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curvy_tracker9.py
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import pdblp
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import math
from datetime import datetime
from scipy.optimize import minimize # optimization function
from pandas.tseries.offsets import BDay, BMonthBegin
from pandas.tseries.offsets import BMonthEnd
from tqdm import tqdm # this will just make thing a bit prettier
from scipy.stats import skew
from scipy.stats import kurtosis
import time
from itertools import combinations, product
class CurvyTrade:
"""
This class calculates trackers for FX carry trade and curvy trade.
Enters in a 1 month fwd position and rebalances monthly. MtM is daily.
"""
# TODO DONE put a default folder for data as attribute
# TODO DONE build a USD index
# TODO DONE and corrected: study disparity btw monthly and daily
# TODO DONE allow k greater than 4
# TODO betas of strategies Vs USD index
# TODO Breakdown of interest return and spot return
# TODO start date indicator
# TODO refactor code on strategies, so it can be standized by number
# Available currencies
# TODO DONE add 'BRL', 'CLP', 'COP', 'HUF', 'KRW', 'MXN', 'PHP', 'PLN', 'SGD', 'TRY', 'TWD', 'ZAR'
currency_list = ['AUD', 'CAD', 'CHF', 'DEM', 'EUR', 'GBP', 'JPY', 'NOK', 'NZD', 'SEK', 'BRL', 'CLP', 'COP', 'MXN',
'CZK', 'HUF', 'PLN', 'TRY', 'RUB', 'INR', 'HKD', 'KRW', 'SGD', 'TWD', 'ZAR']
currency_list_curve = ['AUD', 'CAD', 'CHF', 'DEM', 'EUR', 'GBP', 'JPY', 'NOK', 'NZD', 'SEK', 'USD', 'BRL', 'CLP', 'COP', 'MXN',
'CZK', 'HUF', 'PLN', 'TRY', 'RUB', 'INR', 'HKD', 'KRW', 'SGD', 'TWD', 'ZAR']
currency_list_DM = ['AUD', 'CAD', 'CHF', 'DEM', 'EUR', 'GBP', 'JPY', 'NOK', 'NZD', 'SEK']
# REMINDER
# Dates: start and end dates are used for strategy period. Price/market data will start from vol_date (hardcoded)
# Default data folder
data_folder = 'data'
# BBG connection
con = pdblp.BCon(debug=True, port=8194, timeout=5000)
con.debug = False
con.start()
# tickers, start date, end date
bbg_field_last = ['PX_LAST']
bbg_field_bid = ['PX_LAST']
bbg_field_ask = ['PX_LAST']
bbg_periodicity_spot = 'DAILY'
bbg_periodicity_interest_curve = 'DAILY'
# ini_date_bbg = '19890101' # extended period for vol calculation
# data_ini = '19910101'
# end_date_bbg = '20190702'
# data_fim = '20190527'
# TODO outliers
# BRL: start from 1999-05
# TRY: from 2001-08??
# ### Spots e fwd points
#
# We consider all G10 currencies vis-a-vis the US dollar, including the Australian dollar, the British pound, the Canadian dollar, the euro (Deutsche mark before 1999), the Japanese yen, the New Zealand dollar, the Norwegian krone, the Swiss franc and the Swedish krona.
#
# We use monthly end-of-period data, with the sample spanning the period from January 1991 to December 2015.
# TODO allow for today as end_date
fwd_dict = {'1W BGN Curncy': 7.0,
'2W BGN Curncy': 14.0,
'3W BGN Curncy': 21.0,
'1M BGN Curncy': 31.0,
'2M BGN Curncy': 61.0,
'3M BGN Curncy': 91.0,
'4M BGN Curncy': 121.0,
'5M BGN Curncy': 151.0,
'6M BGN Curncy': 181.0,
'1W CMPN Curncy': 7.0,
'2W CMPN Curncy': 14.0,
'3W CMPN Curncy': 21.0,
'1M CMPN Curncy': 31.0,
'2M CMPN Curncy': 61.0,
'3M CMPN Curncy': 91.0,
'4M CMPN Curncy': 121.0,
'5M CMPN Curncy': 151.0,
'6M CMPN Curncy': 181.0
}
dict_fwdpts_source = {'AUD': 'BGN',
'CAD': 'BGN',
'CHF': 'BGN',
'DEM': 'CMPN',
'EUR': 'BGN',
'GBP': 'BGN',
'JPY': 'BGN',
'NOK': 'BGN',
'NZD': 'BGN',
'SEK': 'BGN',
'BRL': 'BGN',
'CLP': 'BGN',
'COP': 'BGN',
'MXN': 'BGN',
'CZK': 'BGN',
'HUF': 'BGN',
'PLN': 'BGN',
'TRY': 'BGN',
'RUB': 'BGN',
'INR': 'BGN',
'HKD': 'BGN',
'KRW': 'BGN',
'PHP': 'BGN',
'SGD': 'BGN',
'TWD': 'BGN',
'ZAR': 'BGN'}
dict_FX_NDF = {'AUD': 'AUD',
'CAD': 'CAD',
'CHF': 'CHF',
'DEM': 'DEM',
'EUR': 'EUR',
'GBP': 'GBP',
'JPY': 'JPY',
'NOK': 'NOK',
'NZD': 'NZD',
'SEK': 'SEK',
'BRL': 'BCN',
'CLP': 'CHN',
'COP': 'CLN',
'MXN': 'MXN',
'CZK': 'CZK',
'HUF': 'HUF',
'PLN': 'PLN',
'TRY': 'TRY',
'RUB': 'RUB',
'INR': 'IRN',
'HKD': 'HKD',
'KRW': 'KWN',
'PHP': 'PPN',
'SGD': 'SGD',
'TWD': 'NTN',
'ZAR': 'ZAR'
}
def __init__(self, start_date='1991-01-01', end_date='2019-07-02', k_max=4):
# Start/End date of strategy
time_start = time.time()
self.total_strategy_number = 11
self.k_max = k_max
self.double_sorting_groups = 3
self.double_sorting_subgroups = 2
self.signal_moving_avg = 5
self.k_range, self.k_list = self._build_k_list()
self.ini_date = pd.to_datetime(start_date)
self.end_date = pd.to_datetime(end_date)
# Initial date for getting prices/mkt data
self.ini_date_prices = pd.to_datetime('1989-01-01')
# Dates in BBG format (string)
self.ini_date_bbg = datetime.strftime(self.ini_date, '%Y%m%d')
self.end_date_bbg = datetime.strftime(self.end_date, '%Y%m%d')
self.ini_date_prices_bbg = datetime.strftime(self.ini_date_prices, '%Y%m%d')
# Calendars
# Celendar for strategy
self.daily_calendar = self._build_bday_calendars(self.ini_date, self.end_date)
self.monthly_calendar = self._build_monthly_calendars(self.ini_date, self.end_date)
# Calendar for prices / mkt data
self.daily_calendar_extended = self._build_bday_calendars(self.ini_date_prices, self.end_date)
# self.list_daily_pnl_dates = pd.DataFrame(index=self.daily_calendar).loc['1991-01-31':].index
def run_constructor_in_parts1(self):
time_start = time.time()
# Tickers
self.ticker_spot_bbg = [c + ' Curncy' for c in self.currency_list]
# Conversion dictionaries
self.dict_FX_spot = dict(zip(self.currency_list, self.ticker_spot_bbg))
self.dict_spot_FX = dict(zip(self.ticker_spot_bbg, self.currency_list))
self.dict_NDF_FX = dict(zip(self.dict_FX_NDF.values(), self.dict_FX_NDF.keys()))
self.df_strategy = self.build_strategy_table()
try:
# Try to read df_tickers.xlsx, otherwise get from BBG and write to Excel
df_tickers = pd.read_excel(self.data_folder + '\\' + 'df_tickers.xlsx')
