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# 本代码由可视化策略环境自动生成 2020年4月13日 09:51
# 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,
    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=''
)

[2020-04-11 18:19:40.978805] INFO: bigquant: instruments.v2 开始运行..

[2020-04-11 18:19:41.102277] INFO: bigquant: instruments.v2 运行完成[0.123442s].

[2020-04-11 18:19:41.109764] INFO: bigquant: advanced_auto_labeler.v2 开始运行..

[2020-04-11 18:19:43.595941] INFO: 自动标注(股票): 加载历史数据: 2642813 行

[2020-04-11 18:19:43.597710] INFO: 自动标注(股票): 开始标注 ..

[2020-04-11 18:19:47.345151] INFO: bigquant: advanced_auto_labeler.v2 运行完成[6.235362s].

[2020-04-11 18:19:47.348321] INFO: bigquant: input_features.v1 开始运行..

[2020-04-11 18:19:47.376364] INFO: bigquant: 命中缓存

[2020-04-11 18:19:47.377408] INFO: bigquant: input_features.v1 运行完成[0.029088s].

[2020-04-11 18:19:47.401994] INFO: bigquant: general_feature_extractor.v7 开始运行..

[2020-04-11 18:19:49.652083] INFO: 基础特征抽取: 年份 2009, 特征行数=95020

[2020-04-11 18:19:52.575865] INFO: 基础特征抽取: 年份 2010, 特征行数=431567

[2020-04-11 18:19:55.321343] INFO: 基础特征抽取: 年份 2011, 特征行数=511455

[2020-04-11 18:19:58.579002] INFO: 基础特征抽取: 年份 2012, 特征行数=565675

[2020-04-11 18:20:01.738328] INFO: 基础特征抽取: 年份 2013, 特征行数=564168

[2020-04-11 18:20:03.283742] INFO: 基础特征抽取: 年份 2014, 特征行数=569948

[2020-04-11 18:20:03.968561] INFO: 基础特征抽取: 总行数: 2737833

[2020-04-11 18:20:03.990888] INFO: bigquant: general_feature_extractor.v7 运行完成[16.588882s].

[2020-04-11 18:20:03.997820] INFO: bigquant: derived_feature_extractor.v3 开始运行..

[2020-04-11 18:20:05.120896] INFO: derived_feature_extractor: 提取完成 avg_amount_0/avg_amount_5, 0.014s

[2020-04-11 18:20:05.128520] INFO: derived_feature_extractor: 提取完成 avg_amount_5/avg_amount_20, 0.006s

[2020-04-11 18:20:05.134053] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_0/rank_avg_amount_5, 0.005s

[2020-04-11 18:20:05.139505] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_5/rank_avg_amount_10, 0.005s

[2020-04-11 18:20:05.144603] INFO: derived_feature_extractor: 提取完成 rank_return_0/rank_return_5, 0.004s

[2020-04-11 18:20:05.153154] INFO: derived_feature_extractor: 提取完成 rank_return_5/rank_return_10, 0.008s

[2020-04-11 18:20:05.846659] INFO: derived_feature_extractor: /y_2009, 95020

[2020-04-11 18:20:05.981892] INFO: derived_feature_extractor: /y_2010, 431567

[2020-04-11 18:20:06.426659] INFO: derived_feature_extractor: /y_2011, 511455

[2020-04-11 18:20:06.791934] INFO: derived_feature_extractor: /y_2012, 565675

[2020-04-11 18:20:07.180785] INFO: derived_feature_extractor: /y_2013, 564168

[2020-04-11 18:20:07.587379] INFO: derived_feature_extractor: /y_2014, 569948

[2020-04-11 18:20:09.679414] INFO: bigquant: derived_feature_extractor.v3 运行完成[5.681573s].

[2020-04-11 18:20:09.687464] INFO: bigquant: join.v3 开始运行..

