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# 本代码由可视化策略环境自动生成 2022年12月11日 11:37
# 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 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

# 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


m1 = M.instruments.v2(
    start_date='2014-01-01',
    end_date='2017-12-31',
    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.HIX',
    drop_na_label=True,
    cast_label_int=True
)

m3 = M.input_features.v1(
    features="""# #号开始的表示注释
# 多个特征，每行一个，可以包含基础特征和衍生特征
return_5
return_10
return_20
return_120
return_240
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_5
rank_return_10
rank_return_20
rank_return_120
rank_return_240
ta_bias(close_0, 5)
ta_bias(close_0, 10)
ta_bias(close_0, 20)
rank_market_cap_float_0-shift(rank_market_cap_float_0, 1)
pe_ttm_0
ta_rsi_14_0
ta_rsi_28_0
sum(price_limit_status_0==3, 20)
sum(price_limit_status_0==3, 60)
sum(price_limit_status_0==3, 120)
sum(price_limit_status_0==3, 250)
return_5/return_10
(return_5/return_10)/shift(return_5/return_10, 1)
return_5/return_20
(return_5/return_20)/shift(return_5/return_20, 1)
ta_sma_5_0/shift(ta_sma_5_0, 1)
rank_return_0-shift(rank_return_0, 1)
rank_amount_0-shift(rank_amount_0, 1)
rank_turn_0-shift(rank_turn_0, 1)
close_0/ts_min(close_0, 120)
close_0/ts_max(close_0, 120)
close_0/ts_min(close_0, 250)
close_0/ts_max(close_0, 250)"""
)

m15 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=90
)

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', '2018-01-01'),
    end_date=T.live_run_param('trading_date', '2021-12-31'),
    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=90
)

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='',
    initialize=m19_initialize_bigquant_run,
    handle_data=m19_handle_data_bigquant_run,
    prepare=m19_prepare_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'
)

[2022-12-11 11:27:45.995515] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-12-11 11:27:46.006924] INFO: moduleinvoker: 命中缓存

[2022-12-11 11:27:46.008649] INFO: moduleinvoker: instruments.v2 运行完成[0.013141s].

[2022-12-11 11:27:46.018370] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2022-12-11 11:27:46.029158] INFO: moduleinvoker: 命中缓存

[2022-12-11 11:27:46.030992] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[0.01262s].

[2022-12-11 11:27:46.035919] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-12-11 11:27:46.063016] INFO: moduleinvoker: input_features.v1 运行完成[0.027081s].

[2022-12-11 11:27:46.079942] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-12-11 11:27:47.547711] INFO: 基础特征抽取: 年份 2013, 特征行数=143272

[2022-12-11 11:27:51.378590] INFO: 基础特征抽取: 年份 2014, 特征行数=569948

[2022-12-11 11:27:55.331427] INFO: 基础特征抽取: 年份 2015, 特征行数=569698

[2022-12-11 11:27:59.650129] INFO: 基础特征抽取: 年份 2016, 特征行数=641546

[2022-12-11 11:28:04.937175] INFO: 基础特征抽取: 年份 2017, 特征行数=743233

[2022-12-11 11:28:05.121467] INFO: 基础特征抽取: 总行数: 2667697

[2022-12-11 11:28:05.128521] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[19.048586s].

[2022-12-11 11:28:05.136902] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-12-11 11:28:11.362611] INFO: derived_feature_extractor: 提取完成 avg_amount_0/avg_amount_5, 0.013s

[2022-12-11 11:28:11.372172] INFO: derived_feature_extractor: 提取完成 avg_amount_5/avg_amount_20, 0.007s

[2022-12-11 11:28:11.378889] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_0/rank_avg_amount_5, 0.005s

[2022-12-11 11:28:11.384852] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_5/rank_avg_amount_10, 0.004s

[2022-12-11 11:28:16.084513] INFO: derived_feature_extractor: 提取完成 ta_bias(close_0, 5), 4.698s

[2022-12-11 11:28:21.177695] INFO: derived_feature_extractor: 提取完成 ta_bias(close_0, 10), 5.091s

[2022-12-11 11:28:26.820195] INFO: derived_feature_extractor: 提取完成 ta_bias(close_0, 20), 5.641s

