Boll指标研究 -- 知识铺

<span><span>def</span> <span>get_boll</span><span>(security, start_date=None, end_date=None, time_count=<span>10</span>)</span>:</span>
    hs_data=get_price(security,start_date=start_date,end_date=end_date,frequency=<span>'daily'</span>,fields=<span>None</span>,skip_paused=<span>False</span>,fq=<span>'pre'</span>)
    stock_data = get_price(securirty, end_date= end_date, frequency= <span>'daily'</span>, fields= <span>'close'</span>, skip_paused= <span>True</span>, fq= <span>'pre'</span>, count= (len(hs_data[<span>'close'</span>])+time_count<span>-1</span>))
    upper, middle, lower = talib.BBANDS(stock_data[<span>'close'</span>].values, timeperiod=time_count,nbdevup=<span>2</span>,nbdevdn=<span>2</span>,matype=<span>0</span>)
    stock_data = pd.DataFrame({<span>'upper'</span>: upper, <span>'middle'</span>: middle, <span>'lower'</span>: lower}, index= stock_data.index)
    stock_data = stock_data.dropna()
    stock_data[<span>'close'</span>] = hs_data[<span>'close'</span>]
    <span>return</span> stock_data

<span><span>def</span> <span>show_boll</span><span>(stock_data)</span>:</span>
    plt.figure(figsize=(<span>20</span>, <span>5</span>))
    plt.grid()
    plt.plot(stock_data[<span>'upper'</span>], <span>'-'</span>, color=<span>'r'</span>)
    plt.plot(stock_data[<span>'lower'</span>], <span>'-'</span>, color=<span>'r'</span>)
    plt.plot(stock_data[<span>'middle'</span>], <span>'-.'</span>, color=<span>'b'</span>)
    plt.show()

Boll指标研究

布林线指标，即BOLL指标，其英文全称是“Bollinger Bands”，布林线(BOLL)由约翰·布林先生创造，其利用统计原理，一般而言，股价的运动总是围绕某一价值中枢（如均线、成本线等）在一定的范围内变动，布林线指标正是在上述条件的基础上，引进了“股价信道”的概念，其认为股价信道的宽窄随着股价波动幅度的大小而变化，而且股价信道又具有变异性，它会随着股价的变化而自动调整，求出股价的标准差及其信赖区间，从而确定股价的波动范围及未来走势，利用波带显示股价的安全高低价位。

原理：

根据统计学中的标准差原理设计，股价的波动利用标准差方式来表达，配合均线做为中位线，从而变成了偏离情况的写照。

公式：

一、日BOLL指标的计算公式:

中轨线=N日的移动平均线
上轨线=中轨线+两倍的N日标准差
下轨线=中轨线－两倍的N日标准差

二、日BOLL指标的计算过程:

(1)、计算MA

MA=N日内的收盘价之和÷N

(2)、计算标准差MD

MD=平方根N日的（C－MA）的两次方之和除以N

(3)、计算MB、UP、DN线、K倍

MB=（N－1）日的MA
UP=MB+K×MD
DN=MB-K×MD

正常使用方法：

当股价穿越上限压力线时，卖点信号；
当股价穿越下限支撑线时，买点信号；

目的：

寻找最优中轨参数：N
寻找最优搭配参数：K

导入需要模块

<span>"""导入常用模块"""</span>
<span>import</span> numpy <span>as</span> np
<span>import</span> pandas <span>as</span> pd
<span>import</span> matplotlib.pyplot <span>as</span> plt
<span>import</span> datetime
<span>import</span> talib
<span>from</span> jqdata <span>import</span> *
<span>from</span> jqlib.technical_analysis <span>import</span> *
<span>from</span> environment <span>import</span> * <span># 导入大树工作室开发的回测模块</span>

