import ccxt
import time
import pandas as pd
from ta.momentum import RSIIndicator
from ta.trend import MACD, SMAIndicator
from ta.volatility import BollingerBands
from threading import Timer

# Initialize the CCXT Binance Futures client
exchange = ccxt.binance({
    "apiKey": "your_api_key",  # Replace with your actual API key
    "secret": "your_api_secret",  # Replace with your actual API secret
    "options": {"defaultType": "future"}
})

# Get leverage settings
def set_leverage(pair, leverage):
    try:
        exchange.fapiPrivate_post_leverage({'symbol': pair, 'leverage': leverage})
        print(f"Set leverage for {pair} to {leverage}")
    except Exception as e:
        print(f"Error setting leverage: {e}")

# Function to fetch historical data for a given pair
def collect_data(coin_pair, interval='1h', limit=500):
    try:
        klines = exchange.fetch_ohlcv(coin_pair, timeframe=interval, limit=limit)
        data = pd.DataFrame(klines, columns=[
            'timestamp', 'open', 'high', 'low', 'close', 'volume'
        ])
        data['timestamp'] = pd.to_datetime(data['timestamp'], unit='ms')
        data.set_index('timestamp', inplace=True)
        data = data.astype(float)
        return data
    except Exception as e:
        print(f"Error fetching data for {coin_pair}: {e}")
        return pd.DataFrame()

# Function to analyze data with indicators
def analyze_data(df):
    try:
        df['SMA50'] = SMAIndicator(df['close'], window=50).sma_indicator()
        df['RSI'] = RSIIndicator(df['close']).rsi()
        macd = MACD(df['close'])
        df['MACD'] = macd.macd()
        df['MACD_Signal'] = macd.macd_signal()
        bollinger = BollingerBands(df['close'])
        df['BB_High'] = bollinger.bollinger_hband()
        df['BB_Low'] = bollinger.bollinger_lband()
        return df
    except Exception as e:
        print(f"Error analyzing data: {e}")
        return df

# Function to generate trading signals
def trading_logic(df):
    try:
        signals = ['HOLD']
        for i in range(1, len(df)):
            if df['MACD'][i] > df['MACD_Signal'][i] and df['RSI'][i] < 70 and df['close'][i] > df['BB_Low'][i]:
                signals.append('BUY')
            elif df['MACD'][i] < df['MACD_Signal'][i] and df['RSI'][i] > 30 and df['close'][i] < df['BB_High'][i]:
                signals.append('SELL')
            else:
                signals.append('HOLD')
        return signals
    except Exception as e:
        print(f"Error generating trading signals: {e}")
        return []

# Function to simulate trading and track profit/loss
def simulate_trading(df, signals, balance, leverage):
    try:
        initial_balance = balance  # Starting with the user-defined balance
        position = None
        position_price = 0

        for i in range(len(signals)):
            if signals[i] == 'BUY' and position is None:  # Open Long
                position = 'LONG'
                position_price = df['close'][i]
                print(f"Opening LONG position at {position_price}")
            elif signals[i] == 'SELL' and position == 'LONG':  # Close Long
                balance += (df['close'][i] - position_price) * leverage
                position = None
                position_price = 0
                print(f"Closing LONG position at {df['close'][i]}, New Balance: {balance}")
            elif signals[i] == 'SELL' and position is None:  # Open Short
                position = 'SHORT'
                position_price = df['close'][i]
                print(f"Opening SHORT position at {position_price}")
            elif signals[i] == 'BUY' and position == 'SHORT':  # Close Short
                balance += (position_price - df['close'][i]) * leverage
                position = None
                position_price = 0
                print(f"Closing SHORT position at {df['close'][i]}, New Balance: {balance}")

        profit_loss = balance - initial_balance
        return profit_loss
    except Exception as e:
        print(f"Error simulating trading: {e}")
        return 0

