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克隆策略

In [9]:

zz = m3.data.read()
zz[(zz['date']=='2022-07-01')&(zz['instrument'] == '300472.SZA')]

Out[9]:

	close_0	date	instrument	mom1
2461454	29.006531	2022-07-01	300472.SZA	-0.195004

In [10]:

qq = zz[zz['instrument'] == '300472.SZA']

In [20]:

qq.iloc[-44:]['close_0'].mean()/qq.iloc[-1]['close_0'] - 1

Out[20]:

-0.19500418733153357

In [8]:

# 本代码由可视化策略环境自动生成 2022年7月25日 16:43
# 本代码单元只能在可视化模式下编辑。您也可以拷贝代码，粘贴到新建的代码单元或者策略，然后修改。


# 回测引擎：初始化函数，只执行一次
def m7_initialize_bigquant_run(context):
    context.indicator_data = context.options['data'].read_df()
    context.set_commission(PerOrder(buy_cost=0.0001, sell_cost=0.001, min_cost=5))
    context.stock_num = 50
    context.rebalance_days = 22
    if 'index' not in context.extension:
        context.extension['index'] = 0
 
    

# 回测引擎：每日数据处理函数，每天执行一次
def m7_handle_data_bigquant_run(context, data):
    #------------------------------------------止损模块START--------------------------------------------
    date = data.current_dt.strftime('%Y-%m-%d')
    positions = {e.symbol: p.cost_basis  for e, p in context.portfolio.positions.items()}
    current_stoploss_stock = [] 
    if len(positions) > 0:
        for i in positions.keys():
            stock_cost = positions[i] 
            stock_market_price = data.current(context.symbol(i), 'price') 
            if (stock_market_price - stock_cost) / stock_cost <= -0.1:   
                context.order_target_percent(context.symbol(i),0)     
                current_stoploss_stock.append(i)
                print('日期：',date,'股票：',i,'出现止损状况')
    #-------------------------------------------止损模块END--------------------------------------------------    
    context.extension['index'] += 1
    if  context.extension['index'] % context.rebalance_days != 0:
        return 
    
    # 当前的日期
    date = data.current_dt.strftime('%Y-%m-%d')
    
    cur_data = context.indicator_data[context.indicator_data['date'] == date]
    # 根据日期获取调仓需要买入的股票的列表
    stock_to_buy = list(cur_data.instrument[:context.stock_num])
    # 通过positions对象，使用列表生成式的方法获取目前持仓的股票列表
    stock_hold_now = [equity.symbol for equity in context.portfolio.positions]
    # 继续持有的股票：调仓时，如果买入的股票已经存在于目前的持仓里，那么应继续持有
    no_need_to_sell = [i for i in stock_hold_now if i in stock_to_buy]
    # 需要卖出的股票
    stock_to_sell = [i for i in stock_hold_now if i not in no_need_to_sell]
  
    # 卖出
    for stock in stock_to_sell:
        if stock in current_stoploss_stock:
            continue
        if data.can_trade(context.symbol(stock)):
            context.order_target_percent(context.symbol(stock), 0)
            
    # 如果当天没有买入的股票，就返回
    if len(stock_to_buy) == 0:
        return
    
    weight =  1 / len(stock_to_buy)    
    # 买入
    for stock in stock_to_buy:
        if data.can_trade(context.symbol(stock)):
            context.order_target_percent(context.symbol(stock), weight)
 
# 回测引擎：准备数据，只执行一次
def m7_prepare_bigquant_run(context):
    pass

# 回测引擎：每个单位时间开始前调用一次，即每日开盘前调用一次。
def m7_before_trading_start_bigquant_run(context, data):
    pass


m1 = M.input_features.v1(
    features="""mom1 = mean(close_0,44)/close_0 - 1

"""
)

m8 = M.factorlens.v1(
    features=m1.data,
    title='因子分析: {factor_name}',
    start_date='2020-01-01',
    end_date='2022-07-01',
    rebalance_period=22,
    delay_rebalance_days=0,
    rebalance_price='close_0',
    stock_pool='全市场',
    quantile_count=5,
    commission_rate=0.0016,
    returns_calculation_method='累乘',
    benchmark='无',
    drop_new_stocks=True,
    drop_price_limit_stocks=True,
    drop_st_stocks=True,
    drop_suspended_stocks=True,
    normalization=True,
    neutralization=[],
    metrics=['因子表现概览', '因子分布', '因子行业分布', '因子市值分布', 'IC分析', '买入信号重合分析', '因子估值分析', '因子拥挤度分析', '因子值最大/最小股票', '表达式因子值', '多因子相关性分析'],
    factor_coverage=0.5,
    user_data_merge='left'
)

m2 = M.instruments.v2(
    start_date='2020-01-01',
    end_date='2022-07-01',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m4 = M.general_feature_extractor.v7(
    instruments=m2.data,
    features=m1.data,
    start_date='',
    end_date='',
    before_start_days=90
)

m3 = M.derived_feature_extractor.v3(
    input_data=m4.data,
    features=m1.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions={}
)

m5 = M.dropnan.v2(
    input_data=m3.data
)

m6 = M.sort.v5(
    input_ds=m5.data,
    sort_by='mom1',
    group_by='date',
    keep_columns='--',
    ascending=True
)

m7 = M.trade.v4(
    instruments=m2.data,
    options_data=m6.sorted_data,
    start_date='',
    end_date='',
    initialize=m7_initialize_bigquant_run,
    handle_data=m7_handle_data_bigquant_run,
    prepare=m7_prepare_bigquant_run,
    before_trading_start=m7_before_trading_start_bigquant_run,
    volume_limit=0.025,
    order_price_field_buy='open',
    order_price_field_sell='open',
    capital_base=1000000,
    auto_cancel_non_tradable_orders=True,
    data_frequency='daily',
    price_type='后复权',
    product_type='股票',
    plot_charts=True,
    backtest_only=False,
    benchmark=''
)

[2022-07-25 16:38:13.088060] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-07-25 16:38:13.100704] INFO: moduleinvoker: 命中缓存

[2022-07-25 16:38:13.102608] INFO: moduleinvoker: input_features.v1 运行完成[0.014605s].

[2022-07-25 16:38:13.108624] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-07-25 16:38:13.119059] INFO: moduleinvoker: 命中缓存

[2022-07-25 16:38:13.122307] INFO: moduleinvoker: instruments.v2 运行完成[0.013671s].

[2022-07-25 16:38:13.156734] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-07-25 16:38:13.173849] INFO: moduleinvoker: 命中缓存

[2022-07-25 16:38:13.184364] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.027617s].

[2022-07-25 16:38:13.204790] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-07-25 16:38:13.214305] INFO: moduleinvoker: 命中缓存

[2022-07-25 16:38:13.216825] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.012034s].