[2020-04-11 18:20:11.299205] INFO: join: /y_2009, 行数=0/95020, 耗时=0.888358s

[2020-04-11 18:20:12.574365] INFO: join: /y_2010, 行数=431028/431567, 耗时=1.272406s

[2020-04-11 18:20:14.090445] INFO: join: /y_2011, 行数=510922/511455, 耗时=1.498508s

[2020-04-11 18:20:15.486742] INFO: join: /y_2012, 行数=564582/565675, 耗时=1.383319s

[2020-04-11 18:20:16.983499] INFO: join: /y_2013, 行数=563132/564168, 耗时=1.474996s

[2020-04-11 18:20:18.612129] INFO: join: /y_2014, 行数=555191/569948, 耗时=1.607865s

[2020-04-11 18:20:19.964604] INFO: join: 最终行数: 2624855

[2020-04-11 18:20:19.983768] INFO: bigquant: join.v3 运行完成[10.296295s].

[2020-04-11 18:20:19.989638] INFO: bigquant: dropnan.v1 开始运行..

[2020-04-11 18:20:20.096282] INFO: dropnan: /y_2009, 0/0

[2020-04-11 18:20:20.611804] INFO: dropnan: /y_2010, 423739/431028

[2020-04-11 18:20:21.311372] INFO: dropnan: /y_2011, 504726/510922

[2020-04-11 18:20:21.983790] INFO: dropnan: /y_2012, 561109/564582

[2020-04-11 18:20:22.668366] INFO: dropnan: /y_2013, 563102/563132

[2020-04-11 18:20:23.458601] INFO: dropnan: /y_2014, 553408/555191

[2020-04-11 18:20:25.355673] INFO: dropnan: 行数: 2606084/2624855

[2020-04-11 18:20:25.382988] INFO: bigquant: dropnan.v1 运行完成[5.393342s].

[2020-04-11 18:20:25.384651] INFO: bigquant: instruments.v2 开始运行..

[2020-04-11 18:20:25.403942] INFO: bigquant: 命中缓存

[2020-04-11 18:20:25.404838] INFO: bigquant: instruments.v2 运行完成[0.020181s].

[2020-04-11 18:20:25.424032] INFO: bigquant: general_feature_extractor.v7 开始运行..

[2020-04-11 18:20:25.449279] INFO: bigquant: 命中缓存

[2020-04-11 18:20:25.450180] INFO: bigquant: general_feature_extractor.v7 运行完成[0.026146s].

[2020-04-11 18:20:25.451668] INFO: bigquant: derived_feature_extractor.v3 开始运行..

[2020-04-11 18:20:26.012120] INFO: derived_feature_extractor: 提取完成 avg_amount_0/avg_amount_5, 0.003s

[2020-04-11 18:20:26.016941] INFO: derived_feature_extractor: 提取完成 avg_amount_5/avg_amount_20, 0.004s

[2020-04-11 18:20:26.020793] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_0/rank_avg_amount_5, 0.003s

[2020-04-11 18:20:26.025651] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_5/rank_avg_amount_10, 0.004s

[2020-04-11 18:20:26.029124] INFO: derived_feature_extractor: 提取完成 rank_return_0/rank_return_5, 0.003s

[2020-04-11 18:20:26.032562] INFO: derived_feature_extractor: 提取完成 rank_return_5/rank_return_10, 0.003s

[2020-04-11 18:20:26.417293] INFO: derived_feature_extractor: /y_2014, 141569

[2020-04-11 18:20:26.676388] INFO: derived_feature_extractor: /y_2015, 569698

[2020-04-11 18:20:27.175637] INFO: derived_feature_extractor: /y_2016, 641546

[2020-04-11 18:20:28.458474] INFO: bigquant: derived_feature_extractor.v3 运行完成[3.00676s].

[2020-04-11 18:20:28.461406] INFO: bigquant: dropnan.v1 开始运行..

