Remove nested directory structure that was confusing Hugging Face

hf_space/.gitattributes

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-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
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hf_space/README.md

@@ -1,34 +0,0 @@
----
-title: Trading_Dashboard
-app_file: app.py
-sdk: gradio
-sdk_version: 5.35.0
----
-# Stock-Trader
- Line of best fit stock trader test
-
-
-## ENV MANAGER:
->To CREATE/UPDATE YAML (from PC to file) go to reg. terminal:
->conda env export > env.yml
->
->To create env (from file to PC):
->conda env create --file=env.yml
->
->To update ENV (FROM FILE TO PC) (run in conda terminal) (if i remove --prune it works in terminal?):
->conda env update --file env.yml --prune
-
-
-
-
-### THIS WORKED TO FIX QT ISSUE:
-    from PyQt5.QtCore import QCoreApplication, Qt
-
-    # Clear any cached Qt plugins
-    QCoreApplication.setAttribute(Qt.AA_DisableHighDpiScaling, True)
-
-    app = QCoreApplication([])
-
-
-
-# This might not be needed since we are using conda... (TO ACTIVATE ENV: source venv/bin/activate)

hf_space/app.py

@@ -1,598 +0,0 @@
-#!/usr/bin/env python3
-"""
-Premium Trading Dashboard - Full Featured
-Beautiful Vercel-style dashboard with VM data integration
-"""
-
-import os
-import pandas as pd
-import gradio as gr
-import plotly.graph_objects as go
-import plotly.express as px
-from datetime import datetime, timedelta, timezone
-import logging
-import requests
-from alpaca.trading.client import TradingClient
-from alpaca.trading.requests import GetOrdersRequest, GetPortfolioHistoryRequest
-from alpaca.trading.enums import OrderStatus
-from alpaca.data.timeframe import TimeFrame
-from alpaca.data.historical import StockHistoricalDataClient
-
-# Get API keys and VM URL from environment variables
-API_KEY = os.getenv('ALPACA_API_KEY', 'PK2FD9B2S86LHR7ZBHG1')
-SECRET_KEY = os.getenv('ALPACA_SECRET_KEY', 'QPmGPDgbPArvHv6cldBXc7uWddapYcIAnBhtkuBW')
-VM_API_URL = os.getenv('VM_API_URL', 'http://34.56.193.18:8090')  # Set this in Hugging Face
-
-# Configure logging
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-# Initialize Alpaca clients
-trading_client = TradingClient(api_key=API_KEY, secret_key=SECRET_KEY)
-data_client = StockHistoricalDataClient(API_KEY, SECRET_KEY)
-
-# Modern color scheme
-COLORS = {
-    'primary': '#0070f3',
-    'success': '#00d647',
-    'error': '#ff0080',
-    'warning': '#f5a623',
-    'neutral': '#8b949e',
-    'background': '#fafafa',
-    'surface': '#ffffff',
-    'text': '#000000',
-    'text_secondary': '#666666',
-    'border': '#eaeaea'
-}
-
-def fetch_from_vm(endpoint, default_value=None):
-    """Fetch data from VM API server"""
-    try:
-        response = requests.get(f"{VM_API_URL}/api/{endpoint}", timeout=10)
-        if response.status_code == 200:
-            return response.json()
-        else:
-            logger.warning(f"VM API {endpoint} returned {response.status_code}")
-            return default_value
-    except Exception as e:
-        logger.error(f"Error fetching from VM {endpoint}: {e}")
-        return default_value
-
-def get_account_info():
-    """Get current account information from Alpaca"""
-    try:
-        account = trading_client.get_account()
-        return {
-            'portfolio_value': float(account.portfolio_value),
-            'buying_power': float(account.buying_power),
-            'cash': float(account.cash),
-            'equity': float(account.equity),
-            'day_change': float(getattr(account, 'unrealized_pl', 0)) if hasattr(account, 'unrealized_pl') else 0,
-            'day_change_percent': float(getattr(account, 'unrealized_plpc', 0)) * 100 if hasattr(account, 'unrealized_plpc') else 0,
-            'last_equity': float(account.last_equity) if account.last_equity else 0
-        }
-    except Exception as e:
-        logger.error(f"Error fetching account info: {e}")
-        return {
-            'portfolio_value': 0, 'buying_power': 0, 'cash': 0, 'equity': 0,
-            'day_change': 0, 'day_change_percent': 0, 'last_equity': 0
-        }
-
