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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'].read_df()\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.hold_days = 5\n","type":"Literal","bound_global_parameter":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 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.hold_days # 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 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# 本代码由可视化策略环境自动生成 2021年10月28日 16:18
# 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 m13_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 m13_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 m13_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 m13_before_trading_start_bigquant_run(context, data):
    pass


m1 = M.instruments.v2(
    start_date='2010-01-01',
    end_date='2014-12-31',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m8 = 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=False,
    user_functions={},
    m_cached=False
)

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
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
"""
)

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

m6 = 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={}
)

m9 = M.join.v3(
    data1=m8.data,
    data2=m6.data,
    on='date,instrument',
    how='inner',
    sort=False
)

m11 = M.dropnan.v1(
    input_data=m9.data
)

m4 = M.instruments.v2(
    start_date='2015-01-01',
    end_date='2017-01-01',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m5 = M.general_feature_extractor.v7(
    instruments=m4.data,
    features=m2.data,
    start_date='',
    end_date='',
    before_start_days=90
)

m7 = M.derived_feature_extractor.v3(
    input_data=m5.data,
    features=m2.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions={}
)

m10 = M.dropnan.v1(
    input_data=m7.data
)

m12 = M.random_forest_regressor.v1(
    training_ds=m11.data,
    features=m2.data,
    predict_ds=m10.data,
    iterations=10,
    feature_fraction=1,
    max_depth=30,
    min_samples_per_leaf=200,
    key_cols='date,instrument',
    workers=1,
    random_state=0,
    other_train_parameters={"random_state":0}
)

m14 = M.sort.v4(
    input_ds=m12.predictions,
    sort_by='pred_label',
    group_by='date',
    keep_columns='--',
    ascending=False
)

m13 = M.trade.v4(
    instruments=m4.data,
    options_data=m14.sorted_data,
    start_date='',
    end_date='',
    initialize=m13_initialize_bigquant_run,
    handle_data=m13_handle_data_bigquant_run,
    prepare=m13_prepare_bigquant_run,
    before_trading_start=m13_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=''
)

[2021-10-22 10:43:33.315657] INFO: moduleinvoker: instruments.v2 开始运行..

[2021-10-22 10:43:33.489704] INFO: moduleinvoker: 命中缓存

[2021-10-22 10:43:33.491669] INFO: moduleinvoker: instruments.v2 运行完成[0.176026s].

[2021-10-22 10:43:33.500760] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2021-10-22 10:43:35.592362] INFO: 自动标注(股票): 加载历史数据: 2642813 行

[2021-10-22 10:43:35.594038] INFO: 自动标注(股票): 开始标注 ..

[2021-10-22 10:43:38.569316] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[5.068578s].

[2021-10-22 10:43:38.581286] INFO: moduleinvoker: input_features.v1 开始运行..

[2021-10-22 10:43:38.591990] INFO: moduleinvoker: 命中缓存

[2021-10-22 10:43:38.593799] INFO: moduleinvoker: input_features.v1 运行完成[0.012523s].

[2021-10-22 10:43:38.608956] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2021-10-22 10:43:38.621025] INFO: moduleinvoker: 命中缓存

[2021-10-22 10:43:38.622571] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.013639s].

[2021-10-22 10:43:38.630650] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2021-10-22 10:43:38.640798] INFO: moduleinvoker: 命中缓存

[2021-10-22 10:43:38.642555] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.011904s].

[2021-10-22 10:43:38.656853] INFO: moduleinvoker: join.v3 开始运行..

[2021-10-22 10:43:43.428061] INFO: join: /y_2009, 行数=0/95020, 耗时=0.791252s

[2021-10-22 10:43:45.434440] INFO: join: /y_2010, 行数=431028/431567, 耗时=2.004348s

[2021-10-22 10:43:47.625282] INFO: join: /y_2011, 行数=510922/511455, 耗时=2.186906s

[2021-10-22 10:43:50.143942] INFO: join: /y_2012, 行数=564582/565675, 耗时=2.512683s

[2021-10-22 10:43:52.595209] INFO: join: /y_2013, 行数=563132/564168, 耗时=2.445148s

[2021-10-22 10:43:55.229274] INFO: join: /y_2014, 行数=555191/569948, 耗时=2.628388s

[2021-10-22 10:43:55.301045] INFO: join: 最终行数: 2624855

[2021-10-22 10:43:55.325343] INFO: moduleinvoker: join.v3 运行完成[16.668473s].

