Python talib.MA Examples

def technical_indicators_df(self, daily_data):
        """
        Assemble a dataframe of technical indicator series for a single stock
        """
        o = daily_data['Open'].values
        c = daily_data['Close'].values
        h = daily_data['High'].values
        l = daily_data['Low'].values
        v = daily_data['Volume'].astype(float).values
        # define the technical analysis matrix

        # Most data series are normalized by their series' mean
        ta = pd.DataFrame()
        ta['MA5'] = tb.MA(c, timeperiod=5) / tb.MA(c, timeperiod=5).mean()
        ta['MA10'] = tb.MA(c, timeperiod=10) / tb.MA(c, timeperiod=10).mean()
        ta['MA20'] = tb.MA(c, timeperiod=20) / tb.MA(c, timeperiod=20).mean()
        ta['MA60'] = tb.MA(c, timeperiod=60) / tb.MA(c, timeperiod=60).mean()
        ta['MA120'] = tb.MA(c, timeperiod=120) / tb.MA(c, timeperiod=120).mean()
        ta['MA5'] = tb.MA(v, timeperiod=5) / tb.MA(v, timeperiod=5).mean()
        ta['MA10'] = tb.MA(v, timeperiod=10) / tb.MA(v, timeperiod=10).mean()
        ta['MA20'] = tb.MA(v, timeperiod=20) / tb.MA(v, timeperiod=20).mean()
        ta['ADX'] = tb.ADX(h, l, c, timeperiod=14) / tb.ADX(h, l, c, timeperiod=14).mean()
        ta['ADXR'] = tb.ADXR(h, l, c, timeperiod=14) / tb.ADXR(h, l, c, timeperiod=14).mean()
        ta['MACD'] = tb.MACD(c, fastperiod=12, slowperiod=26, signalperiod=9)[0] / \
                     tb.MACD(c, fastperiod=12, slowperiod=26, signalperiod=9)[0].mean()
        ta['RSI'] = tb.RSI(c, timeperiod=14) / tb.RSI(c, timeperiod=14).mean()
        ta['BBANDS_U'] = tb.BBANDS(c, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)[0] / \
                         tb.BBANDS(c, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)[0].mean()
        ta['BBANDS_M'] = tb.BBANDS(c, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)[1] / \
                         tb.BBANDS(c, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)[1].mean()
        ta['BBANDS_L'] = tb.BBANDS(c, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)[2] / \
                         tb.BBANDS(c, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)[2].mean()
        ta['AD'] = tb.AD(h, l, c, v) / tb.AD(h, l, c, v).mean()
        ta['ATR'] = tb.ATR(h, l, c, timeperiod=14) / tb.ATR(h, l, c, timeperiod=14).mean()
        ta['HT_DC'] = tb.HT_DCPERIOD(c) / tb.HT_DCPERIOD(c).mean()
        ta["High/Open"] = h / o
        ta["Low/Open"] = l / o
        ta["Close/Open"] = c / o

        self.ta = ta 

def CCI(self, period: int, bars: list):
        """
        Return CCI (Commodity Chanel Index) for n bars close price.
​
        CCI = (Typical Price − MA) / 0.015 * Mean Deviation

        where:
            Typical Price = ∑P((H + L + C) / 3))
            P = number of bars (period)
            MA = Moving Average = (∑P Typical Price) / P
            Mean Deviation=(∑P | Typical Price - MA |) / P
        """

        self.check_bars_type(bars)

        cci = ta.CCI(
            bars['high'], bars['low'], bars['close'], timeperiod=period)

        return cci 

def _getAtrExtreme(cls, highs, lows, closes, atrPeriod=14, slowPeriod=30, fastPeriod=3):
        """
            获取TTI ATR Exterme通道, which is based on 《Volatility-Based Technical Analysis》
            TTI is 'Trading The Invisible'

