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# 本代码由可视化策略环境自动生成 2022年11月18日 11:20
# 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()
#     context.ranker_prediction = context.options['data'].read_df().sort_values('pred_label',ascending=False)
#     context.ranker_prediction = context.options['data'].read_df().sort_values('classes_prob_1.0', ascending=False)


    

    # 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='2015-01-01',
    end_date='2016-01-01',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m4 = 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)
where(shift(close,-5) / shift(open,-1)>1,1,0)

# 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={}
)

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
"""
)

m5 = 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=m5.data,
    features=m2.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions={}
)

m7 = M.join.v3(
    data1=m4.data,
    data2=m6.data,
    on='date,instrument',
    how='inner',
    sort=False
)

m8 = M.dropnan.v2(
    input_data=m7.data
)

m12 = M.linear_sgd_train.v2(
    training_ds=m8.data,
    features=m2.data,
    loss='auto',
    penalty='l2',
    alpha=0.0001,
    n_iter=5,
    shuffle=True,
    eta0=0.1,
    algo='classifier'
)

m3 = M.instruments.v2(
    start_date='2016-01-01',
    end_date='2017-12-31',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m9 = M.general_feature_extractor.v7(
    instruments=m3.data,
    features=m2.data,
    start_date='',
    end_date='',
    before_start_days=90
)

m10 = M.derived_feature_extractor.v3(
    input_data=m9.data,
    features=m2.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions={}
)

m11 = M.dropnan.v2(
    input_data=m10.data
)

m14 = M.linear_sgd_predict.v2(
    model=m12.model,
    data=m11.data
)

m13 = M.trade.v4(
    instruments=m3.data,
    options_data=m14.predictions,
    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='000300.HIX'
)

[2022-11-18 11:05:27.090279] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-11-18 11:05:27.235926] INFO: moduleinvoker: instruments.v2 运行完成[0.145663s].

[2022-11-18 11:05:27.249904] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2022-11-18 11:05:29.797784] INFO: 自动标注(股票): 加载历史数据: 569698 行

[2022-11-18 11:05:29.800940] INFO: 自动标注(股票): 开始标注 ..

[2022-11-18 11:05:31.803859] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[4.553938s].

[2022-11-18 11:05:31.816702] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-11-18 11:05:31.826541] INFO: moduleinvoker: 命中缓存

[2022-11-18 11:05:31.829389] INFO: moduleinvoker: input_features.v1 运行完成[0.012737s].

[2022-11-18 11:05:31.863760] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-11-18 11:05:33.600620] INFO: 基础特征抽取: 年份 2014, 特征行数=141569

[2022-11-18 11:05:37.331707] INFO: 基础特征抽取: 年份 2015, 特征行数=569698

[2022-11-18 11:05:38.392378] INFO: 基础特征抽取: 年份 2016, 特征行数=0

[2022-11-18 11:05:38.471131] INFO: 基础特征抽取: 总行数: 711267

[2022-11-18 11:05:38.476114] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[6.612369s].

[2022-11-18 11:05:38.501829] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-11-18 11:05:40.301008] INFO: derived_feature_extractor: 提取完成 avg_amount_0/avg_amount_5, 0.004s

[2022-11-18 11:05:40.308651] INFO: derived_feature_extractor: 提取完成 avg_amount_5/avg_amount_20, 0.005s

[2022-11-18 11:05:40.315310] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_0/rank_avg_amount_5, 0.004s

[2022-11-18 11:05:40.321804] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_5/rank_avg_amount_10, 0.004s

[2022-11-18 11:05:40.328267] INFO: derived_feature_extractor: 提取完成 rank_return_0/rank_return_5, 0.004s

[2022-11-18 11:05:40.378237] INFO: derived_feature_extractor: 提取完成 rank_return_5/rank_return_10, 0.047s

[2022-11-18 11:05:40.913043] INFO: derived_feature_extractor: /y_2014, 141569

[2022-11-18 11:05:42.438598] INFO: derived_feature_extractor: /y_2015, 569698

[2022-11-18 11:05:43.054666] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[4.552786s].

[2022-11-18 11:05:43.084503] INFO: moduleinvoker: join.v3 开始运行..

[2022-11-18 11:05:45.607444] INFO: join: /y_2014, 行数=0/141569, 耗时=0.764954s

[2022-11-18 11:05:49.481909] INFO: join: /y_2015, 行数=560635/569698, 耗时=3.869551s

[2022-11-18 11:05:49.579429] INFO: join: 最终行数: 560635

[2022-11-18 11:05:49.594475] INFO: moduleinvoker: join.v3 运行完成[6.509969s].

