import pandas as pd
import numpy as np
import talib

class CryptoTradingBot:
    def init(self):
        pass  # Add your initialization code here

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

        # Detect simple candlestick patterns
        try:
            if self.is_bullish_engulfing(df):
                signals.append(‘bullish_engulfing’)
        except Exception as e:
            print(f"Error detecting bullish engulfing: {e}“)

        try:
            if self.is_bearish_engulfing(df):
                signals.append(‘bearish_engulfing’)
        except Exception as e:
            print(f"Error detecting bearish engulfing: {e}”)

        try:
            if self.is_hammer(df):
                signals.append(‘hammer’)
        except Exception as e:
            print(f"Error detecting hammer: {e}“)

        try:
            if self.is_shooting_star(df):
                signals.append(‘shooting_star’)
        except Exception as e:
            print(f"Error detecting shooting star: {e}”)

        try:
            if self.is_bullish_harami(df):
                signals.append(‘bullish_harami’)
        except Exception as e:
            print(f"Error detecting bullish harami: {e}“)

        try:
            if self.is_bearish_harami(df):
                signals.append(‘bearish_harami’)
        except Exception as e:
            print(f"Error detecting bearish harami: {e}”)

        try:
            if self.is_piercing_pattern(df):
                signals.append(‘piercing_pattern’)
        except Exception as e:
            print(f"Error detecting piercing pattern: {e}“)

        try:
            if self.is_dark_cloud_cover(df):
                signals.append(‘dark_cloud_cover’)
        except Exception as e:
            print(f"Error detecting dark cloud cover: {e}”)

        try:
            if self.is_three_white_soldiers(df):
                signals.append(‘three_white_soldiers’)
        except Exception as e:
            print(f"Error detecting three white soldiers: {e}“)

        try:
            if self.is_three_black_crows(df):
                signals.append(‘three_black_crows’)
        except Exception as e:
            print(f"Error detecting three black crows: {e}”)

        # 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():
            try:
                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)
            except Exception as e:
                print(f"Error detecting pattern {pattern_name}: {e}“)

        return signals

    def multi_candle_check(self, df, num_candles):
        “””
        Ensure dataframe has at least the required number of candles.
        “”“
        return len(df) >= num_candles

    def is_bullish_engulfing(self, df):
        “””
        Identify Bullish Engulfing pattern.
        “”“
        if not self.multi_candle_check(df, 2):
            return False
        
        prev_candle = df.iloc[-2]
        current_candle = df.iloc[-1]
        
        return (prev_candle[‘close’] < prev_candle[‘open’]) and <br/>               (current_candle[‘close’] > current_candle[‘open’]) and <br/>               (current_candle[‘close’] > prev_candle[‘open’]) and <br/>               (current_candle[‘open’] < prev_candle[‘close’])

    def is_bearish_engulfing(self, df):
        “””
        Identify Bearish Engulfing pattern.
        “”“
        if not self.multi_candle_check(df, 2):
            return False
        
        prev_candle = df.iloc[-2]
        current_candle = df.iloc[-1]
        
        return (prev_candle[‘close’] > prev_candle[‘open’]) and <br/>               (current_candle[‘close’] < current_candle[‘open’]) and <br/>               (current_candle[‘close’] < prev_candle[‘open’]) and <br/>               (current_candle[‘open’] > prev_candle[‘close’])

    def is_hammer(self, df):
        “””
        Identify Hammer pattern.
        “”“
        if not self.multi_candle_check(df, 1):
            return False
        
        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.
        “”“
        if not self.multi_candle_check(df, 1):
            return False
        
