Update app.py

app.py

@@ -1,13 +1,22 @@
+# app.py
 from fastapi import FastAPI
 from pydantic import BaseModel
 import pandas as pd
 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')
-
+import logging
+import os
+
+# -----------------------
+# Logging
+# -----------------------
+logging.basicConfig(level=logging.INFO, format="%(asctime)s — %(levelname)s — %(message)s")
+logger = logging.getLogger("modelb_api")
+
+# -----------------------
+# App & config
+# -----------------------
 app = FastAPI(
     title="Model B – EMA & Dynamic Scaling API",
     description="API untuk menghitung EMA, normalisasi, dan analisis tren otomatis berdasarkan data yfinance",
@@ -15,63 +24,83 @@ app = FastAPI(
 )
 
 PAIR = "EURUSD=X"
-BASE_WINDOW = 60
+BASE_WINDOW = 60  # jumlah hari historical yang diambil untuk jaga-jaga agar EMA50 bisa dihitung
 
+# -----------------------
+# Request schema
+# -----------------------
 class DateRange(BaseModel):
-    start_date: str
-    end_date: str
+    start_date: str  # ISO date 'YYYY-MM-DD'
+    end_date: str    # ISO date 'YYYY-MM-DD'
 
+# -----------------------
+# Utility functions
+# -----------------------
 def ema_manual(prices, span):
-    logging.debug(f"Memulai ema_manual untuk span={span}. Jumlah harga: {len(prices)}")
-    if len(prices) < span:
-        logging.warning(f"Tidak cukup data untuk menghitung EMA span {span}. Hanya {len(prices)} tersedia.")
-        return [np.nan] * len(prices)
+    """
+    Manual EMA calculation:
+      - prices: iterable numeric
+      - span: int (e.g. 20 or 50)
+    Returns list of same length; first (span-1) entries are NaN, entry at index span-1 is SMA seed.
+    """
+    prices = np.asarray(prices, dtype=float)
+    n = len(prices)
+    if n == 0:
+        return [np.nan] * 0
+    if span <= 0:
+        raise ValueError("span must be > 0")
+    ema = [np.nan] * n
+    alpha = 2.0 / (span + 1.0)
+
+    # Not enough data -> return NaNs (keputusan: tetap mengembalikan NaN untuk indeks sebelum span-1)
+    if n < span:
+        logger.warning(f"Not enough data for EMA span={span} (have {n} < needed {span}), returning NaNs.")
+        return ema
+
+    # seed with SMA at index span-1
+    seed = float(np.mean(prices[:span]))
+    ema[span - 1] = seed
+
+    # recursive EMA
+    for i in range(span, n):
+        ema[i] = alpha * prices[i] + (1.0 - alpha) * ema[i - 1]
 
-    ema = [np.nan] * len(prices)
-    alpha = 2 / (span + 1)
+    return ema
 
+def get_dynamic_minmax(pair=PAIR, window_days=BASE_WINDOW):
+    """Download recent window and return min/max of Close to use for normalization."""
+    today = datetime.utcnow().date()
+    start = today - timedelta(days=window_days)
+    logger.info(f"Fetching recent data for dynamic min/max: {start} -> {today}")
     try:
-        for i in range(len(prices)):
-            if i < span - 1:
-                ema[i] = np.nan
-            elif i == span - 1:  # ✅ diperbaiki di sini
-                # EMA pertama = rata-rata N pertama
-                ema[i] = np.mean(prices[:span])
-                logging.debug(f"EMA awal (span={span}) dihitung: {ema[i]:.6f}")
-            else:
-                ema[i] = alpha * prices[i] + (1 - alpha) * ema[i - 1]
+        df = yf.download(pair, start=start, end=today + timedelta(days=1), auto_adjust=True, progress=False)
     except Exception as e:
-        logging.error(f"Error dalam loop ema_manual (span={span}, index={i}): {e}", exc_info=True)
+        logger.error("yfinance download failed for dynamic minmax: %s", e, exc_info=True)
         raise
 
-    logging.debug(f"ema_manual selesai untuk span={span}.")
-    return ema
+    if df is None or df.empty:
+        raise ValueError("Failed to fetch recent prices for dynamic min/max")
 
