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加载股票指标数据,数据继承自m6模块\n context.indicator_data = context.options['data'].read_df()\n\n # 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\n context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))\n \n # 设置股票数量\n context.stock_num = 30\n \n # 调仓天数,22个交易日大概就是一个月。可以理解为一个月换仓一次\n context.rebalance_days = 22\n \n # 如果策略运行中，需要将数据进行保存，可以借用extension这个对象，类型为dict\n # 比如当前运行的k线的索引，比如个股持仓天数、买入均价\n if 'index' not in context.extension:\n context.extension['index'] = 0\n \n \n","type":"Literal","bound_global_parameter":null},{"name":"handle_data","value":"# 回测引擎：每日数据处理函数，每天执行一次\ndef bigquant_run(context, data):\n \n \n context.extension['index'] += 1\n # 不在换仓日就return,相当于后面的代码只会一个月运行一次，买入的股票会持有1个月\n if context.extension['index'] % context.rebalance_days != 0:\n return \n \n # 当前的日期\n date = data.current_dt.strftime('%Y-%m-%d')\n \n cur_data = context.indicator_data[context.indicator_data['date'] == date]\n # 根据日期获取调仓需要买入的股票的列表\n stock_to_buy = list(cur_data.instrument[:context.stock_num])\n # 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\n stock_hold_now = [equity.symbol for equity in context.portfolio.positions]\n # 继续持有的股票：调仓时，如果买入的股票已经存在于目前的持仓里，那么应继续持有\n no_need_to_sell = [i for i in stock_hold_now if i in stock_to_buy]\n # 需要卖出的股票\n stock_to_sell = [i for i in stock_hold_now if i not in no_need_to_sell]\n \n # 卖出\n for stock in stock_to_sell:\n # 如果该股票停牌，则没法成交。因此需要用can_trade方法检查下该股票的状态\n # 如果返回真值，则可以正常下单，否则会出错\n # 因为stock是字符串格式，我们用symbol方法将其转化成平台可以接受的形式：Equity格式\n\n if data.can_trade(context.symbol(stock)):\n # order_target_percent是平台的一个下单接口，表明下单使得该股票的权重为0，\n # 即卖出全部股票，可参考回测文档\n context.order_target_percent(context.symbol(stock), 0)\n \n # 如果当天没有买入的股票，就返回\n if len(stock_to_buy) == 0:\n return\n\n # 等权重买入 \n weight = 1 / len(stock_to_buy)\n \n # 买入\n for stock in stock_to_buy:\n if data.can_trade(context.symbol(stock)):\n # 下单使得某只股票的持仓权重达到weight，因为\n # weight大于0,因此是等权重买入\n context.order_target_percent(context.symbol(stock), weight)\n ","type":"Literal","bound_global_parameter":null},{"name":"prepare","value":"# 回测引擎：准备数据，只执行一次\ndef bigquant_run(context):\n pass\n","type":"Literal","bound_global_parameter":null},{"name":"before_trading_start","value":"# 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。\ndef bigquant_run(context, data):\n pass\n","type":"Literal","bound_global_parameter":null},{"name":"volume_limit","value":0.025,"type":"Literal","bound_global_parameter":null},{"name":"order_price_field_buy","value":"open","type":"Literal","bound_global_parameter":null},{"name":"order_price_field_sell","value":"open","type":"Literal","bound_global_parameter":null},{"name":"capital_base","value":1000000,"type":"Literal","bound_global_parameter":null},{"name":"auto_cancel_non_tradable_orders","value":"True","type":"Literal","bound_global_parameter":null},{"name":"data_frequency","value":"daily","type":"Literal","bound_global_parameter":null},{"name":"price_type","value":"后复权","type":"Literal","bound_global_parameter":null},{"name":"product_type","value":"股票","type":"Literal","bound_global_parameter":null},{"name":"plot_charts","value":"True","type":"Literal","bound_global_parameter":null},{"name":"backtest_only","value":"False","type":"Literal","bound_global_parameter":null},{"name":"benchmark","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-41"},{"name":"options_data","node_id":"-41"},{"name":"history_ds","node_id":"-41"},{"name":"benchmark_ds","node_id":"-41"},{"name":"trading_calendar","node_id":"-41"}],"output_ports":[{"name":"raw_perf","node_id":"-41"}],"cacheable":false,"seq_num":6,"comment":"回测","comment_collapsed":true}],"node_layout":"<node_postions><node_position Node='-6' Position='45,51,200,200'/><node_position Node='-14' Position='450,48,200,200'/><node_position Node='-17' Position='231,182,200,200'/><node_position Node='-24' Position='241,281,200,200'/><node_position Node='-32' Position='238,372,200,200'/><node_position Node='-41' Position='210,463,200,200'/></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 17:27
# 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

