app.py

import logging
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')

app = FastAPI(
    title="Model B – EMA & Dynamic Scaling API",
    description="API untuk menghitung EMA, normalisasi, dan analisis tren otomatis berdasarkan data yfinance",
    version="2.1"
)

PAIR = "EURUSD=X"
BASE_WINDOW = 60

class DateRange(BaseModel):
    start_date: str
    end_date: str

def ema_manual(prices, span):
    ema = [np.nan] * len(prices)
    alpha = 2 / (span + 1)
    for i in range(len(prices)):
        if i < span - 1:
            ema[i] = np.nan
        elif i == span - 1:
            ema[i] = np.mean(prices[:span])
        else:
            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)
    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"]

    if ema20 > ema50:
        trend = "bullish"
    elif ema20 < ema50:
        trend = "bearish"
    else:
        trend = "neutral"

    diff = abs(ema20 - ema50) / ema50 * 100
    if diff > 0.3:
        strength = "strong"
    elif diff > 0.1:
        strength = "moderate"
    else:
        strength = "weak"

    if close > ema20 and close > ema50:
        price_position = "above both EMA — possible continuation"
    elif close < ema20 and close < ema50:
        price_position = "below both EMA — possible correction"
    else:
        price_position = "between EMAs — indecision zone"

    return {
        "trend": trend,
        "strength": strength,
        "price_position": price_position,
        "ema_gap_percent": round(diff, 3)
    }

@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)
        extended_start = start_date - timedelta(days=60)

        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)

        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)
        df["EMA50"] = ema_manual(df["close"], 50)
        df = df.dropna().reset_index(drop=True)

        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
        }

    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)}

@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), 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)
        df["EMA50"] = ema_manual(df["close"], 50)
        df = df.dropna().reset_index(drop=True)

        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
        }

    except Exception as e:
        logging.error(f"Error di /summary: {e}", exc_info=True)
        return {"status": "error", "message": str(e)}