from collections import OrderedDict
from typing import Dict, Iterable, List


FEATURE_VERSION = "qohlc_v2"
FEATURE_VERSION_ID = 2
WINDOW_SECONDS = 5
SEGMENT_SECONDS = 300
TOKENS_PER_SEGMENT = SEGMENT_SECONDS // WINDOW_SECONDS
LOOKBACK_SECONDS = [15, 30, 60, 120]


FEATURE_NAMES: List[str] = [
    "cum_log_return",
    "mean_log_return_1s",
    "std_log_return_1s",
    "max_up_1s",
    "max_down_1s",
    "realized_vol",
    "window_range_frac",
    "close_to_close_slope",
    "accel_proxy",
    "frac_pos_1s",
    "frac_neg_1s",
]

for lookback in LOOKBACK_SECONDS:
    prefix = f"lb_{lookback}s"
    FEATURE_NAMES.extend([
        f"{prefix}_dist_high",
        f"{prefix}_dist_low",
        f"{prefix}_drawdown_high",
        f"{prefix}_rebound_low",
        f"{prefix}_pos_in_range",
        f"{prefix}_range_width",
        f"{prefix}_compression_ratio",
        f"{prefix}_breakout_high",
        f"{prefix}_breakdown_low",
        f"{prefix}_reclaim_breakdown",
        f"{prefix}_rejection_breakout",
    ])

FEATURE_NAMES.extend([
    "nearest_support_dist",
    "nearest_resistance_dist",
    "support_touch_count",
    "resistance_touch_count",
    "support_age_sec",
    "resistance_age_sec",
    "support_strength",
    "resistance_strength",
    "inside_support_zone",
    "inside_resistance_zone",
    "support_swept",
    "resistance_swept",
    "support_reclaim",
    "resistance_reject",
    "keylevel_breakout_up",
    "keylevel_breakout_down",
    "keylevel_hold_above",
    "keylevel_hold_below",
    "keylevel_failed_breakout_up",
    "keylevel_failed_breakout_down",
    "keylevel_flip_to_support",
    "keylevel_flip_to_resistance",
    "keylevel_upper_distance",
    "keylevel_lower_distance",
    "keylevel_zone_width_frac",
    "keylevel_density",
    "lower_trendline_slope",
    "upper_trendline_slope",
    "dist_to_lower_line",
    "dist_to_upper_line",
    "trend_channel_width",
    "trend_convergence",
    "trend_breakout_upper",
    "trend_breakdown_lower",
    "trend_reentry",
    "ema_fast",
    "ema_medium",
    "sma_fast",
    "sma_medium",
    "price_minus_ema_fast",
    "price_minus_ema_medium",
    "ema_spread",
    "price_zscore",
    "mean_reversion_score",
    "rolling_vol_zscore",
])

FEATURE_NAMES.extend([
    "sr_available",
    "trendline_available",
])

FEATURE_INDEX = {name: idx for idx, name in enumerate(FEATURE_NAMES)}
NUM_QUANT_OHLC_FEATURES = len(FEATURE_NAMES)

FEATURE_GROUPS = OrderedDict([
    ("price_path", [
        "cum_log_return",
        "mean_log_return_1s",
        "std_log_return_1s",
        "max_up_1s",
        "max_down_1s",
        "realized_vol",
        "window_range_frac",
        "close_to_close_slope",
        "accel_proxy",
        "frac_pos_1s",
        "frac_neg_1s",
    ]),
    ("relative_structure", [name for name in FEATURE_NAMES if name.startswith("lb_")]),
    ("levels_breaks", [
        "nearest_support_dist",
        "nearest_resistance_dist",
        "support_touch_count",
        "resistance_touch_count",
        "support_age_sec",
        "resistance_age_sec",
        "support_strength",
        "resistance_strength",
        "inside_support_zone",
        "inside_resistance_zone",
        "support_swept",
        "resistance_swept",
        "support_reclaim",
        "resistance_reject",
        "keylevel_breakout_up",
        "keylevel_breakout_down",
        "keylevel_hold_above",
        "keylevel_hold_below",
        "keylevel_failed_breakout_up",
        "keylevel_failed_breakout_down",
        "keylevel_flip_to_support",
        "keylevel_flip_to_resistance",
        "keylevel_upper_distance",
        "keylevel_lower_distance",
        "keylevel_zone_width_frac",
        "keylevel_density",
    ]),
    ("trendlines", [
        "lower_trendline_slope",
        "upper_trendline_slope",
        "dist_to_lower_line",
        "dist_to_upper_line",
        "trend_channel_width",
        "trend_convergence",
        "trend_breakout_upper",
        "trend_breakdown_lower",
        "trend_reentry",
    ]),
    ("rolling_quant", [
        "ema_fast",
        "ema_medium",
        "sma_fast",
        "sma_medium",
        "price_minus_ema_fast",
        "price_minus_ema_medium",
        "ema_spread",
        "price_zscore",
        "mean_reversion_score",
        "rolling_vol_zscore",
    ]),
    ("availability", [
        "sr_available",
        "trendline_available",
    ]),
])


def empty_feature_dict() -> Dict[str, float]:
    return {name: 0.0 for name in FEATURE_NAMES}


def feature_dict_to_vector(features: Dict[str, float]) -> List[float]:
    out: List[float] = []
    for name in FEATURE_NAMES:
        value = features.get(name, 0.0)
        try:
            out.append(float(value))
        except Exception:
            out.append(0.0)
    return out


def group_feature_indices(group_names: Iterable[str]) -> List[int]:
    indices: List[int] = []
    for group_name in group_names:
        for feature_name in FEATURE_GROUPS[group_name]:
            indices.append(FEATURE_INDEX[feature_name])
    return sorted(set(indices))