self.df_tickers = df_tickers
except FileNotFoundError:
self.df_tickers = self._build_df_tickers()
self.df_tickers.to_excel(self.data_folder + '\\' + 'df_tickers.xlsx')
self.ticker_fwdpts_1m = list(self.df_tickers['fwdpts'])
self.ticker_scale = self.df_tickers['scale']
self.ticker_inverse = self.df_tickers['inverse']
try:
# Try to read df_tickers_curve.xlsx, otherwise get from BBG and write to Excel
df_tickers_curve = pd.read_excel(self.data_folder + '\\' + 'df_tickers_curve.xlsx', index_col=[0,1])
self.df_tickers_curve = df_tickers_curve
except FileNotFoundError:
self.df_tickers_curve = self._build_df_tickers_curve()
self.df_tickers_curve.to_excel(self.data_folder + '\\' + 'df_tickers_curve.xlsx')
self.ticker_year_dict = self._build_dict_ticker_year()
print('tickers OK - ', time.time() - time_start, ' seconds')
time_start = time.time()
# Prices
# Last price
time_start = time.time()
try:
# Try to read spot_last.xlsx, otherwise get from BBG and write to Excel
df_spot_last = pd.read_excel(self.data_folder + '\\' + 'spot_last.xlsx')
self.spot_last = df_spot_last
# Adjusting DEM and EUR
self.spot_last.loc['1998-12':]['DEM'] = np.NaN
self.spot_last.loc[:'1998-12']['EUR'] = np.NaN
except FileNotFoundError:
self.spot_last = self._get_spot_data(self.bbg_field_last)
# Adjusting DEM and EUR
self.spot_last.loc['1998-12':]['DEM'] = np.NaN
self.spot_last.loc[:'1998-12']['EUR'] = np.NaN
self.spot_last.to_excel(self.data_folder + '\\' + 'spot_last.xlsx')
print('spot_last OK - ', time.time() - time_start, ' seconds')
# Bid and Ask of spot price
time_start = time.time()
try:
# Try to read spot_bid.xlsx, otherwise get from BBG and write to Excel
df_spot_bid = pd.read_excel(self.data_folder + '\\' + 'spot_bid.xlsx')
df_spot_ask = pd.read_excel(self.data_folder + '\\' + 'spot_ask.xlsx')
self.spot_bid = df_spot_bid
self.spot_ask = df_spot_ask
except FileNotFoundError:
self.spot_bid = self._get_spot_data(self.bbg_field_bid)
self.spot_ask = self._get_spot_data(self.bbg_field_ask)
self.adjust_spot_bid_ask()
self.spot_bid.to_excel(self.data_folder + '\\' + 'spot_bid.xlsx')
self.spot_ask.to_excel(self.data_folder + '\\' + 'spot_ask.xlsx')
print('spot_bid and ask OK - ', time.time() - time_start, ' seconds')
# Fwd points last
time_start = time.time()
try:
# Try to read fwdpts_last.xlsx, otherwise get from BBG and write to Excel
df_fwdpts_last = pd.read_excel(self.data_folder + '\\' + 'fwdpts_last.xlsx')
self.fwdpts_last = df_fwdpts_last
# Adjusting DEM and EUR
self.fwdpts_last.loc['1998-12':]['DEM'] = np.NaN
self.fwdpts_last.loc[:'1998-12']['EUR'] = np.NaN
except FileNotFoundError:
self.fwdpts_last = self._get_fwdpts_data(self.bbg_field_last)
# Adjusting DEM and EUR
self.fwdpts_last.loc['1998-12':]['DEM'] = np.NaN
self.fwdpts_last.loc[:'1998-12']['EUR'] = np.NaN
self.fwdpts_last.to_excel(self.data_folder + '\\' + 'fwdpts_last.xlsx')
print('fwdpts_last OK - ', time.time() - time_start, ' seconds')
# Fwd points bid and ask
time_start = time.time()
try:
# Try to read fwdpts_bid.xlsx, otherwise get from BBG and write to Excel
# Try to read fwdpts_ask.xlsx, otherwise get from BBG and write to Excel
df_fwdpts_bid = pd.read_excel(self.data_folder + '\\' + 'fwdpts_bid.xlsx')
df_fwdpts_ask = pd.read_excel(self.data_folder + '\\' + 'fwdpts_ask.xlsx')
self.fwdpts_bid = df_fwdpts_bid
self.fwdpts_ask = df_fwdpts_ask
except FileNotFoundError:
self.fwdpts_bid = self._get_fwdpts_data(self.bbg_field_bid)
self.fwdpts_ask = self._get_fwdpts_data(self.bbg_field_ask)
self.adjust_fwdpts_bid_ask()
self.fwdpts_bid.to_excel(self.data_folder + '\\' + 'fwdpts_bid.xlsx')
self.fwdpts_ask.to_excel(self.data_folder + '\\' + 'fwdpts_ask.xlsx')
print('fwdpts_bid and ask OK - ', time.time() - time_start, ' seconds')
# Outright forward, last price
time_start = time.time()
self.fwd_last = self._get_fwd_outright(self.spot_last, self.fwdpts_last, self.ticker_scale)
print('fwd_last OK - ', time.time() - time_start, ' seconds')
# Outright forward, bid price
time_start = time.time()
self.fwd_bid = self._get_fwd_outright(self.spot_bid, self.fwdpts_bid, self.ticker_scale)
print('fwd_bid OK - ', time.time() - time_start, ' seconds')
# Outright forward, ask price
time_start = time.time()
self.fwd_ask = self._get_fwd_outright(self.spot_ask, self.fwdpts_ask, self.ticker_scale)
print('fwd_ask OK - ', time.time() - time_start, ' seconds')
# prices in XXXUSD terms
time_start = time.time()
self.spot_last_XXXUSD = self.spot_last ** self.ticker_inverse
self.spot_bid_XXXUSD = self.spot_bid ** self.ticker_inverse
self.spot_ask_XXXUSD = self.spot_ask ** self.ticker_inverse
self.fwd_last_XXXUSD = self.fwd_last ** self.ticker_inverse
self.fwd_bid_XXXUSD = self.fwd_bid ** self.ticker_inverse
self.fwd_ask_XXXUSD = self.fwd_ask ** self.ticker_inverse
self.fwd_discount_last = (self.spot_last_XXXUSD / self.fwd_last_XXXUSD - 1) * 12 # signal fwd premium
self.tradeable_fx = (~np.isnan(self.fwd_discount_last))
print('fwd_discount_last OK - ', time.time() - time_start, ' seconds')
time_start = time.time()
# self.vols = self._ewma_vol(ewma_lambda=0.94) # discontinued after version 8
self.vols = self._std_vol(window=126)
self.adjust_vol()
self.vols.to_excel(self.data_folder + '\\' + 'vols.xlsx')
print('vol EWMA OK - ', time.time() - time_start, ' seconds')
self.currency_list_EM = self.build_currency_list_EM()
time_start = time.time()
try:
# Try to read USD_index.xlsx, otherwise calculate it and write to Excel
df_USD_index = pd.read_excel(self.data_folder + '\\' + 'USD_index.xlsx')
self.USD_index = df_USD_index
except FileNotFoundError:
self.USD_index = self.run_USD_index_all()
self.USD_index.to_excel(self.data_folder + '\\' + 'USD_index.xlsx')
print('USD index OK - ', time.time() - time_start, ' seconds')
