魔改版小市值策略

策略思路

最近几年，小市值策略一直都收益不错（当然，不包含17年和18年）。小市值因子对收益的影响是很大的。特别是行情不好的时候，大家都忙着炒作热点，那么这时候符合题材的小市值更加符合炒作标准了。

为什么说是魔改版的小市值策略呢，因为最终选取小市值的方法真的很玄学。

上收益效果图：

三年多，有7.45倍的收益，实属夸张了哈。有这个收益，还要什么自行车啊。

那么，这个策略玄学在哪里呢。

核心想法：

1、选取100个市值最小的股票，剔除ST股、次新股、科创板、北交所股票等等。

2、从剩下的股票中对证券代码进行排序，这就是最玄学的地方。然后选取排序靠前的5只股票。

3、每周第一个交易日早上进行轮换。

核心代码

def monthly_filter(context):
    
    today = context.current_dt
    yesterday = today -  datetime.timedelta(days=1)
    start_day = today -  datetime.timedelta(days=375)
    yesterday = yesterday.strftime('%Y-%m-%d')
    # 选出小市值的股票
    q = query(
            valuation.code,
            valuation.circulating_market_cap
        ).filter(
            valuation.circulating_market_cap.between(0,1000)
        ).order_by(
            valuation.circulating_market_cap.asc()).limit(100)
    codes = get_fundamentals(q).code.tolist()
    

    # 筛选出主板、创业板股票
    codes = [code for code in codes if code[:2] in ('60','00','30')]
    # log.info("Top 10 小市值:" + str(codes[:10]))
    # 过滤ST股票
    df = get_extras('is_st', codes, end_date=yesterday,count=1)
    df = df.T
    df.columns = ['is_st']
    df=df[df['is_st']==0]
    codes = df.index.tolist()
    # 过滤次新股
    q = query(finance.STK_LIST.code).filter(
        finance.STK_LIST.start_date <=start_day,
        finance.STK_LIST.code.in_(codes)
        )
    codes = list(finance.run_query(q).code)
    g.pools = set(codes)


def weekly_filter(context):
    today = context.current_dt
    yesterday = today -  datetime.timedelta(days=1)
    start_day = today -  datetime.timedelta(days=375)
    yesterday = yesterday.strftime('%Y-%m-%d')
    
    codes = list(g.pools)
    
    if codes != []:
        # 过滤ST股票
        df = get_extras('is_st', codes, end_date=yesterday,count=1)
        df = df.T
        df.columns = ['is_st']
        df=df[df['is_st']==0]
        codes = df.index.tolist()
        # codes = filter_st_stock(codes)
        g.pools = set(codes)
    else:
        # 选出小市值的股票
        q = query(
                valuation.code,
                valuation.circulating_market_cap
            ).filter(
                valuation.circulating_market_cap.between(0,1000)
            ).order_by(
                valuation.circulating_market_cap.asc()).limit(100)
        codes = get_fundamentals(q).code.tolist()
        
        # 筛选出主板、创业板股票
        codes = [code for code in codes if code[:2] in ('60','00','30')]
        # 过滤ST股票
        df = get_extras('is_st', codes, end_date=yesterday,count=1)
        df = df.T
        df.columns = ['is_st']
        df=df[df['is_st']==0]
        codes = df.index.tolist()
        # 过滤次新股
        q = query(finance.STK_LIST.code).filter(
            finance.STK_LIST.start_date <= start_day,
            finance.STK_LIST.code.in_(codes)
            )
        codes = list(finance.run_query(q).code)
        g.pools = set(codes)
    # log.info('每周五选定的股票:', g.pools)
    
#1-1 准备股票池
def prepare_stock_list(context):

    #获取已持有列表
    g.hold_list= list(context.portfolio.positions.keys())
    #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 = []
        
    # 获取今天的日期
    today = context.current_dt
    today_Year = today.strftime('%Y')
    NewYears = {'2010': '02-14', '2011': '02-03', '2012': '01-23', '2013': '02-10', '2014': '01-31', '2015': '02-19', '2016': '02-08', 
                '2017': '01-28', '2018': '02-16', '2019': '02-05', '2020': '01-25', '2021': '02-12', '2022': '02-01', '2023': '01-22', 
                '2024': '02-10', '2025': '01-29', '2026': '02-17', '2027': '02-06', '2028': '01-26', '2029': '02-13', '2030': '02-03'}
    spring_festival_date = NewYears[today_Year]
    #判断今天是否为账户资金再平衡的日期 或者 元旦到春节之间的日期
    g.no_trading_today_signal = today_is_between(context, '04-05', '04-30') or today_is_between(context, '01-01', spring_festival_date)


def get_stock_list(context):
    final_list = set()
    initial_list = list(g.pools)
    if len(initial_list) == 0:
        return []
    initial_list = filter_paused_stock(initial_list)
    initial_list = filter_st_stock(initial_list)
    
    today = context.current_dt
    # 通过timedelta算出前一天的日期
    delta = datetime.timedelta(days=1)
    yesterday = today - delta
    yesterday = yesterday.strftime('%Y-%m-%d')

    q = query(valuation.code, 
        valuation.circulating_market_cap).filter(valuation.code.in_(initial_list)).limit(g.stock_num)

    codes = get_fundamentals(q).code.tolist()
    circulating_market_cap_list = get_fundamentals(q).circulating_market_cap.tolist()

    log.info('数据库拉取的股票数量：{}'.format(len(codes)))
    log.info('最小市值：{}， 最大市值：{}'.format(min(circulating_market_cap_list), max(circulating_market_cap_list)))
        
    final_list = codes.copy()
    print('final_list数量:{}'.format(len(final_list)))

    final_list = filter_paused_stock(final_list)
    print('过滤停牌后的股票数量:{}'.format(len(final_list)))
    final_list = filter_st_stock(final_list)
    log.info('每周一买入的股票数量:'.format(len(final_list)))

    return final_list

#1-3 整体调整持仓
def weekly_adjustment(context):
    if g.no_trading_today_signal:
        return

    #获取应买入列表 
    target_list = get_stock_list(context)
    #调仓卖出
    for stock in g.hold_list:
        if (stock not in target_list) and (stock not in g.yesterday_HL_list):
            log.info("卖出[%s]" % (stock))
            order_target(stock, 0)
        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