{"description":"实验创建于2022/7/29","graph":{"edges":[{"to_node_id":"-124:instruments","from_node_id":"-111:data"},{"to_node_id":"-871:instruments","from_node_id":"-111:data"},{"to_node_id":"-124:features","from_node_id":"-119:data"},{"to_node_id":"-554:features","from_node_id":"-119:data"},{"to_node_id":"-554:input_data","from_node_id":"-124:data"},{"to_node_id":"-563:input_1","from_node_id":"-554:data"},{"to_node_id":"-860:input_ds","from_node_id":"-563:data"},{"to_node_id":"-871:options_data","from_node_id":"-860:sorted_data"}],"nodes":[{"node_id":"-111","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2018-01-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2020-01-01","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":"-111"}],"output_ports":[{"name":"data","node_id":"-111"}],"cacheable":true,"seq_num":1,"comment":"","comment_collapsed":true},{"node_id":"-119","module_id":"BigQuantSpace.input_features.input_features-v1","parameters":[{"name":"features","value":"\n# #号开始的表示注释，注释需单独一行\n# 多个特征，每行一个，可以包含基础特征和衍生特征，特征须为本平台特征\nfactor = arctan(close_5-close_10)\n","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-119"}],"output_ports":[{"name":"data","node_id":"-119"}],"cacheable":true,"seq_num":2,"comment":"","comment_collapsed":true},{"node_id":"-124","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":90,"type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-124"},{"name":"features","node_id":"-124"}],"output_ports":[{"name":"data","node_id":"-124"}],"cacheable":true,"seq_num":3,"comment":"","comment_collapsed":true},{"node_id":"-554","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":"-554"},{"name":"features","node_id":"-554"}],"output_ports":[{"name":"data","node_id":"-554"}],"cacheable":true,"seq_num":4,"comment":"","comment_collapsed":true},{"node_id":"-563","module_id":"BigQuantSpace.standardlize.standardlize-v10","parameters":[{"name":"standard_func","value":"ZScoreNorm","type":"Literal","bound_global_parameter":null},{"name":"columns_input","value":"factor","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_1","node_id":"-563"},{"name":"input_2","node_id":"-563"}],"output_ports":[{"name":"data","node_id":"-563"}],"cacheable":true,"seq_num":5,"comment":"","comment_collapsed":true},{"node_id":"-860","module_id":"BigQuantSpace.sort.sort-v5","parameters":[{"name":"sort_by","value":"factor","type":"Literal","bound_global_parameter":null},{"name":"group_by","value":"date","type":"Literal","bound_global_parameter":null},{"name":"keep_columns","value":"--","type":"Literal","bound_global_parameter":null},{"name":"ascending","value":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_ds","node_id":"-860"},{"name":"sort_by_ds","node_id":"-860"}],"output_ports":[{"name":"sorted_data","node_id":"-860"}],"cacheable":true,"seq_num":6,"comment":"","comment_collapsed":true},{"node_id":"-871","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":"# 回测引擎：初始化函数，只执行一次\ndef bigquant_run(context):\n # 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是否在建仓期间（前 hold_days 天）\n cash_avg = context.portfolio.portfolio_value / context.hold_days\n cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)\n cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)\n positions = {e.symbol: p.amount * p.last_sale_price\n for e, p in context.perf_tracker.position_tracker.positions.items()}\n\n # 2. 生成卖出订单：hold_days天之后才开始卖出；对持仓的股票，按StockRanker预测的排序末位淘汰\n if not is_staging and cash_for_sell > 0:\n equities = {e.symbol: e for e, p in context.perf_tracker.position_tracker.positions.items()}\n instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(\n lambda x: x in equities and not context.has_unfinished_sell_order(equities[x]))])))\n # print('rank order for sell %s' % instruments)\n for instrument in instruments:\n context.order_target(context.symbol(instrument), 0)\n cash_for_sell -= positions[instrument]\n if cash_for_sell <= 0:\n break\n\n # 3. 生成买入订单：按StockRanker预测的排序，买入前面的stock_count只股票\n buy_cash_weights = context.stock_weights\n buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])\n max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument\n for i, instrument in enumerate(buy_instruments):\n cash = cash_for_buy * buy_cash_weights[i]\n if cash > max_cash_per_instrument - positions.get(instrument, 0):\n # 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# 本代码由可视化策略环境自动生成 2022年7月29日 19:06
# 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):
    # 加载预测数据
    context.ranker_prediction = context.options['data'].read_df()