            @return: fasts, slows
        """
        # talib 的源码，它的 ATR 不是 N 日简单平均，而是类似 EMA 的方法计算的指数平均
        atr = talib.ATR(highs, lows, closes, timeperiod=atrPeriod)

        highsMean = talib.EMA(highs, 5)
        lowsMean = talib.EMA(lows, 5)
        closesMean = talib.EMA(closes, 5)

        atrExtremes = np.where(closes > closesMean,
                               ((highs - highsMean)/closes * 100) * (atr/closes * 100),
                               ((lows - lowsMean)/closes * 100) * (atr/closes * 100)
                               )

        fasts = talib.MA(atrExtremes, fastPeriod)
        slows = talib.EMA(atrExtremes, slowPeriod)

        return fasts, slows, np.std(atrExtremes[-slowPeriod:]) 

def zscore(candles: np.ndarray, period=14, matype=0, nbdev=1, source_type="close", sequential=False) -> Union[
    float, np.ndarray]:
    """
    zScore

    :param candles: np.ndarray
    :param period: int - default: 14
    :param matype: int - default: 0
    :param nbdev: int - default: 1
    :param source_type: str - default: "close"
    :param sequential: bool - default=False

    :return: float | np.ndarray
    """
    if not sequential and len(candles) > 240:
        candles = candles[-240:]

    source = get_candle_source(candles, source_type=source_type)
    means = talib.MA(source, timeperiod=period, matype=matype)
    sigmas = talib.STDDEV(source, timeperiod=period, nbdev=nbdev)
    zScores = (source - means) / sigmas

    return zScores if sequential else zScores[-1] 

def reinit(self,pingzhongname,filename,dailyresultname,jsonname):
        """更换品种，重新初始化"""
        # K线界面
        ui.loadKLineSetting(jsonname)
        ui.KLtitle.setText(pingzhongname+'   '+ui.StrategyName ,size='10pt',color='FFFF00')
        ui.loadData(pd.DataFrame.from_csv(filename))
        ui.loadData_listsig(pd.DataFrame.from_csv(dailyresultname))
        # 初始化界面显示
        self.SHORT_TERM_SHOW_ALL =False
        self.SHORT_TERM_SHOW_FIRST =False
        self.SHORT_TERM_SHOW_LIMIT =False
        ui.initIndicator(u'MA SHORT')
        ui.initIndicator(u'MA LONG')
        ui.initIndicator(u'KLINE')
        ui.initIndicator(u'SHORT TERM(First)')
        ui.initIndicator(u'SHORT TERM(All)')
        ui.initIndicator(u'SHORT TERM(Limit)')
        if ui.signal_show == True :
            ui.initIndicator(u'信号显示')
        else:
            ui.initIndicator(u'信号隐藏')
        ui.refreshAll()
    #----------------------------------------------------------------------
    #  画图相关 
    #---------------------------------------------------------------------- 

def before_trading(context):
    prices = history_bars(context.s1, context.window, '1d', fields=['high', 'low', 'close', 'open'])
    highP = prices['high']
    lowP = prices['low']
    closeP = prices['close']
    openP = prices['open']

    context.ADX = ta.ADXR(highP, lowP, closeP, timeperiod=14)
    context.Pdi = ta.PLUS_DI(highP, lowP, closeP, timeperiod=14)
    context.Ndi = ta.MINUS_DI(highP, lowP, closeP, timeperiod=14)

    context.MA_tw = ta.MA(closeP, timeperiod=20)[-5:]
    context.MA_fi = ta.MA(closeP, timeperiod=50)[-5:]
    context.MA_fork = context.MA_tw > context.MA_fi

    context.SAR = ta.SAR(highP, lowP, acceleration=context.acceleration, maximum=0.2)

    # context.JQ_selOpen = (context.ADX[-1]>=20) #& (context.ADX[-2]>=20) & (context.ADX[-1]<=30) & (context.ADX[-2]<=30)
    context.JW_selOpen = (context.Pdi[-1] <= context.Ndi[-1]) & (context.Pdi[-2] >= context.Ndi[-2])
    context.JE_selOpen = (context.MA_fork[-1]) & (context.MA_fork[-2]) & (not context.MA_fork[-3])
    context.JR_selOpen = (context.SAR[-1] >= 0.95 * openP[-1]) & (context.SAR[-2] <= 1.05 * closeP[-2])
    context.J_selOpen = context.JQ_selOpen & context.JW_selOpen & context.JE_selOpen & context.JR_selOpen