-def get_portfolio_history():
-    """Get portfolio value history from Alpaca"""
-    try:
-        portfolio_history_request = GetPortfolioHistoryRequest(
-            period="1M",
-            timeframe="1D",
-            extended_hours=False
-        )
-        
-        portfolio_history = trading_client.get_portfolio_history(portfolio_history_request)
-        
-        timestamps = [datetime.fromtimestamp(ts, tz=timezone.utc) for ts in portfolio_history.timestamp]
-        equity_values = portfolio_history.equity
-        
-        df = pd.DataFrame({
-            'timestamp': timestamps,
-            'equity': equity_values
-        })
-        
-        return df.dropna()
-    except Exception as e:
-        logger.error(f"Error fetching portfolio history: {e}")
-        return pd.DataFrame()
-
-def get_current_positions():
-    """Get current positions"""
-    try:
-        positions = trading_client.get_all_positions()
-        position_data = []
-        
-        for position in positions:
-            position_data.append({
-                'symbol': position.symbol,
-                'qty': float(position.qty),
-                'market_value': float(position.market_value),
-                'cost_basis': float(position.cost_basis),
-                'unrealized_pl': float(position.unrealized_pl),
-                'unrealized_plpc': float(position.unrealized_plpc) * 100,
-                'current_price': float(position.current_price) if position.current_price else 0
-            })
-        
-        return position_data
-    except Exception as e:
-        logger.error(f"Error fetching positions: {e}")
-        return []
-
-def create_portfolio_chart():
-    """Create beautiful portfolio value chart"""
-    portfolio_df = get_portfolio_history()
-    
-    if portfolio_df.empty:
-        fig = go.Figure()
-        fig.add_annotation(
-            text="No portfolio history available",
-            x=0.5, y=0.5,
-            xref="paper", yref="paper",
-            showarrow=False,
-            font=dict(size=16, color=COLORS['text_secondary'])
-        )
-    else:
-        fig = go.Figure()
-        
-        fig.add_trace(go.Scatter(
-            x=portfolio_df['timestamp'],
-            y=portfolio_df['equity'],
-            mode='lines',
-            name='Portfolio Value',
-            line=dict(color=COLORS['primary'], width=3),
-            fill='tonexty',
-            fillcolor=f"rgba(0, 112, 243, 0.1)",
-            hovertemplate='<b>%{y:$,.2f}</b><br>%{x}<extra></extra>'
-        ))
-        
-        if len(portfolio_df) > 0:
-            current_value = portfolio_df['equity'].iloc[-1]
-            fig.add_annotation(
-                x=portfolio_df['timestamp'].iloc[-1],
-                y=current_value,
-                text=f"${current_value:,.2f}",
-                showarrow=True,
-                arrowhead=2,
-                arrowcolor=COLORS['primary'],
-                bgcolor="white",
-                bordercolor=COLORS['primary'],
-                borderwidth=2,
-                font=dict(size=12, color=COLORS['text'])
-            )
-    
-    fig.update_layout(
-        title=dict(
-            text="Portfolio Value (Last 30 Days)",
-            font=dict(size=24, color=COLORS['text'], family="Inter"),
-            x=0.02
-        ),
-        xaxis=dict(
-            title="Date",
-            showgrid=True,
-            gridcolor=COLORS['border'],
-            color=COLORS['text_secondary']
-        ),
-        yaxis=dict(
-            title="Portfolio Value ($)",
-            showgrid=True,
-            gridcolor=COLORS['border'],
-            color=COLORS['text_secondary'],
-            tickformat='$,.0f'
-        ),
-        plot_bgcolor='white',
-        paper_bgcolor='white',
-        height=400,
-        margin=dict(l=60, r=40, t=60, b=60),
-        hovermode='x unified',
-        showlegend=False
-    )
-    
-    return fig
-
-def create_ipo_discovery_chart():
-    """Create IPO discovery chart with investment decisions"""
-    ipos = fetch_from_vm('ipos?limit=30', [])
-    
-    if not ipos:
-        fig = go.Figure()
-        fig.add_annotation(
-            text="No IPO data available from VM",
-            x=0.5, y=0.5,
-            xref="paper", yref="paper",
-            showarrow=False,
-            font=dict(size=16, color=COLORS['text_secondary'])
-        )
-    else:
-        # Count by status
-        status_counts = {}
-        for ipo in ipos:
-            status = ipo.get('investment_status', 'UNKNOWN')
-            status_counts[status] = status_counts.get(status, 0) + 1
-        
-        # Create pie chart
-        labels = list(status_counts.keys())
-        values = list(status_counts.values())
-        
-        # Map status to colors
-        color_map = {
-            'INVESTED': COLORS['success'],
-            'ELIGIBLE_NOT_INVESTED': COLORS['warning'], 
-            'WRONG_TYPE': COLORS['neutral'],
-            'UNKNOWN': COLORS['error']
-        }
-        colors = [color_map.get(label, COLORS['neutral']) for label in labels]
-        
-        fig = go.Figure(data=[go.Pie(
-            labels=labels,
-            values=values,
-            hole=0.4,
-            marker=dict(colors=colors),
-            textinfo='label+percent',
-            textposition='outside'
-        )])
-    
-    fig.update_layout(
-        title=dict(
-            text="IPO Investment Decisions",
-            font=dict(size=24, color=COLORS['text'], family="Inter"),
-            x=0.5
-        ),
-        plot_bgcolor='white',
-        paper_bgcolor='white',
-        height=400,
-        margin=dict(l=60, r=60, t=60, b=60),
-        showlegend=True
-    )
-    
-    return fig
-
-def refresh_account_overview():
-    """Refresh account overview display"""
-    account = get_account_info()
-    
-    portfolio_value = f"${account['portfolio_value']:,.2f}"
-    buying_power = f"${account['buying_power']:,.2f}"
-    cash = f"${account['cash']:,.2f}"
-    
-    day_change_value = account['day_change']
-    day_change_percent = account['day_change_percent']
-    if day_change_value > 0:
-        day_change = f"↗️ +${day_change_value:,.2f} (+{day_change_percent:.2f}%)"
-    elif day_change_value < 0:
-        day_change = f"↘️ ${day_change_value:,.2f} ({day_change_percent:.2f}%)"
-    else:
-        day_change = f"➡️ ${day_change_value:,.2f} ({day_change_percent:.2f}%)"
-    
-    equity = f"${account['equity']:,.2f}"
-    
-    return portfolio_value, buying_power, cash, day_change, equity
-
-def refresh_positions_table():
-    """Refresh current positions table"""
-    positions = get_current_positions()
-    if not positions:
-        return pd.DataFrame(columns=['Symbol', 'Quantity', 'Market Value', 'Unrealized P&L', 'Unrealized %'])
-    
-    df_data = []
-    for pos in positions:
-        pnl_indicator = "🟢" if pos['unrealized_pl'] > 0 else "🔴" if pos['unrealized_pl'] < 0 else "⚪"
-        df_data.append({
-            'Symbol': f"{pnl_indicator} {pos['symbol']}",
-            'Quantity': f"{pos['qty']:.0f}",
-            'Market Value': f"${pos['market_value']:,.2f}",
-            'Unrealized P&L': f"${pos['unrealized_pl']:,.2f}",
-            'Unrealized %': f"{pos['unrealized_plpc']:.2f}%"
-        })
-    
-    return pd.DataFrame(df_data)
-
-def refresh_ipo_discoveries_table():
-    """Refresh IPO discoveries table with investment decisions"""
-    ipos = fetch_from_vm('ipos?limit=100', [])
-    
-    if not ipos:
-        return pd.DataFrame(columns=['Status', 'Symbol', 'Security Type', 'Price', 'Detected At'])
-    
-    df_data = []
-    for ipo in ipos:
-        status_emoji = ipo.get('status_emoji', '⚪')
-        status = ipo.get('investment_status', 'UNKNOWN')
-        
-        # Clean up status for display
-        display_status = {
-            'INVESTED': '🟢 INVESTED',
-            'ELIGIBLE_NOT_INVESTED': '🟡 ELIGIBLE', 
-            'WRONG_TYPE': '⚪ WRONG TYPE',
-            'UNKNOWN': '🔴 UNKNOWN'
-        }.get(status, '⚪ UNKNOWN')
-        
-        df_data.append({
-            'Status': display_status,
-            'Symbol': ipo.get('symbol', 'N/A'),
-            'Security Type': ipo.get('security_type', 'N/A'),
