copyTrader.py

import pandas as pd
import json
import time
from textblob import TextBlob
import ccxt

# Configuration
API_KEY = 'your_api_key'  # Exchange API key
API_SECRET = 'your_api_secret'  # Exchange API secret
EXCHANGE_ID = 'binance'  # Exchange platform (e.g., 'binance', 'kraken')

# Blockchain Explorer API Key (for wallet monitoring)
BLOCKCHAIN_API_KEY = 'your_blockchain_api_key'

# Copy trading wallets to monitor
COPY_TRADE_WALLETS = ['wallet_address_1', 'wallet_address_2']

# Trade Parameters
TRADE_AMOUNT = 0.001  # Base trade amount (adjust dynamically as needed)
TAKE_PROFIT_PERCENT = 1.5  # Take profit percentage
STOP_LOSS_PERCENT = 1.0  # Stop loss percentage

# Sniping Conditions
SNIPING_CONDITIONS = {
    "price_change": 5.0,  # Minimum percentage price change for sniping
    "volume_spike": 2.0,  # Volume spike multiplier for sniping
}

# Logging Configuration
LOG_LEVEL = 'DEBUG'  # Set to 'DEBUG', 'INFO', or 'ERROR'
LOG_FILE_PATH = 'trading_bot.log'  # Path to log file

# Timeframe Configuration
TIMEFRAME = '1m'  # Default timeframe for fetching candles
DAILY_TIMEFRAME = '1d'  # Timeframe for daily trend analysis
VOLATILITY_PERIOD = 20  # Period for volatility calculation
MOMENTUM_PERIOD = 10  # Period for momentum calculation

# Initialize the exchange outside of conditional block
exchange = None
if CCXT_AVAILABLE:
    exchange = getattr(ccxt, EXCHANGE_ID)({
        'apiKey': API_KEY,
        'secret': API_SECRET,
        'enableRateLimit': True,
    })

# Dynamic data population from CSV or JSON
def fetch_trading_symbols_from_file(file_path, file_type='csv'):
    """Fetch and filter trading symbols from a file (CSV or JSON)."""
    try:
        if file_type == 'csv':
            df = pd.read_csv(file_path)
            symbols = df['symbol'].tolist()
        elif file_type == 'json':
            with open(file_path, 'r') as f:
                data = json.load(f)
                symbols = data.get('symbols', [])
        else:
            raise ValueError("Unsupported file type. Use 'csv' or 'json'.")

        print(f"Fetched {len(symbols)} symbols from {file_path}.")
        return symbols
    except Exception as e:
        print(f"Error fetching symbols from file: {e}")
        return []

# Load symbols dynamically from a file (replace 'symbols.csv' with your file path)
tokens = fetch_trading_symbols_from_file('symbols.csv', file_type='csv')

# Ensure fallback logic in case file-based fetching fails
if not tokens:
    print("Failed to load symbols from file. Falling back to default dynamic fetching.")
    def fetch_trading_symbols(quote_currency='USDT', max_symbols=10):
        """Fetch and filter trading symbols dynamically from the exchange."""
        if not exchange:
            print("Exchange is not initialized. Cannot fetch symbols.")
            return []

        try:
            exchange.load_markets()
            symbols = [symbol for symbol in exchange.symbols if symbol.endswith(f"/{quote_currency}")]
            print(f"Fetched {len(symbols)} symbols matching {quote_currency}.")
            return symbols[:max_symbols]  # Limit the number of symbols
        except Exception as e:
            print(f"Error fetching trading symbols: {e}")
            return []

    tokens = fetch_trading_symbols()

# Initialize data structure
data = {
    'symbol': tokens,
    'open_side': [None] * len(tokens),
    'index_pos': list(range(len(tokens))),
    'open_size': [0] * len(tokens),
    'open_bool': [False] * len(tokens),
    'long': [None] * len(tokens)
}
positions_df = pd.DataFrame(data)

# Helper functions

def fetch_latest_candle(symbol, timeframe=TIMEFRAME):
    """Fetch the latest candle for the given symbol and timeframe."""
    if not exchange:
        print("Exchange is not initialized. Cannot fetch candle data.")
        return None
    candles = exchange.fetch_ohlcv(symbol, timeframe=timeframe, limit=2)
    return candles[-1]  # Return the most recent candle

def calculate_daily_trend(symbol):
    """Analyze the daily trend for better-informed trading decisions."""
    if not exchange:
        print("Exchange is not initialized. Cannot calculate daily trend.")
        return None
    candles = exchange.fetch_ohlcv(symbol, timeframe=DAILY_TIMEFRAME, limit=5)  # Fetch last 5 daily candles
    if len(candles) < 5:
        return None

