Python talib.ROCR Examples

The following are 7 code examples of talib.ROCR(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module talib , or try the search function .
Example #1
Source File: apriori.py    From cryptotrader with MIT License 6 votes vote down vote up 
def rebalance(self, obs):
        """
        Performs portfolio rebalance within environment
        :param obs: pandas DataFrame: Environment observation
        :return: numpy array: Portfolio vector
        """
        if not self.init:
            n_pairs = obs.columns.levels[0].shape[0]
            action = np.ones(n_pairs)
            action[-1] = 0
            self.crp = array_normalize(action)
            self.init = True

        if self.step:
            x = self.predict(obs)
            price_relative = obs.xs('open', level=1, axis=1).apply(lambda x: ta.ROCR(x, timeperiod=1),
                                                                   raw=True).fillna(1.0)
            cov_mat = price_relative.cov()
            return self.update(cov_mat, x)
        else:
            return self.crp
Example #2
Source File: apriori.py    From cryptotrader with MIT License 6 votes vote down vote up 
def rebalance(self, obs):
        """
        Performs portfolio rebalance within environment
        :param obs: pandas DataFrame: Environment observation
        :return: numpy array: Portfolio vector
        """
        if not self.init:
            n_pairs = obs.columns.levels[0].shape[0]
            action = np.ones(n_pairs)
            action[-1] = 0
            self.crp = array_normalize(action)
            self.init = True

        if self.step:
            x = self.predict(obs)
            # x[self.fiat] = 1 * (1 - x.std())
            price_relative = obs.xs('open', level=1, axis=1).apply(lambda x: ta.ROCR(x, timeperiod=1),
                                                                   raw=True).fillna(1.0)
            # price_relative[self.fiat] = 1 * (1 - price_relative.std(axis=1))
            cov_mat = price_relative.cov()
            return self.update(cov_mat, x, self.target_return)
        else:
            return self.crp
Example #3
Source File: rocr.py    From jesse with MIT License 6 votes vote down vote up 
def rocr(candles: np.ndarray, period=10, source_type="close", sequential=False) -> Union[float, np.ndarray]:
    """
    ROCR - Rate of change ratio: (price/prevPrice)

    :param candles: np.ndarray
    :param period: int - default=10
    :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)
    res = talib.ROCR(source, timeperiod=period)

    if sequential:
        return res
    else:
        return None if np.isnan(res[-1]) else res[-1]
Example #4
Source File: kline_data.py    From klineyes with MIT License 6 votes vote down vote up 
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
Example #5
Source File: apriori.py    From cryptotrader with MIT License 5 votes vote down vote up 
def price_relative(obs, period=1):
    prices = obs.xs('open', level=1, axis=1).astype(np.float64)
    price_relative = prices.apply(ta.ROCR, timeperiod=period, raw=True).fillna(1.0)
    return price_relative
Example #6
Source File: ta.py    From dash-technical-charting with MIT License 5 votes vote down vote up 
def add_ROCR(self, timeperiod=10,
             type='line', color='tertiary', **kwargs):
    """Rate of Change (Ratio)."""

    if not self.has_close:
        raise Exception()

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

    name = 'ROCR({})'.format(str(timeperiod))
    self.sec[name] = dict(type=type, color=color)
    self.ind[name] = talib.ROCR(self.df[self.cl].values,
                                timeperiod)
Example #7
Source File: talib_indicators.py    From qtpylib with Apache License 2.0 5 votes vote down vote up 
def ROCR(data, **kwargs):
    _check_talib_presence()
    prices = _extract_series(data)
    return talib.ROCR(prices, **kwargs)