Delete app.py

app.py

@@ -1,208 +0,0 @@
-# ============================================================
-# PORTFOLIO INTELLIGENCE ENGINE - FULL APP.PY
-# Live + Historical Data Enabled
-# ============================================================
-
-import yfinance as yf
-import numpy as np
-import pandas as pd
-from datetime import datetime, timedelta
-from scipy.optimize import minimize
-from scipy import stats
-from dataclasses import dataclass
-from typing import Dict, List
-import warnings
-
-warnings.filterwarnings("ignore")
-
-# ============================================================
-# DATA ENGINE
-# ============================================================
-
-class MarketDataLoader:
-    def __init__(self, lookback_days=365, interval="1d"):
-        self.lookback_days = lookback_days
-        self.interval = interval
-
-    def fetch(self, symbols: List[str]) -> Dict[str, pd.DataFrame]:
-        end = datetime.now()
-        start = end - timedelta(days=self.lookback_days)
-
-        data = {}
-        for s in symbols:
-            df = yf.download(
-                s,
-                start=start.strftime("%Y-%m-%d"),
-                end=end.strftime("%Y-%m-%d"),
-                interval=self.interval,
-                auto_adjust=True,
-                progress=False
-            )
-            if not df.empty:
-                data[s] = df[["Open", "High", "Low", "Close", "Volume"]].dropna()
-        return data
-
-
-# ============================================================
-# AI FORECAST ENGINE (PROXY)
-# ============================================================
-
-class SimpleAIForecaster:
-    def forecast(self, prices: pd.Series, horizon=30) -> Dict:
-        returns = np.log(prices / prices.shift(1)).dropna()
-        mu = returns.mean() * horizon
-        sigma = returns.std() * np.sqrt(horizon)
-
-        samples = np.random.normal(mu, sigma, 1000)
-
-        return {
-            "samples": samples,
-            "expected_return": mu,
-            "confidence": float(np.clip(1 / (1 + sigma * 10), 0.3, 0.9))
-        }
-
-
-# ============================================================
-# REGIME DETECTOR
-# ============================================================
-
-class RegimeDetector:
-    def detect(self, returns: pd.Series) -> Dict:
-        vol = returns.std() * np.sqrt(252)
-        momentum = returns.rolling(20).mean().iloc[-1]
-
-        if momentum > 0 and vol < 0.25:
-            return {"regime": "bull", "confidence": 0.7}
-        elif momentum < 0:
-            return {"regime": "bear", "confidence": 0.7}
-        else:
-            return {"regime": "sideways", "confidence": 0.6}
-
-
-# ============================================================
-# FEATURE ENGINE
-# ============================================================
-
-@dataclass
-class AssetFeature:
-    symbol: str
-    expected_return: float
-    confidence: float
-    volatility: float
-    returns: pd.Series
-    regime: str
-
-
-class FeatureBuilder:
-    def build(self, symbol, df, forecast, regime) -> AssetFeature:
-        returns = np.log(df["Close"] / df["Close"].shift(1)).dropna()
-        vol = returns.std() * np.sqrt(252)
-
-        return AssetFeature(
-            symbol=symbol,
-            expected_return=forecast["expected_return"] * forecast["confidence"],
-            confidence=forecast["confidence"],
-            volatility=vol,
-            returns=returns,
-            regime=regime["regime"]
-        )
-
-
-# ============================================================
-# OPTIMIZATION ENGINE
-# ============================================================
-
-class Optimizer:
-    def optimize(self, features: List[AssetFeature]) -> np.ndarray:
-        returns = np.array([f.expected_return for f in features])
-        returns = np.clip(returns, -0.5, 0.5)
-
-        returns_df = pd.concat(
-            [f.returns for f in features],
-            axis=1
-        )
-        returns_df.columns = [f.symbol for f in features]
-
-        cov = returns_df.cov().values
-        n = len(features)
-
-        def objective(w):
-            port_ret = w @ returns
-            port_vol = np.sqrt(w @ cov @ w)
-            return -(port_ret / (port_vol + 1e-8))
-
-        bounds = [(0.05, 0.5) for _ in range(n)]
-        cons = [{"type": "eq", "fun": lambda w: np.sum(w) - 1}]
-        w0 = np.ones(n) / n
-
-        res = minimize(objective, w0, bounds=bounds, constraints=cons)
-        return res.x if res.success else w0
-
-
-# ============================================================
-# RISK ANALYTICS
-# ============================================================
-
-class RiskEngine:
-    def analyze(self, weights, features):
-        returns = np.array([f.expected_return for f in features])
-        returns_df = pd.concat([f.returns for f in features], axis=1)
-        cov = returns_df.cov().values
-
-        port_ret = weights @ returns
-        port_vol = np.sqrt(weights @ cov @ weights)
-        sharpe = (port_ret - 0.04) / port_vol if port_vol > 0 else 0
-
-        return {
-            "expected_return": port_ret,
-            "volatility": port_vol,
-            "sharpe": sharpe
-        }
-
-
-# ============================================================
-# MAIN ORCHESTRATOR
-# ============================================================
-
-class PortfolioApp:
-    def __init__(self, symbols):
-        self.symbols = symbols
-        self.data_loader = MarketDataLoader()
-        self.forecaster = SimpleAIForecaster()
-        self.regime_detector = RegimeDetector()
-        self.feature_builder = FeatureBuilder()
-        self.optimizer = Optimizer()
-        self.risk_engine = RiskEngine()
-
-    def run(self):
-        market_data = self.data_loader.fetch(self.symbols)
-        features = []
-
-        for s, df in market_data.items():
-            forecast = self.forecaster.forecast(df["Close"])
-            returns = np.log(df["Close"] / df["Close"].shift(1)).dropna()
-            regime = self.regime_detector.detect(returns)
-            feature = self.feature_builder.build(s, df, forecast, regime)
-            features.append(feature)
-
-        weights = self.optimizer.optimize(features)
-        risk = self.risk_engine.analyze(weights, features)
-
-        print("\n📊 PORTFÖY SONUCU\n")
-        for i, f in enumerate(features):
-            print(f"{f.symbol:5s} | Weight: {weights[i]:.2%} | Regime: {f.regime}")
-
-        print("\n--- RISK METRICS ---")
-        print(f"Expected Return : {risk['expected_return']:.2%}")
-        print(f"Volatility      : {risk['volatility']:.2%}")
-        print(f"Sharpe Ratio    : {risk['sharpe']:.2f}")
-
-
-# ============================================================
-# RUN
-# ============================================================
-
-if __name__ == "__main__":
-    symbols = ["XOM", "GOOGL", "WMT", "JNJ"]
-    app = PortfolioApp(symbols)
-    app.run()