#!/usr/bin/env python3
# allow-simple-sharpe: pattern research uses forward returns without duration tracking
"""Historical formation pattern analysis across granularities (Polars).

Research question: Do historical formation patterns at 50/200 dbps predict
100 dbps forward returns?

Unlike previous multi-factor analysis which used CURRENT alignment,
this tests HISTORICAL formations - the sequence of bars leading up
to the current bar at different granularities.

GitHub Issue: #52 (extension)
"""

from __future__ import annotations

import json
import math
import sys
from collections import Counter
from datetime import datetime, timezone
from pathlib import Path

import polars as pl

# =============================================================================
# NDJSON Logging
# =============================================================================


def log_json(level: str, message: str, **kwargs: object) -> None:
    """Log a message in NDJSON format."""
    entry = {
        "timestamp": datetime.now(tz=timezone.utc).isoformat(),
        "level": level,
        "message": message,
        **kwargs,
    }
    print(json.dumps(entry), flush=True)


def log_info(message: str, **kwargs: object) -> None:
    """Log INFO level message."""
    log_json("INFO", message, **kwargs)


# =============================================================================
# Data Loading
# =============================================================================


def load_open_deviation_bars_polars(
    symbol: str,
    threshold_dbps: int,
    start_date: str,
    end_date: str,
) -> pl.LazyFrame:
    """Load open deviation bars from cache as Polars LazyFrame."""
    from opendeviationbar import get_open_deviation_bars

    log_info(
        "Loading open deviation bars", symbol=symbol, threshold_dbps=threshold_dbps
    )

    pdf = get_open_deviation_bars(
        symbol,
        start_date=start_date,
        end_date=end_date,
        threshold_decimal_bps=threshold_dbps,
        use_cache=True,
        fetch_if_missing=False,
    )

    pdf = pdf.reset_index()
    pdf = pdf.rename(columns={pdf.columns[0]: "timestamp"})

    return pl.from_pandas(pdf).lazy()


# =============================================================================
# Formation Pattern Analysis
# =============================================================================


def add_bar_direction(df: pl.LazyFrame) -> pl.LazyFrame:
    """Add bar direction (U for up, D for down)."""
    return df.with_columns(
        pl.when(pl.col("Close") >= pl.col("Open"))
        .then(pl.lit("U"))
        .otherwise(pl.lit("D"))
        .alias("direction")
    )


def add_formation_patterns(df: pl.LazyFrame, lookback: int = 3) -> pl.LazyFrame:
    """Add historical formation patterns (lookback bars before current)."""
    # Build formation from previous bars (not including current)
    cols = [pl.col("direction").shift(i) for i in range(lookback, 0, -1)]

    formation_expr = cols[0]
    for col in cols[1:]:
        formation_expr = formation_expr + col

    return df.with_columns(formation_expr.alias(f"formation_{lookback}"))


def compute_t_stat(returns: pl.Series) -> float:
    """Compute t-statistic for returns."""
    n = len(returns)
    if n < 2:
        return 0.0

    mean = returns.mean()
    std = returns.std()

    if std is None or std == 0 or mean is None:
        return 0.0

    return float(mean / (std / math.sqrt(n)))


def check_odd_robustness(
    df: pl.DataFrame,
    pattern_col: str,
    return_col: str,
    min_samples: int = 100,
    min_t_stat: float = 5.0,
) -> dict:
    """Check ODD robustness for a pattern column."""
    # Add quarterly period
    df = df.with_columns(
        (
            pl.col("timestamp").dt.year().cast(pl.Utf8)
            + "-Q"
            + ((pl.col("timestamp").dt.month() - 1) // 3 + 1).cast(pl.Utf8)
        ).alias("period")
    )

    patterns = df.select(pattern_col).unique().to_series().drop_nulls().to_list()
    results = {}

    for pattern in patterns:
        if pattern is None:
            continue

        pattern_subset = df.filter(pl.col(pattern_col) == pattern)
        periods = (
            pattern_subset.select("period").unique().to_series().drop_nulls().to_list()
        )

        period_stats = []
        for period_label in periods:
            period_data = pattern_subset.filter(pl.col("period") == period_label)
            returns = period_data.select(return_col).drop_nulls().to_series()
            count = len(returns)

            if count < min_samples:
                continue

            mean_ret = returns.mean()
            t_stat = compute_t_stat(returns)

