def __init__(self, market_data_generator=None): self.fxconv = FXConv() self.cache = {} self._calculations = Calculations() self._market_data_generator = market_data_generator return
def __init__(self, market_data_generator=None): self.logger = LoggerManager().getLogger(__name__) self.fxconv = FXConv() self.cache = {} self.calculations = Calculations() self.market_data_generator = market_data_generator return
class FXCrossFactory(object): """Generates FX spot time series and FX total return time series (assuming we already have total return indices available from xxxUSD form) from underlying series. Can also produce cross rates from the USD crosses. """ def __init__(self, market_data_generator=None): self.fxconv = FXConv() self.cache = {} self._calculations = Calculations() self._market_data_generator = market_data_generator return def get_fx_cross_tick(self, start, end, cross, cut="NYC", data_source="dukascopy", cache_algo='internet_load_return', type='spot', environment='backtest', fields=['bid', 'ask']): if isinstance(cross, str): cross = [cross] market_data_request = MarketDataRequest( gran_freq="tick", freq_mult=1, freq='tick', cut=cut, fields=['bid', 'ask', 'bidv', 'askv'], cache_algo=cache_algo, environment=environment, start_date=start, finish_date=end, data_source=data_source, category='fx') market_data_generator = self._market_data_generator data_frame_agg = None for cr in cross: if (type == 'spot'): market_data_request.tickers = cr cross_vals = market_data_generator.fetch_market_data( market_data_request) if cross_vals is not None: # If user only wants 'close' calculate that from the bid/ask fields if fields == ['close']: cross_vals = cross_vals[[cr + '.bid', cr + '.ask']].mean(axis=1) cross_vals.columns = [cr + '.close'] else: filter = Filter() filter_columns = [cr + '.' + f for f in fields] cross_vals = filter.filter_time_series_by_columns( filter_columns, cross_vals) if data_frame_agg is None: data_frame_agg = cross_vals else: data_frame_agg = data_frame_agg.join(cross_vals, how='outer') if data_frame_agg is not None: # Strip the nan elements data_frame_agg = data_frame_agg.dropna() return data_frame_agg def get_fx_cross(self, start, end, cross, cut="NYC", data_source="bloomberg", freq="intraday", cache_algo='internet_load_return', type='spot', environment='backtest', fields=['close']): if data_source == "gain" or data_source == 'dukascopy' or freq == 'tick': return self.get_fx_cross_tick(start, end, cross, cut=cut, data_source=data_source, cache_algo=cache_algo, type='spot', fields=fields) if isinstance(cross, str): cross = [cross] market_data_request_list = [] freq_list = [] type_list = [] for cr in cross: market_data_request = MarketDataRequest(freq_mult=1, cut=cut, fields=['close'], freq=freq, cache_algo=cache_algo, start_date=start, finish_date=end, data_source=data_source, environment=environment) market_data_request.type = type market_data_request.cross = cr if freq == 'intraday': market_data_request.gran_freq = "minute" # intraday elif freq == 'daily': market_data_request.gran_freq = "daily" # daily market_data_request_list.append(market_data_request) data_frame_agg = [] # Depends on the nature of operation as to whether we should use threading or multiprocessing library if constants.market_thread_technique is "thread": from multiprocessing.dummy import Pool else: # Most of the time is spend waiting for Bloomberg to return, so can use threads rather than multiprocessing # must use the multiprocess library otherwise can't pickle objects correctly # note: currently not very stable from multiprocess import Pool thread_no = constants.market_thread_no['other'] if market_data_request_list[ 0].data_source in constants.market_thread_no: thread_no = constants.market_thread_no[ market_data_request_list[0].data_source] # Fudge, issue with multithreading and accessing HDF5 files # if self._market_data_generator.__class__.__name__ == 'CachedMarketDataGenerator': # thread_no = 0 thread_no = 0 if (thread_no > 0): pool = Pool(thread_no) # Open the market data downloads in their own threads and return the results df_list = pool.map_async(self._get_individual_fx_cross, market_data_request_list).get() data_frame_agg = self._calculations.iterative_outer_join(df_list) # data_frame_agg = self._calculations.pandas_outer_join(result.get()) try: pool.close() pool.join() except: pass else: for md_request in market_data_request_list: data_frame_agg.append( self._get_individual_fx_cross(md_request)) data_frame_agg = self._calculations.pandas_outer_join( data_frame_agg) # Strip the nan elements data_frame_agg = data_frame_agg.dropna(how='all') # self.speed_cache.put_dataframe(key, data_frame_agg) return data_frame_agg def _get_individual_fx_cross(self, market_data_request): cr = market_data_request.cross type = market_data_request.type freq = market_data_request.freq 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 market_data_request.tickers = base_USD market_data_request.category = 'fx' base_vals = self._market_data_generator.fetch_market_data( market_data_request) # Download