import pandas as pd
import numpy as np
from binance.client import Client
from binance.exceptions import BinanceAPIException
import time
import logging
import traceback
from concurrent.futures import ThreadPoolExecutor

# Set up logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

class PriceActionTradingBot:
    def __init__(self, api_key, api_secret, symbol, interval, lookback, quantity):
        self.client = Client(api_key, api_secret)
        self.symbol = symbol
        self.interval = interval
        self.lookback = lookback
        self.quantity = quantity
        self.df = None
        self.position = None

    def get_historical_data(self):
        try:
            bars = self.client.get_historical_klines(self.symbol, self.interval, self.lookback + " ago UTC")
            self.df = pd.DataFrame(bars, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume', 'close_time', 'quote_asset_volume', 'number_of_trades', 'taker_buy_base_asset_volume', 'taker_buy_quote_asset_volume', 'ignore'])
            self.df['timestamp'] = pd.to_datetime(self.df['timestamp'], unit='ms')
            for col in ['open', 'high', 'low', 'close', 'volume']:
                self.df[col] = self.df[col].astype(float)
            self.df.set_index('timestamp', inplace=True)
        except BinanceAPIException as e:
            logger.error(f"Error fetching historical data: {e}")

    def price_action_analysis(self, df):
        try:
            recent_df = df.iloc[-100:]  # Increased lookback for more robust analysis
            recent_df.dropna(inplace=True)

            if len(recent_df) < 20:
                return None

            highs = recent_df['high'].values
            lows = recent_df['low'].values
            closes = recent_df['close'].values

            # Trend Analysis
            short_term_trend = np.polyfit(range(20), closes[-20:], 1)[0]
            medium_term_trend = np.polyfit(range(50), closes[-50:], 1)[0]
            long_term_trend = np.polyfit(range(100), closes[-100:], 1)[0]

            # Support and Resistance
            support = np.min(lows[-20:])
            resistance = np.max(highs[-20:])

            # Volatility
            volatility = np.std(closes[-20:])

            # Price Action Patterns
            bullish_engulfing = (closes[-1] > opens[-1]) and (closes[-2] < opens[-2]) and (closes[-1] > opens[-2]) and (closes[-1] - opens[-1] > opens[-2] - closes[-2])
            bearish_engulfing = (closes[-1] < opens[-1]) and (closes[-2] > opens[-2]) and (closes[-1] < opens[-2]) and (opens[-1] - closes[-1] > closes[-2] - opens[-2])
            
            pin_bar_bullish = (highs[-1] - max(opens[-1], closes[-1])) > 2 * abs(opens[-1] - closes[-1]) and (min(opens[-1], closes[-1]) - lows[-1]) < abs(opens[-1] - closes[-1])
            pin_bar_bearish = (max(opens[-1], closes[-1]) - lows[-1]) > 2 * abs(opens[-1] - closes[-1]) and (highs[-1] - min(opens[-1], closes[-1])) < abs(opens[-1] - closes[-1])

            # Inside bar
            inside_bar = highs[-1] < highs[-2] and lows[-1] > lows[-2]

            # Decision making
            if short_term_trend > 0 and medium_term_trend > 0 and long_term_trend > 0:
                if closes[-1] > resistance or bullish_engulfing or pin_bar_bullish:
                    signal = 'STRONG_BUY'
                elif inside_bar:
                    signal = 'POTENTIAL_BREAKOUT'
                else:
                    signal = 'WEAK_BUY'
            elif short_term_trend < 0 and medium_term_trend < 0 and long_term_trend < 0:
                if closes[-1] < support or bearish_engulfing or pin_bar_bearish:
                    signal = 'STRONG_SELL'
                elif inside_bar:
                    signal = 'POTENTIAL_BREAKDOWN'
                else:
                    signal = 'WEAK_SELL'
            else:
                signal = 'NO_SIGNAL'

            last_row = recent_df.iloc[-1].copy()
            last_row['Signal'] = signal
            last_row['Volatility'] = volatility
            last_row['Support'] = support
            last_row['Resistance'] = resistance

            return last_row.to_frame().T

        except Exception as e:
            logger.error(f"Error in price action analysis: {e}\n{traceback.format_exc()}")
            return None

    def advanced_price_action_analysis(self, df):
        try:
            recent_df = df.iloc[-200:]  # Extended lookback for more comprehensive analysis
            recent_df.dropna(inplace=True)

            if len(recent_df) < 50:
                return None

            highs = recent_df['high'].values
            lows = recent_df['low'].values
            closes = recent_df['close'].values
            opens = recent_df['open'].values