            period_stats.append(
                {
                    "period": period_label,
                    "count": count,
                    "mean_return": float(mean_ret) if mean_ret is not None else 0.0,
                    "t_stat": t_stat,
                }
            )

        if len(period_stats) < 2:
            results[pattern] = {
                "is_odd_robust": False,
                "reason": "insufficient_periods",
                "n_periods": len(period_stats),
            }
            continue

        t_stats = [s["t_stat"] for s in period_stats]
        all_significant = all(abs(t) >= min_t_stat for t in t_stats)
        same_sign = all(t > 0 for t in t_stats) or all(t < 0 for t in t_stats)

        is_odd_robust = all_significant and same_sign

        total_count = sum(s["count"] for s in period_stats)
        avg_return = (
            sum(s["mean_return"] * s["count"] for s in period_stats) / total_count
            if total_count > 0
            else 0
        )

        results[pattern] = {
            "is_odd_robust": is_odd_robust,
            "n_periods": len(period_stats),
            "all_significant": all_significant,
            "same_sign": same_sign,
            "min_t_stat": min(abs(t) for t in t_stats),
            "max_t_stat": max(abs(t) for t in t_stats),
            "total_count": total_count,
            "avg_return_bps": avg_return * 10000,
        }

    return results


def run_formation_analysis(
    symbol: str,
    start_date: str,
    end_date: str,
    lookback: int = 3,
    min_samples: int = 100,
    min_t_stat: float = 5.0,
) -> dict:
    """Run historical formation pattern analysis for a symbol."""
    log_info("Starting formation analysis", symbol=symbol, lookback=lookback)

    # Load data at different granularities
    df_50 = load_open_deviation_bars_polars(symbol, 50, start_date, end_date)
    df_100 = load_open_deviation_bars_polars(symbol, 100, start_date, end_date)
    df_200 = load_open_deviation_bars_polars(symbol, 200, start_date, end_date)

    # Add directions
    df_50 = add_bar_direction(df_50)
    df_100 = add_bar_direction(df_100)
    df_200 = add_bar_direction(df_200)

    # Add formations
    df_50 = add_formation_patterns(df_50, lookback)
    df_200 = add_formation_patterns(df_200, lookback)

    # Add forward return to 100 dbps
    df_100 = df_100.with_columns(
        (pl.col("Close").shift(-1) / pl.col("Close") - 1).alias("fwd_ret_1")
    )

    # Collect
    df_50_c = df_50.select(["timestamp", f"formation_{lookback}"]).collect()
    df_100_c = df_100.select(["timestamp", "Close", "fwd_ret_1"]).collect()
    df_200_c = df_200.select(["timestamp", f"formation_{lookback}"]).collect()

    log_info(
        "Data loaded",
        symbol=symbol,
        bars_50=len(df_50_c),
        bars_100=len(df_100_c),
        bars_200=len(df_200_c),
    )

    # Rename formation columns to distinguish granularities
    df_50_c = df_50_c.rename({f"formation_{lookback}": f"formation_50_{lookback}"})
    df_200_c = df_200_c.rename({f"formation_{lookback}": f"formation_200_{lookback}"})

    # Join formations to 100 dbps using asof join (most recent formation before 100 dbps bar)
    df_100_c = df_100_c.sort("timestamp")
    df_50_c = df_50_c.sort("timestamp")
    df_200_c = df_200_c.sort("timestamp")

    # asof join: for each 100 dbps bar, get most recent 50 dbps formation
    merged = df_100_c.join_asof(
        df_50_c,
        on="timestamp",
        strategy="backward",
    )

    # asof join: add 200 dbps formation
    merged = merged.join_asof(
        df_200_c,
        on="timestamp",
        strategy="backward",
    )

    # Create combined formation patterns
    merged = merged.with_columns(
        [
            (
                pl.col(f"formation_50_{lookback}")
                + "|"
                + pl.col(f"formation_200_{lookback}")
            ).alias("combined_formation"),
        ]
    )

    log_info("Data merged", symbol=symbol, merged_rows=len(merged))

    # Test ODD robustness for each formation type
    results = {
        "symbol": symbol,
        "lookback": lookback,
        "total_bars": len(merged),
    }

