Create app.py

app.py

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+import gradio as gr
+import ccxt
+import pandas as pd
+import pandas_ta as ta
+import numpy as np
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.preprocessing import MinMaxScaler
+import joblib
+import os
+import json
+from datetime import datetime
+
+# Konfigurasi
+SYMBOL = "ETH/USDT"
+TIMEFRAME = "4h"
+FEE = 0.0015
+WINDOW = 60
+MODEL_PATH = "rf_model.pkl"
+SCALER_PATH = "scaler.pkl"
+
+def fetch_data(symbol: str, timeframe: str, limit: int = 2000) -> pd.DataFrame:
+    """Ambil data dari Binance"""
+    try:
+        exchange = ccxt.binance({'enableRateLimit': True})
+        ohlcv = exchange.fetch_ohlcv(symbol, timeframe, limit=limit)
+        df = pd.DataFrame(ohlcv, columns=["timestamp", "open", "high", "low", "close", "volume"])
+        df["timestamp"] = pd.to_datetime(df["timestamp"], unit="ms")
+        return df
+    except Exception as e:
+        return pd.DataFrame()
+
+def prepare_dataset(df: pd.DataFrame):
+    """Siapkan dataset dengan indikator teknikal"""
+    if df.empty:
+        return None, None, None, None, None
+        
+    df = df.copy()
+    
+    try:
+        df.ta.rsi(length=14, append=True)
+        df.ta.macd(fast=12, slow=26, signal=9, append=True)
+        df.ta.bbands(length=20, append=True)
+        df.ta.cci(length=14, append=True)
+    except Exception as e:
+        return None, None, None, None, None
+    
+    df.dropna(inplace=True)
+    
+    df["next_close"] = df["close"].shift(-1)
+    df["label"] = (df["next_close"] > df["close"] * (1 + FEE)).astype(int)
+    df.dropna(inplace=True)
+    
+    X_list, y_list, idx_list = [], [], []
+    feature_cols = [col for col in df.columns if col not in ["timestamp", "next_close", "label"]]
+    
+    for i in range(WINDOW, len(df)):
+        window = df.iloc[i-WINDOW:i][feature_cols]
+        X_list.append(window.values.flatten())
+        y_list.append(df.iloc[i]["label"])
+        idx_list.append(i)
+    
+    X = np.array(X_list)
+    y = np.array(y_list)
+    scaler = MinMaxScaler()
+    X_scaled = scaler.fit_transform(X)
+    
+    return X_scaled, y, idx_list, df, scaler
+
+def train_model():
+    """Training model dan backtest"""
+    if os.path.exists(MODEL_PATH):
+        return "Model sudah ada. Hapus file model untuk retrain."
+    
+    df = fetch_data(SYMBOL, TIMEFRAME)
+    if df.empty:
+        return "❌ Gagal mengambil data"
+    
+    X, y, idx, df_full, scaler = prepare_dataset(df)
+    if X is None:
+        return "❌ Gagal menyiapkan dataset"
+    
+    split = int(len(X) * 0.95)
+    X_train, X_test = X[:split], X[split:]
+    y_train, y_test = y[:split], y[split:]
+    
+    model = RandomForestClassifier(n_estimators=500, max_depth=None, random_state=42, n_jobs=-1)
+    model.fit(X_train, y_train)
+    acc = float(model.score(X_test, y_test))
+    
+    joblib.dump(model, MODEL_PATH)
+    joblib.dump(scaler, SCALER_PATH)
+    
+    # Backtest
+    test_indices = idx[split:]
+    df_test = df_full.loc[test_indices].copy()
+    df_test["pred"] = model.predict(X_test)
+    
+    pnl_list = []
+    for i in range(len(df_test) - 1):
+        if df_test.iloc[i]["pred"] == 1:
+            entry = df_test.iloc[i]["close"]
+            exit_ = df_test.iloc[i + 1]["close"]
+            ret = (exit_ - entry) / entry
+            pnl = ret - FEE
+            pnl_list.append(pnl)
+    
+    wins = sum(1 for p in pnl_list if p > 0)
+    losses = len(pnl_list) - wins
+    gross_profit = sum(p for p in pnl_list if p > 0)
+    gross_loss = sum(abs(p) for p in pnl_list if p <= 0)
+    profit_factor = gross_profit / gross_loss if gross_loss > 0 else float("inf")
+    
+    result = f"""
+    ✅ **Training Selesai!**
+    
+    📊 **Hasil Backtest (66 hari):**
+    - Accuracy: {acc:.2%}
+    - Profit Factor: {profit_factor:.3f}
+    - Net Profit: {sum(pnl_list)*100:.2f}%
+    - Win Rate: {wins/len(pnl_list)*100:.2f}%
+    - Total Trades: {len(pnl_list)}
+    - Wins: {wins} | Losses: {losses}
+    """
+    return result
+
+def predict_next_move():
+    """Prediksi pergerakan harga berikutnya"""
+    if not os.path.exists(MODEL_PATH):
+        return "❌ Model belum dilatih. Klik tombol 'Train Model' terlebih dahulu."
