首板高开-低开-弱转强混合策略（今年收益1138.61%）

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/49499
# 标题：首板高开-低开-弱转强混合策略（今年收益1138.61%）
# 作者：天山灵兔
# 原回测条件：2024-01-01 到 2024-08-13, ￥100000, 每天

# 原文网址：https://www.joinquant.com/post/48680
# 标题：追首板涨停 过去两年年化304%
# 作者：子匀

from jqdata import *
from jqfactor import *
import pandas as pd
from datetime import datetime,timedelta,date

################################### 初始化设置 #############################################
def initialize(context):
    set_option('use_real_price', True)
    log.set_level('system', 'error')
    set_option('avoid_future_data', True)
    
def after_code_changed(context):
    g.n_days_limit_up_list = []   #重新初始化列表
    unschedule_all() # 取消所有定时运行    
    # run_daily(get_stock_list, '9:05')
    run_daily(buy, '09:26')
    run_daily(sell, time='11:25', reference_security='000300.XSHG')
    run_daily(sell, time='14:50', reference_security='000300.XSHG')

def after_trading_end(context):
    print('———————————————————————————————————')
    
## 定义股票池     
def set_stockpool(context):
    yesterday = context.previous_date 
    initial_list = get_all_securities('stock', yesterday).index.tolist()
    return initial_list

##################################  交易函数群 ##################################
def buy(context):
    current_data = get_current_data()
    qualified_stocks =  get_stock_list(context)
    if qualified_stocks:
        value = context.portfolio.available_cash / len(qualified_stocks)
        for s in qualified_stocks:
            # 下单   #至少够买1手
            if context.portfolio.available_cash/current_data[s].last_price>100: 
                order_value(s, value, MarketOrderStyle(current_data[s].day_open))
                print('买入' + s)
                
def sell(context):
    stime = context.current_dt.strftime("%H%M")
    current_data = get_current_data()
    
    for s in list(context.portfolio.positions):
        close_data = attribute_history(s, 4, '1d', ['close'])
        M4=close_data['close'].mean()
        MA5=(M4*4+current_data[s].last_price)/5
        position=context.portfolio.positions[s]
        
        if ((position.closeable_amount != 0) and (current_data[s].last_price < current_data[s].high_limit) and (current_data[s].last_price > 1*position.avg_cost)):#avg_cost当前持仓成本
            order_target_value(s, 0)
            ret=100*(position.price/position.avg_cost-1)
            print('止盈卖出 ' + get_security_info(s).display_name + s + ' 收益率:{:.2f}%'.format(ret,'.2f'))
        
        #跌破5日线止损
        if ((position.closeable_amount != 0) and (current_data[s].last_price < MA5)):
            order_target_value(s, 0)
            ret=100*(position.price/position.avg_cost-1)
            print('止损卖出 ' + get_security_info(s).display_name + s + ' 收益率:{:.2f}%'.format(ret,'.2f'))
        
##################################  选股函数群 ##################################   
# 选股
def get_stock_list(context): 
    target_list, target_list2=prepare_stock_list(context)
    qualified_stocks = []
    sbdk_stocks = []
    sbgk_stocks = []
    rzq_stocks = []
    current_data = get_current_data()
    date_now = context.current_dt.strftime("%Y-%m-%d")
    mid_time1 = ' 09:15:00'
    end_times1 =  ' 09:26:00'
    start = date_now + mid_time1
    end = date_now + end_times1

for s in target_list:
        # 首版低开条件 股票处于一段时间内相对位置<50% 低开3%-4%
        history_data = attribute_history(s, 60, '1d', fields=['close', 'high', 'low', 'money'], skip_paused=True)
        close = history_data['close'][-1]
        high = history_data['high'].max()
        low = history_data['low'].min()
        rp = (close-low) / (high-low)
        money = history_data['money'][-1] 
        if rp<= 0.5 and money>= 1e8 :
            auction_data = get_call_auction(s, start_date=start, end_date=end, fields=['time','current'])
            if not auction_data.empty:
                current_ratio = auction_data['current'][0] / close
                if current_ratio<=0.97 and current_ratio>=0.955:
                    sbdk_stocks.append(s)
                    continue
        
