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System Requirements

Before installing CryptoView Pro, ensure your system meets these requirements:

Python Version

Python 3.8 or higher requiredCheck your version:
python --version

Operating System

  • Windows 10/11
  • macOS 10.14+
  • Linux (Ubuntu 20.04+, Debian, Fedora)

Memory

Minimum 4GB RAM8GB+ recommended for large datasets

Disk Space

500MB for dependenciesAdditional space for data caching

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Installation Steps

1

Install Python

If Python isn’t installed, download it from python.org.
  1. Download the Windows installer
  2. Important: Check “Add Python to PATH” during installation
  3. Verify installation:
python --version
pip --version
2

Clone the Repository

Get the CryptoView Pro source code:
# Using HTTPS
git clone https://github.com/your-username/cryptoview-pro.git
cd cryptoview-pro

# Or using SSH
git clone [email protected]:your-username/cryptoview-pro.git
cd cryptoview-pro
Don’t have Git? Download Git or download the repository as a ZIP file.
3

Create Virtual Environment

Create an isolated Python environment to avoid dependency conflicts:
# Create virtual environment
python -m venv venv

# Activate it
venv\Scripts\activate

# You should see (venv) in your prompt
To deactivate later: Simply run deactivate in your terminal.
4

Install Dependencies

Install all required packages from requirements.txt:
# Upgrade pip first (recommended)
pip install --upgrade pip

# Install all dependencies
pip install -r requirements.txt
This process takes 2-5 minutes depending on your internet connection and system.
If you encounter errors during installation, see the Troubleshooting section below.
5

Verify Installation

Test that all critical packages are installed:
python -c "import streamlit, ccxt, xgboost, prophet, plotly; print('✅ All packages installed successfully!')"
If this command runs without errors, you’re ready to go!

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Dependencies Explained

Here’s what gets installed and why:

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Core Dependencies

streamlit
Streamlit powers the interactive web dashboard.
  • Provides the UI framework
  • Handles real-time updates
  • Manages session state
  • Built-in caching system
Version: Latest (automatically updates)

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Machine Learning Dependencies

xgboost
scikit-learn
joblib
scipy
XGBoost is a gradient boosting framework for short-term predictions.Used for:
  • 1-72 hour forecasts
  • Feature engineering pipeline
  • Model persistence (saving/loading)
Configuration in config/settings.py:
MODELS_CONFIG = {
    'xgboost': {
        'n_estimators': 200,        # Number of trees
        'learning_rate': 0.07,      # Step size
        'max_depth': 6,             # Tree depth
        'subsample': 0.8,           # Sample ratio
        'colsample_bytree': 0.8     # Feature ratio
    }
}
scikit-learn provides:
  • Data scaling (MinMaxScaler)
  • Train/test splitting
  • Cross-validation (TimeSeriesSplit)
  • Evaluation metrics (RMSE, MAE, R²)

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Optional Dependencies

python-telegram-bot
requests
python-telegram-bot enables push notifications.Setup in .env file:
TELEGRAM_BOT_TOKEN=your_bot_token_here
TELEGRAM_CHAT_ID=your_chat_id_here
Alert configuration:
ALERT_THRESHOLDS = {
    'price_change_pct': 5.0,      # Alert on 5% moves
    'rsi_overbought': 70,          # RSI upper threshold
    'rsi_oversold': 30,            # RSI lower threshold
    'volume_spike': 2.0            # 2x average volume
}

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Configuration

After installation, configure CryptoView Pro for your needs:

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Basic Configuration

Edit config/settings.py to customize: 
# Select cryptocurrencies to track
AVAILABLE_CRYPTOS = [
    'BTC/USDT', 'ETH/USDT', 'SOL/USDT',  # Add or remove as needed
]

# Default settings
DEFAULT_CRYPTO = os.getenv('DEFAULT_CRYPTO', 'BTC/USDT')
DEFAULT_TIMEFRAME = os.getenv('DEFAULT_TIMEFRAME', '1h')

# Data collection
DATA_LIMIT = 2000              # Number of historical candles
MIN_DATA_POINTS = 500          # Minimum required for training
CACHE_TTL = 60                 # Cache duration in seconds

# Auto-refresh
AUTO_REFRESH_SECONDS = 30      # Dashboard update frequency

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Exchange Configuration

# No API keys needed for public data
from data.collectors import CryptoDataCollector

collector = CryptoDataCollector('kraken')
df = collector.fetch_ohlcv('BTC/USDT', timeframe='1h')

