mirror of
https://github.com/chrisnov-it/quantumbotx.git
synced 2026-07-28 11:17:44 +00:00
Remove obsolete Streamlit and Vercel demo files
This commit is contained in:
chrisnov-it
+1
-1
@@ -8,5 +8,5 @@ MT5_LOGIN=your_mt5_account_number
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MT5_PASSWORD=your_mt5_password
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MT5_SERVER=your_mt5_server
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# Skip MT5 initialization in deployment environments (Vercel, etc.)
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# Skip MT5 initialization for import checks and local diagnostics.
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SKIP_MT5_INIT=0
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@@ -1,213 +0,0 @@
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# 🚀 QuantumBotX Streamlit Demo Deployment Guide
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## Overview
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Since MetaTrader 5 requires **Windows OS and persistent terminal connections**, enabling Railway or other cloud platforms for live trading is not technically feasible. However, for **public demonstration** purposes, we've created a beautiful Streamlit demo that showcases all QuantumBotX features without requiring MT5.
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## Why Streamlit Demo?
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### ✅ Advantages:
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- **No MT5 Dependency**: Works on any cloud platform (Railway, Vercel, Heroku, etc.)
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- **Interactive Demo**: Realistic trading simulation with live data
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- **Easy Deployment**: Single command deployment with pip installs
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- **Public Showcase**: Perfect for demonstrating capabilities to potential users
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- **Cost Effective**: Free tier available on most platforms
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- **Fast Loading**: Lightweight compared to full Flask app
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### ❌ Limitations:
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- No live trading execution (by design for safety)
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- Simulated data only
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- No MT5 integration
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- Read-only demonstration
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## Quick Deployment Options
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### Option 1: Streamlit Cloud (Easiest)
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1. **Create Account**: Go to [share.streamlit.io](https://share.streamlit.io)
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2. **Connect Repository**: Link your GitHub account
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3. **Deploy**:
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```bash
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git add streamlit_demo.py streamlit_requirements.txt
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git commit -m "Add Streamlit demo for QuantumBotX"
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git push origin main
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```
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4. **Configuration**:
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- Main file path: `streamlit_demo.py`
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- Requirements file: `streamlit_requirements.txt`
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### Option 2: Railway + Streamlit
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1. **Initialize Railway Project**:
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```bash
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railway init
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```
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2. **Create Railway Configuration**:
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```bash
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# railway.toml
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[build]
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builder = "NIXPACKS"
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[deploy]
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startCommand = "streamlit run streamlit_demo.py --server.port $PORT --server.headless true"
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```
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3. **Environment Variables** (optional):
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- No MT5 credentials needed (demo only)
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4. **Deploy**:
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```bash
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git add .
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git commit -m "Add Railway config for Streamlit demo"
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git push origin main
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railway up
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```
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### Option 3: Heroku + Streamlit
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1. **Create Heroku App**:
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```bash
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heroku create quantum-botx-demo
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```
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2. **Create requirements.txt** (use `streamlit_requirements.txt`)
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3. **Create Procfile**:
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```bash
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web: streamlit run streamlit_demo.py --server.port $PORT --server.headless true
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```
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4. **Deploy**:
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```bash
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git push heroku main
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```
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### Option 4: Vercel + Streamlit (Experimental)
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1. **Create vercel.json**:
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```json
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{
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"version": 2,
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"builds": [
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{
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"src": "streamlit_demo.py",
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"use": "@vercel/python"
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}
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],
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"routes": [
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{
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"src": "/(.*)",
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"dest": "streamlit_demo.py"
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}
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]
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}
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```
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2. **Deploy**:
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```bash
