# source: https://raw.githubusercontent.com/JosefVacha/matrix/2b33455ff0882f307ebc946842250fc6bedaa3da/strategies/MatrixStrategy.py
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# isort: skip_file
# type: ignore

"""
MATRIX Strategy - FreqTrade Integration

FreqTrade-compatible strategy that bridges MATRIX prediction pipeline
to trading signals using optimized thresholds.

This is a SKELETON implementation for sandbox backtesting.
NO LIVE TRADING - all exchange interactions disabled.
"""

import logging
from typing import Dict, Optional
import pandas as pd  # type: ignore
from pandas import DataFrame  # type: ignore

from freqtrade.strategy.interface import IStrategy  # type: ignore
from freqtrade.strategy import (  # type: ignore
    BooleanParameter,
    DecimalParameter,
    IntParameter,
)

# MATRIX imports
from src.matrix.strategy.core import to_signals
from src.matrix.telemetry.metrics import (
    log_signal_behavior_metrics,
    log_performance_metrics,
    print_console_summary,
)

logger = logging.getLogger(__name__)


class Github_JosefVacha_matrix__MatrixStrategy__20250920_141609(IStrategy):
    """
    MATRIX Strategy for FreqTrade.

    Integrates MATRIX ML pipeline with FreqTrade backtesting framework.
    Uses threshold-based signal generation from matrix.strategy.core.

    IMPORTANT: This is a SANDBOX strategy for offline backtesting only.
    NO live trading, NO API calls, NO real money at risk.

    THRESHOLDS NOTE: Current hyperopt parameters use placeholder values.
    Real threshold values should be determined using docs/THRESHOLDS.md
    methodology after first sandbox runs and grid sweep analysis.

    Current placeholder thresholds are conservative defaults for initial testing.
    Follow THRESHOLDS.md 3-step optimization after validating pipeline operation.
    """

    # Strategy metadata
    INTERFACE_VERSION = 3

    # Minimal timeframe for MATRIX (5m as per requirements)
    timeframe = "5m"

    # Disable live trading features
    can_short = False  # TODO: Enable after threshold validation
    use_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = False

    # Startup candles for indicator calculation
    startup_candle_count: int = 100

    # Risk management (conservative defaults for sandbox)
    stoploss = -0.05  # 5% stop loss

    # ROI table (disabled - rely on signals)
    minimal_roi = {
        "0": 100  # Effectively disabled - use exit signals
    }

    # Hyperopt parameters for threshold optimization
    # TODO: Integrate with docs/THRESHOLDS.md Grid Sweep methodology

    # UP_THRESHOLD: Minimum prediction for long entry
    buy_threshold_up = DecimalParameter(
        0.001, 0.010, default=0.005, space="buy", decimals=4, optimize=True
    )

    # DOWN_THRESHOLD: Maximum prediction for short entry (when shorting enabled)
    sell_threshold_down = DecimalParameter(
        -0.010, -0.001, default=-0.005, space="sell", decimals=4, optimize=True
    )

    # HYSTERESIS: Buffer to prevent oscillation
    hysteresis_buffer = DecimalParameter(
        0.0005, 0.002, default=0.001, space="buy", decimals=4, optimize=True
    )

    # COOLDOWN: Minimum bars between signal changes
    signal_cooldown = IntParameter(1, 10, default=3, space="buy", optimize=True)

    def __init__(self, config: dict) -> None:
        """Initialize MATRIX strategy with sandbox configuration."""
        super().__init__(config)

        # Strategy state tracking
        self.last_signal_bar = 0
        self.signal_count = {"long": 0, "short": 0, "exit": 0}

        # TODO: Initialize MATRIX pipeline components
        # - FreqAI model loading
        # - Feature engineering setup
        # - Telemetry initialization

        logger.info("MATRIX Strategy initialized (SANDBOX MODE)")

    def informative_pairs(self):
        """
        Define additional, informative pair/interval combinations.

        TODO: Add informative pairs for feature engineering
        - Higher timeframes for trend context
        - Correlated pairs for market regime detection
        """
        return []

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Populate indicators and features for MATRIX pipeline.

        Args:
            dataframe: OHLCV data from FreqTrade
            metadata: Pair metadata

        Returns:
            DataFrame with added indicators and features

        TODO: Integration with MATRIX feature engineering
        - Call src.matrix.feature.engineering functions
        - Add FreqAI prediction columns
        - Implement data quality checks from METRICS_CHECKLIST.md
        - Log telemetry metrics (latency, NaN ratios)

        Current: SKELETON with basic indicators only
        """

        # SKELETON: Add basic indicators for testing
        # TODO: Replace with MATRIX feature engineering pipeline

        # Simple moving averages (placeholder)
        dataframe["sma_20"] = dataframe["close"].rolling(20).mean()
        dataframe["sma_50"] = dataframe["close"].rolling(50).mean()

        # RSI (placeholder)
        delta = dataframe["close"].diff()
        gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
        loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
        rs = gain / loss
        dataframe["rsi"] = 100 - (100 / (1 + rs))

        # MATRIX prediction placeholder
        # TODO: Integrate with FreqAI prediction pipeline
        dataframe["matrix_prediction"] = 0.0  # Placeholder - no predictions yet

        # TODO: Log data quality metrics
        # missing_data_pct = dataframe.isnull().sum().sum() / (len(dataframe) * len(dataframe.columns))
        # log_data_quality_metrics(missing_data_pct, ...)

