"""
Proven Templates v2 — Deterministic alpha generation without LLMs.

These templates use known-valid BRAIN expression structures with
novel fields swapped into the "value leg" or primary signal slot.

NOTE: Weights inside rank() are monotonic — the coefficient values
(e.g., 0.60, 0.40) are mostly decorative. The real signal comes from
which fields are combined, not how they're weighted.
"""
from ..data.brain_fields import (
    FIELD_INDEX, ALL_FIELDS, GOLDMINE_FIELDS, TIER1_MODEL77_FIELDS,
    TIER3_ANALYST_FIELDS, TIER2_NEWS_FIELDS, TIER3_OPTION_FIELDS,
    TIER3_SUPPLY_CHAIN_FIELDS, TIER3_SOCIAL_FIELDS, TIER2_MODEL16_FIELDS,
    BrainField, SignConvention,
    get_backfill_days, get_sign_multiplier,
)
from ..data.brain_groups import get_group_for_expression, PRODUCTION_GROUPS
import random


def _get_sign(field: BrainField) -> str:
    """Get sign prefix for a field based on convention."""
    if field.sign == SignConvention.LONG_LOW:
        return "-"
    return ""


def _get_bf(field: BrainField) -> str:
    """Get ts_backfill wrapper if needed."""
    bf_days = get_backfill_days(field)
    if field.coverage < 0.85:
        return f"ts_backfill({field.id}, {bf_days})"
    return field.id


def generate_alpha15_variant(field: BrainField, group_key: str = None, decay: int = 5) -> str:
    """
    Alpha 15 archetype: intraday position proxy + value/momentum leg.
    
    Structure: rank( zscore(ts_rank((high+low)/2 - close, 252)) + zscore(ts_rank(field, 252)) )
    
    NOTE: The 0.60/0.40 coefficients are inside rank() and are effectively
    decorative — rank() is a monotonic transform. The signal comes from combining
    two orthogonal sources: (1) intraday position (midpoint vs close)
    and (2) a fundamental/momentum field ranked over 252 days.
    """
    if group_key is None:
        group_key = get_group_for_expression(prefer_novel=True)
    
    sign = _get_sign(field)
    field_ref = _get_bf(field)
    
    expr = (
        f"ts_decay_linear("
        f"group_neutralize("
        f"rank("
        f"zscore(ts_rank((high + low) / 2 - close, 252)) "
        f"+ {sign}zscore(ts_rank({field_ref}, 252))"
        f"), {group_key}), {decay})"
    )
    return expr


def generate_alpha6_variant(field: BrainField, group_key: str = None, decay: int = 5) -> str:
    """
    Alpha 6 archetype: microstructure score + value leg.
    
    Microstructure: rank((vwap - close) / close) * rank(volume / ts_mean(volume, 20))
    Value leg: zscore(ts_rank(field, 252))
    
    NOTE: The structure is multiplicative in the micro part and additive
    with the value leg. The coefficients are decorative inside rank().
    """
    if group_key is None:
        group_key = get_group_for_expression(prefer_novel=True)
    
    sign = _get_sign(field)
    field_ref = _get_bf(field)
    
    expr = (
        f"ts_decay_linear("
        f"group_neutralize("
        f"rank("
        f"rank((vwap - close) / close) * rank(volume / ts_mean(volume, 20)) "
        f"+ {sign}zscore(ts_rank({field_ref}, 252))"
        f"), {group_key}), {decay})"
    )
    return expr


def generate_pure_field_rank(field: BrainField, horizon: int = 252, group_key: str = None, decay: int = 5) -> str:
    """
    Simplest possible expression — just rank the field cross-sectionally.
    Often works for high-quality pre-computed model fields (model77).
    """
    if group_key is None:
        group_key = get_group_for_expression(prefer_novel=True)
    
    sign = _get_sign(field)
    field_ref = _get_bf(field)
    
    expr = (
        f"ts_decay_linear("
        f"group_neutralize("
        f"{sign}rank(ts_rank({field_ref}, {horizon}))"
        f", {group_key}), {decay})"
    )
    return expr


def generate_delta_momentum(field: BrainField, horizon: int = 21, group_key: str = None, decay: int = 5) -> str:
    """
    Delta/change momentum — buy stocks where the field is improving.
    Works well for score-type fields (model16 derivatives, earnings revisions).
    """
    if group_key is None:
        group_key = get_group_for_expression(prefer_novel=True)
    
    sign = _get_sign(field)
    field_ref = _get_bf(field)
    
    expr = (
        f"ts_decay_linear("
        f"group_neutralize("
        f"{sign}zscore(ts_delta({field_ref}, {horizon}))"
        f", {group_key}), {decay})"
    )
    return expr


def generate_mean_reversion(field: BrainField, horizon: int = 20, group_key: str = None, decay: int = 5) -> str:
    """
    Mean reversion on a field — short stocks where field is high, long where low.
    Effective for contrarian indicators (option PCR, social buzz).
    """
    if group_key is None:
        group_key = get_group_for_expression(prefer_novel=True)
    
    # Mean reversion: flip the natural sign
    if field.sign == SignConvention.LONG_HIGH:
        sign = "-"
    elif field.sign == SignConvention.LONG_LOW:
        sign = ""
    else:
        sign = "-"
    
    field_ref = _get_bf(field)
    
    expr = (
        f"ts_decay_linear("
        f"group_neutralize("
        f"{sign}zscore(ts_rank({field_ref}, {horizon}))"
        f", {group_key}), {decay})"
    )
    return expr


def generate_batch_from_proven_templates(count: int = 5, decay: int = 5) -> list[dict]:
    """
    Generate a batch of alphas using PROVEN templates with novel fields.
    This is the PRIMARY generation method — guaranteed valid structure.
    
    Args:
        count: Number of alphas to generate.
        decay: Decay parameter for ts_decay_linear (default 5).
    
    Returns list of dicts with expression, field, template, group_key.
    """
    # Priority: goldmine (AC=0) first, then AC=1, then AC≤5
    priority_fields = GOLDMINE_FIELDS + TIER1_MODEL77_FIELDS + TIER2_MODEL16_FIELDS + TIER3_ANALYST_FIELDS
    
    # Shuffle for variety but keep priority ordering
    available = list(priority_fields)
    random.shuffle(available)
    
    templates = [
        ("alpha15", generate_alpha15_variant),
        ("alpha6", generate_alpha6_variant),
        ("pure_rank", generate_pure_field_rank),
        ("delta_momentum", generate_delta_momentum),
        ("mean_reversion", generate_mean_reversion),
    ]
    
    results = []
    used_fields = set()
    
    for i in range(min(count, len(available))):
        field = available[i]
        if field.id in used_fields:
            continue
        used_fields.add(field.id)
        
        # Cycle through templates
        template_name, template_fn = templates[i % len(templates)]
        group_key = get_group_for_expression(prefer_novel=True)
        
        expr = template_fn(field, group_key=group_key, decay=decay)
        
        results.append({
            "expression": expr,
            "field_id": field.id,
            "field_ac": field.alpha_count,
            "template": template_name,
            "group_key": group_key,
            "theme": "proven_template",
            "archetype": template_name,
            "sign": field.sign.value,
            "decay": decay,
            "neutralization": group_key,
        })
    
    return results