        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.
        “”“
        if not self.multi_candle_check(df, 2):
            return False
        
        prev_candle = df.iloc[-2]
        current_candle = df.iloc[-1]
        
        return (prev_candle[‘close’] < prev_candle[‘open’]) and <br/>               (current_candle[‘open’] > prev_candle[‘close’]) and <br/>               (current_candle[‘close’] < prev_candle[‘open’]) and <br/>               (current_candle[‘open’] < current_candle[‘close’])

    def is_bearish_harami(self, df):
        “””
        Identify Bearish Harami pattern.
        “”“
        if not self.multi_candle_check(df, 2):
            return False
        
        prev_candle = df.iloc[-2]
        current_candle = df.iloc[-1]
        
        return (prev_candle[‘close’] > prev_candle[‘open’]) and <br/>               (current_candle[‘open’] < prev_candle[‘close’]) and <br/>               (current_candle[‘close’] > prev_candle[‘open’]) and <br/>               (current_candle[‘open’] > current_candle[‘close’])

    def is_piercing_pattern(self, df):
        “””
        Identify Piercing Pattern.
        “”“
        if not self.multi_candle_check(df, 2):
            return False
        
        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 <br/>               (current_candle[‘open’] < prev_candle[‘low’]) and <br/>               (current_candle[‘close’] > midpoint)

    def is_dark_cloud_cover(self, df):
        “””
        Identify Dark Cloud Cover pattern.
        “”“
        if not self.multi_candle_check(df, 2):
            return False
        
        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 <br/>               (current_candle[‘open’] > prev_candle[‘high’]) and <br/>               (current_candle[‘close’] < midpoint) and <br/>               (current_candle[‘close’] > prev_candle[‘open’])

    def is_three_white_soldiers(self, df):
        “””
        Identify Three White Soldiers pattern.
        “”“
        if not self.multi_candle_check(df, 3):
            return False
        
        candle1, candle2, candle3 = df.iloc[-3], df.iloc[-2], df.iloc[-1]
        
        return (candle1[‘close’] > candle1[‘open’]) and <br/>               (candle2[‘close’] > candle2[‘open’]) and <br/>               (candle3[‘close’] > candle3[‘open’]) and <br/>               (candle2[‘open’] > candle1[‘close’]) and <br/>               (candle3[‘open’] > candle2[‘close’])

    def is_three_black_crows(self, df):
        “””
        Identify Three Black Crows pattern.
        “”“
        if not self.multi_candle_check(df, 3):
            return False
        
        candle1, candle2, candle3 = df.iloc[-3], df.iloc[-2], df.iloc[-1]
        
        return (candle1[‘close’] < candle1[‘open’]) and <br/>               (candle2[‘close’] < candle2[‘open’]) and <br/>               (candle3[‘close’] < candle3[‘open’]) and <br/>               (candle2[‘open’] < candle1[‘close’]) and <br/>               (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)
        
        # Placeholder for further logic for confirming price action signals with technical indicators
        trading_signals = price_action_signals  # For now, assuming confirmation is always true
        
        # Group the 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)
        
        # Multi-timeframe analysis
        grouped_signals = self.confirm_with_multiple_timeframes(grouped_signals, df)

        return grouped_signals

    def confirm_with_multiple_timeframes(self, signals, df):
        “””
        Confirm signals by checking the same patterns in higher timeframes.
        “”“
        # Example to check if signals appear in the 5, 15 minute timeframes
        higher_timeframes = [‘5T’, ‘15T’]
        ohlc_dict = df[[‘open’, ‘high’, ‘low’, ‘close’]]

        for timeframe in higher_timeframes:
            resampled_df = self.resample_ohlc(ohlc_dict, timeframe)
            
            higher_tf_signals = self.identify_price_action_signals(resampled_df)
            
            for key in signals:
                signals[key] = [signal for signal in signals[key] if signal in higher_tf_signals]
        
        return signals

    def resample_ohlc(self, df, timeframe):
        “””
        Resample OHLC data to different timeframes.
        “”“
        return df.resample(timeframe).agg({
            ‘open’: ‘first’,
            ‘high’: ‘max’,
            ‘low’: ‘min’,
            ‘close’: ‘last’
        }).dropna()

    def filter_by_volatility(self, signals, df, threshold):
        “””
        Filter out signals during high volatility periods.
        “”"
        avg_true_range = talib.ATR(
            df[‘high’].values, df[‘low’].values, df[‘close’].values, timeperiod=14)
        