-def get_dynamic_minmax():
-    today = datetime.now().date()
-    start = today - timedelta(days=BASE_WINDOW)
-    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 float(close_min), float(close_max)
+    close_min = float(df["Close"].min())
+    close_max = float(df["Close"].max())
+    logger.info("dynamic min/max: %s / %s", close_min, close_max)
+    return close_min, close_max
 
 def normalize_close(value, close_min, close_max):
     if close_max == close_min:
-        logging.warning(f"Max_close ({close_max}) sama dengan min_close ({close_min}). Mengembalikan 0.5.")
         return 0.5
-    return (value - close_min) / (close_max - close_min)
+    return float((value - close_min) / (close_max - close_min))
 
 def analyze_trend(latest_row):
-    logging.debug(f"Menganalisis tren untuk data terbaru: {latest_row.to_dict()}")
+    """
+    Return simple analysis dict based on EMA20 vs EMA50 and gap percent.
+    """
+    ema20 = latest_row.get("EMA20", np.nan)
+    ema50 = latest_row.get("EMA50", np.nan)
+    close = latest_row.get("close", np.nan)
 
-    ema20 = latest_row["EMA20"]
-    ema50 = latest_row["EMA50"]
-    close = latest_row["close"]
+    if np.isnan(ema20) or np.isnan(ema50):
+        return {"trend": "unknown", "strength": "unknown", "price_position": "unknown", "ema_gap_percent": None}
 
     if ema20 > ema50:
         trend = "bullish"
@@ -80,15 +109,14 @@ def analyze_trend(latest_row):
     else:
         trend = "neutral"
 
-    if ema50 == 0:
-        logging.error("EMA50 adalah nol, tidak dapat menghitung gap persen.")
-        diff = 0
+    if ema50 == 0 or np.isnan(ema50):
+        gap_pct = 0.0
     else:
-        diff = abs(ema20 - ema50) / ema50 * 100
+        gap_pct = abs(ema20 - ema50) / abs(ema50) * 100.0
 
-    if diff > 0.3:
+    if gap_pct > 0.3:
         strength = "strong"
-    elif diff > 0.1:
+    elif gap_pct > 0.1:
         strength = "moderate"
     else:
         strength = "weak"
@@ -100,122 +128,143 @@ def analyze_trend(latest_row):
     else:
         price_position = "between EMAs — indecision zone"
 
-    logging.debug(f"Analisis tren: trend={trend}, strength={strength}, pos={price_position}")
     return {
         "trend": trend,
         "strength": strength,
         "price_position": price_position,
-        "ema_gap_percent": round(diff, 3)
+        "ema_gap_percent": round(gap_pct, 4)
     }
 
+# -----------------------
+# Endpoints
+# -----------------------
 @app.post("/analyze")
 def analyze_ema_endpoint(input_data: DateRange):
+    """
+    Return time series data (date, close, EMA20, EMA50, norm_close) for plotting.
+    If requested window is too short to compute EMA50, endpoint automatically uses earlier data
+    by extending start_date backward by BASE_WINDOW days.
+    """
     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)
-        extended_start = start_date - timedelta(days=60)
-
-        logging.info(f"Mengunduh data dari yfinance: start={extended_start.strftime('%Y-%m-%d')}, end={end_date.strftime('%Y-%m-%d')}")
-        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)
-        logging.debug(f"Jumlah baris setelah download dan rename: {len(df)}")
-
-        if len(df) < 50:
-            logging.error(f"Data terlalu sedikit ({len(df)} hari). Butuh minimal 50 hari untuk EMA50.")
-            return {"status": "error", "message": f"Data terlalu sedikit ({len(df)} hari). Butuh minimal 50 hari untuk EMA50."}
-
-        logging.debug(f"Memanggil ema_manual untuk EMA20 dengan {len(df)} data.")
-        # BARIS INI YANG DIPERBAIKI:
-        df["EMA20"] = ema_manual(df["close"].values.tolist(), 20) 
-        logging.debug(f"Memanggil ema_manual untuk EMA50 dengan {len(df)} data.")
-        # BARIS INI YANG DIPERBAIKI:
-        df["EMA50"] = ema_manual(df["close"].values.tolist(), 50)
-
-        df = df.dropna().reset_index(drop=True)
-        logging.debug(f"Jumlah baris setelah dropna: {len(df)}")
+        logger.info("Received /analyze request: %s -> %s", input_data.start_date, input_data.end_date)
+        start_date = pd.to_datetime(input_data.start_date).date()
+        end_date = pd.to_datetime(input_data.end_date).date()
+    except Exception as e:
+        logger.error("Invalid date format: %s", e)
+        return {"status": "error", "message": "Invalid date format. Use YYYY-MM-DD."}
 