[2021-10-22 10:43:55.338221] INFO: moduleinvoker: dropnan.v1 开始运行..

[2021-10-22 10:43:55.527992] INFO: dropnan: /y_2009, 0/0

[2021-10-22 10:43:56.284359] INFO: dropnan: /y_2010, 423739/431028

[2021-10-22 10:43:57.152677] INFO: dropnan: /y_2011, 504726/510922

[2021-10-22 10:43:58.181114] INFO: dropnan: /y_2012, 561109/564582

[2021-10-22 10:43:59.122163] INFO: dropnan: /y_2013, 563102/563132

[2021-10-22 10:44:00.131496] INFO: dropnan: /y_2014, 553408/555191

[2021-10-22 10:44:00.318514] INFO: dropnan: 行数: 2606084/2624855

[2021-10-22 10:44:00.330379] INFO: moduleinvoker: dropnan.v1 运行完成[4.992154s].

[2021-10-22 10:44:00.336179] INFO: moduleinvoker: instruments.v2 开始运行..

[2021-10-22 10:44:00.345195] INFO: moduleinvoker: 命中缓存

[2021-10-22 10:44:00.347123] INFO: moduleinvoker: instruments.v2 运行完成[0.010952s].

[2021-10-22 10:44:00.368800] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2021-10-22 10:44:00.403459] INFO: moduleinvoker: 命中缓存

[2021-10-22 10:44:00.406223] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.03743s].

[2021-10-22 10:44:00.414921] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2021-10-22 10:44:00.423494] INFO: moduleinvoker: 命中缓存

[2021-10-22 10:44:00.425643] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.010718s].

[2021-10-22 10:44:00.434013] INFO: moduleinvoker: dropnan.v1 开始运行..

[2021-10-22 10:44:00.442708] INFO: moduleinvoker: 命中缓存

[2021-10-22 10:44:00.444477] INFO: moduleinvoker: dropnan.v1 运行完成[0.010469s].

[2021-10-22 10:44:00.460917] INFO: moduleinvoker: random_forest_regressor.v1 开始运行..

[2021-10-22 10:45:32.594888] INFO: moduleinvoker: random_forest_regressor.v1 运行完成[92.133945s].

[2021-10-22 10:45:32.625546] INFO: moduleinvoker: sort.v4 开始运行..

[2021-10-22 10:45:34.842223] INFO: moduleinvoker: sort.v4 运行完成[2.216675s].

[2021-10-22 10:45:34.938122] INFO: moduleinvoker: backtest.v8 开始运行..

[2021-10-22 10:45:34.944081] INFO: backtest: biglearning backtest:V8.5.0

[2021-10-22 10:45:34.945457] INFO: backtest: product_type:stock by specified

[2021-10-22 10:45:35.091395] INFO: moduleinvoker: cached.v2 开始运行..

[2021-10-22 10:45:35.108039] INFO: moduleinvoker: 命中缓存

[2021-10-22 10:45:35.110386] INFO: moduleinvoker: cached.v2 运行完成[0.018999s].

[2021-10-22 10:45:37.181317] INFO: algo: TradingAlgorithm V1.8.5

[2021-10-22 10:45:38.158906] INFO: algo: trading transform...

[2021-10-22 10:45:41.306927] INFO: algo: handle_splits get splits [dt:2015-04-23 00:00:00+00:00] [asset:Equity(2277 [600184.SHA]), ratio:0.5000951886177063]

[2021-10-22 10:45:41.497067] INFO: algo: handle_splits get splits [dt:2015-04-30 00:00:00+00:00] [asset:Equity(2721 [603399.SHA]), ratio:0.9986504912376404]

[2021-10-22 10:45:41.499079] INFO: Position: position stock handle split[sid:2721, orig_amount:7300, new_amount:7309.0, orig_cost:14.900000792630854, new_cost:14.8799, ratio:0.9986504912376404, last_sale_price:14.800000190734863]

[2021-10-22 10:45:41.501024] INFO: Position: after split: PositionStock(asset:Equity(2721 [603399.SHA]), amount:7309.0, cost_basis:14.8799, last_sale_price:14.819999694824219)

[2021-10-22 10:45:41.502936] INFO: Position: returning cash: 12.798

[2021-10-22 10:45:41.846629] INFO: algo: handle_splits get splits [dt:2015-05-14 00:00:00+00:00] [asset:Equity(2164 [300150.SZA]), ratio:0.4970097243785858]