沪深300价量展示Boll

<span>import</span> seaborn <span>as</span> sns
sns.set(font=<span>'serif'</span>)
securirty = <span>'000300.XSHG'</span>
Boll_data = []
HS_data = []
trade_date = get_trade_days(start_date= <span>'2018-9-01'</span>, end_date= <span>'2019-03-01'</span>)
time_count=<span>20</span>
HS_data = get_price(security=<span>'000300.XSHG'</span>, 
                  start_date=<span>'2018-9-01'</span>, 
                  end_date=<span>'2019-03-01'</span>, 
                  frequency=<span>'daily'</span>, 
                  fields=<span>None</span>, 
                  skip_paused=<span>False</span>, 
                  fq=<span>'pre'</span>)

stock_data = get_price(securirty, end_date= <span>'2019-03-01'</span> , frequency= <span>'daily'</span>, fields= <span>'close'</span>, skip_paused= <span>True</span>, fq= <span>'pre'</span>, count= (len(HS_data[<span>'close'</span>])+time_count<span>-1</span>))
upper, middle, lower = talib.BBANDS(stock_data[<span>'close'</span>].values, timeperiod=time_count,nbdevup=<span>2</span>,nbdevdn=<span>2</span>,matype=<span>0</span>)
stock_data = pd.DataFrame({<span>'upper'</span>: upper, <span>'middle'</span>: middle, <span>'lower'</span>: lower}, index= stock_data.index)
stock_data = stock_data.dropna()
stock_data[<span>'close'</span>] = HS_data[<span>'close'</span>]

plt.figure(figsize=(<span>20</span>, <span>5</span>))
plt.grid()
plt.plot(stock_data[<span>'close'</span>], <span>'-'</span>, color=<span>'g'</span>)
plt.plot(stock_data[<span>'upper'</span>], <span>'-'</span>, color=<span>'r'</span>)
plt.plot(stock_data[<span>'lower'</span>], <span>'-'</span>, color=<span>'r'</span>)
plt.plot(stock_data[<span>'middle'</span>], <span>'-.'</span>, color=<span>'b'</span>)
plt.title(<span>'沪深300指数与Boll通道图'</span>)
plt.legend([<span>'指数价格'</span>, <span>'Boll上轨'</span>, <span>'Boll中轨'</span>, <span>'Boll下轨'</span>], loc=<span>2</span>)
plt.show()

上图展示沪深300指数与其Boll通道表现，图例的通道以及价格走势，明显可知，这是一种类价值回归，也与波动性有一定挂钩。对期货不陌生的投资者应该最为了解，因为在CTA部分策略中，最常使用这类的指标。综上所述，我们本次内容，就深入了解Boll指标，做进行布林带探索的[稀饭?哥伦布魏]！

Boll指标择时效应研究

Boll指标用法可谓五花八门，因为三线合一，不同的搭配有不同的口味，而且三线又可各自组合，使得使用起来更加多变。仔细思考，向上突破下轨，向上突破上轨，站于中轨，二次站于中轨等等，只要不脱轨，就很不错了。

本次研究的脉络为：

择时交易择优参-常规择优
择时交易择优参-轨道分化择优
择时交易择优参-轨道综合

择时交易择优参

[稀饭?哥伦布魏]以简单方式带大家分享Boll。择时交易，方式非常容易理解，就是对Boll参数进行筛选，筛选的参数有三类，中轨均线参数，上下轨标准差倍数。

常规择优

展示常规情况下的中轨配置以及上下轨标准差2倍数，本次的稀饭会比较多清汤，但是会加点肉，因为最近猪瘟还是蛮严重的，所以严选最差猪肉！

回测标的：沪深300指数。
回测时间：2010年01月01日到2018年12月31日。
初始资金：100000元。
不考虑对冲成本，尽量投入全部资金。
代码展示：

<span>"""初始化以下内容"""</span>
context = Context() <span># 账户对象</span>
order = Order(context) <span># 下单对象</span>
trade = Trade(context, order) <span># 回测对旬</span>
context.start_date = <span>'2010-01-01'</span>
context.end_date = <span>'2018-12-31'</span>
context.universe = [<span>'000300.XSHG'</span>]
context.base = <span>'000300.XSHG'</span>

