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# 本代码由可视化策略环境自动生成 2022年11月8日 14:07
# 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('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='2011-01-01',
    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
)

m3 = M.instruments.v2(
    start_date='2016-01-01',
    end_date='2018-01-01',
    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
)

m12 = M.mlp_classifier.v1(
    training_ds=m8.data,
    features=m2.data,
    predict_ds=m11.data,
    hidden_layer_sizes='10',
    activation='relu',
    solver='adam',
    alpha=0.0001,
    batch_size=200,
    learning_rate_init=0.001,
    max_iter=200,
    key_cols='date,instrument',
    other_train_parameters={}
)

m13 = M.trade.v4(
    instruments=m3.data,
    options_data=m12.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'
)

[2022-11-08 14:06:24.990623] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-11-08 14:06:25.012077] INFO: moduleinvoker: 命中缓存

[2022-11-08 14:06:25.016032] INFO: moduleinvoker: instruments.v2 运行完成[0.026045s].

[2022-11-08 14:06:25.031022] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2022-11-08 14:06:25.043959] INFO: moduleinvoker: 命中缓存

[2022-11-08 14:06:25.047486] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[0.016435s].

[2022-11-08 14:06:25.054742] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-11-08 14:06:25.067988] INFO: moduleinvoker: 命中缓存

[2022-11-08 14:06:25.074357] INFO: moduleinvoker: input_features.v1 运行完成[0.019663s].

[2022-11-08 14:06:25.105453] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-11-08 14:06:25.119750] INFO: moduleinvoker: 命中缓存

[2022-11-08 14:06:25.122473] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.01704s].

[2022-11-08 14:06:25.134207] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-11-08 14:06:25.145128] INFO: moduleinvoker: 命中缓存

[2022-11-08 14:06:25.147856] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.013643s].

[2022-11-08 14:06:25.168808] INFO: moduleinvoker: join.v3 开始运行..

[2022-11-08 14:06:25.183012] INFO: moduleinvoker: 命中缓存

[2022-11-08 14:06:25.188790] INFO: moduleinvoker: join.v3 运行完成[0.019902s].

[2022-11-08 14:06:25.207464] INFO: moduleinvoker: dropnan.v2 开始运行..

[2022-11-08 14:06:25.220123] INFO: moduleinvoker: 命中缓存

[2022-11-08 14:06:25.225336] INFO: moduleinvoker: dropnan.v2 运行完成[0.017871s].

[2022-11-08 14:06:25.242969] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-11-08 14:06:25.325531] INFO: moduleinvoker: 命中缓存

[2022-11-08 14:06:25.327931] INFO: moduleinvoker: instruments.v2 运行完成[0.084982s].

[2022-11-08 14:06:25.507797] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-11-08 14:06:25.637846] INFO: moduleinvoker: 命中缓存

[2022-11-08 14:06:25.640534] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.132807s].

[2022-11-08 14:06:25.675600] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-11-08 14:06:25.883879] INFO: moduleinvoker: 命中缓存

[2022-11-08 14:06:25.886249] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.210657s].

[2022-11-08 14:06:25.924479] INFO: moduleinvoker: dropnan.v2 开始运行..

[2022-11-08 14:06:26.028895] INFO: moduleinvoker: 命中缓存

[2022-11-08 14:06:26.031971] INFO: moduleinvoker: dropnan.v2 运行完成[0.107475s].

[2022-11-08 14:06:26.078915] INFO: moduleinvoker: mlp_classifier.v1 开始运行..

[2022-11-08 14:06:26.206375] INFO: moduleinvoker: 命中缓存

[2022-11-08 14:06:26.210831] INFO: moduleinvoker: mlp_classifier.v1 运行完成[0.131781s].

[2022-11-08 14:06:26.357449] INFO: moduleinvoker: backtest.v8 开始运行..

[2022-11-08 14:06:26.604002] INFO: backtest: biglearning backtest:V8.6.3

[2022-11-08 14:06:26.605715] INFO: backtest: product_type:stock by specified

[2022-11-08 14:06:26.912563] INFO: moduleinvoker: cached.v2 开始运行..

[2022-11-08 14:06:26.989384] INFO: moduleinvoker: 命中缓存

[2022-11-08 14:06:26.992008] INFO: moduleinvoker: cached.v2 运行完成[0.079459s].