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Model Configuration

Fine-tune model parameters in config/settings.py: 
MODELS_CONFIG = {
    'xgboost': {
        'n_estimators': 200,          # More trees = better fit, slower training
        'learning_rate': 0.07,        # Lower = more conservative, needs more trees
        'max_depth': 6,               # Deeper = more complex patterns, risk overfitting
        'subsample': 0.8,             # Random sampling to prevent overfitting
        'colsample_bytree': 0.8       # Feature sampling per tree
    },
    'prophet': {
        'changepoint_prior_scale': 0.5,   # 0.001-0.5: flexibility for trend changes
        'seasonality_prior_scale': 10,    # 1-20: strength of seasonal patterns
        'seasonality_mode': 'multiplicative',  # or 'additive'
        'daily_seasonality': True,
        'weekly_seasonality': True,
        'yearly_seasonality': False       # Not useful for crypto
    }
}
Start with default values and adjust based on backtesting results. More aggressive parameters can improve short-term accuracy but may reduce generalization.

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Running the Application

Once installed and configured, start CryptoView Pro:
1

Activate Virtual Environment

# Windows
venv\Scripts\activate

# macOS/Linux
source venv/bin/activate
2

Navigate to Project Directory

cd path/to/cryptoview-pro
3

Launch Streamlit

streamlit run app.py
Optional flags:
# Custom port
streamlit run app.py --server.port 8502

# Disable auto-open browser
streamlit run app.py --server.headless true

# Enable CORS for remote access
streamlit run app.py --server.enableCORS false
4

Access Dashboard

Open your browser to:
http://localhost:8501
Or the custom port you specified.

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Troubleshooting Installation Issues

Some packages require C++ build tools.Solution:
  1. Download Microsoft C++ Build Tools
  2. Install “Desktop development with C++”
  3. Restart terminal and retry: pip install -r requirements.txt
Alternative: Install pre-built wheels:
pip install --only-binary :all: -r requirements.txt
Prophet has complex dependencies (pystan) that can fail.Solution:Try installing Prophet separately:
# Option 1: Using conda (recommended)
conda install -c conda-forge prophet

# Option 2: Pre-compiled wheel
pip install prophet --no-cache-dir

# Option 3: Skip Prophet (use XGBoost only)
# Edit requirements.txt and remove the 'prophet' line
CryptoView Pro will work without Prophet - you’ll just be limited to XGBoost and won’t have long-term forecasting.
Dependencies weren’t installed in the active environment.Solution:
# Verify you're in virtual environment (should see (venv))
which python

# Reinstall dependencies
pip install --upgrade pip
pip install -r requirements.txt

# Verify ccxt
python -c "import ccxt; print(ccxt.__version__)"
Network/firewall issues preventing package downloads.Solution:
# Temporary workaround (not recommended for production)
pip install --trusted-host pypi.org --trusted-host files.pythonhosted.org -r requirements.txt

# Better: Fix SSL certificates
pip install --upgrade certifi
Missing system libraries.Solution:
  1. Install Visual C++ Redistributable
  2. Restart your computer
  3. Retry installation
Not enough RAM for compilation.Solution:
# Install packages one at a time
pip install streamlit
pip install ccxt
pip install pandas numpy
pip install plotly
pip install scikit-learn
pip install xgboost
pip install prophet

# Or increase pip's memory limit
pip install --no-cache-dir -r requirements.txt

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Docker Installation (Alternative)

Prefer containerized deployment? Use Docker:
1

Create Dockerfile

Dockerfile
FROM python:3.11-slim

WORKDIR /app

# Install dependencies
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Copy application
COPY . .

# Expose Streamlit port
EXPOSE 8501

# Run application
CMD ["streamlit", "run", "app.py", "--server.address", "0.0.0.0"]
2

Build Image

docker build -t cryptoview-pro .
3

Run Container

docker run -p 8501:8501 \
  -e TELEGRAM_BOT_TOKEN=your_token \
  -e TELEGRAM_CHAT_ID=your_id \
  cryptoview-pro
4

Access Application

Open browser to http://localhost:8501

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Performance Optimization

Increase Cache TTL

# config/settings.py
CACHE_TTL = 300  # 5 minutes
Reduces API calls, faster dashboard updates

Limit Data Points

DATA_LIMIT = 1000  # Instead of 2000
Faster training, lower memory usage

Disable Auto-Refresh

AUTO_REFRESH_SECONDS = 0  # Manual only
Saves CPU, useful for batch analysis

Use XGBoost Only

Remove Prophet from requirements.txtFaster installation, lower dependencies

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Next Steps

Quickstart Guide

Generate your first predictions in 5 minutes

Configuration

Set up exchanges, API keys, and alerts

Model Training

Learn when to use each model type

API Reference

Explore the complete API documentation

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Updating CryptoView Pro

Keep your installation up to date: 
# Pull latest changes
git pull origin main

# Update dependencies
pip install --upgrade -r requirements.txt

# Restart application
streamlit run app.py
Always backup your configuration files (.env, custom settings.py) before updating.

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