Create llm.py

llm.py

@@ -0,0 +1,102 @@
+import os
+import json
+import ccxt
+import pandas as pd
+import pandas_ta as ta
+from openai import OpenAI
+from duckduckgo_search import DDGS
+
+class MarketBrain:
+    def __init__(self):
+        self.client = OpenAI(
+            base_url="https://integrate.api.nvidia.com/v1",
+            api_key=os.environ.get("NVIDIA_API_KEY")
+        )
+        self.exchange = ccxt.okx()
+
+    def get_market_data(self, symbol="BTC/USDT", timeframe='1h', limit=50):
+        try:
+            ohlcv = self.exchange.fetch_ohlcv(symbol, timeframe, limit=limit)
+            df = pd.DataFrame(ohlcv, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])
+            df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
+            
+            # المؤشرات الفنية
+            df['rsi'] = ta.rsi(df['close'], length=14)
+            bb = ta.bbands(df['close'], length=20)
+            df = pd.concat([df, bb], axis=1)
+            df['ema_50'] = ta.ema(df['close'], length=50)
+            
+            # إرجاع آخر صف كبيانات حالية والسجل كامل للرسم
+            return df.iloc[-1].to_dict(), df
+        except Exception as e:
+            print(f"Error fetching market data: {e}")
+            return None, None
+
+    def get_news(self, symbol):
+        # البحث عن أخبار العملة
+        query = f"{symbol} crypto news analysis today"
+        results = []
+        try:
+            with DDGS() as ddgs:
+                for r in ddgs.text(query, max_results=5):
+                    results.append(f"- {r['title']}: {r['body']}")
+        except Exception as e:
+            print(f"News scraping error: {e}")
+            results.append("No news available currently.")
+        return "\n".join(results)
+
+    def analyze_and_decide(self, symbol, current_position=None):
+        market_stats, df = self.get_market_data(symbol)
+        if not market_stats:
+            return {"action": "HOLD", "reason": "Data fetch error"}
+        
+        news_summary = self.get_news(symbol)
+        price = market_stats['close']
+        
+        # صياغة البرومبت
+        system_prompt = f"""
+        You are an elite crypto quantitative trader. Analyze the data below for {symbol}.
+        
+        Technical Data:
+        - Price: {price}
+        - RSI (14): {market_stats.get('rsi', 50)}
+        - Bollinger Upper: {market_stats.get('BBU_20_2.0')}
+        - Bollinger Lower: {market_stats.get('BBL_20_2.0')}
+        - EMA 50: {market_stats.get('ema_50')}
+        
+        Recent News/Sentiment:
+        {news_summary}
+        
+        Current Status: {"WE HAVE A POSITION" if current_position else "NO POSITION"}
+        
+        Task:
+        1. Analyze trend, volatility, and sentiment.
+        2. Decide ACTION: BUY, SELL (if we have position), or HOLD.
+        3. If BUY, provide Take Profit (TP) and Stop Loss (SL).
+        
+        OUTPUT MUST BE STRICT JSON ONLY:
+        {{
+            "reasoning": "short explanation",
+            "action": "BUY" or "SELL" or "HOLD",
+            "confidence": 0-100,
+            "tp_target": number or null,
+            "sl_target": number or null
+        }}
+        """
+
+        try:
+            completion = self.client.chat.completions.create(
+                model="qwen/qwen3-next-80b-a3b-thinking",
+                messages=[{"role": "user", "content": system_prompt}],
+                temperature=0.5,
+                max_tokens=1024,
+                response_format={"type": "json_object"} 
+            )
+            
+            # التعامل مع الرد (قد يكون الـ reasoning منفصل في نماذج التفكير)
+            response_content = completion.choices[0].message.content
+            return json.loads(response_content), df
+            
+        except Exception as e:
+            print(f"LLM Error: {e}")
+            return {"action": "HOLD", "reason": "LLM Error"}, df