{"Description":"实验创建于2017/8/26","Summary":"","Graph":{"EdgesInternal":[{"DestinationInputPortId":"-145:input_data","SourceOutputPortId":"-135:data"},{"DestinationInputPortId":"-135:instruments","SourceOutputPortId":"-143:data"},{"DestinationInputPortId":"-58:instruments","SourceOutputPortId":"-143:data"},{"DestinationInputPortId":"-2750:instruments","SourceOutputPortId":"-143:data"},{"DestinationInputPortId":"-135:features","SourceOutputPortId":"-151:data"},{"DestinationInputPortId":"-135:user_functions","SourceOutputPortId":"-52:functions"},{"DestinationInputPortId":"-2766:data2","SourceOutputPortId":"-145:data"},{"DestinationInputPortId":"-145:features","SourceOutputPortId":"-62:data"},{"DestinationInputPortId":"-2750:features","SourceOutputPortId":"-2745:data"},{"DestinationInputPortId":"-2757:features","SourceOutputPortId":"-2745:data"},{"DestinationInputPortId":"-2757:input_data","SourceOutputPortId":"-2750:data"},{"DestinationInputPortId":"-2766:data1","SourceOutputPortId":"-2757:data"},{"DestinationInputPortId":"-58:options_data","SourceOutputPortId":"-2766:data"}],"ModuleNodes":[{"Id":"-135","ModuleId":"BigQuantSpace.feature_extractor_1m.feature_extractor_1m-v1","ModuleParameters":[{"Name":"start_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"before_start_days","Value":"90","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"workers","Value":"2","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"parallel_mode","Value":"测试","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"table_1m","Value":"level2_bar1m_CN_STOCK_A","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"instruments","NodeId":"-135"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-135"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"user_functions","NodeId":"-135"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-135","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":12,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-143","ModuleId":"BigQuantSpace.instruments.instruments-v2","ModuleParameters":[{"Name":"start_date","Value":"2020-06-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"2020-12-31","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"market","Value":"CN_STOCK_A","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"instrument_list","Value":"000001.SZA\n000002.SZA\n000005.SZA\n600519.SHA","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"max_count","Value":"0","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"rolling_conf","NodeId":"-143"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-143","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":20,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-151","ModuleId":"BigQuantSpace.input_features.input_features-v1","ModuleParameters":[{"Name":"features","Value":"# 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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 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# 本代码由可视化策略环境自动生成 2021年6月24日16:28
# 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。


def m1_func_bigquant_run(df, close, volume):
    vwap=(close*volume).sum()/volume.sum()
    return vwap

# 回测引擎：初始化函数，只执行一次
def m3_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 = 1
    # 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 = 1
    context.hold_days = 5

# 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):
    # 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 m3_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 m3_before_trading_start_bigquant_run(context, data):
    pass


m20 = M.instruments.v2(
    start_date='2020-06-01',
    end_date='2020-12-31',
    market='CN_STOCK_A',
    instrument_list="""000001.SZA
000002.SZA
000005.SZA
600519.SHA""",
    max_count=0
)

m21 = M.input_features.v1(
    features="""# 支持 np=numpy, pd=pandas, ta=talib, math 库，支持 pandas series 内建函数
# _ 开始的表示中间变量，不会出现在最终结果中，可以用于中间复用计算结果，加快速度
# 自定义表达式

ret5m = close.loc[145700] / close.loc[145200] -1 
close_ = close.loc[145700]
vwap(close,volume)
open_ = open.loc[93100]

mom1 = close.loc[103000]/close.loc[93100] - 1
mom2 = close.loc[113000]/close.loc[103000] - 1
mom3 = close.loc[140000]/close.loc[130100] - 1
mom4 = close.loc[145700]/close.loc[140000] - 1
"""
)

m1 = M.feature_extractor_user_function.v1(
    name='vwap',
    func=m1_func_bigquant_run
)

m12 = M.feature_extractor_1m.v1(
    instruments=m20.data,
    features=m21.data,
    user_functions=m1.functions,
    start_date='',
    end_date='',
    before_start_days=90,
    workers=2,
    parallel_mode='测试',
    table_1m='level2_bar1m_CN_STOCK_A'
)

m4 = M.input_features.v1(
    features="""mom0 = open_/close_.shift(1)
ma_mom1 = mean(mom1,22)
ma_mom2 = mean(mom2,22)
ma_mom3 = mean(mom3,22)
ma_mom4 = mean(mom4,22)
 
