HarmonicDivergenceV2

# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401

# --- Do not remove these libs ---
import datetime
import logging
from typing import Dict, List, Optional, Tuple
import numpy as np  # noqa
import pandas as pd  # noqa
pd.options.mode.chained_assignment = None
from pandas import DataFrame, Series
from technical.util import resample_to_interval, resampled_merge
from freqtrade.strategy import IStrategy, merge_informative_pair
from freqtrade.strategy import CategoricalParameter, DecimalParameter, IntParameter


# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from collections import deque


logger = logging.getLogger(__name__)


class PlotConfig():

    def __init__(self):
        self.config = {
            'main_plot': {
                resample('bollinger_upperband') : {'color': 'rgba(4,137,122,0.7)'},
                resample('kc_upperband') : {'color': 'rgba(4,146,250,0.7)'},
                resample('kc_middleband') : {'color': 'rgba(4,146,250,0.7)'},
                resample('kc_lowerband') : {'color': 'rgba(4,146,250,0.7)'},
                resample('bollinger_lowerband') : {
                    'color': 'rgba(4,137,122,0.7)',
                    'fill_to': resample('bollinger_upperband'),
                    'fill_color': 'rgba(4,137,122,0.07)'
                    },
                resample('ema9') : {'color': 'purple'},
                resample('ema20') : {'color': 'yellow'},
                resample('ema50') : {'color': 'red'},
                resample('ema200') : {'color': 'white'},
            },
            'subplots': {
                "ATR" : {
                    resample('atr'):{'color':'firebrick'}
                }
            }
        }

    def add_pivots_in_config(self):
        self.config['main_plot']["pivot_lows"] = {
            "plotly": {
                'mode': 'markers',
                'marker': {
                    'symbol': 'diamond-open',
                    'size': 11,
                    'line': {
                        'width': 2
                    },
                    'color': 'olive'
                }
            }
        }
        self.config['main_plot']["pivot_highs"] = {
            "plotly": {
                'mode': 'markers',
                'marker': {
                    'symbol': 'diamond-open',
                    'size': 11,
                    'line': {
                        'width': 2
                    },
                    'color': 'violet'
                }
            }
        }
        self.config['main_plot']["pivot_highs"] = {
            "plotly": {
                'mode': 'markers',
                'marker': {
                    'symbol': 'diamond-open',
                    'size': 11,
                    'line': {
                        'width': 2
                    },
                    'color': 'violet'
                }
            }
        }
        return self

    def add_divergence_in_config(self, indicator:str):
        # self.config['main_plot']["bullish_divergence_" + indicator + "_occurence"] = {
        #     "plotly": {
        #         'mode': 'markers',
        #         'marker': {
        #             'symbol': 'diamond',
        #             'size': 11,
        #             'line': {
        #                 'width': 2
        #             },
        #             'color': 'orange'
        #         }
        #     }
        # }
        # self.config['main_plot']["bearish_divergence_" + indicator + "_occurence"] = {
        #     "plotly": {
        #         'mode': 'markers',
        #         'marker': {
        #             'symbol': 'diamond',
        #             'size': 11,
        #             'line': {
        #                 'width': 2
        #             },
        #             'color': 'purple'
        #         }
        #     }
        # }
        for i in range(3):
            self.config['main_plot']["bullish_divergence_" + indicator + "_line_" + str(i)] = {
                "plotly": {
                    'mode': 'lines',
                    'line' : {
                        'color': 'green',
                        'dash' :'dash'
                    }
                }
            }   
            self.config['main_plot']["bearish_divergence_" + indicator + "_line_" + str(i)] = {
                "plotly": {
                    'mode': 'lines',
                    'line' : {
                        "color":'crimson',
                        'dash' :'dash'
                    }
                }
            } 
        return self

    def add_total_divergences_in_config(self, dataframe):
        total_bullish_divergences_count = dataframe[resample("total_bullish_divergences_count")]
        total_bullish_divergences_names = dataframe[resample("total_bullish_divergences_names")]
        self.config['main_plot'][resample("total_bullish_divergences")] = {
            "plotly": {
                'mode': 'markers+text',
                'text': total_bullish_divergences_count,
                'hovertext': total_bullish_divergences_names,
                'textfont':{'size': 11, 'color':'green'},
                'textposition':'bottom center',
                'marker': {
                    'symbol': 'diamond',
                    'size': 11,
                    'line': {
                        'width': 2
                    },
                    'color': 'green'
                }
            }
        }
        total_bearish_divergences_count = dataframe[resample("total_bearish_divergences_count")]
        total_bearish_divergences_names = dataframe[resample("total_bearish_divergences_names")]
        self.config['main_plot'][resample("total_bearish_divergences")] = {
            "plotly": {
                'mode': 'markers+text',
                'text': total_bearish_divergences_count,
                'hovertext': total_bearish_divergences_names,
                'textfont':{'size': 11, 'color':'crimson'},
                'textposition':'top center',
                'marker': {
                    'symbol': 'diamond',
                    'size': 11,
                    'line': {
                        'width': 2
                    },
                    'color': 'crimson'
                }
            }
        }
        return self
        
class HarmonicDivergenceV2(IStrategy):
    """
    This is a strategy template to get you started.
    More information in https://www.freqtrade.io/en/latest/strategy-customization/

