import datetime
import lib
from matplotlib import pyplot as plt
import strategies as st
import pandas as pd

if __name__ == "__main__":
    stockList = ["^GSPC"]
    monthly_params = dict(sum=100000, coef=1.005, t_rate=1 + 0.02 / 12)
    # startDate = datetime.datetime.fromisoformat("2000-01-01")
    # endDate = datetime.datetime.fromisoformat("2022-01-01")
    print(stockList[0])
    stockData = lib.get_data(
        stockList[0],
    )
    start_date = stockData.index[0]
    year_step = 5
    print(
        "Investing monthly, increasing {:.2f}%, starting from ${}".format(
            (monthly_params["coef"] * 100) - 100,
            monthly_params["sum"],
        )
    )
    for start_year in range(start_date.year, datetime.datetime.today().year, year_step):
        start_date = datetime.date(start_year, 1, 1)
        end_date = datetime.date(start_year + year_step - 1, 12, 31)
        if (today := datetime.date.today()) < end_date:
            end_date = today
        stockData = lib.get_data(stockList[0], start_date, end_date)
        start, end = lib.get_data_borders(stockData)
        print(
            "{} to {}, {:.1f} years".format(
                start,
                end,
                (end - start).days / 365,
            )
        )
        newdf = pd.concat(
            [
                lib.test_strategy(stockData, strategy, monthly_params)
                for strategy in (st.DCA, st.QDCA)
            ]
        )
        print(newdf.to_string())
        # print(df[["annual%", "froi%", "cost", "total_value", "max_dd%", "max_md"]])
        # print(newdf["annual%"])