{"description":"实验创建于2021/12/6","graph":{"edges":[{"to_node_id":"-227:instruments","from_node_id":"-406:data"}],"nodes":[{"node_id":"-227","module_id":"BigQuantSpace.hftrade.hftrade-v2","parameters":[{"name":"start_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"initialize","value":"# 交易引擎：初始化函数，只执行一次\ndef bigquant_run(context):\n # 加载预测数据\n print(\"initialize\") \n context.ins = context.instruments[0]#从传入参数中获取需要交易的合约\n context.order_num = 100#下单手数\n context.set_universe(context.ins)#设置需要处理的合约\n context.last_grid = 0 # 储存前一个网格所处区间，用来和最新网格所处区间作比较\n context.set_stock_t1(0)\n context.set_commission(PerOrder(buy_cost=0.0008, sell_cost=0.0008, min_cost=0))\n context.first = 0\n\n \n \n \n\n","type":"Literal","bound_global_parameter":null},{"name":"before_trading_start","value":"# 盘前处理函数：每个天开盘前调用一次\ndef bigquant_run(context, data):\n context.subscribe(context.ins) #注册合约\n # 记录上一次交易时网格范围的变化情况（例如从4区到5区，记为4,5）\n context.grid_change_last = [0,0]\n # 以前一日的收盘价为中枢价格\n context.center = data.history(context.ins,[\"close\"],1,\"1d\").iat[0,0]\n \n #订阅股票\n context.subscribe_bar(context.instruments, '1m')\n ","type":"Literal","bound_global_parameter":null},{"name":"handle_tick","value":"# 交易引擎：tick数据处理函数，每个tick执行一次\ndef bigquant_run(context, data):\n pass\n","type":"Literal","bound_global_parameter":null},{"name":"handle_data","value":"#K线处理函数：每个bar调用一次，该策略1分钟为1bar\ndef bigquant_run(context, data):\n cur_date = data.current_dt\n cur_hm = cur_date.strftime('%H:%M') #time \n \n # 分别获取持仓\n position = context.get_position(context.ins)\n # 获取当前价格\n price = data.current(context.ins, \"close\")\n \n #以50%仓位开仓\n if context.first == 0:\n rv = context.order(context.ins, context.order_num*5, price, order_type=OrderType.MARKET)\n print('开仓50%')\n context.first = 1 \n return\n cash = context.portfolio.cash\n cash_num = context.order_num * price \n # 设置网格和当前价格所处的网格区域\n band = np.arange(0.9, 1.011, 0.02)* context.center\n grid = pd.cut([price], band, labels=np.arange(1,len(band)))[0]\n # 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# 本代码由可视化策略环境自动生成 2023年2月7日 21:13
# 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。


# 交易引擎：初始化函数，只执行一次
def m2_initialize_bigquant_run(context):
    # 加载预测数据
    print("initialize")  
    context.ins = context.instruments[0]#从传入参数中获取需要交易的合约
    context.order_num = 100#下单手数
    context.set_universe(context.ins)#设置需要处理的合约
    context.last_grid = 0 # 储存前一个网格所处区间，用来和最新网格所处区间作比较
    context.set_stock_t1(0)
    context.set_commission(PerOrder(buy_cost=0.0008, sell_cost=0.0008, min_cost=0))
    context.first = 0

    
   
   


# 盘前处理函数：每个天开盘前调用一次
def m2_before_trading_start_bigquant_run(context, data):
    context.subscribe(context.ins) #注册合约
    # 记录上一次交易时网格范围的变化情况（例如从4区到5区，记为4,5）
    context.grid_change_last = [0,0]
    # 以前一日的收盘价为中枢价格
    context.center = data.history(context.ins,["close"],1,"1d").iat[0,0]
    
    #订阅股票
    context.subscribe_bar(context.instruments, '1m')
    
# 交易引擎：tick数据处理函数，每个tick执行一次
def m2_handle_tick_bigquant_run(context, data):
    pass

#K线处理函数：每个bar调用一次，该策略1分钟为1bar
def m2_handle_data_bigquant_run(context, data):
    cur_date =  data.current_dt
    cur_hm = cur_date.strftime('%H:%M') #time  
    
