快过年了。不想呆在小市值的，可以拿这个策略躲一躲

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/49995
# 标题：快过节了。搞点东西给大家学习。shui一波积分（狗头保命）。
# 作者：MarioC

# 原文网址：https://www.joinquant.com/post/49995
# 标题：快过节了。搞点东西给大家学习。shui一波积分（狗头保命）。
# 作者：MarioC

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

import talib

# 初始化函数
def initialize(context):
    # 设定基准
    set_benchmark('000300.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='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.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.stock_num = 5
    # 设置交易运行时间
    run_daily(prepare_stock_list, '9:05')
    run_monthly(weekly_adjustment, 1, '9:30')
    run_daily(check_limit_up, '14:00')  # 检查持仓中的涨停股是否需要卖出

# 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 get_stock_list(context):
    # 指定日期防止未来数据
    yesterday = context.previous_date
    today = context.current_dt
    # 获取初始列表
    initial_list = get_index_stocks('000300.XSHG', today)
    initial_list = filter_all_stock2(context, initial_list)
    final_list = []
    #构建查询
    lst = get_delta_stocks(context,initial_list,today)
    q = query(
    valuation.code
    ).filter(
    valuation.code.in_(lst)
    ).order_by(
        valuation.market_cap.desc())
    lst = list(get_fundamentals(q).code)[:g.stock_num*3]
    lst = filter_paused_stock(lst)
    lst = filter_limitup_stock(context, lst)
    lst = filter_limitdown_stock(context, lst)
    lst = lst[:min(g.stock_num, len(lst))]
    for stock in lst:
        if stock not in final_list:
            final_list.append(stock)
    return final_list

#取财务基本面的三角合集
def get_delta_stocks(context,stocklist,today_date):
    lastd_date = context.previous_date
    current_data = get_current_data()
    poollist=[]
    # df = get_history_fundamentals(stocklist,[indicator.roe,indicator.inc_net_profit_year_on_year,indicator.ocf_to_revenue]
    # , watch_date=lastd_date, count=4)  # 连续的4个季度    
    df = get_history_fundamentals(stocklist,[indicator.roe,indicator.inc_total_revenue_year_on_year,indicator.ocf_to_revenue]
    , watch_date=lastd_date, count=4)  # 连续的4个季度
    grouped = df.groupby('code')
    mask = grouped.apply(lambda x: x.isna().any().any())
    df = df[~df['code'].isin(mask[mask].index)]
    grouped = df.groupby('code')
    result = []
    for code, group in grouped:
        group_sorted = group.sort_values('statDate')
        roe_values = group_sorted['roe'].values #股本回报率
        ocf_values = group_sorted['ocf_to_revenue'].values # 经营现金流/收入
        # inc_net_profit_year_on_year = group_sorted['inc_net_profit_year_on_year'].values
        revenue_growth_values = group_sorted['inc_total_revenue_year_on_year'].values #总收入同比增长率

if (
            # (inc_net_profit_year_on_year[-1] > 0  and roe_values[-1] > 0 and ocf_values[-1] >0)
            (revenue_growth_values[-1] > 0.01  and roe_values[-1] > 0.01 and ocf_values[-1] >0.01)
            and
            roe_values[-1] == np.max(roe_values) and
            ocf_values[-1] == np.max(ocf_values) and
            # inc_net_profit_year_on_year[-1] == np.max(inc_net_profit_year_on_year) 
            revenue_growth_values[-1] == np.max(revenue_growth_values)

# roe_values[-1] == np.min(roe_values) and
            # ocf_values[-1] == np.min(ocf_values) and
            # inc_net_profit_year_on_year[-1] == np.min(inc_net_profit_year_on_year) 
            # revenue_growth_values[-1] == np.max(revenue_growth_values)

):
            result.append(code)
    return result

def weekly_adjustment(context):
    today = context.current_dt

target_list = get_stock_list(context)
    for stock in g.hold_list:
        if (stock not in target_list) and (stock not in g.yesterday_HL_list):
            position = context.portfolio.positions[stock]
            close_position(position)
    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 stock not in list(context.portfolio.positions.keys()):
                if open_position(stock, value):
                    if len(context.portfolio.positions) == target_num:
                        break

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))

def filter_all_stock2(context, stock_list):
    # 过滤次新股（新股、老股的分界日期，两种指定方法）
    # 新老股的分界日期, 自然日180天
    # by_date = context.previous_date - datetime.timedelta(days=180)
    # 新老股的分界日期，120个交易日
    by_date = get_trade_days(end_date=context.previous_date, count=375)[0]
    all_stocks = get_all_securities(date=by_date).index.tolist()
    stock_list = list(set(stock_list).intersection(set(all_stocks)))

curr_data = get_current_data()
    return [stock for stock in stock_list if not (
            stock.startswith(( '68', '4', '8','3')) or  # 创业，科创，北交所
            curr_data[stock].paused or
            curr_data[stock].is_st or  # ST
            ('ST' in curr_data[stock].name) or
            ('*' in curr_data[stock].name) or
            ('退' in curr_data[stock].name) or
            (curr_data[stock].day_open == curr_data[stock].high_limit) or  # 涨停开盘, 其它时间用last_price
            (curr_data[stock].day_open == curr_data[stock].low_limit)  # 跌停开盘, 其它时间用last_price
    )]

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)

def open_position(security, value):
    order = order_target_value_(security, value)
    if order != None and order.filled > 0:
        return True
    return False

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

#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]

#2-4 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]
```

发布时间: 2025-02-26 20:58:18

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水滴

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