Python pandas.rolling_mean方法代碼示例

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import rolling_mean [as 別名]
def __init__(self, x, y, timeStepInSeconds, df):

        movingAverageWindow = self.calculateMovingAverageWindow(timeStepInSeconds)

        self.xRolling = "xRolling"
        self.yRolling = "yRolling"

        self.xDiffSq = "xDiffSq"
        self.yDiffSq = "yDiffSq"

        df[self.xRolling] = pd.rolling_mean(df[x], window = movingAverageWindow, min_periods = 1)
        df[self.yRolling] = pd.rolling_mean(df[y], window = movingAverageWindow, min_periods = 1)

        df[self.xDiffSq] = ((df[x] - df[self.xRolling])** 2.0)
        df[self.yDiffSq] = ((df[y] - df[self.yRolling])** 2.0) # this needed in uncertainty?

        CalibrationBase.__init__(self, x, y)

        self.requiredColumns += [self.xDiffSq, self.yDiffSq] 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import rolling_mean [as 別名]
def relative_ts_div_predictor(tickers, rawdata):
    all_divs=pd.concat([get_div_yield(tickname, rawdata) for tickname in tickers], axis=1, names=tickers)
    all_divs.columns=tickers
   
    vol_ts=pd.DataFrame([vols]*len(rawdata.index), rawdata.index, columns=tickers)

    divs_SR=all_divs / vol_ts

    divs_SR_ts_avg=pd.rolling_mean(divs_SR, 600, min_periods=1)
    
    norm_SR=divs_SR - divs_SR_ts_avg

    SR_avg=norm_SR.median(axis=1)
    SR_avg=expand_boring(SR_avg, tickers)

    
    rel_SR=norm_SR - SR_avg

    return rel_SR 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import rolling_mean [as 別名]
def calcute_ma(self, df, avr_short=12, avr_long=40):
        """
        計算ma, ema
        :param df:
        :return:
        """
        if len(df) == 0:
            return

        # print "{} calcute ma".format(df.ix[0,'code'])
        df['ma_' + str(avr_short)] = pd.rolling_mean(df['close'], avr_short)  # 12
        df['ma_' + str(avr_long)] = pd.rolling_mean(df['close'], avr_long)  # 40

        # print "{} calcute ema".format(df.ix[0, 'code'])
        df['ema_' + str(avr_short)] = pd.ewma(df['close'], span=avr_short)  # 12
        df['ema_' + str(avr_long)] = pd.ewma(df['close'], span=avr_long)  # 40

        df = df.replace(np.nan, 0)
        return df 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import rolling_mean [as 別名]
def calcute_ma(df, avr_short=12, avr_long=40):
    """
    計算ma, ema
    :param df:
    :return:
    """
    if len(df) == 0:
        return

    # print "{} calcute ma".format(df.ix[0,'code'])
    df['ma_' + str(avr_short)] = pd.rolling_mean(df['close'], avr_short)  # 12
    df['ma_' + str(avr_long)] = pd.rolling_mean(df['close'], avr_long)  # 40


    # print "{} calcute ema".format(df.ix[0, 'code'])
    df['ema_' + str(avr_short)] = pd.ewma(df['close'], span=avr_short)  # 12
    df['ema_' + str(avr_long)] = pd.ewma(df['close'], span=avr_long)  # 40

    df = df.replace(np.nan, 0)
    return df 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import rolling_mean [as 別名]
def _pd_rolling(pd_object, pd_object_cm, how, *args, **kwargs):
    """
    被_pd_object_covert裝飾，對pandas中的rolling操作，根據pandas version版本自動選擇調用方式
    :param pd_object: 可迭代的序列，pd.Series, pd.DataFrame或者隻是Iterable
    :param pd_object_cm: 與pd_object相同，針對需要兩個pandas對象或者序列執行的操作，如corr，cov等
    :param how: 代表方法操作名稱，eg. mean, std, var
    :return:
    """
    if g_pandas_has_rolling:
        """pandas版本高，使用如pd_object.rolling直接調用"""
        rolling_obj = pd_object.rolling(*args, **kwargs)
        if hasattr(rolling_obj, how):
            if pd_object_cm is None:
                return getattr(rolling_obj, how)()
            # 需要兩個pd_object進行的操作, getattr(rolling_obj, how)(pd_object_cm)
            return getattr(rolling_obj, how)(pd_object_cm)
    else:
        """pandas版本低，使用如pd.rolling_mean方法調用"""
        how_func = 'rolling_{}'.format(how)
        if hasattr(pd, how_func):
            if pd_object_cm is None:
                return getattr(pd, how_func)(pd_object, *args, **kwargs)
            # 需要兩個pd_object進行的操作，getattr(pd, how_func)(pd_object, pd_object_cm, *args, **kwargs)
            return getattr(pd, how_func)(pd_object, pd_object_cm, *args, **kwargs)
    raise RuntimeError('_pd_rolling {} getattr error'.format(how)) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import rolling_mean [as 別名]
def sample_532():
    """
    5.3.2 繪製股票的價格與均線
    :return:
    """
    tsla_df.close.plot()
    # ma 30
    # pd_rolling_mean(tsla_df.close, window=30).plot()
    pd_rolling_mean(tsla_df.close, window=30).plot()
    # ma 60
    # pd.rolling_mean(tsla_df.close, window=60).plot()
    pd_rolling_mean(tsla_df.close, window=60).plot()
    # ma 90
    # pd.rolling_mean(tsla_df.close, window=90).plot()
    pd_rolling_mean(tsla_df.close, window=90).plot()
    # loc='best'即自動尋找適合的位置
    plt.legend(['close', '30 mv', '60 mv', '90 mv'], loc='best')
    plt.show() 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import rolling_mean [as 別名]
def dMakeIngredients(self, dFeeds):
        """
        dMakeIngredients takes a dictionary of feeds dFeeds
        with at least one key from lRequiredFeeds to work on.
        It returns a dictionary of ingredients with the keys in lRequiredIngredients
        and a copy of the config  that it used as the key dIngredientsConfig.
        """
        oC = self.oEnsureConfigObj()
        assert oC is not None
        iLongMa = oC['rLongMa']['iLongMa']
        iShortMa = oC['rShortMa']['iShortMa']
        bUseTalib = oC['rShortMa']['bUseTalib']

