"""
Custom indicator implementations for TradingView indicators not in TA-Lib.

These indicators follow TA-Lib style conventions and integrate seamlessly
with the indicator framework. All implementations are based on well-known,
publicly documented formulas.
"""

import logging
from typing import List, Optional
import numpy as np

from datasource.schema import ColumnInfo
from .base import Indicator
from .schema import (
    ComputeContext,
    ComputeResult,
    IndicatorMetadata,
    IndicatorParameter,
    InputSchema,
    OutputSchema,
)

logger = logging.getLogger(__name__)


class VWAP(Indicator):
    """Volume Weighted Average Price - Most widely used institutional indicator."""

    @classmethod
    def get_metadata(cls) -> IndicatorMetadata:
        return IndicatorMetadata(
            name="VWAP",
            display_name="VWAP",
            description="Volume Weighted Average Price - Average price weighted by volume",
            category="volume",
            parameters=[],
            use_cases=[
                "Institutional reference price",
                "Support/resistance levels",
                "Mean reversion trading"
            ],
            references=["https://www.investopedia.com/terms/v/vwap.asp"],
            tags=["vwap", "volume", "institutional"]
        )

    @classmethod
    def get_input_schema(cls) -> InputSchema:
        return InputSchema(required_columns=[
            ColumnInfo(name="high", type="float", description="High price"),
            ColumnInfo(name="low", type="float", description="Low price"),
            ColumnInfo(name="close", type="float", description="Close price"),
            ColumnInfo(name="volume", type="float", description="Volume"),
        ])

    @classmethod
    def get_output_schema(cls, **params) -> OutputSchema:
        return OutputSchema(columns=[
            ColumnInfo(name="vwap", type="float", description="Volume Weighted Average Price", nullable=True)
        ])

    def compute(self, context: ComputeContext) -> ComputeResult:
        high = np.array([float(v) if v is not None else np.nan for v in context.get_column("high")])
        low = np.array([float(v) if v is not None else np.nan for v in context.get_column("low")])
        close = np.array([float(v) if v is not None else np.nan for v in context.get_column("close")])
        volume = np.array([float(v) if v is not None else np.nan for v in context.get_column("volume")])

        # Typical price
        typical_price = (high + low + close) / 3.0

        # VWAP = cumsum(typical_price * volume) / cumsum(volume)
        cumulative_tp_vol = np.nancumsum(typical_price * volume)
        cumulative_vol = np.nancumsum(volume)

        vwap = cumulative_tp_vol / cumulative_vol

        times = context.get_times()
        result_data = [
            {"time": times[i], "vwap": float(vwap[i]) if not np.isnan(vwap[i]) else None}
            for i in range(len(times))
        ]

        return ComputeResult(data=result_data, is_partial=context.is_incremental)


class VWMA(Indicator):
    """Volume Weighted Moving Average."""

    @classmethod
    def get_metadata(cls) -> IndicatorMetadata:
        return IndicatorMetadata(
            name="VWMA",
            display_name="VWMA",
            description="Volume Weighted Moving Average - Moving average weighted by volume",
            category="overlap",
            parameters=[
                IndicatorParameter(
                    name="length",
                    type="int",
                    description="Period length",
                    default=20,
                    min_value=1,
                    required=False
                )
            ],
            use_cases=["Volume-aware trend following", "Dynamic support/resistance"],
            references=["https://www.investopedia.com/articles/trading/11/trading-with-vwap-mvwap.asp"],
            tags=["vwma", "volume", "moving average"]
        )

    @classmethod
    def get_input_schema(cls) -> InputSchema:
        return InputSchema(required_columns=[
            ColumnInfo(name="close", type="float", description="Close price"),
            ColumnInfo(name="volume", type="float", description="Volume"),
        ])

