"""
====================================================================================================
9.01 Free time series modeling
====================================================================================================
We reproduce here the figure 9.1 of the book, showcasing how to download data an plot the charts for the three selected stocks.
"""

#########################################################################
# Necessary Imports
# ------------------------
import os
import sys

import matplotlib.pyplot as plt
import numpy as np

try:
    CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
except NameError:
    CURRENT_DIR = os.getcwd()
PARENT_DIR = os.path.abspath(os.path.join(CURRENT_DIR, ".."))
sys.path.insert(0, PARENT_DIR)

from utils.ch9.data_utils import df_summary
from utils.ch9.market_data import retrieve_market_data

try:
    current_dir = os.path.dirname(__file__)
    data_path = os.path.join(current_dir, "data")
except NameError:
    current_dir = os.getcwd()
    data_path = os.path.join(current_dir, "data")

#########################################################################
# Parameter definition
# ------------------------


def get_cdpres_param():
    return {
        "rescale_kernel": {"max": 2000, "seed": None},
        "rescale": True,
        "grid_projection": True,
        "reproductibility": False,
        "date_format": "%d/%m/%Y",
        "begin_date": "01/06/2020",
        "end_date": "01/06/2022",
        "today_date": "01/06/2022",
        "symbols": ["AAPL", "GOOGL", "AMZN"],
    }


#########################################################################
# Getting the market data
# ------------------------------------------------
# We call the graphic function created inside the parameters by the `retrieve_market_data` method.
# This displays the historical data for the selected stocks and date range.
params = retrieve_market_data()
params["graphic"](params)
plt.show()
print(df_summary(np.log(params["data"] / params["data"].shift(1)).dropna()))