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回测引擎：初始化函数，只执行一次\ndef bigquant_run(context):\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","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 # 获取今日的日期\n today = data.current_dt.strftime('%Y-%m-%d') \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 = {e.symbol: p.amount * p.last_sale_price\n for e, p in context.perf_tracker.position_tracker.positions.items()}\n \n df = context.options['data'].read_df()\n df = df.loc[df['date'] == today,:]\n \n import random\n num = random.randint(0,len(df)-1)\n if num == 0:\n return\n stock_to_buy = df.iloc[num,:].instrument\n print(f'{today} buy: {stock_to_buy}')\n# breakpoint()\n # 记录用于买入股票的可用现金,因为是早盘卖股票，需要记录卖出的股票市值并在买入下单前更新可用现金；\n # 如果是早盘买尾盘卖，则卖出时不需更新可用现金，因为尾盘卖出股票所得现金无法使用\n cash_for_buy = context.portfolio.cash \n print(f'{today} cash {cash_for_buy}')\n try:\n buy_stock = stock_to_buy # 当日符合买入条件的股票\n except:\n buy_stock=[] # 如果没有符合条件的股票，就设置为空\n \n # 需要卖出的股票:已有持仓中符合卖出条件的股票\n stock_to_sell = [ i for i in stock_hold_now ]\n # 需要买入的股票:没有持仓且符合买入条件的股票\n# stock_to_buy = [ i for i in buy_stock if i not in stock_hold_now ] \n \n # 如果有卖出信号\n if len(stock_to_sell)>0:\n for instrument in stock_to_sell:\n sid = context.symbol(instrument) # 将标的转化为equity格式\n cur_position = context.portfolio.positions[sid].amount # 持仓\n if cur_position > 0 and data.can_trade(sid):\n context.order_target_percent(sid, 0) # 全部卖出 \n # 因为设置的是早盘卖出早盘买入，需要根据卖出的股票更新可用现金；如果设置尾盘卖出早盘买入，则不需更新可用现金(可以删除下面的语句)\n# cash_for_buy += stock_hold_now[instrument]\n# breakpoint()\n # 如果有买入信号/有持仓\n sid = context.symbol(stock_to_buy) # 将标的转化为equity格式\n if data.can_trade(sid):\n context.order_target_percent(sid, 1) # 买入","type":"Literal","bound_global_parameter":null},{"name":"prepare","value":"","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 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#号开始的表示注释，注释需单独一行\n# 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\nprice_limit_status_0\nclose_0","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-306"}],"output_ports":[{"name":"data","node_id":"-306"}],"cacheable":true,"seq_num":3,"comment":"","comment_collapsed":true},{"node_id":"-311","module_id":"BigQuantSpace.general_feature_extractor.general_feature_extractor-v7","parameters":[{"name":"start_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"before_start_days","value":90,"type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-311"},{"name":"features","node_id":"-311"}],"output_ports":[{"name":"data","node_id":"-311"}],"cacheable":true,"seq_num":5,"comment":"","comment_collapsed":true},{"node_id":"-318","module_id":"BigQuantSpace.filter.filter-v3","parameters":[{"name":"expr","value":"price_limit_status_0 == 3","type":"Literal","bound_global_parameter":null},{"name":"output_left_data","value":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-318"}],"output_ports":[{"name":"data","node_id":"-318"},{"name":"left_data","node_id":"-318"}],"cacheable":true,"seq_num":6,"comment":"","comment_collapsed":true}],"node_layout":"<node_postions><node_position Node='-249' Position='325,524,200,200'/><node_position Node='-278' Position='233,639,200,200'/><node_position Node='-298' Position='179,283,200,200'/><node_position Node='-306' Position='478,278,200,200'/><node_position Node='-311' Position='335,371,200,200'/><node_position Node='-318' Position='326,447,200,200'/></node_postions>"},"nodes_readonly":false,"studio_version":"v2"}

# 本代码由可视化策略环境自动生成 2021年9月5日 17:03
# 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):

    # 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 m2_handle_data_bigquant_run(context, data):
    # 获取今日的日期
    today = data.current_dt.strftime('%Y-%m-%d')  
    # 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 = {e.symbol: p.amount * p.last_sale_price
                 for e, p in context.perf_tracker.position_tracker.positions.items()}
    
    df = context.options['data'].read_df()
    df = df.loc[df['date'] == today,:]
    
    import random
    num = random.randint(0,len(df)-1)
    if num == 0:
        return
    stock_to_buy = df.iloc[num,:].instrument
    print(f'{today} buy: {stock_to_buy}')
#     breakpoint()
    # 记录用于买入股票的可用现金,因为是早盘卖股票，需要记录卖出的股票市值并在买入下单前更新可用现金；
    # 如果是早盘买尾盘卖，则卖出时不需更新可用现金，因为尾盘卖出股票所得现金无法使用
    cash_for_buy = context.portfolio.cash    
    print(f'{today} cash {cash_for_buy}')
    try:
        buy_stock = stock_to_buy  # 当日符合买入条件的股票
    except:
        buy_stock=[]  # 如果没有符合条件的股票，就设置为空
    
    # 需要卖出的股票:已有持仓中符合卖出条件的股票
    stock_to_sell = [ i for i in stock_hold_now ]
    # 需要买入的股票:没有持仓且符合买入条件的股票
#     stock_to_buy = [ i for i in buy_stock if i not in stock_hold_now ]  
    