    @classmethod
    def get_output_schema(cls, **params) -> OutputSchema:
        return OutputSchema(columns=[
            ColumnInfo(name="vwma", type="float", description="Volume Weighted Moving Average", nullable=True)
        ])

    def compute(self, context: ComputeContext) -> ComputeResult:
        close = np.array([float(v) if v is not None else np.nan for v in context.get_column("close")])
        volume = np.array([float(v) if v is not None else np.nan for v in context.get_column("volume")])
        length = self.params.get("length", 20)

        vwma = np.full_like(close, np.nan)

        for i in range(length - 1, len(close)):
            window_close = close[i - length + 1:i + 1]
            window_volume = volume[i - length + 1:i + 1]
            vwma[i] = np.sum(window_close * window_volume) / np.sum(window_volume)

        times = context.get_times()
        result_data = [
            {"time": times[i], "vwma": float(vwma[i]) if not np.isnan(vwma[i]) else None}
            for i in range(len(times))
        ]

        return ComputeResult(data=result_data, is_partial=context.is_incremental)


class HullMA(Indicator):
    """Hull Moving Average - Fast and smooth moving average."""

    @classmethod
    def get_metadata(cls) -> IndicatorMetadata:
        return IndicatorMetadata(
            name="HMA",
            display_name="Hull Moving Average",
            description="Hull Moving Average - Reduces lag while maintaining smoothness",
            category="overlap",
            parameters=[
                IndicatorParameter(
                    name="length",
                    type="int",
                    description="Period length",
                    default=9,
                    min_value=1,
                    required=False
                )
            ],
            use_cases=["Low-lag trend following", "Quick trend reversal detection"],
            references=["https://alanhull.com/hull-moving-average"],
            tags=["hma", "hull", "moving average", "low-lag"]
        )

    @classmethod
    def get_input_schema(cls) -> InputSchema:
        return InputSchema(required_columns=[
            ColumnInfo(name="close", type="float", description="Close price"),
        ])

    @classmethod
    def get_output_schema(cls, **params) -> OutputSchema:
        return OutputSchema(columns=[
            ColumnInfo(name="hma", type="float", description="Hull Moving Average", nullable=True)
        ])

    def compute(self, context: ComputeContext) -> ComputeResult:
        close = np.array([float(v) if v is not None else np.nan for v in context.get_column("close")])
        length = self.params.get("length", 9)

        def wma(data, period):
            """Weighted Moving Average."""
            weights = np.arange(1, period + 1)
            result = np.full_like(data, np.nan)
            for i in range(period - 1, len(data)):
                window = data[i - period + 1:i + 1]
                result[i] = np.sum(weights * window) / np.sum(weights)
            return result

        # HMA = WMA(2 * WMA(n/2) - WMA(n)), sqrt(n))
        half_length = length // 2
        sqrt_length = int(np.sqrt(length))

        wma_half = wma(close, half_length)
        wma_full = wma(close, length)
        raw_hma = 2 * wma_half - wma_full
        hma = wma(raw_hma, sqrt_length)

        times = context.get_times()
        result_data = [
            {"time": times[i], "hma": float(hma[i]) if not np.isnan(hma[i]) else None}
            for i in range(len(times))
        ]

        return ComputeResult(data=result_data, is_partial=context.is_incremental)


class SuperTrend(Indicator):
    """SuperTrend - Popular trend following indicator."""

    @classmethod
    def get_metadata(cls) -> IndicatorMetadata:
        return IndicatorMetadata(
            name="SUPERTREND",
            display_name="SuperTrend",
            description="SuperTrend - Volatility-based trend indicator",
            category="overlap",
            parameters=[
                IndicatorParameter(
                    name="length",
                    type="int",
                    description="ATR period",
                    default=10,
                    min_value=1,
                    required=False
                ),
                IndicatorParameter(
                    name="multiplier",
                    type="float",
                    description="ATR multiplier",
                    default=3.0,
                    min_value=0.1,
                    required=False
                )
            ],
            use_cases=["Trend identification", "Stop loss placement", "Trend reversal signals"],
            references=["https://www.investopedia.com/articles/trading/08/supertrend-indicator.asp"],
            tags=["supertrend", "trend", "volatility"]
        )

    @classmethod
    def get_input_schema(cls) -> InputSchema:
        return InputSchema(required_columns=[
            ColumnInfo(name="high", type="float", description="High price"),
            ColumnInfo(name="low", type="float", description="Low price"),
            ColumnInfo(name="close", type="float", description="Close price"),
        ])

