策略-修改了调仓天数为每天-每天选5支

[wcf]

克隆策略

策略简介¶

因子：样例因子（18个）

标注：未来5日涨幅分类，涨幅靠前的为1，涨幅靠后的为0

算法：决策树算法

类型：分类问题

训练集：10-15年

测试集：15-19年

选股依据：根据上涨概率值排序买入

持股数：30

持仓天数：5

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In [1]:

# 本代码由可视化策略环境自动生成 2020年3月5日 09:32
# 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。


# Python 代码入口函数，input_1/2/3 对应三个输入端，data_1/2/3 对应三个输出端
def m6_run_bigquant_run(input_1, input_2, input_3):
    train_df = input_1.read()
    features = input_2.read()
    feature_min = train_df[features].quantile(0.005)
    feature_max = train_df[features].quantile(0.995)
    train_df[features] = train_df[features].clip(feature_min,feature_max,axis=1)
    data_1 = DataSource.write_df(train_df)
    test_df = input_3.read()
    test_df[features] = test_df[features].clip(feature_min,feature_max,axis=1)
    data_2 = DataSource.write_df(test_df)
    return Outputs(data_1=data_1, data_2=data_2, data_3=None)

# 后处理函数，可选。输入是主函数的输出，可以在这里对数据做处理，或者返回更友好的outputs数据格式。此函数输出不会被缓存。
def m6_post_run_bigquant_run(outputs):
    return outputs

# 回测引擎：初始化函数，只执行一次
def m11_initialize_bigquant_run(context):
    # 加载预测数据
  
    context.indicator_data = 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))
    context.rebalance_days = 1
    context.stock_num = 5
    if 'index' not in context.extension:
        context.extension['index'] = 0
     

# 回测引擎：每日数据处理函数，每天执行一次
def m11_handle_data_bigquant_run(context, data):
    
    context.extension['index'] += 1
    # 不在换仓日就return,相当于后面的代码只会一个月运行一次，买入的股票会持有1个月
    if  context.extension['index'] % context.rebalance_days != 0:
        return 
    
    # 当前的日期
    date = data.current_dt.strftime('%Y-%m-%d')
    
    cur_data = context.indicator_data[context.indicator_data['date'] == date]
    # 根据日期获取调仓需要买入的股票的列表
    #stock_to_buy = list(cur_data.instrument[:context.stock_num])
    cur_data = cur_data[cur_data['pred_label'] == 1.0]
    
    stock_to_buy =  list(cur_data.sort_values('classes_prob_1.0',ascending=False).instrument)[:context.stock_num]
    if date == '2017-02-06':
        print(date, len(stock_to_buy), stock_to_buy)
    # 通过positions对象，使用列表生成式的方法获取目前持仓的股票列表
    stock_hold_now = [equity.symbol for equity in context.portfolio.positions]
    # 继续持有的股票：调仓时，如果买入的股票已经存在于目前的持仓里，那么应继续持有
    no_need_to_sell = [i for i in stock_hold_now if i in stock_to_buy]
    # 需要卖出的股票
    stock_to_sell = [i for i in stock_hold_now if i not in no_need_to_sell]
  
    # 卖出
    for stock in stock_to_sell:
        # 如果该股票停牌，则没法成交。因此需要用can_trade方法检查下该股票的状态
        # 如果返回真值，则可以正常下单，否则会出错
        # 因为stock是字符串格式，我们用symbol方法将其转化成平台可以接受的形式：Equity格式

        if data.can_trade(context.symbol(stock)):
            # order_target_percent是平台的一个下单接口，表明下单使得该股票的权重为0，
            #   即卖出全部股票，可参考回测文档
            context.order_target_percent(context.symbol(stock), 0)
    
    # 如果当天没有买入的股票，就返回
    if len(stock_to_buy) == 0:
        return

    # 等权重买入 
    weight =  1 / len(stock_to_buy)
    
    # 买入
    for stock in stock_to_buy:
        if data.can_trade(context.symbol(stock)):
            # 下单使得某只股票的持仓权重达到weight，因为
            # weight大于0,因此是等权重买入
            context.order_target_percent(context.symbol(stock), weight)
 
# 回测引擎：准备数据，只执行一次
def m11_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 m11_before_trading_start_bigquant_run(context, data):
    pass


m1 = M.instruments.v2(
    start_date='2017-01-01',
    end_date='2019-01-01',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m2 = M.advanced_auto_labeler.v2(
    instruments=m1.data,
    label_expr="""shift(close, -5) / shift(open, -1)-1
rank(label)
where(label>=0.75,1,where(label<=0.25, 0, NaN))""",
    start_date='',
    end_date='',
    benchmark='000300.SHA',
    drop_na_label=False,
    cast_label_int=False
)

m3 = M.input_features.v1(
    features="""(close_0-mean(close_0,12))/mean(close_0,12)*100
rank(std(amount_0,15))
rank_avg_amount_0/rank_avg_amount_8
ts_argmin(low_0,20)
rank_return_30
(low_1-close_0)/close_0
ta_bbands_lowerband_14_0
mean(mf_net_pct_s_0,4)
amount_0/avg_amount_3
return_0/return_5
return_1/return_5
rank_avg_amount_7/rank_avg_amount_10
ta_sma_10_0/close_0
sqrt(high_0*low_0)-amount_0/volume_0*adjust_factor_0
avg_turn_15/(turn_0+1e-5)
return_10
mf_net_pct_s_0
(close_0-open_0)/close_1
 """
)

m15 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=0
)

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
)

m9 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2019-01-01'),
    end_date=T.live_run_param('trading_date', '2020-02-26'),
    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=50
)

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
)

m6 = M.cached.v3(
    input_1=m13.data,
    input_2=m3.data,
    input_3=m14.data,
    run=m6_run_bigquant_run,
    post_run=m6_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m8 = M.RobustScaler.v13(
    train_ds=m6.data_1,
    features=m3.data,
    test_ds=m6.data_2,
    scale_type='standard',
    quantile_range_min=0.01,
    quantile_range_max=0.99,
    global_scale=True
)

m10 = M.decision_tree_classifier.v1(
    training_ds=m8.train_data,
    features=m3.data,
    predict_ds=m8.test_data,
    criterion='gini',
    feature_fraction=1,
    max_depth=30,
    min_samples_per_leaf=200,
    key_cols='date,instrument',
    other_train_parameters={}
)

m11 = M.trade.v4(
    instruments=m9.data,
    options_data=m10.predictions,
    start_date='',
    end_date='',
    initialize=m11_initialize_bigquant_run,
    handle_data=m11_handle_data_bigquant_run,
    prepare=m11_prepare_bigquant_run,
    before_trading_start=m11_before_trading_start_bigquant_run,
    volume_limit=0,
    order_price_field_buy='open',
    order_price_field_sell='close',
    capital_base=100000,
    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=''
)

日志 97 条，错误日志  0 条 

[2020-03-05 09:11:28.968177] INFO: bigquant: instruments.v2 开始运行..

[2020-03-05 09:11:29.024410] INFO: bigquant: 命中缓存

[2020-03-05 09:11:29.025516] INFO: bigquant: instruments.v2 运行完成[0.057367s].

