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 = []

        # Basic 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,
        }

        for pattern_name, pattern_func in patterns.items():
            if pattern_func(df[‘open’], df[‘high’], df[‘low’], df[‘close’]).iloc[-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)
        
        return trading_signals

# Example usage
if name == “main”:
    # Replace with your actual API keys
    binance_api_key = ‘your_binance_api_key’
    binance_api_secret = ‘your_binance_api_secret’

    # Create an instance of CryptoTradingBot
    bot = CryptoTradingBot(
        api_key=binance_api_key,
        api_secret=binance_api_secret,
        timeframe=‘1h’  # User can change this to any supported timeframe (e.g., ‘5m’, ‘1d’)
    )

    # Fetch and display USDT trading symbols with optional volume and volatility filtering
    usdt_symbols = bot.get_usdt_symbols()
    print(“Filtered USDT Trading Symbols:”, usdt_symbols)

    # Fetch and display historical OHLCV data for a sample symbol
    sample_data = bot.fetch_data(‘BTC/USDT’)
    if sample_data is not None:
        # Compute indicators (Assume compute_indicators method is defined)
        # sample_data_with_indicators = bot.compute_indicators(sample_data)
        
        # Generate trading signals
        trading_signals = bot.generate_trading_signals(sample_data)
        print(“Trading signals:”, trading_signals)

    # Manually invalidate the cache (optional)
    bot.invalidate_cache()
In above code, Consider combining multiple high-probability patterns that confirm each other for stronger signal confirmation, from two to a maximum of three. Also, explore ways to optimize the pattern recognition process, and refine them based on specific market conditions. Consider multiple timeframes to identify patterns that align across different time scales for signals for short-term and long-term day trading. Implement filters to reduce false positives. For example, you could filter out patterns that occur during periods of high volatility. Lastly, generate a list of signals grouped as bullish (those predicting price to go up), Bearish (those predicting price to go down) and Sideways(those predicting price to move within specified timeframe within the support and resistance level)