[2022-12-11 11:28:27.176924] INFO: derived_feature_extractor: 提取完成 rank_market_cap_float_0-shift(rank_market_cap_float_0, 1), 0.355s

[2022-12-11 11:28:29.044805] INFO: derived_feature_extractor: 提取完成 sum(price_limit_status_0==3, 20), 1.866s

[2022-12-11 11:28:31.058159] INFO: derived_feature_extractor: 提取完成 sum(price_limit_status_0==3, 60), 2.012s

[2022-12-11 11:28:32.865694] INFO: derived_feature_extractor: 提取完成 sum(price_limit_status_0==3, 120), 1.806s

[2022-12-11 11:28:34.792103] INFO: derived_feature_extractor: 提取完成 sum(price_limit_status_0==3, 250), 1.924s

[2022-12-11 11:28:34.800390] INFO: derived_feature_extractor: 提取完成 return_5/return_10, 0.006s

[2022-12-11 11:28:35.261231] INFO: derived_feature_extractor: 提取完成 (return_5/return_10)/shift(return_5/return_10, 1), 0.459s

[2022-12-11 11:28:35.272997] INFO: derived_feature_extractor: 提取完成 return_5/return_20, 0.010s

[2022-12-11 11:28:35.761823] INFO: derived_feature_extractor: 提取完成 (return_5/return_20)/shift(return_5/return_20, 1), 0.487s

[2022-12-11 11:28:36.161949] INFO: derived_feature_extractor: 提取完成 ta_sma_5_0/shift(ta_sma_5_0, 1), 0.398s

[2022-12-11 11:28:36.561341] INFO: derived_feature_extractor: 提取完成 rank_return_0-shift(rank_return_0, 1), 0.398s

[2022-12-11 11:28:36.961199] INFO: derived_feature_extractor: 提取完成 rank_amount_0-shift(rank_amount_0, 1), 0.398s

[2022-12-11 11:28:37.360883] INFO: derived_feature_extractor: 提取完成 rank_turn_0-shift(rank_turn_0, 1), 0.398s

[2022-12-11 11:28:39.366266] INFO: derived_feature_extractor: 提取完成 close_0/ts_min(close_0, 120), 2.004s

[2022-12-11 11:28:41.402282] INFO: derived_feature_extractor: 提取完成 close_0/ts_max(close_0, 120), 2.034s

[2022-12-11 11:28:43.468953] INFO: derived_feature_extractor: 提取完成 close_0/ts_min(close_0, 250), 2.065s

[2022-12-11 11:28:45.465717] INFO: derived_feature_extractor: 提取完成 close_0/ts_max(close_0, 250), 1.995s

[2022-12-11 11:28:47.357723] INFO: derived_feature_extractor: /y_2013, 143272

[2022-12-11 11:28:48.713647] INFO: derived_feature_extractor: /y_2014, 569948

[2022-12-11 11:28:50.835291] INFO: derived_feature_extractor: /y_2015, 569698

[2022-12-11 11:28:53.070802] INFO: derived_feature_extractor: /y_2016, 641546

[2022-12-11 11:28:57.161976] INFO: derived_feature_extractor: /y_2017, 743233

[2022-12-11 11:28:59.351215] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[54.214302s].

[2022-12-11 11:28:59.360509] INFO: moduleinvoker: join.v3 开始运行..

[2022-12-11 11:29:06.328189] INFO: join: /y_2013, 行数=0/143272, 耗时=1.086412s

[2022-12-11 11:29:10.108734] INFO: join: /y_2014, 行数=567874/569948, 耗时=3.778341s

[2022-12-11 11:29:13.793792] INFO: join: /y_2015, 行数=560424/569698, 耗时=3.679548s

[2022-12-11 11:29:17.908387] INFO: join: /y_2016, 行数=637453/641546, 耗时=4.109005s

[2022-12-11 11:29:22.770817] INFO: join: /y_2017, 行数=721150/743233, 耗时=4.856077s

[2022-12-11 11:29:22.846150] INFO: join: 最终行数: 2486901

[2022-12-11 11:29:22.869677] INFO: moduleinvoker: join.v3 运行完成[23.509181s].

[2022-12-11 11:29:22.879251] INFO: moduleinvoker: dropnan.v1 开始运行..