[2022-11-18 11:05:49.627258] INFO: moduleinvoker: dropnan.v2 开始运行..

[2022-11-18 11:05:50.034293] INFO: dropnan: /y_2014, 0/0

[2022-11-18 11:05:51.918391] INFO: dropnan: /y_2015, 558339/560635

[2022-11-18 11:05:51.979126] INFO: dropnan: 行数: 558339/560635

[2022-11-18 11:05:51.992554] INFO: moduleinvoker: dropnan.v2 运行完成[2.365288s].

[2022-11-18 11:05:52.014256] INFO: moduleinvoker: linear_sgd_train.v2 开始运行..

[2022-11-18 11:05:54.792652] INFO: linear_sgd_train: 模型在训练集的分数是:0.45

[2022-11-18 11:05:54.800493] INFO: moduleinvoker: linear_sgd_train.v2 运行完成[2.786235s].

[2022-11-18 11:05:54.808964] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-11-18 11:05:54.822860] INFO: moduleinvoker: 命中缓存

[2022-11-18 11:05:54.825237] INFO: moduleinvoker: instruments.v2 运行完成[0.016267s].

[2022-11-18 11:05:54.843768] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-11-18 11:05:56.577467] INFO: 基础特征抽取: 年份 2015, 特征行数=148558

[2022-11-18 11:06:00.548083] INFO: 基础特征抽取: 年份 2016, 特征行数=641546

[2022-11-18 11:06:05.040978] INFO: 基础特征抽取: 年份 2017, 特征行数=743233

[2022-11-18 11:06:05.152225] INFO: 基础特征抽取: 总行数: 1533337

[2022-11-18 11:06:05.162250] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[10.318497s].

[2022-11-18 11:06:05.170495] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-11-18 11:06:08.762924] INFO: derived_feature_extractor: 提取完成 avg_amount_0/avg_amount_5, 0.011s

[2022-11-18 11:06:08.791939] INFO: derived_feature_extractor: 提取完成 avg_amount_5/avg_amount_20, 0.027s

[2022-11-18 11:06:08.808411] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_0/rank_avg_amount_5, 0.014s

[2022-11-18 11:06:08.816174] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_5/rank_avg_amount_10, 0.005s

[2022-11-18 11:06:08.823962] INFO: derived_feature_extractor: 提取完成 rank_return_0/rank_return_5, 0.005s

[2022-11-18 11:06:08.831201] INFO: derived_feature_extractor: 提取完成 rank_return_5/rank_return_10, 0.005s

[2022-11-18 11:06:09.654803] INFO: derived_feature_extractor: /y_2015, 148558

[2022-11-18 11:06:11.007119] INFO: derived_feature_extractor: /y_2016, 641546

[2022-11-18 11:06:13.028745] INFO: derived_feature_extractor: /y_2017, 743233

[2022-11-18 11:06:13.988547] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[8.818014s].

[2022-11-18 11:06:14.003121] INFO: moduleinvoker: dropnan.v2 开始运行..

[2022-11-18 11:06:15.131938] INFO: dropnan: /y_2015, 148267/148558

[2022-11-18 11:06:17.069223] INFO: dropnan: /y_2016, 636912/641546

[2022-11-18 11:06:20.416686] INFO: dropnan: /y_2017, 733820/743233

[2022-11-18 11:06:20.547441] INFO: dropnan: 行数: 1518999/1533337

[2022-11-18 11:06:20.561548] INFO: moduleinvoker: dropnan.v2 运行完成[6.558423s].

[2022-11-18 11:06:20.577558] INFO: moduleinvoker: linear_sgd_predict.v2 开始运行..

[2022-11-18 11:06:27.204657] INFO: moduleinvoker: linear_sgd_predict.v2 运行完成[6.627056s].

[2022-11-18 11:06:32.253193] INFO: moduleinvoker: backtest.v8 开始运行..

[2022-11-18 11:06:32.262256] INFO: backtest: biglearning backtest:V8.6.3

[2022-11-18 11:06:32.264037] INFO: backtest: product_type:stock by specified

[2022-11-18 11:06:32.537542] INFO: moduleinvoker: cached.v2 开始运行..

[2022-11-18 11:06:51.516990] INFO: backtest: 读取股票行情完成:2528160

[2022-11-18 11:06:54.156668] INFO: moduleinvoker: cached.v2 运行完成[21.619155s].

[2022-11-18 11:07:08.181122] INFO: backtest: algo history_data=DataSource(3eff906dffd048ad9f8a8d3b2de28bc8T)

[2022-11-18 11:07:08.183468] INFO: algo: TradingAlgorithm V1.8.8

[2022-11-18 11:07:12.288772] INFO: algo: trading transform...