[2021-10-22 10:45:42.325181] INFO: algo: handle_splits get splits [dt:2015-06-01 00:00:00+00:00] [asset:Equity(1282 [002534.SZA]), ratio:0.9940923452377319]

[2021-10-22 10:45:42.326889] INFO: Position: position stock handle split[sid:1282, orig_amount:2700, new_amount:2716.0, orig_cost:27.410001896052133, new_cost:27.2481, ratio:0.9940923452377319, last_sale_price:25.240005493164062]

[2021-10-22 10:45:42.328323] INFO: Position: after split: PositionStock(asset:Equity(1282 [002534.SZA]), amount:2716.0, cost_basis:27.2481, last_sale_price:25.39000129699707)

[2021-10-22 10:45:42.329533] INFO: Position: returning cash: 1.1474

[2021-10-22 10:45:42.439682] INFO: algo: handle_splits get splits [dt:2015-06-04 00:00:00+00:00] [asset:Equity(1613 [300262.SZA]), ratio:0.7135816216468811]

[2021-10-22 10:45:42.441240] INFO: Position: position stock handle split[sid:1613, orig_amount:1700, new_amount:2382.0, orig_cost:33.93999868629467, new_cost:24.219, ratio:0.7135816216468811, last_sale_price:26.060001373291016]

[2021-10-22 10:45:42.442771] INFO: Position: after split: PositionStock(asset:Equity(1613 [300262.SZA]), amount:2382.0, cost_basis:24.219, last_sale_price:36.52000045776367)

[2021-10-22 10:45:42.444295] INFO: Position: returning cash: 9.078

[2021-10-22 10:45:42.554973] INFO: algo: handle_splits get splits [dt:2015-06-09 00:00:00+00:00] [asset:Equity(1988 [603019.SHA]), ratio:0.9993464350700378]

[2021-10-22 10:45:42.557086] INFO: Position: position stock handle split[sid:1988, orig_amount:1600, new_amount:1601.0, orig_cost:132.43750028779937, new_cost:132.3509, ratio:0.9993464350700378, last_sale_price:122.33000183105469]

[2021-10-22 10:45:42.558959] INFO: Position: after split: PositionStock(asset:Equity(1988 [603019.SHA]), amount:1601.0, cost_basis:132.3509, last_sale_price:122.41000366210938)

[2021-10-22 10:45:42.560649] INFO: Position: returning cash: 5.6746

[2021-10-22 10:45:42.612661] INFO: algo: handle_splits get splits [dt:2015-06-10 00:00:00+00:00] [asset:Equity(1683 [300159.SZA]), ratio:0.499615341424942]

[2021-10-22 10:45:42.615107] INFO: algo: handle_splits get splits [dt:2015-06-10 00:00:00+00:00] [asset:Equity(1594 [002429.SZA]), ratio:0.9978799223899841]

[2021-10-22 10:45:42.617869] INFO: Position: position stock handle split[sid:1594, orig_amount:14100, new_amount:14129.0, orig_cost:12.980000621163741, new_cost:12.9525, ratio:0.9978799223899841, last_sale_price:14.120000839233398]

[2021-10-22 10:45:42.619663] INFO: Position: after split: PositionStock(asset:Equity(1594 [002429.SZA]), amount:14129.0, cost_basis:12.9525, last_sale_price:14.149999618530273)

[2021-10-22 10:45:42.621073] INFO: Position: returning cash: 13.5073

[2021-10-22 10:45:43.279225] INFO: algo: handle_splits get splits [dt:2015-07-09 00:00:00+00:00] [asset:Equity(3192 [600270.SHA]), ratio:0.9768115878105164]

[2021-10-22 10:45:43.280820] INFO: Position: position stock handle split[sid:3192, orig_amount:2100, new_amount:2149.0, orig_cost:16.419998170402266, new_cost:16.0392, ratio:0.9768115878105164, last_sale_price:16.850000381469727]

[2021-10-22 10:45:43.282098] INFO: Position: after split: PositionStock(asset:Equity(3192 [600270.SHA]), amount:2149.0, cost_basis:16.0392, last_sale_price:17.25)

[2021-10-22 10:45:43.283216] INFO: Position: returning cash: 14.3502

[2021-10-22 10:45:43.499439] INFO: algo: handle_splits get splits [dt:2015-07-17 00:00:00+00:00] [asset:Equity(2292 [600240.SHA]), ratio:0.9919679164886475]