<span>"""策略主体"""</span>
<span><span>def</span> <span>handle</span><span>(context, order)</span>:</span>
    stock = context.universe[<span>0</span>]
    time_count = <span>20</span>
    n = <span>2</span>
    m = <span>2</span>
    HS_da = get_price(security=stock, 
                      end_date= context.current_dt,
                      frequency= <span>'daily'</span>, 
                      fields= <span>None</span>, 
                      skip_paused= <span>False</span>, 
                      fq=<span>'pre'</span>,
                      count= <span>50</span>)
    stock_data = get_price(stock, end_date= context.current_dt , frequency= <span>'daily'</span>, fields= <span>'close'</span>, skip_paused= <span>True</span>, fq= <span>'pre'</span>, count= (len(HS_da[<span>'close'</span>])+time_count<span>-1</span>))
    upper, middle, lower = talib.BBANDS(stock_data[<span>'close'</span>].values, timeperiod=time_count,nbdevup=n,nbdevdn=m,matype=<span>0</span>)
    <span>if</span> lower[<span>-2</span>] &gt; HS_da[<span>'close'</span>][<span>-2</span>] <span>and</span> lower[<span>-1</span>] &lt; HS_da[<span>'close'</span>][<span>-1</span>]:
        <span>if</span> stock <span>in</span> context.position.keys():
            <span>return</span>
        order.buy(stock, HS_da[<span>'close'</span>][<span>-1</span>], context.cash // HS_da[<span>'close'</span>][<span>-1</span>])
    <span>elif</span> lower[<span>-1</span>] &gt; HS_da[<span>'close'</span>][<span>-1</span>] <span>or</span> upper[<span>-1</span>] &lt; HS_da[<span>'close'</span>][<span>-1</span>]:
        <span>if</span> stock <span>not</span> <span>in</span> context.position.keys():
            <span>return</span>
        order.sell(stock, HS_da[<span>'close'</span>][<span>-1</span>], context.position[stock][<span>'count'</span>])

<span>"""执行策略"""</span>
trade.trade(handle)

从正常角度看，整个数据表现得不是很理想，猪肉的质量有点差，猪毛（回撤：0.4754）占面积略大，而且猪肉可食用部位（策略收益：-0.4332）也比较少。咱们还是用多参数来下锅吧。

回测标的：沪深300指数。
回测时间：2010年01月01日到2018年12月31日。
初始资金：100000元。
不考虑对冲成本，尽量投入全部资金。
Boll参数：[5， 10， 15， 20， 25， 30， 35， 40]
代码展示：

trade_list = []
Boll_list = [<span>10</span>, <span>15</span>, <span>20</span>, <span>25</span>, <span>30</span>, <span>35</span>, <span>40</span>, <span>45</span>, <span>50</span>]
<span>for</span> _Boll <span>in</span> Boll_list:
    <span># 策略结构</span>
    context = Context()
    order = Order(context)
    trade = Trade(context, order)
    context.start_date = <span>'2010-01-01'</span>
    context.end_date = <span>'2018-12-31'</span>
    context.universe = [<span>'000300.XSHG'</span>]
    context.base = <span>'000300.XSHG'</span>

    <span>"""策略主体"""</span>
    <span><span>def</span> <span>handle</span><span>(context, order)</span>:</span>
        stock = context.universe[<span>0</span>]
        time_count = _Boll
        n = <span>2</span>
        m = <span>2</span>
        HS_da = get_price(security=stock, 
                          end_date= context.current_dt,
                          frequency= <span>'daily'</span>, 
                          fields= <span>None</span>, 
                          skip_paused= <span>False</span>, 
                          fq=<span>'pre'</span>,
                          count= <span>50</span>)
        stock_data = get_price(stock, end_date= context.current_dt , frequency= <span>'daily'</span>, fields= <span>'close'</span>, skip_paused= <span>True</span>, fq= <span>'pre'</span>, count= (len(HS_da[<span>'close'</span>])+time_count<span>-1</span>))
        upper, middle, lower = talib.BBANDS(stock_data[<span>'close'</span>].values, timeperiod=time_count,nbdevup=n,nbdevdn=m,matype=<span>0</span>)
        <span>if</span> lower[<span>-2</span>] &gt; HS_da[<span>'close'</span>][<span>-2</span>] <span>and</span> lower[<span>-1</span>] &lt; HS_da[<span>'close'</span>][<span>-1</span>]:
            <span>if</span> stock <span>in</span> context.position.keys():
                <span>return</span>
            order.buy(stock, HS_da[<span>'close'</span>][<span>-1</span>], context.cash // HS_da[<span>'close'</span>][<span>-1</span>])
        <span>elif</span> lower[<span>-1</span>] &gt; HS_da[<span>'close'</span>][<span>-1</span>] <span>or</span> upper[<span>-1</span>] &lt; HS_da[<span>'close'</span>][<span>-1</span>]:
            <span>if</span> stock <span>not</span> <span>in</span> context.position.keys():
                <span>return</span>
            order.sell(stock, HS_da[<span>'close'</span>][<span>-1</span>], context.position[stock][<span>'count'</span>])
    trade.trade(handle, <span>False</span>)
    trade_list.append(trade)
<span># 展示</span>
Trade.show_ratio_compare(<span>'Boll'</span>, Boll_list, trade_list, <span>3</span>, <span>3</span>)

