TradeLink

tradelink

Cryptocurrency trading has stirred the financial world, inviting ordinary people into a thrilling yet unpredictable market. However, amidst the victories, we find stories of traders fooled by fake promises and suspicious services. In this unstable field, one question stands out: "How can we identify the real from the fake?" Alexander Bogdanov,  co-founder of TradeLink, states clearly, "We want to create a fair and transparent crypto trading environment. This goal led us to develop TradeLink — a tool that increases investor trust in trading strategies." So, what is TradeLink, and why should you care? Let's delve into our goals, services, and shared dream. The mission of TradeLink is: to support real traders in a crowd of fakers, making sure every crypto enthusiast, beginner or expert, can understand who is trustworthy. We're creating a trading community centered on excellent trading strategies and transparency. TradeLink Passport: Tailored for Genuine Traders Needs states Yuriy Avdeev, co-founder and CEO of TradeLink. "Transparency is the cornerstone of trust, and trust is the cornerstone of TradeLink. Our aim with TradeLink Passport is to provide a platform that is as open and understandable as possible. It is more than just a service—it's a tailor-made tool that lets traders shine," is a vital tool for crypto traders and investors. Here's why: TradeLink Passport We are built on transparency. We gather, calculate, and show each trader's stats in an easy-to-understand portfolio format. This helps users to invest their time wisely, quickly analyzing every trader not by his words, but performance. proof-of-competence At TradeLink, traders can control how visible they are, with the option to keep their portfolios private or accessible via a link. Those who decide to make it public can list their portfolios in our , catching the eye of the entire TradeLink community. rating section TradeLink Passport also lets traders build their professional online image. By labeling trading styles, linking social media channels, and adding posts from their Telegram channels, traders create an identity that fits them. personalizing profiles, Our community includes various trading strategy developers, high-volume fund members, and crypto trading enthusiasts, adding trust to our platform and mission. Looking forward, we imagine a future where all investors can access well-checked, successful crypto trading strategies. Our TradeLink Marketplace aims to , providing a safe place for retail investors tired of misleading traders and average social trading platforms. energize the crypto asset management field TradeLink Marketplace: High-Quality Asset Management Platform for Everyone is offering a safe route to profitable crypto trading strategies from our community. TradeLink Marketplace Here's the great thing - the TradeLink Marketplace is essentially a hub where you can safely follow the best-selected trading strategies collected from TradeLink Passport and profit from your investments. You can rely on the expertise of successful traders without having to learn everything yourself. Our way is transparency, responsibility, and investor control. We only collect, assess, and provide access to proven trading strategies, without storing user’s assets. Investments are made via an API connection to your exchange account, ensuring you fully manage your funds and give us only the necessary permissions. We've introduced a feature to create from any trading strategies listed on our marketplace, giving you the power to spread risk across multiple instruments that fit your goals personalized indices Creating indices is powered by our own development called . It aids the replication of joint results from a group of pre-set trading strategies by following their trading operations on a single exchange account. Joint Execution Technology (JET) TradeLink Marketplace is an uncompromised asset management platform in the unpredictable world of crypto trading. You can follow the best, invest confidently, and keep control over your assets. The only price to pay is a profit-sharing fee; you can learn more by visiting our landing page at the bottom of this article. TradeLink: Pioneering a Fairer Crypto Trading Landscape TradeLink's future isn't based on idle dreams but on real actions to democratize access to successful trading strategies. We're tirelessly working to expand our offering of proven, efficient trading strategies, making them accessible to all traders, and investors regardless of their level of expertise or wealth. Moreover, TradeLink is dedicated to helping quality trading strategies spread among other asset management platforms via white-label mechanisms. We have big plans for TradeLink. One of our planned features includes the development of active indices management. Like active portfolio management in traditional finance, managing indices involves adjusting strategy allocations to optimize gains. In the future, we see a platform where clever individuals can change these indices following the market trends, enhancing their own returns and those of their followers. Moreover, we plan to create an affiliate program and referral system to encourage the growth of our community. We believe that creating a platform based on shared development, knowledge exchange, and mutual benefit is key to transforming the crypto trading landscape. Creating a fairer, more transparent trading world is not a task we can do alone. It's a group effort. That's where you come in. We’re asking for help — take part, tell about TradeLink to your audience, friends, and colleagues, and start conversations about a world where trust and transparency in trading are the rules, not the exception. Based on our knowledge, truly successful traders are always ready to showcase their trading achievements. TradeLink's mission is to help genuine traders distinguish themselves from the masses by providing them with an easily shareable validation of their skill and proficiency. As we continue the development of the TradeLink platform, we aim to support more crypto exchanges. It's a big task, but with your voice amplifying our mission, we can reach the right people, attract the right talent, and make our platform more inclusive. Remember, TradeLink is about you. It's about your empowerment, growth, and voice in the wild world of crypto trading. Join our community, engage in the discussion, and contribute to our shared vision for the future of crypto trading. Your input and support matter, and we can revolutionize this space together. You can follow our updates by visiting TradeLink’s landing page.

Shaping a New Era of Transparent Crypto Trading: TradeLink

Dando forma a una nueva era de criptocomercio transparente: TradeLink

Façonner une nouvelle ère de crypto trading transparent : TradeLink

पारदर्शी क्रिप्टो ट्रेडिंग के एक नए युग को आकार देना: ट्रेडलिंक

透明な仮想通貨取引の新時代を形作る: TradeLink

Moldando uma Nova Era de Negociação Transparente de Criptomoedas: TradeLink

Định hình kỷ nguyên mới của giao dịch tiền điện tử minh bạch: TradeLink

塑造透明加密货币交易的新时代：TradeLink

In this tutorial, we’ll be using Alpaca, Python, Docker, and AWS to create a fully scalable algorithmic crypto trading bot that can execute in your sleep. The strategy that will be used is based on the popular . You can check out all the source code for this article in this or in this . Supertrend indicator GitHub repository Google Colab Without further ado, let’s get started! Step 1: Creating Alpaca and AWS Accounts Setting up Alpaca In order to create the algorithmic trading bot system used in this article, the first step is to create an Alpaca account. If you don’t have an account yet, you can for free! Alpaca offers both a live brokerage as well as a paper trading platform but you can sign up just for paper trading. sign up here After creating an account, you will need to create your API credentials to access Alpaca data via Python. Save both the API Key as well as the Secret Key into a safe place on your computer. These keys will allow us to retrieve live price data for cryptocurrencies as well as execute trades in our paper trading account. Setting up AWS In order to deploy the trading bot, we’ll need an AWS account. You can for a free tier account, which offers more than 100 different services and in our case, 2200 hours of Amazon ECS Anywhere coverage. Fortunately, the sign-up process is relatively straightforward, but to learn more about what the free tier consists of, check out this . sign up link Step 2: Creating Trading Bot in Python Now that we have the accounts necessary to get started, we can start building the trading bot in Python. For this tutorial, we’ll be creating a bot that trades Bitcoin based on the signals of the Supertrend indicator. The Supertrend indicator is a popular tool for crypto swing trading as well as trend following systems but can be adapted to any timeframe. It’s calculated by using the difference between the current average true range and the average price from the most recent price bar. Import Dependencies First, we’ll need to import all of the required dependencies we’ll be using in the bot including smtplib for email communication, pandas-ta for the calculation of the supertrend indicator, and alpaca-trade-api for live market data and paper trading account access. If you have not yet installed some of these dependencies, you can go ahead and pip install them in your terminal. # Import Dependencies
import smtplib
import pandas as pd
import pandas_ta as ta
import alpaca_trade_api as tradeapi
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart Define Variables The next step is to define most of the general variables we’ll be needing throughout the program. For the API and Secret keys, you can paste the values you stored from step 1 of this tutorial when creating an Alpaca account. For the trading strategy, we’ll be buying and selling Bitcoin and each transaction will consist of 1 quantity. # API Credentials
API_KEY='********'
SECRET_KEY='********'
api = tradeapi.REST(API_KEY, SECRET_KEY,'https://paper-api.alpaca.markets')

