{"description":"实验创建于2017/8/26","graph":{"edges":[{"to_node_id":"-202:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-209:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-202:instruments","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"to_node_id":"-53:instruments","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"to_node_id":"-209:input_data","from_node_id":"-202:data"},{"to_node_id":"-184:input_data","from_node_id":"-209:data"},{"to_node_id":"-53:options_data","from_node_id":"-1575:data"},{"to_node_id":"-1575:input_data","from_node_id":"-184:data"}],"nodes":[{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24","module_id":"BigQuantSpace.input_features.input_features-v1","parameters":[{"name":"features","value":"# #号开始的表示注释\n# 多个特征，每行一个，可以包含基础特征和衍生特征\nma1=mean(close_0,5)\nma2=mean(close_0,10)\nma3=mean(close_0,20)\nv_mean=mean(volume_0,5)\nt1=where(((close_0>=ma1) & (close_0>=ma2) & (close_0>=ma3)),1,0)\nt2=where(((close_1<shift(ma1,1)) & (close_1<shift(ma2,1)) & (close_1<shift(ma3,1))),1,0)\nt3=where(volume_0>=3*v_mean,1,0)\nt4=where((close_0-low_0)/(high_0-low_0)>0.7,1,0)\nsignal=where((min(t1,t2,t3,t4)==1),1,0)","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24"}],"output_ports":[{"name":"data","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24"}],"cacheable":true,"seq_num":1,"comment":"","comment_collapsed":true},{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2022-01-01","type":"Literal","bound_global_parameter":"交易日期"},{"name":"end_date","value":"2022-11-01","type":"Literal","bound_global_parameter":"交易日期"},{"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":"287d2cb0-f53c-4101-bdf8-104b137c8601-62"}],"output_ports":[{"name":"data","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62"}],"cacheable":true,"seq_num":2,"comment":"","comment_collapsed":true},{"node_id":"-202","module_id":"BigQuantSpace.general_feature_extractor.general_feature_extractor-v7","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":"30","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-202"},{"name":"features","node_id":"-202"}],"output_ports":[{"name":"data","node_id":"-202"}],"cacheable":true,"seq_num":7,"comment":"","comment_collapsed":true},{"node_id":"-209","module_id":"BigQuantSpace.derived_feature_extractor.derived_feature_extractor-v3","parameters":[{"name":"date_col","value":"date","type":"Literal","bound_global_parameter":null},{"name":"instrument_col","value":"instrument","type":"Literal","bound_global_parameter":null},{"name":"drop_na","value":"False","type":"Literal","bound_global_parameter":null},{"name":"remove_extra_columns","value":"False","type":"Literal","bound_global_parameter":null},{"name":"user_functions","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-209"},{"name":"features","node_id":"-209"}],"output_ports":[{"name":"data","node_id":"-209"}],"cacheable":true,"seq_num":8,"comment":"","comment_collapsed":true},{"node_id":"-53","module_id":"BigQuantSpace.trade.trade-v4","parameters":[{"name":"start_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"initialize","value":"# 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now_data.instrument.unique()\n sell_stock = stock_hold_now.keys()\n\n # 卖出\n for instrument in sell_stock:\n if data.can_trade(context.symbol(instrument)):\n context.order_target_percent(context.symbol(instrument), 0)\n\n # 买入\n stock_to_buy_num = len(buy_stock)\n print(today,buy_stock)\n for instrument in buy_stock:\n # 使用当日可用现金等资金比例下单买入\n cash = cash_for_buy / stock_to_buy_num\n if data.can_trade(context.symbol(instrument)):\n # 整百下单\n current_price = data.current(context.symbol(instrument), 'price')\n amount = math.floor(cash / current_price / 100) * 100\n context.order(context.symbol(instrument), amount)\n 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# 本代码由可视化策略环境自动生成 2022年11月5日 15:13
# 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 m3_initialize_bigquant_run(context):

    # 系统已经设置了默认的交易手续费和滑点，要修改手续费可使用如下函数
    context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))
    context.mydata = context.options['data'].read_df()
    context.mydata = context.mydata[context.mydata.signal==1]

# 回测引擎：每日数据处理函数，每天执行一次
def m3_handle_data_bigquant_run(context, data):
    # 回测引擎：每日数据处理函数，每天执行一次
    today = data.current_dt.strftime('%Y-%m-%d') # 日期
    # 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和对应的最新市值
    stock_hold_now = {e.symbol: p.amount * p.last_sale_price
                 for e, p in context.portfolio.positions.items()}
    hold_num=len(stock_hold_now)
    