[2020-03-05 09:11:29.038688] INFO: bigquant: advanced_auto_labeler.v2 开始运行..

[2020-03-05 09:11:29.059707] INFO: bigquant: 命中缓存

[2020-03-05 09:11:29.062186] INFO: bigquant: advanced_auto_labeler.v2 运行完成[0.023484s].

[2020-03-05 09:11:29.069892] INFO: bigquant: input_features.v1 开始运行..

[2020-03-05 09:11:29.091238] INFO: bigquant: 命中缓存

[2020-03-05 09:11:29.092312] INFO: bigquant: input_features.v1 运行完成[0.022426s].

[2020-03-05 09:11:29.134556] INFO: bigquant: general_feature_extractor.v7 开始运行..

[2020-03-05 09:11:29.156058] INFO: bigquant: 命中缓存

[2020-03-05 09:11:29.157234] INFO: bigquant: general_feature_extractor.v7 运行完成[0.022696s].

[2020-03-05 09:11:29.166703] INFO: bigquant: derived_feature_extractor.v3 开始运行..

[2020-03-05 09:11:29.198270] INFO: bigquant: 命中缓存

[2020-03-05 09:11:29.199401] INFO: bigquant: derived_feature_extractor.v3 运行完成[0.03271s].

[2020-03-05 09:11:29.205044] INFO: bigquant: join.v3 开始运行..

[2020-03-05 09:11:29.224616] INFO: bigquant: 命中缓存

[2020-03-05 09:11:29.225548] INFO: bigquant: join.v3 运行完成[0.020498s].

[2020-03-05 09:11:29.232051] INFO: bigquant: dropnan.v1 开始运行..

[2020-03-05 09:11:29.251219] INFO: bigquant: 命中缓存

[2020-03-05 09:11:29.252134] INFO: bigquant: dropnan.v1 运行完成[0.02008s].

[2020-03-05 09:11:29.253453] INFO: bigquant: instruments.v2 开始运行..

[2020-03-05 09:11:29.327610] INFO: bigquant: instruments.v2 运行完成[0.074126s].

[2020-03-05 09:11:29.349104] INFO: bigquant: general_feature_extractor.v7 开始运行..

[2020-03-05 09:11:34.567962] INFO: 基础特征抽取: 年份 2018, 特征行数=123568

[2020-03-05 09:11:40.262226] INFO: 基础特征抽取: 年份 2019, 特征行数=884646

[2020-03-05 09:11:41.354471] INFO: 基础特征抽取: 年份 2020, 特征行数=127828

[2020-03-05 09:11:41.919098] INFO: 基础特征抽取: 总行数: 1136042

[2020-03-05 09:11:41.922463] INFO: bigquant: general_feature_extractor.v7 运行完成[12.573381s].

[2020-03-05 09:11:41.924025] INFO: bigquant: derived_feature_extractor.v3 开始运行..

[2020-03-05 09:11:46.663145] INFO: derived_feature_extractor: 提取完成 (close_0-mean(close_0,12))/mean(close_0,12)*100, 3.960s

[2020-03-05 09:11:49.105619] INFO: derived_feature_extractor: 提取完成 rank(std(amount_0,15)), 2.441s

[2020-03-05 09:11:49.111901] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_0/rank_avg_amount_8, 0.005s

[2020-03-05 09:11:52.634132] INFO: derived_feature_extractor: 提取完成 ts_argmin(low_0,20), 3.521s

[2020-03-05 09:11:52.656929] INFO: derived_feature_extractor: 提取完成 (low_1-close_0)/close_0, 0.022s

[2020-03-05 09:11:54.626404] INFO: derived_feature_extractor: 提取完成 mean(mf_net_pct_s_0,4), 1.968s

[2020-03-05 09:11:54.659440] INFO: derived_feature_extractor: 提取完成 amount_0/avg_amount_3, 0.032s

[2020-03-05 09:11:54.670107] INFO: derived_feature_extractor: 提取完成 return_0/return_5, 0.009s

[2020-03-05 09:11:54.680650] INFO: derived_feature_extractor: 提取完成 return_1/return_5, 0.009s

[2020-03-05 09:11:54.689500] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_7/rank_avg_amount_10, 0.007s

[2020-03-05 09:11:54.760332] INFO: derived_feature_extractor: 提取完成 ta_sma_10_0/close_0, 0.069s

[2020-03-05 09:11:54.770770] INFO: derived_feature_extractor: 提取完成 sqrt(high_0*low_0)-amount_0/volume_0*adjust_factor_0, 0.009s

[2020-03-05 09:11:54.779307] INFO: derived_feature_extractor: 提取完成 avg_turn_15/(turn_0+1e-5), 0.007s

[2020-03-05 09:11:54.860271] INFO: derived_feature_extractor: 提取完成 (close_0-open_0)/close_1, 0.080s

[2020-03-05 09:11:56.572783] INFO: derived_feature_extractor: /y_2018, 123568

[2020-03-05 09:11:57.051982] INFO: derived_feature_extractor: /y_2019, 884646

[2020-03-05 09:11:58.232296] INFO: derived_feature_extractor: /y_2020, 127828

[2020-03-05 09:11:59.089785] INFO: bigquant: derived_feature_extractor.v3 运行完成[17.165739s].

[2020-03-05 09:11:59.092169] INFO: bigquant: dropnan.v1 开始运行..

[2020-03-05 09:11:59.337183] INFO: dropnan: /y_2018, 56124/123568

[2020-03-05 09:12:01.349086] INFO: dropnan: /y_2019, 878460/884646

[2020-03-05 09:12:01.620636] INFO: dropnan: /y_2020, 126634/127828

[2020-03-05 09:12:02.876978] INFO: dropnan: 行数: 1061218/1136042

[2020-03-05 09:12:02.883825] INFO: bigquant: dropnan.v1 运行完成[3.791633s].

[2020-03-05 09:12:02.891184] INFO: bigquant: cached.v3 开始运行..

[2020-03-05 09:12:14.307494] INFO: bigquant: cached.v3 运行完成[11.41631s].

[2020-03-05 09:12:14.319790] INFO: bigquant: RobustScaler.v13 开始运行..

[2020-03-05 09:12:24.091416] INFO: bigquant: RobustScaler.v13 运行完成[9.771632s].

[2020-03-05 09:12:24.424942] INFO: bigquant: decision_tree_classifier.v1 开始运行..

[2020-03-05 09:12:32.087459] INFO: bigquant: decision_tree_classifier.v1 运行完成[7.662515s].

[2020-03-05 09:12:33.275737] INFO: bigquant: backtest.v8 开始运行..

[2020-03-05 09:12:33.278582] INFO: bigquant: biglearning backtest:V8.3.2

[2020-03-05 09:12:33.280092] INFO: bigquant: product_type:stock by specified

[2020-03-05 09:12:33.411636] INFO: bigquant: cached.v2 开始运行..

[2020-03-05 09:12:42.510913] INFO: bigquant: 读取股票行情完成:1873798

[2020-03-05 09:12:45.467639] INFO: bigquant: cached.v2 运行完成[12.055968s].