    You can:
        :return: a Dataframe with all mandatory indicators for the strategies
    - Rename the class name (Do not forget to update class_name)
    - Add any methods you want to build your strategy
    - Add any lib you need to build your strategy

    You must keep:
    - the lib in the section "Do not remove these libs"
    - the methods: populate_indicators, populate_buy_trend, populate_sell_trend
    You should keep:
    - timeframe, minimal_roi, stoploss, trailing_*
    """
    # Strategy interface version - allow new iterations of the strategy interface.
    # Check the documentation or the Sample strategy to get the latest version.
    INTERFACE_VERSION = 2

    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi".
    minimal_roi = {
        "300" : 0.01,
        "60": 0.02,
        "30": 0.03,
        "0": 0.05,
        # "420" : 0.005,
        # "300" : 0.007,
        # "240" : 0.009,
        #"0": 0.018
        #"0": 0.007
    }

    # Optimal stoploss designed for the strategy.
    # This attribute will be overridden if the config file contains "stoploss".
    stoploss = -0.5

    use_custom_stoploss = True

    # Trailing stoploss
    trailing_stop = True
    trailing_stop_positive = 0.015
    trailing_stop_positive_offset = 0.03  # Disabled / not configured
    trailing_only_offset_is_reached = True

    # Optimal timeframe for the strategy.
    timeframe = '15m'

    # Run "populate_indicators()" only for new candle.
    process_only_new_candles = False

    # These values can be overridden in the "ask_strategy" section in the config.
    use_sell_signal = True
    sell_profit_only = False
    ignore_roi_if_buy_signal = False

    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 30

    # Optional order type mapping.
    order_types = {
        'entry': 'market',
        'exit': 'market',
        'stoploss': 'limit',
        'stoploss_on_exchange': True,
        'stoploss_on_exchange_interval': 60,
        'stoploss_on_exchange_limit_offset': 0.0,
    }

    # Optional order time in force.
    order_time_in_force = {
        'entry': 'gtc',
        'exit': 'gtc'
    }

    # --- Custom stoploss configuration ---
    stoploss_time_tolerance_minutes: int = 15
    stoploss_entry_lookback: int = 500
    stoploss_fallback_pct: float = -0.05
    stoploss_min_pct: float = -0.50
    stoploss_max_pct: float = 0
    stoploss_trigger_buffer: float = 0.0005
    stoploss_emergency_gap: float = 0.002
    stoploss_emergency_gap_step: float = 0.002
    stoploss_emergency_gap_cap: float = 0.01
    stoploss_atr_multiplier: float = 1.0
    stoploss_atr_relative_floor: float = 0.001
    stoploss_atr_relative_cap: float = 0.08

    plot_config = None

    def get_ticker_indicator(self):
        return int(self.timeframe[:-1])

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Adds several different TA indicators to the given DataFrame

        Performance Note: For the best performance be frugal on the number of indicators
        you are using. Let uncomment only the indicator you are using in your strategies
        or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
        :param dataframe: Dataframe with data from the exchange
        :param metadata: Additional information, like the currently traded pair
        :return: a Dataframe with all mandatory indicators for the strategies
        """

        # Get the informative pair
        # informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='15m')
        # informative = resample_to_interval(dataframe, self.get_ticker_indicator() * 15)
        informative = dataframe
        # Momentum Indicators
        # ------------------------------------

        # RSI
        informative['rsi'] = ta.RSI(informative)
        # Stochastic Slow
        informative['stoch'] = ta.STOCH(informative)['slowk']
        # ROC
        informative['roc'] = ta.ROC(informative)
        # Ultimate Oscillator
        informative['uo'] = ta.ULTOSC(informative)
        # Awesome Oscillator
        informative['ao'] = qtpylib.awesome_oscillator(informative)
        # MACD
        informative['macd'] = ta.MACD(informative)['macd']
        # Commodity Channel Index
        informative['cci'] = ta.CCI(informative)
        # CMF
        informative['cmf'] = chaikin_money_flow(informative, 20)
        # OBV
        informative['obv'] = ta.OBV(informative)
        # MFI
        informative['mfi'] = ta.MFI(informative)
        # ADX
        informative['adx'] = ta.ADX(informative)

        # ATR
        informative['atr'] = qtpylib.atr(informative, window=14, exp=False)

        # Keltner Channel
        # keltner = qtpylib.keltner_channel(dataframe, window=20, atrs=1)
        keltner = emaKeltner(informative)
        informative["kc_upperband"] = keltner["upper"]
        informative["kc_middleband"] = keltner["mid"]
        informative["kc_lowerband"] = keltner["lower"]