    # 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))
    # 预测数据，通过options传入进来，使用 read_df 函数，加载到内存 (DataFrame)
    # 设置买入的股票数量，这里买入预测股票列表排名靠前的5只
    stock_count = 5
    # 每只的股票的权重，如下的权重分配会使得靠前的股票分配多一点的资金，[0.339160, 0.213986, 0.169580, ..]
    context.stock_weights = T.norm([1 / math.log(i + 2) for i in range(0, stock_count)])
    # 设置每只股票占用的最大资金比例
    context.max_cash_per_instrument = 0.2
    context.hold_days = 5

# 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):
    # 按日期过滤得到今日的预测数据
    ranker_prediction = context.ranker_prediction[
        context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]

    # 1. 资金分配
    # 平均持仓时间是hold_days，每日都将买入股票，每日预期使用 1/hold_days 的资金
    # 实际操作中，会存在一定的买入误差，所以在前hold_days天，等量使用资金；之后，尽量使用剩余资金（这里设置最多用等量的1.5倍）
    is_staging = context.trading_day_index < context.hold_days # 是否在建仓期间（前 hold_days 天）
    cash_avg = context.portfolio.portfolio_value / context.hold_days
    cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)
    cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)
    positions = {e.symbol: p.amount * p.last_sale_price
                 for e, p in context.perf_tracker.position_tracker.positions.items()}

    # 2. 生成卖出订单：hold_days天之后才开始卖出；对持仓的股票，按StockRanker预测的排序末位淘汰
    if not is_staging and cash_for_sell > 0:
        equities = {e.symbol: e for e, p in context.perf_tracker.position_tracker.positions.items()}
        instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                lambda x: x in equities and not context.has_unfinished_sell_order(equities[x]))])))
        # print('rank order for sell %s' % instruments)
        for instrument in instruments:
            context.order_target(context.symbol(instrument), 0)
            cash_for_sell -= positions[instrument]
            if cash_for_sell <= 0:
                break

    # 3. 生成买入订单：按StockRanker预测的排序，买入前面的stock_count只股票
    buy_cash_weights = context.stock_weights
    buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])
    max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument
    for i, instrument in enumerate(buy_instruments):
        cash = cash_for_buy * buy_cash_weights[i]
        if cash > max_cash_per_instrument - positions.get(instrument, 0):
            # 确保股票持仓量不会超过每次股票最大的占用资金量
            cash = max_cash_per_instrument - positions.get(instrument, 0)
        if cash > 0:
            context.order_value(context.symbol(instrument), cash)

# 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.instruments.v2(
    start_date='2018-01-01',
    end_date='2020-01-01',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m2 = M.input_features.v1(
    features="""
# #号开始的表示注释，注释需单独一行
# 多个特征，每行一个，可以包含基础特征和衍生特征，特征须为本平台特征
factor = arctan(close_5-close_10)
"""
)

m3 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m2.data,
    start_date='',
    end_date='',
    before_start_days=90
)

m4 = M.derived_feature_extractor.v3(
    input_data=m3.data,
    features=m2.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions={}
)

m5 = M.standardlize.v10(
    input_1=m4.data,
    standard_func='ZScoreNorm',
    columns_input='factor'
)

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

m7 = M.trade.v4(
    instruments=m1.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='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'
)

[2022-07-29 19:04:51.673815] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-07-29 19:04:51.786258] INFO: moduleinvoker: 命中缓存

[2022-07-29 19:04:51.812020] INFO: moduleinvoker: instruments.v2 运行完成[0.138659s].

