diff --git a/scheduler/jobs.py b/scheduler/jobs.py
new file mode 100644
index 0000000..212c7cc
--- /dev/null
+++ b/scheduler/jobs.py
@@ -0,0 +1,151 @@
+import logging
+from apscheduler.schedulers.background import BackgroundScheduler
+from data.binance_rest import BinanceRestClient
+from data.news_client import NewsClient
+from data.social_client import SocialClient
+from agents.technical import TechnicalAgent
+from agents.news import NewsAgent
+from agents.social import SocialAgent
+from agents.ai_analyst import AIAgent
+from engine.signal import SignalEngine
+from engine.surge import SurgeDetector
+from engine.portfolio import PortfolioManager
+from data.db import Database
+
+logger = logging.getLogger(__name__)
+
+class AnalysisJob:
+    def __init__(self, binance: BinanceRestClient, news_client: NewsClient,
+                 social_client: SocialClient, ai_agent: AIAgent,
+                 signal_engine: SignalEngine, surge_detector: SurgeDetector,
+                 portfolio: PortfolioManager, db: Database,
+                 monitored_coins: list[str]):
+        self.binance = binance
+        self.news_client = news_client
+        self.social_client = social_client
+        self.ai_agent = ai_agent
+        self.tech_agent = TechnicalAgent()
+        self.news_agent = NewsAgent()
+        self.social_agent = SocialAgent()
+        self.signal_engine = signal_engine
+        self.surge_detector = surge_detector
+        self.portfolio = portfolio
+        self.db = db
+        self.monitored_coins = monitored_coins
+        self.latest_results: dict = {}
+        self.ai_summaries: dict[str, str] = {}
+
+    def run_analysis(self):
+        logger.info("Starting analysis cycle...")
+        try:
+            # 0. Surge detection
+            try:
+                all_tickers = self.binance.client.get_ticker()
+                usdt_tickers = [t for t in all_tickers if t["symbol"].endswith("USDT")]
+                avg_volumes = {t["symbol"]: float(t["quoteVolume"]) * 0.7 for t in usdt_tickers}
+                surged = self.surge_detector.detect(usdt_tickers, avg_volumes)
+                for s in surged:
+                    if s not in self.monitored_coins:
+                        self.monitored_coins.append(s)
+                        logger.info(f"Surge-added: {s}")
+            except Exception as e:
+                logger.warning(f"Surge detection failed: {e}")
+
+            # 1. Fetch data
+            all_news = self.news_client.fetch_all()
+            all_social = self.social_client.fetch_reddit()
+            prices = self.binance.get_all_prices()
+
+            coins_scores = {}
+            coins_for_ai = []
+
+            for symbol in self.monitored_coins:
+                try:
+                    # Technical (multi-timeframe)
+                    df_1h = self.binance.get_ohlcv(symbol, interval="1h", limit=100)
+                    score_1h = self.tech_agent.analyze(df_1h)
+                    try:
+                        df_4h = self.binance.get_ohlcv(symbol, interval="4h", limit=100)
+                        score_4h = self.tech_agent.analyze(df_4h)
+                        df_1d = self.binance.get_ohlcv(symbol, interval="1d", limit=200)
+                        score_1d = self.tech_agent.analyze(df_1d)
+                        tech_score = score_1h * 0.5 + score_4h * 0.3 + score_1d * 0.2
+                    except Exception:
+                        tech_score = score_1h
+
+                    # News
+                    coin_news = self.news_client.filter_by_coin(all_news, symbol)
+                    news_score = self.news_agent.analyze(coin_news)
+
+                    # Social
+                    coin_posts = self.social_client.filter_posts_by_coin(all_social, symbol)
+                    sentiment = self.social_client.simple_sentiment(coin_posts)
+                    social_score = self.social_agent.analyze(sentiment, mention_trend=1.0)
+
+                    coins_scores[symbol] = {
+                        "technical": tech_score,
+                        "news": news_score,
+                        "social": social_score,
+                        "ai": 50,
+                    }
+                    coins_for_ai.append({
+                        "symbol": symbol.replace("USDT", ""),
+                        "price": prices.get(symbol, 0),
+                        "change_pct": 0,
+                        "technical_score": tech_score,
+                        "news_score": news_score,
+                        "social_score": social_score,
+                        "headlines": [a["title"] for a in coin_news[:3]],
+                    })
+                except Exception as e:
+                    logger.warning(f"Analysis failed for {symbol}: {e}")
+                    coins_scores[symbol] = {"technical": 50, "news": 50, "social": 50, "ai": 50}
+
+            # 2. AI batch analysis
+            if coins_for_ai:
+                ai_results = self.ai_agent.analyze_batch(coins_for_ai)
+                for symbol in coins_scores:
+                    short = symbol.replace("USDT", "")
+                    if short in ai_results:
+                        coins_scores[symbol]["ai"] = ai_results[short].get("score", 50)
+                        self.ai_summaries[symbol] = ai_results[short].get("summary", "")
+
+            # 3. Compute signals
+            ranked = self.signal_engine.rank_coins(coins_scores)
+            self.latest_results = {r["symbol"]: r for r in ranked}
+
+            # 4. Store signals
+            for r in ranked:
+                self.db.insert_signal(
+                    r["symbol"], r["technical"], r["news"], r["social"], r["ai"],
+                    r["composite"], r["signal"],
+                )
+
+            # 5. Portfolio actions
+            for r in ranked:
+                symbol = r["symbol"]
+                price = prices.get(symbol, 0)
+                if price <= 0:
+                    continue
+                self.portfolio.check_exit(symbol, price)
+                if r["signal"] == "BUY" and symbol not in self.portfolio.positions:
+                    self.portfolio.buy(symbol, price, r["composite"])
+                elif r["signal"] == "SELL" and symbol in self.portfolio.positions:
+                    self.portfolio.sell(symbol, price, reason="signal-sell")
+
+            # 6. Save portfolio snapshot
+            pv = self.portfolio.get_portfolio_value(prices)
+            self.db.insert_portfolio_snapshot(
+                pv["total_value"], pv["cash"], pv["total_pnl"], pv["pnl_pct"]
+            )
+
+            logger.info(f"Analysis complete. {len(ranked)} coins scored. Portfolio: ${pv['total_value']:.2f}")
+
+        except Exception as e:
+            logger.error(f"Analysis cycle failed: {e}")
+
+
+def create_scheduler(job: AnalysisJob, interval_minutes: int = 15) -> BackgroundScheduler:
+    scheduler = BackgroundScheduler()
+    scheduler.add_job(job.run_analysis, "interval", minutes=interval_minutes, id="analysis")
+    return scheduler