[2021-10-22 10:45:43.500939] INFO: algo: handle_splits get splits [dt:2015-07-17 00:00:00+00:00] [asset:Equity(1278 [002504.SZA]), ratio:0.994036853313446]

[2021-10-22 10:45:43.502307] INFO: Position: position stock handle split[sid:2292, orig_amount:5000, new_amount:5040.0, orig_cost:12.199999813307345, new_cost:12.102, ratio:0.9919679164886475, last_sale_price:12.350000381469727]

[2021-10-22 10:45:43.503508] INFO: Position: after split: PositionStock(asset:Equity(2292 [600240.SHA]), amount:5040.0, cost_basis:12.102, last_sale_price:12.449999809265137)

[2021-10-22 10:45:43.504618] INFO: Position: returning cash: 5.9972

[2021-10-22 10:45:43.506104] INFO: Position: position stock handle split[sid:1278, orig_amount:8100, new_amount:8148.0, orig_cost:31.800002379117792, new_cost:31.6104, ratio:0.994036853313446, last_sale_price:16.669998168945312]

[2021-10-22 10:45:43.507672] INFO: Position: after split: PositionStock(asset:Equity(1278 [002504.SZA]), amount:8148.0, cost_basis:31.6104, last_sale_price:16.770000457763672)

[2021-10-22 10:45:43.509305] INFO: Position: returning cash: 9.8561

[2021-10-22 10:45:43.640862] INFO: algo: handle_splits get splits [dt:2015-07-22 00:00:00+00:00] [asset:Equity(1165 [300422.SZA]), ratio:0.9993225336074829]

[2021-10-22 10:45:43.642643] INFO: Position: position stock handle split[sid:1165, orig_amount:1100, new_amount:1100.0, orig_cost:79.50000251835098, new_cost:79.4461, ratio:0.9993225336074829, last_sale_price:88.51998901367188]

[2021-10-22 10:45:43.644405] INFO: Position: after split: PositionStock(asset:Equity(1165 [300422.SZA]), amount:1100.0, cost_basis:79.4461, last_sale_price:88.58000183105469)

[2021-10-22 10:45:43.646116] INFO: Position: returning cash: 66.011

[2021-10-22 10:45:46.525271] INFO: algo: handle_splits get splits [dt:2015-11-20 00:00:00+00:00] [asset:Equity(621 [600617.SHA]), ratio:0.994547426700592]

[2021-10-22 10:45:46.527028] INFO: Position: position stock handle split[sid:621, orig_amount:6500, new_amount:6535.0, orig_cost:17.230000999625545, new_cost:17.1361, ratio:0.994547426700592, last_sale_price:18.23999786376953]

[2021-10-22 10:45:46.528301] INFO: Position: after split: PositionStock(asset:Equity(621 [600617.SHA]), amount:6535.0, cost_basis:17.1361, last_sale_price:18.339998245239258)

[2021-10-22 10:45:46.529569] INFO: Position: returning cash: 11.6013

[2021-10-22 10:45:50.552756] INFO: algo: handle_splits get splits [dt:2016-04-18 00:00:00+00:00] [asset:Equity(3162 [600116.SHA]), ratio:0.3316228687763214]

[2021-10-22 10:45:51.017391] INFO: algo: handle_splits get splits [dt:2016-05-06 00:00:00+00:00] [asset:Equity(2358 [600654.SHA]), ratio:0.9957947731018066]

[2021-10-22 10:45:51.019686] INFO: Position: position stock handle split[sid:2358, orig_amount:27300, new_amount:27415.0, orig_cost:24.135164995636867, new_cost:24.0337, ratio:0.9957947731018066, last_sale_price:23.68000030517578]

[2021-10-22 10:45:51.021788] INFO: Position: after split: PositionStock(asset:Equity(2358 [600654.SHA]), amount:27415.0, cost_basis:24.0337, last_sale_price:23.780000686645508)

[2021-10-22 10:45:51.023838] INFO: Position: returning cash: 6.8081

[2021-10-22 10:45:51.238208] INFO: algo: handle_splits get splits [dt:2016-05-16 00:00:00+00:00] [asset:Equity(1458 [002671.SZA]), ratio:0.9990801215171814]

[2021-10-22 10:45:51.240578] INFO: Position: position stock handle split[sid:1458, orig_amount:27800, new_amount:27825.0, orig_cost:10.957932482751426, new_cost:10.9479, ratio:0.9990801215171814, last_sale_price:10.860001564025879]