虽然是猪肉，但是还是有好位置的，现在数据表出参数10，整个参数中唯一一个整收益的，还是蛮吃惊的，怎么会出现这样情况呢？因为Boll是类于波动率，进出场都和波动息息相关，只有波动和股价恰到好处，才是最合适的刷单或是价差空间，可谓一物降一物的感觉。

轨道分化择优

中轨参数已经得到相对于其他数据比较优秀的参数，接着就是上下轨的设置，烤猪肉要木叉子，基本两根为主，能够固定方向，把控肉质。吃个稀饭，还烤肉，也是不容易啊。

轨道分化择优-上轨

回测标的：沪深300指数。
回测时间：2010年01月01日到2018年12月31日。
初始资金：100000元。
不考虑对冲成本，尽量投入全部资金。
Boll参数：[5， 10， 15， 20， 25， 30， 35， 40]
n参数：[0.5, 1, 1.5, 2]
代码展示：

_list = []
trade_list = []
Boll_list = [<span>10</span>, <span>15</span>, <span>20</span>, <span>25</span>, <span>30</span>, <span>35</span>, <span>40</span>, <span>45</span>, <span>50</span>]
Boll_list_n = [<span>0.5</span>, <span>1</span>, <span>1.5</span>, <span>2</span>]
<span>for</span> _Boll <span>in</span> Boll_list:
    <span>for</span> n_Boll <span>in</span> Boll_list_n:
        <span># 策略结构</span>
        context = Context()
        order = Order(context)
        trade = Trade(context, order)
        context.start_date = <span>'2010-01-01'</span>
        context.end_date = <span>'2018-12-31'</span>
        context.universe = [<span>'000300.XSHG'</span>]
        context.base = <span>'000300.XSHG'</span>
        _list.append(str(_Boll)+<span>','</span>+str(n_Boll))

        <span>"""策略主体"""</span>
        <span><span>def</span> <span>handle</span><span>(context, order)</span>:</span>
            stock = context.universe[<span>0</span>]
            time_count = _Boll
            n = n_Boll
            m = <span>2</span>
            HS_da = get_price(security=stock, 
                              end_date= context.current_dt,
                              frequency= <span>'daily'</span>, 
                              fields= <span>None</span>, 
                              skip_paused= <span>False</span>, 
                              fq=<span>'pre'</span>,
                              count= <span>50</span>)
            stock_data = get_price(stock, end_date= context.current_dt , frequency= <span>'daily'</span>, fields= <span>'close'</span>, skip_paused= <span>True</span>, fq= <span>'pre'</span>, count= (len(HS_da[<span>'close'</span>])+time_count<span>-1</span>))
            upper, middle, lower = talib.BBANDS(stock_data[<span>'close'</span>].values, timeperiod=time_count,nbdevup=n,nbdevdn=m,matype=<span>0</span>)
            <span>if</span> lower[<span>-2</span>] &gt; HS_da[<span>'close'</span>][<span>-2</span>] <span>and</span> lower[<span>-1</span>] &lt; HS_da[<span>'close'</span>][<span>-1</span>]:
                <span>if</span> stock <span>in</span> context.position.keys():
                    <span>return</span>
                order.buy(stock, HS_da[<span>'close'</span>][<span>-1</span>], context.cash // HS_da[<span>'close'</span>][<span>-1</span>])
            <span>elif</span> lower[<span>-1</span>] &gt; HS_da[<span>'close'</span>][<span>-1</span>] <span>or</span> upper[<span>-1</span>] &lt; HS_da[<span>'close'</span>][<span>-1</span>]:
                <span>if</span> stock <span>not</span> <span>in</span> context.position.keys():
                    <span>return</span>
                order.sell(stock, HS_da[<span>'close'</span>][<span>-1</span>], context.position[stock][<span>'count'</span>])
        trade.trade(handle, <span>False</span>)
        trade_list.append(trade)
<span># 展示</span>
Trade.show_all_ratio(<span>'Boll'</span>, Boll_list, trade_list)