def run_constructor_in_parts2(self):
time_start = time.time()
try:
# Try to read interest_curve.xlsx, otherwise get from BBG and write to Excel
df_interest_curve = pd.read_excel(self.data_folder + '\\' + 'interest_curve.xlsx', index_col=[0,1])
self.interest_curve = df_interest_curve
except FileNotFoundError:
self.interest_curve = self._get_all_interest_curves()
if 'BRL' in self.currency_list:
self.BZ_curve_data() # includes Bz curves
if 'DE<' in self.currency_list:
self.DEM_curve_data() # includes DEM curves
self.interest_curve.to_excel(self.data_folder + '\\' + 'interest_curve.xlsx')
print('interest_curve OK - ', time.time() - time_start, ' seconds')
self.calendar_curves = list(self.interest_curve.index.get_level_values(1).unique()) # available dates (curves)
def run_constructor_in_parts3(self):
time_start = time.time()
# self.nsiegel_betas_all_tenors = self._run_NSiegel_fitting()
try:
# Try to read nsiegel_betas_all_tenors.xlsx, otherwise get from BBG and write to Excel
df_nsiegel_betas = pd.read_excel(self.data_folder + '\\' + 'nsiegel_betas_all_tenors.xlsx', index_col=[0, 1])
self.nsiegel_betas_all_tenors = df_nsiegel_betas
except FileNotFoundError:
self.nsiegel_betas_all_tenors = self._run_NSiegel_fitting_sequential()
self.nsiegel_betas_all_tenors.to_excel(self.data_folder + '\\' + 'nsiegel_betas_all_tenors.xlsx')
print('NS-all tenors OK - ', time.time() - time_start, ' seconds')
def run_constructor_in_parts4(self):
time_start = time.time()
try:
# Try to read nsiegel_betas_3month.xlsx, otherwise get from BBG and write to Excel
df_nsiegel_betas = pd.read_excel(self.data_folder + '\\' + 'nsiegel_betas_3month.xlsx', index_col=[0,1])
self.nsiegel_betas_3month = df_nsiegel_betas
except FileNotFoundError:
self.nsiegel_betas_3month = self._run_NSiegel_fitting_sequential(tenors_greater_than_n_years=0.25)
# filling NaN for BRL series
if 'BRL' in self.currency_list:
nsiegel_betas_3month_aux = self.nsiegel_betas_3month.loc['BRL'].fillna(method='ffill', limit=3)
dates_list = self.nsiegel_betas_3month.loc['BRL'].index
for dt in dates_list:
self.nsiegel_betas_3month.loc['BRL', dt] = nsiegel_betas_3month_aux.loc[dt]
if 'DEM' in self.currency_list:
nsiegel_betas_3month_aux = self.nsiegel_betas_3month.loc['DEM'].fillna(method='ffill', limit=3)
dates_list = self.nsiegel_betas_3month.loc['DEM'].index
for dt in dates_list:
self.nsiegel_betas_3month.loc['DEM', dt] = nsiegel_betas_3month_aux.loc[dt]
self.nsiegel_betas_3month.to_excel(self.data_folder + '\\' + 'nsiegel_betas_3month.xlsx')
print('NS-from 3 month tenor OK - ', time.time() - time_start, ' seconds')
def run_constructor_in_parts5(self):
time_start = time.time()
# self.curvature_all_tenors = self._get_df_specific_beta(df_betas=self.nsiegel_betas_all_tenors, beta='b3')
self.curvature_from_three_month_tenor = self._get_df_specific_beta(df_betas=self.nsiegel_betas_3month, beta='b3')
self.level_from_three_month_tenor = self._get_df_specific_beta(df_betas=self.nsiegel_betas_3month, beta='b1')
self.slope_from_three_month_tenor = self._get_df_specific_beta(df_betas=self.nsiegel_betas_3month, beta='b2')
print('betas OK - ', time.time() - time_start, ' seconds')
time_start = time.time()
self.relative_level = self._relative_level() # signal relative level (N-Siegel)
# Adjusting DEM and EUR
self.relative_level.loc['1998-12':]['DEM'] = np.NaN
self.relative_level.loc[:'1998-12']['EUR'] = np.NaN
print('relative level OK - ', time.time() - time_start, ' seconds')
time_start = time.time()
self.relative_slope = self._relative_slope() # signal relative slope (N-Siegel)
# Adjusting DEM and EUR
self.relative_slope.loc['1998-12':]['DEM'] = np.NaN
self.relative_slope.loc[:'1998-12']['EUR'] = np.NaN
self.relative_curvature = self._relative_curvature() # signal relative curvature (N-Siegel)
# Adjusting DEM and EUR
self.relative_curvature.loc['1998-12':]['DEM'] = np.NaN
self.relative_curvature.loc[:'1998-12']['EUR'] = np.NaN
# self.stdev_curvature = self.standard_deviation_curvature()
try:
# Try to read daily_fwdpts.xlsx, otherwise get from BBG and write to Excel
df_daily_fwdpts = pd.read_csv(self.data_folder + '\\' + 'daily_fwdpts.csv', index_col=[0, 1], header=0, names=[float(x) for x in range(0, 182)])
df_daily_fwdpts.index = pd.MultiIndex.from_product(iterables=[self.currency_list, self.daily_calendar])
# df_daily_fwdpts = pd.read_excel(self.data_folder + '\\' + 'daily_fwdpts.xlsx', index_col=[0,1])
self.daily_fwdpts = df_daily_fwdpts
except FileNotFoundError:
self.daily_fwdpts = self._get_daily_fwdpts_curve_data() # scale adjusted fwd pts
self.daily_fwdpts.to_excel(self.data_folder + '\\' + 'daily_fwdpts.xlsx')
self.daily_fwdpts.to_csv(self.data_folder + '\\' + 'daily_fwdpts.CSV')
print('daily fwdpts OK - ', time.time() - time_start, ' seconds')
time_start = time.time()
self.daily_fwds = self._get_daily_fwd_all_tenors(self.daily_fwdpts) # all tenors (FX spot + forwards)
self.daily_fwds_XXXUSD = self._get_daily_fwd_XXXUSD(self.daily_fwds)
try:
# Try to read px_change_matrix.xlsx, otherwise calculate and write to Excel
df_px_change_matrix = pd.read_excel(self.data_folder + '\\' + 'px_change_matrix.xlsx')
self.px_change_matrix = df_px_change_matrix
except FileNotFoundError:
self.px_change_matrix = self._price_change_matrix()
self.px_change_matrix.to_excel(self.data_folder + '\\' + 'px_change_matrix.xlsx')
print('daily prices OK - ', time.time() - time_start, ' seconds')
# Implementation of rankings
time_start = time.time()
try:
self.fwd_discount_rank = pd.read_excel(self.data_folder + '\\' + 'fwd_discount_rank.xlsx')
self.relative_level_rank = pd.read_excel(self.data_folder + '\\' + 'relative_level_rank.xlsx')
self.relative_slope_rank = pd.read_excel(self.data_folder + '\\' + 'relative_slope_rank.xlsx')