        # 条件一：均价，金额，市值，换手率 收盘获利比例低于7%，成交额小于5.5亿或者大于20亿，或市值小于70亿，大于520亿，过滤
        prev_day_data = attribute_history(s, 1, '1d', fields=['close', 'volume', 'money'], skip_paused=True)
        avg_price_increase_value = prev_day_data['money'][0] / prev_day_data['volume'][0] / prev_day_data['close'][0] *1.1 - 1
        if avg_price_increase_value < 0.07 or prev_day_data['money'][0] < 5.5e8 or prev_day_data['money'][0] > 20e8:
            continue
        turnover_ratio_data=get_valuation(s, start_date=context.previous_date, end_date=context.previous_date, fields=['turnover_ratio', 'market_cap','circulating_market_cap'])
        if turnover_ratio_data.empty or turnover_ratio_data['market_cap'][0] < 70  or turnover_ratio_data['circulating_market_cap'][0] > 520 :
            continue
        
        # 条件二：高开,开比
        auction_data = get_call_auction(s, start_date=start, end_date=end, fields=['time','volume', 'current'])
        #print([s,auction_data['volume'][0],prev_day_data['volume'][-1]])
        if auction_data.empty or auction_data['volume'][0] / prev_day_data['volume'][-1] < 0.03:
            continue
        current_ratio = auction_data['current'][0] / prev_day_data['close'][-1]
        if current_ratio<=1 or current_ratio>=1.06:
            continue        
        
        # 条件三：左压
        hst = attribute_history(s, 101, '1d', fields=['high', 'volume'], skip_paused=True)  # 获取历史数据
        prev_high = hst['high'].iloc[-1]  # 计算前一天的高点
        zyts_0 = next((i-1 for i, high in enumerate(hst['high'][-3::-1], 2) if high >= prev_high), 100)  # 计算zyts_0
        zyts = zyts_0 + 5
        volume_data = hst['volume'][-zyts:]   # 获取高点以来的成交量数据
        # 检查今天的成交量是否同步放大
        if len(volume_data) < 2 or volume_data[-1] <= max(volume_data[:-1]) * 0.9:
            continue
        
        # 如果股票满足所有条件，则添加到列表中
        sbgk_stocks.append(s)
       
    # 弱转强
    for s in target_list2:
        # 过滤前面三天涨幅超过28%的票
        prev_day_data = attribute_history(s, 4, '1d', fields=['open', 'close', 'volume', 'money'], skip_paused=True)
        increase_ratio = (prev_day_data['close'][-1] - prev_day_data['close'][0]) / prev_day_data['close'][0]
        if increase_ratio > 0.28:
            continue
        
        # 过滤前一日收盘价小于开盘价5%以上的票
        open_close_ratio = (prev_day_data['close'][-1] - prev_day_data['open'][-1]) / prev_day_data['open'][-1]
        if open_close_ratio < -0.05:
            continue
        
        # 条件一：均价，金额，市值，换手率 收盘获利比例低于4%，成交额小于3亿或者大于19亿，或市值小于70亿，大于520亿，过滤
        avg_price_increase_value = prev_day_data['money'][-1] / prev_day_data['volume'][-1] / prev_day_data['close'][-1]  - 1
        if avg_price_increase_value < -0.04 or prev_day_data['money'][-1] < 3e8 or prev_day_data['money'][-1] > 19e8:
            continue
        turnover_ratio_data = get_valuation(s, start_date=context.previous_date, end_date=context.previous_date, fields=['turnover_ratio','market_cap','circulating_market_cap'])
        if turnover_ratio_data.empty or turnover_ratio_data['market_cap'][0] < 70  or turnover_ratio_data['circulating_market_cap'][0] > 520 :
            continue
        
        # 条件二：高开,开比
        auction_data = get_call_auction(s, start_date=start, end_date=end, fields=['time','volume', 'current'])
        if auction_data.empty or auction_data['volume'][0] / prev_day_data['volume'][-1] < 0.03:
            continue
        current_ratio = auction_data['current'][0] / (current_data[s].high_limit/1.1) #prev_day_data['close'][-1]
        if current_ratio <= 0.98 or current_ratio >= 1.09:
            continue
        