# 回测引擎：初始化函数，只执行一次
def m6_initialize_bigquant_run(context):
    from zipline.finance.commission import PerOrder
    # 加载股票指标数据,数据继承自m6模块
    context.indicator_data = 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))
    
    # 设置股票数量
    context.stock_num = 30
    
    # 调仓天数,22个交易日大概就是一个月。可以理解为一个月换仓一次
    context.rebalance_days = 22
    
    # 如果策略运行中，需要将数据进行保存，可以借用extension这个对象，类型为dict
    # 比如当前运行的k线的索引，比如个股持仓天数、买入均价
    if 'index' not in context.extension:
        context.extension['index'] = 0
 
    

# 回测引擎：每日数据处理函数，每天执行一次
def m6_handle_data_bigquant_run(context, data):
    
    
    context.extension['index'] += 1
    # 不在换仓日就return,相当于后面的代码只会一个月运行一次，买入的股票会持有1个月
    if  context.extension['index'] % context.rebalance_days != 0:
        return 
    
    # 当前的日期
    date = data.current_dt.strftime('%Y-%m-%d')
    
    cur_data = context.indicator_data[context.indicator_data['date'] == date]
    # 根据日期获取调仓需要买入的股票的列表
    stock_to_buy = list(cur_data.instrument[:context.stock_num])
    # 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 = [equity.symbol for equity in context.portfolio.positions]
    # 继续持有的股票：调仓时，如果买入的股票已经存在于目前的持仓里，那么应继续持有
    no_need_to_sell = [i for i in stock_hold_now if i in stock_to_buy]
    # 需要卖出的股票
    stock_to_sell = [i for i in stock_hold_now if i not in no_need_to_sell]
  
    # 卖出
    for stock in stock_to_sell:
        # 如果该股票停牌，则没法成交。因此需要用can_trade方法检查下该股票的状态
        # 如果返回真值，则可以正常下单，否则会出错
        # 因为stock是字符串格式，我们用symbol方法将其转化成平台可以接受的形式：Equity格式

        if data.can_trade(context.symbol(stock)):
            # order_target_percent是平台的一个下单接口，表明下单使得该股票的权重为0，
            #   即卖出全部股票，可参考回测文档
            context.order_target_percent(context.symbol(stock), 0)
    
    # 如果当天没有买入的股票，就返回
    if len(stock_to_buy) == 0:
        return

    # 等权重买入 
    weight =  1 / len(stock_to_buy)
    
    # 买入
    for stock in stock_to_buy:
        if data.can_trade(context.symbol(stock)):
            # 下单使得某只股票的持仓权重达到weight，因为
            # weight大于0,因此是等权重买入
            context.order_target_percent(context.symbol(stock), weight)
 
