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当前交易日期\n all_data = context.all_data[context.all_data.date == today]\n all_data = pd.DataFrame(all_data)\n if len(all_data) == 0:#过滤没有指标的数据\n return\n \n price = all_data['close'].iloc[0]\n high_line = all_data['bbands_up'].iloc[0]\n low_line = all_data['bbands_low'].iloc[0]\n instrument = context.future_symbol(context.instruments[0]) # 交易标的\n long_position = context.get_account_position(instrument, direction=Direction.LONG).avail_qty#多头持仓\n short_position = context.get_account_position(instrument, direction=Direction.SHORT).avail_qty#空头持仓\n curr_position = short_position + long_position#总持仓\n if short_position > 0:\n if price > high_line:\n context.buy_close(instrument, short_position, price, order_type=OrderType.MARKET)\n context.buy_open(instrument, 4, price, order_type=OrderType.MARKET)\n print(today,'先平空再开多')\n elif long_position > 0:\n if price < low_line:\n context.sell_close(instrument, long_position, price, order_type=OrderType.MARKET)\n context.sell_open(instrument, 4, price, order_type=OrderType.MARKET)\n print(today,'先平多再开空',curr_position)\n elif curr_position==0:\n if price > high_line:\n context.buy_open(instrument, 4, price, order_type=OrderType.MARKET)\n print('空仓开多')\n elif price<low_line:\n context.sell_open(instrument, 4, price, order_type=OrderType.MARKET)\n print('空仓开空')","type":"Literal","bound_global_parameter":null},{"name":"handle_trade","value":"# 交易引擎：成交回报处理函数，每个成交发生时执行一次\ndef bigquant_run(context, data):\n pass\n","type":"Literal","bound_global_parameter":null},{"name":"handle_order","value":"# 交易引擎：委托回报处理函数，每个委托变化时执行一次\ndef bigquant_run(context, data):\n pass\n","type":"Literal","bound_global_parameter":null},{"name":"after_trading","value":"# 交易引擎：盘后处理函数，每日盘后执行一次\ndef bigquant_run(context, data):\n pass\n","type":"Literal","bound_global_parameter":null},{"name":"capital_base","value":1000000,"type":"Literal","bound_global_parameter":null},{"name":"frequency","value":"daily","type":"Literal","bound_global_parameter":null},{"name":"price_type","value":"真实价格","type":"Literal","bound_global_parameter":null},{"name":"product_type","value":"期货","type":"Literal","bound_global_parameter":null},{"name":"before_start_days","value":"","type":"Literal","bound_global_parameter":null},{"name":"benchmark","value":"000300.HIX","type":"Literal","bound_global_parameter":null},{"name":"plot_charts","value":"True","type":"Literal","bound_global_parameter":null},{"name":"disable_cache","value":"False","type":"Literal","bound_global_parameter":null},{"name":"show_debug_info","value":"False","type":"Literal","bound_global_parameter":null},{"name":"backtest_only","value":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-127"},{"name":"history_ds","node_id":"-127"},{"name":"benchmark_ds","node_id":"-127"},{"name":"options_data","node_id":"-127"}],"output_ports":[{"name":"raw_perf","node_id":"-127"}],"cacheable":false,"seq_num":9,"comment":"","comment_collapsed":true}],"node_layout":"<node_postions><node_position Node='-5' Position='293,113,200,200'/><node_position Node='-13' Position='602,109,200,200'/><node_position Node='-62' Position='420,280,200,200'/><node_position Node='-71' Position='258,194,200,200'/><node_position Node='-97' Position='406,357,200,200'/><node_position Node='-127' Position='350,452,200,200'/></node_postions>"},"nodes_readonly":false,"studio_version":"v2"}

# 本代码由可视化策略环境自动生成 2023年5月24日 18:21
# 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。
 
# 显式导入 BigQuant 相关 SDK 模块
from bigdatasource.api import DataSource
from biglearning.api import M
from biglearning.api import tools as T
from biglearning.module2.common.data import Outputs

