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import yfinance as yf
import plotly.graph_objects as go
import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
def forecast_crypto(coin, currency, window_days):
# 1. Ambil Data Kripto Dasar (Selalu dalam USD)
base_ticker = f"{coin}-USD"
data = yf.download(base_ticker, period="1y", interval="1d")
if data.empty:
return None, f"⚠️ Gagal mengambil data untuk {base_ticker}."
if isinstance(data.columns, pd.MultiIndex):
data.columns = data.columns.get_level_values(0)
# 2. DATA ENGINEERING: Konversi Mata Uang Historis
if currency != "USD":
fx_ticker = f"USD{currency}=X"
fx_data = yf.download(fx_ticker, period="1y", interval="1d")
if fx_data.empty:
return None, f"⚠️ Gagal mengambil data kurs {fx_ticker}."
if isinstance(fx_data.columns, pd.MultiIndex):
fx_data.columns = fx_data.columns.get_level_values(0)
# Pasar kripto buka 24/7, tapi pasar Forex tutup saat akhir pekan.
# Kita harus menyelaraskan (reindex) dan mengisi hari yang kosong (ffill/bfill)
fx_close = fx_data['Close'].reindex(data.index).ffill().bfill()
# Kalikan semua harga USD dengan kurs lokal di hari tersebut
for col in ['Open', 'High', 'Low', 'Close']:
if col in data.columns:
data[col] = data[col] * fx_close
# 3. Analisis Tren Historis
data['Trend (SMA)'] = data['Close'].rolling(window=int(window_days)).mean()
# 4. MACHINE LEARNING: Prediksi Harga Besok
# Ambil tren X hari terakhir untuk dipelajari mesin
df_pred = data[['Close']].dropna().tail(int(window_days))
if len(df_pred) < 3:
return None, "⚠️ Data tidak cukup untuk melakukan prediksi."
# Siapkan data untuk model ML
X = np.arange(len(df_pred)).reshape(-1, 1)
y = df_pred['Close'].values
model = LinearRegression()
model.fit(X, y)
# Prediksi 1 hari ke depan
next_day_X = np.array([[len(df_pred)]])
predicted_price = model.predict(next_day_X)[0]
# 5. Visualisasi Interaktif Plotly
fig = go.Figure()
# Garis Harga Asli
fig.add_trace(go.Scatter(x=data.index, y=data['Close'],
mode='lines', name='Harga Aktual',
line=dict(color='#2980b9', width=2)))
# Garis Tren (SMA)
fig.add_trace(go.Scatter(x=data.index, y=data['Trend (SMA)'],
mode='lines', name=f'Garis Tren ({window_days} Hari)',
line=dict(color='#e74c3c', width=2, dash='dash')))
fig.update_layout(
title=f"📈 AI Market Forecasting: {coin} to {currency}",
xaxis_title="Tanggal",
yaxis_title=f"Harga ({currency})",
template="plotly_white",
hovermode="x unified",
legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1)
)
# 6. Ringkasan Eksekutif & Hasil Prediksi
last_price = float(data['Close'].iloc[-1])
current_trend = float(data['Trend (SMA)'].iloc[-1])
status = "🟢 Naik (Bullish)" if last_price > current_trend else "🔴 Turun (Bearish)"
# Format mata uang
simbol = "Rp" if currency == "IDR" else ("$" if currency == "USD" else ("€" if currency == "EUR" else "£"))
summary = f"""
### 📊 Market & Prediction Summary ({coin}/{currency})
* **Harga Penutupan Terakhir:** **{simbol} {last_price:,.2f}**
* **Status Tren Pasar:** **{status}**
* 🤖 **AI Prediksi Harga Besok:** **{simbol} {predicted_price:,.2f}**
*(Catatan: Konversi mata uang dilakukan secara real-time menggunakan data pasar valuta asing. Prediksi dihitung menggunakan Machine Learning berdasarkan momentum {window_days} hari terakhir).*
"""
return fig, summary
# --- UI GRADIO ---
with gr.Blocks(theme=gr.themes.Soft(), title="CryptoProphet AI") as app:
gr.Markdown("# 🤖 CryptoProphet: AI Market Forecasting")
gr.Markdown("Sistem analitik prediktif yang menarik data *real-time* via API, melakukan konversi kurs otomatis, dan menggunakan **Machine Learning** untuk meramalkan harga kripto di hari berikutnya.")
with gr.Row():
with gr.Column(scale=1):
in_coin = gr.Dropdown(choices=["BTC", "ETH", "SOL", "BNB", "DOGE", "XRP", "ADA"],
value="BTC", label="Pilih Koin Kripto")
in_currency = gr.Dropdown(choices=["USD", "IDR", "EUR", "GBP"],
value="IDR", label="Pilih Mata Uang (Fiat)")
in_days = gr.Slider(minimum=7, maximum=90, value=30, step=1, label="Jendela Prediksi ML (Hari)")
btn = gr.Button("🔄 Jalankan Prediksi AI", variant="primary")
with gr.Column(scale=2):
out_summary = gr.Markdown("### 📊 Memuat data prediksi AI...")
with gr.Row():
out_plot = gr.Plot()
btn.click(forecast_crypto, inputs=[in_coin, in_currency, in_days], outputs=[out_plot, out_summary])
app.load(forecast_crypto, inputs=[in_coin, in_currency, in_days], outputs=[out_plot, out_summary])
app.launch() |