"""Constants for RL BTC v4."""
from __future__ import annotations

from dataclasses import dataclass
from pathlib import Path
import pandas as pd

REPO_ROOT = Path(__file__).resolve().parents[1]
DEFAULT_DATA_PATH = (
    REPO_ROOT / "binance-experiment" / "research_datasets" / "btc_updown_5m_augmented_v1.parquet"
)

STARTING_CASH = 50.0
DRAWDOWN_LIMIT = 0.40
DEFAULT_HISTORY_LENGTH = 30  # number of past observations to include in state
DEFAULT_EPISODE_SPAN_DAYS = 30
DEFAULT_EPISODE_STRIDE_DAYS = 15  # overlapping episodes for more data
DEFAULT_REBALANCE_TOLERANCE = 0.05

# ── Fee schedule (Crypto / BTC prediction markets) ────────────────────────
# Taker fee in basis points (0.072 = 7.2 bps).
# Maker fee = 0 (not modelled — all orders assumed market/taker).
# Fee formula: fee_usdc = shares × fee_rate × price × (1 - price)
# Quadratic in price — highest near 0.50 (max uncertainty), zero at 0/1.
# Maker rebate (20%) is not applied since we model taker orders.
TAKER_FEE_RATE = 0.072

# ── Action space ──────────────────────────────────────────────────────────────

@dataclass(frozen=True)
class ActionSpec:
    id: int
    name: str
    kind: str          # "hold" | "target" | "reduce"
    target_side: str | None   # "YES" | "NO" | None
    target_fraction: float    # target exposure as fraction of equity

ACTIONS: tuple[ActionSpec, ...] = (
    ActionSpec(0, "HOLD",       "hold",   None,  0.00),
    ActionSpec(1, "FLAT",       "target", None,  0.00),
    ActionSpec(2, "YES_10",     "target", "YES",  0.10),
    ActionSpec(3, "YES_25",     "target", "YES",  0.25),
    ActionSpec(4, "YES_50",     "target", "YES",  0.50),
    ActionSpec(5, "NO_10",      "target", "NO",   0.10),
    ActionSpec(6, "NO_25",      "target", "NO",   0.25),
    ActionSpec(7, "NO_50",      "target", "NO",   0.50),
)

ACTION_INDEX_BY_NAME: dict[str, int] = {s.name: s.id for s in ACTIONS}
N_ACTIONS: int = len(ACTIONS)

# ── Feature columns ───────────────────────────────────────────────────────────

MARKET_FEATURE_COLUMNS: list[str] = [
    "obs_pos",
    "seconds_since_open",
    "seconds_to_close",
    "market_progress",
    "yes_bid_validated",
    "yes_ask_validated",
    "yes_mid_validated",
    "yes_spread_validated",
    "no_bid_validated",
    "no_ask_validated",
    "no_mid_validated",
    "no_spread_validated",
    "microprice_bias",
    "abs_yes_mid_distance_from_even",
    "price_return_from_open",
    "mark_return_from_open_filled",
    "index_return_from_open_filled",
    "abs_mark_return_from_open_filled",
    "rolling_vol_15m",
    "rolling_vol_60m",
    "taker_buy_ratio",
    "buy_sell_imbalance",
    "rolling_volume_15m",
    "rolling_num_trades_15m",
    "volume",
    "num_trades",
    "return_over_vol_15m",
    "return_over_vol_60m",
    "signed_move_x_time_remaining",
    "imbalance_x_vol",
    "funding_rate",
    "funding_rate_prev",
    "oi_delta_5m",
    "oi_delta_15m",
    "oi_delta_60m",
    "long_short_ratio",
    "hour_sin",
    "hour_cos",
]

PORTFOLIO_FEATURE_COLUMNS: list[str] = [
    "cash_fraction",
    "equity_fraction",
    "drawdown_fraction",
    "position_side",
    "position_fraction",
    "position_shares",
    "avg_entry_price",
    "unrealized_pnl_fraction",
    "steps_held_fraction",
]

# Normalisation stats will be fitted on training data