python类RSI的实例源码

def onInit(self):
        """????????????????"""
        self.writeCtaLog(u'%s?????' %self.name)

        # ???RSI????
        self.rsiBuy = 50 + self.rsiEntry
        self.rsiSell = 50 - self.rsiEntry

        # ????????????????????????
        initData = self.loadBar(self.initDays)
        for bar in initData:
            self.onBar(bar)

        self.putEvent()

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

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_RSI(start_trading_day,end_trading_day,_time,_close):
    point = []
    iday = _time.index(start_trading_day)
    eday = _time.index(end_trading_day)
    rsi = ta.RSI(np.array(_close,dtype='f8'),timeperiod=14)
    rsi = rsi[iday:eday]
    point.append(0)
    for i in range(1,len(rsi)):
        if (rsi[i] <= 30) and (rsi[i - 1] > 30):
            point.append(1)
        elif (rsi[i] >= 50) and (rsi[i - 1] < 50):
            point.append(-1)
        else:
            point.append(0)

    return point

def getStrategy_RSI(start_trading_day,end_trading_day,_time,_close):
    point = []
    iday = _time.index(start_trading_day)
    eday = _time.index(end_trading_day)
    rsi = ta.RSI(np.array(_close,dtype='f8'),timeperiod=14)
    rsi = rsi[iday:eday]
    point.append(0)
    for i in range(1,len(rsi)):
        if (rsi[i] <= 30) and (rsi[i - 1] > 30):
            point.append(1)
        elif (rsi[i] >= 50) and (rsi[i - 1] < 50):
            point.append(-1)
        else:
            point.append(0)

    return point

def KDJ_COM(high, low, close, fastk_period=9, slowk_period=3, slowd_period=3, fixpre=True) :
        len1 = len(high)
        len2 = len(low)
        len3 = len(close)
        if len1 != len2 or len1 != len3:
            print (("KDJ_COM input invalid for len:%s %s %s " %(str(len1),str(len2),str(len3))))
            return np.array([np.nan]),np.array([np.nan]),np.array([np.nan])
        hValue = SecurityDataSrcBase.HHV_COM(high, fastk_period, fixpre)
        lValue = SecurityDataSrcBase.LLV_COM(low, fastk_period, fixpre)
        rsValue = (close - lValue) / (hValue - lValue) * 100
        kValue = SecurityDataSrcBase.SMA_COM(rsValue, slowk_period)
        dValue = SecurityDataSrcBase.SMA_COM(kValue,  slowd_period)
        jValue = 3 * kValue - 2 * dValue
        return kValue, dValue, jValue

    # RSI COMMON

def test_JiShuZhiBiao():
    """??????"""
    pl = publish.Publish()
    code = '002440'
    code = '999999'

    #?????????rsi
    df = FenshiEx(code, '2014-10-8', '2014-10-8').df
    #df = FENSHI_MA(df)
    df = FENSHI_BIAS(df)
    df['rsi'] = RSI(df['p'])
    df1 = df.drop(['v','b','bias','rsi'], axis=1)
    ui.drawDf(pl, df1)
    df2 = df.drop(['v','b','p','avg', 'rsi'], axis=1)
    ui.drawDf(pl, df2)
    df2 = df.drop(['v','b','p','avg', 'bias'], axis=1)
    ui.drawDf(pl, df2)

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

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

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

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

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

    # ????????????loop?????????RSI??
    for stock in context.stocks:
        # ??????
        prices = history_bars(stock, context.TIME_PERIOD+1, '1d', 'close')

        # ?Talib??RSI?
        rsi_data = talib.RSI(prices, timeperiod=context.TIME_PERIOD)[-1]

        cur_position = context.portfolio.positions[stock].quantity
        # ??????30%???????
        target_available_cash = context.portfolio.cash * context.ORDER_PERCENT

        # ?RSI???????????????
        if rsi_data > context.HIGH_RSI and cur_position > 0:
            order_target_value(stock, 0)

