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Dockerfile

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+# syntax=docker/dockerfile:1
+FROM python:3.11-slim
+
+RUN apt-get update && apt-get install -y curl && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /app
+
+COPY requirements.txt ./
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . ./
+
+EXPOSE 7860
+
+CMD ["gunicorn", "--bind", "0.0.0.0:7860", "app:app"]

README.md

@@ -1,11 +1,80 @@
 ---
 title: RapidAgentClient
-emoji: 🏃
-colorFrom: red
-colorTo: red
+emoji: 🤖
+colorFrom: purple
+colorTo: blue
 sdk: docker
+app_port: 7860
 pinned: false
-short_description: Microservice of RapidQuant for get AI assistant for crypto
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🤖 RapidAgentClient - AI Analysis API
+
+API de análisis de mercado con IA. Diseñado para ser consumido por RapidQuant.
+
+## 📡 Endpoints
+
+| Método | Endpoint | Descripción |
+|--------|----------|-------------|
+| GET | `/` | Estado del servicio |
+| GET | `/health` | Health check |
+| POST | `/analyze` | Análisis de mercado |
+
+## 📥 Uso
+
+### Request
+```bash
+curl -X POST https://tu-usuario-RapidAgentClient.huggingface.space/analyze \
+  -H "Content-Type: application/json" \
+  -d '{"symbol": "BTC"}'
+```
+
+### Response
+```json
+{
+  "success": true,
+  "data": {
+    "symbol": "BTC",
+    "price_data": {
+      "price": 50000,
+      "change_24h": 2.5,
+      "market_cap": 1000000000000
+    },
+    "tech_data": {
+      "rsi": 65,
+      "trend": "bullish"
+    },
+    "analysis": "..."
+  }
+}
+```
+
+## 🔧 Configuración
+
+### Variables de Entorno
+- `HF_TOKEN`: Token de HuggingFace (opcional, para IA avanzada)
+
+## 🔍 Verificar Conexión (Terminal)
+
+```bash
+# Estado del servicio
+curl https://tu-usuario-RapidAgentClient.huggingface.space/
+
+# Health check
+curl https://tu-usuario-RapidAgentClient.huggingface.space/health
+
+# Análisis de prueba
+curl -X POST https://tu-usuario-RapidAgentClient.huggingface.space/analyze \
+  -H "Content-Type: application/json" \
+  -d '{"symbol": "ETH"}'
+```
+
+## 📊 Datos que Retorna
+
+- **price_data**: Precio, cambio 24h/7d/30d, market cap, rank
+- **tech_data**: RSI, MA7, MA20, tendencia, volatilidad
+- **analysis**: Señal de trading (comprar/vender/mantener)
+
+## 🔗 Consumido por
+
+- **RapidQuant** - Frontend principal

app.py

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+#!/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)

requirements.txt

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+flask>=3.0.0
+yfinance>=0.2.36
+pandas>=2.1.0
+numpy>=1.26.0
+requests>=2.31.0
+python-dotenv>=1.0.0
+gunicorn>=21.0.0