[2020-03-05 09:12:46.540449] INFO: algo: TradingAlgorithm V1.6.6

[2020-03-05 09:12:47.682801] INFO: algo: trading transform...

[2020-03-05 09:12:50.479485] INFO: algo: handle_splits get splits [dt:2019-05-31 00:00:00+00:00] [asset:Equity(3072 [603877.SHA]), ratio:0.9454952478408813]

[2020-03-05 09:12:50.481443] INFO: Position: position stock handle split[sid:3072, orig_amount:2200, new_amount:2326.0, orig_cost:17.229999542236328, new_cost:16.29, ratio:0.9454952478408813, last_sale_price:17.000003814697266]

[2020-03-05 09:12:50.483057] INFO: Position: after split: PositionStock(asset:Equity(3072 [603877.SHA]), amount:2326.0, cost_basis:16.29, last_sale_price:17.979999542236328)

[2020-03-05 09:12:50.484246] INFO: Position: returning cash: 13.99

[2020-03-05 09:12:50.783175] INFO: algo: handle_splits get splits [dt:2019-06-20 00:00:00+00:00] [asset:Equity(2724 [600573.SHA]), ratio:0.9962263107299805]

[2020-03-05 09:12:50.807283] INFO: algo: handle_splits get splits [dt:2019-06-21 00:00:00+00:00] [asset:Equity(624 [600232.SHA]), ratio:0.9836601614952087]

[2020-03-05 09:12:51.045880] INFO: algo: handle_splits get splits [dt:2019-07-05 00:00:00+00:00] [asset:Equity(720 [603699.SHA]), ratio:0.9485182166099548]

[2020-03-05 09:12:51.046998] INFO: Position: position stock handle split[sid:720, orig_amount:3300, new_amount:3479.0, orig_cost:12.699999809265137, new_cost:12.05, ratio:0.9485182166099548, last_sale_price:12.160002708435059]

[2020-03-05 09:12:51.047811] INFO: Position: after split: PositionStock(asset:Equity(720 [603699.SHA]), amount:3479.0, cost_basis:12.05, last_sale_price:12.819998741149902)

[2020-03-05 09:12:51.048494] INFO: Position: returning cash: 1.35

[2020-03-05 09:12:51.145830] INFO: algo: handle_splits get splits [dt:2019-07-11 00:00:00+00:00] [asset:Equity(3207 [601965.SHA]), ratio:0.9663071036338806]

[2020-03-05 09:12:51.146930] INFO: Position: position stock handle split[sid:3207, orig_amount:5200, new_amount:5381.0, orig_cost:7.490000247955322, new_cost:7.24, ratio:0.9663071036338806, last_sale_price:7.169998645782471]

[2020-03-05 09:12:51.147733] INFO: Position: after split: PositionStock(asset:Equity(3207 [601965.SHA]), amount:5381.0, cost_basis:7.24, last_sale_price:7.420000076293945)

[2020-03-05 09:12:51.148423] INFO: Position: returning cash: 2.24

[2020-03-05 09:12:51.224629] INFO: algo: handle_splits get splits [dt:2019-07-16 00:00:00+00:00] [asset:Equity(3637 [600012.SHA]), ratio:0.9611197113990784]

[2020-03-05 09:12:51.225760] INFO: Position: position stock handle split[sid:3637, orig_amount:6100, new_amount:6346.0, orig_cost:6.429999828338623, new_cost:6.18, ratio:0.9611197113990784, last_sale_price:6.179999351501465]

[2020-03-05 09:12:51.226593] INFO: Position: after split: PositionStock(asset:Equity(3637 [600012.SHA]), amount:6346.0, cost_basis:6.18, last_sale_price:6.429999828338623)

[2020-03-05 09:12:51.227296] INFO: Position: returning cash: 4.72

[2020-03-05 09:12:51.266238] INFO: algo: handle_splits get splits [dt:2019-07-17 00:00:00+00:00] [asset:Equity(2962 [600272.SHA]), ratio:0.9951867461204529]

[2020-03-05 09:12:51.268025] INFO: Position: position stock handle split[sid:2962, orig_amount:4800, new_amount:4823.0, orig_cost:8.300000190734863, new_cost:8.26, ratio:0.9951867461204529, last_sale_price:8.270002365112305]

[2020-03-05 09:12:51.268955] INFO: Position: after split: PositionStock(asset:Equity(2962 [600272.SHA]), amount:4823.0, cost_basis:8.26, last_sale_price:8.3100004196167)

[2020-03-05 09:12:51.270496] INFO: Position: returning cash: 1.78

[2020-03-05 09:12:51.323203] INFO: algo: handle_splits get splits [dt:2019-07-19 00:00:00+00:00] [asset:Equity(495 [603227.SHA]), ratio:0.9920843243598938]

[2020-03-05 09:12:52.367620] INFO: algo: handle_splits get splits [dt:2019-09-23 00:00:00+00:00] [asset:Equity(148 [002612.SZA]), ratio:0.9777531027793884]

[2020-03-05 09:12:54.520050] INFO: Performance: Simulated 278 trading days out of 278.

[2020-03-05 09:12:54.521100] INFO: Performance: first open: 2019-01-02 09:30:00+00:00

[2020-03-05 09:12:54.521872] INFO: Performance: last close: 2020-02-26 15:00:00+00:00

[2020-03-05 09:12:59.143404] INFO: bigquant: backtest.v8 运行完成[25.867669s].

[2020-03-05 09:12:59.145056] INFO: bigquant: trade.v4 运行完成[27.046198s].

• Trade(回测/模拟)
• 收益概况
• 交易详情
• 每日持仓和收益
• 输出日志

• 收益率152.93%
• 年化收益率131.9%
• 基准收益率35.29%
• 阿尔法0.53
• 贝塔1.4
• 夏普比率2.07
• 胜率0.54
• 盈亏比1.16
• 收益波动率43.92%
• 信息比率0.12
• 最大回撤35.09%

bigcharts-data-start/{"__type":"tabs","__id":"bigchart-f4114386ce41486ab1d2c28d8ee1168b"}/bigcharts-data-end

[ghslas]

你好，改成一天一股要改那个地方

[wcf]

context.rebalance_days = 1 context.stock_num = 5

不好意思哈，没看到。

context.rebalance_days = 1
context.stock_num = 5

这两行代码是设置调仓天数为1天，每天选择排名靠前的5只股票买入

[yangziriver]

请教一下，我将交易改为调仓天数为5天，每天买入靠前的30只股票后，回测很正常，可是模拟交易就不出信号，没有股票推荐，请帮忙看一下。不只是调为5天不行，调为2、3、4天都不行，只能是1天。感觉调仓天数为1天时波动太大了。谢谢先！

[yangziriver]

克隆策略

https://bigquant.com/community/t/topic/175467

策略简介¶

因子：样例因子（18个）

标注：未来5日涨幅分类，涨幅靠前的为1，涨幅靠后的为0

算法：决策树算法

类型：分类问题

训练集：10-15年

测试集：15-19年

选股依据：根据上涨概率值排序买入

持股数：30

持仓天数：5

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In [1]:

# 本代码由可视化策略环境自动生成 2020年9月20日 16:01
# 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。


# Python 代码入口函数，input_1/2/3 对应三个输入端，data_1/2/3 对应三个输出端
def m6_run_bigquant_run(input_1, input_2, input_3):
    train_df = input_1.read()
    features = input_2.read()
    feature_min = train_df[features].quantile(0.005)
    feature_max = train_df[features].quantile(0.995)
    train_df[features] = train_df[features].clip(feature_min,feature_max,axis=1)
    data_1 = DataSource.write_df(train_df)
    test_df = input_3.read()
    test_df[features] = test_df[features].clip(feature_min,feature_max,axis=1)
    data_2 = DataSource.write_df(test_df)
    return Outputs(data_1=data_1, data_2=data_2, data_3=None)

# 后处理函数，可选。输入是主函数的输出，可以在这里对数据做处理，或者返回更友好的outputs数据格式。此函数输出不会被缓存。
def m6_post_run_bigquant_run(outputs):
    return outputs

# 回测引擎：初始化函数，只执行一次
def m11_initialize_bigquant_run(context):
    # 加载预测数据
  
    context.indicator_data = 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))
    context.rebalance_days = 5
    context.stock_num = 30
    if 'index' not in context.extension:
        context.extension['index'] = 0
     

# 回测引擎：每日数据处理函数，每天执行一次
def m11_handle_data_bigquant_run(context, data):
    
    context.extension['index'] += 1
    # 不在换仓日就return,相当于后面的代码只会一个月运行一次，买入的股票会持有1个月
    if  context.extension['index'] % context.rebalance_days != 0:
        return 
    
    # 当前的日期
    date = data.current_dt.strftime('%Y-%m-%d')
    
    cur_data = context.indicator_data[context.indicator_data['date'] == date]
    # 根据日期获取调仓需要买入的股票的列表
    #stock_to_buy = list(cur_data.instrument[:context.stock_num])
    cur_data = cur_data[cur_data['pred_label'] == 1.0]
    
    stock_to_buy =  list(cur_data.sort_values('classes_prob_1.0',ascending=False).instrument)[:context.stock_num]
    if date == '2017-02-06':
        print(date, len(stock_to_buy), stock_to_buy)
    # 通过positions对象，使用列表生成式的方法获取目前持仓的股票列表
    stock_hold_now = [equity.symbol for equity in context.portfolio.positions]
    # 继续持有的股票：调仓时，如果买入的股票已经存在于目前的持仓里，那么应继续持有
    no_need_to_sell = [i for i in stock_hold_now if i in stock_to_buy]
    # 需要卖出的股票
    stock_to_sell = [i for i in stock_hold_now if i not in no_need_to_sell]
  
    # 卖出
    for stock in stock_to_sell:
        # 如果该股票停牌，则没法成交。因此需要用can_trade方法检查下该股票的状态
        # 如果返回真值，则可以正常下单，否则会出错
        # 因为stock是字符串格式，我们用symbol方法将其转化成平台可以接受的形式：Equity格式

        if data.can_trade(context.symbol(stock)):
            # order_target_percent是平台的一个下单接口，表明下单使得该股票的权重为0，
            #   即卖出全部股票，可参考回测文档
            context.order_target_percent(context.symbol(stock), 0)
    
    # 如果当天没有买入的股票，就返回
    if len(stock_to_buy) == 0:
        return

    # 等权重买入 
    weight =  1 / len(stock_to_buy)
    
    # 买入
    for stock in stock_to_buy:
        if data.can_trade(context.symbol(stock)):
            # 下单使得某只股票的持仓权重达到weight，因为
            # weight大于0,因此是等权重买入
            context.order_target_percent(context.symbol(stock), weight)
 
# 回测引擎：准备数据，只执行一次
def m11_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 m11_before_trading_start_bigquant_run(context, data):
    pass


m1 = M.instruments.v2(
    start_date='2017-01-01',
    end_date='2019-01-01',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m2 = M.advanced_auto_labeler.v2(
    instruments=m1.data,
    label_expr="""shift(close, -5) / shift(open, -1)-1
rank(label)
where(label>=0.75,1,where(label<=0.25, 0, NaN))""",
    start_date='',
    end_date='',
    benchmark='000300.SHA',
    drop_na_label=False,
    cast_label_int=False
)

m3 = M.input_features.v1(
    features="""(close_0-mean(close_0,12))/mean(close_0,12)*100
rank(std(amount_0,15))
rank_avg_amount_0/rank_avg_amount_8
ts_argmin(low_0,20)
rank_return_30
(low_1-close_0)/close_0
ta_bbands_lowerband_14_0
mean(mf_net_pct_s_0,4)
amount_0/avg_amount_3
return_0/return_5
return_1/return_5
rank_avg_amount_7/rank_avg_amount_10
ta_sma_10_0/close_0
sqrt(high_0*low_0)-amount_0/volume_0*adjust_factor_0
avg_turn_15/(turn_0+1e-5)
return_10
mf_net_pct_s_0
(close_0-open_0)/close_1
 """
)

m15 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=0
)

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
)

m4 = M.dropnan.v2(
    input_data=m7.data
)

m9 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2019-01-01'),
    end_date=T.live_run_param('trading_date', '2020-02-26'),
    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=50
)

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
)

m5 = M.dropnan.v2(
    input_data=m18.data
)

m6 = M.cached.v3(
    input_1=m4.data,
    input_2=m3.data,
    input_3=m5.data,
    run=m6_run_bigquant_run,
    post_run=m6_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m8 = M.RobustScaler.v13(
    train_ds=m6.data_1,
    features=m3.data,
    test_ds=m6.data_2,
    scale_type='standard',
    quantile_range_min=0.01,
    quantile_range_max=0.99,
    global_scale=True
)

m10 = M.decision_tree_classifier.v1(
    training_ds=m8.train_data,
    features=m3.data,
    predict_ds=m8.test_data,
    criterion='gini',
    feature_fraction=1,
    max_depth=30,
    min_samples_per_leaf=200,
    key_cols='date,instrument',
    other_train_parameters={}
)

m11 = M.trade.v4(
    instruments=m9.data,
    options_data=m10.predictions,
    start_date='',
    end_date='',
    initialize=m11_initialize_bigquant_run,
    handle_data=m11_handle_data_bigquant_run,
    prepare=m11_prepare_bigquant_run,
    before_trading_start=m11_before_trading_start_bigquant_run,
    volume_limit=0,
    order_price_field_buy='open',
    order_price_field_sell='close',
    capital_base=100000,
    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=''
)

日志 179 条，错误日志  0 条 

[2020-09-20 15:49:31.371564] INFO: moduleinvoker: instruments.v2 开始运行..

[2020-09-20 15:49:31.390226] INFO: moduleinvoker: 命中缓存

[2020-09-20 15:49:31.391218] INFO: moduleinvoker: instruments.v2 运行完成[0.019664s].