        # ?RSI?????????????cash?????????
        if rsi_data < context.LOW_RSI:
            logger.info("target available cash caled: " + str(target_available_cash))
            # ??????????? - 100shares?????ricequant ?order management system reject?
            order_value(stock, target_available_cash)

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

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

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

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

    # ????????????loop?????????RSI??
    for stock in context.stocks:
        # ??????
        prices = history_bars(stock, context.TIME_PERIOD+1, '1d', 'close')

        # ?Talib??RSI?
        rsi_data = talib.RSI(prices, timeperiod=context.TIME_PERIOD)[-1]

        cur_position = context.portfolio.positions[stock].quantity
        # ??????30%???????
        target_available_cash = context.portfolio.cash * context.ORDER_PERCENT

        # ?RSI???????????????
        if rsi_data > context.HIGH_RSI and cur_position > 0:
            order_target_value(stock, 0)

        # ?RSI?????????????cash?????????
        if rsi_data < context.LOW_RSI:
            logger.info("target available cash caled: " + str(target_available_cash))
            # ??????????? - 100shares?????ricequant ?order management system reject?
            order_value(stock, target_available_cash)

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

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

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

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

    # ????????????loop?????????RSI??
    for stock in context.stocks:
        # ??????
        prices = history_bars(stock, context.TIME_PERIOD+1, '1d', 'close')

        # ?Talib??RSI?
        rsi_data = talib.RSI(prices, timeperiod=context.TIME_PERIOD)[-1]

        cur_position = context.portfolio.positions[stock].quantity
        # ??????30%???????
        target_available_cash = context.portfolio.cash * context.ORDER_PERCENT

        # ?RSI???????????????
        if rsi_data > context.HIGH_RSI and cur_position > 0:
            order_target_value(stock, 0)

        # ?RSI?????????????cash?????????
        if rsi_data < context.LOW_RSI:
            logger.info("target available cash caled: " + str(target_available_cash))
            # ??????????? - 100shares?????ricequant ?order management system reject?
            order_value(stock, target_available_cash)

def handle_data(context, data):
    print('handling bar: {}'.format(data.current_dt))

    price = data.current(context.asset, 'close')
    print('got price {price}'.format(price=price))

    prices = data.history(
        context.asset,
        fields='price',
        bar_count=20,
        frequency='30T'
    )
    last_traded = prices.index[-1]
    print('last candle date: {}'.format(last_traded))

    rsi = talib.RSI(prices.values, timeperiod=14)[-1]
    print('got rsi: {}'.format(rsi))

    # If base_price is not set, we use the current value. This is the
    # price at the first bar which we reference to calculate price_change.
    if context.base_price is None:
        context.base_price = price

    price_change = (price - context.base_price) / context.base_price
    cash = context.portfolio.cash

    # Now that we've collected all current data for this frame, we use
    # the record() method to save it. This data will be available as
    # a parameter of the analyze() function for further analysis.
    record(
        price=price,
        price_change=price_change,
        cash=cash
    )

def RSI(df, n, field='close'):
    return pd.Series(talib.RSI(df[field].values, n), index = df.index, name='RSI%s' % str(n))

def rsi(df, n, field = 'close'):
    RSI_key = 'RSI%s' % str(n)
    df[RSI_key][-1] = talib.RSI(df[field][(-n-1):], n)[-1]

def RSI_F(df, n, field='close'):
    UpMove = df[field] - df[field].shift(1)
    DoMove = df[field].shift(1) - df[field]
    UpD = pd.Series(UpMove)
    DoD = pd.Series(DoMove)
    UpD[(UpMove <= 0)] = 0
    DoD[(DoMove <= 0)] = 0
    PosDI = pd.Series(pd.ewma(UpD, com = n-1), name = "RSI"+str(n)+'_UP')
    NegDI = pd.Series(pd.ewma(DoD, com = n-1), name = "RSI"+str(n)+'_DN')
    RSI = pd.Series(PosDI / (PosDI + NegDI) * 100, name = 'RSI' + str(n))
    return pd.concat([RSI, PosDI, NegDI], join='outer', axis=1)

def rsi_f(df, n, field = 'close'):
    RSI_key = 'RSI%s' % str(n)
    dx = df[field][-1] - df[field][-2]
    alpha = 1.0/n
    if dx > 0:
        upx = dx
        dnx = 0
    else:
        upx = 0
        dnx = -dx
    udi = df[RSI_key + '_UP'][-1] = df[RSI_key + '_UP'][-2] * (1 - alpha) + upx * alpha
    ddi = df[RSI_key + '_DN'][-1] = df[RSI_key + '_DN'][-2] * (1 - alpha) + dnx * alpha
    df[RSI_key][-1] = udi/(udi + ddi) * 100.0

