克隆策略

In [9]:

# 本代码由可视化策略环境自动生成 2021年11月18日 15:18
# 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 m3_initialize_bigquant_run(context):

    # 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))

# 回测引擎：每日数据处理函数，每天执行一次
def m3_handle_data_bigquant_run(context, data):
    # 获取今日的日期
    today = data.current_dt.strftime('%Y-%m-%d')  
    # 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 = {e.symbol: p.amount * p.last_sale_price
                 for e, p in context.perf_tracker.position_tracker.positions.items()}

    # 记录用于买入股票的可用现金,因为是早盘卖股票，需要记录卖出的股票市值并在买入下单前更新可用现金；
    # 如果是早盘买尾盘卖，则卖出时不需更新可用现金，因为尾盘卖出股票所得现金无法使用
    cash_for_buy = context.portfolio.cash    
    
    try:
        buy_stock = context.daily_stock_buy[today]  # 当日符合买入条件的股票
    except:
        buy_stock=[]  # 如果没有符合条件的股票，就设置为空
    
    try:
        sell_stock = context.daily_stock_sell[today]  # 当日符合卖出条件的股票
    except:
        sell_stock=[] # 如果没有符合条件的股票，就设置为空
    
    # 需要卖出的股票:已有持仓中符合卖出条件的股票
    stock_to_sell = [ i for i in stock_hold_now if i in sell_stock ]
    # 需要买入的股票:没有持仓且符合买入条件的股票
    stock_to_buy = [ i for i in buy_stock if i not in stock_hold_now ]  
    # 需要调仓的股票：已有持仓且不符合卖出条件的股票
    stock_to_adjust=[ i for i in stock_hold_now if i not in sell_stock ]
    
    # 如果有卖出信号
    if len(stock_to_sell)>0:
        for instrument in stock_to_sell:
            sid = context.symbol(instrument) # 将标的转化为equity格式
            cur_position = context.portfolio.positions[sid].amount # 持仓
            if cur_position > 0 and data.can_trade(sid):
                context.order_target_percent(sid, 0) # 全部卖出 
                # 因为设置的是早盘卖出早盘买入，需要根据卖出的股票更新可用现金；如果设置尾盘卖出早盘买入，则不需更新可用现金(可以删除下面的语句)
                cash_for_buy += stock_hold_now[instrument]
    
    # 如果有买入信号/有持仓
    if len(stock_to_buy)+len(stock_to_adjust)>0:
        weight = 1/(len(stock_to_buy)+len(stock_to_adjust)) # 每只股票的比重为等资金比例持有
        for instrument in stock_to_buy+stock_to_adjust:
            sid = context.symbol(instrument) # 将标的转化为equity格式
            if  data.can_trade(sid):
                context.order_target_value(sid, weight*cash_for_buy) # 买入
# 回测引擎：准备数据，只执行一次
def m3_prepare_bigquant_run(context):
    # 加载预测数据
    df = context.options['data'].read_df()

    # 函数：求满足开仓条件的股票列表
    def open_pos_con(df):
        return list(df[df['buy_condition']>0].instrument)

    # 函数：求满足平仓条件的股票列表
    def close_pos_con(df):
        return list(df[df['sell_condition']>0].instrument)

    # 每日买入股票的数据框
    context.daily_stock_buy= df.groupby('date').apply(open_pos_con)
    # 每日卖出股票的数据框
    context.daily_stock_sell= df.groupby('date').apply(close_pos_con)

g = T.Graph({

    'm1': 'M.input_features.v1',
    'm1.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
buy_condition=where(mean(close_0,5)>mean(close_0,10),1,0)
sell_condition=where(mean(close_0,5)<mean(close_0,10),1,0)""",
    'm1.m_cached': False,

    'm2': 'M.instruments.v2',
    'm2.start_date': T.live_run_param('trading_date', '2019-03-01'),
    'm2.end_date': T.live_run_param('trading_date', '2021-06-01'),
    'm2.market': 'CN_FUND',
    'm2.instrument_list': '159982.ZOF',
    'm2.max_count': 0,

    'm7': 'M.general_feature_extractor.v7',
    'm7.instruments': T.Graph.OutputPort('m2.data'),
    'm7.features': T.Graph.OutputPort('m1.data'),
    'm7.start_date': '',
    'm7.end_date': '',
    'm7.before_start_days': 60,