数据表给予的信息蛮有非巧合性的，最优的Boll参数依旧为10。最优上轨标准差倍数为0.5，该参数的各项数据对比的排名基本为首位，但是回撤问题一直围绕着，得思考思考，就要考虑下轨问题。不过还是让[稀饭?哥伦布魏]有点尴尬而不失礼节的买了该配置的叉子。

轨道分化择优-下轨

回测标的：沪深300指数。
回测时间：2010年01月01日到2018年12月31日。
初始资金：100000元。
不考虑对冲成本，尽量投入全部资金。
Boll参数：[5， 10， 15， 20， 25， 30， 35， 40]
m参数：[0.5, 1, 1.5, 2]
代码展示：

_list = []
trade_list = []
Boll_list = [<span>10</span>, <span>15</span>, <span>20</span>, <span>25</span>, <span>30</span>, <span>35</span>, <span>40</span>, <span>45</span>, <span>50</span>]
Boll_list_m = [<span>0.5</span>, <span>1</span>, <span>1.5</span>, <span>2</span>]
<span>for</span> _Boll <span>in</span> Boll_list:
    <span>for</span> m_Boll <span>in</span> Boll_list_m:
        <span># 策略结构</span>
        context = Context()
        order = Order(context)
        trade = Trade(context, order)
        context.start_date = <span>'2010-01-01'</span>
        context.end_date = <span>'2018-12-31'</span>
        context.universe = [<span>'000300.XSHG'</span>]
        context.base = <span>'000300.XSHG'</span>
        _list.append(str(_Boll)+<span>','</span>+str(m_Boll))

        <span>"""策略主体"""</span>
        <span><span>def</span> <span>handle</span><span>(context, order)</span>:</span>
            stock = context.universe[<span>0</span>]
            time_count = _Boll
            n = <span>2</span>
            m = m_Boll
            HS_da = get_price(security=stock, 
                              end_date= context.current_dt,
                              frequency= <span>'daily'</span>, 
                              fields= <span>None</span>, 
                              skip_paused= <span>False</span>, 
                              fq=<span>'pre'</span>,
                              count= <span>50</span>)
            stock_data = get_price(stock, end_date= context.current_dt , frequency= <span>'daily'</span>, fields= <span>'close'</span>, skip_paused= <span>True</span>, fq= <span>'pre'</span>, count= (len(HS_da[<span>'close'</span>])+time_count<span>-1</span>))
            upper, middle, lower = talib.BBANDS(stock_data[<span>'close'</span>].values, timeperiod=time_count,nbdevup=n,nbdevdn=m,matype=<span>0</span>)
            <span>if</span> lower[<span>-2</span>] &gt; HS_da[<span>'close'</span>][<span>-2</span>] <span>and</span> lower[<span>-1</span>] &lt; HS_da[<span>'close'</span>][<span>-1</span>]:
                <span>if</span> stock <span>in</span> context.position.keys():
                    <span>return</span>
                order.buy(stock, HS_da[<span>'close'</span>][<span>-1</span>], context.cash // HS_da[<span>'close'</span>][<span>-1</span>])
            <span>elif</span> lower[<span>-1</span>] &gt; HS_da[<span>'close'</span>][<span>-1</span>] <span>or</span> upper[<span>-1</span>] &lt; HS_da[<span>'close'</span>][<span>-1</span>]:
                <span>if</span> stock <span>not</span> <span>in</span> context.position.keys():
                    <span>return</span>
                order.sell(stock, HS_da[<span>'close'</span>][<span>-1</span>], context.position[stock][<span>'count'</span>])
        trade.trade(handle, <span>False</span>)
        trade_list.append(trade)
<span># 展示</span>
Trade.show_all_ratio(<span>'Boll'</span>, Boll_list, trade_list)

