Update app.py

app.py

@@ -1,47 +1,40 @@
-import gradio as gr
-import requests
+import ccxt
 import pandas as pd
-import numpy as np
 import matplotlib.pyplot as plt
 import matplotlib.dates as mdates
 from io import BytesIO
-from datetime import datetime
 from PIL import Image
+import gradio as gr
 
-def get_price_data(symbol="BTCUSDT", interval="1h", limit=100):
-    url = f"https://api.mexc.com/api/v3/klines?symbol={symbol}&interval={interval}&limit={limit}"
-    response = requests.get(url)
-    data = response.json()
-    df = pd.DataFrame(data, columns=[
-        'timestamp', 'open', 'high', 'low', 'close', 'volume',
-        'close_time', 'quote_asset_volume', 'number_of_trades',
-        'taker_buy_base_vol', 'taker_buy_quote_vol', 'ignore'
-    ])
+# Fetch OHLCV from MEXC
+def fetch_ohlcv(symbol='BTC/USDT', timeframe='1h', limit=150):
+    exchange = ccxt.mexc()
+    ohlcv = exchange.fetch_ohlcv(symbol, timeframe=timeframe, limit=limit)
+    df = pd.DataFrame(ohlcv, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])
     df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
-    df = df[['timestamp', 'open', 'high', 'low', 'close', 'volume']].astype(float)
     return df
 
-def calculate_ema(df, period=20):
+# EMA calculation
+def calculate_ema(df, period):
     return df['close'].ewm(span=period, adjust=False).mean()
 
+# SuperTrend indicator
 def calculate_supertrend(df, period=10, multiplier=3):
     hl2 = (df['high'] + df['low']) / 2
-    atr = df['high'].rolling(period).max() - df['low'].rolling(period).min()
-    atr = atr.rolling(period).mean()
-    upper_band = hl2 + (multiplier * atr)
-    lower_band = hl2 - (multiplier * atr)
-    supertrend = [True] * len(df)
-
+    atr = (df['high'] - df['low']).rolling(period).mean()
+    upper = hl2 + multiplier * atr
+    lower = hl2 - multiplier * atr
+    st = [True] * len(df)
     for i in range(1, len(df)):
-        if df['close'][i] > upper_band[i - 1]:
-            supertrend[i] = True
-        elif df['close'][i] < lower_band[i - 1]:
-            supertrend[i] = False
+        if df['close'].iloc[i] > upper.iloc[i - 1]:
+            st[i] = True
+        elif df['close'].iloc[i] < lower.iloc[i - 1]:
+            st[i] = False
         else:
-            supertrend[i] = supertrend[i - 1]
-
-    return pd.Series(supertrend)
+            st[i] = st[i - 1]
+    return pd.Series(st, index=df.index)
 
+# RSI + divergence detection
 def calculate_rsi(df, period=14):
     delta = df['close'].diff()
     gain = delta.clip(lower=0)
@@ -55,71 +48,70 @@ def detect_rsi_divergence(df):
     df['rsi'] = calculate_rsi(df)
     df['bullish_div'] = False
     df['bearish_div'] = False
-
     for i in range(2, len(df)):
-        if (
-            df['close'].iloc[i] < df['close'].iloc[i - 1] < df['close'].iloc[i - 2] and
-            df['rsi'].iloc[i] > df['rsi'].iloc[i - 1] > df['rsi'].iloc[i - 2]
-        ):
+        if (df['close'].iloc[i] < df['close'].iloc[i-1] < df['close'].iloc[i-2] and
+            df['rsi'].iloc[i] > df['rsi'].iloc[i-1] > df['rsi'].iloc[i-2]):
             df.at[df.index[i], 'bullish_div'] = True
-
-        if (
-            df['close'].iloc[i] > df['close'].iloc[i - 1] > df['close'].iloc[i - 2] and
-            df['rsi'].iloc[i] < df['rsi'].iloc[i - 1] < df['rsi'].iloc[i - 2]
-        ):
+        if (df['close'].iloc[i] > df['close'].iloc[i-1] > df['close'].iloc[i-2] and
+            df['rsi'].iloc[i] < df['rsi'].iloc[i-1] < df['rsi'].iloc[i-2]):
             df.at[df.index[i], 'bearish_div'] = True
     return df
 
-def generate_combined_signal(df):
+# Combined signal logic
+def generate_signal(df):
     df['ema_50'] = calculate_ema(df, 50)
     df['ema_200'] = calculate_ema(df, 200)
     df['supertrend'] = calculate_supertrend(df)
     df = detect_rsi_divergence(df)
 
     if df[['ema_50', 'ema_200']].isna().any().any():
-        return "⚠️ Not enough data for EMAs"
-
-    ema_signal = 'BULLISH' if df['ema_50'].iloc[-1] > df['ema_200'].iloc[-1] else 'BEARISH'
-    supertrend_signal = 'BULLISH' if df['supertrend'].iloc[-1] else 'BEARISH'
-
-    signal = ""
-    if ema_signal == 'BULLISH' and supertrend_signal == 'BULLISH':
-        signal = "🔥 STRONG BUY"
-    elif ema_signal == 'BEARISH' and supertrend_signal == 'BEARISH':
-        signal = "💀 STRONG SELL"
-    elif ema_signal == supertrend_signal:
-        signal = f"⚠️ {ema_signal}"
+        return "⚠️ Not enough data for EMA"
+
+    ema_sig = 'BULLISH' if df['ema_50'].iloc[-1] > df['ema_200'].iloc[-1] else 'BEARISH'
+    st_sig = 'BULLISH' if df['supertrend'].iloc[-1] else 'BEARISH'
+
+    if ema_sig == 'BULLISH' and st_sig == 'BULLISH':
+        label = "🔥 STRONG BUY"
+    elif ema_sig == 'BEARISH' and st_sig == 'BEARISH':
+        label = "💀 STRONG SELL"
+    elif ema_sig == st_sig:
+        label = f"⚠️ {ema_sig}"
     else:
-        signal = "🤔 NEUTRAL / HOLD"
+        label = "🤔 NEUTRAL / HOLD"
 
