Create models/forecaster.py

models/forecaster.py

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+python3 << 'PYEOF'
+code = '''
+import numpy as np, pandas as pd, warnings
+from datetime import datetime, timedelta
+warnings.filterwarnings("ignore")
+try:
+    import xgboost as xgb
+    from statsmodels.tsa.arima.model import ARIMA
+    from sklearn.preprocessing import StandardScaler
+    HAS_MODELS = True
+except ImportError:
+    HAS_MODELS = False
+
+def _gen_history(n=120, base=82.0):
+    np.random.seed(42)
+    dates = [datetime.today() - timedelta(days=n-i) for i in range(n)]
+    prices = [base]
+    for _ in range(n-1):
+        shock = np.random.normal(0, 1.2)
+        drift = 0.05*(base - prices[-1])
+        prices.append(max(prices[-1]+drift+shock, 40))
+    return pd.DataFrame({"price": prices}, index=pd.to_datetime(dates))
+
+def _features(df):
+    df = df.copy()
+    for lag in [1,3,7,14]: df[f"lag_{lag}"] = df["price"].shift(lag)
+    df["rm7"]  = df["price"].rolling(7).mean()
+    df["rs7"]  = df["price"].rolling(7).std()
+    df["rm14"] = df["price"].rolling(14).mean()
+    df["pct3"] = df["price"].pct_change(3)
+    df["dow"]  = df.index.dayofweek
+    return df.dropna()
+
+FEAT = ["lag_1","lag_3","lag_7","lag_14","rm7","rs7","rm14","pct3","dow"]
+
+class AegisForecaster:
+    def __init__(self):
+        self.arima = self.xgb = self.scaler = None
+        self.hist = None
+        self.fitted = False
+
+    def fit(self, df=None):
+        self.hist = df if df is not None else _gen_history()
+        if not HAS_MODELS:
+            self.fitted = True
+            return self
+        try:
+            self.arima = ARIMA(self.hist["price"], order=(2,1,2)).fit()
+        except:
+            self.arima = None
+        fd = _features(self.hist)
+        X = fd[FEAT].values
+        y = fd["price"].values
+        self.scaler = StandardScaler()
+        Xs = self.scaler.fit_transform(X)
+        self.xgb = xgb.XGBRegressor(
+            n_estimators=200, max_depth=4,
+            learning_rate=0.05, subsample=0.8,
+            colsample_bytree=0.8, random_state=42,
+            verbosity=0
+        ).fit(Xs, y)
+        self.fitted = True
+        print("Forecaster ready")
+        return self
+
+    def forecast(self, horizon_days=14, crisis_shock=0.0, disruption_factor=0.0):
+        if not self.fitted:
+            self.fit()
+        base = float(self.hist["price"].iloc[-1])
+        shocked = base*(1+crisis_shock/100)
+        dates = [datetime.today()+timedelta(days=i+1) for i in range(horizon_days)]
+        if self.arima:
+            arima_p = list(self.arima.forecast(steps=horizon_days))
+        else:
+            arima_p = [base+np.random.normal(0,1)*(i+1)**0.5 for i in range(horizon_days)]
+        rw = list(self.hist["price"].tail(14).values)
+        if crisis_shock > 0:
+            rw[-1] = shocked
+        xgb_p = []
+        for step in range(horizon_days):
+            pw = rw[-14:]
+            f = np.array([[pw[-1],pw[-3],pw[-7],pw[0],
+                           np.mean(pw[-7:]),np.std(pw[-7:]),np.mean(pw),
+                           (pw[-1]-pw[-4])/pw[-4] if pw[-4]!=0 else 0,
+                           (datetime.today().weekday()+step+1)%7]])
+            pred = float(self.xgb.predict(self.scaler.transform(f))[0])
+            pred *= (1+disruption_factor*0.8*(1-step/horizon_days))
+            xgb_p.append(pred)
+            rw.append(pred)
+        w = 0.7 if crisis_shock > 0 else 0.4
+        prices = [round(w*xgb_p[i]+(1-w)*arima_p[i],2) for i in range(horizon_days)]
+        std = float(self.hist["price"].pct_change().std())*base
+        lower = [round(p-1.96*std*((i+1)**0.4),2) for i,p in enumerate(prices)]
+        upper = [round(p+1.96*std*((i+1)**0.4),2) for i,p in enumerate(prices)]
+        fp = prices[-1]
+        pct = round((fp-base)/base*100,1)
+        return {
+            "base_price": round(base,2),
+            "shocked_price": round(shocked,2),
+            "horizon_days": horizon_days,
+            "forecast": [{"date":d.strftime("%Y-%m-%d"),"price":p,"lower":l,"upper":u}
+                         for d,p,l,u in zip(dates,prices,lower,upper)],
+            "summary": {
+                "final_price": fp, "pct_change": pct,
+                "peak_price": round(max(prices),2),
+                "peak_day": prices.index(max(prices))+1,
+                "risk_score": min(100,round(abs(pct)*1.5+disruption_factor*40+(crisis_shock/100)*30,1)),
+                "delay_prob": min(99,round(disruption_factor*65+(pct/100)*20,1)),
+                "cost_impact": round(pct*0.35+disruption_factor*18,1),
+            },
+            "model": "ARIMA+XGBoost hybrid",
+        }
+
+_fc = None
+def get_forecaster():
+    global _fc
+    if _fc is None:
+        _fc = AegisForecaster().fit()
+    return _fc
+'''
+with open("/opt/aegis/models/forecaster.py","w") as f:
+    f.write(code)
+print("forecaster.py written OK")
+PYEOF
+
+✅ Should print: forecaster.py written OK