卡夫曼自适应移动均线-分钟¶

交易规则¶

我们最终的卡夫曼自适应移动均线AMA如下定义：AMAt=AMAt−1+c⋅(p–AMAt−1)
过滤器被定义为AMA变化的一个小的百分数：filter=percentage⋅std(AMAt−AMAt−1,n)其中，std(series, n)是滑动标准差函数。

策略构建步骤¶

确定股票池和回测时间¶

通过证券代码列表输入回测的起止日期

确定买卖原则¶

当AMA - min(AMA, n) > filter，买入；
当max(AMA, n) – AMA > filter，卖出。

回测¶

通过 trade 模块中的初始化函数定义交易手续费和滑点；
通过 trade 模块中的主函数(handle函数)查看每日的买卖交易信号，按照买卖原则执行相应的买入/卖出/调仓操作。

策略详情¶

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lots, price, order_type=order_type_) \n elif position_long.avail_qty < lots:\n context.buy_open(instrument, lots - position_long.avail_qty, price, order_type=order_type_)\n elif position_short.avail_qty > 0:\n context.buy_close(instrument, position_short.avail_qty, price, order_type=order_type_)\n context.buy_open(instrument, lots, price, order_type=order_type_)\n elif (position_long.avail_qty + position_short.avail_qty) == 0:\n context.buy_open(instrument, lots, price, order_type=order_type_)\n elif lots < 0:\n if position_long.avail_qty > 0:\n context.sell_close(instrument, position_long.avail_qty, price, order_type=order_type_)\n context.sell_open(instrument, abs(lots), price, order_type=order_type_)\n elif position_short.avail_qty > 0:\n if position_short.avail_qty > abs(lots):\n context.buy_close(instrument, position_short.avail_qty - abs(lots) , price, order_type=order_type_)\n elif position_short.avail_qty < abs(lots):\n context.sell_open(instrument, abs(lots) - position_short.avail_qty, price, order_type=order_type_)\n elif (position_long.avail_qty + position_short.avail_qty) == 0:\n context.sell_open(instrument, abs(lots), price, order_type=order_type_) \n elif lots == 0:\n if(position_long.current_qty != 0):\n context.sell_close(instrument, position_long.avail_qty, price, order_type=order_type_)\n\n if(position_short.current_qty != 0):\n context.buy_close(instrument, position_short.avail_qty, price, order_type=order_type_)\n# 交易引擎：bar数据处理函数，每个时间单位执行一次\ndef bigquant_run(context, data):\n import talib as ta\n today = data.current_dt#.strftime('%Y-%m-%d-%H-%M')\n if context.index < context.window: # 在形成均线后才开始交易\n context.index+=1\n return\n if today.hour > 15:\n return\n instrument = context.future_symbol(context.instruments[0]) # 交易标的\n curr_position = context.portfolio.positions[instrument].amount # 持仓数量\n prices = data.history(instrument, 'close', context.window, '1m') # 历史价格\n\n if np.any(np.isnan(prices)):\n return\n n=40\n kama = ta.KAMA(prices, timeperiod=n)\n ft = 0.8*np.std((kama-kama.shift(1))[-n:])\n \n if kama[-1] - 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In [4]:

# 本代码由可视化策略环境自动生成 2021年12月10日 15:38
# 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
     # 设置手续费，买入时万3，卖出是千分之1.3,不足5元以5元计
    context.set_commission(futures_commission=PerContract(cost={'RB':(0.000045, 0.000045, 0.000045)}))
    context.set_margin('RB', 0.07)
    context.index = 1              #日期记录
    context.window = 60
# 交易引擎：每个单位时间开盘前调用一次。
def m2_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 m2_handle_tick_bigquant_run(context, data):
    pass

#平仓
def close_orders(context,data):
    #获取当前时间
    cur_date = data.current_dt.strftime('%Y-%m-%d')

    for ins in context.instruments:
        # 分别获取多头持仓和空头持仓
        position_long = context.get_position(ins, Direction.LONG)
        position_short = context.get_position(ins, Direction.SHORT)
        price = data.current(ins,"close")
        if(position_long.current_qty != 0):
            context.sell_close(ins, position_long.avail_qty, price, order_type=OrderType.MARKET)

        if(position_short.current_qty != 0):
            context.buy_close(ins, position_short.avail_qty, price, order_type=OrderType.MARKET)
             