[2022-12-11 11:29:24.440504] INFO: dropnan: /y_2013, 0/0

[2022-12-11 11:29:26.355533] INFO: dropnan: /y_2014, 98593/567874

[2022-12-11 11:29:28.819028] INFO: dropnan: /y_2015, 502640/560424

[2022-12-11 11:29:31.620095] INFO: dropnan: /y_2016, 596972/637453

[2022-12-11 11:29:34.800954] INFO: dropnan: /y_2017, 631451/721150

[2022-12-11 11:29:34.979357] INFO: dropnan: 行数: 1829656/2486901

[2022-12-11 11:29:34.991311] INFO: moduleinvoker: dropnan.v1 运行完成[12.112044s].

[2022-12-11 11:29:35.001898] INFO: moduleinvoker: stock_ranker_train.v5 开始运行..

[2022-12-11 11:29:42.334980] INFO: StockRanker: 特征预处理 ..

[2022-12-11 11:29:50.364965] INFO: StockRanker: prepare data: training ..

[2022-12-11 11:30:00.037734] INFO: StockRanker: sort ..

[2022-12-11 11:30:36.338657] INFO: StockRanker训练: 08a3c3ec 准备训练: 1829656 行数

[2022-12-11 11:30:36.624553] INFO: StockRanker训练: 正在训练 ..

[2022-12-11 11:35:17.721900] INFO: moduleinvoker: stock_ranker_train.v5 运行完成[342.719985s].

[2022-12-11 11:35:17.728329] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-12-11 11:35:17.788809] INFO: moduleinvoker: 命中缓存

[2022-12-11 11:35:17.791204] INFO: moduleinvoker: instruments.v2 运行完成[0.062877s].

[2022-12-11 11:35:17.808169] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-12-11 11:35:19.642764] INFO: 基础特征抽取: 年份 2017, 特征行数=193398

[2022-12-11 11:35:25.428223] INFO: 基础特征抽取: 年份 2018, 特征行数=816987

[2022-12-11 11:35:31.733492] INFO: 基础特征抽取: 年份 2019, 特征行数=884867

[2022-12-11 11:35:38.597875] INFO: 基础特征抽取: 年份 2020, 特征行数=945961

[2022-12-11 11:35:45.985332] INFO: 基础特征抽取: 年份 2021, 特征行数=1061527

[2022-12-11 11:35:46.158992] INFO: 基础特征抽取: 总行数: 3902740

[2022-12-11 11:35:46.166655] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[28.358502s].

[2022-12-11 11:35:46.174704] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-12-11 11:35:55.223027] INFO: derived_feature_extractor: 提取完成 avg_amount_0/avg_amount_5, 0.012s

[2022-12-11 11:35:55.267174] INFO: derived_feature_extractor: 提取完成 avg_amount_5/avg_amount_20, 0.042s

[2022-12-11 11:35:55.275295] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_0/rank_avg_amount_5, 0.006s

[2022-12-11 11:35:55.283257] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_5/rank_avg_amount_10, 0.006s

[2022-12-11 11:36:03.035728] INFO: derived_feature_extractor: 提取完成 ta_bias(close_0, 5), 7.751s

[2022-12-11 11:36:10.389575] INFO: derived_feature_extractor: 提取完成 ta_bias(close_0, 10), 7.352s

[2022-12-11 11:36:17.928844] INFO: derived_feature_extractor: 提取完成 ta_bias(close_0, 20), 7.537s

[2022-12-11 11:36:18.500961] INFO: derived_feature_extractor: 提取完成 rank_market_cap_float_0-shift(rank_market_cap_float_0, 1), 0.570s

[2022-12-11 11:36:21.686511] INFO: derived_feature_extractor: 提取完成 sum(price_limit_status_0==3, 20), 3.184s

[2022-12-11 11:36:24.637241] INFO: derived_feature_extractor: 提取完成 sum(price_limit_status_0==3, 60), 2.949s

[2022-12-11 11:36:27.826684] INFO: derived_feature_extractor: 提取完成 sum(price_limit_status_0==3, 120), 3.185s

[2022-12-11 11:36:30.996964] INFO: derived_feature_extractor: 提取完成 sum(price_limit_status_0==3, 250), 3.168s

[2022-12-11 11:36:31.007623] INFO: derived_feature_extractor: 提取完成 return_5/return_10, 0.008s