terms USD cross market_data_request.tickers = terms_USD market_data_request.category = 'fx' terms_vals = self._market_data_generator.fetch_market_data( market_data_request) # If quoted USD/base flip to get USD terms if (base_USD[0:3] == 'USD'): base_vals = 1 / base_vals # If quoted USD/terms flip to get USD terms 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'] base_vals.columns = [base_USD + '.close'] terms_vals.columns = [terms_USD + '.close'] else: # if base == 'USD': non_USD = terms # if terms == 'USD': non_USD = base correct_cr = self.fxconv.correct_notation(cr) market_data_request.tickers = correct_cr market_data_request.category = 'fx' cross_vals = self._market_data_generator.fetch_market_data( market_data_request) # Special case for USDUSD! if base + terms == 'USDUSD': if freq == 'daily': cross_vals = pd.DataFrame(1, index=cross_vals.index, columns=cross_vals.columns) filter = Filter() cross_vals = filter.filter_time_series_by_holidays( cross_vals, cal='WEEKDAY') else: # Flip if not convention (eg. JPYUSD) if (correct_cr != cr): cross_vals = 1 / cross_vals # cross_vals = self._market_data_generator.harvest_time_series(market_data_request) cross_vals.columns = [cr + '.close'] elif type[0:3] == "tot": if freq == 'daily': # Download base USD cross market_data_request.tickers = base + 'USD' market_data_request.category = 'fx-' + type if type[0:3] == "tot": base_vals = self._market_data_generator.fetch_market_data( market_data_request) # Download terms USD cross market_data_request.tickers = terms + 'USD' market_data_request.category = 'fx-' + type if type[0:3] == "tot": terms_vals = self._market_data_generator.fetch_market_data( market_data_request) # base_rets = self._calculations.calculate_returns(base_vals) # terms_rets = self._calculations.calculate_returns(terms_vals) # Special case for USDUSD case (and if base or terms USD are USDUSD if base + terms == 'USDUSD': base_rets = self._calculations.calculate_returns(base_vals) cross_rets = pd.DataFrame(0, index=base_rets.index, columns=base_rets.columns) elif base + 'USD' == 'USDUSD': cross_rets = -self._calculations.calculate_returns( terms_vals) elif terms + 'USD' == 'USDUSD': cross_rets = self._calculations.calculate_returns( base_vals) else: base_rets = self._calculations.calculate_returns(base_vals) terms_rets = self._calculations.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 = self._calculations.create_mult_index(cross_rets) cross_vals.columns = [cr + '-' + type + '.close'] elif freq == 'intraday': LoggerManager().getLogger(__name__).info( 'Total calculated returns for intraday not implemented yet' ) return None return cross_vals
class FXCrossFactory(object): def __init__(self, market_data_generator=None): self.logger = LoggerManager().getLogger(__name__) self.fxconv = FXConv() self.cache = {} self.calculations = Calculations() self.market_data_generator = market_data_generator return def flush_cache(self): self.cache = {} def get_fx_cross_tick(self, start, end, cross, cut="NYC", source="dukascopy", cache_algo='internet_load_return', type='spot', environment='backtest', fields=['bid', 'ask']): if isinstance(cross, str): cross = [cross] market_data_request = MarketDataRequest( gran_freq="tick", freq_mult=1, freq='tick', cut=cut, fields=['bid', 'ask', 'bidv', 'askv'], cache_algo=cache_algo, environment=environment, start_date=start, finish_date=end, data_source=source, category='fx') market_data_generator = self.market_data_generator data_frame_agg = None for cr in cross: if (type == 'spot'): market_data_request.tickers = cr cross_vals = market_data_generator.fetch_market_data( market_data_request) # if user only wants 'close' calculate that from the bid/ask fields if fields == ['close']: cross_vals = cross_vals[[cr + '.bid', cr + '.ask']].mean(axis=1) cross_vals.columns = [cr + '.close'] 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 def get_fx_cross(self, start, end, cross, cut="NYC", source="bloomberg", freq="intraday", cache_algo='internet_load_return', type='spot', environment='backtest', fields=['close']): 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, type='spot', fields=fields) if isinstance(cross, str): cross = [cross] market_data_request_list = [] freq_list = [] type_list = [] for cr in cross: market_data_request = MarketDataRequest(freq_mult=1, cut=cut, fields=['close'], freq=freq, cache_algo=cache_algo, start_date=start, finish_date=end, data_source=source, environment=environment) market_data_request.type = type market_data_request.cross = cr if freq == 'intraday': market_data_request.gran_freq = "minute" # intraday elif freq == 'daily': market_data_request.gran_freq = "daily" # daily market_data_request_list.append(market_data_request) data_frame_agg = [] # depends on the nature of operation as to whether we should use threading or multiprocessing library if DataConstants().market_thread_technique is "thread": from multiprocessing.dummy import Pool else: # most of the time is spend waiting for Bloomberg to return, so can use threads rather than multiprocessing # must use the multiprocessing_on_dill library otherwise can't pickle objects correctly # note: currently not very stable from multiprocessing_on_dill import Pool thread_no = DataConstants().market_thread_no['other'] if market_data_request_list[0].data_source in DataConstants( ).market_thread_no: thread_no = DataConstants().market_thread_no[ market_data_request_list[0].data_source] # fudge, issue with multithreading and accessing HDF5 files # if self.market_data_generator.