# source: https://raw.githubusercontent.com/DOUGLASGUEDESATRIA/OSIRIS_TRADE/fe3c09567f3798d1cad6d7a306fa4f6131d91e17/user_data/strategies/OsirisPrometheusStrategy.py
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
"""
OSIRIS PROMETHEUS — Binary Information Physics Strategy
========================================================

A COMPLETELY NOVEL trading strategy that uses ZERO traditional indicators.
Every signal is derived from:
  1. Binary N-gram Pattern Matching (mined from raw OHLCV)
  2. Bayesian Consensus across 4 independent encoding schemes
  3. Information Entropy Gradient (order emerging from chaos)
  4. Kolmogorov-proxy Compressibility (regime detection)

The strategy loads a pattern library mined by osiris_prometheus_miner.py
and matches current market states against historically profitable patterns.

Target: 80% WR | 2-3:1 R:R | 10+ trades/day
"""

import numpy as np
import pandas as pd
import pickle
import lzma
from pathlib import Path
from typing import Dict
from datetime import datetime

from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy import IntParameter, DecimalParameter


# ╔══════════════════════════════════════════════════════════════════╗
# ║  BINARY ENCODING FUNCTIONS                                     ║
# ║  Identical to miner. Pure raw OHLCV → binary state.           ║
# ║  NO moving averages. NO RSI. NO MACD. NO Bollinger.           ║
# ╚══════════════════════════════════════════════════════════════════╝


def encode_2bit(close, open_, high, low, volume):
    """2-bit: direction + momentum → 4 states."""
    n = len(close)
    b0 = (close > open_).astype(np.uint8)
    b1 = np.zeros(n, dtype=np.uint8)
    b1[1:] = (close[1:] > close[:-1]).astype(np.uint8)
    return b0 | (b1 << 1)


def encode_4bit(close, open_, high, low, volume):
    """4-bit: direction + momentum + vol_change + volume_change → 16 states."""
    n = len(close)
    rng = high - low
    b0 = (close > open_).astype(np.uint8)
    b1 = np.zeros(n, dtype=np.uint8)
    b1[1:] = (close[1:] > close[:-1]).astype(np.uint8)
    b2 = np.zeros(n, dtype=np.uint8)
    b2[1:] = (rng[1:] > rng[:-1]).astype(np.uint8)
    b3 = np.zeros(n, dtype=np.uint8)
    b3[1:] = (volume[1:] > volume[:-1]).astype(np.uint8)
    return b0 | (b1 << 1) | (b2 << 2) | (b3 << 3)


def encode_6bit(close, open_, high, low, volume):
    """6-bit: direction + momentum + full_higher + close_pos + range_exp + vol → 64 states."""
    n = len(close)
    rng = high - low
    b0 = (close > open_).astype(np.uint8)
    b1 = np.zeros(n, dtype=np.uint8)
    b1[1:] = (close[1:] > close[:-1]).astype(np.uint8)
    b2 = np.zeros(n, dtype=np.uint8)
    b2[1:] = ((high[1:] > high[:-1]) & (low[1:] > low[:-1])).astype(np.uint8)
    b3 = ((close - low) > (high - close)).astype(np.uint8)
    b4 = np.zeros(n, dtype=np.uint8)
    b4[1:] = (rng[1:] > rng[:-1]).astype(np.uint8)
    b5 = np.zeros(n, dtype=np.uint8)
    b5[1:] = (volume[1:] > volume[:-1]).astype(np.uint8)
    return b0 | (b1 << 1) | (b2 << 2) | (b3 << 3) | (b4 << 4) | (b5 << 5)


def encode_8bit(close, open_, high, low, volume):
    """8-bit: all features → 256 states."""
    n = len(close)
    rng = high - low
    body = np.abs(close - open_)
    safe_rng = np.where(rng > 0, rng, 1e-10)
    b0 = (close > open_).astype(np.uint8)
    b1 = np.zeros(n, dtype=np.uint8)
    b1[1:] = (close[1:] > close[:-1]).astype(np.uint8)
    b2 = np.zeros(n, dtype=np.uint8)
    b2[1:] = (rng[1:] > rng[:-1]).astype(np.uint8)
    b3 = np.zeros(n, dtype=np.uint8)
    b3[1:] = (volume[1:] > volume[:-1]).astype(np.uint8)
    b4 = ((close - low) > (high - close)).astype(np.uint8)
    b5 = np.zeros(n, dtype=np.uint8)
    b5[1:] = (high[1:] > high[:-1]).astype(np.uint8)
    b6 = np.zeros(n, dtype=np.uint8)
    b6[1:] = (low[1:] > low[:-1]).astype(np.uint8)
    b7 = (body > 0.5 * safe_rng).astype(np.uint8)
    return (
        b0 | (b1 << 1) | (b2 << 2) | (b3 << 3)
        | (b4 << 4) | (b5 << 5) | (b6 << 6) | (b7 << 7)
    )


