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# 本代码由可视化策略环境自动生成 2021年7月24日 15:22
# 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：每日数据处理函数，每天执行一次
def m19_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.options['hold_days'] # 是否在建仓期间（前 hold_days 天）
    cash_avg = context.portfolio.portfolio_value / context.options['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.portfolio.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，按机器学习算法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.portfolio.positions.items()}
        instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                lambda x: x in equities)])))

        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：按机器学习算法预测的排序，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 m19_prepare_bigquant_run(context):
    pass

# 回测引擎：初始化函数，只执行一次
def m19_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.options['hold_days'] = 5


m1 = M.instruments.v2(
    start_date='2010-01-01',
    end_date='2015-01-01',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m2 = 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=True
)

m3 = M.input_features.v1(
    features="""(high_0-low_0)/low_0
amount_0
mean(return_0,5)"""
)

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
)

m6 = M.stock_ranker_train.v5(
    training_ds=m13.data,
    features=m3.data,
    learning_algorithm='排序',
    number_of_leaves=30,
    minimum_docs_per_leaf=1000,
    number_of_trees=20,
    learning_rate=0.1,
    max_bins=1023,
    feature_fraction=1,
    m_lazy_run=False
)

m9 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2016-01-01'),
    end_date=T.live_run_param('trading_date', '2017-01-01'),
    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=0
)

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
)

m8 = M.stock_ranker_predict.v5(
    model=m6.model,
    data=m14.data,
    m_lazy_run=False
)

m19 = M.trade.v4(
    instruments=m9.data,
    options_data=m8.predictions,
    start_date='',
    end_date='',
    handle_data=m19_handle_data_bigquant_run,
    prepare=m19_prepare_bigquant_run,
    initialize=m19_initialize_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='后复权',
    product_type='股票',
    plot_charts=True,
    backtest_only=False,
    benchmark='000300.SHA'
)

[2021-07-24 15:10:12.269984] INFO: moduleinvoker: instruments.v2 开始运行..

[2021-07-24 15:10:12.285030] INFO: moduleinvoker: 命中缓存

[2021-07-24 15:10:12.286593] INFO: moduleinvoker: instruments.v2 运行完成[0.016623s].

[2021-07-24 15:10:12.324418] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2021-07-24 15:10:12.333816] INFO: moduleinvoker: 命中缓存

[2021-07-24 15:10:12.352378] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[0.027952s].

[2021-07-24 15:10:12.357778] INFO: moduleinvoker: input_features.v1 开始运行..

[2021-07-24 15:10:12.402935] INFO: moduleinvoker: input_features.v1 运行完成[0.045144s].

[2021-07-24 15:10:12.506970] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2021-07-24 15:10:13.905791] INFO: 基础特征抽取: 年份 2010, 特征行数=431567

[2021-07-24 15:10:15.387889] INFO: 基础特征抽取: 年份 2011, 特征行数=511455

[2021-07-24 15:10:17.051972] INFO: 基础特征抽取: 年份 2012, 特征行数=565675

[2021-07-24 15:10:18.677596] INFO: 基础特征抽取: 年份 2013, 特征行数=564168

[2021-07-24 15:10:20.468308] INFO: 基础特征抽取: 年份 2014, 特征行数=569948

[2021-07-24 15:10:21.080780] INFO: 基础特征抽取: 年份 2015, 特征行数=0

[2021-07-24 15:10:21.801610] INFO: 基础特征抽取: 总行数: 2642813

[2021-07-24 15:10:21.806300] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[9.299356s].

[2021-07-24 15:10:21.812642] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2021-07-24 15:10:30.498471] INFO: derived_feature_extractor: 提取完成 (high_0-low_0)/low_0, 0.006s

[2021-07-24 15:10:32.397768] INFO: derived_feature_extractor: 提取完成 mean(return_0,5), 1.898s

[2021-07-24 15:10:33.836074] INFO: derived_feature_extractor: /y_2010, 431567

[2021-07-24 15:10:35.641603] INFO: derived_feature_extractor: /y_2011, 511455

[2021-07-24 15:10:37.636314] INFO: derived_feature_extractor: /y_2012, 565675

[2021-07-24 15:10:39.694536] INFO: derived_feature_extractor: /y_2013, 564168

[2021-07-24 15:10:41.725791] INFO: derived_feature_extractor: /y_2014, 569948

[2021-07-24 15:10:42.423974] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[20.61132s].

[2021-07-24 15:10:42.431447] INFO: moduleinvoker: join.v3 开始运行..

[2021-07-24 15:10:55.995878] INFO: join: /y_2010, 行数=431028/431567, 耗时=2.56156s

[2021-07-24 15:10:58.806571] INFO: join: /y_2011, 行数=510922/511455, 耗时=2.798477s

[2021-07-24 15:11:01.878094] INFO: join: /y_2012, 行数=564582/565675, 耗时=3.056969s

[2021-07-24 15:11:05.093704] INFO: join: /y_2013, 行数=563132/564168, 耗时=3.200847s

[2021-07-24 15:11:08.283994] INFO: join: /y_2014, 行数=555191/569948, 耗时=3.17432s

[2021-07-24 15:11:08.329866] INFO: join: 最终行数: 2624855

[2021-07-24 15:11:08.413097] INFO: moduleinvoker: join.v3 运行完成[25.98164s].