        # Filter out all signals if the ATR exceeds the threshold
        if avg_true_range.iloc[-1] > threshold:
            return []
        
        return signals
The above code is for price action signal generation. I want you to integrate it with the code below for technical analysis. I want them to work together in generation and confirmation of the signals before final signal is generated. After generating the signals, I want the bot to show a list of them under the 3 categories of bullish, Bearish and sideways. If the user enabled the autotrader, let the bot execute the trades from the first one based on highest probability of success, all the way down until the funds in the account are insufficient, or until the set number of simultaneous bots that can run is reached. Please use a minimum of 3 and a maximum of 6 technical indicators per signal. Also, please just make necessary integrations but write the code separately for the separate functions. I will save the files separately in the project folder. 
import ccxt
import pandas as pd
import logging
import sqlite3
import requests_cache
import json
from concurrent.futures import ThreadPoolExecutor, as_completed
from time import sleep
import numpy as np
import traceback
import talib

class CryptoTradingBot:
    def init(self, api_key, api_secret, timeframe=‘1h’, balance=10000, max_risk=0.02, paper_trading=True, config_file=‘config.json’):
        self.exchange = ccxt.binance({
            ‘apiKey’: api_key,
            ‘secret’: api_secret,
        })
        self.timeframe = timeframe
        self.balance = balance
        self.max_risk = max_risk
        self.paper_trading = paper_trading
        
        # Load configuration
        with open(config_file) as f:
            self.config = json.load(f)
        
        # Setup requests cache to minimize redundant API calls
        self.session = requests_cache.CachedSession(‘ccxt_cache’, backend=‘memory’, expire_after=self.config.get(“cache_expires”, 300))

        # Setup database connection
        self.conn = sqlite3.connect(‘trading_data.db’)
        self.cursor = self.conn.cursor()
        self.setup_database()

        # Fetch symbols
        self.symbols = self.get_usdt_symbols(
            min_volume=self.config.get(“min_volume”),
            min_volatility=self.config.get(“min_volatility”)
        )

    def setup_database(self):
        self.cursor.execute(“”“
        CREATE TABLE IF NOT EXISTS ohlcv (
            symbol TEXT,
            timestamp TEXT,
            open REAL,
            high REAL,
            low REAL,
            close REAL,
            volume REAL,
            PRIMARY KEY (symbol, timestamp)
        )”“”)
        self.cursor.execute(“”“
        CREATE TABLE IF NOT EXISTS indicators (
            symbol TEXT,
            timestamp TEXT,
            indicator_name TEXT,
            indicator_value REAL,
            PRIMARY KEY (symbol, timestamp, indicator_name)
        )”“”)
        self.conn.commit()

    def save_data(self, df, symbol, table_name):
        df[‘symbol’] = symbol
        df.to_sql(table_name, self.conn, if_exists=‘append’, index=False)

    def get_usdt_symbols(self, min_volume=None, min_volatility=None):
        try:
            markets = self.exchange.load_markets()
            usdt_symbols = [symbol for symbol in markets if symbol.endswith(‘/USDT’)]
            filtered_symbols = []

            for symbol in usdt_symbols:
                ticker = self.fetch_real_time_data(symbol)
                if min_volume and ticker[‘quoteVolume’] < min_volume:
                    continue

                if min_volatility:
                    df = self.fetch_data(symbol, limit=100)
                    if df is not None:
                        volatility = df[‘close’].pct_change().std()
                        if volatility < min_volatility:
                            continue
                filtered_symbols.append(symbol)

            logging.info(“Successfully fetched and filtered USDT symbols.”)
            return filtered_symbols
        except (ccxt.ExchangeError, ccxt.NetworkError, Exception) as e:
            logging.error(f"Failed to fetch USDT symbols: {e}“)
            logging.error(traceback.format_exc())
            return []