    # 每日买入金额不超过总资产额一半
    cash_for_buy = min(context.portfolio.cash,context.portfolio.portfolio_value*0.5)
    
    now_data = context.mydata[context.mydata.date==today]
    if len(now_data)>10:
        now_data=now_data.iloc[0:10]
    elif len(now_data)==0: 
        return
#     display(now_data)
    buy_stock = now_data.instrument.unique()
    sell_stock = stock_hold_now.keys()

    # 卖出
    for instrument in sell_stock:
        if data.can_trade(context.symbol(instrument)):
            context.order_target_percent(context.symbol(instrument), 0)

    # 买入
    stock_to_buy_num = len(buy_stock)
    print(today,buy_stock)
    for instrument in buy_stock:
        # 使用当日可用现金等资金比例下单买入
        cash = cash_for_buy / stock_to_buy_num
        if data.can_trade(context.symbol(instrument)):
            # 整百下单
            current_price = data.current(context.symbol(instrument), 'price')
            amount = math.floor(cash / current_price / 100) * 100
            context.order(context.symbol(instrument), amount)
  


m1 = M.input_features.v1(
    features="""# #号开始的表示注释
# 多个特征，每行一个，可以包含基础特征和衍生特征
ma1=mean(close_0,5)
ma2=mean(close_0,10)
ma3=mean(close_0,20)
v_mean=mean(volume_0,5)
t1=where(((close_0>=ma1) & (close_0>=ma2) & (close_0>=ma3)),1,0)
t2=where(((close_1<shift(ma1,1)) & (close_1<shift(ma2,1)) & (close_1<shift(ma3,1))),1,0)
t3=where(volume_0>=3*v_mean,1,0)
t4=where((close_0-low_0)/(high_0-low_0)>0.7,1,0)
signal=where((min(t1,t2,t3,t4)==1),1,0)"""
)

m2 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2022-01-01'),
    end_date=T.live_run_param('trading_date', '2022-11-01'),
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m7 = M.general_feature_extractor.v7(
    instruments=m2.data,
    features=m1.data,
    start_date='',
    end_date='',
    before_start_days=30
)

m8 = M.derived_feature_extractor.v3(
    input_data=m7.data,
    features=m1.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False
)

m5 = M.chinaa_stock_filter.v1(
    input_data=m8.data,
    index_constituent_cond=['全部'],
    board_cond=['上证主板', '深证主板', '创业板'],
    industry_cond=['全部'],
    st_cond=['正常'],
    delist_cond=['非退市'],
    output_left_data=False
)

m4 = M.dropnan.v2(
    input_data=m5.data
)

m3 = M.trade.v4(
    instruments=m2.data,
    options_data=m4.data,
    start_date='',
    end_date='',
    initialize=m3_initialize_bigquant_run,
    handle_data=m3_handle_data_bigquant_run,
    volume_limit=0.025,
    order_price_field_buy='open',
    order_price_field_sell='close',
    capital_base=1000000,
    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='000300.HIX'
)

[2022-11-01 20:40:19.470174] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-11-01 20:40:19.477988] INFO: moduleinvoker: 命中缓存

[2022-11-01 20:40:19.479829] INFO: moduleinvoker: input_features.v1 运行完成[0.009692s].

[2022-11-01 20:40:19.485576] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-11-01 20:40:19.596881] INFO: moduleinvoker: instruments.v2 运行完成[0.111284s].

[2022-11-01 20:40:19.616516] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-11-01 20:40:20.077884] INFO: 基础特征抽取: 年份 2021, 特征行数=102307

[2022-11-01 20:40:21.943611] INFO: 基础特征抽取: 年份 2022, 特征行数=955703

[2022-11-01 20:40:22.020535] INFO: 基础特征抽取: 总行数: 1058010

[2022-11-01 20:40:22.030189] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[2.413672s].