            # Swing High and Swing Low detection
            swing_high = np.where((highs[1:-1] > highs[:-2]) & (highs[1:-1] > highs[2:]))[0] + 1
            swing_low = np.where((lows[1:-1] < lows[:-2]) & (lows[1:-1] < lows[2:]))[0] + 1

            # Higher Highs and Lower Lows
            higher_highs = len(swing_high) >= 2 and highs[swing_high[-1]] > highs[swing_high[-2]]
            lower_lows = len(swing_low) >= 2 and lows[swing_low[-1]] < lows[swing_low[-2]]

            # Fibonacci Retracement
            if len(swing_high) > 0 and len(swing_low) > 0:
                fib_high = highs[swing_high[-1]]
                fib_low = lows[swing_low[-1]]
                fib_diff = fib_high - fib_low
                fib_levels = [fib_low + l * fib_diff for l in [0.236, 0.382, 0.5, 0.618, 0.786]]
                current_price = closes[-1]
                fib_support = max([l for l in fib_levels if l < current_price], default=None)
                fib_resistance = min([l for l in fib_levels if l > current_price], default=None)
            else:
                fib_support = fib_resistance = None

            # Volume Analysis
            volume = recent_df['volume'].values
            avg_volume = np.mean(volume[-20:])
            volume_trend = 'UP' if volume[-1] > avg_volume else 'DOWN'

            # Momentum
            momentum = closes[-1] - closes[-20]

            # Decision making
            if higher_highs and momentum > 0:
                if closes[-1] > fib_resistance and volume_trend == 'UP':
                    signal = 'STRONG_BUY'
                elif closes[-1] > fib_resistance:
                    signal = 'BUY'
                else:
                    signal = 'WEAK_BUY'
            elif lower_lows and momentum < 0:
                if closes[-1] < fib_support and volume_trend == 'UP':
                    signal = 'STRONG_SELL'
                elif closes[-1] < fib_support:
                    signal = 'SELL'
                else:
                    signal = 'WEAK_SELL'
            else:
                signal = 'NO_SIGNAL'

            last_row = recent_df.iloc[-1].copy()
            last_row['Signal'] = signal
            last_row['Volume_Trend'] = volume_trend
            last_row['Momentum'] = momentum
            last_row['Fib_Support'] = fib_support
            last_row['Fib_Resistance'] = fib_resistance

            return last_row.to_frame().T

        except Exception as e:
            logger.error(f"Error in advanced price action analysis: {e}\n{traceback.format_exc()}")
            return None

    def execute_trade(self, side, quantity):
        try:
            if side == 'BUY':
                order = self.client.create_order(symbol=self.symbol, side=Client.SIDE_BUY, type=Client.ORDER_TYPE_MARKET, quantity=quantity)
            else:
                order = self.client.create_order(symbol=self.symbol, side=Client.SIDE_SELL, type=Client.ORDER_TYPE_MARKET, quantity=quantity)
            logger.info(f"Order executed: {order}")
        except Exception as e:
            logger.error(f"An error occurred while executing trade: {e}")

    def run_bot(self):
        while True:
            try:
                self.get_historical_data()
                
                with ThreadPoolExecutor(max_workers=2) as executor:
                    future_pa = executor.submit(self.price_action_analysis, self.df)
                    future_apa = executor.submit(self.advanced_price_action_analysis, self.df)
                    
                    pa_result = future_pa.result()
                    apa_result = future_apa.result()

                if pa_result is not None and apa_result is not None:
                    pa_signal = pa_result.iloc[-1]['Signal']
                    apa_signal = apa_result.iloc[-1]['Signal']
                    
                    if pa_signal in ['STRONG_BUY', 'BUY'] and apa_signal in ['STRONG_BUY', 'BUY']:
                        self.execute_trade('BUY', self.quantity)
                    elif pa_signal in ['STRONG_SELL', 'SELL'] and apa_signal in ['STRONG_SELL', 'SELL']:
                        self.execute_trade('SELL', self.quantity)
                
                time.sleep(60)  # Wait for 1 minute before next iteration
            
            except Exception as e:
                logger.error(f"Error in main loop: {e}\n{traceback.format_exc()}")
                time.sleep(60)  # Wait for 1 minute before retrying

if __name__ == "__main__":
    bot = PriceActionTradingBot('your_api_key', 'your_api_secret', 'BTCUSDT', Client.KLINE_INTERVAL_1HOUR, "1 week", 0.001)
    bot.run_bot()
I know it's hard to tell how accurate the bot would be but based on the features of my bot, can you guess a rough estimate of how accurate the signals of this bot would be in percentage