[2020-09-20 15:49:31.393764] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2020-09-20 15:49:31.405084] INFO: moduleinvoker: 命中缓存

[2020-09-20 15:49:31.407422] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[0.013637s].

[2020-09-20 15:49:31.410592] INFO: moduleinvoker: input_features.v1 开始运行..

[2020-09-20 15:49:31.415766] INFO: moduleinvoker: 命中缓存

[2020-09-20 15:49:31.416744] INFO: moduleinvoker: input_features.v1 运行完成[0.006155s].

[2020-09-20 15:49:31.452418] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2020-09-20 15:49:31.459358] INFO: moduleinvoker: 命中缓存

[2020-09-20 15:49:31.460262] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.007855s].

[2020-09-20 15:49:31.462639] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2020-09-20 15:49:31.470110] INFO: moduleinvoker: 命中缓存

[2020-09-20 15:49:31.470962] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.00831s].

[2020-09-20 15:49:31.473254] INFO: moduleinvoker: join.v3 开始运行..

[2020-09-20 15:49:31.479256] INFO: moduleinvoker: 命中缓存

[2020-09-20 15:49:31.480023] INFO: moduleinvoker: join.v3 运行完成[0.006765s].

[2020-09-20 15:49:31.482146] INFO: moduleinvoker: dropnan.v2 开始运行..

[2020-09-20 15:49:32.864204] INFO: dropnan: /y_2017, 331596/743233

[2020-09-20 15:49:34.311021] INFO: dropnan: /y_2018, 396547/816987

[2020-09-20 15:49:34.913513] INFO: dropnan: 行数: 728143/1560220

[2020-09-20 15:49:34.952724] INFO: moduleinvoker: dropnan.v2 运行完成[3.470556s].

[2020-09-20 15:49:34.954512] INFO: moduleinvoker: instruments.v2 开始运行..

[2020-09-20 15:49:35.008475] INFO: moduleinvoker: instruments.v2 运行完成[0.053906s].

[2020-09-20 15:49:35.015516] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2020-09-20 15:49:37.459991] INFO: 基础特征抽取: 年份 2018, 特征行数=123603

[2020-09-20 15:49:43.202131] INFO: 基础特征抽取: 年份 2019, 特征行数=884867

[2020-09-20 15:49:45.316972] INFO: 基础特征抽取: 年份 2020, 特征行数=127828

[2020-09-20 15:49:45.822237] INFO: 基础特征抽取: 总行数: 1136298

[2020-09-20 15:49:45.828584] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[10.813071s].

[2020-09-20 15:49:45.830420] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2020-09-20 15:49:50.017871] INFO: derived_feature_extractor: 提取完成 (close_0-mean(close_0,12))/mean(close_0,12)*100, 3.525s

[2020-09-20 15:49:52.256600] INFO: derived_feature_extractor: 提取完成 rank(std(amount_0,15)), 2.238s

[2020-09-20 15:49:52.260709] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_0/rank_avg_amount_8, 0.003s

[2020-09-20 15:49:55.501012] INFO: derived_feature_extractor: 提取完成 ts_argmin(low_0,20), 3.239s

[2020-09-20 15:49:55.506008] INFO: derived_feature_extractor: 提取完成 (low_1-close_0)/close_0, 0.004s

[2020-09-20 15:49:57.258227] INFO: derived_feature_extractor: 提取完成 mean(mf_net_pct_s_0,4), 1.751s

[2020-09-20 15:49:57.263014] INFO: derived_feature_extractor: 提取完成 amount_0/avg_amount_3, 0.004s

[2020-09-20 15:49:57.266312] INFO: derived_feature_extractor: 提取完成 return_0/return_5, 0.002s

[2020-09-20 15:49:57.269324] INFO: derived_feature_extractor: 提取完成 return_1/return_5, 0.002s

[2020-09-20 15:49:57.272233] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_7/rank_avg_amount_10, 0.002s

[2020-09-20 15:49:57.275550] INFO: derived_feature_extractor: 提取完成 ta_sma_10_0/close_0, 0.003s

[2020-09-20 15:49:57.282099] INFO: derived_feature_extractor: 提取完成 sqrt(high_0*low_0)-amount_0/volume_0*adjust_factor_0, 0.006s

[2020-09-20 15:49:57.286847] INFO: derived_feature_extractor: 提取完成 avg_turn_15/(turn_0+1e-5), 0.004s

[2020-09-20 15:49:57.290480] INFO: derived_feature_extractor: 提取完成 (close_0-open_0)/close_1, 0.003s

[2020-09-20 15:49:58.112306] INFO: derived_feature_extractor: /y_2018, 123603

[2020-09-20 15:49:58.457003] INFO: derived_feature_extractor: /y_2019, 884867

[2020-09-20 15:49:59.950287] INFO: derived_feature_extractor: /y_2020, 127828

[2020-09-20 15:50:01.613145] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[15.782687s].

[2020-09-20 15:50:01.616044] INFO: moduleinvoker: dropnan.v2 开始运行..

[2020-09-20 15:50:01.900736] INFO: dropnan: /y_2018, 56140/123603

[2020-09-20 15:50:03.685441] INFO: dropnan: /y_2019, 878679/884867

[2020-09-20 15:50:03.925959] INFO: dropnan: /y_2020, 126631/127828

[2020-09-20 15:50:05.141150] INFO: dropnan: 行数: 1061450/1136298

[2020-09-20 15:50:05.146156] INFO: moduleinvoker: dropnan.v2 运行完成[3.530108s].

[2020-09-20 15:50:05.150665] INFO: moduleinvoker: cached.v3 开始运行..

[2020-09-20 15:50:16.155148] INFO: moduleinvoker: cached.v3 运行完成[11.004463s].

[2020-09-20 15:50:16.165023] INFO: moduleinvoker: RobustScaler.v13 开始运行..

[2020-09-20 15:50:22.477641] INFO: moduleinvoker: RobustScaler.v13 运行完成[6.312604s].

[2020-09-20 15:50:22.892614] INFO: moduleinvoker: decision_tree_classifier.v1 开始运行..

[2020-09-20 15:50:28.369451] INFO: moduleinvoker: decision_tree_classifier.v1 运行完成[5.476825s].

[2020-09-20 15:50:29.532508] INFO: moduleinvoker: backtest.v8 开始运行..

[2020-09-20 15:50:29.537116] INFO: backtest: biglearning backtest:V8.4.2

[2020-09-20 15:50:29.538518] INFO: backtest: product_type:stock by specified

[2020-09-20 15:50:29.651924] INFO: moduleinvoker: cached.v2 开始运行..

[2020-09-20 15:50:41.006393] INFO: backtest: 读取股票行情完成:2031612

[2020-09-20 15:50:44.885439] INFO: moduleinvoker: cached.v2 运行完成[15.233503s].

[2020-09-20 15:50:46.126736] INFO: algo: TradingAlgorithm V1.6.9

[2020-09-20 15:50:47.521708] INFO: algo: trading transform...