[2021-07-24 15:11:08.484633] INFO: moduleinvoker: dropnan.v1 开始运行..

[2021-07-24 15:11:08.992959] INFO: dropnan: /y_2010, 422665/431028

[2021-07-24 15:11:09.636256] INFO: dropnan: /y_2011, 509478/510922

[2021-07-24 15:11:10.280641] INFO: dropnan: /y_2012, 563776/564582

[2021-07-24 15:11:10.934919] INFO: dropnan: /y_2013, 563086/563132

[2021-07-24 15:11:11.668760] INFO: dropnan: /y_2014, 554980/555191

[2021-07-24 15:11:12.021998] INFO: dropnan: 行数: 2613985/2624855

[2021-07-24 15:11:12.046205] INFO: moduleinvoker: dropnan.v1 运行完成[3.561563s].

[2021-07-24 15:11:12.463757] INFO: moduleinvoker: stock_ranker_train.v5 开始运行..

[2021-07-24 15:11:15.284440] INFO: StockRanker: 特征预处理 ..

[2021-07-24 15:11:16.314475] INFO: StockRanker: prepare data: training ..

[2021-07-24 15:11:17.298383] INFO: StockRanker: sort ..

[2021-07-24 15:11:43.452224] INFO: StockRanker训练: 540f9fca 准备训练: 2613985 行数

[2021-07-24 15:12:20.517723] INFO: StockRanker训练: 正在训练 ..

[2021-07-24 15:17:32.071106] INFO: moduleinvoker: stock_ranker_train.v5 运行完成[379.60737s].

[2021-07-24 15:17:32.073751] INFO: moduleinvoker: instruments.v2 开始运行..

[2021-07-24 15:17:32.081979] INFO: moduleinvoker: 命中缓存

[2021-07-24 15:17:32.083594] INFO: moduleinvoker: instruments.v2 运行完成[0.009837s].

[2021-07-24 15:17:32.092928] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2021-07-24 15:17:33.840057] INFO: 基础特征抽取: 年份 2016, 特征行数=641546

[2021-07-24 15:17:34.710381] INFO: 基础特征抽取: 年份 2017, 特征行数=0

[2021-07-24 15:17:34.829150] INFO: 基础特征抽取: 总行数: 641546

[2021-07-24 15:17:34.837468] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[2.744535s].

[2021-07-24 15:17:34.840472] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2021-07-24 15:17:36.905317] INFO: derived_feature_extractor: 提取完成 (high_0-low_0)/low_0, 0.007s

[2021-07-24 15:17:37.372643] INFO: derived_feature_extractor: 提取完成 mean(return_0,5), 0.466s

[2021-07-24 15:17:39.295884] INFO: derived_feature_extractor: /y_2016, 641546

[2021-07-24 15:17:39.771249] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[4.930759s].

[2021-07-24 15:17:39.774706] INFO: moduleinvoker: dropnan.v1 开始运行..

[2021-07-24 15:17:40.440396] INFO: dropnan: /y_2016, 629215/641546

[2021-07-24 15:17:40.563061] INFO: dropnan: 行数: 629215/641546

[2021-07-24 15:17:40.587156] INFO: moduleinvoker: dropnan.v1 运行完成[0.812428s].

[2021-07-24 15:17:40.624212] INFO: moduleinvoker: stock_ranker_predict.v5 开始运行..

[2021-07-24 15:17:48.029420] INFO: StockRanker预测: /y_2016 ..

[2021-07-24 15:17:53.789475] INFO: moduleinvoker: stock_ranker_predict.v5 运行完成[13.165265s].

[2021-07-24 15:17:55.430443] INFO: moduleinvoker: backtest.v8 开始运行..

[2021-07-24 15:17:55.435608] INFO: backtest: biglearning backtest:V8.5.0

[2021-07-24 15:17:55.436699] INFO: backtest: product_type:stock by specified

[2021-07-24 15:17:56.170990] INFO: moduleinvoker: cached.v2 开始运行..

[2021-07-24 15:18:10.580015] INFO: backtest: 读取股票行情完成:1516814

[2021-07-24 15:18:15.875499] INFO: moduleinvoker: cached.v2 运行完成[19.704519s].

[2021-07-24 15:18:19.112177] INFO: algo: TradingAlgorithm V1.8.3

[2021-07-24 15:18:19.787678] INFO: algo: trading transform...

[2021-07-24 15:18:26.299084] INFO: Performance: Simulated 244 trading days out of 244.

[2021-07-24 15:18:26.300567] INFO: Performance: first open: 2016-01-04 09:30:00+00:00

[2021-07-24 15:18:26.301612] INFO: Performance: last close: 2016-12-30 15:00:00+00:00

[2021-07-24 15:18:31.031065] INFO: moduleinvoker: backtest.v8 运行完成[35.600625s].

[2021-07-24 15:18:31.032902] INFO: moduleinvoker: trade.v4 运行完成[37.237002s].

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