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)