-        close_min, close_max = get_dynamic_minmax()
-        df["norm_close"] = df["close"].apply(lambda x: normalize_close(x, close_min, close_max))
-
-        chart_data = {
-            "dates": df["date"].dt.strftime("%Y-%m-%d").tolist(),
-            "close": df["close"].round(6).tolist(),
-            "EMA20": df["EMA20"].round(6).tolist(),
-            "EMA50": df["EMA50"].round(6).tolist(),
-            "norm_close": df["norm_close"].round(6).tolist(),
-            "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,
-            "start_date": str(start_date.date()),
-            "end_date": str(end_date.date()),
-            "data_points": len(df),
-            "chart_data": chart_data
-        }
+    # extend start to ensure enough history (for EMA50)
+    extended_start = start_date - timedelta(days=BASE_WINDOW)
 
+    try:
+        df_raw = yf.download(PAIR, start=extended_start, end=end_date + timedelta(days=1), auto_adjust=True, progress=False)
     except Exception as e:
-        logging.error(f"Error TERTANGKAP di endpoint /analyze_ema_endpoint: {e}", exc_info=True)
-        return {"status": "error", "message": str(e)}
+        logger.error("yfinance download failed: %s", e, exc_info=True)
+        return {"status": "error", "message": f"Failed to fetch data from yfinance: {e}"}
+
+    if df_raw is None or df_raw.empty:
+        logger.warning("No data returned from yfinance for the requested range")
+        return {"status": "error", "message": "No price data for requested dates"}
+
+    df = df_raw.reset_index()[["Date", "Close"]].rename(columns={"Date": "date", "Close": "close"})
+    df["date"] = pd.to_datetime(df["date"]).dt.normalize()
+    logger.info("Downloaded rows: %d", len(df))
+
+    if len(df) < 50:
+        msg = f"Insufficient data points after extension ({len(df)}). Need at least 50 for EMA50."
+        logger.error(msg)
+        return {"status": "error", "message": msg}
+
+    # compute EMAs
+    close_list = df["close"].tolist()
+    df["EMA20"] = ema_manual(close_list, 20)
+    df["EMA50"] = ema_manual(close_list, 50)
+
+    # drop rows where EMA values are NaN (i.e., before we have enough seed)
+    df = df.dropna(subset=["EMA20", "EMA50"]).reset_index(drop=True)
+    if df.empty:
+        return {"status": "error", "message": "After computing EMAs no usable rows remain."}
+
+    # dynamic min/max and normalization
+    try:
+        close_min, close_max = get_dynamic_minmax()
+    except Exception as e:
+        logger.warning("Dynamic min/max fetch failed: %s", e)
+        close_min, close_max = float(df["close"].min()), float(df["close"].max())
+
+    df["norm_close"] = df["close"].apply(lambda x: normalize_close(x, close_min, close_max))
+
+    chart_data = {
+        "dates": df["date"].dt.strftime("%Y-%m-%d").tolist(),
+        "close": [float(x) for x in df["close"].tolist()],
+        "ema20": [float(x) for x in df["EMA20"].tolist()],
+        "ema50": [float(x) for x in df["EMA50"].tolist()],
+        "norm_close": [float(x) for x in df["norm_close"].tolist()],
+        "min_close": float(close_min),
+        "max_close": float(close_max),
+    }
+
+    logger.info("Analyze success -> points: %d", len(df))
+    return {
+        "status": "ok",
+        "pair": PAIR,
+        "requested_start": str(start_date),
+        "requested_end": str(end_date),
+        "data_points": len(df),
+        "chart_data": chart_data
+    }
 