def order_target(context,data,instrument,lots,price,order_type_ = OrderType.MARKET):
    #获取多空持仓情况
    position_long = context.get_position(instrument, Direction.LONG)
    position_short = context.get_position(instrument, Direction.SHORT)
    #          ------------------------------------------------------
    if lots > 0:
        if position_long.avail_qty > 0:
            if position_long.avail_qty  > lots:
                context.sell_close(instrument, position_long.avail_qty - lots, price, order_type=order_type_)            
            elif position_long.avail_qty  < lots:
                context.buy_open(instrument, lots - position_long.avail_qty, price, order_type=order_type_)
        elif position_short.avail_qty  > 0:
            context.buy_close(instrument, position_short.avail_qty, price, order_type=order_type_)
            context.buy_open(instrument, lots, price, order_type=order_type_)
        elif (position_long.avail_qty + position_short.avail_qty) == 0:
            context.buy_open(instrument, lots, price, order_type=order_type_)
    elif lots < 0:
        if position_long.avail_qty > 0:
            context.sell_close(instrument, position_long.avail_qty, price, order_type=order_type_)
            context.sell_open(instrument, abs(lots), price, order_type=order_type_)
        elif position_short.avail_qty  > 0:
            if position_short.avail_qty  > abs(lots):
                context.buy_close(instrument, position_short.avail_qty - abs(lots) , price, order_type=order_type_)
            elif position_short.avail_qty  < abs(lots):
                context.sell_open(instrument, abs(lots) - position_short.avail_qty, price, order_type=order_type_)
        elif (position_long.avail_qty + position_short.avail_qty) == 0:
            context.sell_open(instrument, abs(lots), price, order_type=order_type_) 
    elif lots == 0:
        if(position_long.current_qty != 0):
            context.sell_close(instrument, position_long.avail_qty, price, order_type=order_type_)

        if(position_short.current_qty != 0):
            context.buy_close(instrument, position_short.avail_qty, price, order_type=order_type_)
# 交易引擎：bar数据处理函数，每个时间单位执行一次
def m2_handle_data_bigquant_run(context, data):
    import talib as ta
    today = data.current_dt#.strftime('%Y-%m-%d-%H-%M')
    if context.index < context.window: # 在形成均线后才开始交易
        context.index+=1
        return
    if today.hour > 15:
        return
    instrument = context.future_symbol(context.instruments[0]) # 交易标的
    curr_position = context.portfolio.positions[instrument].amount # 持仓数量
    prices = data.history(instrument, 'close', context.window, '1m') # 历史价格

    if np.any(np.isnan(prices)):
        return
    n=40
    kama = ta.KAMA(prices, timeperiod=n)
    ft = 0.8*np.std((kama-kama.shift(1))[-n:])
    
    if kama[-1] - np.min(kama[-n:]) > ft:
        order_target(context,data,instrument = context.instruments[0] ,lots = 2,price = prices[-1],order_type_ = OrderType.MARKET)
    if np.max(kama[-n:]) - kama[-1] > ft:
        order_target(context,data,instrument = context.instruments[0],lots = -2,price = prices[-1],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


m1 = M.instruments.v2(
    start_date='2018-04-20',
    end_date='2018-05-30',
    market='CN_FUTURE',
    instrument_list='RB1901.SHF',
    max_count=0
)

m2 = M.hftrade.v2(
    instruments=m1.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='期货',
    before_start_days='0',
    benchmark='000300.HIX',
    plot_charts=True,
    disable_cache=False,
    show_debug_info=False,
    backtest_only=False
)

[2021-12-10 15:36:34.735893] INFO: moduleinvoker: instruments.v2 开始运行..

[2021-12-10 15:36:34.747149] INFO: moduleinvoker: 命中缓存

[2021-12-10 15:36:34.748851] INFO: moduleinvoker: instruments.v2 运行完成[0.012971s].

[2021-12-10 15:36:34.777238] INFO: moduleinvoker: hfbacktest.v1 开始运行..

[2021-12-10 15:36:34.798234] INFO: moduleinvoker: 命中缓存

[2021-12-10 15:36:35.841055] INFO: moduleinvoker: hfbacktest.v1 运行完成[1.063833s].

[2021-12-10 15:36:35.843640] INFO: moduleinvoker: hftrade.v2 运行完成[1.087883s].

收益率-1.41%
年化收益率-12.4%
基准收益率-1.0%
阿尔法-0.15
贝塔0.01
夏普比率-17.15
胜率0.18
盈亏比0.96
收益波动率0.94%
最大回撤1.41%

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