# Define crypto related variables
symbol = 'BTCUSD'
qty_per_trade = 1 Create Function to Check Account Positions Next, we have to create a function to check whether the trading account currently holds any Bitcoin. If it does, we can return the quantity of Bitcoin that is being held. If there is no Bitcoin, then the function will just return 0. This is important because, in the next function which handles the buying and selling, we can focus on buying only if there is currently no Bitcoin in the account. Otherwise, we’ll monitor the sell signal to see if the position should be closed. # Check Whether Account Currently Holds Symbol
def check_positions(symbol):
    positions = api.list_positions()
    for p in positions:
        if p.symbol == symbol:
            return float(p.qty)
    return 0 Create Function to Trade Bitcoin Finally, we can create a function to retrieve the data, calculate the supertrend indicator signals, and execute the buy/sell orders. First, the function takes in an input of bar data which is used for the live current close price. Using the pandas-ta module discussed earlier, we can calculate the supertrend indicator as a column and concatenate it to the dataframe with pricing information. If the closing price is above the supertrend indicator, then a buy signal is issued. If the closing price is below the supertrend indicator, then a sell signal is issued. So by using the latest values from the dataframe, we can create boolean values for buy and sell signals. We can set the code to buy 1 quantity of Bitcoin if there is currently no position and if a buy signal is true. Likewise, we can set the code to sell 1 quantity of Bitcoin if there is currently a position and if a sell signal is true. Wrapping the entire function in a try/except block ensures that the program will not break due to errors, and will simply print out the error message. Since this is a trading bot and is intended to run throughout market hours, it’s best if the program is continuously running. # Supertrend Indicator Bot Function
def supertrend_bot(bar):
    try:
        # Get the Latest Data
        dataframe = api.get_crypto_bars(symbol, tradeapi.TimeFrame(1, tradeapi.TimeFrameUnit.Minute)).df
        dataframe = dataframe[dataframe.exchange == 'CBSE']
        sti = ta.supertrend(dataframe['high'], dataframe['low'], dataframe['close'], 7, 3)
        dataframe = pd.concat([dataframe, sti], axis=1)

        position = check_positions(symbol=symbol)
        should_buy = bar["c"] > dataframe["SUPERT_7_3.0"][-1]
        should_sell = bar["c"] < dataframe["SUPERT_7_3.0"][-1]
        print(f"Price: {bar["c"]}")
        print("Super Trend Indicator: {}".format(dataframe["SUPERT_7_3.0"][-1]))
        print(f"Position: {position} | Should Buy: {should_buy}")

        # Check if No Position and Buy Signal is True
        if position == 0 and should_buy == True:
            api.submit_order(symbol, qty=qty_per_trade, side='buy')
            message = f'Symbol: {symbol} | Side: Buy | Quantity: {qty_per_trade}'
            print(message)
            # send_mail(message)

        # Check if Long Position and Sell Signal is True
        elif position > 0 and should_sell == True:
            api.submit_order(symbol, qty=qty_per_trade, side='sell')
            message = f'Symbol: {symbol} | Side: Sell | Quantity: {qty_per_trade}'
            print(message)
            # send_mail(message)
        print("-"*20)

    except Exception as e:
        print (e)
        # send_mail(f"Trading Bot failed due to {e}") Step 3: Implementing Email Communication System Create Function for Sending Emails A critical aspect of any algorithmic trading system is having a built-in communication system where updates can be sent about any potential errors, transactions, or status. With Python, it’s extremely easy to set up email messaging using the packages email and smtplib. The first step is to set the variables for the sending and receiving emails. The email that is sending the messages must have the setting set to be off. Due to security reasons, it’s for the best if you create an entirely new email just for this trading strategy. “Less Secure Apps” Next, we can set the “from” tag equal to the algorithm name, subject title equal to our bot, and mail content using the input of text for the function. Using smtplib, we can access the gmail server, log in using the credentials given, and send the email message. You can now uncomment all of the send_mail function calls from the previous step! # Send an Update Email After Every Trade
def send_mail(message_text):
    # Define email related variables
    sender_email = '**************'
    sender_password = '**************'
    receiver_email = '**************'

    # Create a multipart message and set headers
    message = MIMEMultipart()
    message['From'] = 'Crypto Trading Algorithm'
    message['To'] = receiver_email
    message['Subject'] = 'Supertrend Indicator Bot'

    # Add body to email and send
    mail_content = message_text
    message.attach(MIMEText(mail_content, 'plain'))
    session = smtplib.SMTP('smtp.gmail.com', 587)
    session.starttls()
    session.login(sender_email, sender_password)
    text = message.as_string()
    session.sendmail(sender_email, receiver_email, text)
    session.quit()
    print ('Mail Sent')

    return {"Success": True} Step 4: Stream Live Data From Alpaca Set Up Alpaca Live Crypto Data The last step of building the Python bot is to start streaming live market data for Bitcoin from Alpaca. Fortunately, Alpaca makes this process extremely easy. First, we have to create an instance of the data streaming API by calling the Stream method in which we pass the API keys. We can also specify that we want raw data only from the Coinbase exchange. Then, we can create an asynchronous function to receive the live bar data and within this function, we can call the supertrend_bot function we made in step 2. Here, we can send an email that the trading bot is now live, subscribe to crypto data, and start streaming live data! # Create instance of Alpaca data streaming API
alpaca_stream = tradeapi.Stream(API_KEY, SECRET_KEY, raw_data=True, crypto_exchanges=['CBSE'])

# Create handler for receiving live bar data
async def on_crypto_bar(bar):
    print(bar)
    supertrend_bot(bar)

send_mail("Trading Bot is Live")

# Subscribe to data and assign handler
alpaca_stream.subscribe_crypto_bars(on_crypto_bar, symbol)

# Start streaming of data
alpaca_stream.run() Step 5: Dockerizing the Python Program Creating a Dockerfile In the next step, we’ll be deploying the code to AWS Elastic Container Services. To do that, we must first dockerize the python program we have just created. If you have not already done so, the first step is to onto your machine. Once you’ve done so, you should be able to run the following command in your terminal and receive a valid response such as “Docker version 20.10.14, build a224086”. download Docker docker -v Next, you must enter into your project directory in which you have the python program we have created and then create a Dockerfile. Using the terminal, you can simply run the command below! touch Dockerfile So far, the project directory should be something similar to what’s below. .
├── crypto_bot.py
├── Dockerfile Now, we can start populating the Dockerfile with the information it needs to containerize the application. First, we should pull down a tagged Python base image using the FROM command. The version 3.7.6 can be changed to suit your needs. Then, we can ADD the source file to the container’s base folder which in this case is crypto_bot.py. In order to use third-party libraries like alpaca-trade-api and pandas-ta in our docker app, we have to install them using the RUN pip install command. Lastly, we can add the final command that Docker will run once it’s fully set up. At this point, our Dockerfile is complete and we just have to build it within our terminal. FROM python:3.7.6

ADD crypto_bot.py .