# 交易引擎：初始化函数，只执行一次
def m9_initialize_bigquant_run(context):
    context.all_data = context.options["data"].read()
    

# 交易引擎：每个单位时间开盘前调用一次。
def m9_before_trading_start_bigquant_run(context, data):
    pass

# 交易引擎：tick数据处理函数，每个tick执行一次
def m9_handle_tick_bigquant_run(context, data):
    pass

# 交易引擎：bar数据处理函数，每个时间单位执行一次
def m9_handle_data_bigquant_run(context, data):
    import pandas as pd 
    today = data.current_dt.strftime('%Y-%m-%d') # 当前交易日期
    all_data = context.all_data[context.all_data.date == today]
    all_data = pd.DataFrame(all_data)
    if len(all_data) == 0:#过滤没有指标的数据
        return
    
    price = all_data['close'].iloc[0]
    high_line = all_data['bbands_up'].iloc[0]
    low_line = all_data['bbands_low'].iloc[0]
    instrument = context.future_symbol(context.instruments[0]) # 交易标的
    long_position = context.get_account_position(instrument, direction=Direction.LONG).avail_qty#多头持仓
    short_position = context.get_account_position(instrument, direction=Direction.SHORT).avail_qty#空头持仓
    curr_position = short_position + long_position#总持仓
    if short_position > 0:
        if price > high_line:
            context.buy_close(instrument, short_position, price, order_type=OrderType.MARKET)
            context.buy_open(instrument, 4, price, order_type=OrderType.MARKET)
            print(today,'先平空再开多')
    elif long_position > 0:
        if price < low_line:
            context.sell_close(instrument, long_position, price, order_type=OrderType.MARKET)
            context.sell_open(instrument, 4, price, order_type=OrderType.MARKET)
            print(today,'先平多再开空',curr_position)
    elif curr_position==0:
        if price > high_line:
            context.buy_open(instrument, 4, price, order_type=OrderType.MARKET)
            print('空仓开多')
        elif price<low_line:
            context.sell_open(instrument, 4, price, order_type=OrderType.MARKET)
            print('空仓开空')
# 交易引擎：成交回报处理函数，每个成交发生时执行一次
def m9_handle_trade_bigquant_run(context, data):
    pass

# 交易引擎：委托回报处理函数，每个委托变化时执行一次
def m9_handle_order_bigquant_run(context, data):
    pass

# 交易引擎：盘后处理函数，每日盘后执行一次
def m9_after_trading_bigquant_run(context, data):
    pass


m4 = M.instruments.v2(
    start_date='2021-02-17',
    end_date='2021-11-26',
    market='CN_FUTURE',
    instrument_list='JM2201.DCE',
    max_count=0
)

m5 = M.input_features.v1(
    features="""# #号开始的表示注释，注释需单独一行
# 多个特征，每行一个，可以包含基础特征和衍生特征，特征须为本平台特征
bbands_up = ta_bbands_u(close, 20)
bbands = ta_bbands_m(close, 20)
bbands_low = ta_bbands_l(close, 20)"""
)

m7 = M.use_datasource.v1(
    instruments=m4.data,
    features=m5.data,
    datasource_id='bar1d_CN_FUTURE',
    start_date='',
    end_date=''
)

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={}
)

m8 = M.dropnan.v2(
    input_data=m6.data
)

m9 = M.hftrade.v1(
    instruments=m4.data,
    options_data=m8.data,
    start_date='2021-05-14',
    end_date='2021-10-18',
    initialize=m9_initialize_bigquant_run,
    before_trading_start=m9_before_trading_start_bigquant_run,
    handle_tick=m9_handle_tick_bigquant_run,
    handle_data=m9_handle_data_bigquant_run,
    handle_trade=m9_handle_trade_bigquant_run,
    handle_order=m9_handle_order_bigquant_run,
    after_trading=m9_after_trading_bigquant_run,
    capital_base=1000000,
    frequency='daily',
    price_type='真实价格',
    product_type='期货',
    before_start_days='',
    benchmark='000300.HIX',
    plot_charts=True,
    disable_cache=False,
    show_debug_info=False,
    backtest_only=False
)

[2023-05-24 18:13:35.774940] INFO moduleinvoker: instruments.v2 开始运行..

