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# 本代码由可视化策略环境自动生成 2019年8月30日 11:30
# 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 m2_initialize_bigquant_run(context):
    # 加载预测数据

    context.pre_act = 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))
    # 预测数据，通过options传入进来，使用 read_df 函数，加载到内存 (DataFrame)
    # 设置买入的股票数量，这里买入预测股票列表排名靠前的5只
    stock_count = 5
    # 每只的股票的权重，如下的权重分配会使得靠前的股票分配多一点的资金，[0.339160, 0.213986, 0.169580, ..]
    context.stock_weights = T.norm([1 / math.log(i + 2) for i in range(0, stock_count)])
    # 设置每只股票占用的最大资金比例
    context.max_cash_per_instrument = 0.2
    context.hold_days = 5

# 回测引擎：每日数据处理函数，每天执行一次
def m2_handle_data_bigquant_run(context, data):
    sid = symbol('000001.SZA')
    
    action = context.pre_act[context.pre_act['date'] == data.current_dt.strftime('%Y-%m-%d')]
    
    # 持仓
    cur_position = context.portfolio.positions[sid].amount 
    
    if len(action['pre_action'])>0:

        if int(action['pre_action'])==0 and  cur_position == 0:
            context.order(sid, 100)
            
        elif int(action['pre_action'])==1 and cur_position > 0:
            context.order_target_percent(sid, 0)

# 回测引擎：准备数据，只执行一次
def m2_prepare_bigquant_run(context):
    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 m2_before_trading_start_bigquant_run(context, data):
    pass


m7 = M.instruments.v2(
    start_date='2012-01-01',
    end_date='2016-01-01',
    market='CN_STOCK_A',
    instrument_list="""000001.SZA
""",
    max_count=0
)

m8 = M.advanced_auto_labeler.v2(
    instruments=m7.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
#   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/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)
""",
    start_date='',
    end_date='',
    benchmark='000300.SHA',
    drop_na_label=True,
    cast_label_int=True
)

m11 = 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
close_0/mean(close_0,5)
close_0/mean(close_0,10)
close_0/mean(close_0,20)
close_0/open_0
open_0/mean(close_0,5)
open_0/mean(close_0,10)
open_0/mean(close_0,20)"""
)

m9 = M.general_feature_extractor.v7(
    instruments=m7.data,
    features=m11.data,
    start_date='',
    end_date='',
    before_start_days=20
)

m10 = M.derived_feature_extractor.v3(
    input_data=m9.data,
    features=m11.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False
)

m12 = M.join.v3(
    data1=m8.data,
    data2=m10.data,
    on='date,instrument',
    how='inner',
    sort=False
)

m14 = M.instruments.v2(
    start_date='2016-01-01',
    end_date='2017-01-01',
    market='CN_STOCK_A',
    instrument_list='000001.SZA',
    max_count=0
)

m19 = M.advanced_auto_labeler.v2(
    instruments=m14.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
#   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/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)
""",
    start_date='',
    end_date='',
    benchmark='000300.SHA',
    drop_na_label=True,
    cast_label_int=True
)

m17 = 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
close_0/mean(close_0,5)
close_0/mean(close_0,10)
close_0/mean(close_0,20)
close_0/open_0
open_0/mean(close_0,5)
open_0/mean(close_0,10)
open_0/mean(close_0,20)
"""
)

m15 = M.general_feature_extractor.v7(
    instruments=m14.data,
    features=m17.data,
    start_date='',
    end_date='',
    before_start_days=20
)

m16 = M.derived_feature_extractor.v3(
    input_data=m15.data,
    features=m17.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False
)

m18 = M.join.v3(
    data1=m19.data,
    data2=m16.data,
    on='date,instrument',
    how='inner',
    sort=False
)

m4 = M.dropnan.v1(
    input_data=m18.data
)

m1 = M.DQN_model.v9(
    data=m12.data,
    data_2=m4.data
)

m3 = M.dropnan.v1(
    input_data=m1.data_1
)

m2 = M.trade.v4(
    instruments=m14.data,
    options_data=m3.data,
    start_date='',
    end_date='',
    initialize=m2_initialize_bigquant_run,
    handle_data=m2_handle_data_bigquant_run,
    prepare=m2_prepare_bigquant_run,
    before_trading_start=m2_before_trading_start_bigquant_run,
    volume_limit=0.025,
    order_price_field_buy='open',
    order_price_field_sell='close',
    capital_base=100000,
    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=''
)

[2019-08-30 11:18:06.170934] INFO: bigquant: instruments.v2 开始运行..

[2019-08-30 11:18:06.456847] INFO: bigquant: 命中缓存

[2019-08-30 11:18:06.459654] INFO: bigquant: instruments.v2 运行完成[0.288705s].

[2019-08-30 11:18:06.463816] INFO: bigquant: advanced_auto_labeler.v2 开始运行..

[2019-08-30 11:18:06.517928] INFO: bigquant: 命中缓存

[2019-08-30 11:18:06.520944] INFO: bigquant: advanced_auto_labeler.v2 运行完成[0.057121s].

[2019-08-30 11:18:06.524487] INFO: bigquant: input_features.v1 开始运行..

