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, 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 = 1
    label_string = "Investing to {} monthly over {} years, increasing {:.2f}%, starting from ${}".format(
        stockList[0],
        year_step,
        (monthly_params["coef"] * 100) - 100,
        monthly_params["sum"],
    )
    print(label_string)
    plot_param = "annual%"

    _strategies = (
        st.QDCA,
        st.EDCA,
        st.SmaCross,
    )
    strategies = (st.DCA,) + _strategies

    df_comp = pd.DataFrame(index=list(st.__name__ for st in strategies))
    df_vwr = pd.DataFrame(index=list(st.__name__ for st in strategies))
    df_rel_to_dca = pd.DataFrame(index=list(st.__name__ for st in _strategies))
    for start_year in range(
        start_date.year, datetime.datetime.today().year - year_step + 1, 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 strategies
            ]
        )
        print(newdf.to_string())
        df_rel_to_dca[start_year] = list(
            newdf.loc[s.__name__][plot_param] - newdf.loc[st.DCA.__name__][plot_param]
            for s in _strategies
        )
        df_comp[start_year] = newdf[plot_param].values
        # print(df[["annual%", "froi%", "cost", "total_value", "max_dd%", "max_md"]])
        # print(newdf["annual%"])

    print(df_rel_to_dca.to_string())
    df_comp.T.plot()
    for index, s in df_rel_to_dca.iterrows():
        print("{}: avg {:.3f} dev {:.3f}".format(index, s.mean(), s.std()))
    df_rel_to_dca.T.hist(bins=20)
    plt.grid()
    plt.title(label_string)
    plt.ylabel(plot_param)
    plt.xlabel("year")
    plt.show()