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Algorand for Beginners 1 – How to set up a development environment for Algorand? | Nextrope Academy

Paulina Lewandowska

27 Sep 2022
Algorand for Beginners 1 – How to set up a development environment for Algorand? | Nextrope Academy

In this article we start our series of articles that form the Algorand course for beginners. From the series of articles you will learn, among other things, how to set up a development environment for Algorand, how to deploy a smart contract on the Algorand network of your choice, how to write a simple smart contract, and what tools and frameworks you can use to work with the Algorand blockchain.

Algorand is a "green blockchain" launched in 2019, with the overarching goal of solving the blockchain trilemma through transaction speed, security, and a consensus algorithm that ensures full decentralization of the network.

Table of Contents

A list of the necessary tools and components:

  • Visual Studio Code
  • Python 3.6 or later
  • pyTeal library and py-teal-sdk
  • Docker Desktop
  • Algorand Sandbox
  • Skeleton project repository

Visual Studio Code installation

Visual Studio Code will be your IDE, with the help of this program you will be able to write application code.

Visual Studio Code (known as VS Code) is a free and open source text editor from Microsoft. VS Code is available for operating systems: Windows, Linux and macOS. Although the editor is relatively lightweight, it includes several advanced features that have made VS Code one of the most popular development environment tools in recent times.

  • Go to https://code.visualstudio.com/download  and download the installation file compatible with your operating system.
  • Install Visual Studio Code on your computer and proceed to the next step in this tutorial.

Installing Python

Python is a general-purpose, high-level interpreted programming language commonly used for web development, data analysis and automation.

One way to write smart contract logic for the Algorand network is with the Python library pyTeal ( https://pyteal.readthedocs.io/en/stable/ ), which allows you to write smart contract logic in python and compile the code into the TEAL code required by the AVM (Algorand Virtual Machine).

  • TEAL is an assembly language syntax for specifying a program that is eventually converted to AVM (Algorand Virtual Machine) byte code.
  • In VS Code, go to the "Extensions" tab used to install add-ons for Our IDE.

In the search bar, type "Python" and install Python extension for VS Code.

The next step is to install the interpreter for Python. This process varies depending on the operating system you are using.

Windows:

  • Download the installer for the language version of your choice from the official Python website https://www.python.org/downloads/ then go through the standard installation process,
  • An alternative to the above method is to install Python from the Microsoft Store, all the latest versions of python are available there.

MacOS:

  • To install the Python interpreter on macOS, we need to use the Homebrew package manager,
  • If Homebrew is already installed, open the command line and enter the command brew install python3.

On Linux distributions, the Python 3.x interpreter is installed by default.

After installing Python, it's a good idea to check that everything went as expected. To see the currently installed version of the interpreter, enter the command at the command line

python -v

Installing Docker Desktop

Docker Desktop is an easy-to-install application for macOS, Linux and Windows environments that allows you to create and share containerized applications and microservices.

Docker Desktop is required by the Algorand sandbox, a toolkit provided by the Algorand developers that is, a must-have, for any Algorand developer no matter what his or her level of expertise. Without Docker Desktop, you won't be able to run the most important tool for Algorand developers.

Installation files for each environment are available here.

After installation, Docker Desktop will start automatically.

Downloading Algorand Sandbox

Algorand sandbox is a set of tools that facilitate communication and interaction with the Algorand blockchain. Components of the sandbox include indexer, goal and algod.

With sandbox you can run betanet, testnet and mainnet in network mode, create tokens, nodes, execute transactions, create wallet addresses, check account balances, or deploy your applications on the network of your choice.

  • Go to https://github.com/algorand/sandbox and copy the link to the sandbox repository
  • Then open the command line on your computer, navigate to the desired location and enter the command git clone <repository address>
  • The sandbox repository will be cloned to your computer and you can start using it right away

Downloading the repository skeleton

To get started with Algorand easily enough, you should use the project skeleton, prepared by the Algorand development team.

Running Python (venv)

Python venv is a virtual Python environment in which the Python interpreter, libraries and scripts installed in it are isolated from those installed in other virtual environments and (by default) any libraries installed in "system" Python, i.e. one that is installed as part of the operating system.

To run a virtual Python environment follow a few simple steps:

  • Open the previously downloaded project skeleton in VS Code and start the terminal,
  • Make sure you are in the root directory of the project and enter the command python -m venv venv, the python virtual environment will be created with the name "venv",
  • Then depending on the operating system you are using, enter another command to activate the virtual environment:
  • MacOS: source ./venv/bin/activate, 
  • Windows: source ./venv/Scripts/activate,
  • To make sure you are working on the correct virtual environment, check that the name of your virtual environment appears before the command line in the terminal.