        # 条件三：左压
        hst = attribute_history(s, 101, '1d', fields=['high', 'volume'], skip_paused=True)  # 获取历史数据
        prev_high = hst['high'].iloc[-1]  # 计算前一天的高点
        zyts_0 = next((i-1 for i, high in enumerate(hst['high'][-3::-1], 2) if high >= prev_high), 100)  # 计算zyts_0
        zyts = zyts_0 + 5
        volume_data = hst['volume'][-zyts:]   # 获取高点以来的成交量数据
        # 检查今天的成交量是否同步放大
        if len(volume_data) < 2 or volume_data[-1] <= max(volume_data[:-1]) * 0.9:
            continue
        
        rzq_stocks.append(s)
    
    qualified_stocks=sbgk_stocks+sbdk_stocks+rzq_stocks
    if qualified_stocks:
        print('今日选股：'+str(qualified_stocks))
        print('首板高开：'+str(sbgk_stocks))
        print('首板低开：'+str(sbdk_stocks))
        print('弱转强：'+str(rzq_stocks))
        
    return qualified_stocks

# 每日初始股票池
def prepare_stock_list(context):
    today = context.current_dt.date()
    yesterday = context.previous_date
    initial_list = set_stockpool(context)
    initial_list = filter_kcbj_stock(initial_list)
    initial_list = filter_st_paused_stock(initial_list, today)
    initial_list = filter_new_stock(initial_list, today)
    # 首次运行，添加前2天的数据
    if not g.n_days_limit_up_list:
        days = get_trade_days( end_date = yesterday, count=3)[:-1]
        for day in days:
            g.n_days_limit_up_list.append(get_hl_stock(initial_list, day, 1))

hl_list = get_hl_stock(initial_list, yesterday, 1)     # 昨日涨停
    g.n_days_limit_up_list.append(hl_list)

hl1_list = set(g.n_days_limit_up_list[-2])      # 前1日曾涨停
    #hl2_list = set(g.n_days_limit_up_list[-2] + g.n_days_limit_up_list[-3])  # 前2日曾涨停
    hl_list = [stock for stock in hl_list if stock not in hl1_list]

# 昨日曾涨停但未封板
    hl_list2 = get_ever_hl_stock2(initial_list, yesterday)
    hl_list2 = [stock for stock in hl_list2 if stock not in hl1_list]
    
    g.n_days_limit_up_list.pop(0)  # 移除无用的数据
    return hl_list, hl_list2

###################################  其它函数群 ##################################

# 筛选出某一日涨停的股票
def get_hl_stock(stock_list, date1, days=1):
    if not stock_list:return []
    h_s = get_price(stock_list, end_date=date1, frequency='daily', fields=['close', 'high_limit', 'paused'],
                  count=days, panel=False, fill_paused=False, skip_paused=False
                  ).query('close==high_limit and paused==0').groupby('code').size()
    return h_s.index.tolist()
    
# 筛选出某一日曾经涨停的股票，含炸板的
def get_ever_hl_stock(stock_list, date1):
    if not stock_list:return []
    h_s = get_price(stock_list, end_date=date1, frequency='daily', fields=['high', 'high_limit', 'paused'],
                  count=1, panel=False, fill_paused=False, skip_paused=False
                  ).query('high==high_limit and paused==0').groupby('code').size()
    return h_s.index.tolist()
    
# 筛选出某一日曾经涨停但未封板的股票
def get_ever_hl_stock2(stock_list, date1):
    if not stock_list:return []
    h_s = get_price(stock_list, end_date=date1, frequency='daily', fields=['close', 'high', 'high_limit', 'paused'],
                  count=1, panel=False, fill_paused=False, skip_paused=False
                  ).query('close!=high_limit and high==high_limit and paused==0').groupby('code').size()
    return h_s.index.tolist()

# 过滤函数
def filter_new_stock(initial_list, date, days=50):
    return [stock for stock in initial_list if get_security_info(stock).start_date < date - timedelta(days=days)]

def filter_st_paused_stock(initial_list, date):
    current_data = get_current_data()
    return [stock for stock in initial_list if not (
            current_data[stock].is_st or 
            current_data[stock].paused or 
            '退' in current_data[stock].name)]

def filter_kcbj_stock(initial_list):
    return [stock for stock in initial_list if stock[0] != '4'  and stock[0] != '8' and stock[:2] != '68']  #and stock[0] != '3'

### end ###
```

发布时间: 2025-02-26 20:58:17

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