    @classmethod
    def get_output_schema(cls, **params) -> OutputSchema:
        return OutputSchema(columns=[
            ColumnInfo(name="supertrend", type="float", description="SuperTrend value", nullable=True),
            ColumnInfo(name="direction", type="int", description="Trend direction (1=up, -1=down)", nullable=True)
        ])

    def compute(self, context: ComputeContext) -> ComputeResult:
        high = np.array([float(v) if v is not None else np.nan for v in context.get_column("high")])
        low = np.array([float(v) if v is not None else np.nan for v in context.get_column("low")])
        close = np.array([float(v) if v is not None else np.nan for v in context.get_column("close")])

        length = self.params.get("length", 10)
        multiplier = self.params.get("multiplier", 3.0)

        # Calculate ATR
        tr = np.maximum(high - low, np.maximum(np.abs(high - np.roll(close, 1)), np.abs(low - np.roll(close, 1))))
        tr[0] = high[0] - low[0]

        atr = np.full_like(close, np.nan)
        atr[length - 1] = np.mean(tr[:length])
        for i in range(length, len(tr)):
            atr[i] = (atr[i - 1] * (length - 1) + tr[i]) / length

        # Calculate basic bands
        hl2 = (high + low) / 2
        basic_upper = hl2 + multiplier * atr
        basic_lower = hl2 - multiplier * atr

        # Calculate final bands
        final_upper = np.full_like(close, np.nan)
        final_lower = np.full_like(close, np.nan)
        supertrend = np.full_like(close, np.nan)
        direction = np.full_like(close, np.nan)

        for i in range(length, len(close)):
            if i == length:
                final_upper[i] = basic_upper[i]
                final_lower[i] = basic_lower[i]
            else:
                final_upper[i] = basic_upper[i] if basic_upper[i] < final_upper[i - 1] or close[i - 1] > final_upper[i - 1] else final_upper[i - 1]
                final_lower[i] = basic_lower[i] if basic_lower[i] > final_lower[i - 1] or close[i - 1] < final_lower[i - 1] else final_lower[i - 1]

            if i == length:
                supertrend[i] = final_upper[i] if close[i] <= hl2[i] else final_lower[i]
                direction[i] = -1 if close[i] <= hl2[i] else 1
            else:
                if supertrend[i - 1] == final_upper[i - 1] and close[i] <= final_upper[i]:
                    supertrend[i] = final_upper[i]
                    direction[i] = -1
                elif supertrend[i - 1] == final_upper[i - 1] and close[i] > final_upper[i]:
                    supertrend[i] = final_lower[i]
                    direction[i] = 1
                elif supertrend[i - 1] == final_lower[i - 1] and close[i] >= final_lower[i]:
                    supertrend[i] = final_lower[i]
                    direction[i] = 1
                else:
                    supertrend[i] = final_upper[i]
                    direction[i] = -1

        times = context.get_times()
        result_data = [
            {
                "time": times[i],
                "supertrend": float(supertrend[i]) if not np.isnan(supertrend[i]) else None,
                "direction": int(direction[i]) if not np.isnan(direction[i]) else None
            }
            for i in range(len(times))
        ]

        return ComputeResult(data=result_data, is_partial=context.is_incremental)


class DonchianChannels(Indicator):
    """Donchian Channels - Breakout indicator using highest high and lowest low."""

    @classmethod
    def get_metadata(cls) -> IndicatorMetadata:
        return IndicatorMetadata(
            name="DONCHIAN",
            display_name="Donchian Channels",
            description="Donchian Channels - Highest high and lowest low over period",
            category="overlap",
            parameters=[
                IndicatorParameter(
                    name="length",
                    type="int",
                    description="Period length",
                    default=20,
                    min_value=1,
                    required=False
                )
            ],
            use_cases=["Breakout trading", "Volatility bands", "Support/resistance"],
            references=["https://www.investopedia.com/terms/d/donchianchannels.asp"],
            tags=["donchian", "channels", "breakout"]
        )

    @classmethod
    def get_input_schema(cls) -> InputSchema:
        return InputSchema(required_columns=[
            ColumnInfo(name="high", type="float", description="High price"),
            ColumnInfo(name="low", type="float", description="Low price"),
        ])

