python类MACD的实例源码

def handle_bar(context, bar_dict):
    # ?????????????
    # ??????????history_bars??????ndarray????????????
    prices = history_bars(context.s1, context.OBSERVATION, '1d', 'close')

    # ?Talib??MACD?????????????????macd,signal ? hist
    macd, signal, hist = talib.MACD(prices, context.SHORTPERIOD, context.LONGPERIOD, context.SMOOTHPERIOD)

    # macd ?????????signal?macd?????????????????????????????????
    if (macd[-1] - signal[-1] > 0 and macd[-2] - signal[-2] < 0):
        sell_qty = context.portfolio.positions[context.s1].sell_quantity
        # ?????????????????????
        if (sell_qty > 0):
            buy_close(context.s1, 1)
        # ????
        buy_open(context.s1, 1)

    if (macd[-1] - signal[-1] < 0 and macd[-2] - signal[-2] > 0):
        buy_qty = context.portfolio.positions[context.s1].buy_quantity
        # ?????????????????????
        if (buy_qty > 0):
            sell_close(context.s1, 1)
        # ????
        sell_open(context.s1, 1)

def get_indicator(df, indicator):
        ret_df = df
        if 'MACD' in indicator:
            macd, macdsignal, macdhist = ta.MACD(df.close.values, fastperiod=12, slowperiod=26, signalperiod=9)
            ret_df = KlineData._merge_dataframe(pd.DataFrame([macd, macdsignal, macdhist]).T.rename(columns={0: "macddif", 1: "macddem", 2: "macdhist"}), ret_df)
            ret_df = KlineData._merge_dataframe(line_intersections(ret_df, columns=['macddif', 'macddem']), ret_df)
        if 'MFI' in indicator:
            real = ta.MFI(df.high.values, df.low.values, df.close.values, df.volume.values, timeperiod=14)
            ret_df = KlineData._merge_dataframe(pd.DataFrame([real]).T.rename(columns={0: "mfi"}), ret_df)
        if 'ATR' in indicator:
            real = ta.NATR(df.high.values, df.low.values, df.close.values, timeperiod=14)
            ret_df = KlineData._merge_dataframe(pd.DataFrame([real]).T.rename(columns={0: "atr"}), ret_df)
        if 'ROCR' in indicator:
            real = ta.ROCR(df.close.values, timeperiod=10)
            ret_df = KlineData._merge_dataframe(pd.DataFrame([real]).T.rename(columns={0: "rocr"}), ret_df)
        ret_df['date'] = pd.to_datetime(ret_df['date'], format='%Y-%m-%d')
        return ret_df

def handle_bar(context, bar_dict):
    # ?????????????
    # ??????????history_bars??????ndarray????????????
    prices = history_bars(context.s1, context.OBSERVATION, '1d', 'close')

    # ?Talib??MACD?????????????????macd,signal ? hist
    macd, signal, hist = talib.MACD(prices, context.SHORTPERIOD,
                                    context.LONGPERIOD, context.SMOOTHPERIOD)

    # macd ?????????signal?macd?????????????????????????????????
    if macd[-1] - signal[-1] > 0 and macd[-2] - signal[-2] < 0:
        sell_qty = context.portfolio.positions[context.s1].sell_quantity
        # ?????????????????????
        if sell_qty > 0:
            buy_close(context.s1, 1)
        # ????
        buy_open(context.s1, 1)

    if macd[-1] - signal[-1] < 0 and macd[-2] - signal[-2] > 0:
        buy_qty = context.portfolio.positions[context.s1].buy_quantity
        # ?????????????????????
        if buy_qty > 0:
            sell_close(context.s1, 1)
        # ????
        sell_open(context.s1, 1)

def handle_bar(context, bar_dict):
    # ?????????????x
    # ??????????history_bars??????ndarray????????????
    prices = history_bars(context.s1, context.OBSERVATION, '1d', 'close')