# 回测引擎：准备数据，只执行一次
def m6_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 m6_before_trading_start_bigquant_run(context, data):
    pass


m1 = M.instruments.v2(
    start_date='2015-01-01',
    end_date='2018-11-01',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m2 = M.input_features.v1(
    features="""market_cap_float_0
amount_0"""
)

m3 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m2.data,
    start_date='',
    end_date='',
    before_start_days=90
)

m4 = M.sort.v5(
    input_ds=m3.data,
    sort_by='market_cap_float_0',
    group_by='instrument',
    keep_columns='--',
    ascending=True
)

m5 = M.filter.v3(
    input_data=m4.sorted_data,
    expr='amount_0 > 10000',
    output_left_data=False
)

m6 = M.trade.v4(
    instruments=m1.data,
    options_data=m5.data,
    start_date='',
    end_date='',
    initialize=m6_initialize_bigquant_run,
    handle_data=m6_handle_data_bigquant_run,
    prepare=m6_prepare_bigquant_run,
    before_trading_start=m6_before_trading_start_bigquant_run,
    volume_limit=0.025,
    order_price_field_buy='open',
    order_price_field_sell='open',
    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=''
)

[2023-05-24 17:27:53.679074] INFO moduleinvoker: instruments.v2 开始运行..

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

[2023-05-24 17:27:54.010044] INFO moduleinvoker: instruments.v2 运行完成[0.331038s].

INFO:moduleinvoker:instruments.v2 运行完成[0.331038s].

[2023-05-24 17:27:54.019959] INFO moduleinvoker: input_features.v1 开始运行..

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

[2023-05-24 17:27:54.027414] INFO moduleinvoker: 命中缓存

INFO:moduleinvoker:命中缓存

[2023-05-24 17:27:54.030312] INFO moduleinvoker: input_features.v1 运行完成[0.010387s].

INFO:moduleinvoker:input_features.v1 运行完成[0.010387s].

[2023-05-24 17:27:54.064918] INFO moduleinvoker: general_feature_extractor.v7 开始运行..

INFO:moduleinvoker:general_feature_extractor.v7 开始运行..

[2023-05-24 17:27:54.757303] INFO 基础特征抽取: 年份 2014, 特征行数=141569

INFO:基础特征抽取:年份 2014, 特征行数=141569

[2023-05-24 17:27:55.468782] INFO 基础特征抽取: 年份 2015, 特征行数=569698

INFO:基础特征抽取:年份 2015, 特征行数=569698

[2023-05-24 17:27:56.214688] INFO 基础特征抽取: 年份 2016, 特征行数=641546

INFO:基础特征抽取:年份 2016, 特征行数=641546

[2023-05-24 17:27:57.044757] INFO 基础特征抽取: 年份 2017, 特征行数=743233

INFO:基础特征抽取:年份 2017, 特征行数=743233

[2023-05-24 17:27:57.710133] INFO 基础特征抽取: 年份 2018, 特征行数=672348

INFO:基础特征抽取:年份 2018, 特征行数=672348

[2023-05-24 17:27:57.815551] INFO 基础特征抽取: 总行数: 2768394

INFO:基础特征抽取:总行数: 2768394

[2023-05-24 17:27:57.826940] INFO moduleinvoker: general_feature_extractor.v7 运行完成[3.762037s].

INFO:moduleinvoker:general_feature_extractor.v7 运行完成[3.762037s].

[2023-05-24 17:27:57.843588] INFO moduleinvoker: sort.v5 开始运行..

INFO:moduleinvoker:sort.v5 开始运行..

[2023-05-24 17:28:07.514004] INFO moduleinvoker: sort.v5 运行完成[9.670394s].

INFO:moduleinvoker:sort.v5 运行完成[9.670394s].

[2023-05-24 17:28:07.532198] INFO moduleinvoker: filter.v3 开始运行..

INFO:moduleinvoker:filter.v3 开始运行..

[2023-05-24 17:28:07.549278] INFO filter: 使用表达式 amount_0 > 10000 过滤

INFO:filter:使用表达式 amount_0 > 10000 过滤

[2023-05-24 17:28:11.977530] INFO filter: 过滤 /data, 2768343/0/2768394

INFO:filter:过滤 /data, 2768343/0/2768394

[2023-05-24 17:28:12.013380] INFO moduleinvoker: filter.v3 运行完成[4.481152s].

INFO:moduleinvoker:filter.v3 运行完成[4.481152s].

[2023-05-24 17:28:12.092951] INFO moduleinvoker: backtest.v8 开始运行..

