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# 本代码由可视化策略环境自动生成 2019年9月4日 14:47
# 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。


# Python 代码入口函数，input_1/2/3 对应三个输入端，data_1/2/3 对应三个输出端
def m2_run_bigquant_run(input_1, start_date,end_date):
    # 示例代码如下。在这里编写您的代码
    ins=input_1.read_pickle()['instruments']
    start_date=input_1.read_pickle()['start_date']
    end_date=input_1.read_pickle()['end_date']
    df=DataSource('bar1d_CN_FUND').read(ins,start_date=start_date,end_date=end_date)
    data_1 = DataSource.write_df(df)
    return Outputs(data_1=data_1, data_2=None, data_3=None)

# 后处理函数，可选。输入是主函数的输出，可以在这里对数据做处理，或者返回更友好的outputs数据格式。此函数输出不会被缓存。
def m2_post_run_bigquant_run(outputs):
    return outputs

# 回测引擎：初始化函数，只执行一次
def m4_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 m4_handle_data_bigquant_run(context, data):
    # 获取今日的日期
    today = data.current_dt.strftime('%Y-%m-%d')  
    data_today=context.data[context.data['date']==today]
    sid = context.symbol(context.instruments[0]) 
    price = data.current(sid, 'price')
    cash = context.portfolio.cash 
    #cur_position = context.portfolio.positions[sid].amount

    short_avg=np.array(data_today['mean(close,5)'])[0]
    long_avg=np.array(data_today['mean(close,10)'])[0]
    
    
    if short_avg>long_avg :
        context.order(sid,int(cash/price/100)*100)
    if short_avg<long_avg :
        context.order_target_percent(sid, 0)
# 回测引擎：准备数据，只执行一次
def m4_prepare_bigquant_run(context):
    # 加载预测数据
    
    df = context.options['data'].read_df()
    context.data=df
    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 m4_before_trading_start_bigquant_run(context, data):
    pass

m3 = 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

mean(close,5)
mean(close,10)
""",
    m_cached=False
)

m1 = M.instruments.v2(
    start_date='2013-01-16',
    end_date='2018-01-16',
    market='CN_FUND',
    instrument_list='510330.HOF',
    max_count=0
)

m2 = M.cached.v3(
    input_1=m1.data,
    run=m2_run_bigquant_run,
    post_run=m2_post_run_bigquant_run,
    input_ports='',
    params='{\'start\':\'2018-08-29\'}',
    output_ports=''
)

m5 = M.derived_feature_extractor.v3(
    input_data=m2.data_1,
    features=m3.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions={}
)

m6 = M.dropnan.v1(
    input_data=m5.data
)

m4 = M.trade.v4(
    instruments=m1.data,
    options_data=m6.data,
    history_ds=m6.data,
    start_date='2013-01-29',
    end_date='',
    initialize=m4_initialize_bigquant_run,
    handle_data=m4_handle_data_bigquant_run,
    prepare=m4_prepare_bigquant_run,
    before_trading_start=m4_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='后复权',
    product_type='股票',
    plot_charts=True,
    backtest_only=False,
    benchmark=''
)

[2019-09-04 11:52:11.439815] INFO: bigquant: input_features.v1 开始运行..

[2019-09-04 11:52:11.545276] INFO: bigquant: input_features.v1 运行完成[0.105445s].

[2019-09-04 11:52:11.557927] INFO: bigquant: instruments.v2 开始运行..

[2019-09-04 11:52:11.601795] INFO: bigquant: 命中缓存

[2019-09-04 11:52:11.605868] INFO: bigquant: instruments.v2 运行完成[0.047918s].

[2019-09-04 11:52:11.611738] INFO: bigquant: cached.v3 开始运行..

[2019-09-04 11:52:11.670001] INFO: bigquant: 命中缓存

[2019-09-04 11:52:11.672877] INFO: bigquant: cached.v3 运行完成[0.061133s].

[2019-09-04 11:52:11.676410] INFO: bigquant: derived_feature_extractor.v3 开始运行..

[2019-09-04 11:52:11.807049] INFO: derived_feature_extractor: 提取完成 mean(close,5), 0.006s

[2019-09-04 11:52:11.820061] INFO: derived_feature_extractor: 提取完成 mean(close,10), 0.010s

[2019-09-04 11:52:11.894950] INFO: derived_feature_extractor: /data, 1218

[2019-09-04 11:52:12.037040] INFO: bigquant: derived_feature_extractor.v3 运行完成[0.360615s].

[2019-09-04 11:52:12.040540] INFO: bigquant: dropnan.v1 开始运行..

[2019-09-04 11:52:12.124737] INFO: dropnan: /data, 1209/1218

[2019-09-04 11:52:12.232249] INFO: dropnan: 行数: 1209/1218

[2019-09-04 11:52:12.237123] INFO: bigquant: dropnan.v1 运行完成[0.196572s].

[2019-09-04 11:52:12.308856] INFO: bigquant: backtest.v8 开始运行..

[2019-09-04 11:52:12.314999] INFO: bigquant: biglearning backtest:V8.2.10

[2019-09-04 11:52:12.391268] INFO: bigquant: product_type:stock by specified

[2019-09-04 11:52:12.931570] INFO: algo: TradingAlgorithm V1.5.7

[2019-09-04 11:52:13.140278] INFO: algo: trading transform...

[2019-09-04 11:52:18.623442] INFO: Performance: Simulated 1209 trading days out of 1209.

[2019-09-04 11:52:18.625440] INFO: Performance: first open: 2013-01-29 09:30:00+00:00

[2019-09-04 11:52:18.626886] INFO: Performance: last close: 2018-01-16 15:00:00+00:00

[2019-09-04 11:52:22.221260] INFO: bigquant: backtest.v8 运行完成[9.912402s].

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