[2022-12-11 11:36:31.614418] INFO: derived_feature_extractor: 提取完成 (return_5/return_10)/shift(return_5/return_10, 1), 0.605s

[2022-12-11 11:36:31.661166] INFO: derived_feature_extractor: 提取完成 return_5/return_20, 0.045s

[2022-12-11 11:36:32.322383] INFO: derived_feature_extractor: 提取完成 (return_5/return_20)/shift(return_5/return_20, 1), 0.660s

[2022-12-11 11:36:32.908294] INFO: derived_feature_extractor: 提取完成 ta_sma_5_0/shift(ta_sma_5_0, 1), 0.584s

[2022-12-11 11:36:33.498806] INFO: derived_feature_extractor: 提取完成 rank_return_0-shift(rank_return_0, 1), 0.589s

[2022-12-11 11:36:34.110418] INFO: derived_feature_extractor: 提取完成 rank_amount_0-shift(rank_amount_0, 1), 0.610s

[2022-12-11 11:36:34.705046] INFO: derived_feature_extractor: 提取完成 rank_turn_0-shift(rank_turn_0, 1), 0.593s

[2022-12-11 11:36:37.984790] INFO: derived_feature_extractor: 提取完成 close_0/ts_min(close_0, 120), 3.278s

[2022-12-11 11:36:41.261577] INFO: derived_feature_extractor: 提取完成 close_0/ts_max(close_0, 120), 3.275s

[2022-12-11 11:36:44.463918] INFO: derived_feature_extractor: 提取完成 close_0/ts_min(close_0, 250), 3.200s

[2022-12-11 11:36:47.866115] INFO: derived_feature_extractor: 提取完成 close_0/ts_max(close_0, 250), 3.401s

[2022-12-11 11:36:50.609253] INFO: derived_feature_extractor: /y_2017, 193398

[2022-12-11 11:36:52.534088] INFO: derived_feature_extractor: /y_2018, 816987

[2022-12-11 11:36:55.877915] INFO: derived_feature_extractor: /y_2019, 884867

[2022-12-11 11:36:59.389053] INFO: derived_feature_extractor: /y_2020, 945961

[2022-12-11 11:37:03.437158] INFO: derived_feature_extractor: /y_2021, 1061527

[2022-12-11 11:37:05.657060] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[79.482347s].

[2022-12-11 11:37:05.682524] INFO: moduleinvoker: dropnan.v1 开始运行..

[2022-12-11 11:37:08.408197] INFO: dropnan: /y_2017, 0/193398

[2022-12-11 11:37:10.920197] INFO: dropnan: /y_2018, 152317/816987

[2022-12-11 11:37:14.649852] INFO: dropnan: /y_2019, 834634/884867

[2022-12-11 11:37:18.839929] INFO: dropnan: /y_2020, 873999/945961

[2022-12-11 11:37:23.303900] INFO: dropnan: /y_2021, 940726/1061527

[2022-12-11 11:37:23.469212] INFO: dropnan: 行数: 2801676/3902740

[2022-12-11 11:37:23.482351] INFO: moduleinvoker: dropnan.v1 运行完成[17.799829s].

[2022-12-11 11:37:23.492529] INFO: moduleinvoker: stock_ranker_predict.v5 开始运行..

[2022-12-11 11:37:24.060584] ERROR: moduleinvoker: module name: stock_ranker_predict, module version: v5, trackeback: lightgbm.basic.LightGBMError: Wrong line at model file: origin_feature_names=["rank_return_20", "rank_return_120", "ta_bias(close_0, 5)", "rank_return_10", "close_0/ts_min(close_0, 120

bigcharts-data-start/{"__type":"tabs","__id":"bigchart-26dafc6c4e534f25a5e0fc24f7ea6eed"}/bigcharts-data-end

---------------------------------------------------------------------------
LightGBMError                             Traceback (most recent call last)
<ipython-input-9-1054a23ff80d> in <module>
    212 )
    213 
--> 214 m8 = M.stock_ranker_predict.v5(
    215     model=m6.model,
    216     data=m14.data,

LightGBMError: Wrong line at model file: origin_feature_names=["rank_return_20", "rank_return_120", "ta_bias(close_0, 5)", "rank_return_10", "close_0/ts_min(close_0, 120