    def fetch_data(self, symbol, limit=500, retries=3, backoff_factor=1.0):
        for attempt in range(retries):
            try:
                url = self.exchange.urls[‘api’][‘public’] + f”/klines?symbol={symbol.replace(‘/’, ‘’)}&interval={self.timeframe}&limit={limit}“
                response = self.session.get(url)
                response.raise_for_status()
                bars = response.json()
                df = pd.DataFrame(bars, columns=[‘timestamp’, ‘open’, ‘high’, ‘low’, ‘close’, ‘volume’, ‘’, ‘’, ‘’, ‘’, ‘’, ‘’])[[‘timestamp’, ‘open’, ‘high’, ‘low’, ‘close’, ‘volume’]]
                df[‘timestamp’] = pd.to_datetime(df[‘timestamp’], unit=‘ms’)
                if self.validate_data(df):
                    df = self.clean_data(df)
                    self.save_data(df, symbol, ‘ohlcv’)
                    logging.info(f"Successfully fetched data for {symbol} on attempt {attempt + 1}.”)
                    return df
                else:
                    logging.warning(f"Validation failed for data fetched for {symbol}. Attempt {attempt + 1}/{retries}.“)
            except requests.RequestException as e:
                logging.error(f"Network/HTTP error while fetching data for {symbol}: {e}. Retrying in {backoff_factor * (2 ** attempt)} seconds.”)
                logging.error(traceback.format_exc())
            except ccxt.ExchangeError as e:
                logging.error(f"Exchange error while fetching data for {symbol}: {e}. Retrying in {backoff_factor * (2 ** attempt)} seconds.“)
                logging.error(traceback.format_exc())
            except Exception as e:
                logging.error(f"Unexpected error while fetching data for {symbol}: {e}. Retrying in {backoff_factor * (2 ** attempt)} seconds.”)
                logging.error(traceback.format_exc())
            sleep(backoff_factor * (2 ** attempt))  # Exponential backoff
        logging.error(f"Failed to fetch data for {symbol} after {retries} attempts.“)
        return None

    def fetch_real_time_data(self, symbol):
        try:
            ticker = self.exchange.fetch_ticker(symbol)
            logging.info(f"Successfully fetched real-time data for {symbol}.”)
            return ticker
        except (ccxt.NetworkError, ccxt.ExchangeError, Exception) as e:
            logging.error(f"Failed to fetch real-time data for {symbol}: {e}“)
            logging.error(traceback.format_exc())
            return None

    @staticmethod
    def validate_data(df):
        if df.isnull().values.any():
            return False
        if df.empty:
            return False
        return True

    def clean_data(self, df):
        df.replace([np.inf, -np.inf], np.nan, inplace=True)
        df.dropna(inplace=True)
        return df

    def invalidate_cache(self):
        self.session.cache.clear()
        logging.info(“Cache invalidated manually.”)

    def fetch_data_concurrently(self, symbols, limit=500, max_workers=5):
        results = {}
        with ThreadPoolExecutor(max_workers=max_workers) as executor:
            future_to_symbol = {executor.submit(self.fetch_data, symbol, limit): symbol for symbol in symbols}
            for future in as_completed(future_to_symbol):
                symbol = future_to_symbol[future]
                try:
                    data = future.result()
                    if data is not None:
                        results[symbol] = data
                except Exception as e:
                    logging.error(f"Error fetching data for {symbol}: {e}”)
                    logging.error(traceback.format_exc())
        return results