        self.vCheckRequiredFeeds(dFeeds)
        mFeedOhlc = dFeeds['mFeedOhlc']
        
        iBeginValid = max(iLongMa, iShortMa)-1
        iEndOhlc = len(mFeedOhlc)

        if bUseTalib:
            import talib
            aShortMA = talib.SMA(mFeedOhlc.O.values, timeperiod=iShortMa)
            aLongMA = talib.SMA(mFeedOhlc.O.values, timeperiod=iLongMa)
            rShortMa = pandas.Series(aShortMA, name='O',
                                     index=mFeedOhlc.O.index)
            rLongMa = pandas.Series(aLongMA, name='O',
                                    index=mFeedOhlc.O.index)
        else:
            rShortMa = pandas.rolling_mean(mFeedOhlc.O, iShortMa)
            rLongMa = pandas.rolling_mean(mFeedOhlc.O, iLongMa)

        rShortMa = rShortMa[iBeginValid:]
        rLongMa = rLongMa[iBeginValid:]
        mOhlc = mFeedOhlc[iBeginValid:]
        self.oOm.vAppendHdf('recipe/ingredients/rShortMa', rShortMa)
        self.oOm.vAppendHdf('recipe/ingredients/rLongMa', rLongMa)
        self.dIngredients = dict(rShortMa=rShortMa, rLongMa=rLongMa,
                                 mOhlc=mOhlc,
                                 dIngredientsConfig=dict(oC))
        return self.dIngredients 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import rolling_mean [as 別名]
def apply_overlay(x, N_length, period_stdev, costs_SR=0):
    """
    apply an equity curve filter overlay
    
    x is a pd time series of returns
    
    N_length is the mav to apply
    
    Returns a new x with 'flat spots'
    
    """
    if N_length==NO_OVERLAY:
        return x

    cum_x=x.cumsum()
    mav_x=pd.rolling_mean(cum_x, N_length)
    filter_x=pd.TimeSeries([isbelow(cum_x, mav_x, idx) for idx in range(len(x))], x.index)
    
    ## can only apply with a lag (!)
    filtered_x=x*filter_x.shift(1)
    
    if costs_SR>0:
        ## apply costs
        ## first work out the turnover
        ## note everything is adjusted for the period we are in

        turnover=filter_x.diff().abs().mean()/2.0
        
        return_degrade=costs_SR*turnover
        filtered_x = filtered_x - return_degrade
    
    return filtered_x 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import rolling_mean [as 別名]
def get_div_yield(tickname, rawdata):
    price_data=rawdata[tickname+"_PRICE"]
    total_returns=rawdata[tickname+"_TR"]

    tr_return = get_monthly_tr(tickname, rawdata)
    price_return = (price_data / price_data.shift(1)) - 1.0
    div = tr_return - price_return
    div_tr = div * total_returns
    last_year_divs = pd.rolling_mean(div_tr, 12, min_periods=1)*12.0
    div_yield = last_year_divs / total_returns
    
    return div_yield 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import rolling_mean [as 別名]
def yield_forecast_ts(data, vols):
    eqyield = data.SP_Yield
    bondyield = data.Bond_Yield
    
    eqreturn = eqyield / vols[0]
    bondreturn = bondyield / vols[1]

    eqreturn = eqreturn - pd.rolling_mean(eqreturn, 240, min_periods=1)
    bondreturn = bondreturn - pd.rolling_mean(bondreturn, 240, min_periods=1)


