{"Description":"实验创建于2017/8/26","Summary":"","Graph":{"EdgesInternal":[{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-15:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8:data"},{"DestinationInputPortId":"-215:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data1","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-15:data"},{"DestinationInputPortId":"-215:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-222:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-231:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-238:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-172:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-186:input_data","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data"},{"DestinationInputPortId":"-677:data1","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:predictions"},{"DestinationInputPortId":"-231:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"DestinationInputPortId":"-250:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"DestinationInputPortId":"-654:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"DestinationInputPortId":"-222:input_data","SourceOutputPortId":"-215:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data2","SourceOutputPortId":"-222:data"},{"DestinationInputPortId":"-238:input_data","SourceOutputPortId":"-231:data"},{"DestinationInputPortId":"-190:input_data","SourceOutputPortId":"-238:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:model","SourceOutputPortId":"-172:model"},{"DestinationInputPortId":"-172:training_ds","SourceOutputPortId":"-186:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:data","SourceOutputPortId":"-190:data"},{"DestinationInputPortId":"-654:features","SourceOutputPortId":"-649:data"},{"DestinationInputPortId":"-661:features","SourceOutputPortId":"-649:data"},{"DestinationInputPortId":"-661:input_data","SourceOutputPortId":"-654:data"},{"DestinationInputPortId":"-677:data2","SourceOutputPortId":"-661:data"},{"DestinationInputPortId":"-250:options_data","SourceOutputPortId":"-677:data"}],"ModuleNodes":[{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8","ModuleId":"BigQuantSpace.instruments.instruments-v2","ModuleParameters":[{"Name":"start_date","Value":"2010-01-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"2015-01-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"market","Value":"CN_STOCK_A","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"instrument_list","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"max_count","Value":"0","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"rolling_conf","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":1,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-15","ModuleId":"BigQuantSpace.advanced_auto_labeler.advanced_auto_labeler-v2","ModuleParameters":[{"Name":"label_expr","Value":"# 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回测引擎：初始化函数，只执行一次\ndef bigquant_run(context):\n # 加载预测数据\n context.ranker_prediction = context.options['data']\n context.wf_rate = context.options['risk_factor']\n\n # 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\n context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))\n # 预测数据，通过options传入进来，使用 read_df 函数，加载到内存 (DataFrame)\n # 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\n stock_count = 5\n # 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, ..]\n context.stock_weights = T.norm([1 / math.log(i + 2) for i in range(0, stock_count)])\n # 设置每只股票占用的最大资金比例\n context.max_cash_per_instrument = 0.2\n context.options['hold_days'] = 5\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"handle_data","Value":"# 回测引擎：每日数据处理函数，每天执行一次\ndef bigquant_run(context, data):\n # 按日期过滤得到今日的预测数据\n now_date = data.current_dt.strftime('%Y-%m-%d')\n ranker_prediction = context.ranker_prediction[context.ranker_prediction.date == now_date]\n \n # 获取当天上涨/下跌比\n now_wf_rate = context.wf_rate[context.wf_rate == now_date]\n if now_date == \"2016-11-19\":\n print(\"[{}] {}\".format(now_date, now_wf_rate.win_fail_rate.values))\n \n # 1. 资金分配\n # 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\n # 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）\n is_staging = context.trading_day_index < context.options['hold_days'] # 是否在建仓期间（前 hold_days 天）\n cash_avg = context.portfolio.portfolio_value / context.options['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.portfolio.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，按机器学习算法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.portfolio.positions.items()}\n instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(\n lambda x: x in equities)])))\n\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：按机器学习算法预测的排序，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 # 确保股票持仓量不会超过每次股票最大的占用资金量\n cash = max_cash_per_instrument - positions.get(instrument, 0)\n if cash > 0:\n context.order_value(context.symbol(instrument), cash)\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"prepare","Value":"# 回测引擎：准备数据，只执行一次\ndef bigquant_run(context):\n df = context.options['data'].read_df()[[\"date\", \"instrument\", \"where(close_0>close_1, 1, 0)\"]]\n df.rename(columns={\"where(close_0>close_1, 1, 0)\": \"wf_rate\"}, inplace=True)\n context.options[\"risk_factor\"] = df.groupby(\"date\").apply(lambda x: x.wf_rate.sum() / x[x.wf_rate==0].shape[0]).reset_index().rename(columns={0: \"win_fail_rate\"})\n \n context.options['data'] = context.options['data'].read_df()\n 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# 本代码由可视化策略环境自动生成 2021年4月22日16:11
# 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 m19_initialize_bigquant_run(context):
    # 加载预测数据
    context.ranker_prediction = context.options['data']
    context.wf_rate = context.options['risk_factor']

