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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()
@ -76,7 +80,7 @@ quotes_df
# %% Discover data ---- # %% Discover data ----
quotes_df.groupby('symbol')['close'].agg(['count', 'last']) quotes_df.groupby('symbol')['close'].agg(['count', 'last'])
# %% USD/RUB dataset ---- # %% USD/RUB dataset ----
usdrub_df = quotes_df[quotes_df.symbol == 'USD/RUB'] usdrub_df = quotes_df[quotes_df.symbol == 'USD/RUB']
@ -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)
@ -172,7 +177,7 @@ def evaluate_simulation(simulated_returns: pd.Series, last_actual_price: float,
x = price_df.iloc[-1] x = price_df.iloc[-1]
fig, ax = plt.subplots(1, 2, figsize=(14, 4)) fig, ax = plt.subplots(1, 2, figsize=(14, 4))
sns.distplot(x, ax=ax[0]) sns.distplot(x, ax=ax[0])
sns.distplot(x, hist_kws={'cumulative': True}, kde_kws={'cumulative': True}, ax=ax[1]) sns.distplot(x, hist_kws={'cumulative': True}, kde_kws={'cumulative':True}, ax=ax[1])
plt.xlabel('Stock Price') plt.xlabel('Stock Price')
plt.show() plt.show()
@ -189,8 +194,8 @@ def evaluate_simulation(simulated_returns: pd.Series, last_actual_price: float,
usdrub_mc_simulation_df = evaluate_simulation( usdrub_mc_simulation_df = evaluate_simulation(
usdrub_simulated_returns, usdrub_simulated_returns,
last_actual_price = usdrub_df['close'].tail(1), last_actual_price=usdrub_df['close'].tail(1),
n_days = n_days) n_days=n_days)
plt.figure(figsize=(10, 6)) plt.figure(figsize=(10, 6))
@ -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()