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Azmikun1
parent
8817cefa56
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9e96949373
145
app.py
145
app.py
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@ -85,69 +85,104 @@ html, body, [class*="css"] { font-family: "Inter", "DejaVu Sans", sans-serif; }
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@st.cache_data(ttl="1h")
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def load_eth_data():
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"""
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Metode Hybrid (Tumpuk Data):
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1. Baca Data Lama dari CSV.
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2. Download Data Baru (dari tanggal terakhir CSV sampai Hari Ini).
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3. Gabung (Concat) tanpa mengubah/mengisi data kosong.
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"""
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ticker = "ETH-USD"
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df = None
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df_final = None
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# 1. COBA ONLINE
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# --- BAGIAN 1: BACA DATA LAMA (BASE) ---
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try:
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df_base = pd.read_csv("eth_backup.csv")
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df = yf.download(
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ticker,
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start="2024-01-01",
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end=date.today() + timedelta(days=1),
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progress=False,
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auto_adjust=True,
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multi_level_index=False
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)
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# Bersihkan kolom sampah
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if "Unnamed: 0" in df_base.columns:
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df_base = df_base.drop(columns=["Unnamed: 0"])
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if df is not None and not df.empty:
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# Bersihkan Index & Kolom
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df = df.reset_index()
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new_cols = []
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for col in df.columns:
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col_name = col[0] if isinstance(col, tuple) else str(col)
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new_cols.append(col_name)
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df.columns = new_cols
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# Pastikan kolom pertama adalah Date
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if 'Date' not in df.columns:
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df = df.rename(columns={df.columns[0]: 'Date'})
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# Hapus Timezone
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df['Date'] = pd.to_datetime(df['Date']).dt.tz_localize(None)
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# Simpan Backup (Timpa file lama agar fresh)
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try:
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df.to_csv("eth_backup.csv", index=False)
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except:
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pass
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return df, "online"
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except Exception as e:
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print(f"Gagal Online: {e}")
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# 2. COBA BACKUP (JIKA ONLINE GAGAL)
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try:
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df_backup = pd.read_csv("eth_backup.csv")
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# Standarisasi kolom Date
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if "Date" not in df_base.columns:
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# Cek kolom pertama
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df_base = df_base.rename(columns={df_base.columns[0]: "Date"})
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# Bersihkan kolom sampah jika ada
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if "Unnamed: 0" in df_backup.columns:
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df_backup = df_backup.drop(columns=["Unnamed: 0"])
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# Pastikan kolom Date dikenali
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if "Date" in df_backup.columns:
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df_backup["Date"] = pd.to_datetime(df_backup["Date"])
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elif df_backup.columns[0].lower() == "date":
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df_backup = df_backup.rename(columns={df_backup.columns[0]: "Date"})
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df_backup["Date"] = pd.to_datetime(df_backup["Date"])
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return df_backup, "backup"
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df_base["Date"] = pd.to_datetime(df_base["Date"]).dt.tz_localize(None)
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except FileNotFoundError:
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# 3. GAGAL TOTAL
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return None, "error"
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# Jika tidak ada file, buat dataframe kosong
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df_base = pd.DataFrame(columns=["Date", "Open", "High", "Low", "Close", "Volume"])
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# --- BAGIAN 2: DOWNLOAD DATA BARU (INCREMENTAL) ---
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today = date.today()
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# Tentukan tanggal mulai download (Lanjutkan dari data terakhir di CSV)
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if not df_base.empty:
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last_date_csv = df_base["Date"].max()
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start_download = last_date_csv + timedelta(days=1)
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else:
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start_download = pd.to_datetime("2020-01-01")
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# Hanya download jika ada selisih hari
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if start_download.date() <= today:
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try:
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# Download dari tanggal terakhir CSV s/d Hari Ini
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df_new = yf.download(
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ticker,
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start=start_download,
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end=today + timedelta(days=1),
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progress=False,
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auto_adjust=True,
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multi_level_index=False
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)
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if df_new is not None and not df_new.empty:
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df_new = df_new.reset_index()
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# Rapikan kolom (Hapus MultiIndex jika ada)
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new_cols = []
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for col in df_new.columns:
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col_name = col[0] if isinstance(col, tuple) else str(col)
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new_cols.append(col_name)
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df_new.columns = new_cols
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# Pastikan kolom Date benar
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if 'Date' not in df_new.columns:
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df_new = df_new.rename(columns={df_new.columns[0]: 'Date'})
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df_new['Date'] = pd.to_datetime(df_new['Date']).dt.tz_localize(None)
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# --- BAGIAN 3: GABUNGKAN (CONCAT) ---
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# Tumpuk data lama (Base) dengan data baru (New)
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df_final = pd.concat([df_base, df_new], ignore_index=True)
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else:
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# Jika download kosong (misal libur/gagal), pakai data lama saja
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df_final = df_base
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except Exception as e:
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print(f"Gagal update online: {e}")
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df_final = df_base # Jika error, tetap tampilkan data lama (Safety Net)
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else:
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# Data CSV sudah paling update
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df_final = df_base
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# --- FINALISASI ---
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if df_final is not None and not df_final.empty:
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# Hapus duplikat (jika ada irisan tanggal)
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df_final = df_final.drop_duplicates(subset="Date", keep="last")
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# Urutkan berdasarkan tanggal
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df_final = df_final.sort_values("Date").reset_index(drop=True)
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# Filter hanya kolom standar (buang kolom sampah)
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target_cols = ['Date', 'Open', 'High', 'Low', 'Close', 'Volume']
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available = [c for c in target_cols if c in df_final.columns]
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df_final = df_final[available]
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return df_final, "mixed" # Status mixed (Gabungan)
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return None, "error"
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def validate_scaler(scaler):
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"""Validasi scaler agar konsisten dengan training."""
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