-            'Price': f"${ipo.get('trading_price', 0)}" if ipo.get('trading_price') != 'N/A' else 'N/A',
-            'Detected At': ipo.get('detected_at', 'N/A')
-        })
-    
-    return pd.DataFrame(df_data)
-
-def refresh_vm_stats():
-    """Refresh VM statistics"""
-    stats = fetch_from_vm('stats', {})
-    
-    if not stats:
-        return "0", "0", "0", "0%", "No data"
-    
-    return (
-        str(stats.get('total_ipos_detected', 0)),
-        str(stats.get('ipos_invested', 0)),
-        str(stats.get('cs_stocks_detected', 0)),
-        f"{stats.get('investment_rate', 0):.1f}%",
-        stats.get('last_updated', 'N/A')
-    )
-
-def refresh_system_logs():
-    """Refresh system logs from VM"""
-    logs = fetch_from_vm('logs', [])
-    
-    if not logs:
-        return "No logs available from VM"
-    
-    # Format logs for display
-    formatted_logs = []
-    for log in logs:
-        emoji = log.get('emoji', '⚪')
-        timestamp = log.get('timestamp', 'N/A')
-        message = log.get('message', '')
-        formatted_logs.append(f"{emoji} {timestamp} | {message}")
-    
-    return '\n'.join(formatted_logs)
-
-def refresh_raw_logs():
-    """Refresh raw logs from VM"""
-    raw_data = fetch_from_vm('logs/raw?lines=1000', {})
-    
-    if not raw_data:
-        return "No raw logs available from VM"
-    
-    content = raw_data.get('content', 'No content')
-    total_lines = raw_data.get('total_lines', 0)
-    showing_lines = raw_data.get('showing_lines', 0)
-    
-    header = f"=== RAW CRON LOGS ===\nShowing last {showing_lines} of {total_lines} total lines\n\n"
-    return header + content
-
-# Custom CSS for gorgeous design
-custom_css = """
-.gradio-container {
-    font-family: 'Inter', -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif !important;
-    background: #fafafa !important;
-}
-
-.main-header {
-    background: linear-gradient(135deg, #0070f3 0%, #0051a5 100%);
-    color: white;
-    padding: 2rem;
-    border-radius: 16px;
-    margin-bottom: 2rem;
-    box-shadow: 0 10px 40px rgba(0, 112, 243, 0.3);
-}
-
-.metric-card {
-    background: white;
-    border: 1px solid #eaeaea;
-    border-radius: 12px;
-    padding: 1.5rem;
-    box-shadow: 0 4px 16px rgba(0, 0, 0, 0.04);
-    transition: all 0.3s ease;
-}
-
-.metric-card:hover {
-    box-shadow: 0 8px 32px rgba(0, 0, 0, 0.12);
-    transform: translateY(-4px);
-}
-
-.gr-button {
-    background: linear-gradient(135deg, #0070f3 0%, #0051a5 100%) !important;
-    color: white !important;
-    border: none !important;
-    border-radius: 12px !important;
-    font-weight: 600 !important;
-    padding: 1rem 2rem !important;
-    transition: all 0.3s ease !important;
-    box-shadow: 0 4px 16px rgba(0, 112, 243, 0.3) !important;
-}
-
-.gr-button:hover {
-    transform: translateY(-2px) !important;
-    box-shadow: 0 8px 32px rgba(0, 112, 243, 0.4) !important;
-}
-
-.gr-textbox, .gr-dataframe {
-    border: 1px solid #eaeaea !important;
-    border-radius: 12px !important;
-    background: white !important;
-    box-shadow: 0 2px 8px rgba(0, 0, 0, 0.04) !important;
-}
-
-.plotly-graph-div {
-    border-radius: 16px !important;
-    box-shadow: 0 4px 20px rgba(0, 0, 0, 0.08) !important;
-    background: white !important;
-}
-
-.status-invested { color: #00d647 !important; font-weight: 600 !important; }
-.status-eligible { color: #f5a623 !important; font-weight: 600 !important; }
-.status-wrong { color: #8b949e !important; }
-.status-unknown { color: #ff0080 !important; }
-"""
-
-def create_dashboard():
-    with gr.Blocks(
-        title="🚀 Premium Trading Dashboard", 
-        theme=gr.themes.Soft(primary_hue="blue"),
-        css=custom_css
-    ) as demo:
-        
-        # Header
-        gr.HTML("""
-            <div class="main-header">
-                <h1 style="margin: 0; font-size: 3rem; font-weight: 800; text-shadow: 0 2px 4px rgba(0,0,0,0.1);">
-                    🚀 Premium Trading Dashboard
-                </h1>
-                <p style="margin: 1rem 0 0 0; font-size: 1.3rem; opacity: 0.95;">