    # context.JQ_buyOpen = context.JQ_selOpen
    context.JW_buyOpen = (context.Pdi[-1] >= context.Ndi[-1]) & (context.Pdi[-2] <= context.Ndi[-2])
    context.JE_buyOpen = (not context.MA_fork[-1]) & (not context.MA_fork[-2]) & (not context.MA_fork[-3])
    context.JR_buyOpen = (context.SAR[-2] >= 0.95 * openP[-2]) & (context.SAR[-1] <= 1.05 * closeP[-1])
    context.J_buyOpen = context.JQ_buyOpen & context.JW_buyOpen & context.JE_buyOpen & context.JR_buyOpen


# 你选择的期货数据更新将会触发此段逻辑，例如日线或分钟线更新 

def handle_data(context):
    if len(Close()) < p2:
        return
    
    # 使用talib计算均价
    ma1 = talib.MA(Close(), p1)
    ma2 = talib.MA(Close(), p2) 

    # 执行下单操作
    if MarketPosition() <= 0 and ma1[-1] > ma2[-1]:
        Buy(1, Close()[-1])
    if MarketPosition() >= 0 and ma1[-1] < ma2[-1]:
        SellShort(1, Close()[-1])
    
    # 绘制指标图形
    PlotNumeric("ma1", ma1[-1], RGB_Red())
    PlotNumeric("ma2", ma2[-1], RGB_Green())    
    PlotNumeric("fit", NetProfit() + FloatProfit() - TradeCost(), RGB_Red(), False) 

def handle_data(context):
    if len(Close()) < p2:
        return;
        
    ma1 = talib.MA(Close(), p1)
    ma2 = talib.MA(Close(), p2)  
    
    his = BarStatus() != 2
    if his:
        his_trigger(ma1, ma2) 
    else: 
        tim_trigger(ma1, ma2) 
    
    PlotNumeric("ma1", ma1[-1], 0xFF0000)
    PlotNumeric("ma2", ma2[-1], 0x00aa00) 
    fit = NetProfit() + FloatProfit() - TradeCost() if his else A_CoverProfit() + A_ProfitLoss() - A_Cost()
    PlotNumeric("fit", fit, 0x0000FF, False) 

def ma30_cross(data):
        MA5 = talib.MA(data.close, 5)
        MA30 = talib.MA(data.close, 30)
    
        MA30_CROSS_JX = CROSS(MA5, MA30)
        MA30_CROSS_JX_Integral = Timeline_Integral_with_cross_before(MA30_CROSS_JX)
        MA30_CROSS_SX = CROSS(MA30, MA5)
        MA30_CROSS_SX_Integral = Timeline_Integral_with_cross_before(MA30_CROSS_SX)
    
        MA30_CROSS = pd.DataFrame(columns=['MA30_CROSS', 'MA30_CROSS_JX', 'MA30_CROSS_SX', 'MA30_TP_CROSS_JX', 'MA30_TP_CROSS_SX'], index=data.index)
        MA30_CROSS.loc[MA30_CROSS_JX == 1, 'MA30_CROSS'] = 1
        MA30_CROSS.loc[MA30_CROSS_SX == 1, 'MA30_CROSS'] = -1
        MA30_CROSS['MA30_CROSS_JX'] = Timeline_Integral_with_cross_before(MA30_CROSS_JX)
        MA30_CROSS['MA30_CROSS_SX'] = Timeline_Integral_with_cross_before(MA30_CROSS_SX)
    
        # MA30 前29个是 NaN，处理会抛出 Warning，使用 [29:] 则不会计算 NaN，相应的 return_index+29
        MA30_tp_min, MA30_tp_max = signal.argrelextrema(MA30.values[29:], np.less)[0] + 29, signal.argrelextrema(MA30.values[29:], np.greater)[0] + 29
        MA30_TP_CROSS = pd.DataFrame(columns=['MA30_TP_CROSS_JX', 'MA30_TP_CROSS_SX'], index=data.index)
        MA30_TP_CROSS['MA30_TP_CROSS_SX'] = MA30_TP_CROSS['MA30_TP_CROSS_JX'] = 0
        MA30_TP_CROSS.iloc[MA30_tp_min, MA30_TP_CROSS.columns.get_loc('MA30_TP_CROSS_JX')] = 1
        MA30_TP_CROSS.iloc[MA30_tp_max, MA30_TP_CROSS.columns.get_loc('MA30_TP_CROSS_SX')] = 1
        MA30_CROSS['MA30_TP_CROSS_JX'] = Timeline_Integral_with_cross_before(MA30_TP_CROSS['MA30_TP_CROSS_JX'])
        MA30_CROSS['MA30_TP_CROSS_SX'] = Timeline_Integral_with_cross_before(MA30_TP_CROSS['MA30_TP_CROSS_SX'])
        return MA30_CROSS 