        # Bollinger Bands
        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(informative), window=20, stds=2)
        informative['bollinger_upperband'] = bollinger['upper']
        informative['bollinger_lowerband'] = bollinger['lower']

        # EMA - Exponential Moving Average
        informative['ema9'] = ta.EMA(informative, timeperiod=9)
        informative['ema20'] = ta.EMA(informative, timeperiod=20)
        informative['ema50'] = ta.EMA(informative, timeperiod=50)
        informative['ema200'] = ta.EMA(informative, timeperiod=200)        
        
        pivots = pivot_points(informative)
        informative['pivot_lows'] = pivots['pivot_lows']
        informative['pivot_highs'] = pivots['pivot_highs']

        # Use the helper function merge_informative_pair to safely merge the pair
        # Automatically renames the columns and merges a shorter timeframe dataframe and a longer timeframe informative pair
        # use ffill to have the 1d value available in every row throughout the day.
        # Without this, comparisons between columns of the original and the informative pair would only work once per day.
        # Full documentation of this method, see below
 

        initialize_divergences_lists(informative)
        add_divergences(informative, 'rsi')
        add_divergences(informative, 'stoch')
        add_divergences(informative, 'roc')
        add_divergences(informative, 'uo')
        add_divergences(informative, 'ao')
        add_divergences(informative, 'macd')
        add_divergences(informative, 'cci')
        add_divergences(informative, 'cmf')
        add_divergences(informative, 'obv')
        add_divergences(informative, 'mfi')
        add_divergences(informative, 'adx')

        # print("-------------------informative-------------------")
        # print(informative)
        # print("-------------------dataframe-------------------")
        # print(dataframe)
        # dataframe = merge_informative_pair(dataframe, informative, self.timeframe, '15m', ffill=True)

        # dataframe = resampled_merge(dataframe, informative)
        # print(dataframe[resample("total_bullish_divergences_count")])
        # for index, value in enumerate(dataframe[resample("total_bullish_divergences_count")]):
        #     if value < 0.5:
        #         dataframe[resample("total_bullish_divergences_count")][index] = None
        #         dataframe[resample("total_bullish_divergences")][index] = None
        #         dataframe[resample("total_bullish_divergences_names")][index] = None
        #     else:
        #         print(value)
        #         print(dataframe[resample("total_bullish_divergences")][index])
        #         print(dataframe[resample("total_bullish_divergences_names")][index])
        HarmonicDivergenceV2.plot_config = (
            PlotConfig()
            # .add_pivots_in_config()
            # .add_divergence_in_config('rsi')
            # .add_divergence_in_config('stoch')
            # .add_divergence_in_config('roc')
            # .add_divergence_in_config('uo')
            # .add_divergence_in_config('ao')
            # .add_divergence_in_config('macd')
            # .add_divergence_in_config('cci')
            # .add_divergence_in_config('cmf')
            # .add_divergence_in_config('obv')
            # .add_divergence_in_config('mfi')
            # .add_divergence_in_config('adx')
            .add_total_divergences_in_config(dataframe)
            .config)

        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the buy signal for the given dataframe
        :param dataframe: DataFrame populated with indicators
        :param metadata: Additional information, like the currently traded pair
        :return: DataFrame with buy column
        """
        dataframe.loc[
            (
                (dataframe[resample('total_bullish_divergences')].shift() > 0)
                # # & (dataframe['high'] > dataframe['high'].shift())
                # & (
                #     (keltner_middleband_check(dataframe) & (ema_check(dataframe)) & (green_candle(dataframe)))
                #     # (keltner_middleband_check(dataframe) & (green_candle(dataframe)))
                #     | (keltner_lowerband_check(dataframe) & (ema_check(dataframe)))
                #     # | keltner_lowerband_check(dataframe)
                #     # | (keltner_lowerband_check(dataframe) & (green_candle(dataframe)))
                #     | (bollinger_lowerband_check(dataframe) & (ema_check(dataframe)))
                # )
                & two_bands_check(dataframe)
                # # & bollinger_keltner_check(dataframe)
                # & ema_cross_check(dataframe)
                & (dataframe['volume'] > 0)  # Make sure Volume is not 0
            ),
            'buy'] = 1

        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the sell signal for the given dataframe
        :param dataframe: DataFrame populated with indicators
        :param metadata: Additional information, like the currently traded pair
        :return: DataFrame with buy column
        """
        dataframe.loc[
            (
                (dataframe['volume'] > 0)  # Make sure Volume is not 0
            ),
            'sell'] = 0
        return dataframe
        
    def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float,
                    current_profit: float, **kwargs):

        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        takeprofit = 999999
        # self.trailing_stop = False
        
        for i in range(1,len(dataframe['close'])):
            if dataframe.iloc[-i]['date'].to_pydatetime().replace(tzinfo=datetime.timezone.utc) == trade.open_date_utc:
                buy_candle = dataframe.iloc[-i-1].squeeze()
                takeprofit = buy_candle[resample('high')] + buy_candle[resample('atr')]
                break