 @app.post("/summary")
 def ema_summary_endpoint(input_data: DateRange):
+    """
+    Return last available close, EMA20, EMA50 and a short trend analysis dictionary.
+    """
     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), 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)
-        logging.debug(f"Jumlah baris setelah download dan rename: {len(df)}")
-
-
-        if len(df) < 50:
-            logging.error(f"Data terlalu sedikit ({len(df)} hari). Butuh minimal 50 hari untuk EMA50.")
-            return {"status": "error", "message": f"Data terlalu sedikit ({len(df)} hari). Butuh minimal 50 hari untuk EMA50."}
-
-        logging.debug(f"Memanggil ema_manual untuk EMA20 dengan {len(df)} data.")
-        # BARIS INI YANG DIPERBAIKI:
-        df["EMA20"] = ema_manual(df["close"].values.tolist(), 20)
-        logging.debug(f"Memanggil ema_manual untuk EMA50 dengan {len(df)} data.")
-        # BARIS INI YANG DIPERBAIKI:
-        df["EMA50"] = ema_manual(df["close"].values.tolist(), 50)
-        df = df.dropna().reset_index(drop=True)
-        logging.debug(f"Jumlah baris setelah dropna: {len(df)}")
-
-
-        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,
-            "as_of_date": latest["date"].strftime("%Y-%m-%d"),
-            "close": round(float(latest["close"]), 6),
-            "EMA20": round(float(latest["EMA20"]), 6),
-            "EMA50": round(float(latest["EMA50"]), 6),
-            "trend_analysis": analysis
-        }
+        logger.info("Received /summary request: %s -> %s", input_data.start_date, input_data.end_date)
+        start_date = pd.to_datetime(input_data.start_date).date()
+        end_date = pd.to_datetime(input_data.end_date).date()
+    except Exception as e:
+        logger.error("Invalid date input for /summary: %s", e)
+        return {"status": "error", "message": "Invalid date format. Use YYYY-MM-DD."}
 
+    extended_start = start_date - timedelta(days=BASE_WINDOW)
+
+    try:
+        df_raw = yf.download(PAIR, start=extended_start, end=end_date + timedelta(days=1), auto_adjust=True, progress=False)
     except Exception as e:
-        logging.error(f"Error TERTANGKAP di endpoint /ema_summary_endpoint: {e}", exc_info=True)
-        return {"status": "error", "message": str(e)}
+        logger.error("yfinance download failed for /summary: %s", e, exc_info=True)
+        return {"status": "error", "message": f"Failed to fetch data from yfinance: {e}"}
+
+    if df_raw is None or df_raw.empty:
+        return {"status": "error", "message": "No price data for requested dates"}
+
+    df = df_raw.reset_index()[["Date", "Close"]].rename(columns={"Date": "date", "Close": "close"})
+    df["date"] = pd.to_datetime(df["date"]).dt.normalize()
+
+    if len(df) < 50:
+        return {"status": "error", "message": f"Insufficient data (have {len(df)} rows). Need >=50 for EMA50."}
+
+    close_list = df["close"].tolist()
+    df["EMA20"] = ema_manual(close_list, 20)
+    df["EMA50"] = ema_manual(close_list, 50)
+    df = df.dropna(subset=["EMA20", "EMA50"]).reset_index(drop=True)
+
+    if df.empty:
+        return {"status": "error", "message": "No usable rows after EMA computation."}
+
+    latest = df.iloc[-1]
+    analysis = analyze_trend(latest)
+
+    return {
+        "status": "ok",
+        "pair": PAIR,
+        "as_of_date": latest["date"].strftime("%Y-%m-%d"),
+        "close": float(latest["close"]),
+        "ema20": float(latest["EMA20"]),
+        "ema50": float(latest["EMA50"]),
+        "trend_analysis": analysis
+    }
 
 @app.get("/")
 def root():
-    return {"message": "Model B API (EMA + Trend Summary) aktif 🚀"}
+    return {"message": "Model B API (EMA + Trend Summary) aktif 🚀"}