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vercel --prod
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```
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## Demo Features Showcased
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### 📊 Interactive Dashboard:
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- **Live Metrics**: Balance, strategies, profits, bots running
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- **Strategy Showcase**: MA Crossover & Bollinger Band explanations
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- **Charts**: Example price movements with indicators
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- **Trading History**: Filtered historic demo trades
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### 🎯 Strategy Highlights:
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- **Beginner Friendly**: Clear explanations and examples
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- **Risk Management**: ATR-based sizing demonstrations
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- **Multi-Asset**: FOREX, Gold, Crypto, Indices examples
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- **AI Features**: Strategy complexity ratings, mentor system
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### 🚀 Professional Presentation:
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- **Clean UI**: Modern Streamlit interface
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- **Responsive Design**: Works on mobile and desktop
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- **Educational Content**: Feature explanations and guides
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- **Call-to-Action**: Download links and system requirements
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## Files Created
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- **`streamlit_demo.py`**: Complete demo application
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- **`streamlit_requirements.txt`**: Minimal dependencies for cloud deployment
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- **`README_STREAMLIT.md`**: This deployment guide
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## Testing Locally
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Before deploying, test the demo locally:
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```bash
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# Install dependencies
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pip install -r streamlit_requirements.txt
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# Run the demo
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streamlit run streamlit_demo.py
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```
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**Expected Result**: Demo app opens in browser showing QuantumBotX features
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## Deployment Commands
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### Streamlit Cloud (Recommended):
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```bash
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streamlit run streamlit_demo.py --server.port 8501 --server.headless false
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```
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### Railway:
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```bash
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railway init
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railway up
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```
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### Heroku:
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```bash
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heroku create your-app-name
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git push heroku main
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```
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## Cost Comparison
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| Platform | Free Tier | Cost for Demo | Best For |
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|----------|-----------|---------------|----------|
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| **Streamlit Cloud** | 100 hours/month | Free | Best choice |
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| **Railway** | $5/month | $5/month | Good alternative |
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| **Heroku** | 550 hours/month | Free | Simple option |
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| **Vercel** | Generous free | Free | If preferring Vercel |
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## Why Not Live Trading Deployment?
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### Technical Barriers:
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1. **MT5 Windows-Only**: Terminal requires Windows OS
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2. **Persistent Connection**: Needs continuous MT5 session
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3. **GUI Requirement**: MT5 needs display server for login
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4. **License Issues**: MT5 EULA may prohibit containerization
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### Railway Specifically:
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- Railway uses **Linux containers** (Ubuntu/CentOS)
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- **Wine complications**: MT5 + Wine = unreliable connections
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- **No Windows support**: Railway doesn't offer Windows containers
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- **Cost ineffective**: Persistent VMs for MT5 would be expensive
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## Next Steps
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1. **Choose Platform**: Streamlit Cloud for easiest deployment
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2. **Test Demo**: Run locally first
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3. **Deploy**: Push to chosen platform
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4. **Share**: Send demo link to potential users
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5. **Monitor**: Check analytics and user feedback
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## Support
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- **Demo Issues**: Test locally first, then check deployment logs
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- **Streamlit Docs**: [docs.streamlit.io](https://docs.streamlit.io)
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- **Platform Support**: Each platform has detailed documentation
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- **QuantumBotX**: Full version requires Windows + MT5 setup
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---
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**Happy Showcasing! 🎯🤖**
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@@ -33,7 +33,7 @@ def health_check():
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return jsonify({"status": "ok", "message": "Server is running", "mt5": mt5_status})
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if __name__ == '__main__':
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# Skip MT5 initialization if SKIP_MT5_INIT is set (for Vercel deployment)
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# Skip MT5 initialization for import checks and local diagnostics.
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if os.getenv('SKIP_MT5_INIT') == '1':
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logging.info("Skipping MT5 initialization (deployment mode).")
