backtest.py

import argparse
import time
import requests
from datetime import datetime, timedelta
from dotenv import load_dotenv
import os

load_dotenv()

CMC_API_KEY = os.getenv("CMC_API_KEY")
BASE_URL = "https://pro-api.coinmarketcap.com/v1"

def get_historical_prices(symbol: str, days: int = 30):
    """
    Recupera prezzi storici giornalieri da CMC.
    NOTA: L'endpoint OHLCV richiede piano Pro. In questa versione demo,
    simuliamo lo storico usando il prezzo attuale e i change percentuali.
    Per un backtest reale, servirebbe un piano CMC a pagamento o un'altra fonte.
    """
    # Otteniamo il prezzo attuale e i change percentuali
    url = f"{BASE_URL}/cryptocurrency/quotes/latest"
    headers = {"X-CMC_PRO_API_KEY": CMC_API_KEY, "Accept": "application/json"}
    params = {"symbol": symbol.upper(), "convert": "USD"}
    
    try:
        response = requests.get(url, headers=headers, params=params, timeout=10)
        response.raise_for_status()
        data = response.json()
        quote = data.get("data", {}).get(symbol.upper(), {}).get("quote", {}).get("USD", {})
        price = quote.get("price", 0)
        pct_24h = quote.get("percent_change_24h", 0)
        pct_7d = quote.get("percent_change_7d", 0)
        
        # Costruzione di prezzi giornalieri simulati (trend lineare basato sui change)
        # Questo è un placeholder. Per un backtest accurato, servirebbe OHLCV storico.
        prices = []
        today = datetime.now()
        for i in range(days):
            # Simulazione: prezzo decrescente se i change sono negativi
            days_ago = days - i
            if days_ago <= 7:
                change = pct_7d / 7
            else:
                change = pct_24h
            simulated_price = price * (1 + change * (days_ago / 100))
            prices.append(round(simulated_price, 2))
        
        return prices
    except Exception as e:
        print(f"[Backtest] Error: {e}")
        return []

def run_backtest(symbol: str = "BTC", days: int = 30):
    print(f"\n📈 Running BACKTEST for {symbol} over {days} days (CMC data)\n")
    
    prices = get_historical_prices(symbol, days)
    if not prices:
        print("❌ Failed to fetch historical prices. Check API key or upgrade CMC plan.")
        return
    
    # Simulazione di segnali basati su trend
    signals = []
    for i in range(1, len(prices)):
        prev_price = prices[i-1]
        curr_price = prices[i]
        change = (curr_price - prev_price) / prev_price * 100
        
        # Segnale semplice per demo: SHORT se calo > 2%, LONG se salita > 2%
        if change < -2:
            signals.append("SHORT")
        elif change > 2:
            signals.append("LONG")
        else:
            signals.append("HOLD")
    
    short_signals = signals.count("SHORT")
    long_signals = signals.count("LONG")
    hold_signals = signals.count("HOLD")
    
    # Stima di performance fittizia (solo per demo)
    total_return = ((prices[-1] - prices[0]) / prices[0]) * 100
    
    print("=" * 65)
    print(f"📊 BACKTEST RESULTS: {symbol} | {days} days")
    print("=" * 65)
    print(f"   Starting price: ${prices[0]:,.2f}")
    print(f"   Ending price: ${prices[-1]:,.2f}")
    print(f"   Total return: {total_return:+.2f}%")
    print(f"\n   Signal distribution:")
    print(f"      SHORT signals: {short_signals}")
    print(f"      LONG signals: {long_signals}")
    print(f"      HOLD signals: {hold_signals}")
    print("=" * 65)
    
    # Avvertenza sulla limitazione del backtest
    print("\n⚠️ NOTE: This backtest uses simulated prices based on CMC percent changes.")
    print("   For accurate backtesting, upgrade to CMC Pro plan for OHLCV historical data.\n")

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--token", type=str, default="BTC")
    parser.add_argument("--days", type=int, default=30)
    args = parser.parse_args()
    run_backtest(args.token.upper(), args.days)