Earn with Bandwidth Guide 2025 – Complete Bandwidth Sharing Monetization Tutorial

# Download and install Webshare app
curl -O https://webshare.io/download/webshare-bandwidth-app
chmod +x webshare-bandwidth-app
./webshare-bandwidth-app --install

# Configure your sharing preferences
webshare config --location auto --bandwidth-limit 50%

import requests
import json
import time
from selenium import webdriver
from selenium.webdriver.common.by import By

class WebshareBandwidthSetup:
    def __init__(self, email, password):
        self.email = email
        self.password = password
        self.session = requests.Session()

    def register_account(self):
        """Register new Webshare account"""
        register_url = "https://dashboard.webshare.io/auth/register"

        data = {
            "email": self.email,
            "password": self.password,
            "confirm_password": self.password
        }

        response = self.session.post(register_url, json=data)
        return response.json()

    def setup_bandwidth_sharing(self):
        """Configure bandwidth sharing settings"""
        # Login to dashboard
        login_url = "https://dashboard.webshare.io/auth/login"
        login_data = {
            "email": self.email,
            "password": self.password
        }

        self.session.post(login_url, json=login_data)

        # Configure sharing preferences
        config_url = "https://dashboard.webshare.io/api/bandwidth/config"
        config_data = {
            "enabled": True,
            "max_bandwidth": 50,  # 50% of total bandwidth
            "preferred_times": ["00:00-23:59"],  # 24/7 sharing
            "locations": ["auto"]  # Automatic location selection
        }

        response = self.session.post(config_url, json=config_data)
        return response.json()

    def monitor_earnings(self):
        """Check current earnings and statistics"""
        stats_url = "https://dashboard.webshare.io/api/earnings/stats"
        response = self.session.get(stats_url)
        return response.json()

#!/bin/bash

# Multi-platform bandwidth sharing setup
echo "Setting up bandwidth monetization across multiple platforms..."

# Install Webshare
echo "Installing Webshare..."
curl -fsSL https://webshare.io/install | bash

# Install Honeygain
echo "Installing Honeygain..."
wget https://download.honeygain.com/Honeygain.deb
sudo dpkg -i Honeygain.deb

# Install EarnApp
echo "Installing EarnApp..."
wget https://earnapp.com/download/EarnApp.deb
sudo dpkg -i EarnApp.deb

# Configure all services
echo "Configuring services..."
webshare config --auto-start --bandwidth 40%
honeygain config --devices all --background
earnapp config --optimize --notifications

echo "Setup complete! All services are running."

import psutil
import time
import subprocess

class BandwidthManager:
    def __init__(self, max_bandwidth_percent=50):
        self.max_bandwidth_percent = max_bandwidth_percent
        self.baseline_speed = self.measure_baseline_speed()

    def measure_baseline_speed(self):
        """Measure baseline internet speed"""
        try:
            result = subprocess.run(['speedtest-cli', '--simple'],
                                  capture_output=True, text=True, timeout=30)
            lines = result.stdout.strip().split('\n')
            download = float(lines[0].split(':')[1].strip().split()[0])
            upload = float(lines[1].split(':')[1].strip().split()[0])
            return {'download': download, 'upload': upload}
        except:
            # Fallback values
            return {'download': 50, 'upload': 10}

    def get_current_usage(self):
        """Get current network usage"""
        net_io = psutil.net_io_counters()
        return {
            'bytes_sent': net_io.bytes_sent,
            'bytes_recv': net_io.bytes_recv,
            'packets_sent': net_io.packets_sent,
            'packets_recv': net_io.packets_recv
        }

    def should_throttle(self):
        """Check if bandwidth usage is too high"""
        usage = self.get_current_usage()
        # Calculate current bandwidth usage (simplified)
        # In practice, you'd use more sophisticated monitoring
        return False  # Placeholder

    def optimize_sharing(self):
        """Optimize bandwidth sharing based on network conditions"""
        if self.should_throttle():
            # Reduce sharing intensity
            self.adjust_sharing_intensity(0.7)
        else:
            # Increase sharing for maximum earnings
            self.adjust_sharing_intensity(1.0)

    def adjust_sharing_intensity(self, factor):
        """Adjust how much bandwidth is shared"""
        # This would interface with the bandwidth sharing applications
        # Implementation depends on specific platform APIs
        pass

class LocationOptimizer:
    def __init__(self):
        self.location_rates = {
            'US': {'min': 1.00, 'max': 3.00, 'demand': 'high'},
            'EU': {'min': 0.80, 'max': 2.50, 'demand': 'high'},
            'UK': {'min': 0.90, 'max': 2.80, 'demand': 'high'},
            'CA': {'min': 0.70, 'max': 2.00, 'demand': 'medium'},
            'AU': {'min': 0.70, 'max': 2.00, 'demand': 'medium'},
            'ASIA': {'min': 0.30, 'max': 1.50, 'demand': 'low'}
        }

