复制链接
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    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0))\n","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-331"}],"output_ports":[{"name":"data","node_id":"-331"}],"cacheable":true,"seq_num":1,"comment":"","comment_collapsed":true},{"node_id":"-312","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2019-01-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2022-05-26","type":"Literal","bound_global_parameter":null},{"name":"market","value":"CN_STOCK_A","type":"Literal","bound_global_parameter":null},{"name":"instrument_list","value":"","type":"Literal","bound_global_parameter":null},{"name":"max_count","value":"0","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"rolling_conf","node_id":"-312"}],"output_ports":[{"name":"data","node_id":"-312"}],"cacheable":true,"seq_num":2,"comment":"","comment_collapsed":true},{"node_id":"-52","module_id":"BigQuantSpace.feature_extractor_1m.feature_extractor_1m-v2","parameters":[{"name":"start_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"before_start_days","value":"20","type":"Literal","bound_global_parameter":null},{"name":"workers","value":"20","type":"Literal","bound_global_parameter":null},{"name":"parallel_mode","value":"集群","type":"Literal","bound_global_parameter":null},{"name":"table_1m","value":"level2_bar1m_CN_STOCK_A","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-52"},{"name":"features","node_id":"-52"},{"name":"user_functions","node_id":"-52"}],"output_ports":[{"name":"data","node_id":"-52"}],"cacheable":true,"seq_num":7,"comment":"V2","comment_collapsed":true},{"node_id":"-41","module_id":"BigQuantSpace.factorlens.factorlens-v1","parameters":[{"name":"title","value":"因子分析: 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bigquant_run(context):\n \n context.ranker_prediction = context.options['data'].read_df()\n factor_name = [i for i in context.ranker_prediction.columns if i not in ['date','instrument','in_csi500_0']]\n print('因子名称:', factor_name)\n \n context.ranker_prediction.sort_values(['date',factor_name[0]], ascending=[True,False], inplace=True)\n context.ranker_prediction.rename(columns={factor_name[0]:'prediction'}, inplace=True)\n \n # 股票数量 \n context.stock_count = 100\n # 组合权重\n #context.stock_weights = T.norm([1 / math.log(i + 2) for i in range(0, context.stock_count)])\n context.stock_weights = [1/ context.stock_count]* context.stock_count\n # 换仓频率\n context.rebalance_days = 1\n \n # 设置交易费用，买入是万三，卖出是千分之1.3，如果不足5元按5元算\n context.set_commission(PerOrder(buy_cost=0.00015, \n sell_cost=0.00015, min_cost=5))\n \n if 'index' not in context.extension:\n context.extension['index'] = 0","type":"Literal","bound_global_parameter":null},{"name":"handle_data","value":"# 回测引擎：每日数据处理函数，每天执行一次\ndef 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    In [6]:
    # 本代码由可视化策略环境自动生成 2022年7月2日 10:08
# 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。


def m4_func_bigquant_run(df, close, op):
    res = close.pct_change()
    res.iloc[0] = close.iloc[0] / op.iloc[0] - 1
    return res

def m11_initialize_bigquant_run(context):
    
    context.ranker_prediction = context.options['data'].read_df()
    factor_name = [i for i in context.ranker_prediction.columns if i not in ['date','instrument','in_csi500_0']]
    print('因子名称:', factor_name)
     
    context.ranker_prediction.sort_values(['date',factor_name[0]], ascending=[True,False], inplace=True)
    context.ranker_prediction.rename(columns={factor_name[0]:'prediction'}, inplace=True)
    
    # 股票数量        
    context.stock_count = 100
    # 组合权重
    #context.stock_weights = T.norm([1 / math.log(i + 2) for i in range(0, context.stock_count)])
    context.stock_weights = [1/ context.stock_count]* context.stock_count
    # 换仓频率
    context.rebalance_days = 1
    
    # 设置交易费用，买入是万三，卖出是千分之1.3，如果不足5元按5元算
    context.set_commission(PerOrder(buy_cost=0.00015,   
                                        sell_cost=0.00015, min_cost=5))
 