    @classmethod
    def get_output_schema(cls, **params) -> OutputSchema:
        return OutputSchema(columns=[
            ColumnInfo(name="upper", type="float", description="Upper channel", nullable=True),
            ColumnInfo(name="middle", type="float", description="Middle line", nullable=True),
            ColumnInfo(name="lower", type="float", description="Lower channel", nullable=True),
        ])

    def compute(self, context: ComputeContext) -> ComputeResult:
        high = np.array([float(v) if v is not None else np.nan for v in context.get_column("high")])
        low = np.array([float(v) if v is not None else np.nan for v in context.get_column("low")])
        length = self.params.get("length", 20)

        upper = np.full_like(high, np.nan)
        lower = np.full_like(low, np.nan)

        for i in range(length - 1, len(high)):
            upper[i] = np.nanmax(high[i - length + 1:i + 1])
            lower[i] = np.nanmin(low[i - length + 1:i + 1])

        middle = (upper + lower) / 2

        times = context.get_times()
        result_data = [
            {
                "time": times[i],
                "upper": float(upper[i]) if not np.isnan(upper[i]) else None,
                "middle": float(middle[i]) if not np.isnan(middle[i]) else None,
                "lower": float(lower[i]) if not np.isnan(lower[i]) else None,
            }
            for i in range(len(times))
        ]

        return ComputeResult(data=result_data, is_partial=context.is_incremental)


class KeltnerChannels(Indicator):
    """Keltner Channels - ATR-based volatility bands."""

    @classmethod
    def get_metadata(cls) -> IndicatorMetadata:
        return IndicatorMetadata(
            name="KELTNER",
            display_name="Keltner Channels",
            description="Keltner Channels - EMA with ATR-based bands",
            category="volatility",
            parameters=[
                IndicatorParameter(
                    name="length",
                    type="int",
                    description="EMA period",
                    default=20,
                    min_value=1,
                    required=False
                ),
                IndicatorParameter(
                    name="multiplier",
                    type="float",
                    description="ATR multiplier",
                    default=2.0,
                    min_value=0.1,
                    required=False
                ),
                IndicatorParameter(
                    name="atr_length",
                    type="int",
                    description="ATR period",
                    default=10,
                    min_value=1,
                    required=False
                )
            ],
            use_cases=["Volatility bands", "Overbought/oversold", "Trend strength"],
            references=["https://www.investopedia.com/terms/k/keltnerchannel.asp"],
            tags=["keltner", "channels", "volatility", "atr"]
        )

    @classmethod
    def get_input_schema(cls) -> InputSchema:
        return InputSchema(required_columns=[
            ColumnInfo(name="high", type="float", description="High price"),
            ColumnInfo(name="low", type="float", description="Low price"),
            ColumnInfo(name="close", type="float", description="Close price"),
        ])

    @classmethod
    def get_output_schema(cls, **params) -> OutputSchema:
        return OutputSchema(columns=[
            ColumnInfo(name="upper", type="float", description="Upper band", nullable=True),
            ColumnInfo(name="middle", type="float", description="Middle line (EMA)", nullable=True),
            ColumnInfo(name="lower", type="float", description="Lower band", nullable=True),
        ])

    def compute(self, context: ComputeContext) -> ComputeResult:
        high = np.array([float(v) if v is not None else np.nan for v in context.get_column("high")])
        low = np.array([float(v) if v is not None else np.nan for v in context.get_column("low")])
        close = np.array([float(v) if v is not None else np.nan for v in context.get_column("close")])

        length = self.params.get("length", 20)
        multiplier = self.params.get("multiplier", 2.0)
        atr_length = self.params.get("atr_length", 10)

        # Calculate EMA
        alpha = 2.0 / (length + 1)
        ema = np.full_like(close, np.nan)
        ema[0] = close[0]
        for i in range(1, len(close)):
            ema[i] = alpha * close[i] + (1 - alpha) * ema[i - 1]

        # Calculate ATR
        tr = np.maximum(high - low, np.maximum(np.abs(high - np.roll(close, 1)), np.abs(low - np.roll(close, 1))))
        tr[0] = high[0] - low[0]

        atr = np.full_like(close, np.nan)
        atr[atr_length - 1] = np.mean(tr[:atr_length])
        for i in range(atr_length, len(tr)):
            atr[i] = (atr[i - 1] * (atr_length - 1) + tr[i]) / atr_length

        upper = ema + multiplier * atr
        lower = ema - multiplier * atr

        times = context.get_times()
        result_data = [
            {
                "time": times[i],
                "upper": float(upper[i]) if not np.isnan(upper[i]) else None,
                "middle": float(ema[i]) if not np.isnan(ema[i]) else None,
                "lower": float(lower[i]) if not np.isnan(lower[i]) else None,
            }
            for i in range(len(times))
        ]

        return ComputeResult(data=result_data, is_partial=context.is_incremental)


class ChaikinMoneyFlow(Indicator):
    """Chaikin Money Flow - Volume-weighted accumulation/distribution."""

