def _offline_prices(self, dt): """ Return last offline prices """ # Get our last prices from the logs last_gox = pl2.nearest_by_date(self._gox_offline, dt, True) last_ltc = pl2.nearest_by_date(self._ltc_offline, dt, True) last_ltc_depth = pl2.nearest_by_date(self._ltc_depth_offline, dt, True) return last_gox, last_ltc, last_ltc_depth
def _offline_prices( self, dt): """ Return last offline prices """ # Get our last prices from the logs last_gox = pl2.nearest_by_date( self._gox_offline, dt, True) last_ltc = pl2.nearest_by_date( self._ltc_offline, dt, True) last_ltc_depth = pl2.nearest_by_date( self._ltc_depth_offline, dt, True) return last_gox, last_ltc, last_ltc_depth
def _new_ohlc_df( self, lookback, time_str): """ Return a new, trimmed set of OHLC based on last prices """ # get nearest index behind lookback lookback2 = pl2.nearest_by_date( self.lastprice, lookback, True) return self.lastprice.lastprice.ix[lookback2.name:].resample( time_str, how="ohlc")
def _new_ohlc_df(self, lookback, time_str): """ Return a new, trimmed set of OHLC based on last prices """ # get nearest index behind lookback lookback2 = pl2.nearest_by_date(self.lastprice, lookback, True) return self.lastprice.lastprice.ix[lookback2.name:].resample( time_str, how="ohlc")
def _new_df( self, lookback, t, window, time_str): """ Return a new, trimmed set of recent prices for use in rolling means. """ lookback2 = pl2.nearest_by_date( self.lastprice, lookback, True) return pd.rolling_mean( self.lastprice.ix[lookback2.name:].resample( time_str, fill_method="ffill"), window, freq=time_str)
def _new_df(self, lookback, t, window, time_str): """ Return a new, trimmed set of recent prices for use in rolling means. """ lookback2 = pl2.nearest_by_date(self.lastprice, lookback, True) return pd.rolling_mean(self.lastprice.ix[lookback2.name:].resample( time_str, fill_method="ffill"), window, freq=time_str)