    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 m11_handle_data_bigquant_run(context, data):
    # 日期
    date = data.current_dt.strftime('%Y-%m-%d')
    context.extension['index'] += 1
    
    if  context.extension['index'] % context.rebalance_days != 0:
        return 

   
    cur_df = context.ranker_prediction[context.ranker_prediction['date'] == date]
    stock_to_buy = cur_df.instrument.tolist()[: context.stock_count]
        
    # 目前持仓列表    
    stock_hold_now = [equity.symbol for equity in context.portfolio.positions]
    # 继续持有股票列表
    no_need_to_sell = [i for i in stock_hold_now  if i in stock_to_buy]
    # 卖出股票列表 
    stock_to_sell = [i for i in stock_hold_now if i not in no_need_to_sell]
    
    # 执行卖出
    for stock in stock_to_sell:
        if data.can_trade(context.symbol(stock)):
            context.order_target_percent(context.symbol(stock), 0)
    
    if len(stock_to_buy) == 0:
        return
    
    # 执行买入
    for  i in np.arange(len(stock_to_buy)):
        cp = stock_to_buy[i]
        weight =  context.stock_weights[i]
        if data.can_trade(context.symbol(cp)):
            context.order_target_percent(context.symbol(cp), weight)
# 回测引擎：准备数据，只执行一次
def m11_prepare_bigquant_run(context):
    pass

# Python 代码入口函数，input_1/2/3 对应三个输入端，data_1/2/3 对应三个输出端
def m5_run_bigquant_run(input_1, input_2, input_3):
    # 示例代码如下。在这里编写您的代码
    df = input_1.read() 
    f = input_2.read()[0]
    df[f] = df[f] * -1
    data_1 = DataSource.write_df(df)
    return Outputs(data_1=data_1)
# 后处理函数，可选。输入是主函数的输出，可以在这里对数据做处理，或者返回更友好的outputs数据格式。此函数输出不会被缓存。
def m5_post_run_bigquant_run(outputs):
    return outputs


m1 = M.input_features.v1(
    features="""_amt = amount
_ret = ret_sim(close, open)
big_order_ret = np.prod(1 + _ret*where(_amt >= _amt.sort_values(ascending=False).iloc[np.int(240*0.3-1)], 1, 0))
"""
)

m2 = M.instruments.v2(
    start_date='2019-01-01',
    end_date='2022-05-26',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m9 = M.input_features.v1(
    features='big_order_ret'
)

m12 = M.input_features.v1(
    features='in_csi500_0'
)

m13 = M.general_feature_extractor.v7(
    instruments=m2.data,
    features=m12.data,
    start_date='',
    end_date='',
    before_start_days=90
)

m14 = M.filter.v3(
    input_data=m13.data,
    expr='in_csi500_0 == 1',
    output_left_data=False
)

m4 = M.feature_extractor_user_function.v1(
    name='ret_sim',
    func=m4_func_bigquant_run
)

m7 = M.feature_extractor_1m.v2(
    instruments=m2.data,
    features=m1.data,
    user_functions=m4.functions,
    start_date='',
    end_date='',
    before_start_days=20,
    workers=20,
    parallel_mode='集群',
    table_1m='level2_bar1m_CN_STOCK_A'
)

m15 = M.data_join.v3(
    input_1=m7.data,
    input_2=m14.data,
    on='date,instrument',
    how='inner',
    sort=False
)

m11 = M.trade.v4(
    instruments=m2.data,
    options_data=m15.data,
    start_date='',
    end_date='',
    initialize=m11_initialize_bigquant_run,
    handle_data=m11_handle_data_bigquant_run,
    prepare=m11_prepare_bigquant_run,
    volume_limit=0.025,
    order_price_field_buy='open',
    order_price_field_sell='close',
    capital_base=10000000,
    auto_cancel_non_tradable_orders=True,
    data_frequency='daily',
    price_type='真实价格',
    product_type='股票',
    plot_charts=True,
    backtest_only=False,
    benchmark='000905.HIX'
)

m5 = M.cached.v3(
    input_1=m7.data,
    input_2=m9.data,
    run=m5_run_bigquant_run,
    post_run=m5_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m8 = M.factorlens.v1(
    features=m9.data,
    user_factor_data=m5.data_1,
    title='因子分析: {factor_name}',
    start_date='2019-01-01',
    end_date='2022-08-01',
    rebalance_period=2,
    delay_rebalance_days=0,
    rebalance_price='close_0',
    stock_pool='全市场',
    quantile_count=10,
    commission_rate=0.0002,
    returns_calculation_method='累乘',
    benchmark='无',
    drop_new_stocks=60,
    drop_price_limit_stocks=False,
    drop_st_stocks=False,
    drop_suspended_stocks=False,
    normalization=False,
    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='inner'
)

