def get_exchange_history_window(self, exchange_name, assets, end_dt, bar_count, frequency, field, data_frequency, ffill=True): """ Fetching price history window from the exchange bundle. Parameters ---------- exchange: Exchange assets: list[TradingPair] end_dt: datetime bar_count: int frequency: str field: str data_frequency: str ffill: bool Returns ------- DataFrame """ # TODO: verify that the exchange supports the timeframe bundle = self.exchange_bundles[exchange_name] # type: ExchangeBundle freq, candle_size, unit, adj_data_frequency = get_frequency( frequency, data_frequency) adj_bar_count = candle_size * bar_count trailing_bar_count = candle_size - 1 if data_frequency == 'minute' and adj_data_frequency == 'daily': end_dt = end_dt.floor('1D') series = bundle.get_history_window_series_and_load( assets=assets, end_dt=end_dt, bar_count=adj_bar_count, field=field, data_frequency=adj_data_frequency, algo_end_dt=self._last_available_session, trailing_bar_count=trailing_bar_count, ) df = resample_history_df(pd.DataFrame(series), freq, field) return df
def get_history_window_with_bundle(self, assets, end_dt, bar_count, frequency, field, data_frequency=None, ffill=True, force_auto_ingest=False): """ Public API method that returns a dataframe containing the requested history window. Data is fully adjusted. Parameters ---------- assets : list[TradingPair] The assets whose data is desired. end_dt: datetime The date of the last bar. bar_count: int The number of bars desired. frequency: string "1d" or "1m" field: string The desired field of the asset. data_frequency: string The frequency of the data to query; i.e. whether the data is 'daily' or 'minute' bars. # TODO: fill how? ffill: boolean Forward-fill missing values. Only has effect if field is 'price'. Returns ------- DataFrame A dataframe containing the requested data. """ # TODO: this function needs some work, # we're currently using it just for benchmark data freq, candle_size, unit, data_frequency = get_frequency( frequency, data_frequency, supported_freqs=['T', 'D']) adj_bar_count = candle_size * bar_count try: series = self.bundle.get_history_window_series_and_load( assets=assets, end_dt=end_dt, bar_count=adj_bar_count, field=field, data_frequency=data_frequency, force_auto_ingest=force_auto_ingest) except (PricingDataNotLoadedError, NoDataAvailableOnExchange): series = dict() for asset in assets: if asset not in series or series[asset].index[-1] < end_dt: # Adding bars too recent to be contained in the consolidated # exchanges bundles. We go directly against the exchange # to retrieve the candles. start_dt = get_start_dt(end_dt, adj_bar_count, data_frequency) trailing_dt = \ series[asset].index[-1] + get_delta(1, data_frequency) \ if asset in series else start_dt # The get_history method supports multiple asset # Use the original frequency to let each api optimize # the size of result sets trailing_bars = get_periods(trailing_dt, end_dt, freq) candles = self.get_candles( freq=freq, assets=asset, end_dt=end_dt, bar_count=trailing_bars if trailing_bars < 500 else 500, ) last_value = series[asset].iloc(0) if asset in series \ else np.nan # Create a series with the common data_frequency, ffill # missing values candle_series = self.get_series_from_candles( candles=candles, start_dt=trailing_dt, end_dt=end_dt, data_frequency=data_frequency, field=field, previous_value=last_value) if asset in series: series[asset].append(candle_series) else: series[asset] = candle_series df = resample_history_df(pd.DataFrame(series), freq, field) # TODO: consider this more carefully df.dropna(inplace=True) return df
def get_exchange_history_window(self, exchange_name, assets, end_dt, bar_count, frequency, field, data_frequency, ffill=True): """ Fetching price history window from the exchange bundle. Parameters ---------- exchange: Exchange assets: list[TradingPair] end_dt: datetime bar_count: int frequency: str field: str data_frequency: str ffill: bool Returns ------- DataFrame """ # TODO: verify that the exchange supports the timeframe bundle = self.exchange_bundles[exchange_name] # type: ExchangeBundle freq, candle_size, unit, adj_data_frequency = get_frequency( frequency, data_frequency, supported_freqs=['T', 'D']) adj_bar_count = candle_size * bar_count if data_frequency == "minute": # for minute frequency always request data until the # current minute (do not include the current minute) last_dt_for_series = end_dt - datetime.timedelta(minutes=1) # read the minute bundles for daily frequency to # support last partial candle # TODO: optimize this by applying this logic only for the last day if adj_data_frequency == 'daily': adj_data_frequency = 'minute' adj_bar_count = adj_bar_count * 1440 else: # data_frequency == "daily": last_dt_for_series = end_dt series = bundle.get_history_window_series_and_load( assets=assets, end_dt=last_dt_for_series, bar_count=adj_bar_count, field=field, data_frequency=adj_data_frequency, algo_end_dt=self._last_available_session, ) start_dt = get_start_dt(last_dt_for_series, adj_bar_count, adj_data_frequency, False) df = resample_history_df(pd.DataFrame(series), freq, field, start_dt) return df
def get_history_window_with_bundle(self, assets, end_dt, bar_count, frequency, field, data_frequency=None, ffill=True, force_auto_ingest=False): """ Public API method that returns a dataframe containing the requested history window. Data is fully adjusted. Parameters ---------- assets : list[TradingPair] The assets whose data is desired. end_dt: datetime The date of the last bar. bar_count: int The number of bars desired. frequency: string "1d" or "1m" field: string The desired field of the asset. data_frequency: string The frequency of the data to query; i.e. whether the data is 'daily' or 'minute' bars. # TODO: fill how? ffill: boolean Forward-fill missing values. Only has effect if field is 'price'. Returns ------- DataFrame A dataframe containing the requested data. """ # TODO: this function needs some work, we're currently using it just for benchmark data freq, candle_size, unit, data_frequency = get_frequency( frequency, data_frequency ) adj_bar_count = candle_size * bar_count try: series = self.bundle.get_history_window_series_and_load( assets=assets, end_dt=end_dt, bar_count=adj_bar_count, field=field, data_frequency=data_frequency, force_auto_ingest=force_auto_ingest ) except (PricingDataNotLoadedError, NoDataAvailableOnExchange): series = dict() for asset in assets: if asset not in series or series[asset].index[-1] < end_dt: # Adding bars too recent to be contained in the consolidated # exchanges bundles. We go directly against the exchange # to retrieve the candles. start_dt = get_start_dt(end_dt, adj_bar_count, data_frequency) trailing_dt = \ series[asset].index[-1] + get_delta(1, data_frequency) \ if asset in series else start_dt # The get_history method supports multiple asset # Use the original frequency to let each api optimize # the size of result sets trailing_bars = get_periods( trailing_dt, end_dt, freq ) candles = self.get_candles( freq=freq, assets=asset, end_dt=end_dt, bar_count=trailing_bars if trailing_bars < 500 else 500, ) last_value = series[asset].iloc(0) if asset in series \ else np.nan # Create a series with the common data_frequency, ffill # missing values candle_series = self.get_series_from_candles( candles=candles, start_dt=trailing_dt, end_dt=end_dt, data_frequency=data_frequency, field=field, previous_value=last_value ) if asset in series: series[asset].append(candle_series) else: series[asset] = candle_series df = resample_history_df(pd.DataFrame(series), freq, field) # TODO: consider this more carefully df.dropna(inplace=True) return df