[2019-08-30 11:18:06.597905] INFO: bigquant: 命中缓存

[2019-08-30 11:18:06.600660] INFO: bigquant: input_features.v1 运行完成[0.076172s].

[2019-08-30 11:18:06.724005] INFO: bigquant: general_feature_extractor.v7 开始运行..

[2019-08-30 11:18:06.806521] INFO: bigquant: 命中缓存

[2019-08-30 11:18:06.809217] INFO: bigquant: general_feature_extractor.v7 运行完成[0.085216s].

[2019-08-30 11:18:06.817397] INFO: bigquant: derived_feature_extractor.v3 开始运行..

[2019-08-30 11:18:06.892479] INFO: bigquant: 命中缓存

[2019-08-30 11:18:06.895033] INFO: bigquant: derived_feature_extractor.v3 运行完成[0.077634s].

[2019-08-30 11:18:06.898660] INFO: bigquant: join.v3 开始运行..

[2019-08-30 11:18:06.943670] INFO: bigquant: 命中缓存

[2019-08-30 11:18:06.946880] INFO: bigquant: join.v3 运行完成[0.048203s].

[2019-08-30 11:18:06.950578] INFO: bigquant: instruments.v2 开始运行..

[2019-08-30 11:18:07.005675] INFO: bigquant: 命中缓存

[2019-08-30 11:18:07.008215] INFO: bigquant: instruments.v2 运行完成[0.057612s].

[2019-08-30 11:18:07.012450] INFO: bigquant: advanced_auto_labeler.v2 开始运行..

[2019-08-30 11:18:07.058793] INFO: bigquant: 命中缓存

[2019-08-30 11:18:07.061828] INFO: bigquant: advanced_auto_labeler.v2 运行完成[0.049367s].

[2019-08-30 11:18:07.065372] INFO: bigquant: input_features.v1 开始运行..

[2019-08-30 11:18:07.105596] INFO: bigquant: 命中缓存

[2019-08-30 11:18:07.108753] INFO: bigquant: input_features.v1 运行完成[0.043406s].

[2019-08-30 11:18:07.204450] INFO: bigquant: general_feature_extractor.v7 开始运行..

[2019-08-30 11:18:07.277911] INFO: bigquant: 命中缓存

[2019-08-30 11:18:07.280705] INFO: bigquant: general_feature_extractor.v7 运行完成[0.076286s].

[2019-08-30 11:18:07.284510] INFO: bigquant: derived_feature_extractor.v3 开始运行..

[2019-08-30 11:18:07.334442] INFO: bigquant: 命中缓存

[2019-08-30 11:18:07.337187] INFO: bigquant: derived_feature_extractor.v3 运行完成[0.052663s].

[2019-08-30 11:18:07.340825] INFO: bigquant: join.v3 开始运行..

[2019-08-30 11:18:07.389722] INFO: bigquant: 命中缓存

[2019-08-30 11:18:07.392047] INFO: bigquant: join.v3 运行完成[0.051211s].

[2019-08-30 11:18:07.395109] INFO: bigquant: dropnan.v1 开始运行..

[2019-08-30 11:18:07.448686] INFO: bigquant: 命中缓存

[2019-08-30 11:18:07.451032] INFO: bigquant: dropnan.v1 运行完成[0.055911s].

[2019-08-30 11:18:07.459746] INFO: bigquant: DQN_model.v9 开始运行..

[2019-08-30 11:29:52.591182] INFO: bigquant: DQN_model.v9 运行完成[705.131402s].

[2019-08-30 11:29:52.598011] INFO: bigquant: dropnan.v1 开始运行..

[2019-08-30 11:29:52.671448] INFO: dropnan: /data, 233/233

[2019-08-30 11:29:52.731526] INFO: dropnan: 行数: 233/233

[2019-08-30 11:29:52.736153] INFO: bigquant: dropnan.v1 运行完成[0.138139s].

[2019-08-30 11:29:52.778982] INFO: bigquant: backtest.v8 开始运行..

[2019-08-30 11:29:52.782852] INFO: bigquant: biglearning backtest:V8.2.9

[2019-08-30 11:29:52.785020] INFO: bigquant: product_type:stock by specified

[2019-08-30 11:29:52.996823] INFO: bigquant: cached.v2 开始运行..

[2019-08-30 11:29:53.039523] INFO: bigquant: 命中缓存

[2019-08-30 11:29:53.041626] INFO: bigquant: cached.v2 运行完成[0.044796s].

[2019-08-30 11:29:53.081966] INFO: algo: TradingAlgorithm V1.5.6

[2019-08-30 11:29:53.194131] INFO: algo: trading transform...

[2019-08-30 11:29:53.971798] INFO: Performance: Simulated 244 trading days out of 244.

[2019-08-30 11:29:53.973595] INFO: Performance: first open: 2016-01-04 09:30:00+00:00

[2019-08-30 11:29:53.975675] INFO: Performance: last close: 2016-12-30 15:00:00+00:00

[2019-08-30 11:29:55.428566] INFO: bigquant: backtest.v8 运行完成[2.649577s].

episode: 0
total_reward: 181854.07299804688
episode: 1
total_reward: 284361.70330810547
episode: 2
total_reward: 286217.8956298828
episode: 3
total_reward: 286217.8956298828
episode: 4
total_reward: 286217.8956298828

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