    因子分析: big_order_ret

    { "type": "factor-track", "data": { "exprs": ["big_order_ret"], "options": {"BacktestInterval": ["2019-01-01", "2022-08-01"], "Benchmark": "none", "StockPool": "all", "UserDataMerge": "inner", "DropSTStocks": 0, "DropPriceLimitStocks": 0, "DropNewStocks": 60, "DropSuspendedStocks": 0, "QuantileCount": 10, "CommissionRates": 0.0002, "Cutoutliers": 1, "Normalization": 0, "Neutralization": "", "DelayRebalanceDays": 0, "RebalancePeriod": 2, "RebalancePeriodsReturns": 0, "RebalancePrice": "close_0", "ReturnsCalculationMethod": "cumprod", "FactorCoverage": 0.5, "_HASH": "701e60fc1a4abfa6fe0173b644307153"} } }

    因子表现概览

      累计收益 近1年收益 近3月收益 近1月收益 近1周收益 昨日收益 最大回撤 盈亏比 胜率 夏普比率 收益波动率
    最小分位 51.97% 23.57% -6.43% 8.23% 0.38% -0.24% 29.97% 0.97 0.54 0.51 24.79%
    最大分位 -32.01% -20.17% -22.30% -4.90% -1.55% 0.13% 52.12% 0.86 0.52 -0.44 26.91%
    多空组合 46.97% 23.80% 9.43% 6.51% 0.96% -0.19% 7.69% 1.20 0.53 1.24 7.08%

    基本特征分析

    IC分析

    0.01

    0.09

    0.09

    80.89%

    买入信号重合分析

    因子估值分析

    因子拥挤度分析

    因子值最小的20只股票 (2022-05-26)

    股票名称 股票代码 因子值
    国茂股份 603915.SHA -1.2132
    金埔园林 301098.SZA -1.1447
    国瑞科技 300600.SZA -1.1415
    宝塔实业 000595.SZA -1.1366
    震裕科技 300953.SZA -1.1343
    麦迪科技 603990.SHA -1.1313
    青鸟消防 002960.SZA -1.1308
    迈信林 688685.SHA -1.1289
    苏文电能 300982.SZA -1.1284
    上海能源 600508.SHA -1.1282
    盛视科技 002990.SZA -1.1271
    开立医疗 300633.SZA -1.1266
    中文在线 300364.SZA -1.1261
    火炬电子 603678.SHA -1.1166
    退市游久 600652.SHA -1.1163
    禾盛新材 002290.SZA -1.1158
    粤传媒 002181.SZA -1.1150
    陕西建工 600248.SHA -1.1136
    贵人鸟 603555.SHA -1.1129
    德新交运 603032.SHA -1.1124

    因子值最大的20只股票 (2022-05-26)

    股票名称 股票代码 因子值
    奥普家居 603551.SHA -0.9041
    *ST腾信 300392.SZA -0.9041
    闽发铝业 002578.SZA -0.9017
    恒玄科技 688608.SHA -0.9005
    达志科技 300530.SZA -0.8996
    卓郎智能 600545.SHA -0.8982
    众源新材 603527.SHA -0.8975
    数源科技 000909.SZA -0.8932
    汇得科技 603192.SHA -0.8914
    友阿股份 002277.SZA -0.8889
    凤凰光学 600071.SHA -0.8813
    远东传动 002406.SZA -0.8804
    兴民智通 002355.SZA -0.8766
    浙江世宝 002703.SZA -0.8736
    万胜智能 300882.SZA -0.8723
    德力股份 002571.SZA -0.8591
    中岩大地 003001.SZA -0.8545
    浩物股份 000757.SZA -0.8528
    中水渔业 000798.SZA -0.8497
    英特集团 000411.SZA -0.8256