RUN pip install alpaca-trade-api pandas-ta

CMD [ "python", "./crypto_bot.py" ] In order to build the docker app, we can return to our terminal in the same project directory and run the following command. You can replace “python-imagename” with whatever name you’d like to call this docker app! docker build -t python-imagename . In order to build the docker app, we can return to our terminal in the same project directory and run the following command. You can replace “python-imagename” with whatever name you’d like to call this docker image! Once the docker app is done building, you can run the following command to run it and you should see that the Python program will run as usual. Hopefully, you should have received an email that the trading bot is live and that trades can be executed. Keep in mind that with any changes you make to the program, you must rebuild the docker app using the previous command and run it again with the command below. And that’s it, our Python program has been fully dockerized! docker run -t python-imagename Step 6: Deploying to AWS Cloud Provider AWS CLI Configuration In order to get our docker app up and running in the cloud, we first need to if it has not already been installed. download the AWS command-line interface Once the CLI is installed, we can run the following command in our terminal to log in to our AWS account and get started with the deployment. aws configure After running this command, your terminal will ask you to provide your AWS Access Key ID and your AWS Secret Access Keys. Both of these can be found within your AWS account within security credentials. You can leave the default region name and output format as they currently are. AWS ECS Repositories Now that we’ve logged into our account in the terminal, the next step is to get our local docker app into an ECS repository in AWS. To do that, type in “ECS Repositories” in the search bar and click on the first Features link. On the ECS repositories screen, you can go ahead and create a new repository, give it a name, and leave the rest of the settings as their default state. Now, you should have a screen that’s similar to the one below. If you open up the repository and click on the “View push commands” button, you’ll be able to see the commands that are needed to push our local docker app into this repository. Follow the instructions on your screen (similar to the one below) in your local terminal. Your app should now be pushed to AWS! AWS ECS Clusters In the AWS console, navigate to AWS ECS and click the “create cluster” button. For the cluster template, click on “EC2 Linux + Networking” and continue to the next step. On the next page, you can configure the cluster to suit your specific needs. In this particular case, you only need to change six things. Create a name for your cluster Under instance configuration, change the EC2 instance type to t3a.micro Under networking, select the default dropdown value for VPC Under networking, select the default dropdown value for Subnets Under networking, select Enabled for “Auto assign public IP” Under networking, select the default dropdown value for “Security Group” After these steps, you can go ahead and create the cluster. Once, the process has been completed you should see the ECS cluster has been created, the ECS Instance IAM Policy was attached, and the CloudFormation Stack was created. AWS Task Definitions Once your cluster has been created, click on the “Task Definitions” button under Clusters in the left-hand sidebar. Click on create a new task definition, pick EC2 as the launch type compatibility and continue on to the next step. Enter a task name and scroll down to the section named Task size. Here, you can enter whatever memory and CPU values you are comfortable with. Then, click on Add container under “Container definitions.” Enter a name once again and for the image, paste the container image URL that you received from AWS. It should be similar to “https://public.ecr.aws/g9l6a0m7/crypto-bot:latest.” Then, you can scroll to the bottom of the page and create the task definition. AWS Deploying Tasks Finally, return back to the main clusters page so that you can see a screen similar to the one below. Enter into your cluster, and navigate to the Tasks tab. There, you should see an option to run a new task. Click on that button and a new popup wIll appear like the one below. Select EC2 as the launch type, and under task definition, select the name of the task you created in the previous step. Enter in the name of the cluster you just created under the Cluster dropdown. Then, go ahead and run the task. At this point, the app should be up and running on AWS! Conclusion In this tutorial, we covered everything from creating a live trading bot and implementing email communication to dockerizing a Python program and deploying it onto AWS ECS. While the strategy in the article was basic and not intended to be used as a full trading system, the processes to build a fully scalable system around a Python bot are the same so you can now automate anything you’d like! Thanks for reading, and I hope you learned something about creating algorithmic trading bots with the Alpaca Crypto API! Please note that this article is for general informational purposes only. All screenshots are for illustrative purposes only. Alpaca does not recommend any specific cryptocurrencies. Cryptocurrency services are made available by Alpaca Crypto LLC ("Alpaca Crypto"), a FinCEN registered money services business (NMLS # 2160858), and a wholly-owned subsidiary of AlpacaDB, Inc. Alpaca Crypto is not a member of SIPC or FINRA. Cryptocurrencies are not stocks and your cryptocurrency investments are not protected by either FDIC or SIPC. Depending on your location, cryptocurrency services may be provided by West Realm Shires Services, Inc., d/b/a FTX US (NMLS #1957771). This is not an offer, solicitation of an offer, or advice to buy or sell cryptocurrencies, or open a cryptocurrency account in any jurisdiction where Alpaca Crypto, or FTX US respectively, are not registered or licensed, as applicable. Cryptocurrency is highly speculative in nature, involves a high degree of risks, such as volatile market price swings, market manipulation, flash crashes, and cybersecurity risks. Cryptocurrency is not regulated or is lightly regulated in most countries. Cryptocurrency trading can lead to large, immediate and permanent loss of financial value. You should have appropriate knowledge and experience before engaging in cryptocurrency trading. For additional information, please click . here Please see alpaca.markets and Alpaca’s Disclosure Library for more information.

How to Build an Algorithmic Crypto Trading Bot in 6 Steps

Crypto trading remains one of the most popular and effective ways to make money from crypto-assets. At the beginning of the year, the total volume of crypto-trading crossed the $68 billion mark and may come close to $100 billion by the end of the year. High returns on assets and accessibility to trading for almost every user attracts many traders to the market who are in search of investors. We have compiled the top 3 services that every crypto trader needs. Trading account analysis. With the help of special analytical services, investors can track trading accounts on crypto exchanges. For example, the TradeLink platform offers a tool to analyze trading account data of individual crypto traders on various exchanges. The platform analyzes traders' trading data via a read-only API key, providing investors with an independent assessment of their portfolios' performance. Such a system provides accurate account analysis while guaranteeing the protection of traders’ personal data from unauthorized access. TradeLink allows traders to analyze one or more crypto exchange accounts in a portfolio and conduct a detailed analysis of their trading in a personal statistics page, which includes more than 25 indicators. Also, the portfolio can be made public, this makes it convenient to show trading results to investors as proof of your professionalism. Rating of traders. Another important tool that affects the choice of a trader and further cooperation with him is a rating system. This rating system is a list of successful traders on the largest crypto exchanges (Binance, BitMex, FTX, ByBit) with detailed information on each of them. The rating publishes information such as balance, profit, maximum drawdown, Net P/L, PnL, number of days of account tracking, open history ratio, etc. Participation in the rating helps traders to showcase their success and attract investors. Trader's portfolio. Professional traders can compile their own portfolios on various crypto exchanges to make a large-scale statement about their successes and achievements. Users only need to link their account on the exchange to the TradeLink platform and the statistics will be displayed directly on the site. Investors will be able to access the information and make a decision about getting in business with a trader. A complete solution for investors and traders The rapid dynamics of the cryptocurrency market are of great interest to traders and investors, many of whom are very determined to act despite the risks involved. And one of the key factors of the crypto market is its high volatility, expressed in sharp fluctuations in asset prices Another important risk of investing in cryptocurrency trading is the deceptive behaviour of some traders. First, they attract investors with tales of high profits and then leave them with nothing after devastating drawdowns. Moreover, it is difficult for an inexperienced investor to assess the traders' real rates of return without special knowledge and tools. The solution to these problems was the creation of , a platform which offers a wide range of tools for tracking and analyzing trading accounts on cryptocurrencies. TradeLink The platform offers a set of solutions that, on the one hand, help the trader to trade more safely and thereby prolong his "life" (analytics solutions, trader's diary, etc.). On the other hand, we offer a system of gamification of trading with active cashback, which increases the trader's activity and thus increases his trading volume (the main index for any exchange market). The gamification system includes an "Achievement System" based on trading events on the trader's account. The main advantage is that users receive real cashback (remuneration) for completing various achievements. Such a system makes it possible to bring back even traders who have already quit trading, because the Achievement System always creates an "incomplete effect" due to the fact that not all achievements have been obtained. Also, the gamification system includes Tournaments, which can be organized both by a sponsor (exchange, trading platform, etc.) and by community members themselves. All this together gives a strong user motivation system for exchanges and trading platforms, which allows you to multiply the benefits received from each user, as well as make the user experience more interesting and exciting. The main partner and investor of the project is the algorithmic trading and investment fund QuantHill. The tools of the platform allow you to effectively track the performance of each individual trader. This information will help investors to make the right decisions and choose the most effective specialists. TradeLink «The main direction of the platform is not only the creation of tools for traders (statistics, account monitoring, trader's diary) and investors but also work with stock exchanges. Our project can offer unique technologies to increase traders' motivation and increase their "life" time. The problem for crypto exchanges right now is the high cost of client acquisition. The second problem is that this client is mostly inexperienced and his lifetime is very limited, due to the fact that he just "drains" his deposit. After that, the client is usually depressed and his return to "active trading life" may be delayed or not happen at all», - says CPO TradeLink Georgiy Brichkovskiy. «The project is based on the desire to make the crypto trading market more transparent, honest and understandable even for novice investors. We ourselves have often encountered the fact that it is difficult to check the results of traders' trading and this makes the market dangerous for novice investors who entrust their funds to fraudsters or just non-professionals. TradeLink allows you to check any trader and his results in a convenient and simple way. And for traders, the project offers convenient tools, a large community of like-minded people, and the opportunity to compete in the rating and regular tournaments», commented Yuri Avdeev, CoFounder of TradeLink.