[稀饭?哥伦布魏]尴尬一笑，依旧是如此的场景，单一参数，策略正收益，最优下轨参数为2，差评地方为回撤貌似加大许多。

轨道综合

上节内容，应该明白发生什么事情，猪肉还是要好好买好好烤。这节内容，会比较严肃介绍。因为是风险厌恶择优的分析，以个人的风险厌恶程度来分析，做比较。

回测标的：沪深300指数。
回测时间：2010年01月01日到2018年12月31日。
初始资金：100000元。
不考虑对冲成本，尽量投入全部资金。
Boll参数：[5， 10， 15， 20， 25， 30， 35， 40]
n参数：[0.5, 1, 1.5, 2]
m参数：[0.5, 1, 1.5, 2]
代码展示：

_list = []
trade_list = []
Boll_list = [<span>10</span>, <span>15</span>, <span>20</span>, <span>25</span>, <span>30</span>, <span>35</span>, <span>40</span>, <span>45</span>, <span>50</span>]
Boll_list_n = [<span>0.5</span>, <span>1</span>, <span>1.5</span>, <span>2</span>]
Boll_list_m = [<span>0.5</span>, <span>1</span>, <span>1.5</span>, <span>2</span>]
<span>for</span> _Boll <span>in</span> Boll_list:
    <span>for</span> m_Boll <span>in</span> Boll_list_m:
        <span>for</span> n_Boll <span>in</span> Boll_list_n:
            <span># 策略结构</span>
            context = Context()
            order = Order(context)
            trade = Trade(context, order)
            context.start_date = <span>'2010-01-01'</span>
            context.end_date = <span>'2018-12-31'</span>
            context.universe = [<span>'000300.XSHG'</span>]
            context.base = <span>'000300.XSHG'</span>
            _list.append(str(_Boll)+<span>','</span>+str([n_Boll,m_Boll]))

            <span>"""策略主体"""</span>
            <span><span>def</span> <span>handle</span><span>(context, order)</span>:</span>
                stock = context.universe[<span>0</span>]
                time_count = _Boll
                n = n_Boll
                m = m_Boll
                HS_da = get_price(security=stock, 
                                  end_date= context.current_dt,
                                  frequency= <span>'daily'</span>, 
                                  fields= <span>None</span>, 
                                  skip_paused= <span>False</span>, 
                                  fq=<span>'pre'</span>,
                                  count= <span>50</span>)
                stock_data = get_price(stock, end_date= context.current_dt , frequency= <span>'daily'</span>, fields= <span>'close'</span>, skip_paused= <span>True</span>, fq= <span>'pre'</span>, count= (len(HS_da[<span>'close'</span>])+time_count<span>-1</span>))
                upper, middle, lower = talib.BBANDS(stock_data[<span>'close'</span>].values, timeperiod=time_count,nbdevup=n,nbdevdn=m,matype=<span>0</span>)
                <span>if</span> lower[<span>-2</span>] &gt; HS_da[<span>'close'</span>][<span>-2</span>] <span>and</span> lower[<span>-1</span>] &lt; HS_da[<span>'close'</span>][<span>-1</span>]:
                    <span>if</span> stock <span>in</span> context.position.keys():
                        <span>return</span>
                    order.buy(stock, HS_da[<span>'close'</span>][<span>-1</span>], context.cash // HS_da[<span>'close'</span>][<span>-1</span>])
                <span>elif</span> lower[<span>-1</span>] &gt; HS_da[<span>'close'</span>][<span>-1</span>] <span>or</span> upper[<span>-1</span>] &lt; HS_da[<span>'close'</span>][<span>-1</span>]:
                    <span>if</span> stock <span>not</span> <span>in</span> context.position.keys():
                        <span>return</span>
                    order.sell(stock, HS_da[<span>'close'</span>][<span>-1</span>], context.position[stock][<span>'count'</span>])
            trade.trade(handle, <span>False</span>)
            trade_list.append(trade)
<span># 展示</span>
Trade.show_all_ratio(<span>'Boll'</span>, Boll_list, trade_list)