"""
)

m2 = M.derived_feature_extractor.v3(
    input_data=m12.data,
    features=m4.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=True,
    remove_extra_columns=True
)

m6 = 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
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
"""
)

m7 = M.general_feature_extractor.v7(
    instruments=m20.data,
    features=m6.data,
    start_date='',
    end_date='',
    before_start_days=90
)

m8 = M.derived_feature_extractor.v3(
    input_data=m7.data,
    features=m6.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=True,
    user_functions={}
)

m9 = M.join.v3(
    data1=m8.data,
    data2=m2.data,
    on='date,instrument',
    how='inner',
    sort=False
)

m3 = M.trade.v4(
    instruments=m20.data,
    options_data=m9.data,
    start_date='',
    end_date='',
    initialize=m3_initialize_bigquant_run,
    handle_data=m3_handle_data_bigquant_run,
    prepare=m3_prepare_bigquant_run,
    before_trading_start=m3_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=''
)

[2021-06-24 11:10:10.912917] INFO: moduleinvoker: instruments.v2 开始运行..

[2021-06-24 11:10:10.956231] INFO: moduleinvoker: instruments.v2 运行完成[0.043282s].

[2021-06-24 11:10:10.961157] INFO: moduleinvoker: input_features.v1 开始运行..

[2021-06-24 11:10:10.968698] INFO: moduleinvoker: 命中缓存

[2021-06-24 11:10:10.971406] INFO: moduleinvoker: input_features.v1 运行完成[0.010261s].

[2021-06-24 11:10:10.976459] INFO: moduleinvoker: feature_extractor_user_function.v1 运行完成[0.00017s].

[2021-06-24 11:10:10.982535] INFO: moduleinvoker: feature_extractor_1m.v1 开始运行..

[2021-06-24 11:10:10.999263] INFO: 高频特征抽取-分钟到日频: 测试模式运行, ['000001.SZA', '000005.SZA', '600519.SHA']

[2021-06-24 11:10:11.001879] INFO: fe1m_utils: extract chunk 3 instruments, 8 features ..

[2021-06-24 11:10:11.003327] INFO: fe1m_utils: extract chunk 3 instruments, n_jobs=30=(20+40)/2, 并行=False ..

[2021-06-24 11:10:15.480741] INFO: fe1m_utils: extracted chunk 3/3 instruments, (618, 10).

[2021-06-24 11:10:15.615028] WARNING: 高频特征抽取-分钟到日频: no data found for {'000002.SZA'}

[2021-06-24 11:10:15.617027] INFO: 高频特征抽取-分钟到日频: extracted 3/4 instruments, (618, 10)

[2021-06-24 11:10:15.621767] INFO: moduleinvoker: feature_extractor_1m.v1 运行完成[4.63921s].

[2021-06-24 11:10:15.625906] INFO: moduleinvoker: input_features.v1 开始运行..

[2021-06-24 11:10:15.638970] INFO: moduleinvoker: 命中缓存

[2021-06-24 11:10:15.641500] INFO: moduleinvoker: input_features.v1 运行完成[0.015593s].

[2021-06-24 11:10:15.646421] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2021-06-24 11:10:15.699394] INFO: derived_feature_extractor: 提取完成 mom0 = open_/close_.shift(1), 0.002s

[2021-06-24 11:10:15.707822] INFO: derived_feature_extractor: 提取完成 ma_mom1 = mean(mom1,22), 0.006s

[2021-06-24 11:10:15.715312] INFO: derived_feature_extractor: 提取完成 ma_mom2 = mean(mom2,22), 0.006s

[2021-06-24 11:10:15.720461] INFO: derived_feature_extractor: 提取完成 ma_mom3 = mean(mom3,22), 0.004s

[2021-06-24 11:10:15.728248] INFO: derived_feature_extractor: 提取完成 ma_mom4 = mean(mom4,22), 0.006s

[2021-06-24 11:10:15.787325] INFO: derived_feature_extractor: /data, 618

[2021-06-24 11:10:15.872485] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.22604s].