[2022-11-08 14:06:43.050141] INFO: backtest: algo history_data=DataSource(e03d97b804eb43a69b4cf22925e1d215T)

[2022-11-08 14:06:43.052892] INFO: algo: TradingAlgorithm V1.8.8

[2022-11-08 14:06:47.193477] INFO: algo: trading transform...

[2022-11-08 14:06:50.310696] INFO: algo: handle_splits get splits [dt:2016-05-23 00:00:00+00:00] [asset:Equity(1203 [603703.SHA]), ratio:0.39774084091186523]

[2022-11-08 14:06:50.313207] INFO: Position: position stock handle split[sid:1203, orig_amount:600, new_amount:1508.0, orig_cost:64.98000341645474, new_cost:25.8452, ratio:0.39774084091186523, last_sale_price:26.7600040435791]

[2022-11-08 14:06:50.315922] INFO: Position: after split: PositionStock(asset:Equity(1203 [603703.SHA]), amount:1508.0, cost_basis:25.8452, last_sale_price:67.27999877929688)

[2022-11-08 14:06:50.318650] INFO: Position: returning cash: 13.9143

[2022-11-08 14:06:50.762393] INFO: algo: handle_splits get splits [dt:2016-06-16 00:00:00+00:00] [asset:Equity(3049 [603698.SHA]), ratio:0.9917076826095581]

[2022-11-08 14:06:50.764867] INFO: Position: position stock handle split[sid:3049, orig_amount:1400, new_amount:1411.0, orig_cost:25.610003273997204, new_cost:25.3976, ratio:0.9917076826095581, last_sale_price:26.31000518798828]

[2022-11-08 14:06:50.767535] INFO: Position: after split: PositionStock(asset:Equity(3049 [603698.SHA]), amount:1411.0, cost_basis:25.3976, last_sale_price:26.530000686645508)

[2022-11-08 14:06:50.769996] INFO: Position: returning cash: 18.5832

[2022-11-08 14:06:50.939760] INFO: algo: handle_splits get splits [dt:2016-06-24 00:00:00+00:00] [asset:Equity(3672 [603688.SHA]), ratio:0.9947177171707153]

[2022-11-08 14:06:50.942827] INFO: Position: position stock handle split[sid:3672, orig_amount:2700, new_amount:2714.0, orig_cost:19.2200026213262, new_cost:19.1185, ratio:0.9947177171707153, last_sale_price:18.830007553100586]

[2022-11-08 14:06:50.945611] INFO: Position: after split: PositionStock(asset:Equity(3672 [603688.SHA]), amount:2714.0, cost_basis:19.1185, last_sale_price:18.93000030517578)

[2022-11-08 14:06:50.948485] INFO: Position: returning cash: 6.3627

[2022-11-08 14:06:51.242822] INFO: algo: handle_splits get splits [dt:2016-07-08 00:00:00+00:00] [asset:Equity(2925 [603699.SHA]), ratio:0.9860337376594543]

[2022-11-08 14:06:51.246846] INFO: Position: position stock handle split[sid:2925, orig_amount:12400, new_amount:12575.0, orig_cost:17.82718415902479, new_cost:17.5782, ratio:0.9860337376594543, last_sale_price:17.65000343322754]

[2022-11-08 14:06:51.249982] INFO: Position: after split: PositionStock(asset:Equity(2925 [603699.SHA]), amount:12575.0, cost_basis:17.5782, last_sale_price:17.899999618530273)

[2022-11-08 14:06:51.252543] INFO: Position: returning cash: 11.2009

[2022-11-08 14:06:51.728046] INFO: algo: handle_splits get splits [dt:2016-07-29 00:00:00+00:00] [asset:Equity(3885 [603701.SHA]), ratio:0.9957828521728516]

[2022-11-08 14:06:51.737552] INFO: Position: position stock handle split[sid:3885, orig_amount:3100, new_amount:3113.0, orig_cost:76.22903275362324, new_cost:75.9076, ratio:0.9957828521728516, last_sale_price:66.11997985839844]

[2022-11-08 14:06:51.745444] INFO: Position: after split: PositionStock(asset:Equity(3885 [603701.SHA]), amount:3113.0, cost_basis:75.9076, last_sale_price:66.4000015258789)

[2022-11-08 14:06:51.753032] INFO: Position: returning cash: 8.498

[2022-11-08 14:06:57.169425] INFO: algo: handle_splits get splits [dt:2017-05-18 00:00:00+00:00] [asset:Equity(2598 [002547.SZA]), ratio:0.9954296946525574]

