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In [1]:

# 本代码由可视化策略环境自动生成 2022年12月15日 17:25
# 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):
   # 设置是否是结算模式
    context.set_need_settle(False)
    context.set_commission(PerOrder(buy_cost=0.0013, sell_cost=0.0023, 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):
    instruments = context.instruments
    
    # 获取当前分钟的买卖信号
    today_date=data.current_dt.strftime('%Y-%m-%d %H:%M:%S')
    time = data.current_dt.strftime('%H:%M')
    if context.index < 6:  # 日内分钟计数器超过计算窗口后才开始交易
        context.index += 1
        return
    
    conditions = context.condition[context.condition.date==today_date] # 获取最新信号
    
    for instrument in instruments:
        instrument_symbol = context.symbol(instrument) # 交易标的
        curr_position = context.portfolio.positions[instrument_symbol].amount # 持仓数量
        
        try:
            buy_condition = conditions[conditions.instrument==instrument]['buy_condition'].values[0]
        except:
            buy_condition = 0
    
        try:
            daily_close_1 = conditions[conditions.instrument==instrument]['daily_close_1'].values[0]
            new_price = data.current(instrument_symbol, ['price'])
            sell_condition = 1 if new_price['price'] > daily_close_1*1.04 else 0
        except:
            sell_condition = 0   
            
        # 如果当前没有仓位且满足买入条件，执行开仓
        if curr_position == 0 and buy_condition>0 and time<"14:56":
            context.order_target(instrument_symbol, 100)
        elif curr_position >= 0 and (sell_condition>0 or time=='14:57'):
            context.order_target(instrument_symbol, 0)
# 回测引擎：准备数据，只执行一次
def m3_prepare_bigquant_run(context):
    df = context.options['data'].read_df()
    df['date']=df['date'].apply(lambda x:x.strftime('%Y-%m-%d %H:%M:%S'))
    context.condition=df


# 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 m3_before_trading_start_bigquant_run(context, data):
    # 当日分钟K线数量记录变量初始化
    context.index = 1


m2 = M.instruments.v2(
    start_date='2020-01-20 09:01:00',
    end_date='2020-02-20 15:15:00',
    market='CN_STOCK_A',
    instrument_list="""600010.SHA
000001.SZA""",
    max_count=0,
    m_cached=False
)

m7 = M.extract_minute_daily.v3(
    input_1=m2.data,
    features=["daily_close_1=close_1/adjust_factor_1", "adjust_factor=adjust_factor_0"],
    before_days=10
)

m5 = 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
buy_condition=where(close>=ts_max(close,5),1,0)
"""
)

m6 = M.derived_feature_extractor.v3(
    input_data=m7.data,
    features=m5.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions={}
)

m1 = M.dropnan.v2(
    input_data=m6.data
)

m3 = M.trade.v4(
    instruments=m2.data,
    options_data=m1.data,
    history_ds=m7.data,
    start_date='',
    end_date='',
    initialize=m3_initialize_bigquant_run,
    handle_data=m3_handle_data_bigquant_run,
    prepare=m3_prepare_bigquant_run,
    before_trading_start=m3_before_trading_start_bigquant_run,
    volume_limit=0,
    order_price_field_buy='open',
    order_price_field_sell='open',
    capital_base=500001,
    auto_cancel_non_tradable_orders=True,
    data_frequency='minute',
    price_type='真实价格',
    product_type='股票',
    plot_charts=True,
    backtest_only=False,
    benchmark='000300.HIX'
)

[2022-12-15 17:24:52.877190] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-12-15 17:24:52.930655] INFO: moduleinvoker: instruments.v2 运行完成[0.053472s].

[2022-12-15 17:24:56.506468] INFO: moduleinvoker: extract_minute_daily.v3 开始运行..

[2022-12-15 17:24:56.699521] ERROR: moduleinvoker: module name: extract_minute_daily, module version: v3, trackeback: ValueError: All objects passed were None

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-1-9a40dc97b184> in <module>
     66 )
     67 
---> 68 m7 = M.extract_minute_daily.v3(
     69     input_1=m2.data,
     70     features=["daily_close_1=close_1/adjust_factor_1", "adjust_factor=adjust_factor_0"],

ValueError: All objects passed were None

In [7]:

dd = m7.data.read()

In [9]:

dd[dd["date"] == "2020-01-20 09:31:00"]

Out[9]:

	date	instrument	volume	low	close	open	high	daily_close_1	adjust_factor
0	2020-01-20 09:31:00	000001.SZA	1231528	16.42	16.48	16.43	16.51	16.389999	109.169426
4320	2020-01-20 09:31:00	600010.SHA	2878960	1.30	1.30	1.30	1.31	1.300000	8.332732