def __init__(self, country, start_date, end_date):
self.bbg = BBG()
self.country = country
self.start_date = self._assert_date_type(start_date)
self.end_date = self._assert_date_type(end_date)
self.generic_tickers = [self.futures_ticker_dict[country] + str(x) + ' Comdty' for x in range(1, 4)]
self.df_generics = self._get_generic_future_series()
self.df_uc = self._get_underlying_contracts()
self.contract_list = self._get_contracts_list()
self.df_fn = self._get_first_notice_dates()
self.df_prices = self._get_all_prices()
df_tracker = self._build_tracker()
self.tr_index = df_tracker[['er_index']]
self.df_roll_info = df_tracker[['contract_rolling_out', 'roll_out_date', 'holdings']].dropna(how='any')
roll_end_date = days_in_the_month[end_idx] if len(days_in_the_month) > end_idx else days_in_the_month[-1]
elif roll_type == 'backward_from_month_end':
roll_start_date = days_in_the_month[roll_start_bday]
roll_end_date = days_in_the_month[-1]
if d < roll_start_date:
weight_out = 1
elif d > roll_end_date:
weight_out = 0
else:
weight_out = float(len([x for x in days_in_the_month if x > d
and x <= roll_end_date])) / float(roll_window_size)
return [weight_out, 1 - weight_out]
bbg = BBG()
start_date = (pd.to_datetime('2002-01-05') + BDay(1)).date() # for the data
end_date = pd.to_datetime('today').date()
comm_bbg_code = 'CL'
roll_start_bday = 5
roll_window_size = 5
roll_schedule = ['H','K','K','N','N','U','U','X','X','F+','F+','H+']
writer = pd.ExcelWriter(r'G:\Gustavo Amarante\Aulas\df_' + comm_bbg_code + ' v2.xlsx')
# all contracts
contract_list = bbg.fetch_futures_list(generic_ticker=comm_bbg_code + '1 Comdty')
"""
Author: Gustavo Soares
Contributions: Gustavo Amarante
"""
import numpy as np
import pandas as pd
from pandas.tseries.offsets import BDay
from datetime import timedelta
import matplotlib.pyplot as plt
from fhdataapi import BBG
import time
start_time = time.time()
bbg = BBG()
# ===== User Defined Parameters =====
currency = 'AUD' # Currency to build the tracker against the USD
n_trades = 20 # Number of trackers to smooth the changes in holdings
start_date = '1999-01-04'
end_date = '2017-04-10'
# Bloomberg tickers for the currency futures
dict_tickers = {
currency + ' Curncy': 'spot',
currency + '1W Curncy': '1w',
currency + '1M Curncy': '1m',
currency + '2M Curncy': '2m'
}
from fhdataapi import BBG
import pandas as pd
import os
from datetime import date, timedelta
from pandas.tseries.offsets import BDay
import matplotlib.pyplot as plt
import math
import numpy as np
bbg = BBG()
#TODO add ['DKK', 'ISK', 'SKK', 'HKD', 'CNY', 'MYR', 'THB', 'ARS', 'COP', 'PEN'] to the list of currencies
currencies = [
'AUD',
'BRL',
'CAD',
'CHF',
'CLP',
'CNH',
'CZK',
'EUR',
'GBP',
'HUF',
'IDR',
'ILS',
'INR',
'JPY',
'KRW',
'MXN',
'NOK',
class BondFutureTrackerBuilder(object):
futures_ticker_dict = {'US': 'TY',
'GE': 'RX',
'FR': 'OAT',
'IT': 'IK',
'JP': 'JB',
'AU': 'XM',
'UK': 'G ',
'CA': 'CN'}
fx_dict = {'GE': 'EURUSD Curncy',
'UK': 'GBPUSD Curncy',
'CA': 'CADUSD Curncy',
'JP': 'JPYUSD Curncy',
'AU': 'AUDUSD Curncy',
'FR': 'EURUSD Curncy',
'IT': 'EURUSD Curncy',
'US': 'USD Curncy'}
def __init__(self, country, start_date, end_date):
self.bbg = BBG()
self.country = country
self.start_date = self._assert_date_type(start_date)
self.end_date = self._assert_date_type(end_date)
self.generic_tickers = [self.futures_ticker_dict[country] + str(x) + ' Comdty' for x in range(1, 4)]
self.df_generics = self._get_generic_future_series()
self.df_uc = self._get_underlying_contracts()
self.contract_list = self._get_contracts_list()
self.df_fn = self._get_first_notice_dates()
self.df_prices = self._get_all_prices()
df_tracker = self._build_tracker()
self.tr_index = df_tracker[['er_index']]
self.df_roll_info = df_tracker[['contract_rolling_out', 'roll_out_date', 'holdings']].dropna(how='any')
def _get_generic_future_series(self):
df = self.bbg.fetch_series(securities=self.generic_tickers,
fields='PX_LAST',
startdate=self.start_date,
enddate=self.end_date)
return df
def _get_underlying_contracts(self):
df = self.bbg.fetch_series(securities=self.generic_tickers,
fields='FUT_CUR_GEN_TICKER',
startdate=self.start_date,
enddate=self.end_date)
df = df.reindex(self.df_generics.index).fillna(method='ffill')
return df
def _get_contracts_list(self):
contract_list = self.bbg.fetch_futures_list(generic_ticker=self.futures_ticker_dict[self.country] + '1 Comdty')
return contract_list
def _get_first_notice_dates(self):
