Upload 3 files

Dockerfile

@@ -0,0 +1,13 @@
+FROM python:3.10-slim
+
+WORKDIR /app
+
+COPY requirements.txt .
+RUN apt-get update && apt-get install -y libglib2.0-0 libsm6 libxext6 libxrender1 \
+    && pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+EXPOSE 7860
+
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

@@ -0,0 +1,118 @@
+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
+
+app = FastAPI(
+    title="Model B – EMA & Dynamic Scaling API",
+    description="API untuk menghitung EMA, normalisasi, dan update harga min/max otomatis berdasarkan data yfinance",
+    version="2.0"
+)
+
+PAIR = "EURUSD=X"
+BASE_WINDOW = 60  # jumlah hari data terakhir untuk update min/max
+
+
+# ===============================
+# Data Model
+# ===============================
+class DateRange(BaseModel):
+    start_date: str
+    end_date: str
+
+
+# ===============================
+# Helper Functions
+# ===============================
+def ema_manual(prices, span):
+    """Manual calculation of EMA (tanpa pandas ewm)."""
+    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():
+    """Ambil data terbaru dari yfinance untuk menentukan min & max close (update otomatis)."""
+    today = datetime.now().date()
+    start = today - timedelta(days=BASE_WINDOW)
+    df = yf.download(PAIR, start=start, end=today + timedelta(days=1))
+    if df.empty:
+        raise ValueError("Gagal mengambil data harga terbaru.")
+    close_min = df["Close"].min()
+    close_max = df["Close"].max()
+    return close_min, close_max
+
+
+def normalize_close(value, close_min, close_max):
+    """Normalisasi nilai close ke skala 0–1."""
+    return (value - close_min) / (close_max - close_min)
+
+
+# ===============================
+# Endpoint utama
+# ===============================
+@app.post("/analyze")
+def analyze_ema(input_data: DateRange):
+    try:
+        # Ambil tanggal dari input
+        start_date = pd.to_datetime(input_data.start_date)
+        end_date = pd.to_datetime(input_data.end_date)
+
+        if end_date <= start_date:
+            return {"status": "error", "message": "Tanggal akhir harus lebih besar dari tanggal awal"}
+
+        # Download data EUR/USD
+        df = yf.download(PAIR, start=start_date, end=end_date + timedelta(days=1))
+        if df.empty:
+            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)
+
+        # Hitung EMA
+        df["EMA20"] = ema_manual(df["close"], 20)
+        df["EMA50"] = ema_manual(df["close"], 50)
+        df = df.dropna().reset_index(drop=True)
+
+        # Update nilai min dan max (otomatis)
+        close_min, close_max = get_dynamic_minmax()
+
+        # Normalisasi close
+        df["norm_close"] = df["close"].apply(lambda x: normalize_close(x, close_min, close_max))
+
+        # Format respons JSON
+        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),
+        }
+
+        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:
+        return {"status": "error", "message": str(e)}
+
+
+@app.get("/")
+def root():
+    return {"message": "Model B API (EMA + Dynamic Normalization) aktif 🚀"}

requirements.txt

@@ -0,0 +1,5 @@
+fastapi
+uvicorn
+pandas
+numpy
+yfinance