def getAtrRatio(df, period=14):
        """
            平均波动率：ATR(14)/MA(14)
        """
        highs = df['high']
        lows = df['low']
        closes = df['close']

        atr = talib.ATR(highs, lows, closes, timeperiod=period)
        ma = talib.MA(closes, timeperiod=period)

        volatility = atr/ma

        s = pd.Series(volatility, index=df.index, name='volatility').dropna()

        return s 

def _getAtrExtreme(cls, highs, lows, closes, atrPeriod=14, slowPeriod=30, fastPeriod=3):
        """
            获取TTI ATR Exterme通道, which is based on 《Volatility-Based Technical Analysis》
            TTI is 'Trading The Invisible'

            @return: fasts, slows
        """
        # talib 的源码，它的 ATR 不是 N 日简单平均，而是类似 EMA 的方法计算的指数平均
        atr = talib.ATR(highs, lows, closes, timeperiod=atrPeriod)

        highsMean = talib.EMA(highs, 5)
        lowsMean = talib.EMA(lows, 5)
        closesMean = talib.EMA(closes, 5)

        atrExtremes = np.where(closes > closesMean,
                               ((highs - highsMean)/closes * 100) * (atr/closes * 100),
                               ((lows - lowsMean)/closes * 100) * (atr/closes * 100)
                               )

        fasts = talib.MA(atrExtremes, fastPeriod)
        slows = talib.EMA(atrExtremes, slowPeriod)

        return fasts, slows, np.std(atrExtremes[-slowPeriod:]) 

def _calc_ma_from_ta(prices, time_period=10, from_calc=EMACalcType.E_MA_MA):
    """
    使用talib计算ma，即透传talib.MA or talib.EMA计算结果
    :param prices: 收盘价格序列，pd.Series或者np.array
    :param time_period: 移动平均的N值，int
    :param from_calc: EMACalcType enum对象，移动移动平均使用的方法
    """

    import talib
    if isinstance(prices, pd.Series):
        prices = prices.values

    if from_calc == EMACalcType.E_MA_MA:
        ma = talib.MA(prices, timeperiod=time_period)
    else:
        ma = talib.EMA(prices, timeperiod=time_period)
    return ma 

def ma(order, buy_index, sell_index,rate,args):
    close = []
    for i in order:
        close.append(i['close'])
    close = np.array(close)
    result = talib.MA(close, timeperiod=int(args))
    result = [float(x) for x in result]
    if(result[-1] > close[-1]):
        buy_index.append(float(rate))
    elif(result[-1] < close[-1]):
        sell_index.append(float(rate))
    else:
        pass
    print('2') 

def handle_data(context):
    prc_lst1 = Close(code1, bt, bi)
    prc_lst2 = Close(code2, bt, bi)
    if len(prc_lst1) == 0 or len(prc_lst2) == 0:
        return

    # 生成价差序列
    global spds
    spd_c = prc_lst1[-1] - prc_lst2[-1]
    if len(prc_lst1) > len(spds):
        spds.append(spd_c)
    else:
        spds[-1] = spd_c    

    if len(spds) < p2:
        return

    # 计算价差ma
    sma1 = talib.MA(np.array(spds), p1)  
    sma2 = talib.MA(np.array(spds), p2)         

    # 根据两根ma的交叉关系下单
    if sma1[-1] > sma2[-1] + dot * PriceTick() and MarketPosition(code1) <= 0:
        Buy(qty, prc_lst1[-1], code1)
        SellShort(qty, prc_lst2[-1], code2)
    elif sma1[-1] < sma2[-1] - dot * PriceTick() and MarketPosition(code1) >= 0:
        SellShort(qty, prc_lst1[-1], code1)
        Buy(qty, prc_lst2[-1], code2)