[2022-11-18 11:07:16.772522] INFO: algo: handle_splits get splits [dt:2016-05-30 00:00:00+00:00] [asset:Equity(1884 [600478.SHA]), ratio:0.6664623022079468]

[2022-11-18 11:07:16.779372] INFO: Position: position stock handle split[sid:1884, orig_amount:2600, new_amount:3901.0, orig_cost:15.340000337388188, new_cost:10.2235, ratio:0.6664623022079468, last_sale_price:10.869999885559082]

[2022-11-18 11:07:16.781968] INFO: Position: after split: PositionStock(asset:Equity(1884 [600478.SHA]), amount:3901.0, cost_basis:10.2235, last_sale_price:16.309999465942383)

[2022-11-18 11:07:16.784385] INFO: Position: returning cash: 2.1294

[2022-11-18 11:07:17.943300] INFO: algo: handle_splits get splits [dt:2016-07-11 00:00:00+00:00] [asset:Equity(3839 [601616.SHA]), ratio:0.9913793802261353]

[2022-11-18 11:07:17.945985] INFO: Position: position stock handle split[sid:3839, orig_amount:12500, new_amount:12608.0, orig_cost:5.7400021258523255, new_cost:5.6905, ratio:0.9913793802261353, last_sale_price:5.750000476837158]

[2022-11-18 11:07:17.949740] INFO: Position: after split: PositionStock(asset:Equity(3839 [601616.SHA]), amount:12608.0, cost_basis:5.6905, last_sale_price:5.800000190734863)

[2022-11-18 11:07:17.952468] INFO: Position: returning cash: 3.9949

[2022-11-18 11:07:19.227410] INFO: algo: handle_splits get splits [dt:2016-08-23 00:00:00+00:00] [asset:Equity(1202 [601608.SHA]), ratio:0.996330201625824]

[2022-11-18 11:07:19.229177] INFO: Position: position stock handle split[sid:1202, orig_amount:5300, new_amount:5319.0, orig_cost:5.580000429078518, new_cost:5.5595, ratio:0.996330201625824, last_sale_price:5.429999351501465]

[2022-11-18 11:07:19.230947] INFO: Position: after split: PositionStock(asset:Equity(1202 [601608.SHA]), amount:5319.0, cost_basis:5.5595, last_sale_price:5.449999809265137)

[2022-11-18 11:07:19.232989] INFO: Position: returning cash: 2.8321

[2022-11-18 11:07:25.569185] INFO: algo: handle_splits get splits [dt:2017-04-27 00:00:00+00:00] [asset:Equity(2963 [002702.SZA]), ratio:0.5879917144775391]

[2022-11-18 11:07:26.367641] INFO: algo: handle_splits get splits [dt:2017-05-26 00:00:00+00:00] [asset:Equity(2850 [002225.SZA]), ratio:0.9962051510810852]

[2022-11-18 11:07:27.140190] INFO: algo: handle_splits get splits [dt:2017-06-28 00:00:00+00:00] [asset:Equity(2144 [603169.SHA]), ratio:0.9991716742515564]

[2022-11-18 11:07:31.646866] INFO: algo: handle_splits get splits [dt:2017-12-29 00:00:00+00:00] [asset:Equity(3160 [000736.SZA]), ratio:0.9868213534355164]

[2022-11-18 11:07:31.648789] INFO: Position: position stock handle split[sid:3160, orig_amount:5600, new_amount:5674.0, orig_cost:15.283939327173469, new_cost:15.0825, ratio:0.9868213534355164, last_sale_price:15.374676704406738]

[2022-11-18 11:07:31.650430] INFO: Position: after split: PositionStock(asset:Equity(3160 [000736.SZA]), amount:5674.0, cost_basis:15.0825, last_sale_price:15.579999923706055)

[2022-11-18 11:07:31.651829] INFO: Position: returning cash: 12.0845

[2022-11-18 11:07:31.705639] INFO: Performance: Simulated 488 trading days out of 488.

[2022-11-18 11:07:31.708057] INFO: Performance: first open: 2016-01-04 09:30:00+00:00

[2022-11-18 11:07:31.712158] INFO: Performance: last close: 2017-12-29 15:00:00+00:00

[2022-11-18 11:07:37.996123] INFO: moduleinvoker: backtest.v8 运行完成[65.742932s].

[2022-11-18 11:07:37.998468] INFO: moduleinvoker: trade.v4 运行完成[70.768659s].

bigcharts-data-start/{"__type":"tabs","__id":"bigchart-95d79e853a5346c5934f7e6b6298ac9f"}/bigcharts-data-end