INFO:moduleinvoker:instruments.v2 开始运行..

[2023-05-24 18:13:35.783077] INFO moduleinvoker: 命中缓存

INFO:moduleinvoker:命中缓存

[2023-05-24 18:13:35.786808] INFO moduleinvoker: instruments.v2 运行完成[0.011875s].

INFO:moduleinvoker:instruments.v2 运行完成[0.011875s].

[2023-05-24 18:13:35.794403] INFO moduleinvoker: input_features.v1 开始运行..

INFO:moduleinvoker:input_features.v1 开始运行..

[2023-05-24 18:13:35.802062] INFO moduleinvoker: 命中缓存

INFO:moduleinvoker:命中缓存

[2023-05-24 18:13:35.805765] INFO moduleinvoker: input_features.v1 运行完成[0.011345s].

INFO:moduleinvoker:input_features.v1 运行完成[0.011345s].

[2023-05-24 18:13:35.815227] INFO moduleinvoker: use_datasource.v1 开始运行..

INFO:moduleinvoker:use_datasource.v1 开始运行..

[2023-05-24 18:13:35.823086] INFO moduleinvoker: 命中缓存

INFO:moduleinvoker:命中缓存

[2023-05-24 18:13:35.825880] INFO moduleinvoker: use_datasource.v1 运行完成[0.010658s].

INFO:moduleinvoker:use_datasource.v1 运行完成[0.010658s].

[2023-05-24 18:13:35.837408] INFO moduleinvoker: derived_feature_extractor.v3 开始运行..

INFO:moduleinvoker:derived_feature_extractor.v3 开始运行..

[2023-05-24 18:13:35.846364] INFO moduleinvoker: 命中缓存

INFO:moduleinvoker:命中缓存

[2023-05-24 18:13:35.851578] INFO moduleinvoker: derived_feature_extractor.v3 运行完成[0.014142s].

INFO:moduleinvoker:derived_feature_extractor.v3 运行完成[0.014142s].

[2023-05-24 18:13:35.869063] INFO moduleinvoker: dropnan.v2 开始运行..

INFO:moduleinvoker:dropnan.v2 开始运行..

[2023-05-24 18:13:35.877525] INFO moduleinvoker: 命中缓存

INFO:moduleinvoker:命中缓存

[2023-05-24 18:13:35.881349] INFO moduleinvoker: dropnan.v2 运行完成[0.012295s].

INFO:moduleinvoker:dropnan.v2 运行完成[0.012295s].

[2023-05-24 18:13:35.893332] WARNING moduleinvoker: 此模块版本 M.hftrade.v1 已不再维护。你仍然可以使用，但建议升级到最新版本：请更新到 hftrade 最新版本

WARNING:moduleinvoker:此模块版本 M.hftrade.v1 已不再维护。你仍然可以使用，但建议升级到最新版本：请更新到 hftrade 最新版本

[2023-05-24 18:13:35.917281] INFO moduleinvoker: hfbacktest.v1 开始运行..

INFO:moduleinvoker:hfbacktest.v1 开始运行..

[2023-05-24 18:13:35.924136] INFO hfbacktest: biglearning V1.4.21

INFO:hfbacktest:biglearning V1.4.21

[2023-05-24 18:13:35.928490] INFO hfbacktest: bigtrader v1.10.2 2023-05-18

INFO:hfbacktest:bigtrader v1.10.2 2023-05-18

[2023-05-24 18:13:35.951933] INFO moduleinvoker: cached.v2 开始运行..