INFO:moduleinvoker:backtest.v8 开始运行..

[2023-05-24 17:28:12.098740] INFO backtest: biglearning backtest:V8.6.3

INFO:backtest:biglearning backtest:V8.6.3

[2023-05-24 17:28:12.101384] INFO backtest: product_type:stock by specified

INFO:backtest:product_type:stock by specified

[2023-05-24 17:28:12.203448] INFO moduleinvoker: cached.v2 开始运行..

INFO:moduleinvoker:cached.v2 开始运行..

[2023-05-24 17:28:31.924733] INFO backtest: 读取股票行情完成:4079789

INFO:backtest:读取股票行情完成:4079789
/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(['instrument', 'suspended', 'name'], dtype='object')]

  pytables.to_hdf(

[2023-05-24 17:28:34.887773] INFO moduleinvoker: cached.v2 运行完成[22.684263s].

INFO:moduleinvoker:cached.v2 运行完成[22.684263s].

[2023-05-24 17:28:55.278900] INFO backtest: algo history_data=DataSource(bbed37c95e234720b8060373e3eee835T)

INFO:backtest:algo history_data=DataSource(bbed37c95e234720b8060373e3eee835T)

[2023-05-24 17:28:55.285280] INFO algo: TradingAlgorithm V1.8.9

INFO:algo:TradingAlgorithm V1.8.9

[2023-05-24 17:29:09.951617] INFO algo: trading transform...

INFO:algo:trading transform...
/usr/local/python3/lib/python3.8/site-packages/empyrical/stats.py:710: RuntimeWarning: divide by zero encountered in divide
  np.divide(
/var/app/enabled/bigline/zipline/data/us_equity_pricing.py:990: FutureWarning: Indexing a timezone-naive DatetimeIndex with a timezone-aware datetime is deprecated and will raise KeyError in a future version.  Use a timezone-naive object instead.
  return df.loc[dt, field]
/usr/local/python3/lib/python3.8/site-packages/pandas/core/indexing.py:1124: FutureWarning: Indexing a timezone-naive DatetimeIndex with a timezone-aware datetime is deprecated and will raise KeyError in a future version.  Use a timezone-naive object instead.
  return self._get_label(key, axis=axis)

[2023-05-24 17:29:13.151669] WARNING Performance: maybe_close_position no price for asset:Equity(1593 [000024.SZA]), field:price, dt:2015-12-30 15:00:00+00:00

WARNING:Performance:maybe_close_position no price for asset:Equity(1593 [000024.SZA]), field:price, dt:2015-12-30 15:00:00+00:00

[2023-05-24 17:29:18.309195] INFO Performance: Simulated 934 trading days out of 934.

INFO:Performance:Simulated 934 trading days out of 934.

[2023-05-24 17:29:18.312387] INFO Performance: first open: 2015-01-05 09:30:00+00:00

INFO:Performance:first open: 2015-01-05 09:30:00+00:00

[2023-05-24 17:29:18.315226] INFO Performance: last close: 2018-11-01 15:00:00+00:00

INFO:Performance:last close: 2018-11-01 15:00:00+00:00
/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->block5_values] [items->Index(['positions', 'transactions', 'orders', 'LOG', 'TRA_FAC', 'POS_FAC',
       'period_label'],
      dtype='object')]

  pytables.to_hdf(
/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->block2_values] [items->Index(['instrument', 'suspended', 'name'], dtype='object')]

  pytables.to_hdf(
/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 17:29:30.431793] INFO: bigcharts.impl.render:render.py:407:render_chart Data is None, skip loading it to chart.

[2023-05-24 17:29:31.128297] INFO moduleinvoker: backtest.v8 运行完成[79.035337s].

INFO:moduleinvoker:backtest.v8 运行完成[79.035337s].

[2023-05-24 17:29:31.132140] INFO moduleinvoker: trade.v4 运行完成[79.103531s].

INFO:moduleinvoker:trade.v4 运行完成[79.103531s].