    # ?Talib??MACD?????????????????macd,signal ? hist
    macd, signal, hist = talib.MACD(prices, context.SHORTPERIOD,
                                    context.LONGPERIOD, context.SMOOTHPERIOD)

    # macd ?????????signal?macd?????????????????????????????????
    if macd[-1] - signal[-1] > 0 and macd[-2] - signal[-2] < 0:
        sell_qty = context.portfolio.positions[context.s1].sell_quantity
        # ?????????????????????
        if sell_qty > 0:
            buy_close(context.s1, 1)
        # ????
        buy_open(context.s1, 1)

    if macd[-1] - signal[-1] < 0 and macd[-2] - signal[-2] > 0:
        buy_qty = context.portfolio.positions[context.s1].buy_quantity
        # ?????????????????????
        if buy_qty > 0:
            sell_close(context.s1, 1)
        # ????
        sell_open(context.s1, 1)

def handle_bar(context, bar_dict):
    # ?????????????
    # ??????????history_bars??????ndarray????????????
    prices = history_bars(context.s1, context.OBSERVATION, '1d', 'close')

    # ?Talib??MACD?????????????????macd,signal ? hist
    macd, signal, hist = talib.MACD(prices, context.SHORTPERIOD,
                                    context.LONGPERIOD, context.SMOOTHPERIOD)

    # macd ?????????signal?macd?????????????????????????????????
    if macd[-1] - signal[-1] > 0 and macd[-2] - signal[-2] < 0:
        sell_qty = context.portfolio.positions[context.s1].sell_quantity
        # ?????????????????????
        if sell_qty > 0:
            buy_close(context.s1, 1)
        # ????
        buy_open(context.s1, 1)

    if macd[-1] - signal[-1] < 0 and macd[-2] - signal[-2] > 0:
        buy_qty = context.portfolio.positions[context.s1].buy_quantity
        # ?????????????????????
        if buy_qty > 0:
            sell_close(context.s1, 1)
        # ????
        sell_open(context.s1, 1)

def isBuy(context, analysis):
    # Bullish SMA Crossover
    if (getLast(analysis, 'sma_test') == 1):
        # Bullish MACD
        if (getLast(analysis, 'macd_test') == 1):
            return True

    # # Bullish Stochastics
    # if(getLast(analysis, 'stoch_over_sold') == 1):
    #     return True

    # # Bullish RSI
    # if(getLast(analysis, 'rsi_over_sold') == 1):
    #     return True

    return False

def isSell(context, analysis):
    # Bearish SMA Crossover
    if (getLast(analysis, 'sma_test') == 0):
        # Bearish MACD
        if (getLast(analysis, 'macd_test') == 0):
            return True

    # # Bearish Stochastics
    # if(getLast(analysis, 'stoch_over_bought') == 0):
    #     return True

    # # Bearish RSI
    # if(getLast(analysis, 'rsi_over_bought') == 0):
    #     return True

    return False

def getStrategy_MACD(start_trading_day,end_trading_day,_time,_close):
    point = []
    iday = _time.index(start_trading_day)
    eday = _time.index(end_trading_day)
    macd, signal,hist = ta.MACD(np.array(_close,dtype='f8'),fastperiod=12,slowperiod=26,signalperiod=9)
    macd = macd[iday:eday]
    signal = signal[iday:eday]
    point.append(0)
    for i in range(1,len(macd)):
        if (macd[i-1] <= signal[i-1]) and (macd[i] >= signal[i]):
            point.append(1)
        elif (macd[i-1] >= signal[i-1]) and (macd[i] <= signal[i]):
            point.append(-1)
        else:
            point.append(0)
    return point

def getStrategy_MACD(start_trading_day,end_trading_day,_time,_close):
    point = []
    iday = _time.index(start_trading_day)
    eday = _time.index(end_trading_day)
    macd, signal,hist = ta.MACD(np.array(_close,dtype='f8'),fastperiod=12,slowperiod=26,signalperiod=9)
    macd = macd[iday:eday]
    signal = signal[iday:eday]
    point.append(0)
    for i in range(1,len(macd)):
        if (macd[i-1] <= signal[i-1]) and (macd[i] >= signal[i]):
            point.append(1)
        elif (macd[i-1] >= signal[i-1]) and (macd[i] <= signal[i]):
            point.append(-1)
        else:
            point.append(0)
    return point

