分红除以当前总市值计算股息率，并筛选股息率最高的股票。
过滤高波动股票: 从高股息率股票中过滤掉波动率较大的股票，以降低风险。
筛选低负债股票: 从高股息率、低波动率股票中筛选出负债率较低的股票，以提高安全边际。
流通市值轮动: 在满足上述条件的股票中，选择流通市值较小的股票，以获取更大的潜在涨幅。
每周调整持仓: 每周调整一次持仓，将不符合条件的股票卖出，并买入符合条件的新股票。
观察昨日涨停股票: 对昨日涨停的股票进行观察，如果尾盘不涨停，则提前卖出，如果涨停，则继续持有。

2. 策略代码

from jqdata import *
from jqfactor import get_factor_values
import numpy as np
import pandas as pd

# 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)
    # 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='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 = 10
    g.limit_days = 20
    g.limit_up_list = []
    g.hold_list = []
    g.history_hold_list = []
    g.not_buy_again_list = []
    # 设置交易时间，每天运行
    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')
    run_daily(print_position_info, time='15:10', reference_security='000300.XSHG')


# 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

# 1-2 选股模块
def get_stock_list(context):
    yesterday = context.previous_date
    initial_list = get_all_securities().index.tolist()
    initial_list = filter_kcbj_stock(initial_list)
    initial_list = filter_new_stock(context, initial_list, 375)
    initial_list = filter_st_stock(initial_list)
    # 高股息(全市场最大25%)
    dr_list = get_dividend_ratio_filter_list(context, initial_list, False, 0, 0.5)
    # 高波动(dr_list中过滤最小20%)
    tv_list = get_factor_filter_list(context, dr_list, 'turnover_volatility', False, 0, 0.8)
    # 低负债(tv_list中保留最小50%)
    lev_list = get_factor_filter_list(context, tv_list, 'MLEV', True, 0, 0.5)
    # 流通市值轮动
    q = query(valuation.code, valuation.circulating_market_cap).filter(valuation.code.in_(lev_list)).order_by(valuation.circulating_market_cap.asc())
    df = get_fundamentals(q, date=yesterday)
    final_list = list(df.code)[:15]
    return final_list

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(df)):int(p2*len(df))]
    return filter_list


# 1-3 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 = []


# 1-4 整体调整持仓
def weekly_adjustment(context):
    # 获取应买入列表
    target_list = get_stock_list(context)
    target_list = filter_paused_stock(target_list)
    target_list = filter_limitup_stock(context, target_list)
    target_list = filter_limitdown_stock(context, target_list)
    print(len(target_list))
    # 截取不超过最大持仓数的股票量
    target_list = target_list[:min(g.stock_num, len(target_list))]
    # 调仓卖出
    for stock in g.hold_list:
        if (stock not in 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(target_list)
    if target_num > position_count:
        value = context.portfolio.cash / (target_num - position_count)
        for stock in target_list:
            if context.portfolio.positions[stock].total_amount == 0:
                if open_position(stock, value):
                    if len(context.portfolio.positions) == target_num:
                        break

# 1-5 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

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

# 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) == 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————————————————————————————————————————')

3. 策略使用说明

将代码复制到聚宽平台的策略编辑器中。
修改策略参数，例如持仓数量、观察天数等。
点击“运行策略”按钮，开始运行策略。

4. 注意事项

该策略仅供参考，不保证盈利。
策略可能存在风险，请谨慎使用。
在使用策略之前，请仔细阅读策略代码，并了解策略的运行原理。

5. 总结

该策略基于分红率、波动率和负债率因子，结合流通市值轮动，筛选出具有良好投资价值的股票。策略每周进行调整，并对昨日涨停股票进行观察，以提高策略的收益率和风险控制能力。