    # 绘制指标线   
    PlotNumeric("sma1", sma1[-1], 0x0000FF, False)
    PlotNumeric("sma2", sma2[-1], 0xFF0000, False)
    PlotNumeric("fit", NetProfit() - TradeCost(), RGB_Purple(), False, True) 

def handle_data(context):
    ma11 = talib.MA(Close(contA, 'M', 1), p1, 0)
    ma12 = talib.MA(Close(contA, 'M', 1), p2, 0)

    ma31 = talib.MA(Close(contA, 'M', 3), p1, 0)
    ma32 = talib.MA(Close(contA, 'M', 3), p2, 0)

    diff2 = getVn(ma11, 2) - getVn(ma31, 2)    

    PlotNumeric("ma11", ma11[-1], color=RGB_Red())
    PlotNumeric("ma12", ma12[-1], color=RGB_Green())
    PlotNumeric("diff2", diff2, color=RGB_Blue(), main=False) 

def handle_data(context):
    
    vol = talib.MA(Vol().astype(float), timeperiod=20, matype=0)
    PlotNumeric("VOL", vol[-1], main=False, color=RGB_Yellow()) 

def handle_data(context):
   
    ma1 = talib.MA(Close(), timeperiod=5, matype=0)
    ma2 = talib.MA(Close(), timeperiod=20, matype=0)

    diff = ma1[-1] - ma2[-1]

    PlotNumeric("ICON",diff, color=RGB_Brown(),main=False)
    if diff == 0:
        PlotIcon(diff, 14)    
    elif diff > 0: 
        PlotIcon(diff, 15)
    else:
        PlotIcon(diff, 9) 

def handle_data(context):
    global N, keep
    ma1 = talib.MA(Close(), timeperiod=N, matype=0)
    ma2 = talib.MA(Close(), timeperiod=P, matype=0)

    keep += 1

    if keep % 5 == 0:
        color=RGB_Blue()
    else:
        color=RGB_Red() 

    PlotText(Open()[-1], "A", main=True, color=color, barsback=N)
    #UnPlotText(True, N+1) 

def MA(data, **kwargs):
    _check_talib_presence()
    prices = _extract_series(data)
    return talib.MA(prices, **kwargs) 

def MA(series, n=30, matype=0):
    return _series_to_series(series, talib.MA, n, matype) 

def SMA(self, period: int, bars: int):
        """
        Simple moving average of previous n bars close price.

        SMA = (sum of all closes in period) / period.
        """
        self.check_bars_type(bars)

        ma = ta.MA(bars['close'], timeperiod=period, matype=0)

        return ma 

def ma(data,index):
    close_arry = tranform_arry(data['close'])
    index['ma'] = talib.MA(close_arry, timeperiod=10) 

def calculate_cmi_indicator(df):  # RSI

    cmi_period = 30
    cmi_ma_period = 10
    roc = df['close'].diff(cmi_period)
    h1 = ta.MAX(df['high'], cmi_period) - ta.MIN(df['low'], cmi_period)
    cmi = abs(roc / h1) * 100
    cmi_ma = ta.MA(cmi, cmi_ma_period)  # rolling.
    return cmi_ma 

def getRuntimeMa(self, ma_num):
        """
        实时计算MA得值
        :param ma_num:第几条均线, 1，对应inputMa1Len,,,,
        :return:
        """
        if ma_num not in [1, 2, 3]:
            return None

        ma_len = 1
        if ma_num == 1 and self.inputMa1Len > 0:
            ma_len = self.inputMa1Len
        elif ma_num == 2 and self.inputMa2Len > 0:
            ma_len = self.inputMa2Len
        elif ma_num == 3 and self.inputMa3Len > 0:
            ma_len = self.inputMa3Len
        else:
            return None
        ma_data_len = ma_len + 2

        if len(self.lineBar) < ma_data_len:
            self.debugCtaLog(u'数据未充分,当前Bar数据数量：{}，计算实时MA {} 需要：{}'.
                             format(len(self.lineBar), ma_len, ma_data_len))
            return None

