fabiochiu/medium-articles huggingface dataset:
Smart Contract Developer in Jhansi +919870635001 Nadcab Technology
By: Nadcab Software Development
Tags: Smart Contract Tron, Smart Contracts, Nadcab Technology, Blockchain, Tron

Direct Whatsapp- https://bit.ly/2op0VQr

Website Visit- https://bit.ly/2nJJwBV

Smart Contract Development

Nadcab Technology, a leading smart contract, provides a comprehensive range of smart contract development, security audit services, and solutions based on customer requirements. Complete blockchain smart contract development services architecture and code-based evaluation to ensure the security of smart contracts. Our team of experienced developers has the expertise to develop all types of smart contracts across a variety of blockchain platforms including Ethereum, Hyperledger, EOS, and Corda, as well as languages ​​like Solidity, Golang Vyper, Truffle, and more.

Smart Contract Development Company is a virtual contract built using a blockchain platform for stability and privacy purposes. This smart contract can be concluded between two parties without the intervention of a third party, and the terms and conditions are drawn up according to the authority. Once these terms and conditions are met, funds are automatically transferred from one party to another. Smart contract data is stored on the ledger to enhance security.

Why use Smart Contracts to Build Blockchain Applications?

One of the most important features of blockchain technology is its decentralized nature. This means that the information is shared by all parties in the network. Therefore, no intermediaries or intermediaries are required to facilitate operations. This feature is especially useful because it saves one from the possibility of hacking and fraudulent activity. Blockchain technology is feature-rich and provides a fast, inexpensive, and efficient mode of trading. Therefore, most organizations in the government and banking sectors are starting to adopt this top-notch technology.

Advantages of Smart Contract Development

1.Independent

When a smart contract is implemented on a blockchain network, it runs constantly and automatically as scheduled events occur.

2.Security

The secure and consistent execution of smart contracts is provided by the decentralized nature and consensus algorithm of the blockchain network. Neither party can change the smart contract once it is deployed on the blockchain.

3.Efficiency

Blockchain Smart Contract Development Company allows inefficient parties to be excluded from the supply chain. Transactions are executed automatically without further approval from the smart contract party.
Atualização no Roadmap
By: Vinicius Menta
Tags: Smart Contracts, Masternodes, Syscoin

Learn more. Medium is an open platform where 170 million readers come to find insightful and dynamic thinking. Here, expert and undiscovered voices alike dive into the heart of any topic and bring new ideas to the surface. Learn more

Make Medium yours. Follow the writers, publications, and topics that matter to you, and you’ll see them on your homepage and in your inbox. Explore
The Graph: Participating in the Curators program
Tags: Indexing, Defi, GraphQL, Ethereum, Smart Contracts

I met The Graph, when I needed to create a simple Web3 app and had no desire to write specifically for it a couple of smart contracts. Then I stumbled upon The Graph. I didn’t know about graphQL before but quickly figured it out and did what I wanted, and after a few months, I heard about the Curators’ program and decided to participate in it. So why am I contribute to the network and why others should too.

Why is The Graph critical to Web3?

The Graph solves a real problem, which confuses many developers, especially beginners. Instead of wasting your time writing smart contracts to index certain data from the blockchain, you can simply use the convenient subgraph to make a query! It helps to avoid bugs that can occur when writing a smart contract, and to lower the threshold for entering Web3. The Graph makes it much easier to prototype your idea.

Which subgraphs are the most critical for Web3 and why?

The most important subgraphs obvious for the most used protocols, such as: Uniswapv2 (I think it’s hard to imagine defi without it), Compoundv2, Aave, Balancer, MakerDAO, Yearn Finance etc.

All these protocols are basis of the DeFi and fast / convenient access to their data important for normal behaviour of the whole system.

What have you learned during the Curator Program?

I learned a lot of new Web3 stuff, not only about graphQL and how to make queries but also about smart-contracts (read/write), Solidity and TypeScript. I found myself in an excellent community that is constantly evolving, helping newbies and really trying to make the coming of the future (Web3-future :) a little closer.

I think that for those who want to dive themselves into Web3, but do not know where to start, becoming a graph Curator or somehow joining such a community would be a great start. Because here it will already be possible to work in advance on part of a large and useful product inherently related to the DeFi infrastructure.

What are you excited about when curation goes live on mainnet?