Installing additional libraries

In the example project in the requirements.txt folder, there are additional libraries that you need to install to start writing code for your application using the pyTeal library.

PyTeal is a Python language library for constructing Algorand smart contracts. It was created as a community project. The main goal of this library is to make writing contracts even easier and more accessible for programmers who prefer programming in Python.

  • To install additional libraries into your virtual environment you need to put is in the project's root directory in the requirements.txt file,
  • The most important libraries that we will use when writing smart contracts for Algorand are pyTeal and py-teal-sdk,
  • Once you have the requirements.txt file enter the following command to start installing additional libraries, pip install -r ./requirements.txt .

Linking your project to a sandbox

When creating your blockchain application, after some time you may need, for example, to deploy it on a network of your choice. For this task you will need the Algorand sandbox, but first you need to properly connect your project to the sandbox by pointing it to the location of Our Project, among other things.

To do this, navigate to the folder where the Algorand sandbox you downloaded earlier is located and follow the instructions below:

  • Open the docker-compose.yml file and in the services.algod section enter the additional volumes key with three additional parameters:
  • -type: bind,
  • source: <path to your project>,
  • target: /data,
  • example in the graphic below:

Starting the Algorand sandbox

To interact with the Algorand blockchain using the sandbox, you must first launch the sandbox container in Docker Desktop application.

To do so, follow the instructions below:

Open the command line on your computer and navigate to the folder of the sandbox you downloaded earlier,

Then enter the command ./sandbox up to start the sandbox container and place it in Docker,

By default, the sandbox will be started with betanet support. To run the sandbox with support for another network enter  ./sandbox up testnet or ./sandbox up mainnet .

Summary

With all the above steps completed, you have a ready-made environment to start working with the pyTeal library. You can now start writing your first smart contract, and deploy it on the network of your choice.

In the next article, we will introduce you to writing the simplest smart contract, along with deploying it on the Algorand network.

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Nextrope on Economic Forum 2024: Insights from the Event

Kajetan Olas

14 Sep 2024
Nextrope on Economic Forum 2024: Insights from the Event

The 33rd Economic Forum 2024, held in Karpacz, Poland, gathered leaders from across the globe to discuss the pressing economic and technological challenges. This year, the forum had a special focus on Artificial Intelligence (AI and Cybersecurity, bringing together leading experts and policymakers.

Nextrope was proud to participate in the Forum where we showcased our expertise and networked with leading minds in the AI and blockchain fields.

Table of Contents

Economic Forum 2024: A Hub for Innovation and Collaboration

The Economic Forum in Karpacz is an annual event often referred to as the "Polish Davos," attracting over 6,000 participants, including heads of state, business leaders, academics, and experts. This year’s edition was held from September 3rd to 5th, 2024.

Key Highlights of the AI Forum and Cybersecurity Forum

The AI Forum and the VI Cybersecurity Forum were integral parts of the event, organized in collaboration with the Ministry of Digital Affairs and leading Polish universities, including:

  • Cracow University of Technology
  • University of Warsaw
  • Wrocław University of Technology
  • AGH University of Science and Technology
  • Poznań University of Technology

Objectives of the AI Forum

  • Promoting Education and Innovation: The forum aimed to foster education and spread knowledge about AI and solutions to enhance digital transformation in Poland and CEE..
  • Strengthening Digital Administration: The event supported the Ministry of Digital Affairs' mission to build and strengthen the digital administration of the Polish State, encouraging interdisciplinary dialogue on decentralized architecture.
  • High-Level Meetings: The forum featured closed meetings of digital ministers from across Europe, including a confirmed appearance by Volker Wissing, the German Minister for Digital Affairs.

Nextrope's Active Participation in the AI Forum

Nextrope's presence at the AI Forum was marked by our active engagement in various activities in the Cracow University of Technology and University of Warsaw zone. One of the discussion panels we enjoyed the most was "AI in education - threats and opportunities".

Our Key Activities

Networking with Leading AI and Cryptography Researchers.

Nextrope presented its contributions in the field of behavioral profilling in DeFi and established relationships with Cryptography Researchers from Cracow University of Technology and the brightest minds on Polish AI scene, coming from institutions such as Wroclaw University of Technology, but also from startups.

Panel Discussions and Workshops

Our team participated in several panel discussions, covering a variety of topics. Here are some of them

  • Polish Startup Scene.
  • State in the Blockchain Network
  • Artificial Intelligence - Threat or Opportunity for Healthcare?
  • Silicon Valley in Poland – Is it Possible?
  • Quantum Computing - How Is It Changing Our Lives?