-                    Real-time portfolio monitoring with IPO discovery analytics
-                </p>
-            </div>
-        """)
-        
-        with gr.Tabs():
-            # Portfolio Overview Tab
-            with gr.Tab("📊 Portfolio Overview"):
-                gr.Markdown("## 💼 Account Summary")
-                with gr.Row():
-                    portfolio_value = gr.Textbox(label="💰 Portfolio Value", interactive=False, elem_classes=["metric-card"])
-                    buying_power = gr.Textbox(label="💳 Buying Power", interactive=False, elem_classes=["metric-card"])
-                    cash = gr.Textbox(label="💵 Cash", interactive=False, elem_classes=["metric-card"])
-                    day_change = gr.Textbox(label="📈 Day Change", interactive=False, elem_classes=["metric-card"])
-                    equity = gr.Textbox(label="🏦 Total Equity", interactive=False, elem_classes=["metric-card"])
-                
-                gr.Markdown("## 📈 Portfolio Performance")
-                portfolio_chart = gr.Plot(label="Portfolio Value Over Time")
-                
-                refresh_overview_btn = gr.Button("🔄 Refresh Portfolio Data", variant="primary", size="lg")
-            
-            # IPO Discoveries Tab  
-            with gr.Tab("🔍 IPO Discoveries"):
-                gr.Markdown("## 📊 IPO Discovery Analytics")
-                
-                with gr.Row():
-                    total_ipos = gr.Textbox(label="🎯 Total IPOs Detected", interactive=False, elem_classes=["metric-card"])
-                    ipos_invested = gr.Textbox(label="💰 IPOs Invested", interactive=False, elem_classes=["metric-card"])
-                    cs_stocks = gr.Textbox(label="📈 CS Stocks Found", interactive=False, elem_classes=["metric-card"])
-                    investment_rate = gr.Textbox(label="🎲 Investment Rate", interactive=False, elem_classes=["metric-card"])
-                    last_updated = gr.Textbox(label="🕒 Last Updated", interactive=False, elem_classes=["metric-card"])
-                
-                with gr.Row():
-                    with gr.Column(scale=1):
-                        ipo_chart = gr.Plot(label="Investment Decision Breakdown")
-                    
-                    with gr.Column(scale=2):
-                        gr.Markdown("## 🆕 Recent IPO Discoveries")
-                        ipo_table = gr.Dataframe(
-                            label="IPO Discoveries with Investment Decisions",
-                            elem_classes=["gr-dataframe"]
-                        )
-                
-                refresh_ipo_btn = gr.Button("🔄 Refresh IPO Data", variant="primary", size="lg")
-            
-            # Current Positions Tab
-            with gr.Tab("🏦 Current Positions"):
-                gr.Markdown("## 📊 Open Positions")
-                positions_table = gr.Dataframe(label="Current Holdings", elem_classes=["gr-dataframe"])
-                refresh_positions_btn = gr.Button("🔄 Refresh Positions", variant="primary", size="lg")
-            
-            # System Logs Tab
-            with gr.Tab("📋 System Logs"):
-                gr.Markdown("## 🖥️ Trading Bot Activity")
-                
-                with gr.Row():
-                    with gr.Column():
-                        gr.Markdown("### 🎯 Parsed Logs (Color Coded)")
-                        system_logs = gr.Textbox(
-                            label="Recent System Activity",
-                            lines=20,
-                            max_lines=20,
-                            interactive=False,
-                            elem_classes=["gr-textbox"]
-                        )
-                    
-                    with gr.Column():
-                        gr.Markdown("### 📄 Raw Cron Logs")
-                        raw_logs = gr.Textbox(
-                            label="Raw Log Output",
-                            lines=20,
-                            max_lines=20,
-                            interactive=False,
-                            elem_classes=["gr-textbox"]
-                        )
-                
