app.py

import ccxt
import pandas as pd
import plotly.graph_objects as go
import gradio as gr

# 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')
    return df

# Indicators
def calculate_ema(df, period):
    return df['close'].ewm(span=period, adjust=False).mean()

def calculate_supertrend(df, period=10, multiplier=3):
    hl2 = (df['high'] + df['low']) / 2
    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'].iloc[i] > upper.iloc[i - 1]:
            st[i] = True
        elif df['close'].iloc[i] < lower.iloc[i - 1]:
            st[i] = False
        else:
            st[i] = st[i - 1]
    return pd.Series(st, index=df.index)

def calculate_rsi(df, period=14):
    delta = df['close'].diff()
    gain = delta.clip(lower=0)
    loss = -delta.clip(upper=0)
    avg_gain = gain.rolling(period).mean()
    avg_loss = loss.rolling(period).mean()
    rs = avg_gain / avg_loss
    return 100 - (100 / (1 + rs))

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]):
            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]):
            df.at[df.index[i], 'bearish_div'] = True
    return df

# 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)

    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:
        label = "🤔 NEUTRAL / HOLD"

    if df['bullish_div'].iloc[-1]:
        label += " + 📈 Bullish RSI Divergence"
    if df['bearish_div'].iloc[-1]:
        label += " + 📉 Bearish RSI Divergence"

    return label

# Plotly chart
def plot_chart(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)

    fig = go.Figure()

    fig.add_trace(go.Candlestick(
        x=df['timestamp'],
        open=df['open'],
        high=df['high'],
        low=df['low'],
        close=df['close'],
        name='Candles'
    ))

    fig.add_trace(go.Scatter(x=df['timestamp'], y=df['ema_50'], line=dict(color='orange'), name='EMA 50'))
    fig.add_trace(go.Scatter(x=df['timestamp'], y=df['ema_200'], line=dict(color='purple'), name='EMA 200'))

    bull = df[df['bullish_div']]
    bear = df[df['bearish_div']]

    fig.add_trace(go.Scatter(
        x=bull['timestamp'],
        y=bull['low'],
        mode='markers+text',
        text=['📈']*len(bull),
        textposition="bottom center",
        marker=dict(color='green', size=10),
        name='Bullish RSI Divergence'
    ))

    fig.add_trace(go.Scatter(
        x=bear['timestamp'],
        y=bear['high'],
        mode='markers+text',
        text=['📉']*len(bear),
        textposition="top center",
        marker=dict(color='red', size=10),
        name='Bearish RSI Divergence'
    ))

    fig.update_layout(
        title="Interactive Crypto Chart",
        xaxis_rangeslider_visible=False,
        xaxis_title="Time",
        yaxis_title="Price",
        template="plotly_dark",
        margin=dict(t=40, b=40),
        height=600
    )

    return fig

# Gradio handler
def analyze(pair, timeframe):
    try:
        df = fetch_ohlcv(pair, timeframe)
        if len(df) < 50:
            return "⚠️ Not enough data", go.Figure()
        signal = generate_signal(df)
        chart = plot_chart(df)
        return signal, chart
    except Exception as e:
        return f"❌ Error: {e}", go.Figure()

# Available pairs and timeframes
pairs = ['BTC/USDT', 'ETH/USDT', 'SOL/USDT', 'MX/USDT']
timeframes = ['1m', '5m', '15m', '1h', '4h']

with gr.Blocks() as demo:
    gr.Markdown("## 📊 Crypto Signal Generator with Interactive Chart")

    with gr.Row():
        pair_dropdown = gr.Dropdown(choices=pairs, value='BTC/USDT', label="Trading Pair")
        timeframe_dropdown = gr.Dropdown(choices=timeframes, value='1h', label="Timeframe")

    run_button = gr.Button("🔍 Analyze")
    signal_output = gr.Textbox(label="Trading Signal", interactive=False)
    chart_output = gr.Plot(label="Interactive Chart")

    run_button.click(fn=analyze, inputs=[pair_dropdown, timeframe_dropdown], outputs=[signal_output, chart_output])

demo.launch()