[2020-09-20 15:50:49.432374] INFO: algo: handle_splits get splits [dt:2019-04-17 00:00:00+00:00] [asset:Equity(824 [600367.SHA]), ratio:0.9956850409507751]

[2020-09-20 15:50:49.434033] INFO: Position: position stock handle split[sid:824, orig_amount:400, new_amount:401.0, orig_cost:9.319999694824219, new_cost:9.2798, ratio:0.9956850409507751, last_sale_price:9.230000495910645]

[2020-09-20 15:50:49.435367] INFO: Position: after split: PositionStock(asset:Equity(824 [600367.SHA]), amount:401.0, cost_basis:9.2798, last_sale_price:9.270000457763672)

[2020-09-20 15:50:49.436586] INFO: Position: returning cash: 6.7699

[2020-09-20 15:50:49.573442] INFO: algo: handle_splits get splits [dt:2019-04-22 00:00:00+00:00] [asset:Equity(1662 [300395.SZA]), ratio:0.9892646074295044]

[2020-09-20 15:50:49.574577] INFO: Position: position stock handle split[sid:1662, orig_amount:200, new_amount:202.0, orig_cost:18.89000129699707, new_cost:18.6872, ratio:0.9892646074295044, last_sale_price:18.43000030517578]

[2020-09-20 15:50:49.575429] INFO: Position: after split: PositionStock(asset:Equity(1662 [300395.SZA]), amount:202.0, cost_basis:18.6872, last_sale_price:18.630001068115234)

[2020-09-20 15:50:49.576161] INFO: Position: returning cash: 3.1401

[2020-09-20 15:50:49.677032] INFO: algo: handle_splits get splits [dt:2019-04-25 00:00:00+00:00] [asset:Equity(606 [300652.SZA]), ratio:0.9913046360015869]

[2020-09-20 15:50:49.891155] INFO: algo: handle_splits get splits [dt:2019-05-10 00:00:00+00:00] [asset:Equity(482 [603856.SHA]), ratio:0.9825074076652527]

[2020-09-20 15:50:49.892287] INFO: Position: position stock handle split[sid:482, orig_amount:300, new_amount:305.0, orig_cost:10.0, new_cost:9.8251, ratio:0.9825074076652527, last_sale_price:10.110001564025879]

[2020-09-20 15:50:49.893171] INFO: Position: after split: PositionStock(asset:Equity(482 [603856.SHA]), amount:305.0, cost_basis:9.8251, last_sale_price:10.289999961853027)

[2020-09-20 15:50:49.893949] INFO: Position: returning cash: 3.4496

[2020-09-20 15:50:50.049896] INFO: algo: handle_splits get splits [dt:2019-05-16 00:00:00+00:00] [asset:Equity(2926 [002561.SZA]), ratio:0.9605693817138672]

[2020-09-20 15:50:50.051054] INFO: Position: position stock handle split[sid:2926, orig_amount:400, new_amount:416.0, orig_cost:9.0, new_cost:8.6451, ratio:0.9605693817138672, last_sale_price:8.769998550415039]

[2020-09-20 15:50:50.051901] INFO: Position: after split: PositionStock(asset:Equity(2926 [002561.SZA]), amount:416.0, cost_basis:8.6451, last_sale_price:9.130000114440918)

[2020-09-20 15:50:50.052633] INFO: Position: returning cash: 3.6806

[2020-09-20 15:50:50.063822] INFO: algo: handle_splits get splits [dt:2019-05-20 00:00:00+00:00] [asset:Equity(779 [603239.SHA]), ratio:0.9687824249267578]

[2020-09-20 15:50:50.064845] INFO: Position: position stock handle split[sid:779, orig_amount:400, new_amount:412.0, orig_cost:9.980000495910645, new_cost:9.6684, ratio:0.9687824249267578, last_sale_price:9.309999465942383]

[2020-09-20 15:50:50.065689] INFO: Position: after split: PositionStock(asset:Equity(779 [603239.SHA]), amount:412.0, cost_basis:9.6684, last_sale_price:9.610000610351562)

[2020-09-20 15:50:50.066400] INFO: Position: returning cash: 8.2804

[2020-09-20 15:50:50.148031] INFO: algo: handle_splits get splits [dt:2019-05-21 00:00:00+00:00] [asset:Equity(3730 [603813.SHA]), ratio:0.9896173477172852]

[2020-09-20 15:50:50.149288] INFO: Position: position stock handle split[sid:3730, orig_amount:200, new_amount:202.0, orig_cost:18.860000610351562, new_cost:18.6642, ratio:0.9896173477172852, last_sale_price:18.10999870300293]

[2020-09-20 15:50:50.150287] INFO: Position: after split: PositionStock(asset:Equity(3730 [603813.SHA]), amount:202.0, cost_basis:18.6642, last_sale_price:18.30000114440918)

[2020-09-20 15:50:50.151140] INFO: Position: returning cash: 1.7805

[2020-09-20 15:50:50.275503] INFO: algo: handle_splits get splits [dt:2019-05-28 00:00:00+00:00] [asset:Equity(487 [603600.SHA]), ratio:0.9794872999191284]

[2020-09-20 15:50:50.324105] INFO: algo: handle_splits get splits [dt:2019-05-30 00:00:00+00:00] [asset:Equity(2442 [002700.SZA]), ratio:0.9964622855186462]

[2020-09-20 15:50:50.325357] INFO: Position: position stock handle split[sid:2442, orig_amount:400, new_amount:401.0, orig_cost:8.440000534057617, new_cost:8.4101, ratio:0.9964622855186462, last_sale_price:8.450000762939453]

[2020-09-20 15:50:50.326344] INFO: Position: after split: PositionStock(asset:Equity(2442 [002700.SZA]), amount:401.0, cost_basis:8.4101, last_sale_price:8.480000495910645)

[2020-09-20 15:50:50.327159] INFO: Position: returning cash: 3.5499

[2020-09-20 15:50:50.334494] INFO: algo: handle_splits get splits [dt:2019-05-31 00:00:00+00:00] [asset:Equity(1069 [000099.SZA]), ratio:0.9971750974655151]

[2020-09-20 15:50:50.335575] INFO: Position: position stock handle split[sid:1069, orig_amount:500, new_amount:501.0, orig_cost:7.199999809265137, new_cost:7.1797, ratio:0.9971750974655151, last_sale_price:7.059999465942383]

[2020-09-20 15:50:50.336440] INFO: Position: after split: PositionStock(asset:Equity(1069 [000099.SZA]), amount:501.0, cost_basis:7.1797, last_sale_price:7.079999923706055)

[2020-09-20 15:50:50.337206] INFO: Position: returning cash: 2.9402

[2020-09-20 15:50:50.454153] INFO: algo: handle_splits get splits [dt:2019-06-05 00:00:00+00:00] [asset:Equity(1115 [002136.SZA]), ratio:0.9878198504447937]

[2020-09-20 15:50:50.455377] INFO: Position: position stock handle split[sid:1115, orig_amount:400, new_amount:404.0, orig_cost:8.360000610351562, new_cost:8.2582, ratio:0.9878198504447937, last_sale_price:8.110000610351562]

[2020-09-20 15:50:50.456454] INFO: Position: after split: PositionStock(asset:Equity(1115 [002136.SZA]), amount:404.0, cost_basis:8.2582, last_sale_price:8.210000038146973)

