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    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    In [83]:
    # 本代码由可视化策略环境自动生成 2022年9月17日 13:52
# 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。


# 交易引擎：初始化函数，只执行一次
def m19_initialize_bigquant_run(context):
       # 加载股票指标数据,数据继承自m6模块
    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))
    
    # 设置数量
    context.stock_num = 5
    
    # 调仓天数,22个交易日大概就是一个月。可以理解为一个月换仓一次
    context.rebalance_days = 22
    
    # 如果策略运行中，需要将数据进行保存，可以借用extension这个对象，类型为dict
    # 比如当前运行的k线的索引，比如个股持仓天数、买入均价
    context.extension.index = 0
    context.subscribe(context.instruments)
 
# 交易引擎：每个单位时间开盘前调用一次。
def m19_before_trading_start_bigquant_run(context, data):
    pass
# 交易引擎：tick数据处理函数，每个tick执行一次
def m19_handle_tick_bigquant_run(context, data):
    pass

# 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):
    
    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]

    stock_to_buy = list(cur_data.instrument[:context.stock_num])
    # 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 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:
        context.order_target_percent(stock, 0)
    
    # 如果当天没有买入的股票，就返回
    if len(stock_to_buy) == 0:
        print(date,'当天没有买入的股票')
        return

    # 等权重买入 
    weight =  1 / len(stock_to_buy)
    print(stock_to_buy,weight)
    # 买入
    for stock in stock_to_buy:
        context.order_target_percent(stock, weight)
 
# 交易引擎：成交回报处理函数，每个成交发生时执行一次
def m19_handle_trade_bigquant_run(context, data):
    pass

# 交易引擎：委托回报处理函数，每个委托变化时执行一次
def m19_handle_order_bigquant_run(context, data):
    pass

# 交易引擎：盘后处理函数，每日盘后执行一次
def m19_after_trading_bigquant_run(context, data):
    pass


m11 = M.input_features.v1(
    features="""return_5 = close/shift(close, 5)
return_10 = close/shift(close, 10)
return_20 = close/shift(close, 20)
amount/mean(amount,5)
mean(amount,5)/mean(amount,20)
rank(amount)/rank(mean(amount,5))
rank(mean(amount,5))/rank(mean(amount,10))
rank(close/shift(close, 1))
rank(close/shift(close, 5))
rank(close/shift(close, 10))     
rank(close/shift(close, 1))/rank(close/shift(close, 5))    
rank(close/shift(close, 5))/rank(close/shift(close, 10))"""
)

m12 = M.use_datasource.v1(
    datasource_id='bar1d_CN_CONBOND',
    start_date='2017-01-01',
    end_date='2020-12-31',
    m_cached=False
)

m13 = M.derived_feature_extractor.v3(
    input_data=m12.data,
    features=m11.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions={}
)

m14 = M.auto_labeler_on_datasource.v1(
    input_data=m12.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
# 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)
""",
    drop_na_label=True,
    cast_label_int=True,
    date_col='date',
    instrument_col='instrument',
    user_functions={}
)

m15 = M.join.v3(
    data1=m14.data,
    data2=m13.data,
    on='date,instrument',
    how='inner',
    sort=False
)

m18 = M.dropnan.v1(
    input_data=m15.data,
    m_cached=False
)

m16 = M.stock_ranker_train.v6(
    training_ds=m18.data,
    features=m11.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,
    data_row_fraction=1,
    plot_charts=True,
    ndcg_discount_base=1,
    m_lazy_run=False
)

m1 = M.use_datasource.v1(
    datasource_id='market_performance_CN_CONBOND',
    start_date='2021-01-01',
    end_date='2022-09-16',
    m_cached=False
)

m2 = M.trade_data_generation.v1(
    input=m1.data,
    category='CN_STOCK',
    m_cached=False
)

m28 = M.use_datasource.v1(
    instruments=m2.instrument_list,
    datasource_id='bar1d_CN_CONBOND',
    start_date='2018-01-01',
    end_date='2021-09-15'
)

m30 = M.derived_feature_extractor.v3(
    input_data=m28.data,
    features=m11.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions={}
)

m17 = M.stock_ranker_predict.v5(
    model=m16.model,
    data=m30.data,
    m_lazy_run=False
)

