コード例 #1
0
    def get_fx_cross_tick(self, start, end, cross,
                     cut = "NYC", source = "gain", cache_algo='cache_algo_return', type = 'spot'):

        if isinstance(cross, str):
            cross = [cross]

        time_series_request = TimeSeriesRequest()
        time_series_factory = self.time_series_factory
        data_frame_agg = None

        time_series_request.gran_freq = "tick"                  # tick

        time_series_request.freq_mult = 1                       # 1 min
        time_series_request.cut = cut                           # NYC/BGN ticker
        time_series_request.fields = ['bid', 'ask']             # bid/ask field only
        time_series_request.cache_algo = cache_algo             # cache_algo_only, cache_algo_return, internet_load

        time_series_request.environment = 'backtest'
        time_series_request.start_date = start
        time_series_request.finish_date = end
        time_series_request.data_source = source

        time_series_request.category = 'fx'

        for cr in cross:

            if (type == 'spot'):
                time_series_request.tickers = cr

                cross_vals = time_series_factory.harvest_time_series(time_series_request)
                cross_vals.columns = [cr + '.bid', cr + '.ask']

            if data_frame_agg is None:
                data_frame_agg = cross_vals
            else:
                data_frame_agg = data_frame_agg.join(cross_vals, how='outer')

        # strip the nan elements
        data_frame_agg = data_frame_agg.dropna()
        return data_frame_agg
コード例 #2
0
    def get_fx_cross(self, start, end, cross,
                     cut = "NYC", source = "bloomberg", freq = "intraday", cache_algo='cache_algo_return', type = 'spot'):

        if source == "gain" or source == 'dukascopy' or freq == 'tick':
            return self.get_fx_cross_tick(start, end, cross,
                     cut = cut, source = source, cache_algo='cache_algo_return', type = 'spot')

        if isinstance(cross, str):
            cross = [cross]

        time_series_request = TimeSeriesRequest()
        time_series_factory = self.time_series_factory
        time_series_calcs = TimeSeriesCalcs()
        data_frame_agg = None

        if freq == 'intraday':
            time_series_request.gran_freq = "minute"                # intraday

        elif freq == 'daily':
            time_series_request.gran_freq = "daily"                 # intraday

        time_series_request.freq_mult = 1                       # 1 min
        time_series_request.cut = cut                           # NYC/BGN ticker
        time_series_request.fields = 'close'                    # close field only
        time_series_request.cache_algo = cache_algo             # cache_algo_only, cache_algo_return, internet_load

        time_series_request.environment = 'backtest'
        time_series_request.start_date = start
        time_series_request.finish_date = end
        time_series_request.data_source = source

        for cr in cross:
            base = cr[0:3]
            terms = cr[3:6]

            if (type == 'spot'):
                # non-USD crosses
                if base != 'USD' and terms != 'USD':
                    base_USD = self.fxconv.correct_notation('USD' + base)
                    terms_USD = self.fxconv.correct_notation('USD' + terms)

                    # TODO check if the cross exists in the database

                    # download base USD cross
                    time_series_request.tickers = base_USD
                    time_series_request.category = self.fxconv.em_or_g10(base, freq)
                    base_vals = time_series_factory.harvest_time_series(time_series_request)

                    # download terms USD cross
                    time_series_request.tickers = terms_USD
                    time_series_request.category = self.fxconv.em_or_g10(terms, freq)
                    terms_vals = time_series_factory.harvest_time_series(time_series_request)

                    if (base_USD[0:3] == 'USD'):
                        base_vals = 1 / base_vals
                    if (terms_USD[0:3] == 'USD'):
                        terms_vals = 1 / terms_vals

                    base_vals.columns = ['temp']
                    terms_vals.columns = ['temp']
                    cross_vals = base_vals.div(terms_vals, axis = 'index')
                    cross_vals.columns = [cr + '.close']

                else:
                    if base == 'USD': non_USD = terms
                    if terms == 'USD': non_USD = base

                    correct_cr = self.fxconv.correct_notation(cr)

                    time_series_request.tickers = correct_cr
                    time_series_request.category = self.fxconv.em_or_g10(non_USD, freq)
                    cross_vals = time_series_factory.harvest_time_series(time_series_request)