    @classmethod
    def get_metadata(cls) -> IndicatorMetadata:
        return IndicatorMetadata(
            name="CMF",
            display_name="Chaikin Money Flow",
            description="Chaikin Money Flow - Measures buying and selling pressure",
            category="volume",
            parameters=[
                IndicatorParameter(
                    name="length",
                    type="int",
                    description="Period length",
                    default=20,
                    min_value=1,
                    required=False
                )
            ],
            use_cases=["Buying/selling pressure", "Trend confirmation", "Divergence analysis"],
            references=["https://www.investopedia.com/terms/c/chaikinoscillator.asp"],
            tags=["cmf", "chaikin", "volume", "money flow"]
        )

    @classmethod
    def get_input_schema(cls) -> InputSchema:
        return InputSchema(required_columns=[
            ColumnInfo(name="high", type="float", description="High price"),
            ColumnInfo(name="low", type="float", description="Low price"),
            ColumnInfo(name="close", type="float", description="Close price"),
            ColumnInfo(name="volume", type="float", description="Volume"),
        ])

    @classmethod
    def get_output_schema(cls, **params) -> OutputSchema:
        return OutputSchema(columns=[
            ColumnInfo(name="cmf", type="float", description="Chaikin Money Flow", nullable=True)
        ])

    def compute(self, context: ComputeContext) -> ComputeResult:
        high = np.array([float(v) if v is not None else np.nan for v in context.get_column("high")])
        low = np.array([float(v) if v is not None else np.nan for v in context.get_column("low")])
        close = np.array([float(v) if v is not None else np.nan for v in context.get_column("close")])
        volume = np.array([float(v) if v is not None else np.nan for v in context.get_column("volume")])
        length = self.params.get("length", 20)

        # Money Flow Multiplier
        mfm = ((close - low) - (high - close)) / (high - low)
        mfm = np.where(high == low, 0, mfm)

        # Money Flow Volume
        mfv = mfm * volume

        # CMF
        cmf = np.full_like(close, np.nan)
        for i in range(length - 1, len(close)):
            cmf[i] = np.nansum(mfv[i - length + 1:i + 1]) / np.nansum(volume[i - length + 1:i + 1])

        times = context.get_times()
        result_data = [
            {"time": times[i], "cmf": float(cmf[i]) if not np.isnan(cmf[i]) else None}
            for i in range(len(times))
        ]

        return ComputeResult(data=result_data, is_partial=context.is_incremental)


class VortexIndicator(Indicator):
    """Vortex Indicator - Identifies trend direction and strength."""

    @classmethod
    def get_metadata(cls) -> IndicatorMetadata:
        return IndicatorMetadata(
            name="VORTEX",
            display_name="Vortex Indicator",
            description="Vortex Indicator - Trend direction and strength",
            category="momentum",
            parameters=[
                IndicatorParameter(
                    name="length",
                    type="int",
                    description="Period length",
                    default=14,
                    min_value=1,
                    required=False
                )
            ],
            use_cases=["Trend identification", "Trend reversals", "Trend strength"],
            references=["https://www.investopedia.com/terms/v/vortex-indicator-vi.asp"],
            tags=["vortex", "trend", "momentum"]
        )

    @classmethod
    def get_input_schema(cls) -> InputSchema:
        return InputSchema(required_columns=[
            ColumnInfo(name="high", type="float", description="High price"),
            ColumnInfo(name="low", type="float", description="Low price"),
            ColumnInfo(name="close", type="float", description="Close price"),
        ])

