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# 本代码由可视化策略环境自动生成 2020年6月23日 10:46
# 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):

    context.ranker_prediction = context.options['data'].read_df()
    context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))
    context.stock_count = 30
    context.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):
    
    if context.trading_day_index % context.hold_days != 0:
        return 
    
    # 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')]
    
    stock_to_buy = list(ranker_prediction.instrument)[:context.stock_count]
    # 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.symbol 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]
    
    assert type(stock_to_buy) == list, '格式类型不对！'
    
    
    # 卖出
    for stock in stock_to_sell:
        if data.can_trade(context.symbol(stock)):
            # order_target_percent是平台的一个下单接口，表明下单使得该股票的权重为0，
            #   即卖出全部股票，可参考回测文档
            context.order_target_percent(context.symbol(stock), 0)
    
    # 如果当天没有买入的股票，就返回
    if len(stock_to_buy) == 0:
        return

     
    context.stock_weights = T.norm([1 / math.log(i + 2) for i in range(0, len(stock_to_buy))])

    # 买入
    c = 0
    for stock in stock_to_buy:
        
        if data.can_trade(context.symbol(stock)):
            # 下单使得某只股票的持仓权重达到weight，因为
            # weight大于0,因此是等权重买入
            context.order_target_percent(context.symbol(stock), context.stock_weights[c])
        c += 1
# 回测引擎：准备数据，只执行一次
def m19_prepare_bigquant_run(context):
    pass


g = T.Graph({

    'm1': 'M.instruments.v2',
    'm1.start_date': '2014-01-01',
    'm1.end_date': '2015-01-01',
    'm1.market': 'CN_STOCK_A',
    'm1.instrument_list': '',
    'm1.max_count': 0,

    'm2': 'M.advanced_auto_labeler.v2',
    'm2.instruments': T.Graph.OutputPort('m1.data'),
    'm2.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/data_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/big_expr.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)
""",
    'm2.start_date': '',
    'm2.end_date': '',
    'm2.benchmark': '000300.SHA',
    'm2.drop_na_label': True,
    'm2.cast_label_int': True,

    'm3': 'M.input_features.v1',
    'm3.features': 'sum(max(0,high_0-((high_1+low_1+close_1)/3)),26)/sum(max(0,((high_1+low_1+close_1)/3)-low_0),26)*100',

    'm15': 'M.general_feature_extractor.v7',
    'm15.instruments': T.Graph.OutputPort('m1.data'),
    'm15.features': T.Graph.OutputPort('m3.data'),
    'm15.start_date': '',
    'm15.end_date': '',
    'm15.before_start_days': 0,

    'm16': 'M.derived_feature_extractor.v3',
    'm16.input_data': T.Graph.OutputPort('m15.data'),
    'm16.features': T.Graph.OutputPort('m3.data'),
    'm16.date_col': 'date',
    'm16.instrument_col': 'instrument',
    'm16.drop_na': False,
    'm16.remove_extra_columns': False,

    'm7': 'M.join.v3',
    'm7.data1': T.Graph.OutputPort('m2.data'),
    'm7.data2': T.Graph.OutputPort('m16.data'),
    'm7.on': 'date,instrument',
    'm7.how': 'inner',
    'm7.sort': False,

    'm13': 'M.dropnan.v1',
    'm13.input_data': T.Graph.OutputPort('m7.data'),

    'm5': 'M.stock_ranker_train.v6',
    'm5.training_ds': T.Graph.OutputPort('m13.data'),
    'm5.features': T.Graph.OutputPort('m3.data'),
    'm5.learning_algorithm': '排序',
    'm5.number_of_leaves': 30,
    'm5.minimum_docs_per_leaf': 1000,
    'm5.number_of_trees': 20,
    'm5.learning_rate': 0.1,
    'm5.max_bins': 1023,
    'm5.feature_fraction': 1,
    'm5.data_row_fraction': 1,
    'm5.ndcg_discount_base': 1,
    'm5.m_lazy_run': False,

    'm9': 'M.instruments.v2',
    'm9.start_date': T.live_run_param('trading_date', '2017-01-01'),
    'm9.end_date': T.live_run_param('trading_date', '2017-06-01'),
    'm9.market': 'CN_STOCK_A',
    'm9.instrument_list': '',
    'm9.max_count': 0,

    'm17': 'M.general_feature_extractor.v7',
    'm17.instruments': T.Graph.OutputPort('m9.data'),
    'm17.features': T.Graph.OutputPort('m3.data'),
    'm17.start_date': '',
    'm17.end_date': '',
    'm17.before_start_days': 0,

    'm18': 'M.derived_feature_extractor.v3',
    'm18.input_data': T.Graph.OutputPort('m17.data'),
    'm18.features': T.Graph.OutputPort('m3.data'),
    'm18.date_col': 'date',
    'm18.instrument_col': 'instrument',
    'm18.drop_na': False,
    'm18.remove_extra_columns': False,

    'm14': 'M.dropnan.v1',
    'm14.input_data': T.Graph.OutputPort('m18.data'),

    'm8': 'M.stock_ranker_predict.v5',
    'm8.model': T.Graph.OutputPort('m5.model'),
    'm8.data': T.Graph.OutputPort('m14.data'),
    'm8.m_lazy_run': False,

    'm19': 'M.trade.v4',
    'm19.instruments': T.Graph.OutputPort('m9.data'),
    'm19.options_data': T.Graph.OutputPort('m8.predictions'),
    'm19.start_date': '',
    'm19.end_date': '',
    'm19.initialize': m19_initialize_bigquant_run,
    'm19.handle_data': m19_handle_data_bigquant_run,
    'm19.prepare': m19_prepare_bigquant_run,
    'm19.volume_limit': 0.025,
    'm19.order_price_field_buy': 'open',
    'm19.order_price_field_sell': 'open',
    'm19.capital_base': 3000000,
    'm19.auto_cancel_non_tradable_orders': True,
    'm19.data_frequency': 'daily',
    'm19.price_type': '后复权',
    'm19.product_type': '股票',
    'm19.plot_charts': True,
    'm19.backtest_only': False,
    'm19.benchmark': '',
})

# g.run({})


def m4_run_bigquant_run(bq_graph, inputs):
    features =['pe_ttm_0', 'shift(close_0,5)/close_0']

    parameters_list = []
     
    for feature in features:
        parameters = {'m3.features':feature}
        parameters_list.append({'parameters': parameters})
    
    def run(parameters):
        try:
            print(parameters)
            res = g.run(parameters)
            # demo 获取需要的数据，存为csv文档，也可根据实际需求做其他处理
            data = res['m19'].raw_perf.read()[['sharpe', 'win_percent', 'max_drawdown']].tail(1)
            data.to_csv("data.csv", index=False)
            # 显示回测图表
            res['m19'].
            return res
        
        except Exception as e:
            print('ERROR --------', e)
            return None
 
    results = T.parallel_map(run, parameters_list, max_workers=2, remote_run=False, silent=False)

    return results


m4 = M.hyper_run.v1(
    run=m4_run_bigquant_run,
    run_now=True,
    bq_graph=g
)

[2020-06-23 10:44:43.590119] INFO: tools: parallel_map, 开始并行运算..

[2020-06-23 10:45:27.929840] INFO: moduleinvoker: hyper_run.v1 运行完成[44.340126s].

[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.
[Parallel(n_jobs=2)]: Done   1 tasks      | elapsed:   37.6s
[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:   44.3s remaining:    0.0s
[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:   44.3s finished