self.relative_curvature_rank = pd.read_excel(self.data_folder + '\\' + 'relative_curvature_rank.xlsx')
except FileNotFoundError:
self.fwd_discount_rank = self._simple_ranking(df_signals=self.fwd_discount_last)
self.relative_level_rank = self._simple_ranking(df_signals=self.relative_level)
self.relative_slope_rank = self._simple_ranking(df_signals=self.relative_slope)
self.relative_curvature_rank = self._simple_ranking(df_signals=self.relative_curvature)
self.fwd_discount_rank.to_excel(self.data_folder + '\\' + 'fwd_discount_rank.xlsx')
self.relative_level_rank.to_excel(self.data_folder + '\\' + 'relative_level_rank.xlsx')
self.relative_slope_rank.to_excel(self.data_folder + '\\' + 'relative_slope_rank.xlsx')
self.relative_curvature_rank.to_excel(self.data_folder + '\\' + 'relative_curvature_rank.xlsx')
print('ranks OK - ', time.time() - time_start, ' seconds')
def build_strategy_table(self):
list_strat_number = list(range(1, self.total_strategy_number + 1))
df_strategy = pd.DataFrame(data=None, index=list_strat_number)
strategy_name_dict = {1: 'Carry trade',
2: 'Curvature',
3: 'Level',
4: 'Slope',
5: 'Carry (EV)',
6: 'Curvature (EV)',
7: 'Level (EV)',
8: 'Slope (EV)',
9: 'Carry-Curvy DS',
10: 'Curvy-Carry DS',
11: 'Carry to Vol'}
df_strategy['Name'] = [strategy_name_dict[x] for x in list_strat_number]
df_strategy['TR'] = ['TR_df_' + "{:02}".format(x) for x in list_strat_number]
df_strategy['holdings'] = ['holdings_df_' + "{:02}".format(x) for x in list_strat_number]
df_strategy['weights'] = ['weights_df_' + "{:02}".format(x) for x in list_strat_number]
df_strategy['long_short_signals'] = ['long_short_signals_df_' + "{:02}".format(x) for x in list_strat_number]
df_strategy['TR_df_daily'] = ['TR_df_daily_' + "{:02}".format(x) for x in list_strat_number]
return df_strategy
def run_strategy01(self):
time_start = time.time()
# Traditional Carry Trade
strategy_n = 1
name_start = self.data_folder + "\\" + "{:02}".format(strategy_n) + '_'
strategy_criteria = self.fwd_discount_last
try:
# Try to read results from Excel files, otherwise run and write to Excel
df_excel_tr = pd.read_excel(name_start + 'TR.xlsx')
df_excel_holdings = pd.read_excel(name_start + 'Holdings.xlsx', index_col=[0,1])
df_excel_weights = pd.read_excel(name_start + 'Weights.xlsx', index_col=[0,1])
df_excel_ls_signals = pd.read_excel(name_start + 'LS_Signals.xlsx', index_col=[0,1])
df_excel_ir = pd.read_excel(name_start + 'IR.xlsx')
df_excel_sr = pd.read_excel(name_start + 'SR.xlsx')
df_excel_cost = pd.read_excel(name_start + 'Cost.xlsx')
df_excel_pnlfx = pd.read_excel(name_start + 'PctPnlFX.xlsx', index_col=[0,1])
df_excel_irfx = pd.read_excel(name_start + 'PctIRFX.xlsx', index_col=[0,1])
df_excel_srfx = pd.read_excel(name_start + 'PctSRFX.xlsx', index_col=[0,1])
df_excel_costfx = pd.read_excel(name_start + 'PctCostFX.xlsx', index_col=[0,1])
dt_excel_daily_pnl = pd.read_excel(name_start + 'TR_Daily.xlsx')
self.TR_df_01 = df_excel_tr
self.holdings_df_01 = df_excel_holdings
self.weights_df_01 = df_excel_weights
self.long_short_signals_df_01 = df_excel_ls_signals
self.ir_01 = df_excel_ir
self.sr_01 = df_excel_sr
self.cost_01 = df_excel_cost
self.pct_pnlfx_01 = df_excel_pnlfx
self.pct_irfx_01 = df_excel_irfx
self.pct_srfx_01 = df_excel_srfx
self.pct_costfx_01 = df_excel_costfx
self.TR_df_daily_01 = dt_excel_daily_pnl
except FileNotFoundError:
# this function already writes results do excel (just need strategy_n): self.run_equal_vol_monthly_strategy
[self.TR_df_01, self.holdings_df_01, self.weights_df_01, self.long_short_signals_df_01, self.ir_01, self.sr_01,
self.cost_01, self.pct_pnlfx_01, self.pct_irfx_01, self.pct_srfx_01, self.pct_costfx_01, self.blotter_01] = \
self.run_default_monthly_strategy(df_signals=strategy_criteria, func_weight=self._ranking_to_wgt, strategy_n=strategy_n)
# self.TR_df_daily_01 = self.run_daily_pnl(self.holdings_df_01, strategy_n)
[self.TR_df_daily_01, self.TR_df_daily_fx_01] = self.calculate_daily_pnl(df_blotter=self.blotter_01)
(self.TR_df_daily_01.loc['1991-01-31':]+100).to_excel(name_start + 'TR_Daily.xlsx')
(self.TR_df_daily_fx_01 + 100).to_excel(name_start + 'TR_DailyFX.xlsx')
print('Carry trade OK - ', time.time() - time_start, ' seconds')
def run_strategy02(self):
time_start = time.time()
# Traditional CURVY Trade (Nelson-Siegel curvature)
strategy_n = 2
name_start = self.data_folder + "\\" + "{:02}".format(strategy_n) + '_'
strategy_criteria = self.relative_curvature
try:
# Try to read results from Excel files, otherwise run and write to Excel
df_excel_tr = pd.read_excel(name_start + 'TR.xlsx')
df_excel_holdings = pd.read_excel(name_start + 'Holdings.xlsx', index_col=[0,1])
df_excel_weights = pd.read_excel(name_start + 'Weights.xlsx', index_col=[0,1])
df_excel_ls_signals = pd.read_excel(name_start + 'LS_Signals.xlsx', index_col=[0,1])
df_excel_ir = pd.read_excel(name_start + 'IR.xlsx')
df_excel_sr = pd.read_excel(name_start + 'SR.xlsx')
df_excel_cost = pd.read_excel(name_start + 'Cost.xlsx')
df_excel_pnlfx = pd.read_excel(name_start + 'PctPnlFX.xlsx', index_col=[0,1])
df_excel_irfx = pd.read_excel(name_start + 'PctIRFX.xlsx', index_col=[0,1])
df_excel_srfx = pd.read_excel(name_start + 'PctSRFX.xlsx', index_col=[0,1])
df_excel_costfx = pd.read_excel(name_start + 'PctCostFX.xlsx', index_col=[0,1])
dt_excel_daily_pnl = pd.read_excel(name_start + 'TR_Daily.xlsx')
self.TR_df_02 = df_excel_tr
self.holdings_df_02 = df_excel_holdings
self.weights_df_02 = df_excel_weights
self.long_short_signals_df_02 = df_excel_ls_signals
self.ir_02 = df_excel_ir
self.sr_02 = df_excel_sr
self.cost_02 = df_excel_cost
self.pct_pnlfx_02 = df_excel_pnlfx
self.pct_irfx_02 = df_excel_irfx
self.pct_srfx_02 = df_excel_srfx
self.pct_costfx_02 = df_excel_costfx
self.TR_df_daily_02 = dt_excel_daily_pnl
except FileNotFoundError:
# this function already writes results do excel (just need strategy_n): self.run_equal_vol_monthly_strategy
[self.TR_df_02, self.holdings_df_02, self.weights_df_02, self.long_short_signals_df_02, self.ir_02, self.sr_02,
self.cost_02, self.pct_pnlfx_02, self.pct_irfx_02, self.pct_srfx_02, self.pct_costfx_02, self.blotter_02] = \
self.run_default_monthly_strategy(df_signals=strategy_criteria, func_weight=self._ranking_to_wgt, strategy_n=strategy_n)
# self.TR_df_daily_02 = self.run_daily_pnl(self.holdings_df_02, strategy_n)
[self.TR_df_daily_02, self.TR_df_daily_fx_02] = self.calculate_daily_pnl(df_blotter=self.blotter_02)
(self.TR_df_daily_02.loc['1991-01-31':]+100).to_excel(name_start + 'TR_Daily.xlsx')
(self.TR_df_daily_fx_02 + 100).to_excel(name_start + 'TR_DailyFX.xlsx')
print('Curvy trade OK - ', time.time() - time_start, ' seconds')
def run_strategy03(self):
time_start = time.time()
# Traditional level Trade (Nelson-Siegel level)
strategy_n = 3
name_start = self.data_folder + "\\" + "{:02}".format(strategy_n) + '_'
strategy_criteria = self.relative_level
try:
# Try to read results from Excel files, otherwise run and write to Excel
df_excel_tr = pd.read_excel(name_start + 'TR.xlsx')
df_excel_holdings = pd.read_excel(name_start + 'Holdings.xlsx', index_col=[0,1])
df_excel_weights = pd.read_excel(name_start + 'Weights.xlsx', index_col=[0,1])
df_excel_ls_signals = pd.read_excel(name_start + 'LS_Signals.xlsx', index_col=[0,1])
df_excel_ir = pd.read_excel(name_start + 'IR.xlsx')
df_excel_sr = pd.read_excel(name_start + 'SR.xlsx')
df_excel_cost = pd.read_excel(name_start + 'Cost.xlsx')
df_excel_pnlfx = pd.read_excel(name_start + 'PctPnlFX.xlsx', index_col=[0,1])
df_excel_irfx = pd.read_excel(name_start + 'PctIRFX.xlsx', index_col=[0,1])
df_excel_srfx = pd.read_excel(name_start + 'PctSRFX.xlsx', index_col=[0,1])
df_excel_costfx = pd.read_excel(name_start + 'PctCostFX.xlsx', index_col=[0,1])
dt_excel_daily_pnl = pd.read_excel(name_start + 'TR_Daily.xlsx')
self.TR_df_03 = df_excel_tr
self.holdings_df_03 = df_excel_holdings
self.weights_df_03 = df_excel_weights
self.long_short_signals_df_03 = df_excel_ls_signals
self.ir_03 = df_excel_ir
self.sr_03 = df_excel_sr
self.cost_03 = df_excel_cost
self.pct_pnlfx_03 = df_excel_pnlfx
self.pct_irfx_03 = df_excel_irfx
self.pct_srfx_03 = df_excel_srfx
self.pct_costfx_03 = df_excel_costfx
self.TR_df_daily_03 = dt_excel_daily_pnl
except FileNotFoundError:
# this function already writes results do excel (just need strategy_n): self.run_equal_vol_monthly_strategy
[self.TR_df_03, self.holdings_df_03, self.weights_df_03, self.long_short_signals_df_03, self.ir_03, self.sr_03,
self.cost_03, self.pct_pnlfx_03, self.pct_irfx_03, self.pct_srfx_03, self.pct_costfx_03, self.blotter_03] = \
self.run_default_monthly_strategy(df_signals=strategy_criteria, func_weight=self._ranking_to_wgt, strategy_n=strategy_n)
# self.TR_df_daily_03 = self.run_daily_pnl(self.holdings_df_03, strategy_n)
[self.TR_df_daily_03, self.TR_df_daily_fx_03] = self.calculate_daily_pnl(df_blotter=self.blotter_03)
(self.TR_df_daily_03.loc['1991-01-31':]+100).to_excel(name_start + 'TR_Daily.xlsx')
(self.TR_df_daily_fx_03 + 100).to_excel(name_start + 'TR_DailyFX.xlsx')
print('Level trade OK - ', time.time() - time_start, ' seconds')
def run_strategy04(self):
time_start = time.time()
# Traditional slope Trade (Nelson-Siegel slope)
strategy_n = 4
name_start = self.data_folder + "\\" + "{:02}".format(strategy_n) + '_'
strategy_criteria = self.relative_slope
try:
# Try to read results from Excel files, otherwise run and write to Excel
df_excel_tr = pd.read_excel(name_start + 'TR.xlsx')
df_excel_holdings = pd.read_excel(name_start + 'Holdings.xlsx', index_col=[0,1])
df_excel_weights = pd.read_excel(name_start + 'Weights.xlsx', index_col=[0,1])
df_excel_ls_signals = pd.read_excel(name_start + 'LS_Signals.xlsx', index_col=[0,1])
df_excel_ir = pd.read_excel(name_start + 'IR.xlsx')
df_excel_sr = pd.read_excel(name_start + 'SR.xlsx')
df_excel_cost = pd.read_excel(name_start + 'Cost.xlsx')
df_excel_pnlfx = pd.read_excel(name_start + 'PctPnlFX.xlsx', index_col=[0,1])
df_excel_irfx = pd.read_excel(name_start + 'PctIRFX.xlsx', index_col=[0,1])
df_excel_srfx = pd.read_excel(name_start + 'PctSRFX.xlsx', index_col=[0,1])
df_excel_costfx = pd.read_excel(name_start + 'PctCostFX.xlsx', index_col=[0,1])
dt_excel_daily_pnl = pd.read_excel(name_start + 'TR_Daily.xlsx')
self.TR_df_04 = df_excel_tr
self.holdings_df_04 = df_excel_holdings
self.weights_df_04 = df_excel_weights
self.long_short_signals_df_04 = df_excel_ls_signals
self.ir_04 = df_excel_ir
self.sr_04 = df_excel_sr
self.cost_04 = df_excel_cost
self.pct_pnlfx_04 = df_excel_pnlfx
self.pct_irfx_04 = df_excel_irfx
self.pct_srfx_04 = df_excel_srfx
self.pct_costfx_04 = df_excel_costfx
self.TR_df_daily_04 = dt_excel_daily_pnl
except FileNotFoundError:
# this function already writes results do excel (just need strategy_n): self.run_equal_vol_monthly_strategy
[self.TR_df_04, self.holdings_df_04, self.weights_df_04, self.long_short_signals_df_04, self.ir_04, self.sr_04,
self.cost_04, self.pct_pnlfx_04, self.pct_irfx_04, self.pct_srfx_04, self.pct_costfx_04, self.blotter_04] = \
self.run_default_monthly_strategy(df_signals=strategy_criteria, func_weight=self._ranking_to_wgt, strategy_n=strategy_n)
# self.TR_df_daily_04 = self.run_daily_pnl(self.holdings_df_04, strategy_n)
[self.TR_df_daily_04, self.TR_df_daily_fx_04] = self.calculate_daily_pnl(df_blotter=self.blotter_04)
(self.TR_df_daily_04.loc['1991-01-31':]+100).to_excel(name_start + 'TR_Daily.xlsx')
(self.TR_df_daily_fx_04 + 100).to_excel(name_start + 'TR_DailyFX.xlsx')
print('Slope trade OK - ', time.time() - time_start, ' seconds')
def run_strategy05(self):
time_start = time.time()