        # 3、获取前InputN周期(不包含当前周期）的K线
        listClose = [x.close for x in self.lineBar[-ma_data_len:]]
        barMa = ta.MA(np.array(listClose, dtype=float), ma_len)[-1]
        barMa = round(float(barMa), self.round_n)

        return barMa

    #---------------------------------------------------------------------- 

def onBarRatio(self, bar):
        """比率线的OnBar事件"""
        # 获取比率线的长度
        l = len(self.lineRatio.lineStateMean)
        if l > 0:
            ma = self.lineRatio.lineStateMean[-1]
        else:
            ma = bar.close

        if l > 6:
            listKf = [x for x in self.lineRatio.lineStateMean[-7:-1]]
            # MA():？
            # numpy.array(listKf, dtype = float：转浮点型
            malist = ta.MA(numpy.array(listKf, dtype=float), 5)
            ma5 = malist[-1]
            ma5_ref1 = malist[-2]
            if ma5 <= 0 or ma5_ref1 <= 0:
                self.writeCtaLog(u'卡尔曼均线异常')
                return
            # ？
            self.m1_atan = math.atan((ma5 / ma5_ref1 - 1) * 100 * 180 / math.pi)
        # M1的切线队列长度 > 10
        if len(self.m1_atan_list) > 10:
            del self.m1_atan_list[0]
        # m1_atan_list加入m1_atan
        self.m1_atan_list.append(self.m1_atan)
        # displayLastBar()显示最后一个Bar的信息
        self.writeCtaLog(self.lineRatio.displayLastBar())

    # 残差线更新 

def getAtrRatio(df, period=14):
        """
            平均波动率：ATR(14)/MA(14)
        """
        highs = df['high']
        lows = df['low']
        closes = df['close']

        atr = talib.ATR(highs, lows, closes, timeperiod=period)
        ma = talib.MA(closes, timeperiod=period)

        volatility = atr/ma

        s = pd.Series(volatility, index=df.index, name='volatility').dropna()

        return s 

def ifup20_TA(data):
        # TA-lib计算
        return (talib.MA(data.close, 5) - talib.MA(data.close, 20)).dropna() > 0

    # 写个自定义指标 MAX_FACTOR TA-lib计算 

def boll_cross(data):
        BBANDS = QA.TA_BBANDS(data.close, timeperiod=20, nbdevup=2)

        BOLL_CROSS = pd.DataFrame(columns=['min_peak', 'max_peak', 'BOLL_CROSS', 'BOLL_CROSS_JX', 'BOLL_CROSS_SX'], index=data.index)
        
        # 防止插针行情突然搞乱
        data['smooth_low'] = talib.MA(data.low, 2)
        data['smooth_high'] = talib.MA(data.high, 2)

        BOLL_CROSS['min_peak'] = data.apply(lambda x: min(x['open'], x['close'], x['smooth_low']), axis=1)
        BOLL_CROSS['max_peak'] = data.apply(lambda x: max(x['open'], x['close'], x['smooth_high']), axis=1)

        BOLL_CROSS_JX = CROSS(BOLL_CROSS['min_peak'], BBANDS[:,2])
        BOLL_CROSS_SX = CROSS(BBANDS[:,0], BOLL_CROSS['max_peak'])

        BOLL_CROSS.loc[BOLL_CROSS_JX == 1, 'BOLL_CROSS'] = 1
        BOLL_CROSS.loc[BOLL_CROSS_SX == 1, 'BOLL_CROSS'] = -1
        BOLL_TP_CROSS = pd.DataFrame(columns=['BOLL_TP_CROSS_JX', 'BOLL_TP_CROSS_SX'], index=data.index)
        BOLL_TP_CROSS['BOLL_TP_CROSS_SX'] = BOLL_TP_CROSS['BOLL_TP_CROSS_JX'] = 0
        BOLL_TP_CROSS.loc[BOLL_CROSS_JX == 1, 'BOLL_TP_CROSS_JX'] = 1
        BOLL_TP_CROSS.loc[BOLL_CROSS_SX == 1, 'BOLL_TP_CROSS_SX'] = 1