df = self.bbg.fetch_contract_parameter(securities=self.contract_list,
field='FUT_NOTICE_FIRST').sort_values('FUT_NOTICE_FIRST')
return df
def _get_all_prices(self):
tickers = self.contract_list + [self.fx_dict[self.country]]
df = self.bbg.fetch_series(securities=tickers,
fields='PX_LAST',
startdate=self.start_date,
enddate=self.end_date)
df = df.reindex(self.df_generics.index).fillna(method='ffill')
return df
def _build_tracker(self):
df_tracker = pd.DataFrame(index=self.df_generics.index,
columns=['contract_rolling_out', 'er_index', 'roll_out_date', 'holdings'])
# set the values for the initial date
dt_ini = self.df_uc.index[0]
df_tracker.loc[dt_ini, 'er_index'] = 100
contract_rolling_out = self.df_uc.loc[dt_ini, self.futures_ticker_dict[self.country] + '2 Comdty'] + ' Comdty'
df_tracker.loc[dt_ini, 'contract_rolling_out'] = contract_rolling_out
holdings = df_tracker.loc[dt_ini, 'er_index'] / (self.df_generics.loc[dt_ini, self.futures_ticker_dict[self.country] + '2 Comdty'] * self.df_prices[self.fx_dict[self.country]].loc[dt_ini])
df_tracker.loc[dt_ini, 'holdings'] = holdings
roll_out_date = self.df_fn.loc[df_tracker.loc[dt_ini, 'contract_rolling_out'], 'FUT_NOTICE_FIRST'] - BDay(1)
df_tracker.loc[dt_ini, 'roll_out_date'] = roll_out_date
for d, dm1 in zip(df_tracker.index[1:], df_tracker.index[:-1]):
df_tracker.loc[d, 'contract_rolling_out'] = contract_rolling_out
price_dm1 = self.df_prices.loc[dm1, contract_rolling_out]
price_d = self.df_prices.loc[d, contract_rolling_out]
pnl = holdings * (price_d - price_dm1) * self.df_prices[self.fx_dict[self.country]].loc[d]
if math.isnan(pnl):
pnl = 0
df_tracker.loc[d, 'er_index'] = df_tracker.loc[dm1, 'er_index'] + pnl
if d >= roll_out_date.date():
contract_rolling_out = (self.df_uc.loc[d, self.futures_ticker_dict[self.country] + '2 Comdty'] + ' Comdty')
df_tracker.loc[d, 'contract_rolling_out'] = contract_rolling_out
holdings = df_tracker.loc[d, 'er_index'] / (self.df_generics.loc[d, self.futures_ticker_dict[self.country] + '2 Comdty'] * self.df_prices[self.fx_dict[self.country]].loc[d])
df_tracker.loc[d, 'holdings'] = holdings
roll_out_date = self.df_fn.loc[df_tracker.loc[d, 'contract_rolling_out'], 'FUT_NOTICE_FIRST'] - BDay(1)
df_tracker.loc[d, 'roll_out_date'] = roll_out_date
return df_tracker
@staticmethod
def _assert_date_type(date):
if type(date) is pd.Timestamp:
date = date
else:
date = pd.to_datetime(date)
return date
"""
Author: Gustavo Amarante
This script is to show how a bond futures tracker is built
"""
from fhdataapi import BBG
import pandas as pd
from datetime import timedelta
from pandas.tseries.offsets import BDay
import matplotlib.pyplot as plt
import math
bbg = BBG()
start_date = (pd.to_datetime('2004-01-05') + BDay(1)).date() # for the data
end_date = pd.to_datetime('today').date()
# value of the qoutes
df_generics = bbg.fetch_series(securities=['JB1 Comdty', 'JB2 Comdty', 'JB3 Comdty'],
fields='PX_LAST',
startdate=start_date,
enddate=end_date)
# underlying contracts
df_uc = bbg.fetch_series(securities=['JB1 Comdty', 'JB2 Comdty', 'JB3 Comdty'],
fields='FUT_CUR_GEN_TICKER',
startdate=start_date,
enddate=end_date)
df_uc = df_uc.reindex(df_generics.index).fillna(method='ffill')
from fhdataapi import BBG
import pandas as pd
from datetime import date, timedelta
from pandas.tseries.offsets import BDay
import matplotlib.pyplot as plt
import math
import numpy as np
bbg = BBG()
start_date = pd.to_datetime('2000-01-01').date()
end_date = pd.to_datetime('today')
ann_factor = 252
stocks = [
"MMM",
"AXP",
"AAPL",
"BA",
"CAT",
"CVX",
"CSCO",
"KO",
"DOW",
"XOM",
"GS",
"HD",
"IBM",
"INTC",
"JNJ",
"JPM",
from fhdataapi import BBG
import pandas as pd
start_date = '30-mar-2015'
end_date = pd.to_datetime('today')
bbg = BBG()
# Grabs tickers and fields
df = bbg.fetch_series(securities=['BRL Curncy', 'DXY Index'],
fields=['PX_LAST', 'VOLATILITY_90D'],
startdate=start_date,
enddate=end_date)
print(df)
# Grabs cashflow payments of corporate bonds
df = bbg.fetch_cash_flow('EI1436001 Govt', pd.to_datetime('03-jul-2017'))
print(df)
# Grabs weights of the components of an index
df = bbg.fetch_index_weights(index_name='IBOV Index',
ref_date=pd.to_datetime('03-jul-2017'))
print(df)
print(df.sum())
# Grabs all the contracts from a generic series
futures_list = bbg.fetch_futures_list(generic_ticker='TY1 Comdty')
print(futures_list)
# grabs the first notice date for each contract
df_fn = bbg.fetch_contract_parameter(securities=futures_list,