ХОЛОДНЫЙ РАСЧЕТ ∅

ХОЛОДНЫЙ РАСЧЕТ ∅

🦅 Обязательная продажа валюты не впечатлила денежный рынок Ожидания по траектории ключевой ключевой ставке до мая 2024 снизились на в пределах 0.1-0.2пп: пик все еще ~15.5% в апреле, снижение с 2кв24 🧙 Оценка ожидания по ставки из ROISfix @c0ldness

🐍 Вермишельный график ожиданий из свопов ROISfix: Рецепт приготовления

from nelson_siegel_svensson import NelsonSiegelCurve
from nelson_siegel_svensson.calibrate import calibrate_ns_ols
import numpy as np
from datetime import datetime as dt
from datetime import timedelta

df_roisfix = # IMPORT ROISFIX DATA

# FIXED EXCESS RETURN FOR FIXED LEG (ANNUALIZED)
term_prem = 0.028*12

maturities = [1/52,2/52,1/12,2/12,3/12,6/12,1,2]
li_term_prem = [x*term_prem for x in maturities]

df_roisfix_ex_exret = df_roisfix.sub(li_term_prem,axis=1).copy(deep=True)

col_mat = np.linspace(1,180,30)
col_date = df_roisfix_ex_exret_resample.index.to_list()

ix_date = pd.date_range(start=dt(2011,1,1), end=dt(2023,9,13) + timedelta(days=300),freq='D')
df_rates = pd.DataFrame(columns=col_date,index=ix_date)

for ix, row in df_roisfix_ex_exret_resample.iterrows():
 try:
  vals = row.dropna().values
  curve_fit, status = calibrate_ns_ols(np.array( maturities[:len(vals)]),vals) 
  NSS_Fwd = NelsonSiegelCurve.forward(curve_fit,np.array([x/360 for x in col_mat]))
  df_rates.loc[pd.date_range(start=ix+ timedelta(days=6), end=ix + timedelta(days=180),freq='6D'),ix] = NSS_Fwd
  # print(ix)
 except:
  pass

df_rates.tail()

🫡 Спасибо за посещение нашего научного макротелеграм-семинара

@c0ldness

🔥48🫡5

www.group-telegram.com/ru/c0ldness.com/2107

2.55K viewsedited  Oct 12, 2023 at 12:57

🐍 Вермишельный график ожиданий из свопов ROISfix: Рецепт приготовления

from nelson_siegel_svensson import NelsonSiegelCurve
from nelson_siegel_svensson.calibrate import calibrate_ns_ols
import numpy as np
from datetime import datetime as dt
from datetime import timedelta

df_roisfix = # IMPORT ROISFIX DATA

# FIXED EXCESS RETURN FOR FIXED LEG (ANNUALIZED)
term_prem = 0.028*12

maturities = [1/52,2/52,1/12,2/12,3/12,6/12,1,2]
li_term_prem = [x*term_prem for x in maturities]

df_roisfix_ex_exret = df_roisfix.sub(li_term_prem,axis=1).copy(deep=True)

col_mat = np.linspace(1,180,30)
col_date = df_roisfix_ex_exret_resample.index.to_list()

ix_date = pd.date_range(start=dt(2011,1,1), end=dt(2023,9,13) + timedelta(days=300),freq='D')
df_rates = pd.DataFrame(columns=col_date,index=ix_date)

for ix, row in df_roisfix_ex_exret_resample.iterrows():
 try:
  vals = row.dropna().values
  curve_fit, status = calibrate_ns_ols(np.array( maturities[:len(vals)]),vals) 
  NSS_Fwd = NelsonSiegelCurve.forward(curve_fit,np.array([x/360 for x in col_mat]))
  df_rates.loc[pd.date_range(start=ix+ timedelta(days=6), end=ix + timedelta(days=180),freq='6D'),ix] = NSS_Fwd
  # print(ix)
 except:
  pass

df_rates.tail()

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