    def get_optimal_locations(self, user_location=None):
        """Get locations with highest earning potential"""
        if user_location:
            # Prioritize user's actual location for better performance
            return [user_location] + self.get_high_demand_locations()

        return self.get_high_demand_locations()

    def get_high_demand_locations(self):
        """Get locations with high demand"""
        high_demand = [loc for loc, data in self.location_rates.items()
                      if data['demand'] == 'high']
        return sorted(high_demand, key=lambda x: self.location_rates[x]['max'], reverse=True)

    def calculate_potential_earnings(self, location, bandwidth_gb):
        """Calculate potential earnings for given bandwidth"""
        rates = self.location_rates.get(location, {'min': 0.5, 'max': 1.5})
        avg_rate = (rates['min'] + rates['max']) / 2
        return bandwidth_gb * avg_rate

class DeviceManager:
    def __init__(self):
        self.devices = []
        self.max_devices = 10  # Platform limits vary

    def add_device(self, device_info):
        """Add a device to the earning network"""
        if len(self.devices) >= self.max_devices:
            raise Exception("Maximum devices reached")

        device = {
            'id': device_info.get('id'),
            'type': device_info.get('type', 'computer'),
            'bandwidth_limit': device_info.get('bandwidth_limit', 50),
            'active': True,
            'earnings': 0
        }

        self.devices.append(device)
        return device

    def optimize_device_usage(self):
        """Optimize which devices are active for maximum earnings"""
        # Prioritize high-bandwidth devices
        active_devices = [d for d in self.devices if d['active']]

        # Sort by potential earnings (bandwidth * rate)
        sorted_devices = sorted(active_devices,
                              key=lambda x: x['bandwidth_limit'],
                              reverse=True)

        # Activate top performers, deactivate low performers
        for i, device in enumerate(sorted_devices):
            should_be_active = i < 5  # Keep top 5 most capable devices active
            if device['active'] != should_be_active:
                self.toggle_device(device['id'], should_be_active)

    def toggle_device(self, device_id, active):
        """Activate or deactivate a device"""
        for device in self.devices:
            if device['id'] == device_id:
                device['active'] = active
                # Implementation would interface with platform APIs
                break

    def get_total_earnings(self):
        """Calculate total earnings across all devices"""
        return sum(device['earnings'] for device in self.devices)

import datetime

class TimeOptimizer:
    def __init__(self):
        self.peak_hours = {
            'US': [9, 10, 11, 12, 13, 14, 15, 16, 17],  # 9 AM - 6 PM EST
            'EU': [8, 9, 10, 11, 12, 13, 14, 15, 16, 17],  # 8 AM - 6 PM CET
            'ASIA': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]  # Peak Asian hours
        }

    def is_peak_hour(self, timezone='US'):
        """Check if current time is peak earning hour"""
        now = datetime.datetime.now()
        current_hour = now.hour

        if timezone in self.peak_hours:
            return current_hour in self.peak_hours[timezone]

        return False

    def get_optimal_schedule(self, timezone='US'):
        """Get optimal sharing schedule for maximum earnings"""
        peak_hours = self.peak_hours.get(timezone, [])

        schedule = {}
        for hour in range(24):
            if hour in peak_hours:
                schedule[hour] = 'high'  # Maximum sharing during peak
            elif hour in [peak-1, peak+1 for peak in peak_hours]:
                schedule[hour] = 'medium'  # Moderate sharing near peak
            else:
                schedule[hour] = 'low'  # Minimal sharing during off-peak

        return schedule

    def adjust_for_peak_hours(self, platform_configs):
        """Adjust platform settings based on peak hours"""
        current_timezone = self.detect_timezone()
        schedule = self.get_optimal_schedule(current_timezone)

        current_hour = datetime.datetime.now().hour
        intensity = schedule.get(current_hour, 'medium')

        # Adjust platform settings
        if intensity == 'high':
            bandwidth_limit = 80
        elif intensity == 'medium':
            bandwidth_limit = 50
        else:
            bandwidth_limit = 20

        self.apply_bandwidth_limit(platform_configs, bandwidth_limit)

    def detect_timezone(self):
        """Detect user's timezone"""
        # Simplified timezone detection
        offset = datetime.datetime.now() - datetime.datetime.utcnow()
        hours_offset = int(offset.total_seconds() / 3600)

        if -8 <= hours_offset <= -5:
            return 'US'
        elif 0 <= hours_offset <= 3:
            return 'EU'
        elif 5 <= hours_offset <= 12:
            return 'ASIA'
        else:
            return 'US'  # Default

class ReferralManager:
    def __init__(self, referral_code):
        self.referral_code = referral_code
        self.referral_stats = {
            'direct_referrals': 0,
            'indirect_referrals': 0,
            'total_earnings': 0,
            'commission_rate': 0.10  # 10% commission
        }

    def generate_referral_link(self, platform):
        """Generate referral link for specific platform"""
        base_urls = {
            'webshare': 'https://webshare.io/?ref=',
            'honeygain': 'https://r.honeygain.com/',
            'earnapp': 'https://earnapp.com/i/'
        }