def macd_rule(df):
    try:  df = MACD(df)
    except: return False

    input_1 = 0.2
    input_2 = -0.8
    input_3 = 22 * 3
    index = -1
    df['macd_dif_1'] = df['macd_dif'].shift(1)
    df['macd_dea_1'] = df['macd_dea'].shift(1)

    return (abs(df['macd_dea'][index]) < input_1) & \
           (abs(df['macd_dif'][index]) < input_1) & \
           (df['macd_dif'][-input_3:].min() < input_2) & \
           (df['macd_dif'][index] > df['macd_dea'][index]) & \
           ((df['macd_dea_1'][index] > df['macd_dif_1'][index]) | (abs(df['macd_dea_1'][index] - df['macd_dif_1'][index]) < 0.007))

def macd_rule_1(df):
    try:  df = MACD(df)
    except: return False

    input_1 = 0
    input_2 = -0.8
    input_3 = 0.05

    dif_len = len(df['macd_dif'])
    if dif_len < 2: return False

    if abs(df['macd_dif'][-1]) > input_3:
        return False

    for idx in range(dif_len-1, 1, -1):
        if ((df['macd_dif'][idx] - df['macd_dif'][idx-1]) > input_1):
            continue

        if df['macd_dif'][idx] <= input_2:
            return True
        else: return False

def macd_rule(df, index = -1):
    try:  
        if not {'macd_dif', 'macd_dea', 'macd'}.issubset(df.columns):
            df = MACD(df)
    except Exception as e: 
        print(e)
        return False

    input_1 = -3
    input_2 = 0.05

    # df['macd_dif_1'] = df['macd_dif'].shift(1)
    # df['macd_dea_1'] = df['macd_dea'].shift(1)

#(df['macd_dif'][-input_3:].min() < input_2) & \

    return (df['macd_dif'][index] > input_1) & \
           (df['macd_dif'][index] < input_2) & \
           (df['macd_dif'][index] > df['macd_dea'][index]) & \
           ((df['macd_dea'][index-1] > df['macd_dif'][index-1]) | (abs(df['macd_dea'][index-1] - df['macd_dif'][index-1]) < 0.007))

def Unittest_Kline():
    """"""
    kline = Guider("600100", "")
    print(kline.getData(0).date, kline.getLastData().date)

    #kline.myprint()
    obv = kline.OBV()

    pl.figure
    pl.subplot(2,1,1)
    pl.plot(kline.getCloses())
    pl.subplot(2,1,2)
    ma,m2,m3 = kline.MACD()
    pl.plot(ma)
    pl.plot(m2,'r')
    left = np.arange(0, len(m3))
    pl.bar(left,m3)
    #pl.plot(obv, 'y')
    pl.show()


#Unittest_Kstp()    
#
#???????????
#----------------------------------------------------------------------

def compute(self, today, assets, out, close):

            sig_lines = []

            for col in close.T:
                # get signal line only
                try:
                    _, signal_line, _ = talib.MACD(col, fastperiod=12,
                                                   slowperiod=26, signalperiod=10)
                    sig_lines.append(signal_line[-1])
                # if error calculating, return NaN
                except:
                    sig_lines.append(np.nan)
            out[:] = sig_lines

    # 20-day Stochastic Oscillator

def handle_bar(context, bar_dict):
    # ?????????????
    # ??????????history_bars??????ndarray????????????
    prices = history_bars(context.s1, context.OBSERVATION, '1d', 'close')

    # ?Talib??MACD?????????????????macd,signal ? hist
    macd, signal, hist = talib.MACD(prices, context.SHORTPERIOD,
                                    context.LONGPERIOD, context.SMOOTHPERIOD)