    diff = eqreturn - bondreturn
    
    return diff*3.0 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import rolling_mean [as 別名]
def getEMA(close):
    '''
    calculate EMA value
    :param DataFrame close: close price
    :return: DataFrame EMA: EMA value
    '''
    print '''*************************************************************************************
    a kind WARNING from the programmer(not the evil interpreter) function getEMA:
    we have different values for n1,n2,n3 in test code and real code,because the sample file
    may not have sufficient rows for real n1,n2,n3,leading to empty matrix.So be careful of
    the value you choose
    **************************************************************************************
          '''
    # real n1,n2,n3
    n1 = 12
    n2 = 26
    n3 = 9
    # n1,n2,n3 for test
    # n1 = 3
    # n2 = 6
    # n3 = 5
    # calculate MA12
    MA12 = pd.rolling_mean(close, n1)
    # drop np.nan in the first (n1-1) rows
    MA12.dropna(inplace=True)
    # set index with 0,1,2...
    MA12.index = range(MA12.shape[0])
    MA26 = pd.rolling_mean(close, n2)
    MA26.dropna(inplace=True)
    MA26.index = range(MA26.shape[0])
    [row, col] = MA26.shape
    DIF = pd.DataFrame(MA12.iloc[(-row):, :].values) - MA26
    tmp = pd.rolling_mean(DIF, n3)
    tmp.dropna(inplace=True)
    tmp.index = range(tmp.shape[0])
    [row, col] = tmp.shape
    DIF = pd.DataFrame(DIF.iloc[(-row):, :].values)
    EMA = DIF - tmp
    return EMA 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import rolling_mean [as 別名]
def load_timings(path, y="cost2_p_expl", start=0, finish=3000000, window=100, hours=False):
    logging.debug("Loading timings from {}".format(path))
    tm = numpy.load(path)
    num_steps = min(tm['step'], finish)
    df = pandas.DataFrame({k : tm[k] for k in [y, 'time_step']})[start:num_steps]
    one_step = df['time_step'][-window:].median() / 3600.0
    print "Median time for one step is {} hours".format(one_step)
    if hours:
        df.index = (start + numpy.arange(0, df.index.shape[0])) * one_step
    return pandas.rolling_mean(df, window).iloc[window:] 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import rolling_mean [as 別名]
def load_timings(path, args, y):
    logging.debug("Loading timings from {}".format(path))
    tm = numpy.load(path)
    num_steps = min(tm['step'], args.finish)
    df = pandas.DataFrame({k : tm[k] for k in [y, 'time_step']})[args.start:num_steps]
    one_step = df['time_step'].median() / 3600.0
    logging.debug("Median time for one step is {} hours".format(one_step))
    if args.hours:
        df.index = (args.start + numpy.arange(0, df.index.shape[0])) * one_step
    return pandas.rolling_mean(df, args.window).iloc[args.window:] 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import rolling_mean [as 別名]
def results(self, data_frame):
        y_value = data_frame[self.y_data]
        x_value = data_frame[self.x_data]
        if self.lookback >= len(x_value):
            return ([self.value, self.hedge_ratio, self.spread, self.zscore], \
                    [pd.Series(np.nan), pd.Series(np.nan), pd.Series(np.nan), pd.Series(np.nan)])
        ols_result = pd.ols(y=y_value, x=x_value, window=self.lookback)
        hedge_ratio = ols_result.beta['x']
        spread = y_value - hedge_ratio * x_value
        data_frame[self.value] = ols_result.resid
        data_frame[self.hedge_ratio] = hedge_ratio
        data_frame[self.spread] = spread
        data_frame[self.zscore] = (spread - \
                                   pd.rolling_mean(spread, self.lookback)) / \
                                   pd.rolling_std(spread, self.lookback) 

# 需要導入模塊: import pandas [as 別名]
# 或者: from pandas import rolling_mean [as 別名]
def test_multiple_talib_with_args(self):
        zipline_transforms = [ta.MA(timeperiod=10),
                              ta.MA(timeperiod=25)]
        talib_fn = talib.abstract.MA
        algo = TALIBAlgorithm(talib=zipline_transforms, identifiers=[0])
        algo.run(self.source)
        # Test if computed values match those computed by pandas rolling mean.
        sid = 0
        talib_values = np.array([x[sid] for x in
                                 algo.talib_results[zipline_transforms[0]]])
        np.testing.assert_array_equal(talib_values,
                                      pd.rolling_mean(self.panel[0]['price'],
                                                      10).values)
        talib_values = np.array([x[sid] for x in
                                 algo.talib_results[zipline_transforms[1]]])
        np.testing.assert_array_equal(talib_values,
                                      pd.rolling_mean(self.panel[0]['price'],
                                                      25).values)
        for t in zipline_transforms:
            talib_result = np.array(algo.talib_results[t][-1])
            talib_data = dict()
            data = t.window
            # TODO: Figure out if we are clobbering the tests by this
            # protection against empty windows
            if not data:
                continue
            for key in ['open', 'high', 'low', 'volume']:
                if key in data:
                    talib_data[key] = data[key][0].values
            talib_data['close'] = data['price'][0].values
            expected_result = talib_fn(talib_data, **t.call_kwargs)[-1]
            np.testing.assert_allclose(talib_result, expected_result)