        if platform in base_urls:
            return f"{base_urls[platform]}{self.referral_code}"

        return None

    def track_referral_earnings(self, referral_earnings):
        """Track earnings from referrals"""
        self.referral_stats['total_earnings'] += referral_earnings

    def calculate_potential_earnings(self, network_size):
        """Calculate potential earnings from referral network"""
        # Simplified calculation
        monthly_earnings_per_user = 25  # Average earnings per user
        commission_rate = self.referral_stats['commission_rate']

        direct_earnings = network_size * monthly_earnings_per_user * commission_rate
        indirect_earnings = direct_earnings * 0.5  # 50% of direct for indirect

        return {
            'direct': direct_earnings,
            'indirect': indirect_earnings,
            'total': direct_earnings + indirect_earnings
        }

class HardwareROI:
    def __init__(self):
        self.hardware_costs = {
            'raspberry_pi_4': 35,
            'intel_nuc': 300,
            'old_laptop': 0,  # Already owned
            'vps_monthly': 10
        }

        self.avg_monthly_earnings = {
            'raspberry_pi_4': 15,
            'intel_nuc': 50,
            'old_laptop': 25,
            'vps_monthly': 30
        }

    def calculate_roi(self, hardware_type, months=12):
        """Calculate return on investment"""
        initial_cost = self.hardware_costs.get(hardware_type, 0)
        monthly_earnings = self.avg_monthly_earnings.get(hardware_type, 0)

        total_earnings = monthly_earnings * months
        net_profit = total_earnings - initial_cost

        if initial_cost > 0:
            roi_percentage = (net_profit / initial_cost) * 100
        else:
            roi_percentage = float('inf')  # Infinite ROI for free hardware

        payback_months = initial_cost / monthly_earnings if monthly_earnings > 0 else float('inf')

        return {
            'initial_cost': initial_cost,
            'monthly_earnings': monthly_earnings,
            'total_earnings': total_earnings,
            'net_profit': net_profit,
            'roi_percentage': roi_percentage,
            'payback_months': payback_months
        }

import matplotlib.pyplot as plt
import pandas as pd
from datetime import datetime, timedelta

class EarningsDashboard:
    def __init__(self):
        self.earnings_data = []
        self.platforms = ['webshare', 'honeygain', 'earnapp', 'packetstream']

    def record_earnings(self, platform, amount, date=None):
        """Record earnings from a platform"""
        if date is None:
            date = datetime.now()

        entry = {
            'platform': platform,
            'amount': amount,
            'date': date,
            'timestamp': date.timestamp()
        }

        self.earnings_data.append(entry)

    def get_total_earnings(self, days=30):
        """Get total earnings for specified period"""
        cutoff_date = datetime.now() - timedelta(days=days)
        recent_earnings = [e for e in self.earnings_data if e['date'] > cutoff_date]

        return sum(e['amount'] for e in recent_earnings)

    def get_platform_breakdown(self, days=30):
        """Get earnings breakdown by platform"""
        cutoff_date = datetime.now() - timedelta(days=days)
        recent_earnings = [e for e in self.earnings_data if e['date'] > cutoff_date]

        breakdown = {}
        for platform in self.platforms:
            platform_earnings = [e['amount'] for e in recent_earnings if e['platform'] == platform]
            breakdown[platform] = sum(platform_earnings)

        return breakdown

    def generate_report(self, days=30):
        """Generate comprehensive earnings report"""
        total = self.get_total_earnings(days)
        breakdown = self.get_platform_breakdown(days)

        report = {
            'period_days': days,
            'total_earnings': total,
            'daily_average': total / days,
            'platform_breakdown': breakdown,
            'best_platform': max(breakdown.items(), key=lambda x: x[1])[0],
            'trend': self.calculate_trend(days)
        }

        return report

    def calculate_trend(self, days):
        """Calculate earnings trend"""
        # Compare current period with previous period
        current = self.get_total_earnings(days)
        previous = self.get_total_earnings(days * 2) - current

        if previous > 0:
            change_percent = ((current - previous) / previous) * 100
            if change_percent > 10:
                return 'increasing'
            elif change_percent < -10:
                return 'decreasing'
            else:
                return 'stable'
        else:
            return 'new'

    def plot_earnings(self, days=30):
        """Generate earnings visualization"""
        df = pd.DataFrame(self.earnings_data)
        df['date'] = pd.to_datetime(df['timestamp'], unit='s')
        df = df[df['date'] > datetime.now() - timedelta(days=days)]

        # Group by date and platform
        daily_earnings = df.groupby([df['date'].dt.date, 'platform'])['amount'].sum().unstack()

        # Create plot
        daily_earnings.plot(kind='bar', stacked=True, figsize=(12, 6))
        plt.title(f'Earnings by Platform - Last {days} Days')
        plt.xlabel('Date')
        plt.ylabel('Earnings ($)')
        plt.legend(title='Platform')
        plt.tight_layout()

        return plt