                    # flip if not convention
                    if(correct_cr != cr):
                        cross_vals = 1 / cross_vals

                    cross_vals.columns.names = [cr + '.close']

            elif type[0:3] == "tot":
                if freq == 'daily':
                    # download base USD cross
                    time_series_request.tickers = base + 'USD'
                    time_series_request.category = self.fxconv.em_or_g10(base, freq) + '-tot'

                    if type == "tot":
                        base_vals = time_series_factory.harvest_time_series(time_series_request)
                    else:
                        x = 0

                    # download terms USD cross
                    time_series_request.tickers = terms + 'USD'
                    time_series_request.category = self.fxconv.em_or_g10(terms, freq) + '-tot'

                    if type == "tot":
                        terms_vals = time_series_factory.harvest_time_series(time_series_request)
                    else:
                        x = 0

                    base_rets = time_series_calcs.calculate_returns(base_vals)
                    terms_rets = time_series_calcs.calculate_returns(terms_vals)

                    cross_rets = base_rets.sub(terms_rets.iloc[:,0],axis=0)

                    # first returns of a time series will by NaN, given we don't know previous point
                    cross_rets.iloc[0] = 0

                    cross_vals = time_series_calcs.create_mult_index(cross_rets)
                    cross_vals.columns = [cr + '-tot.close']

                elif freq == 'intraday':
                    self.logger.info('Total calculated returns for intraday not implemented yet')
                    return None

            if data_frame_agg is None:
                data_frame_agg = cross_vals
            else:
                data_frame_agg = data_frame_agg.join(cross_vals, how='outer')

        # strip the nan elements
        data_frame_agg = data_frame_agg.dropna()
        return data_frame_agg
コード例 #3
0
            tickers=['USDJPY'],  # ticker (Thalesians)
            fields=['close'],  # which fields to download
            vendor_tickers=['USDJPY BGN Curncy'],  # ticker (Bloomberg)
            vendor_fields=['PX_LAST'],  # which Bloomberg fields to download
            cache_algo='internet_load_return')  # how to return data

        ltsf = LightTimeSeriesFactory()

        df = None
        df = ltsf.harvest_time_series(time_series_request)

        df.columns = [x.replace('.close', '') for x in df.columns.values]

        time_series_request.tickers = ['USDJPY purchases (bn USD)']
        time_series_request.vendor_tickers = ['JPINTDUSDJPY']
        time_series_request.data_source = 'fred'

        df_fred = ltsf.harvest_time_series(time_series_request)
        df_fred.columns = [
            x.replace('.close', '') for x in df_fred.columns.values
        ]

        # convert to USD bn
        # df_fred = (df_fred * 10000000)
        df = df.join(df_fred, how="outer")
        df['USDJPY'] = df['USDJPY'].ffill()

        # data is in 100 million JPY, divide by 10 to get into 1000 million (ie. 1 billion)
        # divide by USD/JPY spot to get into USD
        df['USDJPY purchases (bn USD)'] = (df['USDJPY purchases (bn USD)'] /
                                           df['USDJPY']) / 10
コード例 #4
0
                tickers = ['USDJPY'],                            # ticker (Thalesians)
                fields = ['close'],              # which fields to download
                vendor_tickers = ['USDJPY BGN Curncy'],         # ticker (Bloomberg)
                vendor_fields = ['PX_LAST'],   # which Bloomberg fields to download
                cache_algo = 'internet_load_return')                # how to return data

        ltsf = LightTimeSeriesFactory()

        df = None
        df = ltsf.harvest_time_series(time_series_request)

        df.columns = [x.replace('.close', '') for x in df.columns.values]

        time_series_request.tickers = ['USDJPY purchases (bn USD)']
        time_series_request.vendor_tickers = ['JPINTDUSDJPY']
        time_series_request.data_source = 'fred'

        df_fred = ltsf.harvest_time_series(time_series_request)
        df_fred.columns = [x.replace('.close', '') for x in df_fred.columns.values]

        # convert to USD bn
        # df_fred = (df_fred * 10000000)
        df = df.join(df_fred, how="outer")
        df['USDJPY'] = df['USDJPY'].ffill()

        # data is in 100 million JPY, divide by 10 to get into 1000 million (ie. 1 billion)
        # divide by USD/JPY spot to get into USD
        df['USDJPY purchases (bn USD)'] = (df['USDJPY purchases (bn USD)'] / df['USDJPY']) / 10

        gp = GraphProperties()
        gp.scale_factor = 3