[2020-04-11 18:20:28.773460] INFO: dropnan: /y_2014, 140747/141569

[2020-04-11 18:20:29.316179] INFO: dropnan: /y_2015, 565146/569698

[2020-04-11 18:20:29.973489] INFO: dropnan: /y_2016, 636912/641546

[2020-04-11 18:20:30.782470] INFO: dropnan: 行数: 1342805/1352813

[2020-04-11 18:20:30.812085] INFO: bigquant: dropnan.v1 运行完成[2.350663s].

[2020-04-11 18:20:31.067957] INFO: bigquant: random_forest_regressor.v1 开始运行..

[2020-04-11 18:21:40.103727] INFO: bigquant: random_forest_regressor.v1 运行完成[69.035765s].

[2020-04-11 18:21:40.109027] INFO: bigquant: sort.v4 开始运行..

[2020-04-11 18:21:42.465071] INFO: bigquant: sort.v4 运行完成[2.356029s].

[2020-04-11 18:21:43.480990] INFO: bigquant: backtest.v8 开始运行..

[2020-04-11 18:21:43.505695] INFO: backtest: biglearning backtest:V8.3.3

[2020-04-11 18:21:43.506841] INFO: backtest: product_type:stock by specified

[2020-04-11 18:21:43.687772] INFO: bigquant: cached.v2 开始运行..

[2020-04-11 18:21:43.710185] INFO: bigquant: 命中缓存

[2020-04-11 18:21:43.711518] INFO: bigquant: cached.v2 运行完成[0.023745s].

[2020-04-11 18:21:44.858188] INFO: algo: TradingAlgorithm V1.6.7

[2020-04-11 18:21:45.983963] INFO: algo: trading transform...

[2020-04-11 18:21:47.972808] INFO: algo: handle_splits get splits [dt:2015-04-23 00:00:00+00:00] [asset:Equity(2674 [600184.SHA]), ratio:0.5000951886177063]

[2020-04-11 18:21:48.099390] INFO: algo: handle_splits get splits [dt:2015-04-30 00:00:00+00:00] [asset:Equity(627 [603399.SHA]), ratio:0.9986504912376404]

[2020-04-11 18:21:48.100507] INFO: Position: position stock handle split[sid:627, orig_amount:7300, new_amount:7309.0, orig_cost:14.900000792630854, new_cost:14.88, ratio:0.9986504912376404, last_sale_price:14.800000190734863]

[2020-04-11 18:21:48.101449] INFO: Position: after split: PositionStock(asset:Equity(627 [603399.SHA]), amount:7309.0, cost_basis:14.88, last_sale_price:14.819999694824219)

[2020-04-11 18:21:48.102177] INFO: Position: returning cash: 12.8

[2020-04-11 18:21:48.311974] INFO: algo: handle_splits get splits [dt:2015-05-14 00:00:00+00:00] [asset:Equity(1129 [300150.SZA]), ratio:0.4970097243785858]

[2020-04-11 18:21:48.587414] INFO: algo: handle_splits get splits [dt:2015-06-01 00:00:00+00:00] [asset:Equity(1233 [002534.SZA]), ratio:0.9940923452377319]

[2020-04-11 18:21:48.588539] INFO: Position: position stock handle split[sid:1233, orig_amount:2700, new_amount:2716.0, orig_cost:27.410001896052133, new_cost:27.25, ratio:0.9940923452377319, last_sale_price:25.240005493164062]

[2020-04-11 18:21:48.589413] INFO: Position: after split: PositionStock(asset:Equity(1233 [002534.SZA]), amount:2716.0, cost_basis:27.25, last_sale_price:25.39000129699707)

[2020-04-11 18:21:48.590150] INFO: Position: returning cash: 1.15

[2020-04-11 18:21:48.673954] INFO: algo: handle_splits get splits [dt:2015-06-04 00:00:00+00:00] [asset:Equity(1216 [300262.SZA]), ratio:0.7135816216468811]

[2020-04-11 18:21:48.675165] INFO: Position: position stock handle split[sid:1216, orig_amount:1700, new_amount:2382.0, orig_cost:33.93999868629467, new_cost:24.22, ratio:0.7135816216468811, last_sale_price:26.060001373291016]

