{"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":"-215: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":"-215:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-222:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-231:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-238:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-14684:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24: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":"-250:options_data","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:predictions"},{"to_node_id":"-231:instruments","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"to_node_id":"-250:instruments","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"to_node_id":"-14684:training_ds","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-84:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:data","from_node_id":"-86:data"},{"to_node_id":"-222:input_data","from_node_id":"-215:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data2","from_node_id":"-222:data"},{"to_node_id":"-238:input_data","from_node_id":"-231:data"},{"to_node_id":"-86:input_data","from_node_id":"-238:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:model","from_node_id":"-14684:model"}],"nodes":[{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2018-01-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2020-12-31","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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# 本代码由可视化策略环境自动生成 2023年4月29日 16:56
# 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 m6_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.options['hold_days'] = 5

# 回测引擎：每日数据处理函数，每天执行一次
def m6_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.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 m6_prepare_bigquant_run(context):
    pass


m1 = M.instruments.v2(
    start_date='2018-01-01',
    end_date='2020-12-31',
    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, -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='000500.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
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


hf_value_diff_large_order
hf_close_net_inflow_volume_rate
hf_close_net_inflow_value_rate
hf_volume_diff_large_order
hf_close_netinflow_rate_med_order_act
hf_close_sell_volume_med_order
hf_close_volume_ask
hf_close_netinflow_rate_small_order_act
hf_close_sell_volume_small_order
hf_close_buy_volume_small_order_act
hf_close_buy_volume_med_order_act
hf_close_sell_volume_large_order"""
)

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
)

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

m4 = M.stock_ranker_train.v6(
    training_ds=m13.data,
    features=m3.data,
    learning_algorithm='排序',
    number_of_leaves=40,
    minimum_docs_per_leaf=1000,
    number_of_trees=25,
    learning_rate=0.2,
    max_bins=1023,
    feature_fraction=1,
    data_row_fraction=1,
    plot_charts=True,
    ndcg_discount_base=1,
    m_lazy_run=False
)

m9 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2021-01-01'),
    end_date=T.live_run_param('trading_date', '2022-11-28'),
    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
)

m14 = M.dropnan.v1(
    input_data=m18.data
)

m8 = M.stock_ranker_predict.v5(
    model=m4.model,
    data=m14.data,
    m_lazy_run=False
)

m6 = M.trade.v4(
    instruments=m9.data,
    options_data=m8.predictions,
    start_date='',
    end_date='',
    initialize=m6_initialize_bigquant_run,
    handle_data=m6_handle_data_bigquant_run,
    prepare=m6_prepare_bigquant_run,
    volume_limit=0.025,
    order_price_field_buy='vwap_1',
    order_price_field_sell='vwap_10',
    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'
)

[2023-04-29 16:53:00.520645] INFO: moduleinvoker: instruments.v2 开始运行..

[2023-04-29 16:53:00.535880] INFO: moduleinvoker: 命中缓存

[2023-04-29 16:53:00.538493] INFO: moduleinvoker: instruments.v2 运行完成[0.017862s].

[2023-04-29 16:53:00.549188] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2023-04-29 16:53:00.557056] INFO: moduleinvoker: 命中缓存

[2023-04-29 16:53:01.451861] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[0.902657s].

[2023-04-29 16:53:01.458482] INFO: moduleinvoker: input_features.v1 开始运行..

[2023-04-29 16:53:01.468446] INFO: moduleinvoker: 命中缓存

[2023-04-29 16:53:01.470625] INFO: moduleinvoker: input_features.v1 运行完成[0.012146s].

[2023-04-29 16:53:01.501864] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2023-04-29 16:53:01.511505] INFO: moduleinvoker: 命中缓存

[2023-04-29 16:53:01.513513] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.01168s].

[2023-04-29 16:53:01.522718] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2023-04-29 16:53:01.530918] INFO: moduleinvoker: 命中缓存

[2023-04-29 16:53:01.532875] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.010159s].

[2023-04-29 16:53:01.554106] INFO: moduleinvoker: join.v3 开始运行..

[2023-04-29 16:53:01.561458] INFO: moduleinvoker: 命中缓存

[2023-04-29 16:53:01.563138] INFO: moduleinvoker: join.v3 运行完成[0.009039s].

[2023-04-29 16:53:01.572756] INFO: moduleinvoker: dropnan.v1 开始运行..

[2023-04-29 16:53:01.587991] INFO: moduleinvoker: 命中缓存

[2023-04-29 16:53:01.590022] INFO: moduleinvoker: dropnan.v1 运行完成[0.017265s].

[2023-04-29 16:53:01.601830] INFO: moduleinvoker: stock_ranker_train.v6 开始运行..

[2023-04-29 16:53:01.614217] INFO: moduleinvoker: 命中缓存

[2023-04-29 16:53:02.174197] INFO: moduleinvoker: stock_ranker_train.v6 运行完成[0.572356s].