# Function to find the best trading pair based on profitability
def find_best_pair():
    try:
        markets = exchange.load_markets()
        all_pairs = [pair for pair in markets.keys() if 'USDT' in pair and markets[pair]['type'] == 'future']
        best_pair = None
        best_profit_loss = -float('inf')

        for pair in all_pairs:
            print(f"Analyzing {pair}…")
            df = collect_data(pair, interval='1h', limit=500)
            if df.empty:
                continue
            df = analyze_data(df)
            signals = trading_logic(df)
            profit_loss = simulate_trading(df, signals, balance, leverage)
            print(f"{pair} Profit/Loss: {profit_loss}")

            if profit_loss > best_profit_loss:
                best_profit_loss = profit_loss
                best_pair = pair

        if best_pair:
            print(f"Best pair to trade: {best_pair} with Profit/Loss: {best_profit_loss}")
            return best_pair
        else:
            print("No suitable trading pair found.")
            return None
    except Exception as e:
        print(f"Error finding the best trading pair: {e}")
        return None

# Function to trade the best pair
def trade_best_pair(pair):
    try:
        df = collect_data(pair, interval='1h', limit=500)
        if df.empty:
            return
        df = analyze_data(df)
        signals = trading_logic(df)

        # Setting leverage
        set_leverage(pair, leverage)

        for i in range(1, len(signals)):
            if signals[i] == 'BUY':
                print(f"Executing BUY order for {pair}")
                exchange.create_order(symbol=pair, type='market', side='buy', amount=leverage * balance)  # Adjust amount as needed
            elif signals[i] == 'SELL':
                print(f"Executing SELL order for {pair}")
                exchange.create_order(symbol=pair, type='market', side='sell', amount=leverage * balance)  # Adjust amount as needed
        
        print(f"Trade executed for {pair}.")
        # Setting a timer for 20 minutes for user to decide on further actions

        timer = Timer(1200, find_best_pair)
        timer.start()

        ask_user()

        # Cancel the timer if the user responds in time
        timer.cancel()
    except Exception as e:
        print(f"Error trading the best pair: {e}")

# CLI for user interaction
def ask_user():
    response = input("Do you want to open a new trade? (yes/no): ").strip().lower()
    if response == 'yes':
        best_pair = find_best_pair()
        if best_pair:
            trade_best_pair(best_pair)
    elif response == 'no':
        print("No new trade initiated.")
    else:
        print("Invalid response. Please respond with 'yes' or 'no'.")
        ask_user()  # Recursive call for valid response

def main():
    global balance, leverage, timer

    # Ask user if they want to start the AI bot for automatic trading
    auto_trade = input("Do you want to start the AI bot for automatic trading? (yes/no): ").strip().lower()
    if auto_trade == 'yes':
        # Collect user input for initial balance and leverage
        while True:
            try:
                balance = float(input("Enter your initial balance for trading: "))
                leverage = int(input("Enter the leverage you want to use (e.g., 1 for no leverage): "))
                if balance <= 0 or leverage <= 0:
                    raise ValueError("Balance and leverage must be positive numbers.")
                break
            except ValueError as e:
                print(f"Invalid input: {e}. Please try again.")

        # Find the best trading pair and set leverage
        best_pair = find_best_pair()
        if best_pair:
            trade_best_pair(best_pair)

    else:
        print("AI bot trading is not started. Manual trading options will be provided.")
        ask_user()

    # Set a timer for 20 minutes (1200 seconds) for user response
    timer = Timer(1200, find_best_pair)
    timer.start()

    ask_user()

    # Cancel the timer if the user responds in time
    timer.cancel()

if name == "main":
    main()

Use binance public api and internal paper trading add a keybind so user can check for position of trades allow bot to set limit for buying and selling constantly monitoring and trailing up in real time. Give option to user for which how to trade use all ai potential you can use add your own ideas modify the indicators and add strategies the best ones you can think of.Use binance public api not api one add a keybind so user can check for position of trades allow bot to set limit for buying and selling constantly monitoring and trailing up in real time. Give option to user for which how to trade use all ai potential you can use add your own ideas modify the indicators and add strategies the best ones you can think ofNo the ai decides stoploss and take profit and it will automatically learn and improve itself how ever you can make it do it. It earns negative points for loss and positive points for win that helps it always find earning trades. Reduce user input time from 20 minutes to 2 minutes. Don't create simplified RL make Fully fledged Reinforced learning and check fo errors once