[2020-09-20 15:50:50.457749] INFO: Position: returning cash: 7.5596

[2020-09-20 15:50:50.468590] INFO: algo: handle_splits get splits [dt:2019-06-06 00:00:00+00:00] [asset:Equity(3136 [300219.SZA]), ratio:0.9592391848564148]

[2020-09-20 15:50:50.470331] INFO: Position: position stock handle split[sid:3136, orig_amount:500, new_amount:521.0, orig_cost:7.460000514984131, new_cost:7.1559, ratio:0.9592391848564148, last_sale_price:7.060000419616699]

[2020-09-20 15:50:50.471793] INFO: Position: after split: PositionStock(asset:Equity(3136 [300219.SZA]), amount:521.0, cost_basis:7.1559, last_sale_price:7.360000133514404)

[2020-09-20 15:50:50.473092] INFO: Position: returning cash: 1.7398

[2020-09-20 15:50:50.484317] INFO: algo: handle_splits get splits [dt:2019-06-10 00:00:00+00:00] [asset:Equity(2957 [300712.SZA]), ratio:0.7638605237007141]

[2020-09-20 15:50:50.485562] INFO: Position: position stock handle split[sid:2957, orig_amount:200, new_amount:261.0, orig_cost:14.8100004196167, new_cost:11.3128, ratio:0.7638605237007141, last_sale_price:11.160001754760742]

[2020-09-20 15:50:50.486565] INFO: Position: after split: PositionStock(asset:Equity(2957 [300712.SZA]), amount:261.0, cost_basis:11.3128, last_sale_price:14.609999656677246)

[2020-09-20 15:50:50.487391] INFO: Position: returning cash: 9.2394

[2020-09-20 15:50:50.569783] INFO: algo: handle_splits get splits [dt:2019-06-12 00:00:00+00:00] [asset:Equity(2244 [002809.SZA]), ratio:0.9889011979103088]

[2020-09-20 15:50:50.571342] INFO: Position: position stock handle split[sid:2244, orig_amount:200, new_amount:202.0, orig_cost:17.799999237060547, new_cost:17.6024, ratio:0.9889011979103088, last_sale_price:17.81999969482422]

[2020-09-20 15:50:50.572420] INFO: Position: after split: PositionStock(asset:Equity(2244 [002809.SZA]), amount:202.0, cost_basis:17.6024, last_sale_price:18.020000457763672)

[2020-09-20 15:50:50.573217] INFO: Position: returning cash: 4.3601

[2020-09-20 15:50:50.635890] INFO: algo: handle_splits get splits [dt:2019-06-18 00:00:00+00:00] [asset:Equity(1567 [002791.SZA]), ratio:0.9931880235671997]

[2020-09-20 15:50:50.637185] INFO: algo: handle_splits get splits [dt:2019-06-18 00:00:00+00:00] [asset:Equity(3572 [600338.SHA]), ratio:0.7130165100097656]

[2020-09-20 15:50:50.638204] INFO: Position: position stock handle split[sid:1567, orig_amount:200, new_amount:201.0, orig_cost:15.139999389648438, new_cost:15.0369, ratio:0.9931880235671997, last_sale_price:14.580000877380371]

[2020-09-20 15:50:50.639000] INFO: Position: after split: PositionStock(asset:Equity(1567 [002791.SZA]), amount:201.0, cost_basis:15.0369, last_sale_price:14.680000305175781)

[2020-09-20 15:50:50.639748] INFO: Position: returning cash: 5.42

[2020-09-20 15:50:50.640515] INFO: Position: position stock handle split[sid:3572, orig_amount:100, new_amount:140.0, orig_cost:23.610000610351562, new_cost:16.8343, ratio:0.7130165100097656, last_sale_price:18.459999084472656]

[2020-09-20 15:50:50.641302] INFO: Position: after split: PositionStock(asset:Equity(3572 [600338.SHA]), amount:140.0, cost_basis:16.8343, last_sale_price:25.890003204345703)

[2020-09-20 15:50:50.642077] INFO: Position: returning cash: 4.6004

[2020-09-20 15:50:50.844220] INFO: algo: handle_splits get splits [dt:2019-06-19 00:00:00+00:00] [asset:Equity(614 [601177.SHA]), ratio:0.9979212284088135]

[2020-09-20 15:50:50.845456] INFO: algo: handle_splits get splits [dt:2019-06-19 00:00:00+00:00] [asset:Equity(749 [600452.SHA]), ratio:0.7077869772911072]

[2020-09-20 15:50:50.846362] INFO: algo: handle_splits get splits [dt:2019-06-19 00:00:00+00:00] [asset:Equity(2675 [600708.SHA]), ratio:0.9607073068618774]

[2020-09-20 15:50:50.847262] INFO: Position: position stock handle split[sid:2675, orig_amount:700, new_amount:728.0, orig_cost:5.349999904632568, new_cost:5.1398, ratio:0.9607073068618774, last_sale_price:4.890000343322754]

[2020-09-20 15:50:50.848093] INFO: Position: after split: PositionStock(asset:Equity(2675 [600708.SHA]), amount:728.0, cost_basis:5.1398, last_sale_price:5.090000152587891)

[2020-09-20 15:50:50.848962] INFO: Position: returning cash: 3.0799

[2020-09-20 15:50:50.897728] INFO: algo: handle_splits get splits [dt:2019-06-21 00:00:00+00:00] [asset:Equity(1963 [603159.SHA]), ratio:0.7091598510742188]

[2020-09-20 15:50:50.899342] INFO: Position: position stock handle split[sid:1963, orig_amount:100, new_amount:141.0, orig_cost:20.80000114440918, new_cost:14.7505, ratio:0.7091598510742188, last_sale_price:15.02000617980957]

[2020-09-20 15:50:50.900569] INFO: Position: after split: PositionStock(asset:Equity(1963 [603159.SHA]), amount:141.0, cost_basis:14.7505, last_sale_price:21.18000030517578)

[2020-09-20 15:50:50.901810] INFO: Position: returning cash: 0.1792

[2020-09-20 15:50:50.911650] INFO: algo: handle_splits get splits [dt:2019-06-24 00:00:00+00:00] [asset:Equity(3429 [000521.SZA]), ratio:0.9837397933006287]

[2020-09-20 15:50:50.912787] INFO: Position: position stock handle split[sid:3429, orig_amount:1100, new_amount:1118.0, orig_cost:3.5099997520446777, new_cost:3.4529, ratio:0.9837397933006287, last_sale_price:3.6299996376037598]

[2020-09-20 15:50:50.913742] INFO: Position: after split: PositionStock(asset:Equity(3429 [000521.SZA]), amount:1118.0, cost_basis:3.4529, last_sale_price:3.68999981880188)

[2020-09-20 15:50:50.914594] INFO: Position: returning cash: 0.6602

[2020-09-20 15:50:50.922037] INFO: algo: handle_splits get splits [dt:2019-06-25 00:00:00+00:00] [asset:Equity(3974 [600717.SHA]), ratio:0.8219696283340454]