    # 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.options['hold_days'] = 5

# 回测引擎：每日数据处理函数，每天执行一次
def m19_handle_data_bigquant_run(context, data):
    # 按日期过滤得到今日的预测数据
    now_date = data.current_dt.strftime('%Y-%m-%d')
    ranker_prediction = context.ranker_prediction[context.ranker_prediction.date == now_date]
    
    # 获取当天上涨/下跌比
    now_wf_rate = context.wf_rate[context.wf_rate == now_date]
    if now_date == "2016-11-19":
        print("[{}] {}".format(now_date, now_wf_rate.win_fail_rate.values))
    
    # 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.options['hold_days'] # 是否在建仓期间（前 hold_days 天）
    cash_avg = context.portfolio.portfolio_value / context.options['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.portfolio.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，按机器学习算法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.portfolio.positions.items()}
        instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                lambda x: x in equities)])))

        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：按机器学习算法预测的排序，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 m19_prepare_bigquant_run(context):
    df = context.options['data'].read_df()[["date", "instrument", "where(close_0>close_1, 1, 0)"]]
    df.rename(columns={"where(close_0>close_1, 1, 0)": "wf_rate"}, inplace=True)
    context.options["risk_factor"] = df.groupby("date").apply(lambda x: x.wf_rate.sum() / x[x.wf_rate==0].shape[0]).reset_index().rename(columns={0: "win_fail_rate"})
    
    context.options['data'] = context.options['data'].read_df()
    print("数据准备完毕！")


m1 = M.instruments.v2(
    start_date='2010-01-01',
    end_date='2015-01-01',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m2 = M.advanced_auto_labeler.v2(
    instruments=m1.data,
    label_expr="""# #号开始的表示注释
# 0. 每行一个，顺序执行，从第二个开始，可以使用label字段
# 1. 可用数据字段见 https://bigquant.com/docs/develop/datasource/deprecated/history_data.html
#   添加benchmark_前缀，可使用对应的benchmark数据
# 2. 可用操作符和函数见 `表达式引擎 <https://bigquant.com/docs/develop/bigexpr/usage.html>`_

# 计算收益：5日收盘价(作为卖出价格)除以明日开盘价(作为买入价格)
shift(close, -5) / shift(open, -1)

# 极值处理：用1%和99%分位的值做clip
clip(label, all_quantile(label, 0.01), all_quantile(label, 0.99))

# 将分数映射到分类，这里使用20个分类
all_wbins(label, 20)

# 过滤掉一字涨停的情况 (设置label为NaN，在后续处理和训练中会忽略NaN的label)
where(shift(high, -1) == shift(low, -1), NaN, label)
""",
    start_date='',
    end_date='',
    benchmark='000300.SHA',
    drop_na_label=True,
    cast_label_int=True
)

m3 = 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
return_5
return_10
return_20
avg_amount_0/avg_amount_5
avg_amount_5/avg_amount_20
rank_avg_amount_0/rank_avg_amount_5
rank_avg_amount_5/rank_avg_amount_10
rank_return_0
rank_return_5
rank_return_10
rank_return_0/rank_return_5
rank_return_5/rank_return_10
pe_ttm_0
"""
)

m15 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=90
)

m16 = M.derived_feature_extractor.v3(
    input_data=m15.data,
    features=m3.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False
)

m7 = M.join.v3(
    data1=m2.data,
    data2=m16.data,
    on='date,instrument',
    how='inner',
    sort=False
)

m5 = M.dropnan.v2(
    input_data=m7.data
)

m4 = M.stock_ranker_train.v6(
    training_ds=m5.data,
    features=m3.data,
    learning_algorithm='排序',
    number_of_leaves=30,
    minimum_docs_per_leaf=1000,
    number_of_trees=20,
    learning_rate=0.1,
    max_bins=1023,
    feature_fraction=1,
    data_row_fraction=1,
    ndcg_discount_base=1,
    m_lazy_run=False
)

m9 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2015-01-01'),
    end_date=T.live_run_param('trading_date', '2017-01-01'),
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m17 = M.general_feature_extractor.v7(
    instruments=m9.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=90
)

m18 = M.derived_feature_extractor.v3(
    input_data=m17.data,
    features=m3.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False
)

m10 = M.dropnan.v2(
    input_data=m18.data
)

m8 = M.stock_ranker_predict.v5(
    model=m4.model,
    data=m10.data,
    m_lazy_run=False
)

m6 = M.input_features.v1(
    features="""# 换手率
turn_0
# 市值（流通市值）
market_cap_float_0
# 每天上涨/下跌比
where(close_0>close_1, 1, 0)"""
)

m11 = M.general_feature_extractor.v7(
    instruments=m9.data,
    features=m6.data,
    start_date='',
    end_date='',
    before_start_days=90
)

m12 = M.derived_feature_extractor.v3(
    input_data=m11.data,
    features=m6.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False
)

m14 = M.join.v3(
    data1=m8.predictions,
    data2=m12.data,
    on='date,instrument',
    how='inner',
    sort=False
)

m19 = M.trade.v4(
    instruments=m9.data,
    options_data=m14.data,
    start_date='',
    end_date='',
    initialize=m19_initialize_bigquant_run,
    handle_data=m19_handle_data_bigquant_run,
    prepare=m19_prepare_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.SHA'
)

[2021-04-22 15:49:55.654194] INFO: moduleinvoker: instruments.v2 开始运行..

