main.py

import warnings

import matplotlib.pyplot as plt
import pandas as pd
import okama as ok

import os

os.environ["PYTHONWARNINGS"] = "ignore::FutureWarning"
warnings.simplefilter(action="ignore", category=FutureWarning)

pd.set_option("display.float_format", lambda x: "%.2f" % x)

l = ["SPY.US", "GLD.US", "AGG.US"]
w=[0.20, 0.30, 0.50]

pf = ok.Portfolio(l, weights=w, ccy="EUR", rebalancing_period="none")



# Fixed Percentage strategy
pc = ok.PercentageStrategy(pf)
pc.initial_investment = 10_000
pc.frequency = "year"
pc.percentage = -0.08

# # Fixed Amount strategy
# ind = ok.IndexationStrategy(pf)
# ind.initial_investment = 10_000
# ind.frequency = "year"
# ind.amount = -500
# ind.indexation = "inflation"

# # TimeSeries strategy
# d = {
#     "2025-02": 1_000,
#     "2029-03": -2_000,
# }
#
# ts = ok.TimeSeriesStrategy(pf)
# ts.initial_investment = 10_000
# ts.time_series_dic = d

# Assign a strategy
pf.dcf.cashflow_parameters = pc
# pf.dcf.discount_rate = 0.10
# pf.dcf.use_discounted_values = True

# Set Monte Carlo
pf.dcf.set_mc_parameters(distribution="norm", period=30, number=100)

w = pf.dcf.find_the_largest_withdrawals_size(
    withdrawal_steps=10,   # The number of intermediate steps during the iteration of values fom max to min of the withdrawal size
    confidence_level=0.50,  # Confidence level defines the percentile of Monte Carlo time series. The 25th percentile is a negative scenario.
    goal="maintain_balance",  # The goal of the strategy in this case is to keep the portfolio's real balance
)
print(w)

# pf.dcf.plot_forecast_monte_carlo(backtest=True)
#
# plt.yscale("log")  # log or linear
# plt.legend("")
# # plt.savefig('time_series.png')
# plt.show()