__class__.__name__ == 'CachedMarketDataGenerator': # thread_no = 0 if (thread_no > 0): pool = Pool(thread_no) # open the market data downloads in their own threads and return the results result = pool.map_async(self._get_individual_fx_cross, market_data_request_list) data_frame_agg = self.calculations.iterative_outer_join( result.get()) # data_frame_agg = self.calculations.pandas_outer_join(result.get()) # pool would have already been closed earlier # try: # pool.close() # pool.join() # except: pass else: for md_request in market_data_request_list: data_frame_agg.append( self._get_individual_fx_cross(md_request)) data_frame_agg = self.calculations.pandas_outer_join( data_frame_agg) # strip the nan elements data_frame_agg = data_frame_agg.dropna() return data_frame_agg def _get_individual_fx_cross(self, market_data_request): cr = market_data_request.cross type = market_data_request.type freq = market_data_request.freq 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 market_data_request.tickers = base_USD market_data_request.category = 'fx' if base_USD + '.close' in self.cache: base_vals = self.cache[base_USD + '.close'] else: base_vals = self.market_data_generator.fetch_market_data( market_data_request) self.cache[base_USD + '.close'] = base_vals # download terms USD cross market_data_request.tickers = terms_USD market_data_request.category = 'fx' if terms_USD + '.close' in self.cache: terms_vals = self.cache[terms_USD + '.close'] else: terms_vals = self.market_data_generator.fetch_market_data( market_data_request) self.cache[terms_USD + '.close'] = terms_vals # if quoted USD/base flip to get USD terms if (base_USD[0:3] == 'USD'): if 'USD' + base in '.close' in self.cache: base_vals = self.cache['USD' + base + '.close'] else: base_vals = 1 / base_vals self.cache['USD' + base + '.close'] = base_vals # if quoted USD/terms flip to get USD terms if (terms_USD[0:3] == 'USD'): if 'USD' + terms in '.close' in self.cache: terms_vals = self.cache['USD' + terms + '.close'] else: terms_vals = 1 / terms_vals self.cache['USD' + terms + '.close'] = base_vals base_vals.columns = ['temp'] terms_vals.columns = ['temp'] cross_vals = base_vals.div(terms_vals, axis='index') cross_vals.columns = [cr + '.close'] base_vals.columns = [base_USD + '.close'] terms_vals.columns = [terms_USD + '.close'] else: # if base == 'USD': non_USD = terms # if terms == 'USD': non_USD = base correct_cr = self.fxconv.correct_notation(cr) market_data_request.tickers = correct_cr market_data_request.category = 'fx' if correct_cr + '.close' in self.cache: cross_vals = self.cache[correct_cr + '.close'] else: cross_vals = self.market_data_generator.fetch_market_data( market_data_request) # flip if not convention if (correct_cr != cr): if cr + '.close' in self.cache: cross_vals = self.cache[cr + '.close'] else: cross_vals = 1 / cross_vals self.cache[cr + '.close'] = cross_vals self.cache[correct_cr + '.close'] = cross_vals # cross_vals = self.market_data_generator.harvest_time_series(market_data_request) cross_vals.columns.names = [cr + '.close'] elif type[0:3] == "tot": if freq == 'daily': # download base USD cross market_data_request.tickers = base + 'USD' market_data_request.category = 'fx-tot' if type == "tot": base_vals = self.market_data_generator.fetch_market_data( market_data_request) else: x = 0 # download terms USD cross market_data_request.tickers = terms + 'USD' market_data_request.category = 'fx-tot' if type == "tot": terms_vals = self.market_data_generator.fetch_market_data( market_data_request) else: pass base_rets = self.calculations.calculate_returns(base_vals) terms_rets = self.calculations.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 = self.calculations.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 return cross_vals