Top 3 tools every crypto trader needs

TerraGreen Purpose The general thought behind is to use blockchain technology and cryptocurrency to make the earth (Terra) a greener place. The focus of TerraGreen is to produce renewable energy that is generated from renewable materials and waste productions instead of using fossil fuels like oil, coal and natural gas. In the end, TerraGreen’s purpose is to create a truly sustainable and clean community which will allow consumers to directly participate in the renewable energy industry positively. TerraGreen TerraGreen intends to break down the walls between energy producers, investors, consumers, and utilities so that people get involved and influence their energy is produced. TerraGreen Motivation. Environmental stress to earth has induced an urgent quest for innovation in energy structure on a global scale. As evidenced by the 148 nations who ratified the Paris Climate Accord, there is profound interest in fostering a cleaner future as these nations agreed to fulfil its responsibility by capping GHG emissions as soon as possible. The seriousness of global warming is due to a disconnection between the energy producers and energy users. TerraGreen is all about bringing reform in the current energy supply structure and transition to a new energy system with the help of blockchain technology, that can fundamentally reduce GHG emissions and mitigate waste pollution. TerraGreen is an ecosystem, where renewable energies are tokenized and can be used by the community in exchange for fiat currency or as a payment for energy consumed. But we are not stopping here, we want to use blockchain technology in a way that users can monitor how the energy is produced and what it is used for. Renewable Energy Technology Collaborations. TerraGreen has been in collaborations with companies inside and outside the energy sector to produce renewable energy technologies in more efficient, reliable and sustainable ways. Such collaborations have led to the development of new technologies namely biomass gasification system and biofuel hydrolyzer. The collaboration partners are a. AVI Renewable Sdn Bhd b. Augustina Tradelink Pvt. Ltd. c. Hydrogreen Amplifier Pvt. Ltd. By introducing the hydrolyzer onto the TerraGreen platform, we create a direct connection to the customer and a possibility for everyone to reduce fossil fuel consumption. Usage of the Hydrolyzer can be tracked and Reduction of fuel consumption can be transformed into green energy token, which is connected to the TerraGreen platform. TerraGreen Stages. TerraGreen will be developed in 4 stages. Starting with use cases in stage 1 and 2. TerraGreen is looking to revolutionize the energy generation and distribution system in the long run. But we are beginning with the use cases of real projects to create trust and start the development of the platform. Stage One. The first stage is made to create the first energy tokens and develop the TerraGreen platform. In the first stage and use case of TerraGreen, we are looking at the Energy Token creation for producers. Stage Two through Four. In the second stage, TerraGreen can set up partnerships with energy distributor companies, to accept specific energy tokens as a payment from the customers. Stage 3 will combine stage 1 and 2 to connect the energy token price that consumer is buying with the token creation from the renewable energy power plants. This introduces the TerraGreen exchange on the platform. In the last stage of TerraGreen, we are expanding the energy network to the transmission company. Energy payments will be done with the various tokens. Creation, transmission, and usage of energy can be tracked on the blockchain in real time. Use Case for Stage One: 2.4MWe Biomass Gasification Power Plant in Terengganu, Malaysia by Ajil Biofuel Sdn Bhd Malaysia Renewable Energy Act 2011 In Accordance with Renewable Energy Act 2011, The Malaysia government has introduced a Feed-In Tariff (FiT) mechanism to promote the growth of the renewable energy sector which implementation is administered, managed, monitored and reviewed by the Sustainable Energy Development Authority (SEDA) Malaysia. 2.4MWe Biomass Gasification Power Plant The technology to be employed in the power plant is a well proven fluidized bed gasification system. The use case plant will be using feedstocks in the form of forest and agricultural wastes. It is designed to achieve an availability factor minimum of 85% throughout the entire 16 years of operation. The plant converts treated biomass wastes into medium calorific product gas. Here the produced gas is combusted in a gas engine for the production of electricity. The plant is capable for the production of 2.4 MW of electricity. Biomass Fuel Production Facility. The biomass fuel production facility is designed to produce briquettes from forest and agriculture waste materials. The briquettes are used in the biomass gasification plant for the production of electricity. Use-Case: Biofuel Hydrolyzer Application in India by Hydrogreen Amplifier Pvt. Ltd. The state of Gujarat, In India, is one of the fastest growing metropolitan areas in the world. The number of registered motor vehicles in Gujarat was about 19 million for 2015. TerraGreen counters rising fuel demand and emission with the latest technology. For this reason, we are going to roll out the implementation of the Hydrolyzer first where it is needed most. Use Case Stage 2 — Electricity Transaction Validation Services. TerraGreen in partnership with electricity distribution companies in Cambodia, Malaysia, and Central Africa Republic (CAR), plans to implement electricity retail DApps for the following purposes: - Validation of electricity consumption - Validation of payments - Improve efficiencies in the electricity value chain. Blockchain Technology. The native blockchain of TerraGreen will allow to the Deployment of different DApps on the system. Since it is a platform for green energy, we do not support the massive waste done by proof of work consensus. For this reason, we will implement the latest dedicated proof of stake consensus to make our blockchain safe and efficient. With the applications being deployed on the blockchain, we can transmit, store and analyze energy data in real time. It will feature lightning fast, multilayer protocol, easy to use and unlimited side chains which makes the platform extravagant. Tokenomics — Market, Growth, and Revenue. The (TGN) is connected in every token transaction to a small percentage and therefore “running” the system. This connection is done by smart contracts which are implemented into the blockchain technology. TerraGreen platform coin Each stage of TerraGreen has its own market and creates a new demand for the coin. Additionally, each stage will consist of multiple projects and markets where TerraGreen will be introduced. In the first stage, the renewable energy projects are supported through the TerraGreen platform. The second stage introduces the consumers with energy tokens. In the third and fourth stage, final mass adoption will come later into the picture, pushing the TGN demand and value. Token Distribution, Sales and Supply. Use of fund The total supply of TerraGreen token (TGN) is set at 270 million. Only about 50% will be unlocked at first after the ICO. 20% is reserved for rewards for the master nodes and the rest of TGN is locked investing contracts to be released in line with demand growth performance. Conclusion TerraGreen is about to bring a Revolutionary change with the use of blockchain technology in its platform. Its main aim is to remove the wall between consumers, producers, and investors and build a bridge between them. TerraGreen is an ecosystem where renewable energies are tokenized and can be used by the community in exchange for fiat currency or as a payment for energy consumed. But we are not stopping here, we want to use blockchain technology in a way that users can monitor how the energy is produced and what it is used for. TerraGreen will introduce Hydrolyzer to create a direct connection to the customer and a possibility for everyone to reduce fossil fuel consumption. Usage of the Hydrolyzer can be tracked and transformed into green energy token, which is connected to the TerraGreen platform. TerraGreen will be developed in 4 stages. Starting with use cases in stage 1 and 2. TerraGreen is looking to revolutionize the energy generation and distribution system in the long run. But we are beginning with the use cases of real projects to create trust and start the development of the platform. With the applications being deployed on the blockchain, we can transmit, store and analyze energy data in real time. It will feature lightning fast, multilayer protocol, easy to use and unlimited side chains which makes the platform extravagant. The TerraGreen platform coin (TGN) is connected in every token transaction to a small percentage and therefore “running” the system. This connection is done by smart contracts which are implemented into the blockchain technology. Each stage of TerraGreen has its own market and creates a new demand for the coin. Additionally, each stage will consist of multiple projects and markets where TerraGreen will be introduced.