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app.run(
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@@ -1,218 +0,0 @@
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import streamlit as st
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import pandas as pd
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from datetime import datetime, timedelta
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import random
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# Page configuration
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st.set_page_config(
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page_title="QuantumBotX Trading Bot Demo",
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page_icon="🤖",
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layout="wide",
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initial_sidebar_state="expanded"
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)
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# Header
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st.title("🤖 QuantumBotX - AI Trading Bot Demo")
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st.markdown("*Experience the power of algorithmic trading without risking real money*")
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# Portfolio Overview
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col1, col2, col3, col4 = st.columns(4)
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with col1:
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st.metric("Total Balance", "$10,000", "+2.3%")
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with col2:
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st.metric("Active Strategies", "4", "+1")
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with col3:
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st.metric("Total Profit", "$234.56", "+5.2%")
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with col4:
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st.metric("Bots Running", "3", "Online")
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# Demo Data Generation
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def generate_demo_data():
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# Generate realistic trading data
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symbols = ['EURUSD', 'GBPUSD', 'XAUUSD', 'BTCUSD']
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strategies = ['MA Crossover', 'RSI Momentum', 'Quantum Velocity', 'Ichimoku Cloud']
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data = []
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base_date = datetime.now() - timedelta(days=30)
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for i in range(30):
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for symbol in symbols[:2]: # Only EURUSD and GBPUSD for demo
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profit = random.uniform(-50, 150) if random.random() > 0.3 else random.uniform(-100, 200)
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data.append({
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'date': (base_date + timedelta(days=i)).strftime('%Y-%m-%d'),
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'symbol': symbol,
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'strategy': random.choice(strategies),
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'profit': round(profit, 2),
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'status': 'Closed' if random.random() > 0.2 else 'Open'
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})
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return pd.DataFrame(data)
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# Strategy Showcase
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st.header("🎯 Featured Trading Strategies")
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col1, col2 = st.columns(2)
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with col1:
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st.subheader("📈 MA Crossover Strategy")
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st.write("""
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**Perfect for beginners!** This classic strategy identifies trend changes using moving average crossovers.
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- Uses 50 & 200 period moving averages
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- Works beautifully in trending markets
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- Risk management: 1% per trade
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- EURUSD & GBPUSD optimized
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""")
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# Example chart placeholder
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chart_data = pd.DataFrame({
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'Price': [1.0850, 1.0875, 1.0920, 1.0885, 1.0910, 1.0935, 1.0960, 1.0945],
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'SMA50': [1.0800, 1.0815, 1.0830, 1.0845, 1.0860, 1.0875, 1.0890, 1.0905],
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'SMA200': [1.0750, 1.0765, 1.0780, 1.0795, 1.0810, 1.0825, 1.0840, 1.0855]
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})
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st.line_chart(chart_data)
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with col2:
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st.subheader("🎪 Bollinger Band Reversal")
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st.write("""
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**Advanced mean reversion strategy** that profits from price returning to the mean.
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- Uses Bollinger Bands for entry signals
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- RSI confirmation for momentum timing
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- Excellent in ranging markets
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- Perfect for FOREX pairs
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""")
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# Bollinger bands visualization
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bb_data = pd.DataFrame({
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'Price': [1.0850, 1.0835, 1.0860, 1.0825, 1.0875, 1.0800, 1.0885, 1.0840],
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'Upper BB': [1.0910, 1.0925, 1.0900, 1.0935, 1.0920, 1.0945, 1.0930, 1.0915],
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'Lower BB': [1.0790, 1.0775, 1.0800, 1.0765, 1.0780, 1.0755, 1.0770, 1.0785]
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})
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st.line_chart(bb_data)
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# Live Demo Section
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st.header("📊 Live Bot Performance")
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# Demo trading data
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demo_data = generate_demo_data()
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# Filter options
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col1, col2, col3 = st.columns(3)
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with col1:
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symbol_filter = st.selectbox("Filter by Symbol:", ["All"] + demo_data['symbol'].unique().tolist())
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with col2:
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strategy_filter = st.selectbox("Filter by Strategy:", ["All"] + demo_data['strategy'].unique().tolist())
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with col3:
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status_filter = st.selectbox("Filter by Status:", ["All"] + demo_data['status'].unique().tolist())
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# Apply filters
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filtered_data = demo_data.copy()
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if symbol_filter != "All":
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filtered_data = filtered_data[filtered_data['symbol'] == symbol_filter]
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if strategy_filter != "All":