    # macd ?????????signal?macd?????????????????????????????????
    if macd[-1] - signal[-1] > 0 and macd[-2] - signal[-2] < 0:
        sell_qty = context.portfolio.positions[context.s1].sell_quantity
        # ?????????????????????
        if sell_qty > 0:
            buy_close(context.s1, 1)
        # ????
        buy_open(context.s1, 1)

    if macd[-1] - signal[-1] < 0 and macd[-2] - signal[-2] > 0:
        buy_qty = context.portfolio.positions[context.s1].buy_quantity
        # ?????????????????????
        if buy_qty > 0:
            sell_close(context.s1, 1)
        # ????
        sell_open(context.s1, 1)

def handle_bar(context, bar_dict):
    # ?????????????
    # ??????????history_bars??????ndarray????????????
    prices = history_bars(context.s1, context.OBSERVATION, '1d', 'close')

    # ?Talib??MACD?????????????????macd,signal ? hist
    macd, signal, hist = talib.MACD(prices, context.SHORTPERIOD, context.LONGPERIOD, context.SMOOTHPERIOD)

    # macd ?????????signal?macd?????????????????????????????????
    if (macd[-1] - signal[-1] > 0 and macd[-2] - signal[-2] < 0):
        sell_qty = context.portfolio.positions[context.s1].sell_quantity
        # ?????????????????????
        if (sell_qty > 0):
            buy_close(context.s1, 1)
        # ????
        buy_open(context.s1, 1)

    if (macd[-1] - signal[-1] < 0 and macd[-2] - signal[-2] > 0):
        buy_qty = context.portfolio.positions[context.s1].buy_quantity
        # ?????????????????????
        if (buy_qty > 0):
            sell_close(context.s1, 1)
        # ????
        sell_open(context.s1, 1)

def __init__(self, selector):
        self.selector = selector
        self.supported = {"ROCP", "OROCP", "HROCP", "LROCP", "MACD", "RSI", "VROCP", "BOLL", "MA", "VMA", "PRICE_VOLUME"}
        self.feature = []

def init(context):
    context.s1 = "000001.XSHE"

    # ??MACD?????????macd??????
    context.SHORTPERIOD = 12
    context.LONGPERIOD = 26
    context.SMOOTHPERIOD = 9
    context.OBSERVATION = 100


# ???????????????????????????????????????????

def handle_bar(context, bar_dict):
    # ?????????????

    # bar_dict[order_book_id] ?????????bar??
    # context.portfolio ???????????????

    # ??order_shares(id_or_ins, amount)??????

    # TODO: ??????????

    # ?????????sma??????????????????????ema????????????????????????????
    prices = history_bars(context.s1, context.OBSERVATION,'1d','close')

    # ?Talib??MACD?????????????????macd, signal ? hist
    macd, signal, hist = talib.MACD(prices, context.SHORTPERIOD,
                                    context.LONGPERIOD, context.SMOOTHPERIOD)

    plot("macd", macd[-1])
    plot("macd signal", signal[-1])

    # macd ?????????signal?macd??????macd?????????????????macd???signal??????

    # ??macd??????macd_signal

    if macd[-1] - signal[-1] < 0 and macd[-2] - signal[-2] > 0:
        # ????portfolio??????
        curPosition = context.portfolio.positions[context.s1].quantity
        #????
        if curPosition > 0:
            order_target_value(context.s1, 0)

    # ????????????????????
    if macd[-1] - signal[-1] > 0 and macd[-2] - signal[-2] < 0:
        # ????
        order_target_percent(context.s1, 1)

def init(context):
    # context???????s1?????????
    context.s1 = 'IF1606'

    # ??MACD?????????macd??????
    context.SHORTPERIOD = 12
    context.LONGPERIOD = 26
    context.SMOOTHPERIOD = 9
    context.OBSERVATION = 50