A Reliable Blockchain Platform for Renewable Energy Sector

The Financial Token and Trading Platform Image Caption: Before reading this article, please read our full disclaimer found and at the bottom of this page. This Paid Story is brought to you by trade.io here Introduction is a Swiss company that wants to revolutionize the exchange trading system, through a decentralized, traceable, transparent platform, that enables its users to trade through the blockchain. The platform eventually wants to enable users to trade both digital, and physical assets, such as cryptocurrencies, precious metals, oil, and other commodities. Trade.io The team has a long history of working on trading. The advisors of trade.io have founded successful brokerage firms, such as , and Primus Capital Markets UK. The former started 9 years ago, and is a regulated foreign exchange and CFD trading platform with over $180B worth of transactions traded per year. FXPRIMUS At the time of writing, Trade.io is going through their Pre-ICO. The company is looking to raise 200,000M ETH in exchange of 200M TIO (their Trade Tokens), or roughly $100M USD. Unsold tokens from the Pre-ICO + 75M more TIO tokens will be added to the ICO, scheduled December 5th for a total possible value of over . $100M USD Note: The company’s White Paper defers a lot from the numbers on their website. After an interview we verified that the website numbers are the most up to date and accurate in terms of token pricing and quantity. The Idea Trade.io is introduced in the white paper as a replacement trading platform for the existing system. It aims to be more efficient and transparent by leveraging a decentralized network such as the blockchain. The system is building a shared liquidity pool, where users are able to invest their assets through their trade.io membership (by the use of the token), trade.io will in return, share 50% of the profits generated through this pool, but will also share the losses. Joining this network requires the user to own 2,500 TIO Trade Tokens (approx $875 USD pre-ICO or $1200 USD post-ICO). The company is looking to provide startups with a number of investment and trading options, including the possibility of enabling companies to ICO and IPO through the platform. There is little information about how the company will handle commissions received from those activities, or what the current status is, in the accreditation to enable IPOs or ICOs to happen through the platform. The company is looking to provide multiple trading and investing features, including: : Their trading token acts as a membership to use the trading platform, in order to trade, a user has to hold 2,500 Trade Tokens Trading : Wins and losses from the trading activities are shared amongst traders Liquidity Pool : The platform will invest for the users and distributes wins and losses at 50% Investment Banking : Every day the platform will distribute the results of its investment amongst the users holding a membership Daily Distributions : The company claims to be (or will be) compliant with securities rules, and regulations in multiple jurisdictions, as well as Know Your Customer, and Anti money Laundry regulations Compliance There is currently not a lot of information regarding how the profits will be generated in the first place. The company will also be able to trade physical assets by virtualizing them on the network through their own token. This is a very similar practice to the one we discussed on , and asset-backed cryptocurrencies. Stable Tokens Token The pre-ICO launches the trade token which is an ERC 20 token and follows the standard specification required by the ethereum clients. It is used as the internal currency while performing trading and investment actions on the trade.io platform. Trade.io drives the usage of trade tokens over other cryptocurrencies by providing preferential payouts for investments with trade tokens over other cryptocurrencies or fiat. The smart contracts that underlie this token are written using the Majoolr system. The team at Majoolr are well versed in decentralized applications and have a good understanding of the technical side of things. However, due to the ambitious nature of this project it is highly unlikely that just four developers would be able to produce the required platform according to the timelines given by trade.io. It is also unclear if they have a plan in place to attract the requisite blockchain engineers to work on the platform. Since technical talent is highly sought after in this industry, this is a very important question that must be addressed with high priority. Use of Funds One of the most complicated aspects of this ICO, is the fact that the amount of money raised is completely dependent on when the users buy the tokens. The price of the Trade Token during the Pre-ICO is a fixed amount, while the price during the ICO will change depending on the time the user purchases the token. This is not that uncommon, what’s strange is that there seems to be a huge disconnect between the White Paper and the website. The White Paper states that each ETH will give the user 1000Trade Tokens. On their website, trade.io provides 1000 Trade Tokens per ETH during the Pre-ICO, and 625 Trade Tokens per 1 ETH during the ICO . This complex and unclear system forces trade.io to give an estimate on the use of funds based on a percentage of how much money they raised, instead of an amount. The reason this system is difficult, is that companies scale their budget by percentages. Having twice as many funds doesn’t mean you need to spend twice as much in legal, audits, or advisory. We wished that the company had 3–5 different scenarios, and the use of funds on each scenario, say, one for raising $5–10M USD, another for $50–99M USD, and one for > $200M USD. do not The following is the estimated use of funds for the platform: Image Caption: Image from: Page 26 of trade.io White Paper Since the White Paper, trade.io has expanded their use of funds through their website. The website now displays a set of goals or milestones that are reached, as funds increase. Image Caption: Image from: trade.io website as of November 22nd, 2017 Token Distribution The tokens of this ICO will be distributed based on the following chart: Image Caption: Image from: Page 23 of trade.io White Paper The company has an interesting strategy to distribute tokens during the Pre-ICO and ICO. Their plan is to launch 200 million tokens during the Pre-ICO and if there are any unsold tokens, they will proceed to add the leftovers to the ICO and increase the prices, which is a very common practice. The ICO itself will have 75 million tokens plus the leftovers of the Pre-ICO. Any tokens that are not purchased through the ICO (concluding December 15th, 2017) will be destroyed. Assuring that the platform has a stable supply of tokens. Pre-ICO Details The Pre-ICO period for token.io is November 7th to November 25th, 2017. During this period they have raised over $9M USD. Based on their websites statement, each 1 ETH = 1,000 Trade Tokens. With a price of $360USD, this means that over 25 million Trade Tokens have been sold, out of the possible 200 Million. ICO Details As previously mentioned, all the leftover Trade Tokens from the Pre-ICO + 75 Million will be for sale during the December 5th ICO. As it stands, there should be around 175 Million Trade Tokens remaining from the Pre-ICO plus another 75 million, or approximately 250 million Trade Tokens will be available for sale. Blockchain Test the decentralization of transactions allows for shifting of trust from a central organization (like a bank) to the entire node network, and this is the prime use of the blockchain. A trading platform also can benefit from the same trust transfer, and can avoid dangerous situations where large amounts of funds are lost due to hacking or improper asset management by the trading platform (like Mt. Gox). Certainly Trade.io’s trading platform is definitely a very good use of a blockchain and utilizes its decentralized nature to the fullest extent. However, how they will execute certain classical trading features like marginal trades and stop loss remains to be seen. (There is no existing implementation of marginal trading on a decentralized exchange). Counter to this however is Trade.io’s liquidity pool which requires entrusting liquid assets (trade tokens) to the trading platform (something which entirely negates the decentralized aspect), and while trades can still happen in a decentralized manner, the storage of the shared liquidity pool is still subject to hacks and arbitrary draining by the signing keyholders of the liquidity pool (i.e the platform). Traction One of the weakest parts of the trade.io White Paper is the lack of information on traction. From the company’s timeline, the intend is to launch the trade.io exchange by April 2018. With only 5 months to go, we would like to see an established MVP or some sort of technical update. Team The team at trade.io has clearly worked in the trading industries for a number of years. The brokerage firms and Primus Capital Markets UK were started by some of their advisors. This means that the founders have a good understanding of what is required in order to trade, and the regulations and due diligence that they will possibly be facing. FXPRIMUS The White Paper references 15 members of the management team, and board. As well as 10 advisors. Some of their team members are based in Cyprus, EU. Transparency The company claims to use blockchain technology to increase transparency for transactions. However, we have certain doubts about complete transparency regarding as claimed by their promise of no backroom trades. The Raiden network (and any off-chain networks) enable backroom trades, and while they publish the final transaction to the blockchain, it still enables some manipulation to happen. However, we recognize that off-chain payment channels are necessary to enable thousands of transactions to happen in a blockchain-hybrid environment. Moving forward, the platform could demonstrate how they are planning to achieve this, or the steps they are taking to improve transparency. Conclusion The trade.io whitepaper is one of the most ambitious ICOs we have seen. The document leaves a lot of questions in terms of regulations, current status of the token and platform, and a clear plan of how they are planning on mitigating risks and delays. Furthermore, there currently exist decentralized exchanges with a platform that has been built and thus competition in this category is quite stiff, and the business plan for trade.io has failed to address how they will retain market-share when they are late to market. There are also questions regarding regulation and status, while trade.io White Paper explains how the token is a utility and not a security, we still recommend this decision to be stated by a securities regulator, or at least some information from securities attorneys. Furthermore the raiden network is currently untested and would require heavy development to be able to launch to fit in the timeline provided by trade.io. The company is also looking to go public in only 12 months. That’s approximately 8 months after their launch, and less than 6 before the anticipated swiss banking license and within 6 months of the MetaTrader 4 platform incorporation. Our concern will be that without having the platform fully launched for even 12 months, it will be very difficult for investors on the public market to determine the price of the company and expectation of sales. It is also currently unclear what would happen in the case that the company IPOs in December 2018, what would happen to the tokens, and how would token holders be rewarded? What if the token is regarded to be a security? What if they don’t receive swiss banking license? Or if the MT4 platform connection has security vulnerabilities, like last week’s $300M Ethereum Hack. If the platform is successful, it would be a monumental development on the financial system, as creating a physical/virtual token system could potentially revolutionize the ways people trade, crowdfund, and support companies. Looking to help? 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Trade.io White Paper Review

