app.py

#!/usr/bin/env python3
"""
RapidAgentClient - API Terminal
API para análisis de mercado. Diseñado para ser consumido por otros Spaces.
"""

from flask import Flask, request, jsonify
import os
import requests
from dotenv import load_dotenv

load_dotenv()

import yfinance as yf
import pandas as pd
import numpy as np

app = Flask(__name__)

HF_TOKEN = os.getenv("HF_TOKEN", "")

COINGECKO_API = "https://api.coingecko.com/api/v3"

SYMBOL_MAP = {
    'BTC': 'bitcoin', 'ETH': 'ethereum', 'SOL': 'solana',
    'ADA': 'cardano', 'DOT': 'polkadot', 'AVAX': 'avalanche-2',
    'MATIC': 'matic-network', 'LINK': 'chainlink', 'XRP': 'ripple',
    'DOGE': 'dogecoin', 'BNB': 'binancecoin', 'LTC': 'litecoin',
}

@app.route("/", methods=["GET"])
def index():
    return jsonify({"success": True, "message": "RapidAgentClient API running"})

@app.route("/health", methods=["GET"])
def health():
    return jsonify({"success": True, "status": "healthy"})

@app.route("/analyze", methods=["POST"])
def analyze():
    data = request.get_json()
    symbol = data.get("symbol", "BTC").upper()
    
    price_data = get_price_data(symbol)
    tech_data = get_technical_data(symbol)
    
    analysis = generate_analysis(symbol, price_data, tech_data)
    
    return jsonify({
        "success": True,
        "data": {
            "symbol": symbol,
            "price_data": price_data,
            "tech_data": tech_data,
            "analysis": analysis
        }
    })

def get_price_data(symbol):
    try:
        coin_id = SYMBOL_MAP.get(symbol, symbol.lower())
        url = f"{COINGECKO_API}/coins/{coin_id}"
        params = {'localization': 'false', 'tickers': 'false', 'community_data': 'false', 'developer_data': 'false', 'sparkline': 'false'}
        response = requests.get(url, params=params, timeout=15)
        if response.status_code == 200:
            data = response.json()
            return {
                'success': True,
                'price': data.get('market_data', {}).get('current_price', {}).get('usd', 0),
                'change_24h': data.get('market_data', {}).get('price_change_percentage_24h', 0),
                'change_7d': data.get('market_data', {}).get('price_change_percentage_7d', 0),
                'change_30d': data.get('market_data', {}).get('price_change_percentage_30d', 0),
                'market_cap': data.get('market_data', {}).get('market_cap', {}).get('usd', 0),
                'volume_24h': data.get('market_data', {}).get('total_volume', {}).get('usd', 0),
                'rank': data.get('market_cap_rank', 0),
            }
    except:
        pass
    return {'success': False}

def get_technical_data(symbol):
    try:
        ticker = yf.Ticker(f"{symbol}-USD")
        hist = ticker.history(period="1mo")
        if hist.empty:
            return {'success': False}
        
        current_price = hist['Close'].iloc[-1]
        hist['MA7'] = hist['Close'].rolling(window=7).mean()
        hist['MA20'] = hist['Close'].rolling(window=20).mean()
        
        delta = hist['Close'].diff()
        gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
        loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
        rs = gain / loss
        hist['RSI'] = 100 - (100 / (1 + rs))
        
        volatility = hist['Close'].pct_change().std() * 100
        
        return {
            'success': True,
            'price': float(current_price),
            'rsi': float(hist['RSI'].iloc[-1]) if not pd.isna(hist['RSI'].iloc[-1]) else 50.0,
            'ma7': float(hist['MA7'].iloc[-1]) if not pd.isna(hist['MA7'].iloc[-1]) else float(current_price),
            'ma20': float(hist['MA20'].iloc[-1]) if not pd.isna(hist['MA20'].iloc[-1]) else float(current_price),
            'volatility': float(volatility * 100),
            'trend': 'bullish' if current_price > hist['MA7'].iloc[-1] else 'bearish' if current_price < hist['MA7'].iloc[-1] else 'neutral',
        }
    except:
        return {'success': False}

def generate_analysis(symbol, price_data, tech_data):
    rsi = tech_data.get('rsi', 50)
    trend = tech_data.get('trend', 'neutral')
    change_24h = price_data.get('change_24h', 0)
    
    if rsi > 70:
        signal = "vender"
        risk = "alto"
    elif rsi < 30:
        signal = "comprar"
        risk = "medio"
    elif trend == "bullish" and change_24h > 0:
        signal = "comprar"
        risk = "bajo"
    elif trend == "bearish" and change_24h < 0:
        signal = "vender"
        risk = "medio"
    else:
        signal = "mantener"
        risk = "bajo"
    
    return f'{{"tendencia": "{trend}", "senal": "{signal}", "riesgo": "{risk}", "accion": "Considerar {signal} {symbol}"}}'

if __name__ == "__main__":
    app.run(host="0.0.0.0", port=7860)