-                refresh_logs_btn = gr.Button("🔄 Refresh All Logs", variant="primary", size="lg")
-        
-        # Footer
-        gr.HTML("""
-            <div style="text-align: center; padding: 2rem; color: #666; border-top: 1px solid #eaeaea; margin-top: 3rem; background: white; border-radius: 16px;">
-                <p style="font-size: 1.1rem;"><strong>🤖 Automated Trading Dashboard</strong></p>
-                <p style="font-size: 0.95rem;">Real-time data from Alpaca Markets + VM Analytics | Built with ❤️</p>
-            </div>
-        """)
-        
-        # Event Handlers
-        
-        # Portfolio tab
-        refresh_overview_btn.click(
-            fn=refresh_account_overview,
-            outputs=[portfolio_value, buying_power, cash, day_change, equity]
-        )
-        refresh_overview_btn.click(
-            fn=create_portfolio_chart,
-            outputs=[portfolio_chart]
-        )
-        
-        # IPO tab
-        refresh_ipo_btn.click(
-            fn=refresh_vm_stats,
-            outputs=[total_ipos, ipos_invested, cs_stocks, investment_rate, last_updated]
-        )
-        refresh_ipo_btn.click(
-            fn=create_ipo_discovery_chart,
-            outputs=[ipo_chart]
-        )
-        refresh_ipo_btn.click(
-            fn=refresh_ipo_discoveries_table,
-            outputs=[ipo_table]
-        )
-        
-        # Positions tab
-        refresh_positions_btn.click(
-            fn=refresh_positions_table,
-            outputs=[positions_table]
-        )
-        
-        # Logs tab
-        refresh_logs_btn.click(
-            fn=refresh_system_logs,
-            outputs=[system_logs]
-        )
-        refresh_logs_btn.click(
-            fn=refresh_raw_logs,
-            outputs=[raw_logs]
-        )
-        
-        # Initial data load
-        demo.load(
-            fn=refresh_account_overview,
-            outputs=[portfolio_value, buying_power, cash, day_change, equity]
-        )
-        demo.load(fn=create_portfolio_chart, outputs=[portfolio_chart])
-        demo.load(fn=refresh_positions_table, outputs=[positions_table])
-        demo.load(
-            fn=refresh_vm_stats,
-            outputs=[total_ipos, ipos_invested, cs_stocks, investment_rate, last_updated]
-        )
-        demo.load(fn=create_ipo_discovery_chart, outputs=[ipo_chart])
-        demo.load(fn=refresh_ipo_discoveries_table, outputs=[ipo_table])
-    
-    return demo
-
-# Create and launch
-demo = create_dashboard()
-
-if __name__ == "__main__":
-    demo.launch()

hf_space/requirements.txt

@@ -1,9 +0,0 @@
-# Trading Dashboard Requirements - Full Featured
-gradio>=5.0.0
-plotly>=6.0.0
-pandas>=2.0.0
-numpy>=1.20.0
-alpaca-py>=0.8.0
-requests>=2.28.0
-flask>=2.0.0
-flask-cors>=4.0.0

hf_space/vm_data_server.py

@@ -1,277 +0,0 @@
-#!/usr/bin/env python3
-"""
-VM Data Server
-Exposes local trading bot data via API for dashboard consumption
-Runs on the VM alongside your trading bot
-"""
-
-import os
-import json
-import pandas as pd
-from datetime import datetime, timedelta
-from flask import Flask, jsonify, request
-from flask_cors import CORS
-import logging
-
-app = Flask(__name__)
-CORS(app)  # Allow dashboard to connect from anywhere
-
-# Configure logging
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-# File paths
-PORTFOLIO_FILE = 'portfolio.txt'
-NEW_TICKERS_LOG_FILE = 'new_tickers_log.csv'
-SCRIPT_LOG_FILE = 'script.log'
-BUY_QUEUE_FILE = 'buy_queue.json'
-CURRENT_TICKERS_FILE = 'current_tickers.txt'
-
-def load_portfolio_data():
-    """Load portfolio CSV data"""
-    try:
-        if os.path.exists(PORTFOLIO_FILE):
-            df = pd.read_csv(PORTFOLIO_FILE)
-            return df.to_dict('records')
-        return []
-    except Exception as e:
-        logger.error(f"Error loading portfolio: {e}")
-        return []
-
-def load_new_tickers_with_decisions():
-    """Load IPO discoveries with investment decisions"""
-    try:
-        if not os.path.exists(NEW_TICKERS_LOG_FILE):
-            return []
-        
-        df = pd.read_csv(NEW_TICKERS_LOG_FILE)
-        portfolio_data = load_portfolio_data()