        # if takeprofit < current_rate:
            # self.trailing_stop = True
            # return True

    def _get_trade_custom_info(self, trade: 'Trade') -> Dict[str, Dict]:
        info = trade.custom_info if isinstance(getattr(trade, 'custom_info', None), dict) else {}
        trade.custom_info = info
        return info

    def _update_stoploss_state(self, trade: 'Trade', state: Dict[str, object]) -> None:
        info = self._get_trade_custom_info(trade)
        info['stoploss_state'] = state
        trade.custom_info = info

    def _log_once(self, trade: 'Trade', key: str, message: str, level: int = logging.WARNING) -> None:
        info = self._get_trade_custom_info(trade)
        flags = info.setdefault('_log_once', {})
        if not flags.get(key):
            logger.log(level, message)
            flags[key] = True
        trade.custom_info = info

    def _to_utc(self, dt_obj: datetime.datetime) -> datetime.datetime:
        if dt_obj.tzinfo is None:
            return dt_obj.replace(tzinfo=datetime.timezone.utc)
        return dt_obj.astimezone(datetime.timezone.utc)

    def _resolve_stoploss_source(self, trade: 'Trade', dataframe: DataFrame) -> Tuple[Optional[Series], str]:
        tolerance_seconds = self.stoploss_time_tolerance_minutes * 60
        lookback = min(len(dataframe), self.stoploss_entry_lookback)
        entry_time = self._to_utc(trade.open_date_utc)

        for i in range(1, lookback):
            candle = dataframe.iloc[-i]
            candle_time = pd.Timestamp(candle['date']).to_pydatetime()
            candle_time = self._to_utc(candle_time)
            if abs((candle_time - entry_time).total_seconds()) <= tolerance_seconds:
                idx = len(dataframe) - i
                # 如果仍在使用最新（未收盘）的入场蜡烛，则临时回退上一根
                if idx == len(dataframe) - 1 and len(dataframe) >= 2:
                    return dataframe.iloc[-2].squeeze(), 'entry_pending'
                if 0 <= idx < len(dataframe):
                    return dataframe.iloc[idx].squeeze(), 'entry'
                break

        if len(dataframe) >= 2:
            return dataframe.iloc[-2].squeeze(), 'latest'

        return None, 'no-data'

    def _fallback_stoploss(self, pair: str, trade: 'Trade', current_time: datetime.datetime,
                           reason_key: str, message: Optional[str] = None) -> float:
        if message:
            self._log_once(trade, reason_key, message, logging.WARNING)

        state = {
            'reason': reason_key,
            'effective_pct': self.stoploss_fallback_pct,
            'timeout_count': getattr(trade, '_stoploss_timeouts', 0),
            'timestamp': self._to_utc(current_time).isoformat(),
        }
        self._update_stoploss_state(trade, state)
        return self.stoploss_fallback_pct

    def _reset_stoploss_timeout(self, trade: 'Trade') -> None:
        if hasattr(trade, '_stoploss_timeouts'):
            setattr(trade, '_stoploss_timeouts', 0)

    def _increment_stoploss_timeout(self, trade: 'Trade') -> int:
        current = getattr(trade, '_stoploss_timeouts', 0) + 1
        setattr(trade, '_stoploss_timeouts', current)
        return current

    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime.datetime,
                            current_rate: float, current_profit: float, **kwargs) -> float:

        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        if dataframe is None or len(dataframe) < 2:
            return self._fallback_stoploss(
                pair,
                trade,
                current_time,
                'stoploss_no_dataframe',
                f"[{pair}] 止损缺少K线数据，使用备用止损({self.stoploss_fallback_pct:.2%})",
            )

        source_candle, source_label = self._resolve_stoploss_source(trade, dataframe)
        if source_candle is None:
            return self._fallback_stoploss(
                pair,
                trade,
                current_time,
                'stoploss_no_entry_candle',
                f"[{pair}] 未找到入场K线，使用备用止损({self.stoploss_fallback_pct:.2%})",
            )

        try:
            low_val = float(source_candle[resample('low')])
            atr_val = float(source_candle[resample('atr')])
        except (KeyError, ValueError, TypeError):
            return self._fallback_stoploss(
                pair,
                trade,
                current_time,
                'stoploss_invalid_candle',
                f"[{pair}] 止损参考K线数据异常，使用备用止损({self.stoploss_fallback_pct:.2%})",
            )

        if not np.isfinite(low_val) or not np.isfinite(atr_val) or atr_val <= 0 or trade.open_rate <= 0:
            return self._fallback_stoploss(
                pair,
                trade,
                current_time,
                'stoploss_invalid_values',
                f"[{pair}] 止损参考数值无效，使用备用止损({self.stoploss_fallback_pct:.2%})",
            )

