import pandas as pd
import numpy as np
import talib

class CryptoTradingBot:
    # Initialization and other methods…

    def identify_price_action_signals(self, df):
        “”“
        Analyze price action to identify trading signals.
        “””
        signals = []

        # Detect simple candlestick patterns
        if self.is_bullish_engulfing(df):
            signals.append(‘bullish_engulfing’)
        
        if self.is_bearish_engulfing(df):
            signals.append(‘bearish_engulfing’)
        
        if self.is_hammer(df):
            signals.append(‘hammer’)
        
        if self.is_shooting_star(df):
            signals.append(‘shooting_star’)
        
        if self.is_bullish_harami(df):
            signals.append(‘bullish_harami’)

        if self.is_bearish_harami(df):
            signals.append(‘bearish_harami’)

        if self.is_piercing_pattern(df):
            signals.append(‘piercing_pattern’)
        
        if self.is_dark_cloud_cover(df):
            signals.append(‘dark_cloud_cover’)
        
        if self.is_three_white_soldiers(df):
            signals.append(‘three_white_soldiers’)

        if self.is_three_black_crows(df):
            signals.append(‘three_black_crows’)

        # Advanced patterns using TA-Lib
        patterns = {
            ‘doji’: talib.CDLDOJI,
            ‘long_legged_doji’: talib.CDLLONGLEGGEDDOJI,
            ‘gravestone_doji’: talib.CDLGRAVESTONEDOJI,
            ‘dragonfly_doji’: talib.CDLDRAGONFLYDOJI,
            ‘spinning_top’: talib.CDLSPINNINGTOP,
            ‘marubozu’: talib.CDLMARUBOZU,
            ‘inside_bar’: talib.CDLINSIDE,
            ‘outside_bar’: talib.CDLOUTSIDE,
            ‘morning_star’: talib.CDLMORNINGSTAR,
            ‘evening_star’: talib.CDLEVENINGSTAR,
            ‘abandoned_baby’: talib.CDLABANDONEDBABY,
            ‘tweezer_top’: talib.CDLTWEEZERTOP,
            ‘tweezer_bottom’: talib.CDLTWEEZERBOTTOM,
        }

        ohlc_dict = df[[‘open’, ‘high’, ‘low’, ‘close’]].to_dict(orient=‘list’)
        for pattern_name, pattern_func in patterns.items():
            result = pattern_func(
                np.array(ohlc_dict[‘open’]),
                np.array(ohlc_dict[‘high’]),
                np.array(ohlc_dict[‘low’]),
                np.array(ohlc_dict[‘close’])
            )
            if result[-1] != 0:
                signals.append(pattern_name)
        
        return signals

    def is_bullish_engulfing(self, df):
        “”“
        Identify Bullish Engulfing pattern
        “””
        prev_candle = df.iloc[-2]
        current_candle = df.iloc[-1]
        
        return (prev_candle[‘close’] < prev_candle[‘open’]) and (current_candle[‘close’] > current_candle[‘open’]) and (current_candle[‘close’] > prev_candle[‘open’]) and (current_candle[‘open’] < prev_candle[‘close’])

    def is_bearish_engulfing(self, df):
        “”“
        Identify Bearish Engulfing pattern
        “””
        prev_candle = df.iloc[-2]
        current_candle = df.iloc[-1]
        
        return (prev_candle[‘close’] > prev_candle[‘open’]) and (current_candle[‘close’] < current_candle[‘open’]) and (current_candle[‘close’] < prev_candle[‘open’]) and (current_candle[‘open’] > prev_candle[‘close’])

    def is_hammer(self, df):
        “”“
        Identify Hammer pattern
        “””
        current_candle = df.iloc[-1]
        body = abs(current_candle[‘close’] - current_candle[‘open’])
        lower_shadow = current_candle[‘low’] - min(current_candle[‘open’], current_candle[‘close’])
        upper_shadow = max(current_candle[‘open’], current_candle[‘close’]) - current_candle[‘high’]
        
        return (lower_shadow > 2 * body) and (upper_shadow < body)

    def is_shooting_star(self, df):
        “”“
        Identify Shooting Star pattern
        “””
        current_candle = df.iloc[-1]
        body = abs(current_candle[‘close’] - current_candle[‘open’])
        upper_shadow = current_candle[‘high’] - max(current_candle[‘close’], current_candle[‘open’])
        lower_shadow = min(current_candle[‘close’], current_candle[‘open’]) - current_candle[‘low’]
        
        return (upper_shadow > 2 * body) and (lower_shadow < body)

    def is_bullish_harami(self, df):
        “”“
        Identify Bullish Harami pattern
        “””
        prev_candle = df.iloc[-2]
        current_candle = df.iloc[-1]
        
        return (prev_candle[‘close’] < prev_candle[‘open’]) and (current_candle[‘open’] > prev_candle[‘close’]) and (current_candle[‘close’] < prev_candle[‘open’]) and (current_candle[‘open’] < current_candle[‘close’])

    def is_bearish_harami(self, df):
        “”“
        Identify Bearish Harami pattern
        “””
        prev_candle = df.iloc[-2]
        current_candle = df.iloc[-1]
        