    # Simple trend analysis: compare the closing prices over 5 days
    closes = [c[4] for c in candles]
    trend = "up" if closes[-1] > closes[0] else "down"
    print(f"Daily trend for {symbol}: {trend}")
    return trend

def calculate_volatility(prices, period=VOLATILITY_PERIOD):
    """Calculate volatility using standard deviation."""
    if len(prices) < period:
        return None
    return pd.Series(prices).pct_change().rolling(period).std().iloc[-1]

def calculate_rsi(prices, period=14):
    """Calculate the Relative Strength Index (RSI)."""
    if len(prices) < period:
        return None

    gains = [prices[i] - prices[i - 1] for i in range(1, len(prices)) if prices[i] > prices[i - 1]]
    losses = [prices[i - 1] - prices[i] for i in range(1, len(prices)) if prices[i] < prices[i - 1]]

    avg_gain = sum(gains[-period:]) / period
    avg_loss = sum(losses[-period:]) / period

    if avg_loss == 0:
        return 100

    rs = avg_gain / avg_loss
    rsi = 100 - (100 / (1 + rs))
    return rsi

def perform_sentiment_analysis(text):
    """Perform sentiment analysis on a given text."""
    analysis = TextBlob(text)
    sentiment = analysis.sentiment.polarity
    return sentiment

def calculate_momentum(prices, period=MOMENTUM_PERIOD):
    """Calculate momentum for trend-following logic."""
    if len(prices) < period:
        return None
    return prices[-1] - prices[-period]  # Momentum as price difference

def place_order(side, amount, symbol):
    """Place a market order."""
    if not exchange:
        print("Exchange is not initialized. Cannot place order.")
        return None
    try:
        order = exchange.create_order(
            symbol=symbol,
            type='market',
            side=side,
            amount=amount
        )
        return order
    except Exception as e:
        print(f"Error placing {side} order: {e}")
        return None

def monitor_copy_trade_wallets():
    """Monitor selected wallets and copy their trades."""
    print("Monitoring wallets for copy trading...")
    for wallet in COPY_TRADE_WALLETS:
        try:
            trades = get_wallet_trades(wallet)  # Fetch trades from blockchain
            for trade in trades:
                print(f"Wallet {wallet} executed trade: {trade}")
                place_order(trade['side'], trade['amount'], trade['symbol'])
        except Exception as e:
            print(f"Error monitoring wallet {wallet}: {e}")

def get_wallet_trades(wallet):
    """Fetch trades from the blockchain for a given wallet."""
    # Replace the following with real API or blockchain explorer integration
    # Example: Using Etherscan API or similar for fetching wallet transactions
    try:
        transactions = []  # Replace with real API call result

        # Simulate parsing transactions into trade actions
        trades = []
        for tx in transactions:
            if tx['to'] in COPY_TRADE_WALLETS:  # Example condition
                trades.append({
                    'symbol': tx['token'],
                    'side': 'buy' if tx['type'] == 'incoming' else 'sell',
                    'amount': tx['amount']
                })
        return trades
    except Exception as e:
        print(f"Error fetching trades for wallet {wallet}: {e}")
        return []

def pairs_trading(symbols):
    """Execute pairs trading strategy based on price spreads."""
    prices = [fetch_latest_candle(symbol)[4] for symbol in symbols]
    if len(prices) == 2:
        spread = prices[0] - prices[1]
        print(f"Pair Spread: {spread}")
        if spread > PAIR_SPREAD_THRESHOLD:  # Arbitrary threshold
            print("Spread too wide: Short first asset, Long second asset")
            place_order('sell', TRADE_AMOUNT, symbols[0])
            place_order('buy', TRADE_AMOUNT, symbols[1])
        elif spread < -PAIR_SPREAD_THRESHOLD:
            print("Spread too negative: Long first asset, Short second asset")
            place_order('buy', TRADE_AMOUNT, symbols[0])
            place_order('sell', TRADE_AMOUNT, symbols[1])

def find_open_positions(df):
    """Find positions where open_bool is True and return the symbols."""
    open_positions = df[df['open_bool'] == True]
    symbols = open_positions['symbol'].tolist()
    print(f"Open positions found: {symbols}")
    return symbols

def token_sniping(symbols):
    """Sniping logic to monitor tokens for specific conditions."""
    for symbol in symbols:
        candle = fetch_latest_candle(symbol)
        if candle is None:
            continue
        price_change = ((candle[4] - candle[1]) / candle[1]) * 100  # % price change
        volume_spike = candle[5] / sum([c[5] for c in exchange.fetch_ohlcv(symbol, timeframe=TIMEFRAME, limit=20)])  # Volume spike ratio

        if price_change >= SNIPING_CONDITIONS["price_change"]:
            print(f"Sniping opportunity detected for {symbol}: Price change {price_change:.2f}%")
            place_order('buy', TRADE_AMOUNT, symbol)

        if volume_spike >= SNIPING_CONDITIONS["volume_spike"]:
            print(f"Volume spike detected for {symbol}: {volume_spike:.2f}x average volume")
            place_order('buy', TRADE_AMOUNT, symbol)

def dollar_cost_averaging(symbol):
    """Implement Dollar-Cost Averaging (DCA) logic."""
    print(f"Executing DCA for {symbol} with amount {DCA_AMOUNT}")
    place_order('buy', DCA_AMOUNT, symbol)

pnl_tracker = {"realized_pnl": 0.0, "unrealized_pnl": 0.0}

def update_pnl(symbol, entry_price, current_price, amount, side):
    """Update PnL tracking."""
    if side == "buy":
        pnl_tracker["unrealized_pnl"] = (current_price - entry_price) * amount
    elif side == "sell":
        pnl_tracker["realized_pnl"] += (current_price - entry_price) * amount
    print(f"PnL Update: Realized: {pnl_tracker['realized_pnl']:.2f}, Unrealized: {pnl_tracker['unrealized_pnl']:.2f}")

def trading_logic():
    """Main trading logic with enhanced features."""
    print("Starting trading bot...")
    monitor_copy_trade_wallets()
    # Add additional logic for trading here

if __name__ == "__main__":
    trading_logic()