[2023-04-29 16:53:02.180640] INFO: moduleinvoker: instruments.v2 开始运行..

[2023-04-29 16:53:02.259507] INFO: moduleinvoker: instruments.v2 运行完成[0.078859s].

[2023-04-29 16:53:02.272933] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2023-04-29 16:53:05.083240] INFO: 基础特征抽取: 年份 2020, 特征行数=243745

[2023-04-29 16:53:13.592598] INFO: 基础特征抽取: 年份 2021, 特征行数=1058862

[2023-04-29 16:53:22.313604] INFO: 基础特征抽取: 年份 2022, 特征行数=1029063

[2023-04-29 16:53:22.436874] INFO: 基础特征抽取: 总行数: 2331670

[2023-04-29 16:53:22.447892] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[20.174974s].

[2023-04-29 16:53:22.458966] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2023-04-29 16:53:28.729879] INFO: derived_feature_extractor: 提取完成 avg_amount_0/avg_amount_5, 0.011s

[2023-04-29 16:53:28.740154] INFO: derived_feature_extractor: 提取完成 avg_amount_5/avg_amount_20, 0.008s

[2023-04-29 16:53:28.747873] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_0/rank_avg_amount_5, 0.005s

[2023-04-29 16:53:28.754684] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_5/rank_avg_amount_10, 0.005s

[2023-04-29 16:53:28.761672] INFO: derived_feature_extractor: 提取完成 rank_return_0/rank_return_5, 0.005s

[2023-04-29 16:53:28.768760] INFO: derived_feature_extractor: 提取完成 rank_return_5/rank_return_10, 0.005s

[2023-04-29 16:53:30.386145] INFO: derived_feature_extractor: /y_2020, 243745

[2023-04-29 16:53:33.275213] INFO: derived_feature_extractor: /y_2021, 1058862

[2023-04-29 16:53:37.274712] INFO: derived_feature_extractor: /y_2022, 1029063

[2023-04-29 16:53:38.855544] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[16.396585s].

[2023-04-29 16:53:38.865942] INFO: moduleinvoker: dropnan.v1 开始运行..

[2023-04-29 16:53:40.654564] INFO: dropnan: /y_2020, 136305/243745

[2023-04-29 16:53:44.460807] INFO: dropnan: /y_2021, 865852/1058862

[2023-04-29 16:53:48.254510] INFO: dropnan: /y_2022, 1020622/1029063

[2023-04-29 16:53:48.395622] INFO: dropnan: 行数: 2022779/2331670

[2023-04-29 16:53:48.409199] INFO: moduleinvoker: dropnan.v1 运行完成[9.543256s].

[2023-04-29 16:53:48.425452] INFO: moduleinvoker: stock_ranker_predict.v5 开始运行..

[2023-04-29 16:53:49.900746] INFO: StockRanker预测: /y_2020 ..

[2023-04-29 16:53:52.581730] INFO: StockRanker预测: /y_2021 ..

[2023-04-29 16:53:57.309411] INFO: StockRanker预测: /y_2022 ..

[2023-04-29 16:54:03.171779] INFO: moduleinvoker: stock_ranker_predict.v5 运行完成[14.74633s].

[2023-04-29 16:54:07.092108] INFO: moduleinvoker: backtest.v8 开始运行..

[2023-04-29 16:54:07.099508] INFO: backtest: biglearning backtest:V8.6.3

[2023-04-29 16:54:07.101570] INFO: backtest: product_type:stock by specified

[2023-04-29 16:54:07.186183] INFO: moduleinvoker: cached.v2 开始运行..

[2023-04-29 16:54:26.160991] INFO: backtest: 读取股票行情完成:3367732

[2023-04-29 16:54:29.946489] INFO: moduleinvoker: cached.v2 运行完成[22.760326s].

[2023-04-29 16:54:56.933243] INFO: backtest: algo history_data=DataSource(9f779bbbd05a4ddd984511148649b484T)

[2023-04-29 16:54:56.935746] INFO: algo: TradingAlgorithm V1.8.9

[2023-04-29 16:55:02.838094] INFO: algo: trading transform...

[2023-04-29 16:55:18.067192] WARNING: Performance: maybe_close_position no price for asset:Equity(4589 [300325.SZA]), field:price, dt:2022-06-28 15:00:00+00:00

[2023-04-29 16:55:22.062255] INFO: Performance: Simulated 461 trading days out of 461.

[2023-04-29 16:55:22.064118] INFO: Performance: first open: 2021-01-04 09:30:00+00:00

[2023-04-29 16:55:22.065826] INFO: Performance: last close: 2022-11-28 15:00:00+00:00

[2023-04-29 16:55:28.241710] INFO: moduleinvoker: backtest.v8 运行完成[81.149595s].

[2023-04-29 16:55:28.243507] INFO: moduleinvoker: trade.v4 运行完成[85.061606s].

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