[2020-04-11 18:21:48.676226] INFO: Position: after split: PositionStock(asset:Equity(1216 [300262.SZA]), amount:2382.0, cost_basis:24.22, last_sale_price:36.52000045776367)

[2020-04-11 18:21:48.677067] INFO: Position: returning cash: 9.08

[2020-04-11 18:21:48.750156] INFO: algo: handle_splits get splits [dt:2015-06-09 00:00:00+00:00] [asset:Equity(919 [603019.SHA]), ratio:0.9993464350700378]

[2020-04-11 18:21:48.751297] INFO: Position: position stock handle split[sid:919, orig_amount:1600, new_amount:1601.0, orig_cost:132.43750028779937, new_cost:132.35, ratio:0.9993464350700378, last_sale_price:122.33000183105469]

[2020-04-11 18:21:48.752179] INFO: Position: after split: PositionStock(asset:Equity(919 [603019.SHA]), amount:1601.0, cost_basis:132.35, last_sale_price:122.41000366210938)

[2020-04-11 18:21:48.752921] INFO: Position: returning cash: 5.67

[2020-04-11 18:21:48.777967] INFO: algo: handle_splits get splits [dt:2015-06-10 00:00:00+00:00] [asset:Equity(4 [300159.SZA]), ratio:0.499615341424942]

[2020-04-11 18:21:48.778914] INFO: algo: handle_splits get splits [dt:2015-06-10 00:00:00+00:00] [asset:Equity(67 [002429.SZA]), ratio:0.9978799223899841]

[2020-04-11 18:21:48.779757] INFO: Position: position stock handle split[sid:67, orig_amount:14100, new_amount:14129.0, orig_cost:12.980000621163741, new_cost:12.95, ratio:0.9978799223899841, last_sale_price:14.120000839233398]

[2020-04-11 18:21:48.780529] INFO: Position: after split: PositionStock(asset:Equity(67 [002429.SZA]), amount:14129.0, cost_basis:12.95, last_sale_price:14.149999618530273)

[2020-04-11 18:21:48.781262] INFO: Position: returning cash: 13.51

[2020-04-11 18:21:49.227038] INFO: algo: handle_splits get splits [dt:2015-07-09 00:00:00+00:00] [asset:Equity(477 [600270.SHA]), ratio:0.9768115878105164]

[2020-04-11 18:21:49.228183] INFO: Position: position stock handle split[sid:477, orig_amount:2100, new_amount:2149.0, orig_cost:16.419998170402266, new_cost:16.04, ratio:0.9768115878105164, last_sale_price:16.850000381469727]

[2020-04-11 18:21:49.229035] INFO: Position: after split: PositionStock(asset:Equity(477 [600270.SHA]), amount:2149.0, cost_basis:16.04, last_sale_price:17.25)

[2020-04-11 18:21:49.229876] INFO: Position: returning cash: 14.35

[2020-04-11 18:21:49.379650] INFO: algo: handle_splits get splits [dt:2015-07-17 00:00:00+00:00] [asset:Equity(2199 [600240.SHA]), ratio:0.9919679164886475]

[2020-04-11 18:21:49.380750] INFO: algo: handle_splits get splits [dt:2015-07-17 00:00:00+00:00] [asset:Equity(1112 [002504.SZA]), ratio:0.994036853313446]

[2020-04-11 18:21:49.381675] INFO: Position: position stock handle split[sid:2199, orig_amount:5000, new_amount:5040.0, orig_cost:12.199999813307345, new_cost:12.1, ratio:0.9919679164886475, last_sale_price:12.350000381469727]

[2020-04-11 18:21:49.382466] INFO: Position: after split: PositionStock(asset:Equity(2199 [600240.SHA]), amount:5040.0, cost_basis:12.1, last_sale_price:12.449999809265137)