    'm8': 'M.derived_feature_extractor.v3',
    'm8.input_data': T.Graph.OutputPort('m7.data'),
    'm8.features': T.Graph.OutputPort('m1.data'),
    'm8.date_col': 'date',
    'm8.instrument_col': 'instrument',
    'm8.drop_na': False,
    'm8.remove_extra_columns': False,

    'm4': 'M.dropnan.v2',
    'm4.input_data': T.Graph.OutputPort('m8.data'),

    'm3': 'M.trade.v4',
    'm3.instruments': T.Graph.OutputPort('m2.data'),
    'm3.options_data': T.Graph.OutputPort('m4.data'),
    'm3.start_date': '',
    'm3.end_date': '',
    'm3.initialize': m3_initialize_bigquant_run,
    'm3.handle_data': m3_handle_data_bigquant_run,
    'm3.prepare': m3_prepare_bigquant_run,
    'm3.volume_limit': 0.025,
    'm3.order_price_field_buy': 'open',
    'm3.order_price_field_sell': 'open',
    'm3.capital_base': 1000000,
    'm3.auto_cancel_non_tradable_orders': True,
    'm3.data_frequency': 'daily',
    'm3.price_type': '后复权',
    'm3.product_type': '股票',
    'm3.plot_charts': True,
    'm3.backtest_only': False,
    'm3.benchmark': '',
})

# g.run({})


def m5_param_grid_builder_bigquant_run():
    import itertools
    param_grid = {}
    
    fast_period = [5,6,7,8,9,10]
    slow_period = [20,30,40,50]
    
    combination = list(itertools.product(fast_period,slow_period))
    feature_list = [
    '''
    buy_condition=where(mean(close_0,{0})>mean(close_0,{1}),1,0)
    sell_condition=where(mean(close_0,{0})<mean(close_0,{1}),1,0)
    '''.format(comb[0],comb[1]) for comb in combination
    ]
    # 在这里设置需要调优的参数备选
    param_grid['m1.features'] = feature_list
    

    return param_grid

def m5_scoring_bigquant_run(result):
    import empyrical
    returns = result.get('m3').read_raw_perf()['returns'].tolist()
    sharp_ratio = empyrical.sharpe_ratio(np.array(returns),0.03/252)
    return {'score': sharp_ratio}


m5 = M.hyper_parameter_search.v1(
    param_grid_builder=m5_param_grid_builder_bigquant_run,
    scoring=m5_scoring_bigquant_run,
    search_algorithm='网格搜索',
    search_iterations=10,
    workers=4,
    worker_distributed_run=True,
    worker_silent=True,
    run_now=True,
    bq_graph=g
)

[2021-11-18 15:15:32.146551] INFO: moduleinvoker: input_features.v1 开始运行..

[2021-11-18 15:15:32.173531] INFO: moduleinvoker: input_features.v1 运行完成[0.026955s].

[2021-11-18 15:15:32.197334] INFO: moduleinvoker: instruments.v2 开始运行..

[2021-11-18 15:15:32.207936] INFO: moduleinvoker: 命中缓存

[2021-11-18 15:15:32.209665] INFO: moduleinvoker: instruments.v2 运行完成[0.012354s].

[2021-11-18 15:15:32.224367] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2021-11-18 15:15:32.258818] WARNING: bigdatasource: cannot find filed [close] table in field_table_map!

[2021-11-18 15:15:32.261021] INFO: 基础特征抽取: 年份 2018, 特征行数=0

[2021-11-18 15:15:32.267341] WARNING: bigdatasource: cannot find filed [close] table in field_table_map!

[2021-11-18 15:15:32.271544] INFO: 基础特征抽取: 年份 2019, 特征行数=0

[2021-11-18 15:15:32.278224] WARNING: bigdatasource: cannot find filed [close] table in field_table_map!

[2021-11-18 15:15:32.282293] INFO: 基础特征抽取: 年份 2020, 特征行数=0

[2021-11-18 15:15:32.288148] WARNING: bigdatasource: cannot find filed [close] table in field_table_map!

[2021-11-18 15:15:32.291799] INFO: 基础特征抽取: 年份 2021, 特征行数=0

[2021-11-18 15:15:32.318521] ERROR: moduleinvoker: module name: general_feature_extractor, module version: v7, trackeback: Exception: no features extracted.

---------------------------------------------------------------------------
Exception                                 Traceback (most recent call last)
<ipython-input-9-6a9c46a60d8c> in <module>
     19 )
     20 
---> 21 m7 = M.general_feature_extractor.v7(
     22     instruments=m2.data,
     23     features=m1.data,

Exception: no features extracted.