"""
RL BTC v4 — Offline Implicit Q-Learning for Bitcoin Trading.

Based on: "Offline Reinforcement Learning with Implicit Q-Learning" (Kostrikov et al., 2021)
https://hf.co/papers/2110.06169

Key innovation: learns from logged historical data without environment interaction.
Uses upper expectile value function to estimate the value of the best actions
without ever explicitly querying out-of-distribution actions.
"""
from .constants import (
    DEFAULT_DATA_PATH,
    MARKET_FEATURE_COLUMNS,
    PORTFOLIO_FEATURE_COLUMNS,
    ACTIONS,
    N_ACTIONS,
    ACTION_INDEX_BY_NAME,
    STARTING_CASH,
    DRAWDOWN_LIMIT,
)
from .env import BTCTradingEnv
from .dataset import build_offline_rl_dataset, OfflineRLDataset
from .iql_trainer import IQLTrainer, IQLConfig