[2022-11-01 20:40:22.042064] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-11-01 20:40:25.212063] INFO: derived_feature_extractor: 提取完成 ma1=mean(close_0,5), 0.905s

[2022-11-01 20:40:26.072429] INFO: derived_feature_extractor: 提取完成 ma2=mean(close_0,10), 0.859s

[2022-11-01 20:40:26.906388] INFO: derived_feature_extractor: 提取完成 ma3=mean(close_0,20), 0.832s

[2022-11-01 20:40:27.806776] INFO: derived_feature_extractor: 提取完成 v_mean=mean(volume_0,5), 0.899s

[2022-11-01 20:40:27.816315] INFO: derived_feature_extractor: 提取完成 t1=where(((close_0>=ma1) & (close_0>=ma2) & (close_0>=ma3)),1,0), 0.007s

[2022-11-01 20:40:28.276596] INFO: derived_feature_extractor: 提取完成 t2=where(((close_1
[2022-11-01 20:40:28.284116] INFO: derived_feature_extractor: 提取完成 t3=where(volume_0>=3*v_mean,1,0), 0.005s
[2022-11-01 20:40:28.291197] INFO: derived_feature_extractor: 提取完成 t4=where((close_0-low_0)/(high_0-low_0)>0.7,1,0), 0.005s
[2022-11-01 20:40:28.306281] INFO: derived_feature_extractor: 提取完成 signal=where((min(t1,t2,t3,t4)==1),1,0), 0.013s
[2022-11-01 20:40:28.675947] INFO: derived_feature_extractor: /y_2021, 102307
[2022-11-01 20:40:30.462253] INFO: derived_feature_extractor: /y_2022, 955703
[2022-11-01 20:40:31.036161] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[8.994098s].
[2022-11-01 20:40:31.047699] INFO: moduleinvoker: chinaa_stock_filter.v1 开始运行..
[2022-11-01 20:40:32.203722] INFO: A股股票过滤: 过滤 /y_2021, 88527/0/102307
[2022-11-01 20:40:39.755830] INFO: A股股票过滤: 过滤 /y_2022, 820410/0/955703
[2022-11-01 20:40:39.759923] INFO: A股股票过滤: 过滤完成, 908937 + 0
[2022-11-01 20:40:39.784053] INFO: moduleinvoker: chinaa_stock_filter.v1 运行完成[8.736368s].
[2022-11-01 20:40:39.796368] INFO: moduleinvoker: dropnan.v2 开始运行..
[2022-11-01 20:40:40.190712] INFO: dropnan: /y_2021, 11986/88527
[2022-11-01 20:40:42.387289] INFO: dropnan: /y_2022, 816694/820410
[2022-11-01 20:40:42.481518] INFO: dropnan: 行数: 828680/908937
[2022-11-01 20:40:42.491674] INFO: moduleinvoker: dropnan.v2 运行完成[2.695311s].
[2022-11-01 20:40:42.553015] INFO: moduleinvoker: backtest.v8 开始运行..
[2022-11-01 20:40:42.559637] INFO: backtest: biglearning backtest:V8.6.3
[2022-11-01 20:40:42.562017] INFO: backtest: product_type:stock by specified
[2022-11-01 20:40:54.418703] INFO: backtest: 读取股票行情完成:2236747
[2022-11-01 20:40:54.451500] INFO: backtest: algo history_data=None
[2022-11-01 20:40:54.454215] INFO: algo: TradingAlgorithm V1.8.8
[2022-11-01 20:40:56.602670] INFO: algo: trading transform...
[2022-11-01 20:40:57.620072] INFO: algo: handle_splits get splits [dt:2022-04-08 00:00:00+00:00] [asset:Equity(5512 [300801.SZA]), ratio:0.9822062253952026]
[2022-11-01 20:40:57.622384] INFO: Position: position stock handle split[sid:5512, orig_amount:21900, new_amount:22296.0, orig_cost:23.500003637639217, new_cost:23.0818, ratio:0.9822062253952026, last_sale_price:22.079998016357422]
[2022-11-01 20:40:57.624494] INFO: Position: after split: PositionStock(asset:Equity(5512 [300801.SZA]), amount:22296.0, cost_basis:23.0818, last_sale_price:22.48000144958496)
[2022-11-01 20:40:57.626115] INFO: Position: returning cash: 16.4104
[2022-11-01 20:40:58.402639] INFO: algo: handle_splits get splits [dt:2022-08-25 00:00:00+00:00] [asset:Equity(2323 [600482.SHA]), ratio:0.9950000047683716]
[2022-11-01 20:40:58.807856] INFO: Performance: Simulated 199 trading days out of 199.
[2022-11-01 20:40:58.809923] INFO: Performance: first open: 2022-01-04 09:30:00+00:00
[2022-11-01 20:40:58.811257] INFO: Performance: last close: 2022-11-01 15:00:00+00:00
[2022-11-01 20:41:01.961630] INFO: moduleinvoker: backtest.v8 运行完成[19.408635s].
[2022-11-01 20:41:01.963631] INFO: moduleinvoker: trade.v4 运行完成[19.463533s].