Of course, I’m looking forward to see (mainnet was launched already, but the system itself needs some time to start to work on the full power) how all these components (indexers, curators, delegators, users) will work together. The Graph economic model is not trivial (as well as technical side), but I think that by gradually developing the network, it will be possible soon to fully enjoy completely decentralized indexing and query system, in which everyone can contribute, be it an indexer or a regular user. Don’t forget, if you passionate about Web3-future by contributing to the Graph, you are investing in the DeFi ecosystem itself.
How do I attract more people into my business? #1
By: O. Elite Leader
Tags: Incomatrix Team, Smart Contract Tron, Online Business, Smart Contracts, Incomatrix

“How do I attract more people to my business?” — This is a question, every successful entrepreneur had to ask him-/herself at least once. We’ll break it down for you. And at the end of this series, you will be able to apply the given techniques to any kind of business that needs promotion. Let’s go!

Part #1:

WRITE A BLOG

This is one of the easiest ways to attract partners, and meanwhile a very effective way! You don’t have to directly write about the business you are trying to promote. Just write about the very process of building your business. Write about the lucky breaks, the failures, and what kept you going. Through this way, you can build a trusted relationship with your readers, which will directly help you to increase your number of partners.

There are a lot of different ways to start your own blog. I will list a couple of the best Blogging Sites for you to make it easier for you to choose a comfortable platform!

Wordpress.org — [It’s free and very easy to set up. Good for Business and Personal Blogs. I might also write an extra article for setting up WordPress.]

Wix.com — [Wix is good for self-hosting, but you are a bit limited in the free Version. The Paid Version starts at 13$/month.]

Medium.com — [Medium is a very popular community for bloggers and journalists. You just have to sign up and can start writing! And it’s free.]

Part #2 is going to be released: 24. December.2020

Join our Incomatrix Global Elite (Group will be closed soon)

Contact me directly:
Announcing the native integration of API3 to Fantom
Tags: API, Smart Contracts

API3 is pleased to announce a collaboration with the Fantom Foundation and a native integration with the Fantom Opera Network. The API3 DAO will build and provide Airnode compatibility with Fantom’s Opera Network for expanded use in decentralized applications.

What is Fantom?

The Fantom Foundation is building a highly scalable blockchain ecosystem that facilitates technologies that are decentralized, scalable, and secure. Fantom’s team is made up of engineers, scientists, researchers, designers, and entrepreneurs all around the globe who all share the same vision for a world powered by Web 3.0.

Fantom’s Opera Network is a fully permissionless and open-source network powered by Fantom’s own consensus algorithm. It is secure, fast, and fully compatible with the Ethereum Virtual Machine and provides full smart-contract support through Solidity. Through the EVM, developers can utilize smart contracts on top of Fantom, and it allows projects to port their existing Ethereum dApps over to Fantom easily.

Native Integration

The Fantom network will integrate API3’s decentralized API’s and Airnode-enabled API’s into its platform. This will give developers building on the Fantom network access to all of the data that API3 provides through these solutions. API3 is excited to work together with Fantom through Airnode compatibility and pursue opportunities for further collaboration in building decentralized applications.

We’re incredibly excited to collaborate with the API3 DAO in creating Airnode compatibility with Fantom’s Opera Network, as it allows API providers to host Opera-native first-party oracles that developers can use in their decentralized applications. From an ecosystem developer perspective, it’s also a great addition to the existing array of oracle options on top of Opera, as dAPI’s give us access to data that is aggregated right from the source.

-Michael Kong, CEO/CIO Fantom Foundation

API3 is thrilled about the native integration with Fantom’s Opera Network and excited to provide the Fantom network with new data feed solutions. We look forward to collaborating further with Fantom Foundation and supporting their efforts to enable the creation of new, data-rich and scalable decentralized applications.

-Heikki Vänttinen, API3 co-founder.

What is API3?

API3 is building decentrally governed and quantifiably secure data feeds that power Web 3.0 applications without employing third-party intermediaries. API3’s dAPI’s are fully decentralized and blockchain-native APIs with quantifiable security.

API3’s dAPIs — which will be set up, managed, and monetized at scale by the API3 DAO — will be the next evolutionary step to meet the inevitably strict decentralization requirements of Web 3.0. Read our full whitepaper here.