[2022-07-29 19:04:51.831806] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-07-29 19:04:51.843554] INFO: moduleinvoker: 命中缓存

[2022-07-29 19:04:51.846834] INFO: moduleinvoker: input_features.v1 运行完成[0.015027s].

[2022-07-29 19:04:51.901973] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-07-29 19:04:51.924593] INFO: moduleinvoker: 命中缓存

[2022-07-29 19:04:51.932236] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.030244s].

[2022-07-29 19:04:51.958309] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-07-29 19:04:51.980776] INFO: moduleinvoker: 命中缓存

[2022-07-29 19:04:51.983614] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.025317s].

[2022-07-29 19:04:52.002073] INFO: moduleinvoker: standardlize.v10 开始运行..

[2022-07-29 19:05:03.645167] INFO: moduleinvoker: standardlize.v10 运行完成[11.643075s].

[2022-07-29 19:05:03.807404] INFO: moduleinvoker: sort.v5 开始运行..

[2022-07-29 19:05:11.719587] INFO: moduleinvoker: sort.v5 运行完成[7.912176s].

[2022-07-29 19:05:22.355655] INFO: moduleinvoker: backtest.v8 开始运行..

[2022-07-29 19:05:22.372532] INFO: backtest: biglearning backtest:V8.6.2

[2022-07-29 19:05:22.379477] INFO: backtest: product_type:stock by specified

[2022-07-29 19:05:22.702626] INFO: moduleinvoker: cached.v2 开始运行..

[2022-07-29 19:05:46.474011] INFO: backtest: 读取股票行情完成:2905202

[2022-07-29 19:06:00.852827] INFO: moduleinvoker: cached.v2 运行完成[38.150206s].

[2022-07-29 19:06:07.920705] INFO: algo: TradingAlgorithm V1.8.8

[2022-07-29 19:06:12.737693] INFO: algo: trading transform...

[2022-07-29 19:06:15.531668] INFO: algo: handle_splits get splits [dt:2018-05-02 00:00:00+00:00] [asset:Equity(457 [600588.SHA]), ratio:0.7661136984825134]

[2022-07-29 19:06:15.533721] INFO: Position: position stock handle split[sid:457, orig_amount:4500, new_amount:5873.0, orig_cost:36.13422029908706, new_cost:27.6829, ratio:0.7661136984825134, last_sale_price:28.170001983642578]

[2022-07-29 19:06:15.535076] INFO: Position: after split: PositionStock(asset:Equity(457 [600588.SHA]), amount:5873.0, cost_basis:27.6829, last_sale_price:36.77000045776367)

[2022-07-29 19:06:15.536547] INFO: Position: returning cash: 22.5859

[2022-07-29 19:06:16.084026] INFO: algo: handle_splits get splits [dt:2018-05-30 00:00:00+00:00] [asset:Equity(4151 [600276.SHA]), ratio:0.7681827545166016]

[2022-07-29 19:06:16.181795] INFO: algo: handle_splits get splits [dt:2018-06-04 00:00:00+00:00] [asset:Equity(3798 [002153.SZA]), ratio:0.9964677095413208]

[2022-07-29 19:06:16.184905] INFO: Position: position stock handle split[sid:3798, orig_amount:1100, new_amount:1103.0, orig_cost:29.190000544398593, new_cost:29.0869, ratio:0.9964677095413208, last_sale_price:28.21000099182129]

[2022-07-29 19:06:16.186789] INFO: Position: after split: PositionStock(asset:Equity(3798 [002153.SZA]), amount:1103.0, cost_basis:29.0869, last_sale_price:28.309999465942383)

[2022-07-29 19:06:16.188337] INFO: Position: returning cash: 25.3691

[2022-07-29 19:06:16.437934] INFO: algo: handle_splits get splits [dt:2018-06-15 00:00:00+00:00] [asset:Equity(2030 [600519.SHA]), ratio:0.9860074520111084]