Authors:
(1) Chengrun Yang, Google DeepMind and Equal contribution;
(2) Xuezhi Wang, Google DeepMind;
(3) Yifeng Lu, Google DeepMind;
(4) Hanxiao Liu, Google DeepMind;
(5) Quoc V. Le, Google DeepMind;
(6) Denny Zhou, Google DeepMind;
(7) Xinyun Chen, Google DeepMind and Equal contribution. Table of Links Abstract and 1. Introduction 2 Opro: Llm as the Optimizer and 2.1 Desirables of Optimization by Llms 2.2 Meta-Prompt Design 3 Motivating Example: Mathematical Optimization and 3.1 Linear Regression 3.2 Traveling Salesman Problem (TSP) 4 Application: Prompt Optimization and 4.1 Problem Setup 4.2 Meta-Prompt Design 5 Prompt Optimization Experiments and 5.1 Evaluation Setup 5.2 Main Results 5.3 Ablation Studies 5.4 Overfitting Analysis in Prompt Optimization and 5.5 Comparison with Evoprompt 6 Related Work 7 Conclusion, Acknowledgments and References A Some Failure Cases B Prompting Formats for Scorer Llm C Meta-Prompts and C.1 Meta-Prompt for Math Optimization C.2 Meta-Prompt for Prompt Optimization D Prompt Optimization Curves on the Remaining Bbh Tasks E Prompt Optimization on Bbh Tasks – Tabulated Accuracies and Found Instructions 3.2 TRAVELING SALESMAN PROBLEM (TSP) Next, we consider the Traveling Salesman Problem (TSP) (Jünger et al., 1995; Gutin & Punnen, 2006), a classical combinatorial optimization problem with numerous algorithms proposed in literature, including heuristic algorithms and solvers (Rosenkrantz et al., 1977; Golden et al., 1980; Optimization et al., 2020; Applegate et al., 2006; Helsgaun, 2017), and approaches based on training deep neural networks (Kool et al., 2019; Deudon et al., 2018; Chen & Tian, 2019; Nazari et al., 2018). Specifically, given a set of n nodes with their coordinates, the TSP task is to find the shortest route that traverses all nodes from the starting node and finally returns to the starting node. Our optimization process with LLMs starts from 5 randomly generated solutions, and each optimization step produces at most 8 new solutions. We present the meta-prompt in Figure 20 of Appendix C.1. We generate the problem instances by sampling n nodes with both x and y coordinates in [−100, 100]. We use the Gurobi solver (Optimization et al., 2020) to construct the oracle solutions and compute the optimality gap for all approaches, where the optimality gap is defined as the difference between the distance in the solution constructed by the evaluated approach and the distance achieved by the oracle solution, divided by the distance of the oracle solution. Besides evaluating OPRO with different LLMs including text-bison, gpt-3.5-turbo and gpt-4, we also compare OPRO to the following heuristics: On the other hand, the performance of OPRO degrades dramatically on problems with larger sizes. When n = 10, all LLMs find the optimal solutions for every evaluated problem; as the problem size gets larger, the OPRO optimality gaps increase quickly, and the farthest insertion heuristic starts to outperform all LLMs in the optimality gap. Limitations. We would like to note that OPRO is designed for neither outperforming the stateof-the-art gradient-based optimization algorithms for continuous mathematical optimization, nor surpassing the performance of specialized solvers for classical combinatorial optimization problems such as TSP. Instead, the goal is to demonstrate that LLMs are able to optimize different kinds of objective functions simply through prompting, and reach the global optimum for some smallscale problems. Our evaluation reveals several limitations of OPRO for mathematical optimization. Specifically, the length limit of the LLM context window makes it hard to fit large-scale optimization problem descriptions in the prompt, e.g., linear regression with high-dimensional data, and traveling salesman problems with a large set of nodes to visit. In addition, the optimization landscape of some objective functions are too bumpy for the LLM to propose a correct descending direction, causing the optimization to get stuck halfway. We further elaborate our observed failure cases in Appendix A. This paper is available on arxiv under CC0 1.0 DEED license. Authors: (1) Chengrun Yang, Google DeepMind and Equal contribution; (2) Xuezhi Wang, Google DeepMind; (3) Yifeng Lu, Google DeepMind; (4) Hanxiao Liu, Google DeepMind; (5) Quoc V. Le, Google DeepMind; (6) Denny Zhou, Google DeepMind; (7) Xinyun Chen, Google DeepMind and Equal contribution. Authors: Authors: (1) Chengrun Yang, Google DeepMind and Equal contribution; (2) Xuezhi Wang, Google DeepMind; (3) Yifeng Lu, Google DeepMind; (4) Hanxiao Liu, Google DeepMind; (5) Quoc V. Le, Google DeepMind; (6) Denny Zhou, Google DeepMind; (7) Xinyun Chen, Google DeepMind and Equal contribution. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 Opro: Llm as the Optimizer and 2.1 Desirables of Optimization by Llms 2 Opro: Llm as the Optimizer and 2.1 Desirables of Optimization by Llms 2.2 Meta-Prompt Design 2.2 Meta-Prompt Design 3 Motivating Example: Mathematical Optimization and 3.1 Linear Regression 3 Motivating Example: Mathematical Optimization and 3.1 Linear Regression 3.2 Traveling Salesman Problem (TSP) 3.2 Traveling Salesman Problem (TSP) 4 Application: Prompt Optimization and 4.1 Problem Setup 4 Application: Prompt Optimization and 4.1 Problem Setup 4.2 Meta-Prompt Design 4.2 Meta-Prompt Design 5 Prompt Optimization Experiments and 5.1 Evaluation Setup 5 Prompt Optimization Experiments and 5.1 Evaluation Setup 5.2 Main Results 5.2 Main Results 5.3 Ablation Studies 5.3 Ablation Studies 5.4 Overfitting Analysis in Prompt Optimization and 5.5 Comparison with Evoprompt 5.4 Overfitting Analysis in Prompt Optimization and 5.5 Comparison with Evoprompt 6 Related Work 6 Related Work 7 Conclusion, Acknowledgments and References 7 Conclusion, Acknowledgments and References A Some Failure Cases A Some Failure Cases B Prompting Formats for Scorer Llm B Prompting Formats for Scorer Llm C Meta-Prompts and C.1 Meta-Prompt for Math Optimization C Meta-Prompts and C.1 Meta-Prompt for Math Optimization C.2 Meta-Prompt for Prompt Optimization C.2 Meta-Prompt for Prompt Optimization D Prompt Optimization Curves on the Remaining Bbh Tasks D Prompt Optimization Curves on the Remaining Bbh Tasks E Prompt Optimization on Bbh Tasks – Tabulated Accuracies and Found Instructions E Prompt Optimization on Bbh Tasks – Tabulated Accuracies and Found Instructions 3.2 TRAVELING SALESMAN PROBLEM (TSP) Next, we consider the Traveling Salesman Problem (TSP) (Jünger et al., 1995; Gutin & Punnen, 2006), a classical combinatorial optimization problem with numerous algorithms proposed in literature, including heuristic algorithms and solvers (Rosenkrantz et al., 1977; Golden et al., 1980; Optimization et al., 2020; Applegate et al., 2006; Helsgaun, 2017), and approaches based on training deep neural networks (Kool et al., 2019; Deudon et al., 2018; Chen & Tian, 2019; Nazari et al., 2018). Specifically, given a set of n nodes with their coordinates, the TSP task is to find the shortest route that traverses all nodes from the starting node and finally returns to the starting node. Our optimization process with LLMs starts from 5 randomly generated solutions, and each optimization step produces at most 8 new solutions. We present the meta-prompt in Figure 20 of Appendix C.1. We generate the problem instances by sampling n nodes with both x and y coordinates in [−100, 100]. We use the Gurobi solver (Optimization et al., 2020) to construct the oracle solutions and compute the optimality gap for all approaches, where the optimality gap is defined as the difference between the distance in the solution constructed by the evaluated approach and the distance achieved by the oracle solution, divided by the distance of the oracle solution. Besides evaluating OPRO with different LLMs including text-bison, gpt-3.5-turbo and gpt-4, we also compare OPRO to the following heuristics: On the other hand, the performance of OPRO degrades dramatically on problems with larger sizes. When n = 10, all LLMs find the optimal solutions for every evaluated problem; as the problem size gets larger, the OPRO optimality gaps increase quickly, and the farthest insertion heuristic starts to outperform all LLMs in the optimality gap. Limitations. We would like to note that OPRO is designed for neither outperforming the stateof-the-art gradient-based optimization algorithms for continuous mathematical optimization, nor surpassing the performance of specialized solvers for classical combinatorial optimization problems such as TSP. Instead, the goal is to demonstrate that LLMs are able to optimize different kinds of objective functions simply through prompting, and reach the global optimum for some smallscale problems. Our evaluation reveals several limitations of OPRO for mathematical optimization. Specifically, the length limit of the LLM context window makes it hard to fit large-scale optimization problem descriptions in the prompt, e.g., linear regression with high-dimensional data, and traveling salesman problems with a large set of nodes to visit. In addition, the optimization landscape of some objective functions are too bumpy for the LLM to propose a correct descending direction, causing the optimization to get stuck halfway. We further elaborate our observed failure cases in Appendix A. Limitations. This paper is available on arxiv under CC0 1.0 DEED license. This paper is available on arxiv under CC0 1.0 DEED license. available on arxiv