-        
-        # Get symbols we actually invested in
-        invested_symbols = set()
-        for trade in portfolio_data:
-            invested_symbols.add(trade.get('symbol', ''))
-        
-        # Add investment decision to each IPO
-        enriched_ipos = []
-        for _, row in df.iterrows():
-            symbol = row.get('Symbol', '')
-            security_type = row.get('Security_Type', '')
-            
-            # Determine investment status
-            if symbol in invested_symbols:
-                investment_status = 'INVESTED'
-                status_color = 'success'
-                status_emoji = '🟢'
-            elif security_type == 'CS':
-                investment_status = 'ELIGIBLE_NOT_INVESTED'
-                status_color = 'warning'
-                status_emoji = '🟡'
-            elif security_type in ['SP', 'WARRANT', 'UNIT']:
-                investment_status = 'WRONG_TYPE'
-                status_color = 'neutral'
-                status_emoji = '⚪'
-            else:
-                investment_status = 'UNKNOWN'
-                status_color = 'error'
-                status_emoji = '🔴'
-            
-            enriched_ipos.append({
-                'symbol': symbol,
-                'security_type': security_type,
-                'trading_price': row.get('Trading_Price', 'N/A'),
-                'detected_at': row.get('Detected_At', 'N/A'),
-                'investment_status': investment_status,
-                'status_color': status_color,
-                'status_emoji': status_emoji
-            })
-        
-        # Sort by detection date (newest first)
-        enriched_ipos.sort(key=lambda x: x['detected_at'], reverse=True)
-        
-        return enriched_ipos
-    except Exception as e:
-        logger.error(f"Error loading IPO data: {e}")
-        return []
-
-def load_script_logs(lines=100):
-    """Load recent script logs"""
-    try:
-        if not os.path.exists(SCRIPT_LOG_FILE):
-            return []
-        
-        with open(SCRIPT_LOG_FILE, 'r') as f:
-            all_lines = f.readlines()
-        
-        # Get recent lines
-        recent_lines = all_lines[-lines:] if len(all_lines) > lines else all_lines
-        
-        # Parse log lines
-        logs = []
-        for line in recent_lines:
-            line = line.strip()
-            if line:
-                # Try to parse timestamp and level
-                parts = line.split(' - ', 2)
-                if len(parts) >= 3:
-                    timestamp = parts[0]
-                    level = parts[1]
-                    message = parts[2]
-                    
-                    # Determine log type for color coding
-                    if 'ERROR' in level:
-                        log_type = 'error'
-                        emoji = '🔴'
-                    elif 'WARNING' in level or 'WARN' in level:
-                        log_type = 'warning'
-                        emoji = '🟡'
-                    elif 'Buy order placed' in message or 'Sold' in message:
-                        log_type = 'trade'
-                        emoji = '💰'
-                    elif 'Found' in message and 'new ticker' in message:
-                        log_type = 'discovery'
-                        emoji = '🔍'
-                    elif 'INFO' in level:
-                        log_type = 'info'
-                        emoji = '🔵'
-                    else:
-                        log_type = 'default'
-                        emoji = '⚪'
-                    
-                    logs.append({
-                        'timestamp': timestamp,
-                        'level': level,
-                        'message': message,
-                        'log_type': log_type,
-                        'emoji': emoji,
-                        'full_line': line
-                    })
-                else:
-                    # Fallback for unparseable lines
-                    logs.append({
-                        'timestamp': 'N/A',
-                        'level': 'RAW',
-                        'message': line,