    @classmethod
    def get_output_schema(cls, **params) -> OutputSchema:
        return OutputSchema(columns=[
            ColumnInfo(name="vi_plus", type="float", description="Positive Vortex", nullable=True),
            ColumnInfo(name="vi_minus", type="float", description="Negative Vortex", nullable=True),
        ])

    def compute(self, context: ComputeContext) -> ComputeResult:
        high = np.array([float(v) if v is not None else np.nan for v in context.get_column("high")])
        low = np.array([float(v) if v is not None else np.nan for v in context.get_column("low")])
        close = np.array([float(v) if v is not None else np.nan for v in context.get_column("close")])
        length = self.params.get("length", 14)

        # Vortex Movement
        vm_plus = np.abs(high - np.roll(low, 1))
        vm_minus = np.abs(low - np.roll(high, 1))
        vm_plus[0] = 0
        vm_minus[0] = 0

        # True Range
        tr = np.maximum(high - low, np.maximum(np.abs(high - np.roll(close, 1)), np.abs(low - np.roll(close, 1))))
        tr[0] = high[0] - low[0]

        # Vortex Indicator
        vi_plus = np.full_like(close, np.nan)
        vi_minus = np.full_like(close, np.nan)

        for i in range(length - 1, len(close)):
            sum_vm_plus = np.sum(vm_plus[i - length + 1:i + 1])
            sum_vm_minus = np.sum(vm_minus[i - length + 1:i + 1])
            sum_tr = np.sum(tr[i - length + 1:i + 1])

            if sum_tr != 0:
                vi_plus[i] = sum_vm_plus / sum_tr
                vi_minus[i] = sum_vm_minus / sum_tr

        times = context.get_times()
        result_data = [
            {
                "time": times[i],
                "vi_plus": float(vi_plus[i]) if not np.isnan(vi_plus[i]) else None,
                "vi_minus": float(vi_minus[i]) if not np.isnan(vi_minus[i]) else None,
            }
            for i in range(len(times))
        ]

        return ComputeResult(data=result_data, is_partial=context.is_incremental)


class AwesomeOscillator(Indicator):
    """Awesome Oscillator - Bill Williams' momentum indicator."""

    @classmethod
    def get_metadata(cls) -> IndicatorMetadata:
        return IndicatorMetadata(
            name="AO",
            display_name="Awesome Oscillator",
            description="Awesome Oscillator - Difference between 5 and 34 period SMAs of midpoint",
            category="momentum",
            parameters=[],
            use_cases=["Momentum shifts", "Trend reversals", "Divergence trading"],
            references=["https://www.investopedia.com/terms/a/awesomeoscillator.asp"],
            tags=["awesome", "oscillator", "momentum", "williams"]
        )

    @classmethod
    def get_input_schema(cls) -> InputSchema:
        return InputSchema(required_columns=[
            ColumnInfo(name="high", type="float", description="High price"),
            ColumnInfo(name="low", type="float", description="Low price"),
        ])

    @classmethod
    def get_output_schema(cls, **params) -> OutputSchema:
        return OutputSchema(columns=[
            ColumnInfo(name="ao", type="float", description="Awesome Oscillator", nullable=True)
        ])

    def compute(self, context: ComputeContext) -> ComputeResult:
        high = np.array([float(v) if v is not None else np.nan for v in context.get_column("high")])
        low = np.array([float(v) if v is not None else np.nan for v in context.get_column("low")])

        midpoint = (high + low) / 2

        # SMA 5
        sma5 = np.full_like(midpoint, np.nan)
        for i in range(4, len(midpoint)):
            sma5[i] = np.mean(midpoint[i - 4:i + 1])

        # SMA 34
        sma34 = np.full_like(midpoint, np.nan)
        for i in range(33, len(midpoint)):
            sma34[i] = np.mean(midpoint[i - 33:i + 1])

        ao = sma5 - sma34

        times = context.get_times()
        result_data = [
            {"time": times[i], "ao": float(ao[i]) if not np.isnan(ao[i]) else None}
            for i in range(len(times))
        ]

        return ComputeResult(data=result_data, is_partial=context.is_incremental)


class AcceleratorOscillator(Indicator):
    """Accelerator Oscillator - Rate of change of Awesome Oscillator."""