Broadening Horizons

Besides tuning in to topics that strictly overlap with our professional expertise we decided to broaden our horizons and participated in panels about national security and cross-border cooperation.

Meeting with clients:

We had a pleasure to deepen relationships with our institutional clients and discuss plans for the future.

Networking with Experts in AI and Blockchain

A major highlight of the Economic Forum in Karpacz was the opportunity to network with experts from academia, industry, and government.

Collaborations with Academia:

We engaged with scholars from leading universities such as the Cracow University of Technology and the University of Warsaw. These interactions laid the groundwork for potential research collaborations and joint projects.

Building Strategic Partnerships:

Our team connected with industry leaders, exploring opportunities for partnerships in regard to building the future of education. We met many extremely smart, yet humble people interested in joining advisory board of one of our projects - HackZ.

Exchanging Knowledge with VCs and Policymakers:

We had fruitful discussions with policymakers and very knowledgable representatives of Venture Capital. The discussions revolved around blockchain and AI regulation, futuristic education methods and dillemas regarding digital transformation in companies. These exchanges provided us with very interesting insights as well as new friendships.

Looking Ahead: Nextrope's Future in AI and Blockchain

Nextrope's participation in the Economic Forum Karpacz 2024 has solidified our position as one of the leading, deep-tech software houses in CEE. By fostering connections with academia, industry experts, and policymakers, we are well-positioned to consult our clients on trends and regulatory needs as well as implementing cutting edge DeFi software.

What's Next for Nextrope?

Continuing Innovation:

We remain committed to developing cutting-edge software solutions and designing token economies that leverage the power of incentives and advanced cryptography.

Deepening Academic Collaborations:

The partnerships formed at the forum will help us stay at the forefront of technological advancements, particularly in AI and blockchain.

Expanding Our Global Reach:

The international connections made at the forum enable us to expand our influence both in CEE and outside of Europe. This reinforces Nextrope's status as a global leader in technology innovation.

If you're looking to create a robust blockchain system and go through institutional-grade testing please reach out to contact@nextrope.com. Our team is ready to help you with the token engineering process and ensure your project’s resilience in the long term.

Tagi

Monte Carlo Simulations in Tokenomics

Kajetan Olas

01 May 2024
Monte Carlo Simulations in Tokenomics

As the web3 field grows in complexity, traditional analytical tools often fall short in capturing the dynamics of digital markets. This is where Monte Carlo simulations come into play, offering a mathematical technique to model systems fraught with uncertainty.

Monte Carlo simulations employ random sampling to understand probable outcomes in processes that are too complex for straightforward analytic solutions. By simulating thousands, or even millions, of scenarios, Monte Carlo methods can provide insights into the likelihood of different outcomes, helping stakeholders make informed decisions under conditions of uncertainty.

In this article, we will explore the role of Monte Carlo simulations within the context of tokenomics.  illustrating how they are employed to forecast market dynamics, assess risk, and optimize strategies in the volatile realm of cryptocurrencies. By integrating this powerful tool, businesses and investors can enhance their analytical capabilities, paving the way for more resilient and adaptable economic models in the digital age.

Table of Contents

Understanding Monte Carlo Simulations

The Monte Carlo method is an approach to solving problems that involve random sampling to understand probable outcomes. This technique was first developed in the 1940s by scientists working on the atomic bomb during the Manhattan Project. The method was designed to simplify the complex simulations of neutron diffusion, but it has since evolved to address a broad spectrum of problems across various fields including finance, engineering, and research.

Random Sampling and Statistical Experimentation

At the heart of Monte Carlo simulations is the concept of random sampling from a probability distribution to compute results. This method does not seek a singular precise answer but rather a probability distribution of possible outcomes. By performing a large number of trials with random variables, these simulations mimic the real-life fluctuations and uncertainties inherent in complex systems.

Role of Randomness and Probability Distributions in Simulations

Monte Carlo simulations leverage the power of probability distributions to model potential scenarios in processes where exact outcomes cannot be determined due to uncertainty. Each simulation iteration uses randomly generated values that follow a specific statistical distribution to model different outcomes. This method allows analysts to quantify and visualize the probability of different scenarios occurring.

The strength of Monte Carlo simulations lies in the insight they offer into potential risks. They allow modelers to see into the probabilistic "what-if" scenarios that more closely mimic real-world conditions.