m19 = M.hftrade.v2(
    instruments=m2.instrument_list,
    options_data=m17.predictions,
    start_date='',
    end_date='',
    initialize=m19_initialize_bigquant_run,
    before_trading_start=m19_before_trading_start_bigquant_run,
    handle_tick=m19_handle_tick_bigquant_run,
    handle_data=m19_handle_data_bigquant_run,
    handle_trade=m19_handle_trade_bigquant_run,
    handle_order=m19_handle_order_bigquant_run,
    after_trading=m19_after_trading_bigquant_run,
    capital_base=1000001,
    frequency='daily',
    price_type='真实价格',
    product_type='可转债',
    before_start_days='0',
    volume_limit=1,
    order_price_field_buy='close',
    order_price_field_sell='close',
    benchmark='000300.HIX',
    plot_charts=True,
    disable_cache=False,
    replay_bdb=False,
    show_debug_info=False,
    backtest_only=False
)
    设置评估测试数据集，查看训练曲线
    [视频教程]StockRanker训练曲线
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-816774ca1e0a4f2f8fd867aae4944e28"}/bigcharts-data-end
    2022-09-17 13:50:34.547520 init history datas... 
2022-09-17 13:50:34.548647 init history datas done. 
2022-09-17 13:50:34.568683 run_backtest() capital_base:1000001, frequency:1d, product_type:conbond, date:2021-01-04 ~ 2022-09-02 
2022-09-17 13:50:34.568981 run_backtest() running... 
2022-09-17 13:50:34.578033 initial contracts len=0 
2022-09-17 13:50:34.578194 backtest inited. 
2022-09-17 13:50:34.843408 backtest transforming 1d, bars=1... 
2022-09-17 13:50:34.844104 transform start_trading_day=2021-01-04 00:00:00, simulation period=2021-01-04 ~ 2022-09-02 
2022-09-17 13:50:34.844140 transform source=None, before_start_days=0 
2022-09-17 13:50:34.844168 transform replay_func=<cyfunction BacktestEngine.transform.<locals>.replay_bars_dt at 0x7f36e4b2c6c0> 
['128103.ZCB', '113587.HCB', '113559.HCB', '113525.HCB', '110058.HCB'] 0.2
['123086.ZCB', '110048.HCB', '110058.HCB', '110061.HCB', '113040.HCB'] 0.2
['110057.HCB', '128072.ZCB', '128070.ZCB', '128014.ZCB', '113546.HCB'] 0.2
['123048.ZCB', '123053.ZCB', '123091.ZCB', '113589.HCB', '123028.ZCB'] 0.2
['113505.HCB', '128101.ZCB', '128041.ZCB', '128118.ZCB', '128050.ZCB'] 0.2
['123104.ZCB', '110076.HCB', '123056.ZCB', '123114.ZCB', '128014.ZCB'] 0.2
['113545.HCB', '128035.ZCB', '110041.HCB', '110073.HCB', '113013.HCB'] 0.2
['113051.HCB', '113505.HCB', '113580.HCB', '113603.HCB', '123083.ZCB'] 0.2
['113570.HCB', '123078.ZCB', '123113.ZCB', '128131.ZCB', '123073.ZCB'] 0.2
['113566.HCB', '113597.HCB', '113609.HCB', '113624.HCB', '123011.ZCB'] 0.2
['113504.HCB', '123088.ZCB', '127005.ZCB', '128122.ZCB', '113606.HCB'] 0.2
['123011.ZCB', '113606.HCB', '127016.ZCB', '123071.ZCB', '128141.ZCB'] 0.2
['128132.ZCB', '113053.HCB', '113618.HCB', '123018.ZCB', '128128.ZCB'] 0.2
['113584.HCB', '123117.ZCB', '113519.HCB', '110059.HCB', '127043.ZCB'] 0.2
['113549.HCB', '123093.ZCB', '123097.ZCB', '123129.ZCB', '123132.ZCB'] 0.2
['113047.HCB', '123137.ZCB', '128014.ZCB', '128042.ZCB', '128109.ZCB'] 0.2
['113624.HCB', '113638.HCB', '123044.ZCB', '123050.ZCB', '123120.ZCB'] 0.2
['113608.HCB', '113530.HCB', '113596.HCB', '113624.HCB', '127062.ZCB'] 0.2
2022-09-17 13:50:37.114012 backtest run end! 
2022-09-17 13:50:38.216441 run_backtest() finished! time cost 3.647s!
    • 收益率39.69%
    • 年化收益率22.05%
    • 基准收益率-23.62%
    • 阿尔法0.23
    • 贝塔0.09
    • 夏普比率1.09
    • 胜率0.68
    • 盈亏比4.7
    • 收益波动率17.68%
    • 信息比率0.09
    • 最大回撤9.99%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-dec83af86eeb42b4b0bf472f6f4fd8d0"}/bigcharts-data-end