# Equal volatility Carry Trade (each FX position is sized to have the same vol)
# Target vol used is 9%. A good guess to get strategy volatility that is similar to traditional carry trade.
strategy_n = 5
self.carry_trade_target_vol = 0.09
name_start = self.data_folder + "\\" + "{:02}".format(strategy_n) + '_'
strategy_criteria = self.fwd_discount_last
try:
# Try to read results from Excel files, otherwise run and write to Excel
df_excel_tr = pd.read_excel(name_start + 'TR.xlsx')
df_excel_holdings = pd.read_excel(name_start + 'Holdings.xlsx', index_col=[0,1])
df_excel_weights = pd.read_excel(name_start + 'Weights.xlsx', index_col=[0,1])
df_excel_ls_signals = pd.read_excel(name_start + 'LS_Signals.xlsx', index_col=[0,1])
df_excel_ir = pd.read_excel(name_start + 'IR.xlsx')
df_excel_sr = pd.read_excel(name_start + 'SR.xlsx')
df_excel_cost = pd.read_excel(name_start + 'Cost.xlsx')
df_excel_pnlfx = pd.read_excel(name_start + 'PctPnlFX.xlsx', index_col=[0,1])
df_excel_irfx = pd.read_excel(name_start + 'PctIRFX.xlsx', index_col=[0,1])
df_excel_srfx = pd.read_excel(name_start + 'PctSRFX.xlsx', index_col=[0,1])
df_excel_costfx = pd.read_excel(name_start + 'PctCostFX.xlsx', index_col=[0,1])
dt_excel_daily_pnl = pd.read_excel(name_start + 'TR_Daily.xlsx')
self.TR_df_05 = df_excel_tr
self.holdings_df_05 = df_excel_holdings
self.weights_df_05 = df_excel_weights
self.long_short_signals_df_05 = df_excel_ls_signals
self.ir_05 = df_excel_ir
self.sr_05 = df_excel_sr
self.cost_05 = df_excel_cost
self.pct_pnlfx_05 = df_excel_pnlfx
self.pct_irfx_05 = df_excel_irfx
self.pct_srfx_05 = df_excel_srfx
self.pct_costfx_05 = df_excel_costfx
self.TR_df_daily_05 = dt_excel_daily_pnl
except FileNotFoundError:
# this function already writes results do excel (just need strategy_n): self.run_equal_vol_monthly_strategy
[self.TR_df_05, self.holdings_df_05, self.weights_df_05, self.long_short_signals_df_05, self.ir_05, self.sr_05,
self.cost_05, self.pct_pnlfx_05, self.pct_irfx_05, self.pct_srfx_05, self.pct_costfx_05, self.blotter_05] = \
self.run_equal_vol_monthly_strategy(df_signals=strategy_criteria, target_vol=self.carry_trade_target_vol, strategy_n=strategy_n)
# self.TR_df_daily_05 = self.run_daily_pnl(self.holdings_df_05, strategy_n)
[self.TR_df_daily_05, self.TR_df_daily_fx_05] = self.calculate_daily_pnl(df_blotter=self.blotter_05)
(self.TR_df_daily_05.loc['1991-01-31':]+100).to_excel(name_start + 'TR_Daily.xlsx')
(self.TR_df_daily_fx_05 + 100).to_excel(name_start + 'TR_DailyFX.xlsx')
print('Equal vol Carry trade OK - ', time.time() - time_start, ' seconds')
def run_strategy06(self):
time_start = time.time()
# Equal vol CURVY Trade
strategy_n = 6
self.curvy_trade_target_vol = 0.09
name_start = self.data_folder + "\\" + "{:02}".format(strategy_n) + '_'
strategy_criteria = self.relative_curvature
try:
# Try to read results from Excel files, otherwise run and write to Excel
df_excel_tr = pd.read_excel(name_start + 'TR.xlsx')
df_excel_holdings = pd.read_excel(name_start + 'Holdings.xlsx', index_col=[0,1])
df_excel_weights = pd.read_excel(name_start + 'Weights.xlsx', index_col=[0,1])
df_excel_ls_signals = pd.read_excel(name_start + 'LS_Signals.xlsx', index_col=[0,1])
df_excel_ir = pd.read_excel(name_start + 'IR.xlsx')
df_excel_sr = pd.read_excel(name_start + 'SR.xlsx')
df_excel_cost = pd.read_excel(name_start + 'Cost.xlsx')
df_excel_pnlfx = pd.read_excel(name_start + 'PctPnlFX.xlsx', index_col=[0,1])
df_excel_irfx = pd.read_excel(name_start + 'PctIRFX.xlsx', index_col=[0,1])
df_excel_srfx = pd.read_excel(name_start + 'PctSRFX.xlsx', index_col=[0,1])
df_excel_costfx = pd.read_excel(name_start + 'PctCostFX.xlsx', index_col=[0,1])
dt_excel_daily_pnl = pd.read_excel(name_start + 'TR_Daily.xlsx')
self.TR_df_06 = df_excel_tr
self.holdings_df_06 = df_excel_holdings
self.weights_df_06 = df_excel_weights
self.long_short_signals_df_06 = df_excel_ls_signals
self.ir_06 = df_excel_ir
self.sr_06 = df_excel_sr
self.cost_06 = df_excel_cost
self.pct_pnlfx_06 = df_excel_pnlfx
self.pct_irfx_06 = df_excel_irfx
self.pct_srfx_06 = df_excel_srfx
self.pct_costfx_06 = df_excel_costfx
self.TR_df_daily_06 = dt_excel_daily_pnl
except FileNotFoundError:
# this function already writes results do excel (just need strategy_n): self.run_equal_vol_monthly_strategy
[self.TR_df_06, self.holdings_df_06, self.weights_df_06, self.long_short_signals_df_06, self.ir_06, self.sr_06,
self.cost_06, self.pct_pnlfx_06, self.pct_irfx_06, self.pct_srfx_06, self.pct_costfx_06, self.blotter_06] = \
self.run_equal_vol_monthly_strategy(df_signals=strategy_criteria, target_vol=self.carry_trade_target_vol, strategy_n=strategy_n)
# self.TR_df_daily_06 = self.run_daily_pnl(self.holdings_df_06, strategy_n)
[self.TR_df_daily_06, self.TR_df_daily_fx_06] = self.calculate_daily_pnl(df_blotter=self.blotter_06)
(self.TR_df_daily_06.loc['1991-01-31':]+100).to_excel(name_start + 'TR_Daily.xlsx')
(self.TR_df_daily_fx_06 + 100).to_excel(name_start + 'TR_DailyFX.xlsx')
print('Equal vol Curvy trade OK - ', time.time() - time_start, ' seconds')
def run_strategy07(self):
time_start = time.time()
# Equal vol level Trade
strategy_n = 7
self.level_trade_target_vol = 0.09
name_start = self.data_folder + "\\" + "{:02}".format(strategy_n) + '_'
strategy_criteria = self.relative_level
try:
# Try to read results from Excel files, otherwise run and write to Excel
df_excel_tr = pd.read_excel(name_start + 'TR.xlsx')
df_excel_holdings = pd.read_excel(name_start + 'Holdings.xlsx', index_col=[0,1])
df_excel_weights = pd.read_excel(name_start + 'Weights.xlsx', index_col=[0,1])
df_excel_ls_signals = pd.read_excel(name_start + 'LS_Signals.xlsx', index_col=[0,1])
df_excel_ir = pd.read_excel(name_start + 'IR.xlsx')
df_excel_sr = pd.read_excel(name_start + 'SR.xlsx')
df_excel_cost = pd.read_excel(name_start + 'Cost.xlsx')
df_excel_pnlfx = pd.read_excel(name_start + 'PctPnlFX.xlsx', index_col=[0,1])
df_excel_irfx = pd.read_excel(name_start + 'PctIRFX.xlsx', index_col=[0,1])
df_excel_srfx = pd.read_excel(name_start + 'PctSRFX.xlsx', index_col=[0,1])
df_excel_costfx = pd.read_excel(name_start + 'PctCostFX.xlsx', index_col=[0,1])
dt_excel_daily_pnl = pd.read_excel(name_start + 'TR_Daily.xlsx')
self.TR_df_07 = df_excel_tr
self.holdings_df_07 = df_excel_holdings
self.weights_df_07 = df_excel_weights
self.long_short_signals_df_07 = df_excel_ls_signals
self.ir_07 = df_excel_ir
self.sr_07 = df_excel_sr
