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    {"description":"实验创建于2017/8/26","graph":{"edges":[{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-15:instruments","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-29:instruments","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data1","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-15:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-29:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-35:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-76:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-70:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-503:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-510:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-35:input_data","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-29:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data2","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-35:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-84:input_data","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-70:instruments","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-81:instruments","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-76:input_data","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-70:data"},{"to_node_id":"-86:input_data","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-76:data"},{"to_node_id":"-361:input_1","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-84:data"},{"to_node_id":"-522:input_1","from_node_id":"-86:data"},{"to_node_id":"-1181:input_data","from_node_id":"-361:data_1"},{"to_node_id":"-503:instruments","from_node_id":"-494:data"},{"to_node_id":"-510:input_data","from_node_id":"-503:data"},{"to_node_id":"-516:input_data","from_node_id":"-510:data"},{"to_node_id":"-519:input_1","from_node_id":"-516:data"},{"to_node_id":"-1155:features","from_node_id":"-519:data_1"},{"to_node_id":"-1174:data","from_node_id":"-522:data_1"},{"to_node_id":"-1174:model","from_node_id":"-1155:model"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-81:options_data","from_node_id":"-1174:predictions"},{"to_node_id":"-1155:training_ds","from_node_id":"-1181:data"},{"to_node_id":"-1155:test_ds","from_node_id":"-1181:left_data"}],"nodes":[{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2010-01-01","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":"287d2cb0-f53c-4101-bdf8-104b137c8601-8"}],"output_ports":[{"name":"data","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8"}],"cacheable":true,"seq_num":1,"comment":"","comment_collapsed":true},{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-15","module_id":"BigQuantSpace.advanced_auto_labeler.advanced_auto_labeler-v2","parameters":[{"name":"label_expr","value":"# 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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 ranker_prediction = context.ranker_prediction[\n context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]\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.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.perf_tracker.position_tracker.positions.items()}\n instruments = 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ain.GBDT_train-v1","parameters":[{"name":"num_boost_round","value":120,"type":"Literal","bound_global_parameter":null},{"name":"early_stopping_rounds","value":"","type":"Literal","bound_global_parameter":null},{"name":"objective","value":"reg:linear","type":"Literal","bound_global_parameter":null},{"name":"num_class","value":"","type":"Literal","bound_global_parameter":null},{"name":"eval_metric","value":"error","type":"Literal","bound_global_parameter":null},{"name":"booster","value":"gbtree","type":"Literal","bound_global_parameter":null},{"name":"eta","value":0.1,"type":"Literal","bound_global_parameter":null},{"name":"gamma","value":0.0001,"type":"Literal","bound_global_parameter":null},{"name":"_lambda","value":0,"type":"Literal","bound_global_parameter":null},{"name":"lambda_bias","value":0,"type":"Literal","bound_global_parameter":null},{"name":"alpha","value":0,"type":"Literal","bound_global_parameter":null},{"name":"max_depth","value":6,"type":"Literal","bound_global_parameter":null},{"name":"max_leaf_nodes","value":30,"type":"Literal","bound_global_parameter":null},{"name":"subsample","value":0.8,"type":"Literal","bound_global_parameter":null},{"name":"xgb_param","value":"{}","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"training_ds","node_id":"-1155"},{"name":"features","node_id":"-1155"},{"name":"test_ds","node_id":"-1155"}],"output_ports":[{"name":"model","node_id":"-1155"}],"cacheable":true,"seq_num":6,"comment":"","comment_collapsed":true},{"node_id":"-1174","module_id":"BigQuantSpace.GBDT_predict.GBDT_predict-v1","parameters":[{"name":"date_col","value":"date","type":"Literal","bound_global_parameter":null},{"name":"instrument_col","value":"instrument","type":"Literal","bound_global_parameter":null},{"name":"sort","value":"True","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"model","node_id":"-1174"},{"name":"data","node_id":"-1174"}],"output_ports":[{"name":"predictions","node_id":"-1174"}],"cacheable":true,"seq_num":8,"comment":"","comment_collapsed":true},{"node_id":"-1181","module_id":"BigQuantSpace.filter.filter-v3","parameters":[{"name":"expr","value":"date<\"2015-01-01\"","type":"Literal","bound_global_parameter":null},{"name":"output_left_data","value":"True","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-1181"}],"output_ports":[{"name":"data","node_id":"-1181"},{"name":"left_data","node_id":"-1181"}],"cacheable":true,"seq_num":22,"comment":"","comment_collapsed":true}],"node_layout":"<node_postions><node_position 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    In [1]:
    # 本代码由可视化策略环境自动生成 2022年6月5日 22:02
# 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：每日数据处理函数，每天执行一次
def m12_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.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.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.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 m12_prepare_bigquant_run(context):
    pass