    # 分别获取持仓
    position = context.get_position(context.ins)
    # 获取当前价格
    price = data.current(context.ins, "close")
    
    #以50%仓位开仓
    if context.first == 0:
         rv = context.order(context.ins, context.order_num*5, price, order_type=OrderType.MARKET)
         print('开仓50%')
         context.first = 1 
         return
    cash = context.portfolio.cash
    cash_num = context.order_num * price    
    # 设置网格和当前价格所处的网格区域
    band = np.arange(0.9, 1.011, 0.02)* context.center
    grid = pd.cut([price], band, labels=np.arange(1,len(band)))[0]
    # 如果新的价格所处网格区间和前一个价格所处的网格区间不同，说明触碰到了网格线，需要进行交易
    # 如果新网格大于前一天的网格，做空或平多
    if context.last_grid < grid:
        # 记录新旧格子范围（按照大小排序）
        grid_change_new = [context.last_grid,grid]
        # 几种例外：
        # 当last_grid = 0 时是初始阶段，不构成信号
        if context.last_grid == 0:
            context.last_grid = grid
            return
        if context.last_grid != 0:
            # 如果前一次开仓是4-5，这一次是5-4，算是没有突破，不成交
            if grid_change_new != context.grid_change_last:
                # 更新前一次的数据
                context.last_grid = grid
                context.grid_change_last = grid_change_new
                # 如果有仓位，卖出1份
                if position.current_qty != 0:
                    rv = context.order(context.ins,  context.order_num*(-1), order_type=OrderType.MARKET) 
    # 如果新网格小于前一天的网格，开仓
    if context.last_grid > grid:
        # 记录新旧格子范围（按照大小排序）
        grid_change_new = [grid,context.last_grid]
        # 几种例外：
        # 当last_grid = 0 时是初始阶段，不构成信号
        if context.last_grid == 0:
            context.last_grid = grid
            return
        if context.last_grid != 0:
            # 如果前一次开仓是4-5，这一次是5-4，算是没有突破，不成交
            if grid_change_new != context.grid_change_last:
                # 更新前一次的数据
                context.last_grid = grid
                context.grid_change_last = grid_change_new
                if cash > cash_num+1000:
                    rv = context.order(context.ins, context.order_num, order_type=OrderType.MARKET)
    # 设计一个条件：当持仓量达到10手，卖出部分
    if context.get_position(context.ins).current_qty >= 10*context.order_num:
        rv = context.order(context.ins, context.order_num*(-3), order_type=OrderType.MARKET)
        return
    # 设计一个条件：当持仓量为0，买入部分    
    if(context.get_position(context.ins).current_qty == 0):
        rv = context.order(context.ins, context.order_num*(3), order_type=OrderType.MARKET)
# 交易引擎：成交回报处理函数，每个成交发生时执行一次
def m2_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 m2_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 m2_after_trading_bigquant_run(context, data):
    pass


m3 = M.instruments.v2(
    start_date='2021-01-01',
    end_date='2021-12-31',
    market='CN_STOCK_A',
    instrument_list="""600519.SHA
""",
    max_count=0,
    m_cached=False
)

m2 = M.hftrade.v2(
    instruments=m3.data,
    start_date='',
    end_date='',
    initialize=m2_initialize_bigquant_run,
    before_trading_start=m2_before_trading_start_bigquant_run,
    handle_tick=m2_handle_tick_bigquant_run,
    handle_data=m2_handle_data_bigquant_run,
    handle_trade=m2_handle_trade_bigquant_run,
    handle_order=m2_handle_order_bigquant_run,
    after_trading=m2_after_trading_bigquant_run,
    capital_base=1000000,
    frequency='minute',
    price_type='真实价格',
    product_type='股票',
    before_start_days='0',
    volume_limit=1,
    order_price_field_buy='open',
    order_price_field_sell='close',
    benchmark='002594.SZA',
    plot_charts=True,
    disable_cache=False,
    replay_bdb=False,
    show_debug_info=False,
    backtest_only=False
)

[2023-02-07 21:13:26.424061] INFO: moduleinvoker: instruments.v2 开始运行..

[2023-02-07 21:13:26.474862] INFO: moduleinvoker: instruments.v2 运行完成[0.050806s].

[2023-02-07 21:13:26.523020] INFO: moduleinvoker: hfbacktest.v1 开始运行..

[2023-02-07 21:13:26.531322] INFO: hfbacktest: biglearning V1.4.19

[2023-02-07 21:13:26.534167] INFO: hfbacktest: bigtrader v1.9.12 2023-02-01

[2023-02-07 21:13:26.560816] INFO: moduleinvoker: cached.v2 开始运行..

[2023-02-07 21:13:26.572966] INFO: moduleinvoker: 命中缓存

[2023-02-07 21:13:26.575816] INFO: moduleinvoker: cached.v2 运行完成[0.015009s].

[2023-02-07 21:13:26.658409] INFO: moduleinvoker: cached.v2 开始运行..

[2023-02-07 21:13:26.670451] INFO: moduleinvoker: 命中缓存

[2023-02-07 21:13:26.673526] INFO: moduleinvoker: cached.v2 运行完成[0.015107s].

initialize
开仓50%