def ngram_key(states, start, n):
    """Convert N consecutive states into a single integer key."""
    key = 0
    for k in range(n):
        key = key * 256 + int(states[start + k])
    return key


def rolling_binary_entropy(states, window=30):
    """Shannon entropy of binary state distribution in a sliding window."""
    n = len(states)
    n_states = int(states.max()) + 1
    entropy = np.full(n, np.nan)
    for i in range(window, n):
        w = states[i - window : i]
        counts = np.bincount(w, minlength=n_states).astype(np.float64)
        probs = counts / window
        probs = probs[probs > 0]
        H = -np.sum(probs * np.log2(probs))
        max_H = np.log2(n_states)
        entropy[i] = H / max_H if max_H > 0 else 0
    return entropy


# ╔══════════════════════════════════════════════════════════════════╗
# ║  THE STRATEGY                                                  ║
# ╚══════════════════════════════════════════════════════════════════╝


class Github_DOUGLASGUEDESATRIA_OSIRIS_TRADE__OsirisPrometheusStrategy__20260329_060843(IStrategy):
    """
    OSIRIS PROMETHEUS — Binary Information Physics Engine.

    Uses pattern matching across 4 independent binary encoding schemes
    with Bayesian consensus to achieve high win-rate entries.
    """

    INTERFACE_VERSION = 3
    can_short = False
    timeframe = "5m"

    # ── Hyperopt Parameters ──
    # Consensus scoring
    buy_score_min = DecimalParameter(0.1, 5.0, default=0.3, decimals=1, space="buy")
    buy_diversity_min = IntParameter(1, 4, default=1, space="buy")
    buy_max_wr_min = DecimalParameter(0.55, 0.90, default=0.65, decimals=2, space="buy")
    buy_count_min = IntParameter(1, 8, default=1, space="buy")
    # Entropy filter
    buy_entropy_max = DecimalParameter(0.50, 0.98, default=0.95, decimals=2, space="buy")

    # Stoploss: ~1R for 5m crypto (0.3-0.8%)
    stoploss = -0.008

    # Trailing stop: lock profits after 1R move
    trailing_stop = True
    trailing_stop_positive = 0.003
    trailing_stop_positive_offset = 0.006
    trailing_only_offset_is_reached = True

    # ROI: time-based take profit (2-3R targets)
    # R ≈ 0.3-0.5% → 2R ≈ 0.6-1.0% → 3R ≈ 0.9-1.5%
    minimal_roi = {
        "0": 0.015,    # 1.5% = ~3R (best case)
        "15": 0.010,   # 1.0% = ~2R (after 15 min)
        "30": 0.006,   # 0.6% = ~1.5R (after 30 min)
        "60": 0.003,   # 0.3% = ~1R (after 1 hour)
        "120": 0.001,  # Near breakeven (2 hours)
        "240": 0,      # Breakeven at 4 hours
    }

    use_custom_stoploss = False

    # Protections
    process_only_new_candles = True
    startup_candle_count = 50

    # ── Pattern Library ──
    _pattern_lib = None
    _meta = None
    _thresholds = None
    _best_r_mult = 2.0

    def __init__(self, config: dict) -> None:
        super().__init__(config)
        self._load_pattern_library()

    def _load_pattern_library(self):
        """Load mined pattern library from pickle."""
        pkl_path = Path(__file__).parent / "prometheus_patterns.pkl"
        if not pkl_path.exists():
            raise FileNotFoundError(
                f"Pattern library not found: {pkl_path}. "
                "Run osiris_prometheus_miner.py first."
            )
        with open(pkl_path, "rb") as f:
            data = pickle.load(f)
        self._pattern_lib = data["library"]
        self._meta = data.get("meta", {})
        self._thresholds = data.get("thresholds", {})
        self._best_r_mult = self._meta.get("best_r_mult", 2.0)

        # Count total patterns
        total = sum(
            len(patterns)
            for enc in self._pattern_lib.values()
            for patterns in enc.values()
        )
        self._total_patterns = total
        print(f"PROMETHEUS: Loaded {total} patterns (R:R={self._best_r_mult}:1)")

    # ── ENCODING CONFIG ──
    # Each encoding has: name, encoder function, N-gram lengths to check
    ENCODING_CONFIG = [
        ("2bit", encode_2bit, [5, 6, 7, 8]),
        ("4bit", encode_4bit, [3, 4, 5, 6]),
        ("6bit", encode_6bit, [3, 4, 5]),
        ("8bit", encode_8bit, [2, 3, 4]),
    ]

    def populate_indicators(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame:
        """
        Compute binary encodings, pattern consensus scores, and
        information physics metrics. ZERO traditional indicators.
        """
        close = dataframe["close"].values.astype(np.float64)
        open_ = dataframe["open"].values.astype(np.float64)
        high = dataframe["high"].values.astype(np.float64)
        low = dataframe["low"].values.astype(np.float64)
        volume = dataframe["volume"].values.astype(np.float64)
        n_candles = len(dataframe)