    # ??????????????????????handle_bar?????
    subscribe(context.s1)


# ?????????????????????????????

def init(context):
    context.s1 = "000001.XSHE"

    # ??MACD?????????macd??????
    context.SHORTPERIOD = 12
    context.LONGPERIOD = 26
    context.SMOOTHPERIOD = 9
    context.OBSERVATION = 100


# ???????????????????????????????????????????

def init(context):
    # context???????s1?????????
    context.s1 = 'IF1606'

    # ??MACD?????????macd??????
    context.SHORTPERIOD = 12
    context.LONGPERIOD = 26
    context.SMOOTHPERIOD = 9
    context.OBSERVATION = 50

    #??????????????????????handle_bar?????
    subscribe(context.s1)


# ?????????????????????????????

def init(context):
    # context???????s1?????????
    context.s1 = "IF88"

    # ??MACD?????????macd??????
    context.SHORTPERIOD = 12
    context.LONGPERIOD = 26
    context.SMOOTHPERIOD = 9
    context.OBSERVATION = 50

    # ??????????????????????handle_bar?????
    subscribe(context.s1)


# ?????????????????????????????

def init(context):
    context.s1 = "000001.XSHE"

    # ??MACD?????????macd??????
    context.SHORTPERIOD = 12
    context.LONGPERIOD = 26
    context.SMOOTHPERIOD = 9
    context.OBSERVATION = 100


# ???????????????????????????????????????????

def init(context):
    # context???????s1?????????
    context.s1 = 'IF1606'

    # ??MACD?????????macd??????
    context.SHORTPERIOD = 12
    context.LONGPERIOD = 26
    context.SMOOTHPERIOD = 9
    context.OBSERVATION = 50

    # ??????????????????????handle_bar?????
    subscribe(context.s1)


# ?????????????????????????????

def pre_data(stick_code,ktype='D'):
    # ktype in ('D','W','M')
    global df
    db = m_db2()
    try:
        if ktype == 'D':
            df = db.get_data("select * from t_stick_data_d where code = '"+stick_code+"'  and date > '2015-09-01';")#and date>'2015-05-01'
        elif ktype == 'W':
            df = db.get_data("select * from t_stick_data_w where code = '"+stick_code+"'  ;")#and date>'2015-05-01'
        elif ktype == 'M':
            df = db.get_data("select * from t_stick_data_m where code = '" + stick_code + "'  ;")  # and date>'2015-05-01'

    except Exception as e:
        print('ERR:',e)
        return
    df['cci'] = ta.CCI(df['high'].values.astype('double'),df['low'].values.astype('double'),df['close'].values.astype('double'))
    df['diff'],df['dea'],df['macd'] = ta.MACD(df['close'].values.astype('double'),fastperiod=12, slowperiod=26, signalperiod=9)
    df['obv'] = ta.OBV(df['close'].values.astype('double'),df['vol'].values.astype('double'))
    df['volma5']=ta.MA(df['vol'].values.astype('double'),5);
    df['volma20'] = ta.MA(df['vol'].values.astype('double'), 20);
    df['MA20'] = ta.MA(df['close'].values.astype('double'), 20)
    df['MA60'] = ta.MA(df['close'].values.astype('double'), 60)
    df['cwbili']=0
    df['pricebili']=0
    return   df
# draw

def pre_data(stick_code,ktype='D',today=''):
    # ktype in ('D','W','M')
    #today='2010-01-01'
    if '' == today:
        today = datetime.date.today().strftime('%Y-%m-%d')
#        begindate = datetime.date.today() - datetime.timedelta(days=13)

    global df
    db = m_db2()
    try:
        if ktype == 'D':
            df = db.get_data("select * from t_stick_data_d where code = '"+stick_code+"'  and date > '2015-09-01' and date <='"+today+"' order by date asc;")#and date>'2015-05-01'
        elif ktype == 'W':
            df = db.get_data("select * from t_stick_data_w where code = '"+stick_code+"'  ;")#and date>'2015-05-01'
        elif ktype == 'M':
            df = db.get_data("select * from t_stick_data_m where code = '" + stick_code + "'  ;")  # and date>'2015-05-01'