INFO:moduleinvoker:cached.v2 开始运行..

[2023-05-24 18:13:35.959159] INFO moduleinvoker: 命中缓存

INFO:moduleinvoker:命中缓存

[2023-05-24 18:13:35.962078] INFO moduleinvoker: cached.v2 运行完成[0.01022s].

INFO:moduleinvoker:cached.v2 运行完成[0.01022s].

[2023-05-24 18:13:36.075972] INFO moduleinvoker: cached.v2 开始运行..

INFO:moduleinvoker:cached.v2 开始运行..

[2023-05-24 18:13:36.084166] INFO moduleinvoker: 命中缓存

INFO:moduleinvoker:命中缓存

[2023-05-24 18:13:36.086814] INFO moduleinvoker: cached.v2 运行完成[0.010881s].

INFO:moduleinvoker:cached.v2 运行完成[0.010881s].

2023-05-24 18:13:36.309464 init history datas... 
2023-05-24 18:13:36.313229 init history datas done. 
2023-05-24 18:13:36.318151 run_backtest() capital_base:1000000, frequency:1d, product_type:future, date:2021-05-14 ~ 2021-10-18 
2023-05-24 18:13:36.321194 run_backtest() running... 
2023-05-24 18:13:36.331095 initial contracts len=0 
2023-05-24 18:13:36.334330 backtest inited. 
2023-05-24 18:13:36.433310 backtest transforming 1d, bars=1... 
2023-05-24 18:13:36.439869 transform start_trading_day=2021-05-14 00:00:00, simulation period=2021-05-14 ~ 2021-10-18 
2023-05-24 18:13:36.443326 transform before_start_days=0 
2023-05-24 18:13:36.446396 transform replay_func=<cyfunction BacktestEngine.transform.<locals>.replay_bars_dt at 0x7f0e9047f380> 
空仓开空
2021-07-16 先平空再开多
2023-05-24 18:13:36.818118 backtest run end! 

/usr/local/python3/lib/python3.8/site-packages/empyrical/stats.py:710: RuntimeWarning: divide by zero encountered in divide
  np.divide(
/usr/local/python3/lib/python3.8/site-packages/empyrical/stats.py:799: RuntimeWarning: invalid value encountered in divide
  np.divide(average_annual_return, annualized_downside_risk, out=out)

2023-05-24 18:13:37.121934 run_backtest() finished! time cost 0.798s! 

/usr/local/python3/lib/python3.8/site-packages/pandas/core/generic.py:2605: PerformanceWarning: 
your performance may suffer as PyTables will pickle object types that it cannot
map directly to c-types [inferred_type->mixed,key->block3_values] [items->Index(['orders', 'transactions', 'positions', 'POS_FAC', 'TRA_FAC', 'LOG'], dtype='object')]

  pytables.to_hdf(

[2023-05-24 18:13:38.481640] INFO hfbacktest: backtest done, raw_perf_ds:DataSource(b582eea494694f818d5d27e5b5aaa540T)

INFO:hfbacktest:backtest done, raw_perf_ds:DataSource(b582eea494694f818d5d27e5b5aaa540T)
/usr/local/python3/lib/python3.8/site-packages/pandas/core/indexing.py:1637: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_block(indexer, value, name)
[2023-05-24 18:13:39.455264] INFO: bigcharts.impl.render:render.py:407:render_chart Data is None, skip loading it to chart.

[2023-05-24 18:13:39.931284] INFO moduleinvoker: hfbacktest.v1 运行完成[4.014003s].

INFO:moduleinvoker:hfbacktest.v1 运行完成[4.014003s].

[2023-05-24 18:13:39.935331] INFO moduleinvoker: hftrade.v1 运行完成[4.038216s].

INFO:moduleinvoker:hftrade.v1 运行完成[4.038216s].