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Populate buy signal based on MATRIX predictions and thresholds.

        Args:
            dataframe: DataFrame with OHLCV and indicators
            metadata: Pair metadata

        Returns:
            DataFrame with 'enter_long' column added

        TODO: Integration with matrix.strategy.core.to_signals()
        - Extract predictions from FreqAI
        - Apply threshold-based signal generation
        - Implement cooldown logic
        - Log signal behavior metrics
        """

        # SKELETON: Simple signal logic for testing
        # TODO: Replace with MATRIX threshold-based signals

        conditions = []

        # Placeholder condition (replace with MATRIX signals)
        conditions.append(
            (dataframe["sma_20"] > dataframe["sma_50"]) & (dataframe["rsi"] < 70)
        )

        # TODO: Implement MATRIX signal logic
        # predictions = dataframe['matrix_prediction']
        # context = {
        #     'current_price': dataframe['close'],
        #     'current_position': None,  # TODO: Get from FreqTrade
        #     'market_conditions': {}    # TODO: Add regime detection
        # }
        # signals = to_signals(predictions, context)
        # conditions.append(signals['enter_long'])

        # Apply cooldown logic
        # TODO: Implement signal cooldown from THRESHOLDS.md

        if conditions:
            dataframe.loc[
                conditions[0],  # All conditions combined
                "enter_long",
            ] = 1

        # TODO: Log signal behavior metrics
        # long_trigger_rate = dataframe['enter_long'].mean() * 100
        # log_signal_behavior_metrics(long_trigger_rate, ...)

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Populate sell signal based on MATRIX predictions and thresholds.

        Args:
            dataframe: DataFrame with OHLCV and indicators
            metadata: Pair metadata

        Returns:
            DataFrame with 'exit_long' column added

        TODO: Integration with matrix.strategy.core.to_signals()
        - Implement hysteresis-based exit logic
        - Apply cooldown between signal changes
        - Log exit signal metrics
        """

        # SKELETON: Simple exit logic for testing
        # TODO: Replace with MATRIX threshold-based exits

        conditions = []

        # Placeholder condition (replace with MATRIX signals)
        conditions.append(
            (dataframe["sma_20"] < dataframe["sma_50"]) | (dataframe["rsi"] > 80)
        )

        # TODO: Implement MATRIX exit logic with hysteresis
        # Use (UP_THRESHOLD - HYSTERESIS) for exit threshold
        # Apply cooldown logic from signal_cooldown parameter

        if conditions:
            dataframe.loc[conditions[0], "exit_long"] = 1

        return dataframe

    def custom_exit(
        self, pair: str, trade, current_time, current_rate, current_profit, **kwargs
    ) -> Optional[str]:
        """
        Custom exit logic for advanced MATRIX signals.

        TODO: Implement advanced exit logic
        - Risk-based exits using src.matrix.risk.policy
        - Time-based exits for signal persistence
        - Profit target exits based on prediction confidence

        Args:
            pair: Trading pair
            trade: Current trade object
            current_time: Current timestamp
            current_rate: Current price
            current_profit: Current profit ratio

        Returns:
            Exit reason string or None
        """

        # TODO: Implement custom exit logic
        # - Check risk policy constraints
        # - Apply prediction-based exits
        # - Log exit reasons for analysis

        return None

    def leverage(
        self,
        pair: str,
        current_time,
        current_rate,
        proposed_leverage: float,
        max_leverage: float,
        entry_tag: str,
        side: str,
        **kwargs,
    ) -> float:
        """
        Leverage calculation for MATRIX strategy.

        TODO: Integration with risk management
        - Use src.matrix.risk.policy for position sizing
        - Apply Kelly criterion from advanced metrics
        - Implement regime-based leverage adjustment

        SANDBOX: Return 1.0 (no leverage) for safety
        """
        return 1.0  # No leverage in sandbox mode

    def confirm_trade_entry(
        self,
        pair: str,
        order_type: str,
        amount: float,
        rate: float,
        time_in_force: str,
        current_time,
        entry_tag: str,
        side: str,
        **kwargs,
    ) -> bool:
        """
        Confirm trade entry with MATRIX validation.

        TODO: Implement entry confirmation logic
        - Validate prediction confidence
        - Check risk budget availability
        - Apply market condition filters
        - Log entry confirmations for analysis

        SANDBOX: Always confirm for testing
        """
        return True  # Always confirm in sandbox mode

    def custom_stake_amount(
        self,
        pair: str,
        current_time,
        current_rate,
        proposed_stake: float,
        min_stake: float,
        max_stake: float,
        entry_tag: str,
        side: str,
        **kwargs,
    ) -> float:
        """
        Custom stake amount calculation using MATRIX risk management.

        TODO: Integration with position sizing
        - Use prediction confidence for sizing
        - Apply risk policy constraints
        - Implement Kelly criterion sizing
        - Log position sizes for analysis

        SANDBOX: Use default stake amount
        """
        return proposed_stake  # Use default in sandbox mode