def construct_total_return_index(self, cross_fx, market_df, fx_vol_surface=None, enter_trading_dates=None, fx_options_trading_tenor=None, roll_days_before=None, roll_event=None, roll_months=None, cum_index=None, strike=None, contract_type=None, premium_output=None, position_multiplier=None, fx_options_tenor_for_interpolation=None, freeze_implied_vol=None, depo_tenor_for_option=None, tot_label=None, cal=None, output_calculation_fields=None): if fx_vol_surface is None: fx_vol_surface = self._fx_vol_surface if enter_trading_dates is None: enter_trading_dates = self._enter_trading_dates if fx_options_trading_tenor is None: fx_options_trading_tenor = self._fx_options_trading_tenor if roll_days_before is None: roll_days_before = self._roll_days_before if roll_event is None: roll_event = self._roll_event if roll_months is None: roll_months = self._roll_months if cum_index is None: cum_index = self._cum_index if strike is None: strike = self._strike if contract_type is None: contract_type = self._contact_type if premium_output is None: premium_output = self._premium_output if position_multiplier is None: position_multiplier = self._position_multiplier if fx_options_tenor_for_interpolation is None: fx_options_tenor_for_interpolation = self._fx_options_tenor_for_interpolation if freeze_implied_vol is None: freeze_implied_vol = self._freeze_implied_vol if depo_tenor_for_option is None: depo_tenor_for_option = self._depo_tenor_for_option if tot_label is None: tot_label = self._tot_label if cal is None: cal = self._cal if output_calculation_fields is None: output_calculation_fields = self._output_calculation_fields if not (isinstance(cross_fx, list)): cross_fx = [cross_fx] total_return_index_df_agg = [] # Remove columns where there is no data (because these vols typically aren't quoted) if market_df is not None: market_df = market_df.dropna(how='all', axis=1) fx_options_pricer = FXOptionsPricer(premium_output=premium_output) def get_roll_date(horizon_d, expiry_d, asset_hols, month_adj=0): if roll_event == 'month-end': roll_d = horizon_d + CustomBusinessMonthEnd( roll_months + month_adj, holidays=asset_hols) # Special case so always rolls on month end date, if specify 0 days if roll_days_before > 0: return (roll_d - CustomBusinessDay(n=roll_days_before, holidays=asset_hols)) elif roll_event == 'expiry-date': roll_d = expiry_d # Special case so always rolls on expiry date, if specify 0 days if roll_days_before > 0: return (roll_d - CustomBusinessDay(n=roll_days_before, holidays=asset_hols)) return roll_d for cross in cross_fx: if cal is None: cal = cross # Eg. if we specify USDUSD if cross[0:3] == cross[3:6]: total_return_index_df_agg.append( pd.DataFrame(100, index=market_df.index, columns=[cross + "-option-tot.close"])) else: # Is the FX cross in the correct convention old_cross = cross cross = FXConv().correct_notation(cross) # TODO also specification of non-standard crosses like USDGBP if old_cross != cross: pass if fx_vol_surface is None: fx_vol_surface = FXVolSurface( market_df=market_df, asset=cross, tenors=fx_options_tenor_for_interpolation, depo_tenor=depo_tenor_for_option) market_df = fx_vol_surface.get_all_market_data() horizon_date = market_df.index expiry_date = np.zeros(len(horizon_date), dtype=object) roll_date = np.zeros(len(horizon_date), dtype=object) new_trade = np.full(len(horizon_date), False, dtype=bool) exit_trade = np.full(len(horizon_date), False, dtype=bool) has_position = np.full(len(horizon_date), False, dtype=bool) asset_holidays = self._calendar.get_holidays(cal=cross) # If no entry dates specified, assume we just keep rolling if enter_trading_dates is None: # Get first expiry date expiry_date[ 0] = self._calendar.get_expiry_date_from_horizon_date( pd.DatetimeIndex([horizon_date[0]]), fx_options_trading_tenor, cal=cal, asset_class='fx-vol')[0] # For first month want it to expire within that month (for consistency), hence month_adj=0 ONLY here roll_date[0] = get_roll_date(horizon_date[0], expiry_date[0], asset_holidays, month_adj=0) # New trade => entry at beginning AND on every roll new_trade[0] = True exit_trade[0] = False has_position[0] = True # Get all the expiry dates and roll dates # At each "roll/trade" day we need to reset them for the new contract for i in range(1, len(horizon_date)): has_position[i] = True # If the horizon date has reached the roll date (from yesterday), we're done, and we have a # new roll/trade if (horizon_date[i] - roll_date[i - 1]).days >= 0: new_trade[i] = True else: new_trade[i] = False # If we're entering a new trade/contract (and exiting an old trade) we need to get new expiry and roll dates if new_trade[i]: exp = self._calendar.get_expiry_date_from_horizon_date( pd.DatetimeIndex([horizon_date[i]]), fx_options_trading_tenor, cal=cal, asset_class='fx-vol')[0] # Make sure we don't expire on a date in the history where there isn't market data # It is ok for future values to expire after market data (just not in the backtest!) if exp not in market_df.index: exp_index = market_df.index.searchsorted(exp) if exp_index < len(market_df.index): exp_index = min(exp_index, len(market_df.index)) exp = market_df.index[exp_index] expiry_date[i] = exp roll_date[i] = get_roll_date( horizon_date[i], expiry_date[i], asset_holidays) exit_trade[i] = True else: if horizon_date[i] <= expiry_date[i - 1]: # Otherwise use previous expiry and roll dates, because we're still holding same contract expiry_date[i] = expiry_date[i - 1] roll_date[i] = roll_date[i - 1] exit_trade[i] = False else: exit_trade[i] = True else: new_trade[horizon_date.searchsorted( enter_trading_dates)] = True has_position[horizon_date.searchsorted( enter_trading_dates)] = True # Get first expiry date #expiry_date[0] = \ # self._calendar.get_expiry_date_from_horizon_date(pd.DatetimeIndex([horizon_date[0]]), # fx_options_trading_tenor, cal=cal, # asset_class='fx-vol')[0] # For first month want it to expire within that month (for consistency), hence month_adj=0 ONLY here #roll_date[0] = get_roll_date(horizon_date[0], expiry_date[0], asset_holidays, month_adj=0) # New trade => entry at beginning AND on every roll #new_trade[0] = True #exit_trade[0] = False #has_position[0] = True # Get all the expiry dates and roll dates # At each "roll/trade" day we need to reset them for the new contract for i in range(0, len(horizon_date)): # If we're entering a new trade/contract (and exiting an old trade) we need to get new expiry and roll dates if new_trade[i]: exp = \ self._calendar.get_expiry_date_from_horizon_date(pd.DatetimeIndex([horizon_date[i]]), fx_options_trading_tenor, cal=cal, asset_class='fx-vol')[0] # Make sure we don't expire on a date in the history where there isn't market data # It is ok for future values to expire after market data (just not in the backtest!) if exp not in market_df.index: exp_index = market_df.index.searchsorted(exp) if exp_index < len(market_df.index): exp_index = min(exp_index, len(market_df.index)) exp = market_df.index[exp_index] expiry_date[i] = exp # roll_date[i] = get_roll_date(horizon_date[i], expiry_date[i], asset_holidays) # if i > 0: # Makes the assumption we aren't rolling contracts exit_trade[i] = False else: if i > 0: # Check there's valid expiry on previous day (if not then we're not in an option trade here!) if expiry_date[i - 1] == 0: has_position[i] = False else: if horizon_date[i] <= expiry_date[i - 1]: # Otherwise use previous expiry and roll dates, because we're still holding same contract expiry_date[i] = expiry_date[i - 1] # roll_date[i] = roll_date[i - 1] has_position[i] = True if horizon_date[i] == expiry_date[i]: exit_trade[i] = True else: exit_trade[i] = False # Note: may need to add discount factor when marking to market option mtm = np.zeros(len(horizon_date)) calculated_strike = np.zeros(len(horizon_date)) interpolated_option = np.zeros(len(horizon_date)) implied_vol = np.zeros(len(horizon_date)) delta = np.zeros(len(horizon_date)) # For debugging df_temp = pd.DataFrame() df_temp['expiry-date'] = expiry_date df_temp['horizon-date'] = horizon_date df_temp['roll-date'] = roll_date df_temp['new-trade'] = new_trade df_temp['exit-trade'] = exit_trade df_temp['has-position'] = has_position if has_position[0]: # Special case: for first day of history (given have no previous positions) option_values_, spot_, strike_, vol_, delta_, expiry_date_, intrinsic_values_ = \ fx_options_pricer.price_instrument(cross, horizon_date[0], strike, expiry_date[0], contract_type=contract_type, tenor=fx_options_trading_tenor, fx_vol_surface=fx_vol_surface, return_as_df=False) interpolated_option[0] = option_values_ calculated_strike[0] = strike_ implied_vol[0] = vol_ mtm[0] = 0 # Now price options for rest of history # On rolling dates: MTM will be the previous option contract (interpolated) # On non-rolling dates: it will be the current option contract for i in range(1, len(horizon_date)): if exit_trade[i]: # Price option trade being exited option_values_, spot_, strike_, vol_, delta_, expiry_date_, intrinsic_values_ = \ fx_options_pricer.price_instrument(cross, horizon_date[i], calculated_strike[i-1], expiry_date[i-1], contract_type=contract_type, tenor=fx_options_trading_tenor, fx_vol_surface=fx_vol_surface, return_as_df=False) # Store as MTM mtm[i] = option_values_ delta[ i] = 0 # Note: this will get overwritten if there's a new trade calculated_strike[i] = calculated_strike[ i - 1] # Note: this will get overwritten if there's a new trade if new_trade[i]: # Price new option trade being entered option_values_, spot_, strike_, vol_, delta_, expiry_date_, intrinsic_values_ = \ fx_options_pricer.price_instrument(cross, horizon_date[i], strike, expiry_date[i], contract_type=contract_type, tenor=fx_options_trading_tenor, fx_vol_surface=fx_vol_surface, return_as_df=False) calculated_strike[ i] = strike_ # option_output[cross + '-strike.close'].values implied_vol[i] = vol_ interpolated_option[i] = option_values_ delta[i] = delta_ elif has_position[i] and not (exit_trade[i]): # Price current option trade # - strike/expiry the same as yesterday # - other market inputs taken live, closer to expiry calculated_strike[i] = calculated_strike[i - 1] if freeze_implied_vol: frozen_vol = implied_vol[i - 1] else: frozen_vol = None option_values_, spot_, strike_, vol_, delta_, expiry_date_, intrinsic_values_ = \ fx_options_pricer.price_instrument(cross, horizon_date[i], calculated_strike[i], expiry_date[i], vol=frozen_vol, contract_type=contract_type, tenor=fx_options_trading_tenor, fx_vol_surface=fx_vol_surface, return_as_df=False) interpolated_option[i] = option_values_ implied_vol[i] = vol_ mtm[i] = interpolated_option[i] delta[i] = delta_ # Calculate delta hedging P&L spot_rets = (market_df[cross + ".close"] / market_df[cross + ".close"].shift(1) - 1).values if tot_label == '': tot_rets = spot_rets else: tot_rets = ( market_df[cross + "-" + tot_label + ".close"] / market_df[cross + "-" + tot_label + ".close"].shift(1) - 1).values # Remember to take the inverted sign, eg. if call is +20%, we need to -20% of spot to flatten delta # Also invest for whether we are long or short the option delta_hedging_pnl = -np.roll( delta, 1) * tot_rets * position_multiplier delta_hedging_pnl[0] = 0 # Calculate options P&L (given option premium is already percentage, only need to subtract) # Again need to invert if we are short option option_rets = (mtm - np.roll(interpolated_option, 1)) * position_multiplier option_rets[0] = 0 # Calculate option + delta hedging P&L option_delta_rets = delta_hedging_pnl + option_rets if cum_index == 'mult': cum_rets = 100 * np.cumprod(1.0 + option_rets) cum_delta_rets = 100 * np.cumprod(1.0 + delta_hedging_pnl) cum_option_delta_rets = 100 * np.cumprod(1.0 + option_delta_rets) elif cum_index == 'add': cum_rets = 100 + 100 * np.cumsum(option_rets) cum_delta_rets = 100 + 100 * np.cumsum(delta_hedging_pnl) cum_option_delta_rets = 100 + 100 * np.cumsum( option_delta_rets) total_return_index_df = pd.DataFrame( index=horizon_date, columns=[cross + "-option-tot.close"]) total_return_index_df[cross + "-option-tot.close"] = cum_rets if output_calculation_fields: total_return_index_df[ cross + '-interpolated-option.close'] = interpolated_option total_return_index_df[cross + '-mtm.close'] = mtm total_return_index_df[cross + ".close"] = market_df[ cross + ".close"].values total_return_index_df[cross + '-implied-vol.close'] = implied_vol total_return_index_df[cross + '-new-trade.close'] = new_trade total_return_index_df[cross + '.roll-date'] = roll_date total_return_index_df[cross + '-exit-trade.close'] = exit_trade total_return_index_df[cross + '.expiry-date'] = expiry_date total_return_index_df[ cross + '-calculated-strike.close'] = calculated_strike total_return_index_df[cross + '-option-return.close'] = option_rets total_return_index_df[cross + '-spot-return.close'] = spot_rets total_return_index_df[cross + '-tot-return.close'] = tot_rets total_return_index_df[cross + '-delta.close'] = delta total_return_index_df[ cross + '-delta-pnl-return.close'] = delta_hedging_pnl total_return_index_df[ cross + '-delta-pnl-index.close'] = cum_delta_rets total_return_index_df[ cross + '-option-delta-return.close'] = option_delta_rets total_return_index_df[ cross + '-option-delta-tot.close'] = cum_option_delta_rets total_return_index_df_agg.append(total_return_index_df) return self._calculations.join(total_return_index_df_agg, how='outer')
def fetch_continuous_time_series(self, md_request, market_data_generator, fx_vol_surface=None, enter_trading_dates=None, fx_options_trading_tenor=None, roll_days_before=None, roll_event=None, construct_via_currency=None, fx_options_tenor_for_interpolation=None, base_depos_tenor=None, roll_months=None, cum_index=None, strike=None, contract_type=None, premium_output=None, position_multiplier=None, depo_tenor_for_option=None, freeze_implied_vol=None, tot_label=None, cal=None, output_calculation_fields=None): if fx_vol_surface is None: fx_vol_surface = self._fx_vol_surface if enter_trading_dates is None: enter_trading_dates = self._enter_trading_dates if market_data_generator is None: market_data_generator = self._market_data_generator if fx_options_trading_tenor is None: fx_options_trading_tenor = self._fx_options_trading_tenor if roll_days_before is None: roll_days_before = self._roll_days_before if roll_event is None: roll_event = self._roll_event if construct_via_currency is None: construct_via_currency = self._construct_via_currency if fx_options_tenor_for_interpolation is None: fx_options_tenor_for_interpolation = self._fx_options_tenor_for_interpolation if base_depos_tenor is None: base_depos_tenor = self._base_depos_tenor if roll_months is None: roll_months = self._roll_months if strike is None: strike = self._strike if contract_type is None: contract_type = self._contact_type if premium_output is None: premium_output = self._premium_output if position_multiplier is None: position_multiplier = self._position_multiplier if depo_tenor_for_option is None: depo_tenor_for_option = self._depo_tenor_for_option if freeze_implied_vol is None: freeze_implied_vol = self._freeze_implied_vol if tot_label is None: tot_label = self._tot_label if cal is None: cal = self._cal if output_calculation_fields is None: output_calculation_fields = self._output_calculation_fields # Eg. we construct EURJPY via EURJPY directly (note: would need to have sufficient options/forward data for this) if construct_via_currency == 'no': if fx_vol_surface is None: # Download FX spot, FX forwards points and base depos etc. market = Market(market_data_generator=market_data_generator) md_request_download = MarketDataRequest(md_request=md_request) fx_conv = FXConv() # CAREFUL: convert the tickers to correct notation, eg. USDEUR => EURUSD, because our data # should be fetched in correct convention md_request_download.tickers = [ fx_conv.correct_notation(x) for x in md_request.tickers ] md_request_download.category = 'fx-vol-market' md_request_download.fields = 'close' md_request_download.abstract_curve = None md_request_download.fx_options_tenor = fx_options_tenor_for_interpolation md_request_download.base_depos_tenor = base_depos_tenor # md_request_download.base_depos_currencies = [] forwards_market_df = market.fetch_market(md_request_download) else: forwards_market_df = None # Now use the original tickers return self.construct_total_return_index( md_request.tickers, forwards_market_df, fx_vol_surface=fx_vol_surface, enter_trading_dates=enter_trading_dates, fx_options_trading_tenor=fx_options_trading_tenor, roll_days_before=roll_days_before, roll_event=roll_event, fx_options_tenor_for_interpolation= fx_options_tenor_for_interpolation, roll_months=roll_months, cum_index=cum_index, strike=strike, contract_type=contract_type, premium_output=premium_output, position_multiplier=position_multiplier, freeze_implied_vol=freeze_implied_vol, depo_tenor_for_option=depo_tenor_for_option, tot_label=tot_label, cal=cal, output_calculation_fields=output_calculation_fields) else: # eg. we calculate via your domestic currency such as USD, so returns will be in your domestic currency # Hence AUDJPY would be calculated via AUDUSD and JPYUSD (subtracting the difference in returns) total_return_indices = [] for tick in md_request.tickers: base = tick[0:3] terms = tick[3:6] md_request_base = MarketDataRequest(md_request=md_request) md_request_base.tickers = base + construct_via_currency md_request_terms = MarketDataRequest(md_request=md_request) md_request_terms.tickers = terms + construct_via_currency # Construct the base and terms separately (ie. AUDJPY => AUDUSD & JPYUSD) base_vals = self.fetch_continuous_time_series( md_request_base, market_data_generator, fx_vol_surface=fx_vol_surface, enter_trading_dates=enter_trading_dates, fx_options_trading_tenor=fx_options_trading_tenor, roll_days_before=roll_days_before, roll_event=roll_event, fx_options_tenor_for_interpolation= fx_options_tenor_for_interpolation, base_depos_tenor=base_depos_tenor, roll_months=roll_months, cum_index=cum_index, strike=strike, contract_type=contract_type, premium_output=premium_output, position_multiplier=position_multiplier, depo_tenor_for_option=depo_tenor_for_option, freeze_implied_vol=freeze_implied_vol, tot_label=tot_label, cal=cal, output_calculation_fields=output_calculation_fields, construct_via_currency='no') terms_vals = self.fetch_continuous_time_series( md_request_terms, market_data_generator, fx_vol_surface=fx_vol_surface, enter_trading_dates=enter_trading_dates, fx_options_trading_tenor=fx_options_trading_tenor, roll_days_before=roll_days_before, roll_event=roll_event, fx_options_tenor_for_interpolation= fx_options_tenor_for_interpolation, base_depos_tenor=base_depos_tenor, roll_months=roll_months, cum_index=cum_index, strike=strike, contract_type=contract_type, position_multiplier=position_multiplier, depo_tenor_for_option=depo_tenor_for_option, freeze_implied_vol=freeze_implied_vol, tot_label=tot_label, cal=cal, output_calculation_fields=output_calculation_fields, construct_via_currency='no') # Special case for USDUSD case (and if base or terms USD are USDUSD if base + terms == construct_via_currency + construct_via_currency: base_rets = self._calculations.calculate_returns(base_vals) cross_rets = pd.DataFrame(0, index=base_rets.index, columns=base_rets.columns) elif base + construct_via_currency == construct_via_currency + construct_via_currency: cross_rets = -self._calculations.calculate_returns( terms_vals) elif terms + construct_via_currency == construct_via_currency + construct_via_currency: cross_rets = self._calculations.calculate_returns( base_vals) else: base_rets = self._calculations.calculate_returns(base_vals) terms_rets = self._calculations.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 = self._calculations.create_mult_index(cross_rets) cross_vals.columns = [tick + '-option-tot.close'] total_return_indices.append(cross_vals) return self._calculations.join(total_return_indices, how='outer')
def construct_total_return_index(self, cross_fx, forwards_market_df, fx_forwards_trading_tenor=None, roll_days_before=None, roll_event=None, roll_months=None, fx_forwards_tenor_for_interpolation=None, cum_index=None, output_calculation_fields=False): if not (isinstance(cross_fx, list)): cross_fx = [cross_fx] if fx_forwards_trading_tenor is None: fx_forwards_trading_tenor = self._fx_forwards_trading_tenor if roll_days_before is None: roll_days_before = self._roll_days_before if roll_event is None: roll_event = self._roll_event if roll_months is None: roll_months = self._roll_months if fx_forwards_tenor_for_interpolation is None: fx_forwards_tenor_for_interpolation = self._fx_forwards_tenor_for_interpolation if cum_index is None: cum_index = self._cum_index total_return_index_df_agg = [] # Remove columns where there is no data (because these points typically aren't quoted) forwards_market_df = forwards_market_df.dropna(how='all', axis=1) fx_forwards_pricer = FXForwardsPricer() def get_roll_date(horizon_d, delivery_d, asset_hols, month_adj=1): if roll_event == 'month-end': roll_d = horizon_d + CustomBusinessMonthEnd(roll_months + month_adj, holidays=asset_hols) elif roll_event == 'delivery-date': roll_d = delivery_d return (roll_d - CustomBusinessDay(n=roll_days_before, holidays=asset_hols)) for cross in cross_fx: # Eg. if we specify USDUSD if cross[0:3] == cross[3:6]: total_return_index_df_agg.append( pd.DataFrame(100, index=forwards_market_df.index, columns=[cross + "-forward-tot.close"])) else: # Is the FX cross in the correct convention old_cross = cross cross = FXConv().correct_notation(cross) horizon_date = forwards_market_df.index delivery_date = [] roll_date = [] new_trade = np.full(len(horizon_date), False, dtype=bool) asset_holidays = self._calendar.get_holidays(cal=cross) # Get first delivery date delivery_date.append( self._calendar.get_delivery_date_from_horizon_date(horizon_date[0], fx_forwards_trading_tenor, cal=cross, asset_class='fx')[0]) # For first month want it to expire within that month (for consistency), hence month_adj=0 ONLY here roll_date.append(get_roll_date(horizon_date[0], delivery_date[0], asset_holidays, month_adj=0)) # New trade => entry at beginning AND on every roll new_trade[0] = True # Get all the delivery dates and roll dates # At each "roll/trade" day we need to reset them for the new contract for i in range(1, len(horizon_date)): # If the horizon date has reached the roll date (from yesterday), we're done, and we have a # new roll/trade if (horizon_date[i] - roll_date[i-1]).days == 0: new_trade[i] = True # else: # new_trade[i] = False # If we're entering a new trade/contract, we need to get new delivery and roll dates if new_trade[i]: delivery_date.append(self._calendar.get_delivery_date_from_horizon_date(horizon_date[i], fx_forwards_trading_tenor, cal=cross, asset_class='fx')[0]) roll_date.append(get_roll_date(horizon_date[i], delivery_date[i], asset_holidays)) else: # Otherwise use previous delivery and roll dates, because we're still holding same contract delivery_date.append(delivery_date[i-1]) roll_date.append(roll_date[i-1]) interpolated_forward = fx_forwards_pricer.price_instrument(cross, horizon_date, delivery_date, market_df=forwards_market_df, fx_forwards_tenor_for_interpolation=fx_forwards_tenor_for_interpolation)[cross + '-interpolated-outright-forward.close'].values # To record MTM prices mtm = np.copy(interpolated_forward) # Note: may need to add discount factor when marking to market forwards? # Special case: for very first trading day # mtm[0] = interpolated_forward[0] # On rolling dates, MTM will be the previous forward contract (interpolated) # otherwise it will be the current forward contract for i in range(1, len(horizon_date)): if new_trade[i]: mtm[i] = fx_forwards_pricer.price_instrument(cross, horizon_date[i], delivery_date[i-1], market_df=forwards_market_df, fx_forwards_tenor_for_interpolation=fx_forwards_tenor_for_interpolation) \ [cross + '-interpolated-outright-forward.close'].values # else: # mtm[i] = interpolated_forward[i] # Eg. if we asked for USDEUR, we first constructed spot/forwards for EURUSD # and then need to invert it if old_cross != cross: mtm = 1.0 / mtm interpolated_forward = 1.0 / interpolated_forward forward_rets = mtm / np.roll(interpolated_forward, 1) - 1.0 forward_rets[0] = 0 if cum_index == 'mult': cum_rets = 100 * np.cumprod(1.0 + forward_rets) elif cum_index == 'add': cum_rets = 100 + 100 * np.cumsum(forward_rets) total_return_index_df = pd.DataFrame(index=horizon_date, columns=[cross + "-forward-tot.close"]) total_return_index_df[cross + "-forward-tot.close"] = cum_rets if output_calculation_fields: total_return_index_df[cross + '-interpolated-outright-forward.close'] = interpolated_forward total_return_index_df[cross + '-mtm.close'] = mtm total_return_index_df[cross + '-roll.close'] = new_trade total_return_index_df[cross + '.roll-date'] = roll_date total_return_index_df[cross + '.delivery-date'] = delivery_date total_return_index_df[cross + '-forward-return.close'] = forward_rets total_return_index_df_agg.append(total_return_index_df) return self._calculations.pandas_outer_join(total_return_index_df_agg)
# for logging from findatapy.util.loggermanager import LoggerManager # for signal generation from finmarketpy.economics import TechIndicator, TechParams # for plotting from chartpy import Chart, Style logger = LoggerManager().getLogger(__name__) import datetime backtest = Backtest() br = BacktestRequest() fxconv = FXConv() # get all asset data br.start_date = "02 Jan 1990" br.finish_date = datetime.datetime.utcnow() br.spot_tc_bp = 2.5 # 2.5 bps bid/ask spread br.ann_factor = 252 # have vol target for each signal br.signal_vol_adjust = True br.signal_vol_target = 0.05 br.signal_vol_max_leverage = 3 br.signal_vol_periods = 60 br.signal_vol_obs_in_year = 252 br.signal_vol_rebalance_freq = 'BM' br.signal_vol_resample_freq = None