[2021-06-24 11:10:15.876931] INFO: moduleinvoker: input_features.v1 开始运行..

[2021-06-24 11:10:15.882562] INFO: moduleinvoker: 命中缓存

[2021-06-24 11:10:15.884453] INFO: moduleinvoker: input_features.v1 运行完成[0.00753s].

[2021-06-24 11:10:15.892576] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2021-06-24 11:10:17.864891] INFO: 基础特征抽取: 年份 2020, 特征行数=824

[2021-06-24 11:10:17.910180] INFO: 基础特征抽取: 总行数: 824

[2021-06-24 11:10:17.922213] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[2.029631s].

[2021-06-24 11:10:17.928252] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2021-06-24 11:10:18.000412] INFO: derived_feature_extractor: 提取完成 avg_amount_0/avg_amount_5, 0.001s

[2021-06-24 11:10:18.003760] INFO: derived_feature_extractor: 提取完成 avg_amount_5/avg_amount_20, 0.001s

[2021-06-24 11:10:18.007052] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_0/rank_avg_amount_5, 0.001s

[2021-06-24 11:10:18.009845] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_5/rank_avg_amount_10, 0.001s

[2021-06-24 11:10:18.013398] INFO: derived_feature_extractor: 提取完成 rank_return_0/rank_return_5, 0.001s

[2021-06-24 11:10:18.016420] INFO: derived_feature_extractor: 提取完成 rank_return_5/rank_return_10, 0.001s

[2021-06-24 11:10:18.090960] INFO: derived_feature_extractor: /y_2020, 824

[2021-06-24 11:10:18.175256] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.246986s].

[2021-06-24 11:10:18.180457] INFO: moduleinvoker: join.v3 开始运行..

[2021-06-24 11:10:18.308446] INFO: join: /data, 行数=555/555, 耗时=0.055297s

[2021-06-24 11:10:18.368127] INFO: join: 最终行数: 555

[2021-06-24 11:10:18.377204] INFO: moduleinvoker: join.v3 运行完成[0.196773s].

[2021-06-24 11:10:18.411944] INFO: moduleinvoker: backtest.v8 开始运行..

[2021-06-24 11:10:18.416838] INFO: backtest: biglearning backtest:V8.5.0

[2021-06-24 11:10:18.418166] INFO: backtest: product_type:stock by specified

[2021-06-24 11:10:18.802142] INFO: moduleinvoker: cached.v2 开始运行..

[2021-06-24 11:10:25.337683] INFO: backtest: 读取股票行情完成:120945

[2021-06-24 11:10:25.604908] INFO: moduleinvoker: cached.v2 运行完成[6.802816s].

[2021-06-24 11:10:25.818130] INFO: algo: TradingAlgorithm V1.8.3

[2021-06-24 11:10:26.053361] INFO: algo: trading transform...

[2021-06-24 11:10:27.359175] INFO: Performance: Simulated 146 trading days out of 146.

[2021-06-24 11:10:27.362665] INFO: Performance: first open: 2020-06-01 09:30:00+00:00

[2021-06-24 11:10:27.366435] INFO: Performance: last close: 2020-12-31 15:00:00+00:00

[2021-06-24 11:10:28.580296] INFO: moduleinvoker: backtest.v8 运行完成[10.168341s].

[2021-06-24 11:10:28.582611] INFO: moduleinvoker: trade.v4 运行完成[10.199106s].

bigcharts-data-start/{"__type":"tabs","__id":"bigchart-0ca6094104ba4eadb041b9a215a12038"}/bigcharts-data-end