diff --git a/src/cryptocurrency_portfolio__assets_selection.py b/src/cryptocurrency_portfolio__assets_selection.py
new file mode 100644
index 0000000..fdf26f9
--- /dev/null
+++ b/src/cryptocurrency_portfolio__assets_selection.py
@@ -0,0 +1,188 @@
+#!/usr/bin/python3
+
+
+"""Crypto Currency Portfolio: Assets Selection.
+
+Description:
+    Crypto Currency Selection using monte Carlo simulation.
+"""
+
+# %% Import dependencies ----
+# core
+import os
+import gc
+
+# data science
+import pandas as pd
+import numpy as np
+from scipy.stats import norm
+
+# Cloud integration
+from azureml.core import Workspace, Dataset, VERSION as aml_version
+print(f'Azure ML SDK v{aml_version}')
+
+# network
+import ssl
+ssl._create_default_https_context = ssl._create_unverified_context
+
+# plots
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+# show info about python env
+from IPython import sys_info
+print(sys_info())
+
+import warnings
+warnings.filterwarnings("ignore")
+
+
+# %% Set params ----
+symbols = ['BTCUSDT', 'ETHUSDT', 'BNBUSDT', 'SOLUSDT', 'MATICUSDT', 'UNIUSDT']
+
+n_days = int(252) # US market has 252 trading days in a year
+n_iterations = int(1e4)
+
+
+
+# %% Load quotes ----
+def get_quotes(symbol: str) -> pd.DataFrame:
+    df = pd.read_csv(f'https://www.cryptodatadownload.com/cdd/Binance_{symbol}_d.csv', skiprows=[0])
+    df = df.set_index('date')
+    df = df.sort_values(by = 'date')
+
+    return df[['symbol', 'open', 'high', 'low', 'close']]
+
+quotes_data = [get_quotes(s) for s in symbols]
+# row-wise union:
+# pd.concat([get_quotes(s) for s in symbols])
+# column-wise: 
+# pd.concat(list1, axis=1, ignore_index=False)
+
+
+btcusdt_df = quotes_data[0]
+
+pd.concat([
+    btcusdt_df['close'].head(5),
+    btcusdt_df['close'].tail(5)
+])
+
+
+
+# %% Calculate Return
+def get_returns(close_prices) -> pd.Series:
+    return (close_prices/close_prices.shift()) - 1
+
+
+btcusdt_df['diff'] = btcusdt_df['close'].diff()
+btcusdt_df['return'] = get_returns(btcusdt_df['close'])
+
+btcusdt_df[['close', 'diff', 'return']].tail(10)
+
+
+
+# %% Calculate LogReturn
+def get_log_returns(return_prices) -> pd.Series:
+    return np.log(1 + return_prices)
+
+btcusdt_df['log_return'] = btcusdt_df['return'].apply(lambda x: get_log_returns(x))
+btcusdt_df[['close', 'diff', 'return', 'log_return']].tail(10)
+
+
+
+# %% Simulate possible LogReturns
+
+def get_simulated_returns(log_returns: pd.Series, n_days: int, n_iterations: int) -> pd.Series:
+    u = log_returns.mean()
+    var = log_returns.var()
+    stdev = log_returns.std()
+
+    drift = u - (0.5*var)
+    Z = norm.ppf(np.random.rand(n_days, n_iterations))
+    
+    return np.exp(drift + stdev*Z)
+
+
+btcusd_logreturns = btcusdt_df['log_return'].dropna()
+btcusd_simulated_returns = get_simulated_returns(btcusd_logreturns, n_days, n_iterations)
+
+assert(
+    btcusd_simulated_returns.shape == (n_days, n_iterations)
+)
+
+
+
+# %% Monte carlo simulation functions ----
+def get_breakeven_prob(predicted, threshold = 0):
+    """
+    This function calculated the probability of a stock being above a certain threshhold, which can be defined as a value (final stock price) or return rate (percentage change)
+    """
+    predicted0 = predicted.iloc[0,0]
+    predicted = predicted.iloc[-1]
+    predList = list(predicted)
+
+    over = [(i*100)/predicted0 for i in predList if ((i-predicted0)*100)/predicted0 >= threshold]
+    less = [(i*100)/predicted0 for i in predList if ((i-predicted0)*100)/predicted0 < threshold]
+
+    return (len(over)/(len(over) + len(less)))
+
+
+def monte_carlo_simulation(simulated_returns: pd.Series, last_actual_price: float, n_days: int, plot=True):
+    # Create empty matrix
+    price_list = np.zeros_like(simulated_returns)
+
+    # Put the last actual price in the first row of matrix
+    price_list[0] = last_actual_price
+
+    # Calculate the price of each day
+    for t in range(1, n_days):
+        price_list[t] = price_list[t-1]*simulated_returns[t]
+    
+    # Plot
+    if plot == True:
+        x = pd.DataFrame(price_list).iloc[-1]
+        fig, ax = plt.subplots(1, 2, figsize=(14,4))
+        sns.distplot(x, ax=ax[0])
+        sns.distplot(x, hist_kws={'cumulative': True}, kde_kws={'cumulative': True}, ax=ax[1])
+        plt.xlabel('Stock Price')
+        plt.show()
+    
+
+    print(f"Investment period: {n_days-1}")
+    print(f"Expected Value: ${round(pd.DataFrame(price_list).iloc[-1].mean(),2)}")
+    print(f"Return: {round(100*(pd.DataFrame(price_list).iloc[-1].mean()-price_list[0,1])/pd.DataFrame(price_list).iloc[-1].mean(),2)}%")
+    print(f"Probability of Breakeven: {get_breakeven_prob(pd.DataFrame(price_list))}")
+          
+    return pd.DataFrame(price_list)
+
+
+# %% Run Monte carlo simulation and estimate result
+
+simulated_prices_df = monte_carlo_simulation(
+    btcusd_simulated_returns, 
+    quotes_data[0]['close'].tail(1), 
+    n_days)
+
+
+plt.figure(figsize=(10,6))
+plt.plot(simulated_prices_df.iloc[:, 1:10])
+plt.show()
+
+
+
+# %% Monte Carlo simulation pipeline for multiple tokens ----
+
+n_iterations = int(1e4) #! WARN: set simulations number
+
+returns_data = [get_returns(df['close']) for df in quotes_data]
+log_returns_data = [get_log_returns(r) for r in returns_data]
+simulated_returns_data =  [get_simulated_returns(lr, n_days, n_iterations) for lr in log_returns_data]
+
+
+for i in range(len(simulated_returns_data)):
+    print(f'Starting Monte-Carlo simulation for {symbols[i]} symbol...')
+    prices_ms = monte_carlo_simulation(simulated_returns_data[i], quotes_data[i]['close'].tail(1), n_days, plot=True)
+    
+    plt.figure(figsize=(10,6))
+    plt.plot(prices_ms.iloc[:, 1:50])
+    plt.show()
diff --git a/src/currency_portf.py b/src/fx_currency_portfolio__assets_selection.py
similarity index 100%
rename from src/currency_portf.py
rename to src/fx_currency_portfolio__assets_selection.py