Update app.py

app.py

@@ -1,3 +1,4 @@
+import logging
 from fastapi import FastAPI
 from pydantic import BaseModel
 import pandas as pd
@@ -5,6 +6,9 @@ import numpy as np
 import yfinance as yf
 from datetime import datetime, timedelta
 
+# Konfigurasi logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+
 app = FastAPI(
     title="Model B – EMA & Dynamic Scaling API",
     description="API untuk menghitung EMA, normalisasi, dan analisis tren otomatis berdasarkan data yfinance",
@@ -12,20 +16,12 @@ app = FastAPI(
 )
 
 PAIR = "EURUSD=X"
-BASE_WINDOW = 60  # jumlah hari data untuk update min/max otomatis
-
+BASE_WINDOW = 60
 
-# ===============================
-# Data Model
-# ===============================
 class DateRange(BaseModel):
     start_date: str
     end_date: str
 
-
-# ===============================
-# Helper Functions
-# ===============================
 def ema_manual(prices, span):
     ema = [np.nan] * len(prices)
     alpha = 2 / (span + 1)
@@ -38,28 +34,29 @@ def ema_manual(prices, span):
             ema[i] = alpha * prices[i] + (1 - alpha) * ema[i - 1]
     return ema
 
-
 def get_dynamic_minmax():
     today = datetime.now().date()
     start = today - timedelta(days=BASE_WINDOW)
-    df = yf.download(PAIR, start=start, end=today + timedelta(days=1))
+    logging.info(f"Mengunduh data untuk min/max: start={start}, end={today + timedelta(days=1)}")
+    df = yf.download(PAIR, start=start, end=today + timedelta(days=1), auto_adjust=True)
     if df.empty:
+        logging.error("Gagal mengambil data harga terbaru untuk min/max.")
         raise ValueError("Gagal mengambil data harga terbaru.")
     close_min = df["Close"].min()
     close_max = df["Close"].max()
+    logging.info(f"Min/Max Close: {close_min}/{close_max}")
     return close_min, close_max
 
-
 def normalize_close(value, close_min, close_max):
+    if close_max == close_min: # Hindari pembagian dengan nol
+        return 1.0 # Atau nilai default lain yang masuk akal
     return (value - close_min) / (close_max - close_min)
 
-
 def analyze_trend(latest_row):
     ema20 = latest_row["EMA20"]
     ema50 = latest_row["EMA50"]
     close = latest_row["close"]
 
-    # Analisis arah tren
     if ema20 > ema50:
         trend = "bullish"
     elif ema20 < ema50:
@@ -67,7 +64,6 @@ def analyze_trend(latest_row):
     else:
         trend = "neutral"
 
-    # Momentum
     diff = abs(ema20 - ema50) / ema50 * 100
     if diff > 0.3:
         strength = "strong"
@@ -76,7 +72,6 @@ def analyze_trend(latest_row):
     else:
         strength = "weak"
 
-    # Posisi harga terhadap EMA
     if close > ema20 and close > ema50:
         price_position = "above both EMA — possible continuation"
     elif close < ema20 and close < ema50:
@@ -91,31 +86,26 @@ def analyze_trend(latest_row):
         "ema_gap_percent": round(diff, 3)
     }
 
-
-# ===============================
-# Endpoint: /analyze (grafik & data)
-# ===============================
-# ===============================
-# Endpoint: /analyze (grafik & data)
-# ===============================
 @app.post("/analyze")
 def analyze_ema(input_data: DateRange):
     try:
+        logging.info(f"Menerima permintaan /analyze dengan start_date={input_data.start_date}, end_date={input_data.end_date}")
         start_date = pd.to_datetime(input_data.start_date)
         end_date = pd.to_datetime(input_data.end_date)
-
-        # Tambahkan 60 hari ke belakang agar cukup untuk EMA50
         extended_start = start_date - timedelta(days=60)
 
-        df = yf.download(PAIR, start=extended_start, end=end_date + timedelta(days=1))
+        logging.info(f"Mengunduh data dari yfinance: start={extended_start}, end={end_date + timedelta(days=1)}")
+        df = yf.download(PAIR, start=extended_start, end=end_date + timedelta(days=1), auto_adjust=True)
+        
         if df.empty:
+            logging.warning("Data tidak ditemukan untuk rentang tanggal yang diperluas.")
             return {"status": "error", "message": "Data tidak ditemukan untuk rentang tanggal tersebut"}
 
         df = df.reset_index()[["Date", "Close"]]
         df.rename(columns={"Date": "date", "Close": "close"}, inplace=True)
 
-        # Pastikan minimal 50 data
         if len(df) < 50:
+            logging.warning(f"Data terlalu sedikit ({len(df)} hari) untuk EMA50.")
             return {"status": "error", "message": f"Data terlalu sedikit ({len(df)} hari). Butuh minimal 50 hari untuk EMA50."}
 
         df["EMA20"] = ema_manual(df["close"], 20)
@@ -134,7 +124,7 @@ def analyze_ema(input_data: DateRange):
             "min_close": float(close_min),
             "max_close": float(close_max),
         }
-
+        logging.info(f"Analisis /analyze berhasil, {len(df)} data point.")
         return {
             "status": "ok",
             "pair": PAIR,
@@ -145,27 +135,27 @@ def analyze_ema(input_data: DateRange):
         }
 
     except Exception as e:
+        logging.error(f"Error di /analyze: {e}", exc_info=True) # exc_info=True akan mencetak traceback
         return {"status": "error", "message": str(e)}
 
-
-# ===============================
-# Endpoint: /summary (analisis tren)
-# ===============================
 @app.post("/summary")
 def ema_summary(input_data: DateRange):
     try:
+        logging.info(f"Menerima permintaan /summary dengan start_date={input_data.start_date}, end_date={input_data.end_date}")
         start_date = pd.to_datetime(input_data.start_date)
         end_date = pd.to_datetime(input_data.end_date)
         extended_start = start_date - timedelta(days=60)
 
-        df = yf.download(PAIR, start=extended_start, end=end_date + timedelta(days=1))
+        df = yf.download(PAIR, start=extended_start, end=end_date + timedelta(days=1), auto_adjust=True)
         if df.empty:
+            logging.warning("Data tidak ditemukan untuk rentang tanggal yang diperluas.")
             return {"status": "error", "message": "Data tidak ditemukan"}
 
         df = df.reset_index()[["Date", "Close"]]
         df.rename(columns={"Date": "date", "Close": "close"}, inplace=True)
 
         if len(df) < 50:
+            logging.warning(f"Data terlalu sedikit ({len(df)} hari) untuk EMA50.")
             return {"status": "error", "message": f"Data terlalu sedikit ({len(df)} hari). Butuh minimal 50 hari untuk EMA50."}
 
         df["EMA20"] = ema_manual(df["close"], 20)
@@ -174,7 +164,7 @@ def ema_summary(input_data: DateRange):
 
         latest = df.iloc[-1]
         analysis = analyze_trend(latest)
-
+        logging.info(f"Analisis /summary berhasil, tanggal terakhir: {latest['date'].strftime('%Y-%m-%d')}")
         return {
             "status": "ok",
             "pair": PAIR,
@@ -186,8 +176,5 @@ def ema_summary(input_data: DateRange):
         }
 
     except Exception as e:
-        return {"status": "error", "message": str(e)}
-
-@app.get("/")
-def root():
-    return {"message": "Model B API (EMA + Trend Summary) aktif 🚀"}
+        logging.error(f"Error di /summary: {e}", exc_info=True)
+        return {"status": "error", "message": str(e)}