# 回测引擎：初始化函数，只执行一次
def m12_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 = 8
    # 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 = 0.4
    context.options['hold_days'] = 2


m1 = M.instruments.v2(
    start_date='2010-01-01',
    end_date='2020-01-01',
    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 {{web_host_url}}docs/data_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 <{{web_host_url}}docs/big_expr.html>`_

# 计算收益：2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, -2) / 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.HIX',
    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
return_40
rank_return_5
avg_turn_10
avg_turn_20
avg_turn_40
fs_common_equity_0
market_cap_0
rank_market_cap_0
market_cap_float_0
rank_market_cap_float_0
pe_ttm_0
rank_pe_ttm_0
pb_lf_0
rank_pb_lf_0
ps_ttm_0
rank_ps_ttm_0
mf_net_amount_5
mf_net_amount_10
mf_net_amount_20
avg_mf_net_amount_5
avg_mf_net_amount_10
avg_mf_net_amount_20
rank_avg_mf_net_amount_5
fs_net_profit_yoy_0
rank_fs_net_profit_yoy_0
fs_net_profit_qoq_0
rank_fs_net_profit_qoq_0
fs_operating_revenue_yoy_0
fs_roe_ttm_0
fs_roa_0
fs_roa_ttm_0
fs_eps_0
fs_bps_0
fs_cash_ratio_0
fs_net_cash_flow_0
fs_net_cash_flow_ttm_0
fs_current_assets_0
fs_non_current_assets_0
fs_current_liabilities_0
list_board_0
company_found_date_0
st_status_0
"""
)

m4 = M.general_feature_extractor.v6(
    instruments=m1.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=20
)

m5 = M.derived_feature_extractor.v2(
    input_data=m4.data,
    features=m3.data,
    date_col='date',
    instrument_col='instrument'
)

m7 = M.join.v3(
    data1=m2.data,
    data2=m5.data,
    on='date,instrument',
    how='inner',
    sort=False
)

m13 = M.dropnan.v1(
    input_data=m7.data
)

m16 = M.filtet_st_stock_tomo.v3(
    input_1=m13.data
)

m22 = M.filter.v3(
    input_data=m16.data_1,
    expr='date<"2015-01-01"',
    output_left_data=True
)

m9 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2015-01-01'),
    end_date=T.live_run_param('trading_date', '2020-06-01'),
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m10 = M.general_feature_extractor.v6(
    instruments=m9.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=20
)

m11 = M.derived_feature_extractor.v2(
    input_data=m10.data,
    features=m3.data,
    date_col='date',
    instrument_col='instrument'
)

m14 = M.dropnan.v1(
    input_data=m11.data
)

m21 = M.filtet_st_stock_tomo.v3(
    input_1=m14.data
)

m15 = M.instruments.v2(
    start_date='2020-01-01',
    end_date='2022-06-01',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m17 = M.general_feature_extractor.v6(
    instruments=m15.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=20
)

m18 = M.derived_feature_extractor.v2(
    input_data=m17.data,
    features=m3.data,
    date_col='date',
    instrument_col='instrument'
)

m19 = M.dropnan.v1(
    input_data=m18.data
)

m20 = M.filtet_st_stock_tomo.v3(
    input_1=m19.data
)

m6 = M.GBDT_train.v1(
    training_ds=m22.data,
    features=m20.data_1,
    test_ds=m22.left_data,
    num_boost_round=120,
    objective='reg:linear',
    eval_metric='error',
    booster='gbtree',
    eta=0.1,
    gamma=0.0001,
    _lambda=0,
    lambda_bias=0,
    alpha=0,
    max_depth=6,
    max_leaf_nodes=30,
    subsample=0.8,
    xgb_param={}
)

m8 = M.GBDT_predict.v1(
    model=m6.model,
    data=m21.data_1,
    date_col='date',
    instrument_col='instrument',
    sort=True
)

m12 = M.trade.v3(
    instruments=m9.data,
    options_data=m8.predictions,
    start_date='2010-01-01',
    end_date='2022-06-01',
    handle_data=m12_handle_data_bigquant_run,
    prepare=m12_prepare_bigquant_run,
    initialize=m12_initialize_bigquant_run,
    volume_limit=0.025,
    order_price_field_buy='open',
    order_price_field_sell='close',
    capital_base=10000,
    benchmark='000300.HIX',
    auto_cancel_non_tradable_orders=True,
    data_frequency='daily',
    price_type='后复权',
    plot_charts=True,
    backtest_only=False
)

    ---------------------------------------------------------------------------
UnpicklingError                           Traceback (most recent call last)
<ipython-input-1-c766fc43f66f> in <module>
    249 )
    250 
--> 251 m6 = M.GBDT_train.v1(
    252     training_ds=m22.data,
    253     features=m20.data_1,

UnpicklingError: invalid load key, 'H'.