排头数据列表给予的信息比较明确，最优Boll参数为10，最优上轨标准差倍数为0.5，最优下轨标准差倍数为[0.5, 2],为何上轨标准差倍数是单个，而最优下轨标准差倍数是两个。从看收益来解释，正常的投资，是想保稳定收益，出场可以说是最简单也是最难的，因为心态和欲望控制、风险厌恶程度，因此上轨选择最稳定，而且小空间上轨等于中轨，有点价值回归的味道。
下轨为何会两个，这就关系到风险程度以及市场情绪变化、质变，下轨0.5与2是四倍的关系，从回撤角度，2的回撤几乎快是0.5的一倍，胜率提高了0.1，但是收益下降了一倍多，这就是进场风险选择的关键，总所周知，对于股价，每个人都希望走得稳定，但是却不能如愿，一旦股价走向极端，恰恰是引起质变效果，市场的情绪变化导致我们的心理变化，一波冲击波的情绪量瞬间扩大动能，然而使股价回归的另一波动能却不一定能使得股价回归，因为承接力以及大盘环境因素、股价自身情况。
Boll的运用，可以从多方面去切入，既然是中位线与价格的标准差，可以尝试使用标准差为重心去思考，如何使得中位线与偏离程度能更合适搭配我们的策略，配合量能去做搭配。总的一句话：波动之间最稳定的是平稳，而且不能急躁，慢工出细活。

总结

Boll是以波动率和价值回归为基础，数据告诉我们，谨慎是最安全的，以敏感的Boll参数10配合稳定小空间上下轨标准差倍数参数0.5，实际运用中，参数0.5的运用可能受限，但是可以在更小的周期里面运用。
市场的变化，是情绪得变化，多重变化，会引起质变，极端市场的行情，在Boll中是一种机会，但是也是一种风险，因为股票的性质会市场对待的情绪改变了。

下面展示本次研究效果最好的参数

回测标的：沪深300指数。
回测时间：2010年01月01日到2018年12月31日。
初始资金：100000元。
不考虑对冲成本，尽量投入全部资金。
代码展示：

<span>"""初始化以下内容"""</span>
context = Context() <span># 账户对象</span>
order = Order(context) <span># 下单对象</span>
trade = Trade(context, order) <span># 回测对旬</span>
context.start_date = <span>'2010-01-01'</span>
context.end_date = <span>'2018-12-31'</span>
context.universe = [<span>'000300.XSHG'</span>]
context.base = <span>'000300.XSHG'</span>

<span>"""策略主体"""</span>
<span><span>def</span> <span>handle</span><span>(context, order)</span>:</span>
    stock = context.universe[<span>0</span>]
    time_count = <span>10</span>
    n = <span>0.5</span>
    m = <span>0.5</span>
    HS_da = get_price(security=stock, 
                      end_date= context.current_dt,
                      frequency= <span>'daily'</span>, 
                      fields= <span>None</span>, 
                      skip_paused= <span>False</span>, 
                      fq=<span>'pre'</span>,
                      count= <span>50</span>)
    stock_data = get_price(stock, end_date= context.current_dt , frequency= <span>'daily'</span>, fields= <span>'close'</span>, skip_paused= <span>True</span>, fq= <span>'pre'</span>, count= (len(HS_da[<span>'close'</span>])+time_count<span>-1</span>))
    upper, middle, lower = talib.BBANDS(stock_data[<span>'close'</span>].values, timeperiod=time_count,nbdevup=n,nbdevdn=m,matype=<span>0</span>)
    <span>if</span> lower[<span>-2</span>] &gt; HS_da[<span>'close'</span>][<span>-2</span>] <span>and</span> lower[<span>-1</span>] &lt; HS_da[<span>'close'</span>][<span>-1</span>]:
        <span>if</span> stock <span>in</span> context.position.keys():
            <span>return</span>
        order.buy(stock, HS_da[<span>'close'</span>][<span>-1</span>], context.cash // HS_da[<span>'close'</span>][<span>-1</span>])
    <span>elif</span> lower[<span>-1</span>] &gt; HS_da[<span>'close'</span>][<span>-1</span>] <span>or</span> upper[<span>-1</span>] &lt; HS_da[<span>'close'</span>][<span>-1</span>]:
        <span>if</span> stock <span>not</span> <span>in</span> context.position.keys():
            <span>return</span>
        order.sell(stock, HS_da[<span>'close'</span>][<span>-1</span>], context.position[stock][<span>'count'</span>])

<span>"""执行策略"""</span>
trade.trade(handle)

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