LLMs vs. Heuristics: Tackling the Traveling Salesman Problem (TSP)

Beginnings are hard, but they're also the spark that ignites the journey ahead. As we type our first words here on Hackernoon, we're thrilled to welcome you to the . Nifty Sparks community We're diving into the metaverse, uncovering immersive experiences, and sharing the latest insights and updates with you. Why Nifty Sparks, Why Now Why are we here? Because we believe in the power of the metaverse to transform the way we interact, connect, and innovate. The digital realm is evolving, and the time is ripe to spark conversations about all things metaverse. Our passion for this realm has fueled our drive to create a space where we can explore, learn, and grow together. Building a Vibrant Community This isn't just a newsletter; it's a community. We're creating a space where like-minded individuals can gather to discuss, share, and collaborate. Whether you're a creator, a tech enthusiast, a brand looking to make a mark, or simply curious about the metaverse, you've found your tribe here. What to Expect Clarity is key, and we're all about delivering that. Expect regular posts that dive deep into metaverse trends, share how-to guides, showcase success stories, and provide insights into the latest developments. There's no rulebook here. We encourage you to explore, engage, and experiment. If we're having fun, we know you will too. Feel free to share your thoughts, ideas, and suggestions—our community's growth is driven by your participation. Thank you for joining us on this exciting adventure. The metaverse holds endless possibilities, and together, we're ready to unlock its full potential. Stay tuned for immersive experiences, insightful guides, and a whole lot of metaverse magic! Here's to the beginning of something truly nifty! ✨ — The Nifty Sparks Team 🔗 🔗 Visit Niftysparks.com Also published . here