[2020-04-11 18:21:49.383216] INFO: Position: returning cash: 6.0

[2020-04-11 18:21:49.383974] INFO: Position: position stock handle split[sid:1112, orig_amount:8100, new_amount:8148.0, orig_cost:31.800002379117792, new_cost:31.61, ratio:0.994036853313446, last_sale_price:16.669998168945312]

[2020-04-11 18:21:49.384726] INFO: Position: after split: PositionStock(asset:Equity(1112 [002504.SZA]), amount:8148.0, cost_basis:31.61, last_sale_price:16.770000457763672)

[2020-04-11 18:21:49.385452] INFO: Position: returning cash: 9.86

[2020-04-11 18:21:49.461595] INFO: algo: handle_splits get splits [dt:2015-07-22 00:00:00+00:00] [asset:Equity(2350 [300422.SZA]), ratio:0.9993225336074829]

[2020-04-11 18:21:49.462731] INFO: Position: position stock handle split[sid:2350, orig_amount:1100, new_amount:1100.0, orig_cost:79.50000251835098, new_cost:79.45, ratio:0.9993225336074829, last_sale_price:88.51998901367188]

[2020-04-11 18:21:49.463605] INFO: Position: after split: PositionStock(asset:Equity(2350 [300422.SZA]), amount:1100.0, cost_basis:79.45, last_sale_price:88.58000183105469)

[2020-04-11 18:21:49.464461] INFO: Position: returning cash: 66.01

[2020-04-11 18:21:51.305428] INFO: algo: handle_splits get splits [dt:2015-11-20 00:00:00+00:00] [asset:Equity(1559 [600617.SHA]), ratio:0.994547426700592]

[2020-04-11 18:21:51.306538] INFO: Position: position stock handle split[sid:1559, orig_amount:6500, new_amount:6535.0, orig_cost:17.230000999625133, new_cost:17.14, ratio:0.994547426700592, last_sale_price:18.23999786376953]

[2020-04-11 18:21:51.307396] INFO: Position: after split: PositionStock(asset:Equity(1559 [600617.SHA]), amount:6535.0, cost_basis:17.14, last_sale_price:18.339998245239258)

[2020-04-11 18:21:51.308111] INFO: Position: returning cash: 11.6

[2020-04-11 18:21:53.340368] INFO: algo: handle_splits get splits [dt:2016-04-18 00:00:00+00:00] [asset:Equity(1211 [600116.SHA]), ratio:0.3316228687763214]

[2020-04-11 18:21:53.597869] INFO: algo: handle_splits get splits [dt:2016-05-06 00:00:00+00:00] [asset:Equity(1630 [600654.SHA]), ratio:0.9957947731018066]

[2020-04-11 18:21:53.598982] INFO: Position: position stock handle split[sid:1630, orig_amount:27300, new_amount:27415.0, orig_cost:24.135164995636863, new_cost:24.03, ratio:0.9957947731018066, last_sale_price:23.68000030517578]

[2020-04-11 18:21:53.599848] INFO: Position: after split: PositionStock(asset:Equity(1630 [600654.SHA]), amount:27415.0, cost_basis:24.03, last_sale_price:23.780000686645508)

[2020-04-11 18:21:53.600598] INFO: Position: returning cash: 6.81

[2020-04-11 18:21:53.734313] INFO: algo: handle_splits get splits [dt:2016-05-16 00:00:00+00:00] [asset:Equity(944 [002671.SZA]), ratio:0.9990801215171814]

[2020-04-11 18:21:53.735445] INFO: Position: position stock handle split[sid:944, orig_amount:27800, new_amount:27825.0, orig_cost:10.957932482751426, new_cost:10.95, ratio:0.9990801215171814, last_sale_price:10.860001564025879]

[2020-04-11 18:21:53.736297] INFO: Position: after split: PositionStock(asset:Equity(944 [002671.SZA]), amount:27825.0, cost_basis:10.95, last_sale_price:10.870000839233398)