+    
+    df = fetch_data(SYMBOL, TIMEFRAME, limit=WINDOW + 10)
+    _, _, _, df_full, scaler = prepare_dataset(df)
+    
+    if df_full is None or len(df_full) < WINDOW:
+        return "❌ Data tidak cukup untuk prediksi"
+    
+    feature_cols = [col for col in df_full.columns if col not in ["timestamp", "next_close", "label"]]
+    latest_window = df_full.iloc[-WINDOW:][feature_cols].values.flatten().reshape(1, -1)
+    latest_scaled = scaler.transform(latest_window)
+    
+    model = joblib.load(MODEL_PATH)
+    pred = int(model.predict(latest_scaled)[0])
+    proba = model.predict_proba(latest_scaled)[0]
+    
+    signal = "🟢 **BUY** (Hold 4 jam)" if pred == 1 else "🔴 **HOLD/SELL**"
+    
+    result = f"""
+    📈 **Prediksi {SYMBOL} - {TIMEFRAME}**
+    
+    Signal: {signal}
+    
+    **Probabilitas:**
+    - Naik (>0.15%): {proba[1]*100:.2f}%
+    - Turun/Stagnan: {proba[0]*100:.2f}%
+    - Confidence: {max(proba)*100:.2f}%
+    
+    **Waktu Prediksi:** {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
+    """
+    return result
+
+def get_price_chart():
+    """Generate price chart data"""
+    df = fetch_data(SYMBOL, TIMEFRAME, limit=200)
+    if df.empty:
+        return None
+    
+    # Buat simple chart data
+    chart_data = df[['timestamp', 'close']].tail(100).copy()
+    return chart_data
+
+# Gradio Interface
+with gr.Blocks(title="Crypto ML Trading Strategy", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("""
+    #  Crypto ML Trading Strategy
+    **Paper Reference:** [2105.06827v3](https://arxiv.org/abs/2105.06827)
+    
+    Prediksi arah harga cryptocurrency menggunakan Machine Learning (Random Forest) dengan indikator teknikal.
+    """)
+    
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown("### 📊 Kontrol")
+            train_btn = gr.Button("🎯 Train Model", variant="primary")
+            predict_btn = gr.Button("🔮 Prediksi Sekarang")
+            chart_btn = gr.Button("📈 Lihat Chart")
+        
+        with gr.Column():
+            gr.Markdown("### 💡 Informasi")
+            gr.Markdown("""
+            - **Asset:** ETH/USDT
+            - **Timeframe:** 4 Jam
+            - **Model:** Random Forest
+            - **Threshold:** 0.15% (fee Binance)
+            - **Data:** 2017-2021
+            """)
+    
+    output = gr.Textbox(label="Hasil", lines=15)
+    chart_output = gr.Dataframe(label="Price Data")
+    
+    train_btn.click(fn=train_model, inputs=None, outputs=output)
+    predict_btn.click(fn=predict_next_move, inputs=None, outputs=output)
+    chart_btn.click(fn=get_price_chart, inputs=None, outputs=chart_output)
+    
+    gr.Markdown("""
+    ---
+    **Disclaimer:** Ini adalah proof-of-concept akademis. Bukan saran finansial. 
+    Trading cryptocurrency berisiko tinggi.
+    """)
+
+if __name__ == "__main__":
+    demo.launch()