self.cost_07 = df_excel_cost
self.pct_pnlfx_07 = df_excel_pnlfx
self.pct_irfx_07 = df_excel_irfx
self.pct_srfx_07 = df_excel_srfx
self.pct_costfx_07 = df_excel_costfx
self.TR_df_daily_07 = dt_excel_daily_pnl
except FileNotFoundError:
# this function already writes results do excel (just need strategy_n): self.run_equal_vol_monthly_strategy
[self.TR_df_07, self.holdings_df_07, self.weights_df_07, self.long_short_signals_df_07, self.ir_07, self.sr_07,
self.cost_07, self.pct_pnlfx_07, self.pct_irfx_07, self.pct_srfx_07, self.pct_costfx_07, self.blotter_07] = \
self.run_equal_vol_monthly_strategy(df_signals=strategy_criteria, target_vol=self.carry_trade_target_vol, strategy_n=strategy_n)
# self.TR_df_daily_07 = self.run_daily_pnl(self.holdings_df_07, strategy_n)
[self.TR_df_daily_07, self.TR_df_daily_fx_07] = self.calculate_daily_pnl(df_blotter=self.blotter_07)
(self.TR_df_daily_07.loc['1991-01-31':]+100).to_excel(name_start + 'TR_Daily.xlsx')
(self.TR_df_daily_fx_07 + 100).to_excel(name_start + 'TR_DailyFX.xlsx')
print('Equal vol Level trade OK - ', time.time() - time_start, ' seconds')
def run_strategy08(self):
time_start = time.time()
# Equal vol slope Trade
strategy_n = 8
self.slope_trade_target_vol = 0.09
name_start = self.data_folder + "\\" + "{:02}".format(strategy_n) + '_'
strategy_criteria = self.relative_slope
try:
# Try to read results from Excel files, otherwise run and write to Excel
df_excel_tr = pd.read_excel(name_start + 'TR.xlsx')
df_excel_holdings = pd.read_excel(name_start + 'Holdings.xlsx', index_col=[0,1])
df_excel_weights = pd.read_excel(name_start + 'Weights.xlsx', index_col=[0,1])
df_excel_ls_signals = pd.read_excel(name_start + 'LS_Signals.xlsx', index_col=[0,1])
df_excel_ir = pd.read_excel(name_start + 'IR.xlsx')
df_excel_sr = pd.read_excel(name_start + 'SR.xlsx')
df_excel_cost = pd.read_excel(name_start + 'Cost.xlsx')
df_excel_pnlfx = pd.read_excel(name_start + 'PctPnlFX.xlsx', index_col=[0,1])
df_excel_irfx = pd.read_excel(name_start + 'PctIRFX.xlsx', index_col=[0,1])
df_excel_srfx = pd.read_excel(name_start + 'PctSRFX.xlsx', index_col=[0,1])
df_excel_costfx = pd.read_excel(name_start + 'PctCostFX.xlsx', index_col=[0,1])
dt_excel_daily_pnl = pd.read_excel(name_start + 'TR_Daily.xlsx')
self.TR_df_08 = df_excel_tr
self.holdings_df_08 = df_excel_holdings
self.weights_df_08 = df_excel_weights
self.long_short_signals_df_08 = df_excel_ls_signals
self.ir_08 = df_excel_ir
self.sr_08 = df_excel_sr
self.cost_08 = df_excel_cost
self.pct_pnlfx_08 = df_excel_pnlfx
self.pct_irfx_08 = df_excel_irfx
self.pct_srfx_08 = df_excel_srfx
self.pct_costfx_08 = df_excel_costfx
self.TR_df_daily_08 = dt_excel_daily_pnl
except FileNotFoundError:
# this function already writes results do excel (just need strategy_n): self.run_equal_vol_monthly_strategy
[self.TR_df_08, self.holdings_df_08, self.weights_df_08, self.long_short_signals_df_08, self.ir_08, self.sr_08,
self.cost_08, self.pct_pnlfx_08, self.pct_irfx_08, self.pct_srfx_08, self.pct_costfx_08, self.blotter_08] = \
self.run_equal_vol_monthly_strategy(df_signals=strategy_criteria, target_vol=self.carry_trade_target_vol, strategy_n=strategy_n)
# self.TR_df_daily_08 = self.run_daily_pnl(self.holdings_df_08, strategy_n)
[self.TR_df_daily_08, self.TR_df_daily_fx_08] = self.calculate_daily_pnl(df_blotter=self.blotter_08)
(self.TR_df_daily_08.loc['1991-01-31':]+100).to_excel(name_start + 'TR_Daily.xlsx')
(self.TR_df_daily_fx_08 + 100).to_excel(name_start + 'TR_DailyFX.xlsx')
print('Equal vol Slope trade OK - ', time.time() - time_start, ' seconds')
def run_constructor_in_parts6(self):
print('Starting strategies')
[getattr(self, 'run_strategy' + "{:02}".format(strategy_n))() for strategy_n in range(1, self.total_strategy_number + 1)]
def _build_k_list(self):
k_range = range(1, self.k_max + 1)
k_list = ['k' + str(i) for i in k_range]
return k_range, k_list
@staticmethod
def _build_bday_calendars(start_date, end_date):
# TODO DONE: work with all business days, repeating prices (ffill) and rebalancing beggining of month.
daily_calendar = pd.date_range(start=start_date, end=end_date, freq=BDay())
return daily_calendar
@staticmethod
def _build_monthly_calendars(start_date, end_date):
# TODO DONE: work with all business days, repeating prices (ffill) and rebalancing beggining of month.
monthly_calendar = pd.date_range(start=start_date, end=end_date, freq=BMonthEnd())
return monthly_calendar
def _build_df_tickers(self):
cod_fwdpts = [self.dict_FX_NDF[c] + '1M BGN Curncy' for c in self.currency_list]
df_tickers = pd.DataFrame(index=self.currency_list, data=self.ticker_spot_bbg, columns=['spot'])
df_tickers['fwdpts'] = cod_fwdpts
# only for DEM, replace BGN for CMPN
try:
df_tickers.loc['DEM', 'fwdpts'] = df_tickers.loc['DEM', 'fwdpts'].replace('BGN', 'CMPN')
except KeyError:
print('Warning: DEM is not in currency list.')
# scale column
df_scale = self.con.ref(list(self.ticker_spot_bbg), 'FWD_SCALE').set_index(keys='ticker', drop=True)
df_scale.index = [x.replace(' Curncy', '') for x in df_scale.index]
df_tickers['scale'] = 10 ** df_scale['value']
# inverse column
df_inverse = self.con.ref(self.ticker_spot_bbg, 'INVERSE_QUOTED').set_index(keys='ticker', drop=True)
df_inverse.index = [x.replace(' Curncy', '') for x in df_inverse.index]
df_inverse.loc[df_inverse['value'] == "Y", 'value'] = 1
df_inverse.loc[df_inverse['value'] == "N", 'value'] = -1
df_tickers['inverse'] = df_inverse['value']
# Forward curve tickers
df_tickers['1w'] = [self.dict_FX_NDF[c] + '1W ' + self.dict_fwdpts_source[c] + ' Curncy' for c in self.currency_list]
df_tickers['2w'] = [self.dict_FX_NDF[c] + '2W ' + self.dict_fwdpts_source[c] + ' Curncy' for c in self.currency_list]
df_tickers['3w'] = [self.dict_FX_NDF[c] + '3W ' + self.dict_fwdpts_source[c] + ' Curncy' for c in self.currency_list]
df_tickers['1m'] = [self.dict_FX_NDF[c] + '1M ' + self.dict_fwdpts_source[c] + ' Curncy' for c in self.currency_list]
df_tickers['2m'] = [self.dict_FX_NDF[c] + '2M ' + self.dict_fwdpts_source[c] + ' Curncy' for c in self.currency_list]
df_tickers['3m'] = [self.dict_FX_NDF[c] + '3M ' + self.dict_fwdpts_source[c] + ' Curncy' for c in self.currency_list]
df_tickers['4m'] = [self.dict_FX_NDF[c] + '4M ' + self.dict_fwdpts_source[c] + ' Curncy' for c in self.currency_list]
df_tickers['5m'] = [self.dict_FX_NDF[c] + '5M ' + self.dict_fwdpts_source[c] + ' Curncy' for c in self.currency_list]