[2021-04-22 15:49:55.662830] INFO: moduleinvoker: 命中缓存

[2021-04-22 15:49:55.666520] INFO: moduleinvoker: instruments.v2 运行完成[0.012354s].

[2021-04-22 15:49:55.671127] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

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[2021-04-22 15:49:55.679617] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[0.008502s].

[2021-04-22 15:49:55.682501] INFO: moduleinvoker: input_features.v1 开始运行..

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[2021-04-22 15:49:55.696122] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

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[2021-04-22 15:49:55.704180] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.008086s].

[2021-04-22 15:49:55.706727] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2021-04-22 15:49:55.711390] INFO: moduleinvoker: 命中缓存

[2021-04-22 15:49:55.712348] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.005624s].

[2021-04-22 15:49:55.714805] INFO: moduleinvoker: join.v3 开始运行..

[2021-04-22 15:49:55.719530] INFO: moduleinvoker: 命中缓存

[2021-04-22 15:49:55.720570] INFO: moduleinvoker: join.v3 运行完成[0.005765s].

[2021-04-22 15:49:55.723121] INFO: moduleinvoker: dropnan.v2 开始运行..

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[2021-04-22 15:49:55.732652] INFO: moduleinvoker: dropnan.v2 运行完成[0.009494s].

[2021-04-22 15:49:55.739866] INFO: moduleinvoker: stock_ranker_train.v6 开始运行..

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[2021-04-22 15:49:56.091623] INFO: moduleinvoker: stock_ranker_train.v6 运行完成[0.351735s].

[2021-04-22 15:49:56.095780] INFO: moduleinvoker: instruments.v2 开始运行..

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[2021-04-22 15:49:56.105340] INFO: moduleinvoker: instruments.v2 运行完成[0.009554s].

[2021-04-22 15:49:56.112291] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2021-04-22 15:49:56.117493] INFO: moduleinvoker: 命中缓存

[2021-04-22 15:49:56.118564] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.006279s].

[2021-04-22 15:49:56.120879] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2021-04-22 15:49:56.126378] INFO: moduleinvoker: 命中缓存

[2021-04-22 15:49:56.127546] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.006633s].

[2021-04-22 15:49:56.134481] INFO: moduleinvoker: dropnan.v2 开始运行..

[2021-04-22 15:49:56.142613] INFO: moduleinvoker: 命中缓存

[2021-04-22 15:49:56.145686] INFO: moduleinvoker: dropnan.v2 运行完成[0.011186s].

[2021-04-22 15:49:56.153381] INFO: moduleinvoker: stock_ranker_predict.v5 开始运行..

[2021-04-22 15:49:56.163854] INFO: moduleinvoker: 命中缓存

[2021-04-22 15:49:56.166489] INFO: moduleinvoker: stock_ranker_predict.v5 运行完成[0.013101s].

[2021-04-22 15:49:56.170877] INFO: moduleinvoker: input_features.v1 开始运行..

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[2021-04-22 15:49:56.179649] INFO: moduleinvoker: input_features.v1 运行完成[0.008747s].

[2021-04-22 15:49:56.189377] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2021-04-22 15:49:56.195406] INFO: moduleinvoker: 命中缓存

[2021-04-22 15:49:56.196992] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.007622s].

[2021-04-22 15:49:56.200182] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2021-04-22 15:49:56.253793] INFO: moduleinvoker: 命中缓存

[2021-04-22 15:49:56.257126] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.056901s].

[2021-04-22 15:49:56.263120] INFO: moduleinvoker: join.v3 开始运行..

[2021-04-22 15:49:56.303463] INFO: moduleinvoker: 命中缓存

[2021-04-22 15:49:56.305054] INFO: moduleinvoker: join.v3 运行完成[0.04195s].

[2021-04-22 15:49:56.359405] INFO: moduleinvoker: backtest.v8 开始运行..

[2021-04-22 15:49:56.369031] INFO: moduleinvoker: 命中缓存

[2021-04-22 15:49:57.746993] INFO: moduleinvoker: backtest.v8 运行完成[1.387592s].

[2021-04-22 15:49:57.749027] INFO: moduleinvoker: trade.v4 运行完成[1.441668s].

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