[2020-09-20 15:50:50.923149] INFO: Position: position stock handle split[sid:3974, orig_amount:500, new_amount:608.0, orig_cost:7.700000286102295, new_cost:6.3292, ratio:0.8219696283340454, last_sale_price:6.509999752044678]

[2020-09-20 15:50:50.924230] INFO: Position: after split: PositionStock(asset:Equity(3974 [600717.SHA]), amount:608.0, cost_basis:6.3292, last_sale_price:7.920000076293945)

[2020-09-20 15:50:50.925059] INFO: Position: returning cash: 1.9203

[2020-09-20 15:50:51.265002] INFO: algo: handle_splits get splits [dt:2019-07-11 00:00:00+00:00] [asset:Equity(1388 [300253.SZA]), ratio:0.9985784888267517]

[2020-09-20 15:50:51.266155] INFO: Position: position stock handle split[sid:1388, orig_amount:200, new_amount:200.0, orig_cost:14.130000114440918, new_cost:14.1099, ratio:0.9985784888267517, last_sale_price:14.050000190734863]

[2020-09-20 15:50:51.267002] INFO: Position: after split: PositionStock(asset:Equity(1388 [300253.SZA]), amount:200.0, cost_basis:14.1099, last_sale_price:14.070000648498535)

[2020-09-20 15:50:51.267740] INFO: Position: returning cash: 4.0001

[2020-09-20 15:50:51.284878] INFO: algo: handle_splits get splits [dt:2019-07-16 00:00:00+00:00] [asset:Equity(903 [600012.SHA]), ratio:0.9611197113990784]

[2020-09-20 15:50:51.285900] INFO: algo: handle_splits get splits [dt:2019-07-16 00:00:00+00:00] [asset:Equity(3806 [000078.SZA]), ratio:0.9942857027053833]

[2020-09-20 15:50:51.286768] INFO: Position: position stock handle split[sid:903, orig_amount:600, new_amount:624.0, orig_cost:6.440000057220459, new_cost:6.1896, ratio:0.9611197113990784, last_sale_price:6.179999351501465]

[2020-09-20 15:50:51.287561] INFO: Position: after split: PositionStock(asset:Equity(903 [600012.SHA]), amount:624.0, cost_basis:6.1896, last_sale_price:6.429999828338623)

[2020-09-20 15:50:51.288282] INFO: Position: returning cash: 1.6802

[2020-09-20 15:50:51.289046] INFO: Position: position stock handle split[sid:3806, orig_amount:1100, new_amount:1106.0, orig_cost:3.43999981880188, new_cost:3.4203, ratio:0.9942857027053833, last_sale_price:3.4800000190734863]

[2020-09-20 15:50:51.289810] INFO: Position: after split: PositionStock(asset:Equity(3806 [000078.SZA]), amount:1106.0, cost_basis:3.4203, last_sale_price:3.5)

[2020-09-20 15:50:51.290522] INFO: Position: returning cash: 1.12

[2020-09-20 15:50:51.405137] INFO: algo: handle_splits get splits [dt:2019-07-19 00:00:00+00:00] [asset:Equity(4025 [603227.SHA]), ratio:0.9920843243598938]

[2020-09-20 15:50:51.406317] INFO: Position: position stock handle split[sid:4025, orig_amount:1000, new_amount:1007.0, orig_cost:3.8500001430511475, new_cost:3.8195, ratio:0.9920843243598938, last_sale_price:3.7599997520446777]

[2020-09-20 15:50:51.407898] INFO: Position: after split: PositionStock(asset:Equity(4025 [603227.SHA]), amount:1007.0, cost_basis:3.8195, last_sale_price:3.7900002002716064)

[2020-09-20 15:50:51.409476] INFO: Position: returning cash: 3.6804

[2020-09-20 15:50:51.530727] INFO: algo: handle_splits get splits [dt:2019-07-25 00:00:00+00:00] [asset:Equity(3307 [300641.SZA]), ratio:0.9932316541671753]

[2020-09-20 15:50:51.532003] INFO: Position: position stock handle split[sid:3307, orig_amount:600, new_amount:604.0, orig_cost:5.650000095367432, new_cost:5.6118, ratio:0.9932316541671753, last_sale_price:5.869998931884766]

[2020-09-20 15:50:51.533071] INFO: Position: after split: PositionStock(asset:Equity(3307 [300641.SZA]), amount:604.0, cost_basis:5.6118, last_sale_price:5.909999847412109)

[2020-09-20 15:50:51.534086] INFO: Position: returning cash: 0.5206

[2020-09-20 15:50:51.780971] INFO: algo: handle_splits get splits [dt:2019-08-09 00:00:00+00:00] [asset:Equity(1432 [600635.SHA]), ratio:0.9883721470832825]

[2020-09-20 15:50:51.782112] INFO: Position: position stock handle split[sid:1432, orig_amount:700, new_amount:708.0, orig_cost:5.229999542236328, new_cost:5.1692, ratio:0.9883721470832825, last_sale_price:5.099999904632568]

[2020-09-20 15:50:51.783172] INFO: Position: after split: PositionStock(asset:Equity(1432 [600635.SHA]), amount:708.0, cost_basis:5.1692, last_sale_price:5.159999847412109)

[2020-09-20 15:50:51.783950] INFO: Position: returning cash: 1.1998

[2020-09-20 15:50:53.091717] INFO: algo: handle_splits get splits [dt:2019-11-07 00:00:00+00:00] [asset:Equity(1580 [603365.SHA]), ratio:0.987631618976593]

[2020-09-20 15:50:53.092867] INFO: Position: position stock handle split[sid:1580, orig_amount:200, new_amount:202.0, orig_cost:16.189998626708984, new_cost:15.9898, ratio:0.987631618976593, last_sale_price:15.970003128051758]

[2020-09-20 15:50:53.093720] INFO: Position: after split: PositionStock(asset:Equity(1580 [603365.SHA]), amount:202.0, cost_basis:15.9898, last_sale_price:16.170000076293945)

[2020-09-20 15:50:53.094451] INFO: Position: returning cash: 8.0593

[2020-09-20 15:50:54.705955] INFO: Performance: Simulated 278 trading days out of 278.

[2020-09-20 15:50:54.707243] INFO: Performance: first open: 2019-01-02 09:30:00+00:00

[2020-09-20 15:50:54.708404] INFO: Performance: last close: 2020-02-26 15:00:00+00:00

[2020-09-20 15:51:02.905701] INFO: moduleinvoker: backtest.v8 运行完成[33.373184s].

[2020-09-20 15:51:02.907701] INFO: moduleinvoker: trade.v4 运行完成[34.528961s].

• Trade(回测/模拟)
• 收益概况
• 交易详情
• 每日持仓和收益
• 输出日志

• 收益率20.14%
• 年化收益率18.1%
• 基准收益率35.29%
• 阿尔法-0.06
• 贝塔0.83
• 夏普比率0.69
• 胜率0.53
• 盈亏比1.13
• 收益波动率23.88%
• 信息比率-0.04
• 最大回撤23.03%

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