        atr_ratio = atr_val / trade.open_rate
        if not np.isfinite(atr_ratio) or atr_ratio <= 0:
            return self._fallback_stoploss(
                pair,
                trade,
                current_time,
                'stoploss_invalid_atr_ratio',
                f"[{pair}] ATR 相对值无效，使用备用止损({self.stoploss_fallback_pct:.2%})",
            )

        atr_ratio = max(atr_ratio, self.stoploss_atr_relative_floor)
        atr_ratio = min(atr_ratio, self.stoploss_atr_relative_cap)
        atr_used = atr_ratio * trade.open_rate * self.stoploss_atr_multiplier

        base_price_raw = low_val - atr_used
        base_pct_raw = (base_price_raw / trade.open_rate) - 1

        base_pct = max(base_pct_raw, self.stoploss_min_pct)
        if getattr(self, 'stoploss_max_pct', None) is not None:
            base_pct = min(base_pct, self.stoploss_max_pct)
        base_price = trade.open_rate * (1 + base_pct)

        timeout_count = getattr(trade, '_stoploss_timeouts', 0)
        latest_low_val = dataframe[resample('low')].iloc[-1]
        latest_low = float(latest_low_val) if pd.notna(latest_low_val) else None

        crossed = False
        if latest_low is not None and latest_low <= base_price:
            crossed = True
        if isinstance(current_rate, (int, float)) and np.isfinite(current_rate) and current_rate <= base_price:
            crossed = True

        gap = 0.0
        emergency_applied = False
        effective_pct = base_pct

        if not crossed and timeout_count:
            self._reset_stoploss_timeout(trade)
            timeout_count = 0

        if crossed and timeout_count > 0:
            gap = self.stoploss_emergency_gap + (timeout_count * self.stoploss_emergency_gap_step)
            gap = max(0.0, min(gap, self.stoploss_emergency_gap_cap))
            if isinstance(current_rate, (int, float)) and np.isfinite(current_rate) and current_rate > 0:
                adjusted_price = min(base_price, current_rate * (1 - gap))
            else:
                adjusted_price = base_price
            effective_pct = (adjusted_price / trade.open_rate) - 1
            effective_pct = max(effective_pct, self.stoploss_min_pct)
            if getattr(self, 'stoploss_max_pct', None) is not None:
                effective_pct = min(effective_pct, self.stoploss_max_pct)
            emergency_applied = True

        effective_price = trade.open_rate * (1 + effective_pct)

        state = {
            'source': source_label,
            'base_price': round(base_price, 8),
            'base_pct': round(base_pct, 6),
            'base_pct_raw': round(base_pct_raw, 6),
            'effective_price': round(effective_price, 8),
            'effective_pct': round(effective_pct, 6),
            'atr': round(atr_used, 8),
            'atr_ratio': round(atr_ratio, 6),
            'timeout_count': timeout_count,
            'crossed': crossed,
            'gap': round(gap, 6),
            'emergency_applied': emergency_applied,
            'timestamp': self._to_utc(current_time).isoformat(),
        }
        if source_label != 'entry':
            state['fallback_reason'] = source_label

        self._update_stoploss_state(trade, state)

        return effective_pct

    def check_exit_timeout(self, pair: str, trade: 'Trade', order: dict, **kwargs) -> bool:
        if order.get('ft_order_tag') != 'stoploss':
            return False

        order_price = order.get('price') or order.get('limit')
        if order_price is None:
            return False

        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        if dataframe is None or len(dataframe) == 0:
            return False

        current_low_val = dataframe[resample('low')].iloc[-1]
        current_low = float(current_low_val) if pd.notna(current_low_val) else None
        if current_low is None or current_low > order_price:
            return False

        timeout_count = self._increment_stoploss_timeout(trade)

        info = self._get_trade_custom_info(trade)
        state = info.get('stoploss_state', {})
        state['timeout_count'] = timeout_count
        state['last_timeout'] = datetime.datetime.utcnow().replace(tzinfo=datetime.timezone.utc).isoformat()
        info['stoploss_state'] = state
        trade.custom_info = info

        self._log_once(
            trade,
            f'stoploss_timeout_{timeout_count}',
            f"[{pair}] 止损单第 {timeout_count} 次超时，准备扩大保护间隔",
            logging.WARNING,
        )

        return True

def resample(indicator):
    # return "resample_15_" + indicator
    return indicator

def two_bands_check(dataframe):
    check = (
    # ((dataframe['low'] < dataframe['bollinger_lowerband']) & (dataframe['high'] > dataframe['kc_lowerband'])) |
    ((dataframe[resample('low')] < dataframe[resample('kc_lowerband')]) & (dataframe[resample('high')] > dataframe[resample('kc_upperband')])) # 1
    #  ((dataframe['low'] < dataframe['kc_lowerband']) & (dataframe['high'] > dataframe['kc_middleband'])) # 2
    # | ((dataframe['low'] < dataframe['kc_middleband']) & (dataframe['high'] > dataframe['kc_upperband'])) # 2
    )
    return ~check

