十年收益高达2247.22%的中证1000选股策略

回测日期：2014-12-5 至 2025-05-31

选股策略：

选取中证1000指数中的1000只股票

当日涨跌幅 3-5

当日换手率 3-10

当日均线 MA5 > MA10

止损止盈条件 收益 > 5 或者 收益-3卖出

# 中证1000选股策略
# 作者 但盼风雨来
# 选股策略：
#   当日涨跌幅 3-5 
#   当日换手率 3-10
#   当日均线 MA5 > MA10
#   止损止盈条件 收益 > 5 或者 收益-3卖出


import pandas as pd
# 初始化函数,全局只运行一次
def init(context):
    # 设置基准收益：沪深300指数
    set_benchmark('000300.SH')
    # 打印日志
    log.info('策略开始运行,初始化函数全局只运行一次')
    # 设置股票每笔交易的手续费为万分之二(手续费在买卖成交后扣除,不包括税费,税费在卖出成交后扣除)
    set_commission(PerShare(type='stock',cost=0.0000854,min_trade_cost=0))
    # 设置股票交易双边滑点0.5%,表示买入价为实际价格乘1.0025,卖出价为实际价格乘0.9975
    set_slippage(PriceSlippage(0.005))
    # 设置日级最大成交比例25%,分钟级最大成交比例50%
    # 日频运行时，下单数量超过当天真实成交量25%,则全部不成交
    # 分钟频运行时，下单数量超过当前分钟真实成交量50%,则全部不成交
    set_volume_limit(0.25,0.5)
    # 设置要操作的股票：同花顺
    context.security = ['300033.SZ']
    # 回测区间、初始资金、运行频率请在右上方设置
    context.stock_list = ['300033.SZ','000718.SZ']
  
    run_daily(function)
  
  

#每日开盘前9:00被调用一次,用于储存自定义参数、全局变量,执行盘前选股等
def before_trading(context):

    # 获取日期
    date = get_datetime().strftime('%Y-%m-%d %H:%M:%S')

    # 打印日期
    log.info('{} 盘前运行'.format(date))

  

## 开盘时运行函数
def handle_bar(context, bar_dict):
    # 获取时间
    time = get_datetime().strftime('%Y-%m-%d %H:%M:%S')
  
    log.info(bar_dict)
    # 买卖策略
    positions = context.stock_account.positions
    to_sell = []
    for position in positions:
        shouyi = positions[position].profit_rate * 100
        # log.info(f"{positions[position].symbol}的收益率为{shouyi}%")
        stock = positions[position].symbol
        if shouyi > 6 or shouyi <-3:
            to_sell.append(stock)
    for stock in to_sell:
        order_target(stock,0)
        log.warn(f"已清仓了{stock}")
  

    # 打印时间
    log.info('{} 盘中运行'.format(time))
    stock_list = context.stock_list
    log.info("下单的股票为",str(stock_list))
    for stock in stock_list:
        price = get_current(stock)
   
        current_price = price[stock].open
        # log.info(f"股票代码{stock},价格{current_price}")
        order(stock,1000,price = current_price)
        date = get_datetime()
        # log.info('{} 运行'.format(date))
        log.info(f"已购买{stock},价格为{current_price}")

## 收盘后运行函数,用于储存自定义参数、全局变量,执行盘后选股等
def after_trading(context):

    # 获取时间
    time = get_datetime().strftime('%Y-%m-%d %H:%M:%S')
    # 打印时间
    log.info('{} 盘后运行'.format(time))
    log.info('一天结束')
  



def function(context,*args):
    get_all_stock(context)
    get_rate_stock(context)
    change_hand(context)
    avg_stock(context)
  
# 获取所有股票
def get_all_stock(context):
    # 获取时间
    time = get_datetime()
  
    # 获取市场所有股票
    # all_stock = get_all_securities(ty='stock', date=time)
    # df = all_stock
    # df = df[~df['display_name'].str.contains('ST', na=False)]
    # df = df[~df['order_book_id'].str.contains('BJ')]
    # stock_list = df['order_book_id'].tolist()
  
    # 获取中证1000
    stock_list= get_index_stocks('000852.SH',time)
    log.info("已获取到所有股票代码")
    context.stock_list = stock_list
    log.info(f"中证1000股票数量为{len(stock_list)}")
    log.info("已获取中证1000股票")
  
# 筛选符合涨幅条件的股票
def get_rate_stock(context):
  
    time = get_datetime().strftime('%Y-%m-%d')
    stock_list = context.stock_list
    null_list = []
    fields = ['quote_rate','open']
    current = get_price(
                stock_list,
                start_date=None,
                end_date=time,
                fre_step='1d',
                fields=fields,
                skip_paused=False,
                fq='pre',
                bar_count=1,
                is_panel=False,
                )
   
    for stock in stock_list:
        if (current[stock].quote_rate[0] > 3) & (current[stock].quote_rate[0] < 5):
            null_list.append(stock)
    context.stock_list = null_list    
    log.info(f"符合涨跌幅限制的股票数量为{len(null_list)}")
    log.info("执行筛选符合涨幅条件的股票")


# 筛选符合换手率的股票
def change_hand(context):
    current_stock = context.stock_list
    hand_list = []
  
    time = get_datetime().strftime('%Y%m%d')  
    fields = ['turnover_rate']
    change_hand = get_price(
        current_stock,
        start_date=None,
        end_date=time,
        fre_step='1d',
        fields=fields,
        skip_paused=False,
        fq='pre',
        bar_count=1,
        is_panel=False,
    )
    for stock in current_stock:
        if (float(change_hand[stock]['turnover_rate'])>3)&(float(change_hand[stock]['turnover_rate'])<10):
            hand_list.append(stock)
    context.stock_list = hand_list
    log.info(f"符合换手率的股票数量为{len(hand_list)}")
    log.info("执行筛选符合换手率的股票")



# 筛选符合均线的股票
def avg_stock(context):
    time = get_datetime().strftime('%Y%m%d')
    stock_list = context.stock_list
    avg_list = []
    price = get_price(
    stock_list,
    start_date=None,
    end_date=time,
    fre_step='1d',
    fields=['close'],
    skip_paused=False,
    fq='pre',
    bar_count=20,
    is_panel=False,
    )
    for stock in stock_list:
        MA5 = price[stock]['close'].iloc[-5:].mean()
        MA10 = price[stock]['close'].iloc[-10:].mean()
        if MA5 > MA10 :
            avg_list.append(stock)
    context.stock_list = avg_list
    log.info(f"符合均线策略的股票数量为{len(avg_list)}")
    log.info("执行筛选符合均线的股票")