[2020-04-11 18:21:53.737020] INFO: Position: returning cash: 6.47

[2020-04-11 18:21:54.032814] INFO: algo: handle_splits get splits [dt:2016-06-03 00:00:00+00:00] [asset:Equity(889 [600687.SHA]), ratio:0.9981751441955566]

[2020-04-11 18:21:54.033977] INFO: Position: position stock handle split[sid:889, orig_amount:18400, new_amount:18433.0, orig_cost:16.600002306748827, new_cost:16.57, ratio:0.9981751441955566, last_sale_price:16.40999984741211]

[2020-04-11 18:21:54.034878] INFO: Position: after split: PositionStock(asset:Equity(889 [600687.SHA]), amount:18433.0, cost_basis:16.57, last_sale_price:16.440000534057617)

[2020-04-11 18:21:54.035657] INFO: Position: returning cash: 10.48

[2020-04-11 18:21:54.134104] INFO: algo: handle_splits get splits [dt:2016-06-13 00:00:00+00:00] [asset:Equity(83 [600477.SHA]), ratio:0.7642706632614136]

[2020-04-11 18:21:54.135236] INFO: Position: position stock handle split[sid:83, orig_amount:12700, new_amount:16617.0, orig_cost:9.530002000703572, new_cost:7.28, ratio:0.7642706632614136, last_sale_price:7.229999542236328]

[2020-04-11 18:21:54.136093] INFO: Position: after split: PositionStock(asset:Equity(83 [600477.SHA]), amount:16617.0, cost_basis:7.28, last_sale_price:9.459999084472656)

[2020-04-11 18:21:54.136851] INFO: Position: returning cash: 1.08

[2020-04-11 18:21:54.287635] INFO: algo: handle_splits get splits [dt:2016-06-22 00:00:00+00:00] [asset:Equity(2710 [300192.SZA]), ratio:0.9957805871963501]

[2020-04-11 18:21:54.288739] INFO: Position: position stock handle split[sid:2710, orig_amount:9200, new_amount:9238.0, orig_cost:11.800002975366494, new_cost:11.75, ratio:0.9957805871963501, last_sale_price:11.800000190734863]

[2020-04-11 18:21:54.289589] INFO: Position: after split: PositionStock(asset:Equity(2710 [300192.SZA]), amount:9238.0, cost_basis:11.75, last_sale_price:11.850000381469727)

[2020-04-11 18:21:54.290318] INFO: Position: returning cash: 11.6

[2020-04-11 18:21:54.580341] INFO: algo: handle_splits get splits [dt:2016-07-07 00:00:00+00:00] [asset:Equity(2213 [600039.SHA]), ratio:0.9874686002731323]

[2020-04-11 18:21:54.581704] INFO: algo: handle_splits get splits [dt:2016-07-07 00:00:00+00:00] [asset:Equity(715 [600804.SHA]), ratio:0.9913420081138611]

[2020-04-11 18:21:54.583079] INFO: Position: position stock handle split[sid:2213, orig_amount:27100, new_amount:27443.0, orig_cost:3.9600009209720777, new_cost:3.91, ratio:0.9874686002731323, last_sale_price:3.93999981880188]

[2020-04-11 18:21:54.584398] INFO: Position: after split: PositionStock(asset:Equity(2213 [600039.SHA]), amount:27443.0, cost_basis:3.91, last_sale_price:3.990000009536743)

[2020-04-11 18:21:54.585594] INFO: Position: returning cash: 3.59

[2020-04-11 18:21:57.124683] INFO: Performance: Simulated 488 trading days out of 488.

[2020-04-11 18:21:57.125763] INFO: Performance: first open: 2015-01-05 09:30:00+00:00

[2020-04-11 18:21:57.126565] INFO: Performance: last close: 2016-12-30 15:00:00+00:00

[2020-04-11 18:22:03.223486] INFO: bigquant: backtest.v8 运行完成[19.742499s].

[2020-04-11 18:22:03.225100] INFO: bigquant: trade.v4 运行完成[20.756333s].

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