    def compute_indicators(self, df):
        try:
            indicator_count = 0
            all_indicators = {
                ‘macd’: lambda: self.add_macd(df),
                ‘rsi’: lambda: self.add_rsi(df),
                ‘ema’: lambda: self.add_ema(df),
                ‘atr’: lambda: self.add_atr(df),
                ‘sma50’: lambda: self.add_sma(df, ‘sma50’, 50),
                ‘sma200’: lambda: self.add_sma(df, ‘sma200’, 200),
                ‘bollinger’: lambda: self.add_bollinger_bands(df),
                ‘stochastic’: lambda: self.add_stochastic(df),
                ‘ichimoku’: lambda: self.add_ichimoku_cloud(df),
                ‘aroon’: lambda: self.add_aroon(df),
                ‘cci’: lambda: self.add_cci(df),
                ‘obv’: lambda: self.add_obv(df),
                ‘volatility’: lambda: self.add_volatility(df),
                ‘price_range’: lambda: self.add_price_range(df),
                ‘adx’: lambda: self.add_adx(df),
                ‘williams_r’: lambda: self.add_williams_r(df),
            }
            selected_indicators = self.config.get(“indicators”, [])
            for indicator in selected_indicators[:5]:
                if indicator in all_indicators:
                    all_indicatorsindicator
                    indicator_count += 1
            if self.config.get(“lag_features”, False):
                df = self.add_lag_features(df)

            df.dropna(inplace=True)
            logging.info(f"Successfully computed {indicator_count} indicators.“)
            return df
        except Exception as e:
            logging.error(f"Failed to compute indicators: {e}”)
            logging.error(traceback.format_exc())
            return df

    @staticmethod
    def add_macd(df):
        df[‘macd’], df[‘macd_signal’], _ = talib.MACD(df[‘close’], fastperiod=12, slowperiod=26, signalperiod=9)

    @staticmethod
    def add_rsi(df):
        df[‘rsi’] = talib.RSI(df[‘close’], timeperiod=14)

    @staticmethod
    def add_ema(df):
        df[‘ema’] = talib.EMA(df[‘close’], timeperiod=20)

    @staticmethod
    def add_atr(df):
        df[‘atr’] = talib.ATR(df[‘high’], df[‘low’], df[‘close’], timeperiod=14)

    @staticmethod
    def add_sma(df, column_name, timeperiod):
        df[column_name] = talib.SMA(df[‘close’], timeperiod=timeperiod)

    @staticmethod
    def add_bollinger_bands(df):
        df[‘bollinger_upper’], df[‘bollinger_middle’], df[‘bollinger_lower’] = talib.BBANDS(df[‘close’], timeperiod=20)

    @staticmethod
    def add_stochastic(df):
        df[‘stoch_k’], df[‘stoch_d’] = talib.STOCH(df[‘high’], df[‘low’], df[‘close’])

    def add_ichimoku_cloud(self, df):
        df[‘ichimoku_conversion’], df[‘ichimoku_base’], df[‘ichimoku_span_a’], df[‘ichimoku_span_b’] = self.ichimoku_cloud(df)

    @staticmethod
    def add_aroon(df):
        df[‘aroon_down’], df[‘aroon_up’] = talib.AROON(df[‘high’], df[‘low’], timeperiod=25)
        df[‘aroon_oscillator’] = df[‘aroon_up’] - df[‘aroon_down’]

    @staticmethod
    def add_cci(df):
        df[‘cci’] = talib.CCI(df[‘high’], df[‘low’], df[‘close’], timeperiod=20)

    @staticmethod
    def add_obv(df):
        df[‘obv’] = talib.OBV(df[‘close’], df[‘volume’])

    @staticmethod
    def add_volatility(df):
        df[‘volatility’] = df[‘close’].pct_change().rolling(window=20).std()

    @staticmethod
    def add_price_range(df):
        df[‘price_range’] = df[‘high’] - df[‘low’]

    @staticmethod
    def add_adx(df):
        df[‘adx’] = talib.ADX(df[‘high’], df[‘low’], df[‘close’], timeperiod=14)

