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Update Currency Portfolio Assets Selection script

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codez0mb1e 2022-04-04 17:18:11 +00:00
parent 1dc01127a8
commit 3590c25aae
2 changed files with 1334 additions and 12 deletions
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src/fx_currency_portfolio__assets_selection.ipynb Normal file
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src/fx_currency_portfolio__assets_selection.py
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@ -3,13 +3,14 @@
"""Currency Portfolio: Assets Selection. """Currency Portfolio: Assets Selection.
Description: Description:
Currency Selection in anti-crisis portfolio using monte Carlo simulation. Currency Selection in anti-crisis portfolio using Monte Carlo simulation.
""" """
# %% Import dependencies ---- # %% Import dependencies ----
# core # core
import sys import sys
import warnings import warnings
import gc
from IPython import sys_info from IPython import sys_info
# data science # data science
@ -30,10 +31,14 @@ warnings.filterwarnings("ignore")
# %% Set params ---- # %% Set params ----
symbols = ['USD/CHF', 'USD/CNY', 'USD/EUR', 'USD/GBP', 'USD/HKD', 'USD/JPY', 'USD/KZT', 'USD/RUB']
n_days = int(252) # US market has 252 trading days in a year n_days = int(252) # US market has 252 trading days in a year
n_simulations = int(1e4) n_simulations = int(1e4) # number of Monte-Carlo simulations
# The most promised currencies (copy this list from fx_currencies_analysis.Rmd)
symbols = [
'USD/AED', 'USD/AUD', 'USD/CHF', 'USD/CNY', 'USD/EUR', 'USD/GBP',
'USD/HKD', 'USD/JPY', 'USD/KZT', 'USD/MXN', 'USD/RUB', 'USD/SEK', 'USD/SGD'
]
# %% Connect to Azure ML workspace ---- # %% Connect to Azure ML workspace ----
@ -57,7 +62,6 @@ print(f'Size of Azure ML dataset object: {sys.getsizeof(currencies_ds)} bytes.')
# %% Preprocessing ---- # %% Preprocessing ----
quotes_df = (currencies_ds quotes_df = (currencies_ds
# materialize # materialize
.to_pandas_dataframe() .to_pandas_dataframe()
@ -101,6 +105,7 @@ def calc_log_returns(return_prices: pd.Series) -> pd.Series:
return np.log(1 + return_prices) return np.log(1 + return_prices)
usdrub_df['log_return'] = usdrub_df['return'].apply(lambda x: calc_log_returns(x)) usdrub_df['log_return'] = usdrub_df['return'].apply(lambda x: calc_log_returns(x))
usdrub_df[['close', 'diff', 'return', 'log_return']].tail(10) usdrub_df[['close', 'diff', 'return', 'log_return']].tail(10)
@ -203,8 +208,6 @@ plt.show()
# %% Monte Carlo simulation pipeline for multiple tokens ---- # %% Monte Carlo simulation pipeline for multiple tokens ----
# 0. set simulation params
n_simulations = int(1e5)
# 1. prepare # 1. prepare
quotes_data = [quotes_df.query('symbol == @s') for s in quotes_df.symbol.unique()] quotes_data = [quotes_df.query('symbol == @s') for s in quotes_df.symbol.unique()]
@ -235,4 +238,5 @@ for i in range(len(symbols_list)):
# %% Completed ---- # %% Completed ----
gc() gc.collect()