[2022-11-08 14:06:57.172150] INFO: Position: position stock handle split[sid:2598, orig_amount:4400, new_amount:4420.0, orig_cost:10.939999611230244, new_cost:10.89, ratio:0.9954296946525574, last_sale_price:10.890000343322754]

[2022-11-08 14:06:57.174979] INFO: Position: after split: PositionStock(asset:Equity(2598 [002547.SZA]), amount:4420.0, cost_basis:10.89, last_sale_price:10.9399995803833)

[2022-11-08 14:06:57.177448] INFO: Position: returning cash: 2.1962

[2022-11-08 14:06:57.289784] INFO: algo: handle_splits get splits [dt:2017-05-24 00:00:00+00:00] [asset:Equity(3486 [002553.SZA]), ratio:0.9808059930801392]

[2022-11-08 14:06:57.291998] INFO: Position: position stock handle split[sid:3486, orig_amount:17100, new_amount:17434.0, orig_cost:12.287840054636956, new_cost:12.052, ratio:0.9808059930801392, last_sale_price:10.219998359680176]

[2022-11-08 14:06:57.294046] INFO: Position: after split: PositionStock(asset:Equity(3486 [002553.SZA]), amount:17434.0, cost_basis:12.052, last_sale_price:10.420000076293945)

[2022-11-08 14:06:57.295936] INFO: Position: returning cash: 6.5471

[2022-11-08 14:06:57.409131] INFO: algo: handle_splits get splits [dt:2017-06-01 00:00:00+00:00] [asset:Equity(3957 [002550.SZA]), ratio:0.9866665601730347]

[2022-11-08 14:06:57.411939] INFO: Position: position stock handle split[sid:3957, orig_amount:6000, new_amount:6081.0, orig_cost:6.070001257230948, new_cost:5.9891, ratio:0.9866665601730347, last_sale_price:5.919999122619629]

[2022-11-08 14:06:57.413945] INFO: Position: after split: PositionStock(asset:Equity(3957 [002550.SZA]), amount:6081.0, cost_basis:5.9891, last_sale_price:6.0)

[2022-11-08 14:06:57.415972] INFO: Position: returning cash: 0.4839

[2022-11-08 14:06:57.615621] INFO: algo: handle_splits get splits [dt:2017-06-09 00:00:00+00:00] [asset:Equity(3762 [002548.SZA]), ratio:0.9883177280426025]

[2022-11-08 14:06:57.618247] INFO: Position: position stock handle split[sid:3762, orig_amount:3100, new_amount:3136.0, orig_cost:12.830003305584741, new_cost:12.6801, ratio:0.9883177280426025, last_sale_price:12.6899995803833]

[2022-11-08 14:06:57.621155] INFO: Position: after split: PositionStock(asset:Equity(3762 [002548.SZA]), amount:3136.0, cost_basis:12.6801, last_sale_price:12.84000015258789)

[2022-11-08 14:06:57.623274] INFO: Position: returning cash: 8.1612

[2022-11-08 14:06:58.185816] INFO: algo: handle_splits get splits [dt:2017-07-07 00:00:00+00:00] [asset:Equity(3477 [002551.SZA]), ratio:0.661980390548706]

[2022-11-08 14:06:58.188566] INFO: Position: position stock handle split[sid:3477, orig_amount:12200, new_amount:18429.0, orig_cost:19.714755773057586, new_cost:13.0508, ratio:0.661980390548706, last_sale_price:10.829999923706055]

[2022-11-08 14:06:58.190727] INFO: Position: after split: PositionStock(asset:Equity(3477 [002551.SZA]), amount:18429.0, cost_basis:13.0508, last_sale_price:16.360000610351562)

[2022-11-08 14:06:58.193388] INFO: Position: returning cash: 5.9449

[2022-11-08 14:07:01.761051] INFO: Performance: Simulated 488 trading days out of 488.

[2022-11-08 14:07:01.762964] INFO: Performance: first open: 2016-01-04 09:30:00+00:00

[2022-11-08 14:07:01.765081] INFO: Performance: last close: 2017-12-29 15:00:00+00:00

[2022-11-08 14:07:09.158446] INFO: moduleinvoker: backtest.v8 运行完成[42.801012s].

[2022-11-08 14:07:09.161030] INFO: moduleinvoker: trade.v4 运行完成[42.929686s].

bigcharts-data-start/{"__type":"tabs","__id":"bigchart-33437f4507184b6d956a49af69f49c6b"}/bigcharts-data-end