    except Exception as e:
        #print('ERR:',e)
        return
    df['cci'] = ta.CCI(df['high'].values.astype('double'),df['low'].values.astype('double'),df['close'].values.astype('double'))
    df['diff'],df['dea'],df['macd'] = ta.MACD(df['close'].values.astype('double'),fastperiod=12, slowperiod=26, signalperiod=9)
    df['obv'] = ta.OBV(df['close'].values.astype('double'),df['vol'].values.astype('double'))
    df['volma5']=ta.MA(df['vol'].values.astype('double'),5);
    df['volma13'] = ta.MA(df['vol'].values.astype('double'), 13);
    df['volma20'] = ta.MA(df['vol'].values.astype('double'), 20);
    df['volma34'] = ta.MA(df['vol'].values.astype('double'), 34);
    df['MA20'] = ta.MA(df['close'].values.astype('double'), 20)
    df['MA60'] = ta.MA(df['close'].values.astype('double'), 60)
    df['MA5'] = ta.MA(df['close'].values.astype('double'), 5)
    df['MA13'] = ta.MA(df['close'].values.astype('double'), 13)
    df['MA34'] = ta.MA(df['close'].values.astype('double'), 34)
    df['MA89'] = ta.MA(df['close'].values.astype('double'), 89)
    df['MA144'] = ta.MA(df['close'].values.astype('double'), 144)
    df['cwbili']=0
    df['pricebili']=0
    return   df
# draw

def pre_data(stick_code,ktype='D'):
    # ktype in ('D','W','M')
    global df
    db = m_db2()
    try:
        if ktype == 'D':
            df = db.get_data("select * from t_stick_data_d where code = '"+stick_code+"'  and date > '2015-09-01';")#and date>'2015-05-01'
        elif ktype == 'W':
            df = db.get_data("select * from t_stick_data_w where code = '"+stick_code+"'  ;")#and date>'2015-05-01'
        elif ktype == 'M':
            df = db.get_data("select * from t_stick_data_m where code = '" + stick_code + "'  ;")  # and date>'2015-05-01'

    except Exception as e:
        print('ERR:',e)
        return
    df['cci'] = ta.CCI(df['high'].values.astype('double'),df['low'].values.astype('double'),df['close'].values.astype('double'))
    df['diff'],df['dea'],df['macd'] = ta.MACD(df['close'].values.astype('double'),fastperiod=12, slowperiod=26, signalperiod=9)
    df['obv'] = ta.OBV(df['close'].values.astype('double'),df['vol'].values.astype('double'))
    df['volma5']=ta.MA(df['vol'].values.astype('double'),5);
    df['volma20'] = ta.MA(df['vol'].values.astype('double'), 20);
    df['MA20'] = ta.MA(df['close'].values.astype('double'), 5)

    #print(df)
    #i= ta.CDLCONCEALBABYSWALL(df['open'].values.astype('double'),df['high'].values.astype('double'),
    #                       df['low'].values.astype('double'),df['close'].values.astype('double'),)
    #print(i)
    return  df
# draw

def can_buy(stock, day_count=3):
    DIF, DEA, macd = MACD(stock)
    for i in range(1, day_count + 1):
        if (DIF[-i] > DEA[-i] and DIF[-i - 1] < DEA[-i - 1]):
            return True
    return False


# ??????????

def can_buy_prior(stock, day_count=3):
    DIF, DEA, macd = MACD(stock)
    count = 0
    for i in range(1, len(macd) - 2):
        if DIF[-i] > 0 or DEA[-i] > 0:
            return False
        if (DIF[-i] - DEA[-i] > 0 and DIF[-i - 1] - DEA[-i - 1] < 0):
            count += 1
            if count >= 2:
                return True
        if i >= day_count and count == 0:
            return False


# ?????????????????? ??