app.py

import dash
from dash import html, dcc, Input, Output
import plotly.graph_objs as go
import pandas as pd
import ccxt
from datetime import datetime
from plotly.subplots import make_subplots

# Init MEXC
exchange = ccxt.mexc()
markets = exchange.load_markets()
symbols = sorted([symbol for symbol in markets if "/USDT" in symbol])
timeframes = list(exchange.timeframes.keys())

DEFAULT_SYMBOL = "BTC/USDT"
DEFAULT_TIMEFRAME = "1h"

def fetch_ohlcv(symbol, timeframe):
    try:
        ohlcv = exchange.fetch_ohlcv(symbol, timeframe)
        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:
        print(f"❌ Error: {e}")
        return pd.DataFrame()

def add_indicators(df):
    df = df.copy()
    df['ema12'] = df['close'].ewm(span=12).mean()
    df['ema26'] = df['close'].ewm(span=26).mean()
    df['macd'] = df['ema12'] - df['ema26']
    df['macd_signal'] = df['macd'].ewm(span=9).mean()
    
    delta = df['close'].diff()
    gain = delta.clip(lower=0)
    loss = -delta.clip(upper=0)
    avg_gain = gain.rolling(14).mean()
    avg_loss = loss.rolling(14).mean()
    rs = avg_gain / avg_loss
    df['rsi'] = 100 - (100 / (1 + rs))
    return df

def add_signals(df):
    df['buy'] = (df['macd'] > df['macd_signal']) & (df['macd'].shift(1) <= df['macd_signal'].shift(1)) & (df['rsi'] < 50)
    df['sell'] = (df['macd'] < df['macd_signal']) & (df['macd'].shift(1) >= df['macd_signal'].shift(1)) & (df['rsi'] > 50)
    return df

def create_chart(df):
    fig = make_subplots(rows=3, cols=1, shared_xaxes=True,
                        vertical_spacing=0.02, row_heights=[0.6, 0.2, 0.2])

    fig.add_trace(go.Candlestick(x=df['timestamp'], open=df['open'], high=df['high'],
                                 low=df['low'], close=df['close'], name='Candles'), row=1, col=1)

    fig.add_trace(go.Scatter(x=df['timestamp'], y=df['rsi'], name='RSI',
                             line=dict(color='orange')), row=2, col=1)

    fig.add_trace(go.Scatter(x=df['timestamp'], y=df['macd'], name='MACD',
                             line=dict(color='blue')), row=3, col=1)

    fig.add_trace(go.Scatter(x=df['timestamp'], y=df['macd_signal'], name='MACD Signal',
                             line=dict(color='red')), row=3, col=1)

    # Signal markers
    buys = df[df['buy']]
    sells = df[df['sell']]
    fig.add_trace(go.Scatter(x=buys['timestamp'], y=buys['close'], mode='markers',
                             name='Buy', marker=dict(color='green', size=10, symbol='triangle-up')), row=1, col=1)
    fig.add_trace(go.Scatter(x=sells['timestamp'], y=sells['close'], mode='markers',
                             name='Sell', marker=dict(color='red', size=10, symbol='triangle-down')), row=1, col=1)

    fig.update_layout(height=800, margin=dict(t=30, b=20), showlegend=True)
    return fig

# Dash App
app = dash.Dash(__name__)
app.title = "MEXC AI Signals"

app.layout = html.Div([
    html.H2("📈 MEXC Crypto Signal Chart", style={'textAlign': 'center'}),

    html.Div([
        html.Label("Select Pair:"),
        dcc.Dropdown(id="pair-dropdown", options=[{"label": s, "value": s} for s in symbols],
                     value=DEFAULT_SYMBOL, style={"width": "100%"})
    ], style={"width": "30%", "display": "inline-block"}),

    html.Div([
        html.Label("Timeframes:"),
        html.Div([
            html.Button(tf, id={'type': 'tf-button', 'index': tf}, n_clicks=0,
                        style={"margin": "2px", "width": "50px"}) for tf in timeframes
        ], id="tf-button-row")
    ], style={"width": "68%", "display": "inline-block"}),

    dcc.Store(id="selected-timeframe", data=DEFAULT_TIMEFRAME),
    dcc.Graph(id="chart", config={"displayModeBar": True}),

    html.Div(id="signal-box", style={"marginTop": "10px", "textAlign": "center", "fontSize": "16px"})
])

@app.callback(
    Output("selected-timeframe", "data"),
    Input({'type': 'tf-button', 'index': dash.dependencies.ALL}, 'n_clicks')
)
def update_timeframe(n_clicks_list):
    ctx = dash.callback_context
    if not ctx.triggered:
        return DEFAULT_TIMEFRAME
    triggered_id = ctx.triggered[0]["prop_id"].split(".")[0]
    timeframe = eval(triggered_id)["index"]
    return timeframe

@app.callback(
    [Output("chart", "figure"), Output("signal-box", "children")],
    [Input("pair-dropdown", "value"), Input("selected-timeframe", "data")]
)
def update_chart(symbol, timeframe):
    df = fetch_ohlcv(symbol, timeframe)
    if df.empty:
        return go.Figure(), "❌ No data available"
    df = add_indicators(df)
    df = add_signals(df)

    latest_signal = "🔍 No recent signal"
    if df['buy'].iloc[-1]:
        latest_signal = f"🟢 Latest Signal: BUY at {df['close'].iloc[-1]:.2f} on {df['timestamp'].iloc[-1]}"
    elif df['sell'].iloc[-1]:
        latest_signal = f"🔴 Latest Signal: SELL at {df['close'].iloc[-1]:.2f} on {df['timestamp'].iloc[-1]}"

    fig = create_chart(df)
    return fig, latest_signal

if __name__ == "__main__":
    app.run(host="0.0.0.0", port=7860)