    # 如果有卖出信号
    if len(stock_to_sell)>0:
        for instrument in stock_to_sell:
            sid = context.symbol(instrument) # 将标的转化为equity格式
            cur_position = context.portfolio.positions[sid].amount # 持仓
            if cur_position > 0 and data.can_trade(sid):
                context.order_target_percent(sid, 0) # 全部卖出 
                # 因为设置的是早盘卖出早盘买入，需要根据卖出的股票更新可用现金；如果设置尾盘卖出早盘买入，则不需更新可用现金(可以删除下面的语句)
#                 cash_for_buy += stock_hold_now[instrument]
#     breakpoint()
    # 如果有买入信号/有持仓
    sid = context.symbol(stock_to_buy) # 将标的转化为equity格式
    if  data.can_trade(sid):
        context.order_target_percent(sid, 1) # 买入
# 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


m1 = M.instruments.v2(
    start_date='2021-09-01',
    end_date='2021-09-03',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m3 = 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
price_limit_status_0
close_0"""
)

m5 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=90
)

m6 = M.filter.v3(
    input_data=m5.data,
    expr='price_limit_status_0 == 3',
    output_left_data=False
)

m4 = M.chinaa_stock_filter.v1(
    input_data=m6.data,
    index_constituent_cond=['全部'],
    board_cond=['上证主板', '深证主板'],
    industry_cond=['全部'],
    st_cond=['正常'],
    delist_cond=['全部'],
    output_left_data=False
)

m2 = M.trade.v4(
    instruments=m1.data,
    options_data=m4.data,
    start_date='',
    end_date='',
    initialize=m2_initialize_bigquant_run,
    handle_data=m2_handle_data_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=30000,
    auto_cancel_non_tradable_orders=True,
    data_frequency='daily',
    price_type='真实价格',
    product_type='股票',
    plot_charts=True,
    backtest_only=False,
    benchmark=''
)

[2021-09-05 16:58:47.292144] INFO: moduleinvoker: instruments.v2 开始运行..

[2021-09-05 16:58:47.299171] INFO: moduleinvoker: 命中缓存

[2021-09-05 16:58:47.300704] INFO: moduleinvoker: instruments.v2 运行完成[0.008564s].

[2021-09-05 16:58:47.304255] INFO: moduleinvoker: input_features.v1 开始运行..

[2021-09-05 16:58:47.309615] INFO: moduleinvoker: 命中缓存

[2021-09-05 16:58:47.310969] INFO: moduleinvoker: input_features.v1 运行完成[0.006715s].

[2021-09-05 16:58:47.324307] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2021-09-05 16:58:47.330519] INFO: moduleinvoker: 命中缓存

[2021-09-05 16:58:47.332455] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.00815s].

[2021-09-05 16:58:47.342322] INFO: moduleinvoker: filter.v3 开始运行..

[2021-09-05 16:58:47.350243] INFO: moduleinvoker: 命中缓存

[2021-09-05 16:58:47.352136] INFO: moduleinvoker: filter.v3 运行完成[0.009807s].

[2021-09-05 16:58:47.360630] INFO: moduleinvoker: chinaa_stock_filter.v1 开始运行..

[2021-09-05 16:58:48.470389] INFO: A股股票过滤: 过滤 /y_2021, 3666/0/5840

[2021-09-05 16:58:48.473140] INFO: A股股票过滤: 过滤完成, 3666 + 0

[2021-09-05 16:58:48.486166] INFO: moduleinvoker: chinaa_stock_filter.v1 运行完成[1.125523s].

[2021-09-05 16:58:48.542497] INFO: moduleinvoker: backtest.v8 开始运行..

[2021-09-05 16:58:48.546645] INFO: backtest: biglearning backtest:V8.5.0

[2021-09-05 16:58:48.547927] INFO: backtest: product_type:stock by specified

[2021-09-05 16:58:48.928078] INFO: moduleinvoker: cached.v2 开始运行..

[2021-09-05 16:58:48.933802] INFO: moduleinvoker: 命中缓存

[2021-09-05 16:58:48.935276] INFO: moduleinvoker: cached.v2 运行完成[0.007209s].

[2021-09-05 16:58:53.282902] INFO: algo: TradingAlgorithm V1.8.5

[2021-09-05 16:58:54.256359] INFO: algo: trading transform...

[2021-09-05 16:58:54.645269] INFO: Performance: Simulated 3 trading days out of 3.

[2021-09-05 16:58:54.647296] INFO: Performance: first open: 2021-09-01 09:30:00+00:00

[2021-09-05 16:58:54.648680] INFO: Performance: last close: 2021-09-03 15:00:00+00:00

[2021-09-05 16:59:00.181171] INFO: moduleinvoker: backtest.v8 运行完成[11.638664s].

[2021-09-05 16:59:00.184508] INFO: moduleinvoker: trade.v4 运行完成[11.69162s].

2021-09-01 buy: 000681.SZA
2021-09-01 cash 30000.0
2021-09-02 buy: 002665.SZA
2021-09-02 cash 1051.3155543635548
2021-09-03 buy: 000931.SZA
2021-09-03 cash 217.38322465434612

bigcharts-data-start/{"__type":"tabs","__id":"bigchart-25daf9191edc4b0a963195c0479df6c8"}/bigcharts-data-end