2022-01-04 ['300303.SZA']
2022-01-11 ['301078.SZA']
2022-01-12 ['300933.SZA']
2022-01-17 ['000158.SZA']
2022-01-18 ['600315.SHA']
2022-01-24 ['603839.SHA']
2022-01-25 ['601069.SHA']
2022-01-26 ['603955.SHA']
2022-02-07 ['300284.SZA' '300330.SZA']
2022-02-14 ['301007.SZA']
2022-02-15 ['002703.SZA']
2022-02-16 ['000755.SZA' '300946.SZA']
2022-02-18 ['300738.SZA']
2022-02-21 ['603558.SHA']
2022-02-23 ['000532.SZA' '000987.SZA' '300706.SZA']
2022-02-24 ['000659.SZA']
2022-02-25 ['002040.SZA' '002679.SZA' '601008.SHA' '601188.SHA']
2022-03-02 ['600639.SHA']
2022-03-07 ['001206.SZA' '601811.SHA']
2022-03-09 ['600186.SHA' '601368.SHA']
2022-03-10 ['600829.SHA' '603015.SHA']
2022-03-16 ['002666.SZA']
2022-03-17 ['003018.SZA']
2022-03-18 ['300234.SZA' '603578.SHA']
2022-03-23 ['300423.SZA']
2022-03-25 ['300582.SZA']
2022-03-28 ['300793.SZA']
2022-03-30 ['300801.SZA']

2022-04-08 ['600243.SHA']
2022-04-11 ['300240.SZA' '600073.SHA']
2022-04-12 ['000848.SZA']
2022-04-14 ['300218.SZA']
2022-04-15 ['300991.SZA']
2022-04-19 ['300411.SZA']
2022-04-20 ['000008.SZA' '605138.SHA']
2022-04-22 ['002763.SZA' '300840.SZA' '300879.SZA' '603221.SHA']
2022-04-25 ['300062.SZA' '300564.SZA' '600805.SHA' '600909.SHA']
2022-04-26 ['600578.SHA']
2022-05-25 ['000931.SZA']
2022-06-08 ['600023.SHA']
2022-06-09 ['600610.SHA']
2022-06-15 ['002445.SZA']
2022-06-20 ['002798.SZA']
2022-06-24 ['600620.SHA']
2022-06-27 ['300027.SZA']
2022-07-01 ['600310.SHA' '605162.SHA']
2022-07-05 ['600935.SHA']
2022-07-06 ['002589.SZA']
2022-07-07 ['000751.SZA']
2022-07-11 ['300006.SZA' '301075.SZA']
2022-07-13 ['002115.SZA' '002134.SZA']
2022-07-15 ['000021.SZA' '300319.SZA' '301196.SZA']
2022-07-18 ['300262.SZA' '300740.SZA']
2022-07-22 ['600509.SHA']
2022-07-25 ['301193.SZA']
2022-07-26 ['002306.SZA']
2022-07-29 ['601619.SHA']
2022-08-01 ['300720.SZA']
2022-08-02 ['000505.SZA' '600127.SHA']
2022-08-03 ['000691.SZA']
2022-08-05 ['601990.SHA']
2022-08-11 ['600070.SHA']
2022-08-19 ['002280.SZA' '603680.SHA']
2022-08-22 ['300950.SZA' '605507.SHA']
2022-08-23 ['000589.SZA' '000597.SZA' '002319.SZA']
2022-08-24 ['600482.SHA' '603991.SHA' '603992.SHA']

2022-08-26 ['300530.SZA']
2022-08-29 ['003020.SZA' '300622.SZA' '301042.SZA']
2022-08-30 ['300134.SZA']
2022-09-01 ['000514.SZA']
2022-09-02 ['300045.SZA']
2022-09-05 ['600722.SHA']
2022-09-06 ['300082.SZA']
2022-09-09 ['002589.SZA' '301033.SZA' '600836.SHA' '601990.SHA']
2022-09-14 ['300715.SZA' '301077.SZA']
2022-09-15 ['300536.SZA' '600192.SHA' '603269.SHA']
2022-09-16 ['002197.SZA' '002701.SZA' '002819.SZA']
2022-09-19 ['300758.SZA']
2022-09-23 ['605336.SHA']
2022-09-26 ['000036.SZA' '002394.SZA']
2022-10-10 ['300967.SZA' '600186.SHA']
2022-10-11 ['300684.SZA' '600369.SHA']
2022-10-12 ['603042.SHA']
2022-10-21 ['600644.SHA']
2022-10-25 ['002790.SZA' '300675.SZA']
2022-10-26 ['000531.SZA']

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