def ema_cross_check(dataframe):
    dataframe['ema20_50_cross'] = qtpylib.crossed_below(dataframe[resample('ema20')],dataframe[resample('ema50')])
    dataframe['ema20_200_cross'] = qtpylib.crossed_below(dataframe[resample('ema20')],dataframe[resample('ema200')])
    dataframe['ema50_200_cross'] = qtpylib.crossed_below(dataframe[resample('ema50')],dataframe[resample('ema200')])
    return ~(
        dataframe['ema20_50_cross'] 
        | dataframe['ema20_200_cross'] 
        | dataframe['ema50_200_cross'] 
        )

def green_candle(dataframe):
    return dataframe[resample('open')] < dataframe[resample('close')]

def keltner_middleband_check(dataframe):
    return (dataframe[resample('low')] < dataframe[resample('kc_middleband')]) & (dataframe[resample('high')] > dataframe[resample('kc_middleband')])

def keltner_lowerband_check(dataframe):
    return (dataframe[resample('low')] < dataframe[resample('kc_lowerband')]) & (dataframe[resample('high')] > dataframe[resample('kc_lowerband')])

def bollinger_lowerband_check(dataframe):
    return (dataframe[resample('low')] < dataframe[resample('bollinger_lowerband')]) & (dataframe[resample('high')] > dataframe[resample('bollinger_lowerband')])

def bollinger_keltner_check(dataframe):
    return (dataframe[resample('bollinger_lowerband')] < dataframe[resample('kc_lowerband')]) & (dataframe[resample('bollinger_upperband')] > dataframe[resample('kc_upperband')])

def ema_check(dataframe):
    check = (
        (dataframe[resample('ema9')] < dataframe[resample('ema20')])
        & (dataframe[resample('ema20')] < dataframe[resample('ema50')])
        & (dataframe[resample('ema50')] < dataframe[resample('ema200')]))
    return ~check

def initialize_divergences_lists(dataframe: DataFrame):
    dataframe["total_bullish_divergences"] = np.empty(len(dataframe['close'])) * np.nan
    dataframe["total_bullish_divergences_count"] = np.empty(len(dataframe['close'])) * np.nan
    dataframe["total_bullish_divergences_count"] = [0 if x != x else x for x in dataframe["total_bullish_divergences_count"]]
    dataframe["total_bullish_divergences_names"] = np.empty(len(dataframe['close'])) * np.nan
    dataframe["total_bullish_divergences_names"] = ['' if x != x else x for x in dataframe["total_bullish_divergences_names"]]
    dataframe["total_bearish_divergences"] = np.empty(len(dataframe['close'])) * np.nan
    dataframe["total_bearish_divergences_count"] = np.empty(len(dataframe['close'])) * np.nan
    dataframe["total_bearish_divergences_count"] = [0 if x != x else x for x in dataframe["total_bearish_divergences_count"]]
    dataframe["total_bearish_divergences_names"] = np.empty(len(dataframe['close'])) * np.nan
    dataframe["total_bearish_divergences_names"] = ['' if x != x else x for x in dataframe["total_bearish_divergences_names"]]

def add_divergences(dataframe: DataFrame, indicator: str):
    (bearish_divergences, bearish_lines, bullish_divergences, bullish_lines) = divergence_finder_dataframe(dataframe, indicator)
    dataframe['bearish_divergence_' + indicator + '_occurence'] = bearish_divergences
    # for index, bearish_line in enumerate(bearish_lines):
    #     dataframe['bearish_divergence_' + indicator + '_line_'+ str(index)] = bearish_line
    dataframe['bullish_divergence_' + indicator + '_occurence'] = bullish_divergences
    # for index, bullish_line in enumerate(bullish_lines):
    #     dataframe['bullish_divergence_' + indicator + '_line_'+ str(index)] = bullish_line

def divergence_finder_dataframe(dataframe: DataFrame, indicator_source: str) -> Tuple[pd.Series, pd.Series]:
    bearish_lines = [np.empty(len(dataframe['close'])) * np.nan]
    bearish_divergences = np.empty(len(dataframe['close'])) * np.nan
    bullish_lines = [np.empty(len(dataframe['close'])) * np.nan]
    bullish_divergences = np.empty(len(dataframe['close'])) * np.nan
    low_iterator = []
    high_iterator = []

    for index, row in enumerate(dataframe.itertuples(index=True, name='Pandas')):
        if np.isnan(row.pivot_lows):
            low_iterator.append(0 if len(low_iterator) == 0 else low_iterator[-1])
        else:
            low_iterator.append(index)
        if np.isnan(row.pivot_highs):
            high_iterator.append(0 if len(high_iterator) == 0 else high_iterator[-1])
        else:
            high_iterator.append(index)

    for index, row in enumerate(dataframe.itertuples(index=True, name='Pandas')):

        bearish_occurence = bearish_divergence_finder(dataframe,
            dataframe[indicator_source],
            high_iterator,
            index)