[2021-10-22 10:45:51.242665] INFO: Position: after split: PositionStock(asset:Equity(1458 [002671.SZA]), amount:27825.0, cost_basis:10.9479, last_sale_price:10.870000839233398)

[2021-10-22 10:45:51.244905] INFO: Position: returning cash: 6.4744

[2021-10-22 10:45:51.811860] INFO: algo: handle_splits get splits [dt:2016-06-03 00:00:00+00:00] [asset:Equity(390 [600687.SHA]), ratio:0.9981751441955566]

[2021-10-22 10:45:51.814136] INFO: Position: position stock handle split[sid:390, orig_amount:18400, new_amount:18433.0, orig_cost:16.600002306748827, new_cost:16.5697, ratio:0.9981751441955566, last_sale_price:16.40999984741211]

[2021-10-22 10:45:51.816065] INFO: Position: after split: PositionStock(asset:Equity(390 [600687.SHA]), amount:18433.0, cost_basis:16.5697, last_sale_price:16.440000534057617)

[2021-10-22 10:45:51.817456] INFO: Position: returning cash: 10.4816

[2021-10-22 10:45:51.963222] INFO: algo: handle_splits get splits [dt:2016-06-13 00:00:00+00:00] [asset:Equity(2599 [600477.SHA]), ratio:0.7642706632614136]

[2021-10-22 10:45:51.965012] INFO: Position: position stock handle split[sid:2599, orig_amount:12700, new_amount:16617.0, orig_cost:9.530002000703572, new_cost:7.2835, ratio:0.7642706632614136, last_sale_price:7.229999542236328]

[2021-10-22 10:45:51.966333] INFO: Position: after split: PositionStock(asset:Equity(2599 [600477.SHA]), amount:16617.0, cost_basis:7.2835, last_sale_price:9.459999084472656)

[2021-10-22 10:45:51.967611] INFO: Position: returning cash: 1.0821

[2021-10-22 10:45:52.183161] INFO: algo: handle_splits get splits [dt:2016-06-22 00:00:00+00:00] [asset:Equity(1076 [300192.SZA]), ratio:0.9957805871963501]

[2021-10-22 10:45:52.184825] INFO: Position: position stock handle split[sid:1076, orig_amount:9200, new_amount:9238.0, orig_cost:11.800002975366494, new_cost:11.7502, ratio:0.9957805871963501, last_sale_price:11.800000190734863]

[2021-10-22 10:45:52.186036] INFO: Position: after split: PositionStock(asset:Equity(1076 [300192.SZA]), amount:9238.0, cost_basis:11.7502, last_sale_price:11.850000381469727)

[2021-10-22 10:45:52.187092] INFO: Position: returning cash: 11.6004

[2021-10-22 10:45:52.576987] INFO: algo: handle_splits get splits [dt:2016-07-07 00:00:00+00:00] [asset:Equity(1843 [600804.SHA]), ratio:0.9913420081138611]

[2021-10-22 10:45:52.578633] INFO: algo: handle_splits get splits [dt:2016-07-07 00:00:00+00:00] [asset:Equity(1492 [600039.SHA]), ratio:0.9874686002731323]

[2021-10-22 10:45:52.579954] INFO: Position: position stock handle split[sid:1492, orig_amount:27100, new_amount:27443.0, orig_cost:3.9600009209720777, new_cost:3.9104, ratio:0.9874686002731323, last_sale_price:3.93999981880188]

[2021-10-22 10:45:52.581076] INFO: Position: after split: PositionStock(asset:Equity(1492 [600039.SHA]), amount:27443.0, cost_basis:3.9104, last_sale_price:3.990000009536743)

[2021-10-22 10:45:52.582163] INFO: Position: returning cash: 3.5878

[2021-10-22 10:45:56.474413] INFO: Performance: Simulated 488 trading days out of 488.

[2021-10-22 10:45:56.476061] INFO: Performance: first open: 2015-01-05 09:30:00+00:00

[2021-10-22 10:45:56.477408] INFO: Performance: last close: 2016-12-30 15:00:00+00:00

[2021-10-22 10:46:04.956195] INFO: moduleinvoker: backtest.v8 运行完成[30.018073s].

[2021-10-22 10:46:04.957965] INFO: moduleinvoker: trade.v4 运行完成[30.10844s].

bigcharts-data-start/{"__type":"tabs","__id":"bigchart-3f88e45884b745d49c43c13a9fb8620b"}/bigcharts-data-end