[2022-07-29 19:06:16.439682] INFO: Position: position stock handle split[sid:2030, orig_amount:200, new_amount:202.0, orig_cost:789.0000000449281, new_cost:777.9599, ratio:0.9860074520111084, last_sale_price:775.1300659179688]

[2022-07-29 19:06:16.441470] INFO: Position: after split: PositionStock(asset:Equity(2030 [600519.SHA]), amount:202.0, cost_basis:777.9599, last_sale_price:786.1300048828125)

[2022-07-29 19:06:16.443460] INFO: Position: returning cash: 649.7323

[2022-07-29 19:06:17.124487] INFO: algo: handle_splits get splits [dt:2018-07-17 00:00:00+00:00] [asset:Equity(1141 [002356.SZA]), ratio:0.5848636031150818]

[2022-07-29 19:06:17.126469] INFO: Position: position stock handle split[sid:1141, orig_amount:1900, new_amount:3248.0, orig_cost:17.480001492730533, new_cost:10.2234, ratio:0.5848636031150818, last_sale_price:10.50999927520752]

[2022-07-29 19:06:17.128008] INFO: Position: after split: PositionStock(asset:Equity(1141 [002356.SZA]), amount:3248.0, cost_basis:10.2234, last_sale_price:17.970001220703125)

[2022-07-29 19:06:17.129325] INFO: Position: returning cash: 6.5234

[2022-07-29 19:06:17.944965] INFO: algo: handle_splits get splits [dt:2018-08-21 00:00:00+00:00] [asset:Equity(149 [600031.SHA]), ratio:0.9823981523513794]

[2022-07-29 19:06:17.947460] INFO: Position: position stock handle split[sid:149, orig_amount:3900, new_amount:3969.0, orig_cost:9.160000804031869, new_cost:8.9988, ratio:0.9823981523513794, last_sale_price:8.929999351501465]

[2022-07-29 19:06:17.949440] INFO: Position: after split: PositionStock(asset:Equity(149 [600031.SHA]), amount:3969.0, cost_basis:8.9988, last_sale_price:9.09000015258789)

[2022-07-29 19:06:17.951219] INFO: Position: returning cash: 7.8332

[2022-07-29 19:06:18.028681] INFO: algo: handle_splits get splits [dt:2018-08-23 00:00:00+00:00] [asset:Equity(2253 [000002.SZA]), ratio:0.9623745083808899]

[2022-07-29 19:06:21.303885] INFO: algo: handle_splits get splits [dt:2019-02-25 00:00:00+00:00] [asset:Equity(2687 [000651.SZA]), ratio:0.9866547584533691]

[2022-07-29 19:06:21.306188] INFO: Position: position stock handle split[sid:2687, orig_amount:1100, new_amount:1114.0, orig_cost:44.79000473288754, new_cost:44.1923, ratio:0.9866547584533691, last_sale_price:44.36000061035156]

[2022-07-29 19:06:21.308386] INFO: Position: after split: PositionStock(asset:Equity(2687 [000651.SZA]), amount:1114.0, cost_basis:44.1923, last_sale_price:44.96000289916992)

[2022-07-29 19:06:21.309990] INFO: Position: returning cash: 38.9623

[2022-07-29 19:06:23.378809] INFO: algo: handle_splits get splits [dt:2019-06-14 00:00:00+00:00] [asset:Equity(1746 [600621.SHA]), ratio:0.9992401003837585]

[2022-07-29 19:06:23.395169] INFO: Position: position stock handle split[sid:1746, orig_amount:14000, new_amount:14010.0, orig_cost:12.437500302961332, new_cost:12.428, ratio:0.9992401003837585, last_sale_price:13.149999618530273]

[2022-07-29 19:06:23.397221] INFO: Position: after split: PositionStock(asset:Equity(1746 [600621.SHA]), amount:14010.0, cost_basis:12.428, last_sale_price:13.15999984741211)

