/summary trend

app.py

@@ -1,118 +1,179 @@
-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 🚀"}
+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 analisis tren otomatis berdasarkan data yfinance",
+    version="2.1"
+)
+
+PAIR = "EURUSD=X"
+BASE_WINDOW = 60  # jumlah hari data untuk update min/max otomatis
+
+
+# ===============================
+# 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)
+    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)
+    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):
+    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:
+        trend = "bearish"
+    else:
+        trend = "neutral"
+
+    # Momentum
+    diff = abs(ema20 - ema50) / ema50 * 100
+    if diff > 0.3:
+        strength = "strong"
+    elif diff > 0.1:
+        strength = "moderate"
+    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:
+        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)
+    }
+
+
+# ===============================
+# Endpoint: /analyze (grafik & data)
+# ===============================
+@app.post("/analyze")
+def analyze_ema(input_data: DateRange):
+    try:
+        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"}
+
+        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)
+
+        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),
+        }
+
+        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)}
+
+
+# ===============================
+# Endpoint: /summary (analisis tren)
+# ===============================
+@app.post("/summary")
+def ema_summary(input_data: DateRange):
+    try:
+        df = yf.download(PAIR, start=input_data.start_date, end=input_data.end_date)
+        if df.empty:
+            return {"status": "error", "message": "Data tidak ditemukan"}
+
+        df = df.reset_index()[["Date", "Close"]]
+        df.rename(columns={"Date": "date", "Close": "close"}, inplace=True)
+        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)
+
+        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:
+        return {"status": "error", "message": str(e)}
+
+
+@app.get("/")
+def root():
+    return {"message": "Model B API (EMA + Trend Summary) aktif 🚀"}