Traveling Through the Metaverse: Welcome to Nifty Sparks! 🥽✨

A Journey to the Center of the Earth by Jules Verne, is part of the HackerNoon Books Series. You can jump to any chapter in this book . TRAVELING IN ICELAND here CHAPTER X - TRAVELING IN ICELAND It ought, one would have thought, to have been night, even in the sixty-fifth parallel of latitude; but still the nocturnal illumination did not surprise me. For in Iceland, during the months of June and July, the sun never sets. The temperature, however, was very much lower than I expected. I was cold, but even that did not affect me so much as ravenous hunger. Welcome indeed, therefore, was the hut which hospitably opened its doors to us. It was merely the house of a peasant, but in the matter of hospitality, it was worthy of being the palace of a king. As we alighted at the door the master of the house came forward, held out his hand, and without any further ceremony, signaled to us to follow him. We followed him, for to accompany him was impossible. A long, narrow, gloomy passage led into the interior of this habitation, made from beams roughly squared by the ax. This passage gave ingress to every room. The chambers were four in number—the kitchen, the workshop, where the weaving was carried on, the general sleeping chamber of the family, and the best room, to which strangers were especially invited. My uncle, whose lofty stature had not been taken into consideration when the house was built, contrived to knock his head against the beams of the roof. We were introduced into our chamber, a kind of large room with a hard earthen floor, and lighted by a window, the panes of which were made of a sort of parchment from the intestines of sheep—very far from transparent. The bedding was composed of dry hay thrown into two long red wooden boxes, ornamented with sentences painted in Icelandic. I really had no idea that we should be made so comfortable. There was one objection to the house, and that was, the very powerful odor of dried fish, of macerated meat, and of sour milk, which three fragrances combined did not at all suit my olfactory nerves. As soon as we had freed ourselves from our heavy traveling costume, the voice of our host was heard calling to us to come into the kitchen, the only room in which the Icelanders ever make any fire, no matter how cold it may be. My uncle, nothing loath, hastened to obey this hospitable and friendly invitation. I followed. The kitchen chimney was made on an antique model. A large stone standing in the middle of the room was the fireplace; above, in the roof, was a hole for the smoke to pass through. This apartment was kitchen, parlor and dining room all in one. On our entrance, our worthy host, as if he had not seen us before, advanced ceremoniously, uttered a word which means "be happy," and then kissed both of us on the cheek. His wife followed, pronounced the same word, with the same ceremonial, then the husband and wife, placing their right hands upon their hearts, bowed profoundly. This excellent Icelandic woman was the mother of nineteen children, who, little and big, rolled, crawled, and walked about in the midst of volumes of smoke arising from the angular fireplace in the middle of the room. Every now and then I could see a fresh white head, and a slightly melancholy expression of countenance, peering at me through the vapor. Both my uncle and myself, however, were very friendly with the whole party, and before we were aware of it, there were three or four of these little ones on our shoulders, as many on our boxes, and the rest hanging about our legs. Those who could speak kept crying out saellvertu in every possible and impossible key. Those who did not speak only made all the more noise. This concert was interrupted by the announcement of supper. At this moment our worthy guide, the eider-duck hunter, came in after seeing to the feeding and stabling of the horses—which consisted in letting them loose to browse on the stunted green of the Icelandic prairies. There was little for them to eat, but moss and some very dry and innutritious grass; next day they were ready before the door, some time before we were. "Welcome," said Hans. Then tranquilly, with the air of an automaton, without any more expression in one kiss than another, he embraced the host and hostess and their nineteen children. This ceremony concluded to the satisfaction of all parties, we all sat down to table, that is twenty-four of us, somewhat crowded. Those who were best off had only two juveniles on their knees. As soon, however, as the inevitable soup was placed on the table, the natural taciturnity, common even to Icelandic babies, prevailed over all else. Our host filled our plates with a portion of lichen soup of Iceland moss, of by no means disagreeable flavor, an enormous lump of fish floating in sour butter. After that there came some skyr, a kind of curds and whey, served with biscuits and juniper-berry juice. To drink, we had blanda, skimmed milk with water. I was hungry, so hungry, that by way of dessert I finished up with a basin of thick oaten porridge. As soon as the meal was over, the children disappeared, whilst the grown people sat around the fireplace, on which was placed turf, heather, cow dung and dried fish-bones. As soon as everybody was sufficiently warm, a general dispersion took place, all retiring to their respective couches. Our hostess offered to pull off our stockings and trousers, according to the custom of the country, but as we graciously declined to be so honored, she left us to our bed of dry fodder. Next day, at five in the morning, we took our leave of these hospitable peasants. My uncle had great difficulty in making them accept a sufficient and proper remuneration. Hans then gave the signal to start. We had scarcely got a hundred yards from Gardar, when the character of the country changed. The soil began to be marshy and boggy, and less favorable to progress. To the right, the range of mountains was prolonged indefinitely like a great system of natural fortifications, of which we skirted the glacis. We met with numerous streams and rivulets which it was necessary to ford, and that without wetting our baggage. As we advanced, the deserted appearance increased, and yet now and then we could see human shadows flitting in the distance. When a sudden turn of the track brought us within easy reach of one of these specters, I felt a sudden impulse of disgust at the sight of a swollen head, with shining skin, utterly without hair, and whose repulsive and revolting wounds could be seen through his rags. The unhappy wretches never came forward to beg; on the contrary, they ran away; not so quick, however, but that Hans was able to salute them with the universal saellvertu. "Spetelsk," said he. "A leper," explained my uncle. The very sound of such a word caused a feeling of repulsion. The horrible affliction known as leprosy, which has almost vanished before the effects of modern science, is common in Iceland. It is not contagious but hereditary, so that marriage is strictly prohibited to these unfortunate creatures. These poor lepers did not tend to enliven our journey, the scene of which was inexpressibly sad and lonely. The very last tufts of grassy vegetation appeared to die at our feet. Not a tree was to be seen, except a few stunted willows about as big as blackberry bushes. Now and then we watched a falcon soaring in the grey and misty air, taking his flight towards warmer and sunnier regions. I could not help feeling a sense of melancholy come over me. I sighed for my own Native Land, and wished to be back with Gretchen. We were compelled to cross several little fjords, and at last came to a real gulf. The tide was at its height, and we were able to go over at once, and reach the hamlet of Alftanes, about a mile farther. That evening, after fording the Alfa and the Heta, two rivers rich in trout and pike, we were compelled to pass the night in a deserted house, worthy of being haunted by all the fays of Scandinavian mythology. The King of Cold had taken up his residence there, and made us feel his presence all night. The following day was remarkable by its lack of any particular incidents. Always the same damp and swampy soil; the same dreary uniformity; the same sad and monotonous aspect of scenery. In the evening, having accomplished the half of our projected journey, we slept at the Annexia of Krosolbt. For a whole mile we had under our feet nothing but lava. This disposition of the soil is called : the crumbled lava on the surface was in some instances like ship cables stretched out horizontally, in others coiled up in heaps; an immense field of lava came from the neighboring mountains, all extinct volcanoes, but whose remains showed what once they had been. Here and there could be made out the steam from hot water springs. hraun There was no time, however, for us to take more than a cursory view of these phenomena. We had to go forward with what speed we might. Soon the soft and swampy soil again appeared under the feet of our horses, while at every hundred yards we came upon one or more small lakes. Our journey was now in a westerly direction; we had, in fact, swept round the great bay of Faxa, and the twin white summits of Sneffels rose to the clouds at a distance of less than five miles. The horses now advanced rapidly. The accidents and difficulties of the soil no longer checked them. I confess that fatigue began to tell severely upon me; but my uncle was as firm and as hard as he had been on the first day. I could not help admiring both the excellent Professor and the worthy guide; for they appeared to regard this rugged expedition as a mere walk! On Saturday, the 20th June, at six o'clock in the evening, we reached Budir, a small town picturesquely situated on the shore of the ocean; and here the guide asked for his money. My uncle settled with him immediately. It was now the family of Hans himself, that is to say, his uncles, his cousins—german, who offered us hospitality. We were exceedingly well received, and without taking too much advantage of the goodness of these worthy people, I should have liked very much to have rested with them after the fatigues of the journey. But my uncle, who did not require rest, had no idea of anything of the kind; and despite the fact that next day was Sunday, I was compelled once more to mount my steed. The soil was again affected by the neighborhood of the mountains, whose granite peered out of the ground like tops of an old oak. We were skirting the enormous base of the mighty volcano. My uncle never took his eyes from off it; he could not keep from gesticulating, and looking at it with a kind of sullen defiance as much as to say "That is the giant I have made up my mind to conquer." After four hours of steady traveling, the horses stopped of themselves before the door of the presbytery of Stapi. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Jules Verne (2006). A Journey to the Center of the Earth. Urbana, Illinois: Project Gutenberg. Retrieved October 2022 https://www.gutenberg.org/files/18857/18857-h/18857-h.htm This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

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