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MC_RISK_ASSESMENT.py
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MC_RISK_ASSESMENT.py
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# -*- coding: utf-8 -*-
"""
Created on Tue Jan 12 20:05:07 2021
@author: alexx
The following program assesses the risk on certain currency pairs
It is used for determining the values in tMatrix in the Markov Chain algorithm
The implementation uses the Kelly Criterion for determining optimal bet sizing
"""
import numpy as np
import pandas as pd
import pandas_datareader as wb
import matplotlib.pyplot as plt
from scipy.stats import norm
GBPJPY = 'GBPJPY'
GBPUSD = 'GBPUSD'
USDJPY = 'USDJPY'
default_pair = GBPJPY
t_intervals = 30
iterations = 25
def read_file_date_price(fileName):
data = pd.read_csv(fileName, index_col = 0, usecols = ['Date', 'Price'])
return data
def sort_values_csv_file(data, sortBy):
data.sort_values([sortBy], inplace = True)
#Settings for Monte Carlo asset data, how long, and how many forecasts
def return_data():
data = read_file_date_price('GBP_JPY Historical Data.csv')
print("read file")
return data
def plot_GBPJPY_AssetData(ticker):
#Forex pair ticker
ticker = 'GBP_JPY'
t_intervals = 30
iterations = 25
#Read data from csv file saved from Investing.com
data = read_file_date_price('GBP_JPY Historical Data.csv')
#pd.read_csv('GBP_JPY Historical Data.csv',index_col=0,usecols=['Date', 'Price'])
data.sort_values(["Date"], inplace=False)
#sort_values_csv_file(data, "Date")
#Preparing log returns from data
data = data.rename(columns={"Price": ticker})
#Plot of asset historical closing price
log_returns = np.log(1 + data.pct_change())
data.plot(figsize=(10, 6));
log_returns.plot(figsize = (10, 6))
print("plotted Asset Data")
#TO DO: Generalization
def return_log(pair):
data = read_file_date_price('GBP_JPY Historical Data.csv')
log_returns = np.log(1+ data.pct_change())
return log_returns
#Setting up drift and random component in relation to asset data
def plot_firstMC_Sim():
log_returns = return_log(default_pair)
data = read_file_date_price('GBP_JPY Historical Data.csv')
u = log_returns.mean()
var = log_returns.var()
drift = u - (0.5 * var)
stdev = log_returns.std()
daily_returns = np.exp(drift.values + stdev.values * norm.ppf(np.random.rand(t_intervals, iterations)))
#Takes last data point as startpoint point for simulation
S0 = data.iloc[-1]
price_list = np.zeros_like(daily_returns)
price_list[0] = S0
#Applies Monte Carlo simulation in pair/security
for t in range(1, t_intervals):
price_list[t] = price_list[t - 1] * daily_returns[t]
print("Plotted First Monte Carlo Simulation")
plt.figure(figsize=(10,6))
plt.plot(price_list)
#return price points for probability algorithm
return price_list
def plot_secondMC_Sim():
data = return_data()
log_returns = return_log(default_pair)
u = log_returns.mean()
var = log_returns.var()
drift = u - (0.5 * var)
stdev = log_returns.std()
daily_returns = np.exp(drift.values + stdev.values * norm.ppf(np.random.rand(t_intervals, iterations)))#Takes last data point as startpoint point for simulation
S0 = data.iloc[-1]
price_list = np.zeros_like(daily_returns)
price_list[0] = S0
#Applies Monte Carlo simulation in pair/security
for t in range(1, t_intervals):
price_list[t] = price_list[t - 1] * daily_returns[t]
print("Plotted Second Monte Carlo Simulation")
plt.figure(figsize=(10,6))
plt.plot(price_list)
#return price points for probability algorithm
return price_list
def plot_security(ticker):
#stock ticker - Apple Inc
ticker = 'AAPL'
#Time intervals for the future forecast
t_intervals = 30
#Number of simulation for mc
iterations = 25
#Acquiring data
data = pd.DataFrame()
#Data Reader takes data
data[ticker] = wb.DataReader(ticker, data_source='yahoo', start='2015-1-1')['Adj Close']
print(ticker)
print("Active days price data: ")
print(data[ticker])
#Preparing log returns from data
log_returns = np.log(1 + data.pct_change())
#Plot of asset historical closing price
data.plot(figsize=(10, 6));
def main():
plot_GBPJPY_AssetData(GBPJPY)
plot_firstMC_Sim()
plot_secondMC_Sim()
plot_security('AAPL')
if __name__ == "__main__":
main()