        if bearish_occurence != None:
            (prev_pivot , current_pivot) = bearish_occurence 
            bearish_prev_pivot = dataframe['close'][prev_pivot]
            bearish_current_pivot = dataframe['close'][current_pivot]
            bearish_ind_prev_pivot = dataframe[indicator_source][prev_pivot]
            bearish_ind_current_pivot = dataframe[indicator_source][current_pivot]
            length = current_pivot - prev_pivot
            bearish_lines_index = 0
            can_exist = True
            while(True):
                can_draw = True
                if bearish_lines_index <= len(bearish_lines):
                    bearish_lines.append(np.empty(len(dataframe['close'])) * np.nan)
                actual_bearish_lines = bearish_lines[bearish_lines_index]
                for i in range(length + 1):
                    point = bearish_prev_pivot + (bearish_current_pivot - bearish_prev_pivot) * i / length
                    indicator_point =  bearish_ind_prev_pivot + (bearish_ind_current_pivot - bearish_ind_prev_pivot) * i / length
                    if i != 0 and i != length:
                        if (point <= dataframe['close'][prev_pivot + i] 
                        or indicator_point <= dataframe[indicator_source][prev_pivot + i]):
                            can_exist = False
                    if not np.isnan(actual_bearish_lines[prev_pivot + i]):
                        can_draw = False
                if not can_exist:
                    break
                if can_draw:
                    for i in range(length + 1):
                        actual_bearish_lines[prev_pivot + i] = bearish_prev_pivot + (bearish_current_pivot - bearish_prev_pivot) * i / length
                    break
                bearish_lines_index = bearish_lines_index + 1
            if can_exist:
                bearish_divergences[index] = row.close
                dataframe["total_bearish_divergences"][index] = row.close
                if index > 30:
                    dataframe["total_bearish_divergences_count"][index-30] = dataframe["total_bearish_divergences_count"][index-30] + 1
                    dataframe["total_bearish_divergences_names"][index-30] = dataframe["total_bearish_divergences_names"][index-30] + indicator_source.upper() + '<br>'

        bullish_occurence = bullish_divergence_finder(dataframe,
            dataframe[indicator_source],
            low_iterator,
            index)
        
        if bullish_occurence != None:
            (prev_pivot , current_pivot) = bullish_occurence
            bullish_prev_pivot = dataframe['close'][prev_pivot]
            bullish_current_pivot = dataframe['close'][current_pivot]
            bullish_ind_prev_pivot = dataframe[indicator_source][prev_pivot]
            bullish_ind_current_pivot = dataframe[indicator_source][current_pivot]
            length = current_pivot - prev_pivot
            bullish_lines_index = 0
            can_exist = True
            while(True):
                can_draw = True
                if bullish_lines_index <= len(bullish_lines):
                    bullish_lines.append(np.empty(len(dataframe['close'])) * np.nan)
                actual_bullish_lines = bullish_lines[bullish_lines_index]
                for i in range(length + 1):
                    point = bullish_prev_pivot + (bullish_current_pivot - bullish_prev_pivot) * i / length
                    indicator_point =  bullish_ind_prev_pivot + (bullish_ind_current_pivot - bullish_ind_prev_pivot) * i / length
                    if i != 0 and i != length:
                        if (point >= dataframe['close'][prev_pivot + i] 
                        or indicator_point >= dataframe[indicator_source][prev_pivot + i]):
                            can_exist = False
                    if not np.isnan(actual_bullish_lines[prev_pivot + i]):
                        can_draw = False
                if not can_exist:
                    break
                if can_draw:
                    for i in range(length + 1):
                        actual_bullish_lines[prev_pivot + i] = bullish_prev_pivot + (bullish_current_pivot - bullish_prev_pivot) * i / length
                    break
                bullish_lines_index = bullish_lines_index + 1
            if can_exist:
                bullish_divergences[index] = row.close
                dataframe["total_bullish_divergences"][index] = row.close
                if index > 30:
                    dataframe["total_bullish_divergences_count"][index-30] = dataframe["total_bullish_divergences_count"][index-30] + 1
                    dataframe["total_bullish_divergences_names"][index-30] = dataframe["total_bullish_divergences_names"][index-30] + indicator_source.upper() + '<br>'
    
    return (bearish_divergences, bearish_lines, bullish_divergences, bullish_lines)

def bearish_divergence_finder(dataframe, indicator, high_iterator, index):
    if high_iterator[index] == index:
        current_pivot = high_iterator[index]
        occurences = list(dict.fromkeys(high_iterator))
        current_index = occurences.index(high_iterator[index])
        for i in range(current_index-1,current_index-6,-1):            
            prev_pivot = occurences[i]
            if np.isnan(prev_pivot):
                return
            if ((dataframe['pivot_highs'][current_pivot] < dataframe['pivot_highs'][prev_pivot] and indicator[current_pivot] > indicator[prev_pivot])
            or (dataframe['pivot_highs'][current_pivot] > dataframe['pivot_highs'][prev_pivot] and indicator[current_pivot] < indicator[prev_pivot])):
                return (prev_pivot , current_pivot)
    return None