df_tickers['6m'] = [self.dict_FX_NDF[c] + '6M ' + self.dict_fwdpts_source[c] + ' Curncy' for c in self.currency_list]
return df_tickers
def _get_spot_data(self, bbg_field=['PX_LAST']):
spot_last = self.con.bdh(self.ticker_spot_bbg, bbg_field, self.ini_date_prices_bbg, self.end_date_bbg, elms=[("periodicitySelection", self.bbg_periodicity_spot)])
spot_last.columns = spot_last.columns.droplevel(1)
spot_last.fillna(method='ffill', inplace=True)
spot_last.columns = [x.replace(' Curncy', '') for x in spot_last.columns]
spot_last.sort_index(axis=1, ascending=True, inplace=True)
# DataFrame with extended calendar
spot_last_extended_calendar = pd.DataFrame(data=None, index=self.daily_calendar_extended, columns=self.currency_list)
_take_second = lambda s1, s2: s2
spot_last_extended_calendar = spot_last_extended_calendar.combine(spot_last, func=_take_second)
spot_last_extended_calendar.fillna(method='ffill', inplace=True)
return spot_last_extended_calendar
def adjust_spot_bid_ask(self):
fx_side = self.df_tickers['inverse']
_bid = self.spot_bid.copy(deep=True)
_ask = self.spot_ask.copy(deep=True)
for curncy in self.currency_list:
if fx_side.loc[curncy] == -1:
self.spot_bid[curncy] = _ask[curncy]
self.spot_ask[curncy] = _bid[curncy]
self.spot_last.loc['1998-12':]['DEM'] = np.NaN
self.spot_last.loc[:'1998-12']['EUR'] = np.NaN
def adjust_fwdpts_bid_ask(self):
fx_side = self.df_tickers['inverse']
_bid = self.fwdpts_bid.copy(deep=True)
_ask = self.fwdpts_ask.copy(deep=True)
for curncy in self.currency_list:
if fx_side.loc[curncy] == -1:
self.fwdpts_bid[curncy] = _ask[curncy]
self.fwdpts_ask[curncy] = _bid[curncy]
self.fwdpts_last.loc['1998-12':]['DEM'] = np.NaN
self.fwdpts_last.loc[:'1998-12']['EUR'] = np.NaN
def adjust_vol(self):
if 'DEM' in self.currency_list:
self.vols['DEM'].loc['1998-12':] = np.NaN
lst_DEM_vol = float(self.vols['DEM'].dropna()[-1])
self.vols['EUR'].loc['1999-01':'1999-06-25'] = lst_DEM_vol
def _get_fwdpts_data(self, bbg_field=['PX_LAST']):
fwd_pts_last = self.con.bdh(self.ticker_fwdpts_1m, bbg_field, self.ini_date_prices_bbg, self.end_date_bbg, elms=[("periodicitySelection", self.bbg_periodicity_spot)])
fwd_pts_last.columns = fwd_pts_last.columns.droplevel(1)
fwd_pts_last.fillna(method='ffill', inplace=True)
fwd_pts_last.columns = [self.dict_NDF_FX[x.replace('1M BGN Curncy', '').replace('1M CMPN Curncy', '')] for x in fwd_pts_last.columns]
fwd_pts_last.sort_index(axis=1, ascending=True, inplace=True)
# DataFrame with extended calendar
fwd_pts_last_extended_calendar = pd.DataFrame(data=None, index=self.daily_calendar_extended, columns=self.currency_list)
_take_second = lambda s1, s2: s2
fwd_pts_last_extended_calendar = fwd_pts_last_extended_calendar.combine(fwd_pts_last, func=_take_second)
fwd_pts_last_extended_calendar.fillna(method='ffill', inplace=True)
return fwd_pts_last_extended_calendar
@staticmethod
def _get_fwd_outright(spot, fwd_pts, fwdpts_scale):
fwd_outright = spot + fwd_pts / fwdpts_scale
fwd_outright.fillna(method='ffill', inplace=True)
return fwd_outright
def plot_fwd_discount(self, figure_size):
self.fwd_discount_last.multiply(100).plot(figsize=figure_size, title='Forward premium (annual rate)')
plt.show()
# Weighting function
# Equal weight
# wgt = 1/k
@staticmethod
def _ranking_to_wgt(df_ranking, k=1):
df_signals = pd.DataFrame(data=None, index=df_ranking.index, columns=df_ranking.columns)
weights = pd.DataFrame(data=None, index=df_ranking.index, columns=df_ranking.columns)
unit_wgt = 1.0 / k
down_limit = k
for d in df_ranking.index:
n = df_ranking.loc[d].max()
if n >= k * 2:
up_limit = df_ranking.loc[d].max() - k
df_signals.loc[d] = (df_ranking.loc[d] > up_limit).multiply(1) - (df_ranking.loc[d] <= down_limit).multiply(1)
weights.loc[d] = df_signals.loc[d] * unit_wgt
else:
# Do not trade if there is not enough assets to trade long/short
df_signals.loc[d] = 0
weights.loc[d] = 0
return weights, df_signals
def _same_wgt(self, df_ranking, k=1):
df_signals = (-np.isnan(self.fwd_discount_last)).multiply(1)
for fx in self.currency_list:
if fx != self.currency_strat12:
df_signals[fx] = 0
if fx == 'DEM':
df_signals.loc['1998-11-30', 'DEM'] = 0
weights = df_signals
return weights, df_signals
@staticmethod
def _ranking_to_wgt_double_sorting(df_ranking, df_ranking_2, groups=3, subgroups=2):
df_signals = pd.DataFrame(data=None, index=df_ranking.index, columns=df_ranking.columns)
weights = pd.DataFrame(data=None, index=df_ranking.index, columns=df_ranking.columns)
for d in df_ranking.index:
# Filters for buying FX
# First filter: divide first criteria in 3 groups (high, medium, low)
up_limit = df_ranking.loc[d].max() * (1 - 1 / groups) # Ranking is ascending, so up limit needs to filter from 2/3 to 3/3.
filter1 = (df_ranking.loc[d] > up_limit) # Series of true or false
# Second filter: divide high and low blocks in two subgroups: high and low (eg. high-high, high-low)
rank_2 = df_ranking_2.loc[d, filter1].rank(ascending=True)
up_limit_2 = rank_2.max() / subgroups
filter2 = (rank_2 > up_limit_2)
filter2 = filter2.loc[filter2].index
n_assets = len(filter2)
df_signals.loc[d] = 0
df_signals.loc[d, filter2] = 1
# Filters for selling FX
# First filter: divide first criteria in 3 groups (high, medium, low)
down_limit = df_ranking.loc[d].max() * (1 / groups)
filter1 = (df_ranking.loc[d] <= down_limit) # Series of true or false
# Second filter: divide high and low blocks in two subgroups: high and low (eg. high-high, high-low)
rank_2 = df_ranking_2.loc[d, filter1].rank(ascending=True)
# down_limit_2 = rank_2.max() / subgroups
# filter2 = (rank_2 <= down_limit_2)
filter2 = (rank_2 <= n_assets)
filter2 = filter2.loc[filter2].index
df_signals.loc[d, filter2] = -1
# Weight calculation follows normal strategy approach (same weight for each FX)
if n_assets == 0:
unit_wgt = 0.0
else:
unit_wgt = 1.0 / n_assets
weights.loc[d] = df_signals.loc[d] * unit_wgt
return weights, df_signals
# Equal vol
# wgt_vol_adjusted = wgt/vol
@staticmethod
def _ranking_to_equalvolwgt(df_ranking, df_vols, target_vol=0.06, k=1):
df_signals = pd.DataFrame(data=None, index=df_ranking.index, columns=df_ranking.columns)
weights = pd.DataFrame(data=None, index=df_ranking.index, columns=df_ranking.columns)
unit_wgt = 1.0 / k
down_limit = k
for d in df_ranking.index:
up_limit = df_ranking.loc[d].max() - k
df_signals.loc[d] = (df_ranking.loc[d] > up_limit).multiply(1) - (df_ranking.loc[d] <= down_limit).multiply(1)