"""
Personal interest to see if bitcoin has seasonality.
"""

import os
from statsmodels.graphics.tsaplots import plot_acf
import pandas as pd
import matplotlib.pyplot as plt
from statsmodels.tsa.seasonal import STL

# Chart config
plt.rcParams["figure.figsize"] = (12, 6)

# Ensure assets directory
os.makedirs("assets", exist_ok=True)

# Load solar data
data = pd.read_csv(
    "../assets/btc_yearly.csv",
    parse_dates=["Date"],
    index_col="Date",
    thousands=",",
    converters={
        "Change %": lambda x: float(x.rstrip("%")) / 100,
        "Vol.": lambda x: pd.to_numeric(x.replace("K", "000").replace("M", "000000").replace('B', '000000000') if x != '' else pd.NA),
    },
)
# cutoff = data.index.max() - pd.DateOffset(years=5)  # pyright: ignore[reportOperatorIssue]
# series = data["Price"]
series = data["Price"].iloc[:365*8]
# print(data['vol'])

# Resample to daily
series_daily = series.resample("D").sum()

# Compute autocorrelation, acf = Auto Correlation Function
# Compares against lags up to a year away
# acf_scores = acf(x=series_daily, nlags=365)

# Compute partial autocorrelation
# pacf_scores = pacf(x=series_daily, nlags=365)

# Convience functions to do math and plot
plot_acf(series_daily, lags=365)
plt.savefig("assets/btc_autocorrelation.png")

result = STL(endog=series_daily, period=365).fit()
result.plot()
plt.savefig("assets/btc_stl_decomposition.png")

# plot_pacf(series_daily, lags=365)
# plt.savefig("assets/partion_autocorrelation.png")