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Dashboard Overview

The XAUUSD Trading Bot dashboard provides a comprehensive interface for market analysis and trade signal generation. Built with Streamlit, it offers real-time data visualization and AI-powered insights.
The dashboard uses a wide layout optimized for viewing multi-timeframe analysis side-by-side.

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Interface Layout

The dashboard is organized into three main sections: 
┌─────────────────┬──────────────────────────────────┐
│   SIDEBAR       │      MAIN CONTENT AREA           │
│                 │                                  │
│ Control Panel   │  📊 Analysis | 🎯 Trading Signal │
│ - Run Analysis  │                                  │
│ - Auto Refresh  │  [Tab Content]                   │
│ - Metrics       │                                  │
│ - Last Update   │                                  │
└─────────────────┴──────────────────────────────────┘

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Control Panel (Sidebar)

The sidebar contains all controls for bot operation.

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Run New Analysis Button

Location: app.py:57 
if st.button("📈 Run New Analysis"):
    with st.spinner("Analyzing market data..."):
        st.session_state['analysis_result'] = bot.run_analysis()
        st.session_state['last_update'] = datetime.now()
Function: Triggers a complete market analysis Process:
  1. Fetches XAUUSD data from MetaTrader 5 across 6 timeframes
  2. Calculates technical indicators (RSI, EMA, ATR)
  3. Sends data to Groq LLM for AI analysis
  4. Generates trading signals
  5. Updates dashboard with results
When to use:
  • On first launch
  • When you want fresh market analysis
  • Before making trading decisions
  • After significant market events
Analysis typically takes 10-30 seconds depending on your internet connection and Groq API response time.

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Auto Refresh Toggle

Location: app.py:55 
auto_refresh = st.toggle("🔄 Auto Refresh (30min)", value=False)
Function: Enables automatic analysis every 30 minutes Features:
  • Visual progress bar showing time until next refresh
  • Countdown timer (MM:SS format)
  • Automatic re-analysis without manual intervention
1

Enable Auto Refresh

Click the toggle switch in the sidebar to activate auto-refresh mode.
2

Monitor Progress

A progress bar appears showing:
[████████░░░░░░░░] Next refresh in: 18:45
The bar fills over 30 minutes, then automatically triggers new analysis.
3

Automatic Update

When the timer reaches 00:00:
  • New analysis runs automatically
  • Dashboard updates with fresh data
  • Timer resets to 30:00
Implementation (app.py:97-115): 
if auto_refresh:
    current_time = datetime.now()
    if 'last_refresh' not in st.session_state:
        st.session_state['last_refresh'] = current_time
    
    time_diff = (current_time - st.session_state['last_refresh']).total_seconds()
    remaining_time = 1800 - time_diff  # 1800 seconds = 30 minutes
    
    if remaining_time > 0:
        progress = (1800 - remaining_time) / 1800
        mins = int(remaining_time // 60)
        secs = int(remaining_time % 60)
        st.sidebar.progress(progress, text=f"Next refresh in: {mins:02d}:{secs:02d}")
Auto-refresh increases Groq API usage. Monitor your API limits if using the free tier.

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Current Spread Metric

Location: app.py:71-72 
if result.get('current_spread'):
    st.sidebar.metric("Current Spread", f"{result['current_spread']} points")
Display: Shows the current bid-ask spread for XAUUSD Importance:
  • Lower spreads = better entry conditions
  • High spreads = avoid trading (increased cost)
  • Typical XAUUSD spread: 20-50 points

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Last Update Timestamp

Location: app.py:92 
st.sidebar.info(f"Last Updated: {st.session_state['last_update'].strftime('%Y-%m-%d %H:%M:%S')}")
Display: Shows when the current analysis was generated Format: YYYY-MM-DD HH:MM:SS Example: 2026-03-03 14:32:15

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Main Content Tabs

The dashboard uses a tabbed interface for organized data presentation. Location: app.py:65 
tab1, tab2 = st.tabs(["📊 Analysis", "🎯 Trading Signal"])

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Tab 1: 📊 Analysis

Displays comprehensive technical analysis and market data.

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Technical Analysis Section

Location: app.py:76-77 
st.markdown("### 📊 Technical Analysis")
st.markdown(format_signal(result['technical_features']))
Content: AI-generated analysis of:
  • RSI (Relative Strength Index) values
  • EMA (Exponential Moving Average) trends
  • ATR (Average True Range) volatility
  • Market structure (bullish/bearish)
  • Key support and resistance levels

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Market Data by Timeframe

Location: app.py:79-84 
st.markdown("### 📈 Market Data by Timeframe")
cols = st.columns(2)
for idx, (tf, data) in enumerate(result['market_data'].items()):
    with cols[idx % 2]:
        display_market_data(data, tf)
Layout: Two-column grid displaying 6 timeframes Timeframes analyzed:
  • D1 (Daily) - Long-term trend
  • H4 (4-Hour) - Swing trading context
  • H1 (1-Hour) - Intraday direction
  • M30 (30-Minute) - Entry timing
  • M15 (15-Minute) - Precise entries
  • M5 (5-Minute) - Scalping reference
Data Display Format (app.py:12-18): 
def display_market_data(data_str, timeframe):
    """Display formatted market data in an expander"""
    with st.expander(f"📊 {timeframe} Market Data", expanded=False):
        lines = data_str.split('\n')
        for line in lines:
            if line.strip():
                st.text(line)
Each timeframe is collapsible (expander) containing:
  • OHLC data (Open, High, Low, Close)
  • Volume
  • Technical indicator values
  • Price patterns
Click on any timeframe expander to view detailed market data. All expanders are collapsed by default for clean presentation.