     if df['bullish_div'].iloc[-1]:
-        signal += " + 📈 Bullish RSI Divergence"
+        label += " + 📈 Bullish RSI Divergence"
     if df['bearish_div'].iloc[-1]:
-        signal += " + 📉 Bearish RSI Divergence"
-    return signal
+        label += " + 📉 Bearish RSI Divergence"
+    return label
 
+# Plot chart and overlays
 def plot_chart(df):
-    df['timestamp'] = pd.to_datetime(df['timestamp'])
-    fig, ax = plt.subplots(figsize=(10, 5))
+    df = df.tail(100).copy()
+    df['ema_50'] = calculate_ema(df, 50)
+    df['ema_200'] = calculate_ema(df, 200)
+    df['supertrend'] = calculate_supertrend(df)
+    df = detect_rsi_divergence(df)
 
-    for i in range(len(df)):
-        color = 'green' if df['close'].iloc[i] >= df['open'].iloc[i] else 'red'
-        ax.plot([df['timestamp'].iloc[i]] * 2, [df['low'].iloc[i], df['high'].iloc[i]], color='black')
-        ax.plot([df['timestamp'].iloc[i]] * 2, [df['open'].iloc[i], df['close'].iloc[i]], color=color, linewidth=5)
+    fig, ax = plt.subplots(figsize=(10, 5))
+    for _, row in df.iterrows():
+        color = 'green' if row['close'] >= row['open'] else 'red'
+        ax.plot([row['timestamp'], row['timestamp']], [row['low'], row['high']], color='black', linewidth=1)
+        ax.plot([row['timestamp'], row['timestamp']], [row['open'], row['close']], color=color, linewidth=4)
 
-    ax.plot(df['timestamp'], calculate_ema(df, 50), label="EMA 50", color='blue')
-    ax.plot(df['timestamp'], calculate_ema(df, 200), label="EMA 200", color='orange')
+    ax.plot(df['timestamp'], df['ema_50'], label='EMA 50', color='orange')
+    ax.plot(df['timestamp'], df['ema_200'], label='EMA 200', color='purple')
 
     for i in range(len(df)):
-        if 'bullish_div' in df.columns and df['bullish_div'].iloc[i]:
-            ax.annotate('↑', (df['timestamp'].iloc[i], df['low'].iloc[i] - 1), color='green', fontsize=12, ha='center')
-        if 'bearish_div' in df.columns and df['bearish_div'].iloc[i]:
-            ax.annotate('↓', (df['timestamp'].iloc[i], df['high'].iloc[i] + 1), color='red', fontsize=12, ha='center')
+        if df['bullish_div'].iloc[i]:
+            ax.annotate('↑', (df['timestamp'].iloc[i], df['low'].iloc[i] - 1), color='green', ha='center')
+        if df['bearish_div'].iloc[i]:
+            ax.annotate('↓', (df['timestamp'].iloc[i], df['high'].iloc[i] + 1), color='red', ha='center')
 
     ax.xaxis.set_major_formatter(mdates.DateFormatter('%m-%d %H:%M'))
-    ax.legend()
     plt.xticks(rotation=45)
-    plt.title("MEXC Price Chart")
+    ax.legend()
+    plt.title("Price - EMA, SuperTrend & RSI Divergence")
     plt.tight_layout()
 
     buf = BytesIO()
@@ -128,25 +120,25 @@ def plot_chart(df):
     plt.close(fig)
     return Image.open(buf)
 
-def analyze(symbol, interval):
+# Gradio interface
+def analyze(pair, timeframe):
     try:
-        df = get_price_data(symbol.upper(), interval)
-        signal = generate_combined_signal(df)
+        df = fetch_ohlcv(pair, timeframe)
+        if df.empty or len(df) < 60:
+            return "❌ Not enough data", None
+        signal = generate_signal(df)
         chart = plot_chart(df)
         return signal, chart
     except Exception as e:
         return f"❌ Error: {e}", None
 
-symbol_input = gr.Textbox(label="Symbol", value="BTCUSDT")
-interval_input = gr.Dropdown(["1m", "5m", "15m", "1h", "4h", "1d"], label="Interval", value="1h")
+pairs = ['BTC/USDT', 'ETH/USDT', 'MX/USDT', 'SOL/USDT']
+timeframes = ['1m', '5m', '15m', '1h', '4h']
 
-iface = gr.Interface(
+gr.Interface(
     fn=analyze,
-    inputs=[symbol_input, interval_input],
-    outputs=["text", "image"],
-    title="📊 Crypto Signal Generator",
-    description="EMA + SuperTrend + RSI Divergence on MEXC Data"
-)
-
-if __name__ == "__main__":
-    iface.launch()
+    inputs=[gr.Dropdown(pairs, label="Trading Pair"), gr.Dropdown(timeframes, label="Timeframe")],
+    outputs=[gr.Text(label="Signal"), gr.Image(label="Chart")],
+    title="Crypto Signal Generator (MEXC EMA + SuperTrend + RSI Div)",
+    description="Select a pair and timeframe to view technical signal and chart."
+).launch()