    # Test 50 dbps formations
    robust_50 = check_odd_robustness(
        merged.drop_nulls(subset=[f"formation_50_{lookback}", "fwd_ret_1"]),
        f"formation_50_{lookback}",
        "fwd_ret_1",
        min_samples=min_samples,
        min_t_stat=min_t_stat,
    )
    robust_50_count = sum(
        1 for v in robust_50.values() if v.get("is_odd_robust", False)
    )
    results["formation_50"] = {
        "total_patterns": len(robust_50),
        "robust_count": robust_50_count,
        "patterns": robust_50,
    }
    log_info(
        "50 dbps formations tested",
        symbol=symbol,
        total=len(robust_50),
        robust=robust_50_count,
    )

    # Test 200 dbps formations
    robust_200 = check_odd_robustness(
        merged.drop_nulls(subset=[f"formation_200_{lookback}", "fwd_ret_1"]),
        f"formation_200_{lookback}",
        "fwd_ret_1",
        min_samples=min_samples,
        min_t_stat=min_t_stat,
    )
    robust_200_count = sum(
        1 for v in robust_200.values() if v.get("is_odd_robust", False)
    )
    results["formation_200"] = {
        "total_patterns": len(robust_200),
        "robust_count": robust_200_count,
        "patterns": robust_200,
    }
    log_info(
        "200 dbps formations tested",
        symbol=symbol,
        total=len(robust_200),
        robust=robust_200_count,
    )

    # Test combined formations
    robust_combined = check_odd_robustness(
        merged.drop_nulls(subset=["combined_formation", "fwd_ret_1"]),
        "combined_formation",
        "fwd_ret_1",
        min_samples=min_samples,
        min_t_stat=min_t_stat,
    )
    robust_combined_count = sum(
        1 for v in robust_combined.values() if v.get("is_odd_robust", False)
    )
    results["formation_combined"] = {
        "total_patterns": len(robust_combined),
        "robust_count": robust_combined_count,
        "patterns": robust_combined,
    }
    log_info(
        "Combined formations tested",
        symbol=symbol,
        total=len(robust_combined),
        robust=robust_combined_count,
    )

    return results


def main() -> int:
    """Main entry point."""
    log_info(
        "Starting historical formation pattern analysis (Polars)",
        script="historical_formation_patterns_polars.py",
    )

    # Configuration
    symbols = ["BTCUSDT", "ETHUSDT", "SOLUSDT", "BNBUSDT"]
    start_date = "2022-01-01"
    end_date = "2026-01-31"
    lookback = 3  # 3-bar formation patterns
    min_samples = 100
    min_t_stat = 5.0

    all_results = {}

    for symbol in symbols:
        try:
            result = run_formation_analysis(
                symbol=symbol,
                start_date=start_date,
                end_date=end_date,
                lookback=lookback,
                min_samples=min_samples,
                min_t_stat=min_t_stat,
            )
            all_results[symbol] = result
        except (ValueError, RuntimeError, OSError, KeyError) as e:
            log_json("ERROR", "Analysis failed", symbol=symbol, error=str(e))

    # Cross-symbol summary
    log_info("=== CROSS-SYMBOL FORMATION PATTERN SUMMARY ===")

    # Count robust patterns by type
    for formation_type in ["formation_50", "formation_200", "formation_combined"]:
        robust_counts = []
        for _symbol, result in all_results.items():
            data = result.get(formation_type, {})
            robust_counts.append(data.get("robust_count", 0))

        log_info(
            f"Formation type: {formation_type}",
            type=formation_type,
            total_robust=sum(robust_counts),
            by_symbol=robust_counts,
        )

    # Find universal patterns (robust across all symbols)
    log_info("=== UNIVERSAL FORMATION PATTERNS ===")

    for formation_type in ["formation_50", "formation_200", "formation_combined"]:
        pattern_counts = Counter()

        for _symbol, result in all_results.items():
            patterns = result.get(formation_type, {}).get("patterns", {})
            for pattern, data in patterns.items():
                if data.get("is_odd_robust", False):
                    pattern_counts[pattern] += 1

        universal = [p for p, c in pattern_counts.items() if c >= len(symbols)]
        log_info(
            f"Universal {formation_type} patterns",
            type=formation_type,
            count=len(universal),
            patterns=universal[:10] if universal else [],
        )

    # Save results
    output_path = Path("/tmp/historical_formation_patterns_polars.json")
    with output_path.open("w") as f:
        json.dump(all_results, f, indent=2, default=str)
    log_info("Results saved", path=str(output_path))

    log_info("Historical formation pattern analysis complete")
    return 0


if __name__ == "__main__":
    sys.exit(main())
