Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import ccxt
+import matplotlib.pyplot as plt
+from ta.momentum import RSIIndicator
+from ta.trend import EMAIndicator, MACD, ADXIndicator
+from ta.volatility import BollingerBands
+from xgboost import XGBClassifier
+from sklearn.model_selection import TimeSeriesSplit
+from sklearn.metrics import classification_report
+
+# --- PAGE SETUP ---
+st.set_page_config(page_title="AI Crypto Signal Detector",
+                   page_icon="💹",
+                   layout="wide")
+
+st.markdown("""
+<style>
+.reportview-container {
+    background: #0e1117;
+    color: #fafafa;
+}
+h1,h2,h3 { color: #0df2c8; }
+.sidebar .sidebar-content {
+    background: #111;
+}
+div.stButton > button {
+    color: white;
+    background-color: #00b894;
+    border-radius: 10px;
+    height: 3em;
+    width: 100%;
+}
+</style>
+""", unsafe_allow_html=True)
+
+# --- HEADER ---
+st.title("💹 AI-Powered Crypto Signal Detector")
+st.markdown("Predict **LONG / SHORT / HOLD** signals using EMA, RSI, MACD and XGBoost.")
+
+# --- USER INPUTS ---
+col1, col2, col3 = st.columns(3)
+with col1:
+    symbol = st.selectbox("Select Crypto Pair", ["BTC/USDT", "ETH/USDT", "BNB/USDT", "SOL/USDT"])
+with col2:
+    timeframe = st.selectbox("Timeframe", ["1h", "4h", "1d"])
+with col3:
+    limit = st.slider("Number of Candles", 500, 5000, 2000, step=500)
+
+st.markdown("---")
+
+# --- FETCH & RUN ---
+if st.button("🚀 Run Model"):
+    st.info("Fetching data and training model … please wait ≈ 30 sec")
+    exchange = ccxt.binance()
+    bars = exchange.fetch_ohlcv(symbol, timeframe=timeframe, limit=limit)
+    df = pd.DataFrame(bars, columns=['ts','open','high','low','close','vol'])
+    df['ts'] = pd.to_datetime(df['ts'], unit='ms')
+    df = df.set_index('ts')
+
+    # --- FEATURES ---
+    df['ret1'] = df['close'].pct_change()
+    df['ema12'] = EMAIndicator(df['close'], window=12).ema_indicator()
+    df['ema26'] = EMAIndicator(df['close'], window=26).ema_indicator()
+    df['ema_diff'] = df['ema12'] - df['ema26']
+    df['rsi14'] = RSIIndicator(df['close'], window=14).rsi()
+    bb = BollingerBands(df['close'])
+    df['bb_high'] = bb.bollinger_hband()
+    df['bb_low'] = bb.bollinger_lband()
+    macd = MACD(df['close'])
+    df['macd'] = macd.macd()
+    df['macd_signal'] = macd.macd_signal()
+    adx = ADXIndicator(df['high'], df['low'], df['close'])
+    df['adx'] = adx.adx()
+    df = df.dropna()
+
+    # --- TARGET ---
+    fwd_horizon = 3
+    future_ret = df['close'].shift(-fwd_horizon) / df['close'] - 1
+    thr = 0.005
+    df['target'] = np.where(future_ret > thr, 1, np.where(future_ret < -thr, -1, 0))
+    df = df.dropna()
+
+    # --- MODEL DATA ---
+    features = ['ema_diff','rsi14','bb_high','bb_low','macd','macd_signal','adx','ret1']
+    X = df[features]
+    y = df['target'].map({-1:0,0:1,1:2})  # map for XGBoost
+
+    tscv = TimeSeriesSplit(n_splits=5)
+    train_idx, test_idx = list(tscv.split(X))[-1]
+    X_train, X_test = X.iloc[train_idx], X.iloc[test_idx]
+    y_train, y_test = y.iloc[train_idx], y.iloc[test_idx]
+
+    # --- MODEL TRAIN ---
+    clf = XGBClassifier(n_estimators=300, learning_rate=0.05, max_depth=6,
+                        subsample=0.8, colsample_bytree=0.8, random_state=42)
+    clf.fit(X_train, y_train)
+
+    # --- PREDICT ---
+    y_pred = clf.predict(X_test)
+    df_test = df.iloc[test_idx].copy()
+    df_test['pred'] = pd.Series(y_pred).map({0:-1,1:0,2:1})
+
+    # --- REPORT ---
+    y_true = pd.Series(y_test).map({0:-1,1:0,2:1})
+    report = classification_report(y_true, df_test['pred'], output_dict=True)
+    st.subheader("📊 Model Performance")
+    st.write(pd.DataFrame(report).transpose().style.background_gradient(cmap='Blues'))
+
+    # --- PLOT SIGNALS ---
+    st.subheader("📈 Signals on Price Chart")
+    fig, ax = plt.subplots(figsize=(12,5))
+    ax.plot(df_test.index, df_test['close'], label='Close Price', color='cyan')
+    ax.scatter(df_test.index[df_test['pred']==1], df_test['close'][df_test['pred']==1],
+               color='lime', label='LONG', marker='^', s=80)
+    ax.scatter(df_test.index[df_test['pred']==-1], df_test['close'][df_test['pred']==-1],
+               color='red', label='SHORT', marker='v', s=80)
+    ax.set_facecolor("#0e1117")
+    ax.legend()
+    st.pyplot(fig)
+
+    # --- SUMMARY ---
+    st.success("✅ Model completed! Scroll up for results and chart.")
+    st.markdown(f"**Dataset size:** {len(df)} rows | **Features:** {len(features)} | **Symbol:** {symbol}")