#True Strength Index

def add_RSI(self, timeperiod=14,
            type='line', color='secondary', **kwargs):
    """Relative Strength Index."""

    if not self.has_close:
        raise Exception()

    utils.kwargs_check(kwargs, VALID_TA_KWARGS)
    if 'kind' in kwargs:
        type = kwargs['kind']

    name = 'RSI({})'.format(str(timeperiod))
    self.sec[name] = dict(type=type, color=color)
    self.ind[name] = talib.RSI(self.df[self.cl].values,
                               timeperiod)

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

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

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

def RSI_CN(close, timeperiod=6):
        return tl.RSI(close, timeperiod)

    # MA

def rsi(self, n, array=False):
        """RSI??"""
        result = talib.RSI(self.close, n)
        if array:
            return result
        return result[-1]

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

def RSI(self):
        """??????? 30????? 70????"""
        closes = self.getCloses()
        return talib.RSI(closes)

def RSI(closes, timeperiod=12):
    """??????? 30????? 70???? return: np.darray"""
    closes = np.array(closes)
    closes = closes[np.isnan(closes) == False]
    return talib.RSI(closes, timeperiod)

def unittest_ma():
    closes = Guider('600100').getCloses()
    print(MA(closes))
    fours = FOUR(closes)
    print( len(closes), len(fours))
    print(fours)
    rsi = RSI(closes)
    #rsi = rsi/100 - 0.5
    rsi -= 50
    fours *= 100
    pl.figure
    #pl.plot(closes)
    pl.plot(fours,'r')
    pl.plot(rsi)
    pl.show()

def test_fenshi_rsi():
    """???????rsi??"""
    code = '300059'
    fenshi = CreateFenshiPd(code, '2015-8-6').resample('1min').mean().dropna()
    print( fenshi)
    closes = fenshi['p']
    print(closes)
    rsi = RSI(closes, 6)
    print(rsi)
    ui.DrawTs(pl, rsi)

def get_rsi_info(df):
    """
    calculate rsi quotation.
    :param df:
    :return: rsi
    """
    close = get_close_data(df)
    rsi = ta.RSI(close, timeperiod=12)
    return pd.DataFrame({u'rsi': rsi[-view_days:]})

def Get_TA(df_Code,Dist):
    operate_array1=[]
    operate_array2=[]
    operate_array3=[]

    count = 0
    for code in df_Code.index:
# index,0 - 6 date??? open???? high???? close???? low???? volume???? price_change????? p_change????
# 7-12 ma5?5??? ma10?10??? ma20:20??? v_ma5:5???v_ma10:10??? v_ma20:20???
        df = ts.get_hist_data(code,start='2014-11-20')
        dflen = df.shape[0]
        count = count + 1       
        if dflen>35:
            try:
                (df,operate1) = Get_MACD(df) 
                (df,operate2) = Get_KDJ(df)
                (df,operate3) = Get_RSI(df)
            except Exception, e:
                 Write_Blog(e,Dist)
                 pass
        operate_array1.append(operate1)  #round(df.iat[(dflen-1),16],2)
        operate_array2.append(operate2)
        operate_array3.append(operate3)
        if count == 0:
            Write_Blog(str(count),Dist)
    df_Code['MACD']=pd.Series(operate_array1,index=df_Code.index)
    df_Code['KDJ']=pd.Series(operate_array2,index=df_Code.index)
    df_Code['RSI']=pd.Series(operate_array3,index=df_Code.index)
    return df_Code

#??MACD??????