    @classmethod
    def get_metadata(cls) -> IndicatorMetadata:
        return IndicatorMetadata(
            name="AC",
            display_name="Accelerator Oscillator",
            description="Accelerator Oscillator - Rate of change of Awesome Oscillator",
            category="momentum",
            parameters=[],
            use_cases=["Early momentum detection", "Trend acceleration", "Divergence signals"],
            references=["https://www.investopedia.com/terms/a/accelerator-oscillator.asp"],
            tags=["accelerator", "oscillator", "momentum", "williams"]
        )

    @classmethod
    def get_input_schema(cls) -> InputSchema:
        return InputSchema(required_columns=[
            ColumnInfo(name="high", type="float", description="High price"),
            ColumnInfo(name="low", type="float", description="Low price"),
        ])

    @classmethod
    def get_output_schema(cls, **params) -> OutputSchema:
        return OutputSchema(columns=[
            ColumnInfo(name="ac", type="float", description="Accelerator Oscillator", nullable=True)
        ])

    def compute(self, context: ComputeContext) -> ComputeResult:
        high = np.array([float(v) if v is not None else np.nan for v in context.get_column("high")])
        low = np.array([float(v) if v is not None else np.nan for v in context.get_column("low")])

        midpoint = (high + low) / 2

        # Calculate AO first
        sma5 = np.full_like(midpoint, np.nan)
        for i in range(4, len(midpoint)):
            sma5[i] = np.mean(midpoint[i - 4:i + 1])

        sma34 = np.full_like(midpoint, np.nan)
        for i in range(33, len(midpoint)):
            sma34[i] = np.mean(midpoint[i - 33:i + 1])

        ao = sma5 - sma34

        # AC = AO - SMA(AO, 5)
        sma_ao = np.full_like(ao, np.nan)
        for i in range(4, len(ao)):
            if not np.isnan(ao[i - 4:i + 1]).any():
                sma_ao[i] = np.mean(ao[i - 4:i + 1])

        ac = ao - sma_ao

        times = context.get_times()
        result_data = [
            {"time": times[i], "ac": float(ac[i]) if not np.isnan(ac[i]) else None}
            for i in range(len(times))
        ]

        return ComputeResult(data=result_data, is_partial=context.is_incremental)


class ChoppinessIndex(Indicator):
    """Choppiness Index - Determines if market is choppy or trending."""

    @classmethod
    def get_metadata(cls) -> IndicatorMetadata:
        return IndicatorMetadata(
            name="CHOP",
            display_name="Choppiness Index",
            description="Choppiness Index - Measures market trendiness vs consolidation",
            category="volatility",
            parameters=[
                IndicatorParameter(
                    name="length",
                    type="int",
                    description="Period length",
                    default=14,
                    min_value=1,
                    required=False
                )
            ],
            use_cases=["Trend vs range identification", "Market regime detection"],
            references=["https://www.tradingview.com/support/solutions/43000501980/"],
            tags=["chop", "choppiness", "trend", "range"]
        )

    @classmethod
    def get_input_schema(cls) -> InputSchema:
        return InputSchema(required_columns=[
            ColumnInfo(name="high", type="float", description="High price"),
            ColumnInfo(name="low", type="float", description="Low price"),
            ColumnInfo(name="close", type="float", description="Close price"),
        ])

    @classmethod
    def get_output_schema(cls, **params) -> OutputSchema:
        return OutputSchema(columns=[
            ColumnInfo(name="chop", type="float", description="Choppiness Index (0-100)", nullable=True)
        ])

    def compute(self, context: ComputeContext) -> ComputeResult:
        high = np.array([float(v) if v is not None else np.nan for v in context.get_column("high")])
        low = np.array([float(v) if v is not None else np.nan for v in context.get_column("low")])
        close = np.array([float(v) if v is not None else np.nan for v in context.get_column("close")])
        length = self.params.get("length", 14)

        # True Range
        tr = np.maximum(high - low, np.maximum(np.abs(high - np.roll(close, 1)), np.abs(low - np.roll(close, 1))))
        tr[0] = high[0] - low[0]

        chop = np.full_like(close, np.nan)

        for i in range(length - 1, len(close)):
            sum_tr = np.sum(tr[i - length + 1:i + 1])
            high_low_diff = np.max(high[i - length + 1:i + 1]) - np.min(low[i - length + 1:i + 1])

            if high_low_diff != 0:
                chop[i] = 100 * np.log10(sum_tr / high_low_diff) / np.log10(length)

        times = context.get_times()
        result_data = [
            {"time": times[i], "chop": float(chop[i]) if not np.isnan(chop[i]) else None}
            for i in range(len(times))
        ]

        return ComputeResult(data=result_data, is_partial=context.is_incremental)


class MassIndex(Indicator):
    """Mass Index - Identifies trend reversals based on range expansion."""