[2022-07-29 19:06:23.399256] INFO: Position: returning cash: 8.5039

[2022-07-29 19:06:24.425481] INFO: algo: handle_splits get splits [dt:2019-08-06 00:00:00+00:00] [asset:Equity(2687 [000651.SZA]), ratio:0.9716874361038208]

[2022-07-29 19:06:24.427151] INFO: Position: position stock handle split[sid:2687, orig_amount:1600, new_amount:1646.0, orig_cost:52.700000765017265, new_cost:51.2079, ratio:0.9716874361038208, last_sale_price:51.47999954223633]

[2022-07-29 19:06:24.428592] INFO: Position: after split: PositionStock(asset:Equity(2687 [000651.SZA]), amount:1646.0, cost_basis:51.2079, last_sale_price:52.97999954223633)

[2022-07-29 19:06:24.430362] INFO: Position: returning cash: 31.9194

[2022-07-29 19:06:24.489383] INFO: algo: handle_splits get splits [dt:2019-08-08 00:00:00+00:00] [asset:Equity(1637 [600601.SHA]), ratio:0.9968454837799072]

[2022-07-29 19:06:24.492738] INFO: Position: position stock handle split[sid:1637, orig_amount:25000, new_amount:25079.0, orig_cost:3.183920765749031, new_cost:3.1739, ratio:0.9968454837799072, last_sale_price:3.1600000858306885]

[2022-07-29 19:06:24.495964] INFO: Position: after split: PositionStock(asset:Equity(1637 [600601.SHA]), amount:25079.0, cost_basis:3.1739, last_sale_price:3.169999837875366)

[2022-07-29 19:06:24.500247] INFO: Position: returning cash: 0.3554

[2022-07-29 19:06:24.648761] INFO: algo: handle_splits get splits [dt:2019-08-14 00:00:00+00:00] [asset:Equity(1069 [600612.SHA]), ratio:0.9802442193031311]

[2022-07-29 19:06:24.650644] INFO: Position: position stock handle split[sid:1069, orig_amount:1600, new_amount:1632.0, orig_cost:52.799999473918554, new_cost:51.7569, ratio:0.9802442193031311, last_sale_price:54.57999801635742]

[2022-07-29 19:06:24.652060] INFO: Position: after split: PositionStock(asset:Equity(1069 [600612.SHA]), amount:1632.0, cost_basis:51.7569, last_sale_price:55.68000030517578)

[2022-07-29 19:06:24.653640] INFO: Position: returning cash: 13.4431

[2022-07-29 19:06:24.835611] INFO: algo: handle_splits get splits [dt:2019-08-20 00:00:00+00:00] [asset:Equity(720 [600547.SHA]), ratio:0.7127849459648132]

[2022-07-29 19:06:24.838365] INFO: Position: position stock handle split[sid:720, orig_amount:3400, new_amount:4770.0, orig_cost:47.987941296129115, new_cost:34.2051, ratio:0.7127849459648132, last_sale_price:35.96000289916992]

[2022-07-29 19:06:24.840264] INFO: Position: after split: PositionStock(asset:Equity(720 [600547.SHA]), amount:4770.0, cost_basis:34.2051, last_sale_price:50.45000076293945)

[2022-07-29 19:06:24.843725] INFO: Position: returning cash: 0.7975

[2022-07-29 19:06:24.901989] INFO: algo: handle_splits get splits [dt:2019-08-22 00:00:00+00:00] [asset:Equity(1042 [000568.SZA]), ratio:0.9821016788482666]

[2022-07-29 19:06:27.356510] INFO: Performance: Simulated 487 trading days out of 487.

[2022-07-29 19:06:27.358278] INFO: Performance: first open: 2018-01-02 09:30:00+00:00

[2022-07-29 19:06:27.359683] INFO: Performance: last close: 2019-12-31 15:00:00+00:00

[2022-07-29 19:06:35.197069] INFO: moduleinvoker: backtest.v8 运行完成[72.841419s].

[2022-07-29 19:06:35.199361] INFO: moduleinvoker: trade.v4 运行完成[83.404099s].

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