def bullish_divergence_finder(dataframe, indicator, low_iterator, index):
    if low_iterator[index] == index:
        current_pivot = low_iterator[index]
        occurences = list(dict.fromkeys(low_iterator))
        current_index = occurences.index(low_iterator[index])
        for i in range(current_index-1,current_index-6,-1):
            prev_pivot = occurences[i]
            if np.isnan(prev_pivot):
                return 
            if ((dataframe['pivot_lows'][current_pivot] < dataframe['pivot_lows'][prev_pivot] and indicator[current_pivot] > indicator[prev_pivot])
            or (dataframe['pivot_lows'][current_pivot] > dataframe['pivot_lows'][prev_pivot] and indicator[current_pivot] < indicator[prev_pivot])):
                return (prev_pivot, current_pivot)
    return None

from enum import Enum
class PivotSource(Enum):
    HighLow = 0
    Close = 1

def pivot_points(dataframe: DataFrame, window: int = 5, pivot_source: PivotSource = PivotSource.Close) -> DataFrame:
    high_source = None
    low_source = None

    if pivot_source == PivotSource.Close:
        high_source = 'close'
        low_source = 'close'
    elif pivot_source == PivotSource.HighLow:
        high_source = 'high'
        low_source = 'low'

    pivot_points_lows = np.empty(len(dataframe['close'])) * np.nan
    pivot_points_highs = np.empty(len(dataframe['close'])) * np.nan
    last_values = deque()
    
    # find pivot points
    for index, row in enumerate(dataframe.itertuples(index=True, name='Pandas')):
        last_values.append(row)
        if len(last_values) >= window * 2 + 1:
            current_value = last_values[window]
            is_greater = True
            is_less = True
            for window_index in range(0, window):
                left = last_values[window_index]
                right = last_values[2 * window - window_index]
                local_is_greater, local_is_less = check_if_pivot_is_greater_or_less(current_value, high_source, low_source, left, right)
                is_greater &= local_is_greater
                is_less &= local_is_less
            if is_greater:
                pivot_points_highs[index - window] = getattr(current_value, high_source)
            if is_less:
                pivot_points_lows[index - window] = getattr(current_value, low_source)
            last_values.popleft()
   
    # find last one
    if len(last_values) >= window + 2:
        current_value = last_values[-2]
        is_greater = True
        is_less = True
        for window_index in range(0, window):
            left = last_values[-2 - window_index - 1]
            right = last_values[-1]
            local_is_greater, local_is_less = check_if_pivot_is_greater_or_less(current_value, high_source, low_source, left, right)
            is_greater &= local_is_greater
            is_less &= local_is_less
        if is_greater:
            pivot_points_highs[index - 1] = getattr(current_value, high_source)
        if is_less:
            pivot_points_lows[index - 1] = getattr(current_value, low_source)

    return pd.DataFrame(index=dataframe.index, data={
        'pivot_lows': pivot_points_lows,
        'pivot_highs': pivot_points_highs
    })

def check_if_pivot_is_greater_or_less(current_value, high_source: str, low_source: str, left, right) -> Tuple[bool, bool]:
    is_greater = True
    is_less = True
    if (getattr(current_value, high_source) < getattr(left, high_source) or
        getattr(current_value, high_source) < getattr(right, high_source)):
        is_greater = False

    if (getattr(current_value, low_source) > getattr(left, low_source) or
        getattr(current_value, low_source) > getattr(right, low_source)):
        is_less = False
    return (is_greater, is_less)

def emaKeltner(dataframe):
    keltner = {}
    atr = qtpylib.atr(dataframe, window=10)
    ema20 = ta.EMA(dataframe, timeperiod=20)
    keltner['upper'] = ema20 + atr
    keltner['mid'] = ema20
    keltner['lower'] = ema20 - atr
    return keltner

def chaikin_money_flow(dataframe, n=20, fillna=False) -> Series:
    """Chaikin Money Flow (CMF)
    It measures the amount of Money Flow Volume over a specific period.
    http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:chaikin_money_flow_cmf
    Args:
        dataframe(pandas.Dataframe): dataframe containing ohlcv
        n(int): n period.
        fillna(bool): if True, fill nan values.
    Returns:
        pandas.Series: New feature generated.
    """
    df = dataframe.copy()
    mfv = ((df['close'] - df['low']) - (df['high'] - df['close'])) / (df['high'] - df['low'])
    mfv = mfv.fillna(0.0)  # float division by zero
    mfv *= df['volume']
    cmf = (mfv.rolling(n, min_periods=0).sum()
           / df['volume'].rolling(n, min_periods=0).sum())
    if fillna:
        cmf = cmf.replace([np.inf, -np.inf], np.nan).fillna(0)
    return Series(cmf, name='cmf')