        return (prev_candle[‘close’] > prev_candle[‘open’]) and (current_candle[‘open’] < prev_candle[‘close’]) and (current_candle[‘close’] > prev_candle[‘open’]) and (current_candle[‘open’] > current_candle[‘close’])

    def is_piercing_pattern(self, df):
        “”“
        Identify Piercing Pattern
        “””
        prev_candle = df.iloc[-2]
        current_candle = df.iloc[-1]
        
        midpoint = (prev_candle[‘open’] + prev_candle[‘close’]) / 2.0
        
        return (prev_candle[‘close’] < prev_candle[‘open’]) and (current_candle[‘open’] < prev_candle[‘low’]) and (current_candle[‘close’] > midpoint)

    def is_dark_cloud_cover(self, df):
        “”“
        Identify Dark Cloud Cover pattern
        “””
        prev_candle = df.iloc[-2]
        current_candle = df.iloc[-1]
        
        midpoint = (prev_candle[‘open’] + prev_candle[‘close’]) / 2.0
        
        return (prev_candle[‘close’] > prev_candle[‘open’]) and (current_candle[‘open’] > prev_candle[‘high’]) and (current_candle[‘close’] < midpoint) and (current_candle[‘close’] > prev_candle[‘open’])

    def is_three_white_soldiers(self, df):
        “”“
        Identify Three White Soldiers pattern
        “””
        if len(df) < 3:
            return False
        candle1, candle2, candle3 = df.iloc[-3], df.iloc[-2], df.iloc[-1]
        
        return (candle1[‘close’] > candle1[‘open’]) and (candle2[‘close’] > candle2[‘open’]) and (candle3[‘close’] > candle3[‘open’]) and (candle2[‘open’] > candle1[‘close’]) and (candle3[‘open’] > candle2[‘close’])

    def is_three_black_crows(self, df):
        “”“
        Identify Three Black Crows pattern
        “””
        if len(df) < 3:
            return False
        candle1, candle2, candle3 = df.iloc[-3], df.iloc[-2], df.iloc[-1]
        
        return (candle1[‘close’] < candle1[‘open’]) and (candle2[‘close’] < candle2[‘open’]) and (candle3[‘close’] < candle3[‘open’]) and (candle2[‘open’] < candle1[‘close’]) and (candle3[‘open’] < candle2[‘close’])

    def generate_trading_signals(self, df):
        “”“
        Generate trading signals considering price action first, then confirming with technical indicators.
        “””
        price_action_signals = self.identify_price_action_signals(df)
        
        # Assuming you have methods to compute and get technical indicator values
        # technical_indicators = self.compute_indicators(df)
        
        trading_signals = []
        for signal in price_action_signals:
            # Implement logic to confirm the price action signals with technical indicators
            # For example:
            # if self.is_confirmed_by_technical_indicators(signal, df, technical_indicators):
            #     trading_signals.append(signal)
            
            # Since technical indicators logic is not included, assume it’s always confirmed for now
            trading_signals.append(signal)
        
        # Optionally group signals
        final_signals = self.group_signals(trading_signals, df)
        
        return final_signals

    def group_signals(self, signals, df):
        “”“
        Group signals into bullish, bearish, and sideways categories.
        “””
        bullish_signals = [‘bullish_engulfing’, ‘hammer’, ‘bullish_harami’, ‘piercing_pattern’, ‘three_white_soldiers’, ‘morning_star’, ‘abandoned_baby’, ‘tweezer_bottom’]
        bearish_signals = [‘bearish_engulfing’, ‘shooting_star’, ‘bearish_harami’, ‘dark_cloud_cover’, ‘three_black_crows’, ‘evening_star’, ‘tweezer_top’]
        sideways_signals = [‘doji’, ‘long_legged_doji’, ‘gravestone_doji’, ‘dragonfly_doji’, ‘spinning_top’, ‘marubozu’, ‘inside_bar’, ‘outside_bar’]

        grouped_signals = {
            ‘bullish’: [],
            ‘bearish’: [],
            ‘sideways’: []
        }

        for signal in signals:
            if signal in bullish_signals:
                grouped_signals[‘bullish’].append(signal)
            elif signal in bearish_signals:
                grouped_signals[‘bearish’].append(signal)
            elif signal in sideways_signals:
                grouped_signals[‘sideways’].append(signal)
        
        # Perform multi-timeframe analysis (placeholder for actual implementation)
        # Example: Confirm signals appear in higher timeframe to reduce false signals
        # e.g., self.confirm_with_multiple_timeframes(grouped_signals, df)

        return grouped_signals

    def confirm_with_indicator(self, signal, df, indicator_func):
        “”“
        Confirm signals with specific technical indicators (e.g., RSI, Moving Averages).
        “””
        return indicator_func(df)  # Placeholder for actual logic

    def filter_by_volatility(self, signals, df, threshold):
        “”“
        Filter out signals during high volatility periods.
        “””
        # Placeholder for actual implementation
        avg_true_range = talib.ATR(
            df[‘high’].values, df[‘low’].values, df[‘close’].values, timeperiod=14)
        if avg_true_range.iloc[-1] > threshold:
            return []  # Filter out all signals

        return signals
Analyze the code and tell me what it does. Do not rewrite the code. Just tell me the functionalities and what we can improve