Monte Carlo Simulations in Tokenomics

Monte Carlo simulations are instrumental tool for token engineers. They're so useful due to their ability to model emergent behaviors. Here are some key areas where these simulations are applied:

Pricing and Valuation of Tokens

Determining the value of a new token can be challenging due to the volatile nature of cryptocurrency markets. Monte Carlo simulations help by modeling various market scenarios and price fluctuations over time, allowing analysts to estimate a token's potential future value under different conditions.

Assessing Market Dynamics and Investor Behavior

Cryptocurrency markets are influenced by a myriad of factors including regulatory changes, technological advancements, and shifts in investor sentiment. Monte Carlo methods allow researchers to simulate these variables in an integrated environment to see how they might impact token economics, from overall market cap fluctuations to liquidity concerns.

Assesing Possible Risks

By running a large number of simulations it’s possible to stress-test the project in multiple scenarios and identify emergent risks. This is perhaps the most important function of Monte Carlo Process, since these risks can’t be assessed any other way.

Source: How to use Monte Carlo simulation for reliability analysis?

Benefits of Using Monte Carlo Simulations

By generating a range of possible outcomes and their probabilities, Monte Carlo simulations help decision-makers in the cryptocurrency space anticipate potential futures and make informed strategic choices. This capability is invaluable for planning token launches, managing supply mechanisms, and designing marketing strategies to optimize market penetration.

Using Monte Carlo simulations, stakeholders in the tokenomics field can not only understand and mitigate risks but also explore the potential impact of different strategic decisions. This predictive power supports more robust economic models and can lead to more stable and successful token launches. 

Implementing Monte Carlo Simulations

Several tools and software packages can facilitate the implementation of Monte Carlo simulations in tokenomics. One of the most notable is cadCAD, a Python library that provides a flexible and powerful environment for simulating complex systems. 

Overview of cadCAD configuration Components

To better understand how Monte Carlo simulations work in practice, let’s take a look at the cadCAD code snippet:

sim_config = {

    'T': range(200),  # number of timesteps

    'N': 3,           # number of Monte Carlo runs

    'M': params       # model parameters

}

Explanation of Simulation Configuration Components

T: Number of Time Steps

  • Definition: The 'T' parameter in CadCAD configurations specifies the number of time steps the simulation should execute. Each time step represents one iteration of the model, during which the system is updated. That update is based on various rules defined by token engineers in other parts of the code. For example: we might assume that one iteration = one day, and define data-based functions that predict token demand on that day.

N: Number of Monte Carlo Runs

  • Definition: The 'N' parameter sets the number of Monte Carlo runs. Each run represents a complete execution of the simulation from start to finish, using potentially different random seeds for each run. This is essential for capturing variability and understanding the distribution of possible outcomes. For example, we can acknowledge that token’s price will be correlated with the broad cryptocurrency market, which acts somewhat unpredictably.

M: Model Parameters

  • Definition: The 'M' key contains the model parameters, which are variables that influence system's behavior but do not change dynamically with each time step. These parameters can be constants or distributions that are used within the policy and update functions to model the external and internal factors affecting the system.

Importance of These Components

Together, these components define the skeleton of your Monte Carlo simulation in CadCAD. The combination of multiple time steps and Monte Carlo runs allows for a comprehensive exploration of the stochastic nature of the modeled system. By varying the number of timesteps (T) and runs (N), you can adjust the depth and breadth of the exploration, respectively. The parameters (M) provide the necessary context and ensure that each simulation is realistic.

Messy graph representing Monte Carlo simulation, source: Bitcoin Monte Carlo Simulation

Conclusion

Monte Carlo simulations represent a powerful analytical tool in the arsenal of token engineers. By leveraging the principles of statistics, these simulations provide deep insights into the complex dynamics of token-based systems. This method allows for a nuanced understanding of potential future scenarios and helps with making informed decisions.

We encourage all stakeholders in the blockchain and cryptocurrency space to consider implementing Monte Carlo simulations. The insights gained from such analytical techniques can lead to more effective and resilient economic models, paving the way for the sustainable growth and success of digital currencies.

If you're looking to create a robust tokenomics model and go through institutional-grade testing please reach out to contact@nextrope.com. Our team is ready to help you with the token engineering process and ensure your project’s resilience in the long term.

FAQ

What is a Monte Carlo simulation in tokenomics context?

  • It's a mathematical method that uses random sampling to predict uncertain outcomes.

What are the benefits of using Monte Carlo simulations in tokenomics?

  • These simulations help foresee potential market scenarios, aiding in strategic planning and risk management for token launches.

Why are Monte Carlo simulations unique in cryptocurrency analysis?

  • They provide probabilistic outcomes rather than fixed predictions, effectively simulating real-world market variability and risk.

Tagi