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Tab 2: 🎯 Trading Signal

Displays AI-generated trading recommendations. Location: app.py:86-89 
with tab2:
    st.markdown("### 🎯 Trading Signal")
    st.markdown(format_signal(result['trading_signal']))
Content includes:
  • Trade direction (BUY/SELL)
  • Entry price
  • Stop-loss level
  • Take-profit target(s)
  • Risk-reward ratio
  • Position sizing (based on 1% risk)
  • Trade reasoning/confluence factors
Signal Formatting (app.py:20-24): 
def format_signal(signal_content):
    """Format and display trading signal content"""
    if isinstance(signal_content, str):
        return signal_content
    return signal_content.content if hasattr(signal_content, 'content') else str(signal_content)

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Custom Styling

The dashboard uses custom CSS for enhanced visuals. Location: app.py:28-47 
st.markdown("""
    <style>
    .stApp {background-color: #0e1117}
    .metric-container {
        background-color: #1f2937;
        padding: 10px;
        border-radius: 5px;
        margin: 5px;
    }
    .stTabs [data-baseweb="tab-list"] {
        gap: 2px;
        background-color: #1f2937;
    }
    .stTabs [data-baseweb="tab"] {
        background-color: #374151;
        padding: 10px 20px;
        border-radius: 5px 5px 0 0;
    }
    </style>
""", unsafe_allow_html=True)
Features:
  • Dark theme optimized for trading
  • Rounded corners on UI elements
  • Consistent color scheme
  • Professional appearance

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Session State Management

The dashboard maintains state across interactions: 
st.session_state['analysis_result']  # Stores latest analysis
st.session_state['last_update']      # Timestamp of last analysis
st.session_state['last_refresh']     # Auto-refresh timer reference
Benefits:
  • Results persist when toggling tabs
  • Auto-refresh timer maintains state
  • No data loss on interaction

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No Analysis State

Location: app.py:94 
else:
    st.warning("⚠️ No analysis results yet. Click 'Run Analysis' to start.")
On first launch, a warning prompts you to run initial analysis.

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Error Handling

Location: app.py:117-118 
except Exception as e:
    st.error(f"Error: {str(e)}")
Any errors during operation display as red error messages with details.

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Workflow Example

1

Launch Dashboard

Run streamlit run app.py and access http://localhost:8501
2

Initial Setup

  • Ensure MT5 is running and logged in
  • Verify Groq API key is configured
  • Dashboard shows “No analysis results yet” warning
3

Run First Analysis

Click 📈 Run New Analysis button
  • Spinner shows “Analyzing market data…”
  • Wait 10-30 seconds for completion
4

Review Technical Analysis

In 📊 Analysis tab:
  • Read AI technical analysis summary
  • Expand timeframe data to see details
  • Check multiple timeframes for confluence
5

Check Trading Signal

Switch to 🎯 Trading Signal tab:
  • Review trade direction and entry
  • Note stop-loss and take-profit levels
  • Assess risk-reward ratio
6

Monitor Spread

Check Current Spread in sidebar:
  • Low spread = good entry timing
  • High spread = wait for better conditions
7

Enable Auto-Refresh (Optional)

Toggle 🔄 Auto Refresh:
  • Dashboard updates every 30 minutes
  • Monitor progress bar countdown
  • Keep browser tab open

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Tips and Best Practices

  • Run new analysis during active market hours (London/New York sessions)
  • Check multiple timeframe confluence before trading
  • Use auto-refresh for swing trading (30-min updates sufficient)
  • Disable auto-refresh if actively scalping (run manual analysis as needed)
  • Keep MT5 running continuously for uninterrupted data
  • Close unused browser tabs to reduce resource usage
  • Clear browser cache if dashboard becomes slow
  • Restart Streamlit periodically for long-running sessions
  • Monitor memory usage if running 24/7
  • Higher timeframes (D1, H4) = overall trend
  • Lower timeframes (M15, M5) = precise entry timing
  • Look for alignment across timeframes (confluence)
  • RSI > 70 = overbought, RSI < 30 = oversold
  • ATR indicates volatility (higher = larger stop-loss needed)

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Next Steps

Trading Disclaimer: The dashboard provides analysis tools, but final trading decisions are your responsibility. Always verify signals and manage risk appropriately. 65% accuracy means 35% of signals may result in losses.

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