-                        'log_type': 'default',
-                        'emoji': '⚪',
-                        'full_line': line
-                    })
-        
-        return logs
-    except Exception as e:
-        logger.error(f"Error loading logs: {e}")
-        return []
-
-def load_buy_queue():
-    """Load current buy queue"""
-    try:
-        if os.path.exists(BUY_QUEUE_FILE):
-            with open(BUY_QUEUE_FILE, 'r') as f:
-                return json.load(f)
-        return []
-    except Exception as e:
-        logger.error(f"Error loading buy queue: {e}")
-        return []
-
-def get_system_stats():
-    """Get system statistics"""
-    try:
-        portfolio_data = load_portfolio_data()
-        ipo_data = load_new_tickers_with_decisions()
-        
-        # Calculate stats
-        total_ipos_detected = len(ipo_data)
-        ipos_invested = len([ipo for ipo in ipo_data if ipo['investment_status'] == 'INVESTED'])
-        current_positions = len(portfolio_data)
-        
-        # Get detection stats by type
-        cs_stocks = len([ipo for ipo in ipo_data if ipo['security_type'] == 'CS'])
-        other_types = total_ipos_detected - cs_stocks
-        
-        return {
-            'total_ipos_detected': total_ipos_detected,
-            'ipos_invested': ipos_invested,
-            'current_positions': current_positions,
-            'cs_stocks_detected': cs_stocks,
-            'other_types_detected': other_types,
-            'investment_rate': round((ipos_invested / cs_stocks * 100) if cs_stocks > 0 else 0, 1),
-            'last_updated': datetime.now().strftime('%Y-%m-%d %H:%M:%S')
-        }
-    except Exception as e:
-        logger.error(f"Error calculating stats: {e}")
-        return {}
-
-# API Endpoints
-
-@app.route('/health')
-def health_check():
-    """Health check endpoint"""
-    return jsonify({'status': 'healthy', 'timestamp': datetime.now().isoformat()})
-
-@app.route('/api/portfolio')
-def get_portfolio():
-    """Get portfolio data"""
-    return jsonify(load_portfolio_data())
-
-@app.route('/api/ipos')
-def get_ipos():
-    """Get IPO discoveries with investment decisions"""
-    limit = request.args.get('limit', 50, type=int)
-    ipos = load_new_tickers_with_decisions()
-    return jsonify(ipos[:limit])
-
-@app.route('/api/logs')
-def get_logs():
-    """Get script logs"""
-    lines = request.args.get('lines', 100, type=int)
-    logs = load_script_logs(lines)
-    return jsonify(logs)
-
-@app.route('/api/buy_queue')
-def get_buy_queue():
-    """Get current buy queue"""
-    return jsonify(load_buy_queue())
-
-@app.route('/api/stats')
-def get_stats():
-    """Get system statistics"""
-    return jsonify(get_system_stats())
-
-@app.route('/api/logs/raw')
-def get_raw_logs():
-    """Get raw log file content"""
-    lines = request.args.get('lines', 200, type=int)
-    try:
-        if not os.path.exists(SCRIPT_LOG_FILE):
-            return jsonify({'content': 'No log file found'})
-        
-        with open(SCRIPT_LOG_FILE, 'r') as f:
-            all_lines = f.readlines()
-        
-        recent_lines = all_lines[-lines:] if len(all_lines) > lines else all_lines
-        content = ''.join(recent_lines)
-        
-        return jsonify({
-            'content': content,
-            'total_lines': len(all_lines),
-            'showing_lines': len(recent_lines)
-        })
-    except Exception as e:
-        return jsonify({'error': str(e), 'content': ''})
-
-if __name__ == '__main__':
-    print("🚀 Starting VM Data Server...")
-    print("📡 This exposes your trading bot data via API")
-    print("🌐 Dashboard can now access:")
-    print("   • IPO discoveries with investment decisions")
-    print("   • Raw cron logs with color coding") 
-    print("   • Portfolio data from VM files")
-    print("   • Buy queue and system stats")
-    print("-" * 50)
-    
-    # Run on all interfaces so dashboard can connect
-    app.run(
-        host='0.0.0.0',  # Allow external connections
-        port=8090,       # Different from dashboard port
-        debug=False
-    )