        BOLL_CROSS = BOLL_CROSS.assign(BOLL_UB=BBANDS[:,0])
        BOLL_CROSS = BOLL_CROSS.assign(BOLL_MA=BBANDS[:,1])
        BOLL_CROSS = BOLL_CROSS.assign(BOLL_LB=BBANDS[:,2])
        BOLL_CROSS['BOLL_CROSS_JX'] = QA.Timeline_Integral_with_cross_before(BOLL_TP_CROSS['BOLL_TP_CROSS_JX'])
        BOLL_CROSS['BOLL_CROSS_SX'] = QA.Timeline_Integral_with_cross_before(BOLL_TP_CROSS['BOLL_TP_CROSS_SX'])
        return BOLL_CROSS 

def boll_cross_lf(data):
        BBANDS = QA.TA_BBANDS(data.close, timeperiod=20, nbdevup=2)

        BOLL_CROSS_JX1 = CROSS(data.open, BBANDS[:,2])
        BOLL_CROSS_JX2 = CROSS(data.close, BBANDS[:,2])
        BOLL_CROSS_JX3 = CROSS(talib.MA(data.low, 2), BBANDS[:,2])
        BOLL_CROSS_SX1 = CROSS(BBANDS[:,0], data.open)
        BOLL_CROSS_SX2 = CROSS(BBANDS[:,0], data.close)
        BOLL_CROSS_SX3 = CROSS(BBANDS[:,0], talib.MA(data.high, 2))

        BOLL_CROSS = pd.DataFrame(columns=['BOLL_CROSS', 'BOLL_CROSS_JX', 'BOLL_CROSS_SX'], index=data.index)
        BOLL_CROSS.loc[BOLL_CROSS_JX1 == 1, 'BOLL_CROSS'] = 1
        BOLL_CROSS.loc[BOLL_CROSS_JX2 == 1, 'BOLL_CROSS'] = 1
        BOLL_CROSS.loc[BOLL_CROSS_JX3 == 1, 'BOLL_CROSS'] = 1
        BOLL_CROSS.loc[BOLL_CROSS_SX1 == 1, 'BOLL_CROSS'] = -1
        BOLL_CROSS.loc[BOLL_CROSS_SX2 == 1, 'BOLL_CROSS'] = -1
        BOLL_CROSS.loc[BOLL_CROSS_SX3 == 1, 'BOLL_CROSS'] = -1
        BOLL_TP_CROSS = pd.DataFrame(columns=['BOLL_TP_CROSS_JX', 'BOLL_TP_CROSS_SX'], index=data.index)
        BOLL_TP_CROSS['BOLL_TP_CROSS_SX'] = BOLL_TP_CROSS['BOLL_TP_CROSS_JX'] = 0
        BOLL_TP_CROSS.loc[(BOLL_CROSS_JX1 | BOLL_CROSS_JX2 | BOLL_CROSS_JX3) == 1, 'BOLL_TP_CROSS_JX'] = 1
        BOLL_TP_CROSS.loc[(BOLL_CROSS_SX1 | BOLL_CROSS_SX2 | BOLL_CROSS_SX3) == 1, 'BOLL_TP_CROSS_SX'] = 1

        BOLL_CROSS = BOLL_CROSS.assign(BOLL_UB=BBANDS[:,0])
        BOLL_CROSS = BOLL_CROSS.assign(BOLL_MA=BBANDS[:,1])
        BOLL_CROSS = BOLL_CROSS.assign(BOLL_LB=BBANDS[:,2])
        BOLL_CROSS['BOLL_CROSS_JX'] = QA.Timeline_Integral_with_cross_before(BOLL_TP_CROSS['BOLL_TP_CROSS_JX'])
        BOLL_CROSS['BOLL_CROSS_SX'] = QA.Timeline_Integral_with_cross_before(BOLL_TP_CROSS['BOLL_TP_CROSS_SX'])
        return BOLL_CROSS 

def ma_close(self, sym, frequency, period=30, ma_type=0):
        if not self.kbars_ready(sym, frequency):
            return []

        closes = self.close(sym, frequency)
        ma = ta.MA(closes, timeperiod=period, matype=ma_type)

        return ma