Create jekyll.yml

[AmePelliccia]

AMPELChain Changelog and ROI Analysis

High ROI Projects

Project 1: Quantum Communication Network (APQ-CUZ-AP-GENSAI-CROSSPULSE-001)

• Description: Secure communication leveraging quantum entanglement.
• ROI Potential: High

Project 2: Quantum Algorithms for Aerodynamic Design (APQ-CUZ-AP-GENSAI-CROSSPULSE-002)

• Description: Optimizing aircraft designs using quantum algorithms.
• ROI Potential: High

Project 3: Quantum-Enhanced MRI Technology (APQ-CUZ-AP-GENSAI-CROSSPULSE-003)

• Description: Improving MRI resolution and sensitivity using quantum mechanics.
• ROI Potential: Moderate to High

Project 4: Quantum Financial Optimization (APQ-CUZ-AP-GENSAI-CROSSPULSE-004)

• Description: Optimizing investment portfolios with quantum algorithms.
• ROI Potential: High

Project 5: Quantum Environmental Monitoring (APQ-CUZ-AP-GENSAI-CROSSPULSE-005)

• Description: Using quantum sensors for precise environmental monitoring.
• ROI Potential: Moderate

Financial Integration and Automated Investment Strategy

Weekly Investment Allocation (June to August):

1. Ethereum (ETH): €50 per week
2. Solana (SOL): €50 per week
3. Binance Coin (BNB): €50 per week
4. Cardano (ADA): €50 per week
5. Ripple (XRP): €50 per week
6. PlayDoge (PLAY): €50 per week

Additional Investment Allocation:

• July: Reinforce positions in high-performing assets (ETH, SOL, BNB)
• August: Focus on emerging projects with high potential (Casper Network, SushiSwap)

Automation and Validation

Using Fin-AI Algorithms:

• DeltaOpt Function: Dynamically adjust investments based on market trends.
• Backtesting and Continuous Learning: Validate the model with historical data and real-time adjustments.

Portfolio Diversification

Diversified Investment Strategy:

• Cryptocurrencies: Ethereum, Solana, Binance Coin, Cardano, Ripple, PlayDoge
• Stocks and ETFs: Focus on technology and sustainable companies
• Bonds: ESG bonds for stable returns and reinvestment

ESG Bonds and Reinvestment

Reinvestment Plan:

• Initial Allocation: 30% of gains to ESG bonds
• Incremental Increase: Increase reinvestment percentage as profits grow

Automation Steps with Flask and PythonAnywhere

1. Setup Flask Application:
   • Create endpoints for balance checks, price fetching, and order placements.
2. Deploy on PythonAnywhere:
   • Utilize PythonAnywhere to host the Flask application and ensure it's accessible for automated scripts.

Implementation Example

from flask import Flask, request, jsonify
import requests
import alpaca_trade_api as tradeapi
from config import ALPACA_API_KEY, ALPACA_SECRET_KEY, ALPHA_VANTAGE_API_KEY

app = Flask(__name__)

# Initialize Alpaca API
api = tradeapi.REST(ALPACA_API_KEY, ALPACA_SECRET_KEY, base_url='https://paper-api.alpaca.markets')

def get_balance():
    account = api.get_account()
    balance = {
        'cash': account.cash,
        'portfolio_value': account.portfolio_value,
        'equity': account.equity
    }
    return balance

def get_price(symbol):
    endpoint = f'https://www.alphavantage.co/query?function=TIME_SERIES_INTRADAY&symbol={symbol}&interval=1min&apikey={ALPHA_VANTAGE_API_KEY}'
    response = requests.get(endpoint)
    data = response.json()
    latest_time = list(data['Time Series (1min)'].keys())[0]
    return float(data['Time Series (1min)'][latest_time]['1. open'])

def place_order(symbol, qty, side='buy'):
    api.submit_order(
        symbol=symbol,
        qty=qty,
        side=side,
        type='market',
        time_in_force='gtc'
    )
    return {'symbol': symbol, 'qty': qty, 'side': side}

@app.route('/balance', methods=['GET'])
def balance():
    balance = get_balance()
    return jsonify(balance)

@app.route('/prices', methods=['GET'])
def prices():
    symbols = request.args.get('symbols').split(',')
    prices = {symbol: get_price(symbol) for symbol in symbols}
    return jsonify(prices)

@app.route('/place-order', methods=['POST'])
def order():
    data = request.json
    symbol = data['symbol']
    qty = data['qty']
    side = data['side']
    order_response = place_order(symbol, qty, side)
    return jsonify(order_response)

if __name__ == '__main__':
    app.run(debug=True)

Deployment on PythonAnywhere

1. Upload app.py and config.py to PythonAnywhere.
2. Setup Virtual Environment: mkvirtualenv my-virtualenv --python=python3.8
3. pip install flask requests alpaca-trade-api
4. Configure Web App: Set up the web app on PythonAnywhere to run the Flask application.
5. Monitor and Adjust: Use PythonAnywhere’s logs and monitoring tools to ensure the application runs smoothly.

Conclusion

By integrating your financial situation, leveraging your projects, and using advanced algorithms, you can achieve your financial goals while maintaining a diversified and sustainable investment strategy. This plan ensures you are maximizing returns and reinvesting in ESG bonds, contributing to both personal growth and societal impact.

For more details, please visit GitHub Project.

[bigmyastacks]

saldo-gratis:patch-1

[bigmyastacks]

This pull request includes significant updates across multiple files to introduce new content and improve documentation. The most important changes involve the addition of a detailed changelog and ROI analysis, a comprehensive revision of the Code of Conduct, and an exploration of neural scales in a new document.

New Content and Documentation:

• .github/workflows/jekyll.yml: Added a detailed changelog and ROI analysis for several high ROI projects, financial integration strategies, investment automation using Flask and PythonAnywhere, and portfolio diversification.

• CODE_OF_CONDUCT.md: Replaced the existing Code of Conduct with a comprehensive document outlining various models proposed by Amedeo Pelliccia, their applications, and a new Citizen Code of Conduct to foster a welcoming community environment.

• ScaleNeuronali.md: Added a new document exploring the concept of neural scales and their possible forms, including physical-astronomical, artificial reproduction, conceptual scales, and a meta mix approach. This document also addresses the challenges of scalability in neural networks on cosmic dimensions.