    @staticmethod
    def add_williams_r(df):
        df[‘williams_r’] = talib.WILLR(df[‘high’], df[‘low’], df[‘close’], timeperiod=14)

    def add_lag_features(self, df):
        lag_features = [‘close’, ‘volume’, ‘rsi’, ‘ema’]  # Example set of features to create lagged versions for
        for feature in lag_features:
            for lag in range(1, 6):  # Create lagged features from 1 to 5
                df[f’{feature}lag{lag}'] = df[feature].shift(lag)
        return df

    def ichimoku_cloud(self, df):
        nine_period_high = df[‘high’].rolling(window=9).max()
        nine_period_low = df[‘low’].rolling(window=9).min()
        conversion_line = (nine_period_high + nine_period_low) / 2

        twenty_six_period_high = df[‘high’].rolling(window=26).max()
        twenty_six_period_low = df[‘low’].rolling(window=26).min()
        base_line = (twenty_six_period_high + twenty_six_period_low) / 2

        leading_span_a = ((conversion_line + base_line) / 2).shift(26)

        fifty_two_period_high = df[‘high’].rolling(window=52).max()
        fifty_two_period_low = df[‘low’].rolling(window=52).min()
        leading_span_b = ((fifty_two_period_high + fifty_two_period_low) / 2).shift(26)

        return conversion_line, base_line, leading_span_a, leading_span_b

    def identify_market_condition(self, df):
        # Market condition criteria
        short_sma = df[‘sma50’].iloc[-1]
        long_sma = df[‘sma200’].iloc[-1]
        atr = df[‘atr’].iloc[-1]
        adx = df[‘adx’].iloc[-1]
        closing_price = df[‘close’].iloc[-1]

        # Volatility threshold for identifying trends
        volatility_threshold = self.config.get(“min_volatility”, 0.02)

        # Bullish markets have short SMA above long SMA, price above long SMA, and low to moderate volatility
        if short_sma > long_sma and closing_price > long_sma and atr < volatility_threshold and adx >= 20:
            return ‘bullish’
        # Bearish markets have short SMA below long SMA, price below long SMA, and low to moderate volatility
        elif short_sma < long_sma and closing_price < long_sma and atr < volatility_threshold and adx >= 20:
            return ‘bearish’
        # Sideways markets have high volatility or low ADX (indicating lack of trend)
        elif atr >= volatility_threshold or adx < 20:
            return ‘sideways’
        # Default to sideways if conditions are unclear
        else:
            return ‘sideways’

    def configure_indicators_for_market_condition(self, df):
        market_condition = self.identify_market_condition(df)
        indicators_by_market_condition = self.config.get(“indicator_selection_criteria”, {}).get(“market_condition”, {})
        selected_indicators = indicators_by_market_condition.get(market_condition, [])

        # Recompute indicators based on the market condition
        for indicator in selected_indicators:
            if indicator == ‘macd’:
                self.add_macd(df)
            elif indicator == ‘rsi’:
                self.add_rsi(df)
            elif indicator == ‘ema’:
                self.add_ema(df)
            elif indicator == ‘atr’:
                self.add_atr(df)
            elif indicator == ‘sma50’:
                self.add_sma(df, ‘sma50’, 50)
            elif indicator == ‘sma200’:
                self.add_sma(df, ‘sma200’, 200)
            elif indicator == ‘bollinger’:
                self.add_bollinger_bands(df)
            elif indicator == ‘stochastic’:
                self.add_stochastic(df)
            elif indicator == ‘ichimoku’:
                self.add_ichimoku_cloud(df)
            elif indicator == ‘aroon’:
                self.add_aroon(df)
            elif indicator == ‘cci’:
                self.add_cci(df)
            elif indicator == ‘obv’:
                self.add_obv(df)
            elif indicator == ‘volatility’:
                self.add_volatility(df)
            elif indicator == ‘price_range’:
                self.add_price_range(df)
            elif indicator == ‘adx’:
                self.add_adx(df)
            elif indicator == ‘williams_r’:
                self.add_williams_r(df)

        df.dropna(inplace=True)
        logging.info(f"Indicators configured for {market_condition} market condition.")
        return df

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:
        sample_data_with_indicators = bot.configure_indicators_for_market_condition(sample_data)
        print(sample_data_with_indicators.head())

    # Manually invalidate the cache (optional)
    bot.invalidate_cache()