"""
The Odds API
============

This example shows OddsApi, the paid odds source that carries time-stamped prices.

Nothing here is bought and nothing is downloaded. The example shows how you configure the source and how it handles
your key. You want to understand both before you spend anything.
"""

# Author: Georgios Douzas <gdouzas@icloud.com>
# Licence: MIT

import os

import matplotlib.pyplot as plt
import numpy as np

from sportsbet.sources import NBAStats, OddsApi, RawItem

# The source reads the key from the named variable. Set a placeholder, so the example runs without a real key and never
# writes yours into the docs.
os.environ['ODDS_API_KEY'] = 'your-key'

# %%
# Configuring it
# --------------
#
# It carries prices with a timestamp. So you can backtest an in-play bet against the odds that were on offer at the
# minute you would have placed it. The free feeds cannot do that, because they publish the closing price only.
#
# Every market, region and moment is a separate request. Each one adds to the work.

odds = OddsApi(key_env='ODDS_API_KEY', markets=['h2h'], regions=['eu'])
odds.name, odds.kind

# %%
# It sells every sport. So it carries no sport of its own and takes the sport of the statistics you pair it with.

{'carries no sport of its own': odds.sport is None}

# %%
# Your key never reaches the data
# -------------------------------
#
# The source adds the key to a request at the moment it makes the request. The key is never part of a `RawItem`, so it
# is never written to disk.

item = RawItem(source='odds_api', key='snapshot', url='https://api.the-odds-api.com/v4/sports?all=true')
{'key in the item': 'your-key' in item.url}

# %%
odds.request_url(item)

# %%
# Using it
# --------
#
# Pair it with free statistics, here the NBA. Read your key from the environment rather than write it into a file that
# could be committed:
#
# ```python
# import os
#
# from sportsbet.dataloaders import DataLoader
# from sportsbet.sources import NBAStats, OddsApi
#
# dataloader = DataLoader(
#     param_grid={'league': ['NBA'], 'year': [2026]},
#     stats=NBAStats(),
#     odds=OddsApi(key_env='ODDS_API_KEY', markets=['h2h']),
# )
# X, Y, O = dataloader.extract_train_data(odds_type='pinnacle')
# ```
#
# Extracting is what spends money. Every market, region and moment is its own request, and the vendor sets what those
# requests cost. Ask the source what it would fetch first, and price it before you commit.

NBAStats().sport, OddsApi(key_env='ODDS_API_KEY').sport

# %%
# What a price implies
# --------------------

odds_range = np.linspace(1.05, 10, 200)

fig, ax = plt.subplots()
ax.plot(odds_range, 1 / odds_range)
ax.set_title('The probability a price implies')
ax.set_xlabel('decimal odds')
ax.set_ylabel('implied probability')