    @classmethod
    def get_metadata(cls) -> IndicatorMetadata:
        return IndicatorMetadata(
            name="MASS",
            display_name="Mass Index",
            description="Mass Index - Identifies reversals when range narrows then expands",
            category="volatility",
            parameters=[
                IndicatorParameter(
                    name="fast_period",
                    type="int",
                    description="Fast EMA period",
                    default=9,
                    min_value=1,
                    required=False
                ),
                IndicatorParameter(
                    name="slow_period",
                    type="int",
                    description="Slow EMA period",
                    default=25,
                    min_value=1,
                    required=False
                )
            ],
            use_cases=["Reversal detection", "Volatility analysis", "Bulge identification"],
            references=["https://www.investopedia.com/terms/m/mass-index.asp"],
            tags=["mass", "index", "volatility", "reversal"]
        )

    @classmethod
    def get_input_schema(cls) -> InputSchema:
        return InputSchema(required_columns=[
            ColumnInfo(name="high", type="float", description="High price"),
            ColumnInfo(name="low", type="float", description="Low price"),
        ])

    @classmethod
    def get_output_schema(cls, **params) -> OutputSchema:
        return OutputSchema(columns=[
            ColumnInfo(name="mass", type="float", description="Mass Index", nullable=True)
        ])

    def compute(self, context: ComputeContext) -> ComputeResult:
        high = np.array([float(v) if v is not None else np.nan for v in context.get_column("high")])
        low = np.array([float(v) if v is not None else np.nan for v in context.get_column("low")])

        fast_period = self.params.get("fast_period", 9)
        slow_period = self.params.get("slow_period", 25)

        hl_range = high - low

        # Single EMA
        alpha1 = 2.0 / (fast_period + 1)
        ema1 = np.full_like(hl_range, np.nan)
        ema1[0] = hl_range[0]
        for i in range(1, len(hl_range)):
            ema1[i] = alpha1 * hl_range[i] + (1 - alpha1) * ema1[i - 1]

        # Double EMA
        ema2 = np.full_like(ema1, np.nan)
        ema2[0] = ema1[0]
        for i in range(1, len(ema1)):
            if not np.isnan(ema1[i]):
                ema2[i] = alpha1 * ema1[i] + (1 - alpha1) * ema2[i - 1]

        # EMA Ratio
        ema_ratio = ema1 / ema2

        # Mass Index
        mass = np.full_like(hl_range, np.nan)
        for i in range(slow_period - 1, len(ema_ratio)):
            mass[i] = np.nansum(ema_ratio[i - slow_period + 1:i + 1])

        times = context.get_times()
        result_data = [
            {"time": times[i], "mass": float(mass[i]) if not np.isnan(mass[i]) else None}
            for i in range(len(times))
        ]

        return ComputeResult(data=result_data, is_partial=context.is_incremental)


# Registry of all custom indicators
CUSTOM_INDICATORS = [
    VWAP,
    VWMA,
    HullMA,
    SuperTrend,
    DonchianChannels,
    KeltnerChannels,
    ChaikinMoneyFlow,
    VortexIndicator,
    AwesomeOscillator,
    AcceleratorOscillator,
    ChoppinessIndex,
    MassIndex,
]


def register_custom_indicators(registry) -> int:
    """
    Register all custom indicators with the registry.

    Args:
        registry: IndicatorRegistry instance

    Returns:
        Number of indicators registered
    """
    registered_count = 0

    for indicator_class in CUSTOM_INDICATORS:
        try:
            registry.register(indicator_class)
            registered_count += 1
            logger.debug(f"Registered custom indicator: {indicator_class.__name__}")
        except Exception as e:
            logger.warning(f"Failed to register custom indicator {indicator_class.__name__}: {e}")

    logger.info(f"Registered {registered_count} custom indicators")
    return registered_count