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filtered_data = filtered_data[filtered_data['strategy'] == strategy_filter]
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if status_filter != "All":
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filtered_data = filtered_data[filtered_data['status'] == status_filter]
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# Display trades table
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st.dataframe(
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filtered_data.sort_values('date', ascending=False),
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use_container_width=True
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)
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# Performance Summary
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st.subheader("📈 Performance Summary")
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col1, col2, col3, col4 = st.columns(4)
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with col1:
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total_trades = len(filtered_data)
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st.metric("Total Trades", total_trades)
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with col2:
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profitable_trades = len(filtered_data[filtered_data['profit'] > 0])
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win_rate = (profitable_trades / total_trades * 100) if total_trades > 0 else 0
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st.metric("Win Rate", ".1f")
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with col3:
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avg_profit = filtered_data['profit'].mean()
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st.metric("Avg Profit/Trade", ".2f")
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with col4:
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total_profit = filtered_data['profit'].sum()
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st.metric("Total P&L", ".2f")
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# Features Showcase
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st.header("🚀 Key Features")
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feature_col1, feature_col2 = st.columns(2)
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with feature_col1:
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st.subheader("🛡️ Risk Management")
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st.write("""
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- **ATR-Based Sizing**: Dynamic position sizing that adapts to volatility
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- **1% Risk Rule**: Conservative approach protects your capital
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- **XAUUSD Protection**: Special safeguards for gold trading
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- **Emergency Brake**: Auto-halting during extreme market conditions
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""")
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st.subheader("🎓 AI Educational System")
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st.write("""
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- **Strategy Complexity Ratings**: 2-12 scale for beginners to experts
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- **Day-by-Day Learning**: Progressive experience from Week 1 to Month 3
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- **AI Mentor**: Indonesian AI mentor with cultural intelligence
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- **Parameter Explanations**: Every setting explained in plain language
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""")
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with feature_col2:
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st.subheader("📊 Multi-Asset Trading")
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st.write("""
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- **FOREX**: EURUSD, GBPUSD, JPY pairs with trend-following
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- **Gold**: Ultra-conservative XAUUSD with ATR limits
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- **Crypto**: BTCUSD/ETHUSD with 24/7 weekend trading
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- **Indices**: US500, US30 with momentum strategies
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- **Multi-Broker**: XM Global, FBS, IC Markets support
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""")
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st.subheader("🌟 Unique Features")
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st.write("""
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- **Strategy Switcher**: AI automatically selects best strategy
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- **Holiday Detection**: Christmas & Ramadan mode activation
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- **Cultural Awareness**: Islamic finance features and Zakat calculator
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- **Live Dashboard**: Real-time monitoring and performance tracking
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""")
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# Call to Action
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st.header("🎯 Ready to Start Your Trading Journey?")
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st.info("""
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**QuantumBotX Demo Limitations:**
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- This is a *simulation* of the actual trading bot
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- Real trading requires Windows + MetaTrader 5 terminal
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- All data shown is generated for demonstration purposes
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- The actual bot provides live MT5 integration and real-time trading
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""")
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st.markdown("""
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### How to Get Started:
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1. **Download the full application** from our GitHub repository
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2. **Install MetaTrader 5** on your Windows computer
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3. **Set up your demo account** with any MT5 broker
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4. **Configure and start trading** with $50 minimum deposit
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### System Requirements:
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- Windows 10/11 (64-bit)
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- MetaTrader 5 terminal
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- Python 3.10 or higher
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- At least 4GB RAM
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""")
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# Footer
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st.markdown("---")
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st.markdown("""
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<div style='text-align: center; color: #666;'>
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<p><strong>QuantumBotX</strong> - Making Algorithmic Trading Accessible for Everyone</p>
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<p>Developed with ❤️ by Chrisnov IT Solutions</p>
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<p><a href='https://github.com/rebarakaz/quantumbotx' target='_blank'>View on GitHub</a> • <a href='#' target='_blank'>Documentation</a></p>
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</div>
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""", unsafe_allow_html=True)
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@@ -1,5 +0,0 @@
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streamlit==1.29.0
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pandas==2.3.1
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numpy==1.23.5
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python-dateutil==2.9.0.post0
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pytz==2025.2
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