        # ── Compute binary states (vectorized) ──
        all_states = {
            "2bit": encode_2bit(close, open_, high, low, volume),
            "4bit": encode_4bit(close, open_, high, low, volume),
            "6bit": encode_6bit(close, open_, high, low, volume),
            "8bit": encode_8bit(close, open_, high, low, volume),
        }

        # ── Pattern matching with Bayesian consensus ──
        total_score = np.zeros(n_candles)
        match_count = np.zeros(n_candles, dtype=np.int32)
        max_wr = np.zeros(n_candles)
        enc_diversity = np.zeros(n_candles, dtype=np.int32)

        # Track which encodings matched per candle
        enc_matched = np.zeros((n_candles, 4), dtype=bool)  # 4 encodings

        base_wr = 0.335  # Baseline WR at 2:1 R:R

        for enc_idx, (enc_name, _encoder, n_values) in enumerate(self.ENCODING_CONFIG):
            enc_patterns = self._pattern_lib.get(enc_name, {})
            states = all_states[enc_name]

            for n in n_values:
                n_patterns = enc_patterns.get(n, enc_patterns.get(str(n), {}))
                if not n_patterns:
                    continue

                for i in range(n - 1, n_candles):
                    key = ngram_key(states, i - n + 1, n)
                    if key in n_patterns:
                        p = n_patterns[key]
                        wr = p.get("combined_wr", p.get("wr", 0.5))
                        total = p.get("combined_total", p.get("total", 1))

                        # Score: edge over baseline × reliability weight
                        edge = wr - base_wr
                        weight = np.sqrt(total)
                        total_score[i] += edge * weight
                        match_count[i] += 1
                        max_wr[i] = max(max_wr[i], wr)
                        enc_matched[i, enc_idx] = True

        # Compute encoding diversity
        enc_diversity = enc_matched.sum(axis=1)

        dataframe["prom_score"] = total_score
        dataframe["prom_count"] = match_count
        dataframe["prom_max_wr"] = max_wr
        dataframe["prom_diversity"] = enc_diversity

        # ── Information Entropy Gradient ──
        # Uses 4-bit encoding (most balanced state count)
        ent = rolling_binary_entropy(all_states["4bit"], window=30)
        dataframe["binary_entropy"] = ent

        # Entropy gradient: negative = order forming from chaos
        ent_series = pd.Series(ent)
        dataframe["entropy_grad"] = ent_series - ent_series.shift(5)

        # ── R unit: median candle range (NOT a traditional indicator) ──
        rng = high - low
        dataframe["median_r"] = pd.Series(rng).rolling(20).median()
        dataframe["median_r"] = dataframe["median_r"].bfill()
        dataframe["r_pct"] = dataframe["median_r"] / close

        # ── Diagnostics ──
        has_match = match_count > 0
        entry_sigs = (
            (total_score >= self.buy_score_min.value)
            & (enc_matched.sum(axis=1) >= self.buy_diversity_min.value)
            & (max_wr >= self.buy_max_wr_min.value)
            & (match_count >= self.buy_count_min.value)
        )
        pair = metadata.get('pair', '?')
        print(
            f"  {pair}: {has_match.sum():,} candles with matches, "
            f"{entry_sigs.sum():,} entry signals, "
            f"avg_score={total_score[has_match].mean():.2f} "
            f"avg_wr={max_wr[has_match].mean():.3f} "
            f"avg_count={match_count[has_match].mean():.1f} "
            f"avg_diversity={enc_matched.sum(axis=1)[has_match].mean():.1f}"
        )

        return dataframe

    def populate_entry_trend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame:
        """
        Enter long when Bayesian consensus is high enough.
        Requires multiple independent encoding schemes to agree.
        """
        dataframe.loc[
            (dataframe["prom_score"] >= self.buy_score_min.value)
            & (dataframe["prom_diversity"] >= self.buy_diversity_min.value)
            & (dataframe["prom_max_wr"] >= self.buy_max_wr_min.value)
            & (dataframe["prom_count"] >= self.buy_count_min.value)
            & (dataframe["binary_entropy"] <= self.buy_entropy_max.value)
            & (dataframe["volume"] > 0),
            "enter_long",
        ] = 1

        return dataframe

    def populate_exit_trend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame:
        """Exits handled by trailing stop + ROI table."""
        return dataframe