API3 Links

Twitter — https://twitter.com/API3DAO

Telegram — https://t.me/API3DAO (Community Chat)

Discord — https://discord.gg/qnRrcfnm5W (Builder Chat)

Github — https://github.com/api3dao

Reddit — https://www.reddit.com/r/API3/

Medium — https://medium.com/api3
Deploying Smart Contract with Truffle
By: Apoorv Maheshwari
Tags: Blockchain Technology, Ethereum Blockchain, Ethereum, Smart Contracts, Blockchain

Truffle, the most popular development tooling for Ethereum programmers. Easily deploy smart contracts and communicate with their underlying state without heavy client side programming. An especially useful library for the testing and iteration of Ethereum smart contracts.

Truffle is a development environment (providing a command-line tool to compile, deploy, test, and build), framework (providing various packages to make it easy to write tests, deployment code, build clients, and so on) and asset pipeline (publishing packages and using packages published by others) to build ethereum-based DApps.

Deploying Smart Contract with Truffle

With Truffle, you get:

● Built-in smart contract compilation, linking, deployment and binary management.

● Automated contract testing with Mocha and Chai.

● Configurable build pipeline with support for custom build processes.

● Scriptable deployment & migrations framework.

● Network management for deploying to many public & private networks.

● Interactive console for direct contract communication.

● Instant rebuilding of assets during development.

● External script runner that executes scripts within a Truffle environment.

To use most Truffle commands, you need to run them against an existing Truffle project. So the first step is to create a Truffle project.

Let’s start by installing truffle:

$ npm install -g truffle

Ensure that it is installed:

$ truffle

Truffle v3.2.1 - a development framework for Ethereum Usage: truffle [options] Commands:

init Initialize new Ethereum project with example contracts and tests

...

Then create the project:

$ mkdir storage_smart_contract_example $ cd storage_smart_contract_example $ truffle init

From there, you can run truffle compile, truffle migrate and truffle test to compile your contracts, deploy those contracts to the network, and run their associated unit tests.

Truffle comes bundled with a local development blockchain server that launches automatically when you invoke the commands above. If you’d like to configure a more advanced development environment we recommend you install the blockchain server separately by running npm install -g ganache-cli at the command line.

ganache-cli: a command-line version of Truffle’s blockchain server.

ganache: A GUI for the server that displays your transaction history and chain state.
Nasz uregulowany plan DeFi i dlaczego jest on ważny?
By: Misang Ryu
Tags: Defi, Proof Of Engagement, Smart Contracts, Blockchain, Rozporządzenie

Learn more. Medium is an open platform where 170 million readers come to find insightful and dynamic thinking. Here, expert and undiscovered voices alike dive into the heart of any topic and bring new ideas to the surface. Learn more

Make Medium yours. Follow the writers, publications, and topics that matter to you, and you’ll see them on your homepage and in your inbox. Explore
Xend Finance TestNet Incentive
By: Xam Borg
Tags: Xend Finance, Defi, Incentivized Testnet, Binance, Smart Contracts

Hey hello everyone, This is CryptoBorg for another great article!

First of all, let’s say that there is a way to obtain some token of a great project see the rules there:

https://medium.com/xendfinance/introducing-the-first-xend-finance-incentivized-testnet-e2bff311c8ab

When I say great project lets just have a brief look at some images cause an image is worth 1000 words we use to say!

Backed by the best !

The next images resume the webpage : https://xend.finance/

OK, now that you have seen the webpage, you must understand that it’s going to be something big enough I’m sure. I want to show you a bit about the reward structure.

Basically, there are so many tasks you can do to get in the leaderbord we can look at them, but first we Will take a look at it.

Now how do you get there?

As I said earlier many things you can do. And when you are ready to try the platform, lets go on my video search the link you will need in the description.

https://youtu.be/4qmZTyeE3zI

But lets look at all what you can do .

First its all about trying the platform and reporting your action in the google doc designated for it (so if you click here you get the doc)

Then, as well you can always write articles as I’m doing in here.

Do social involvement in the Telegram, Twitter…

As well as making video : https://youtu.be/4qmZTyeE3zI

The reward and prize are not only for the first 5 winner.

Look at it deeper.

Hey its worth trying your shot everyone Get In!!!

https://t.me/xendFinance

Get in the telegram and Tell me Hello tagging me @xamborg2 there

lets make Xend finance telegram an active one!

For people who are good at doing memes, and sticker, it’s something easy you can do to contribute, but always send those forms to tell them your involvement

https://docs.google.com/forms/d/e/1FAIpQLSd9kPfOsnDpZBoWbBSDcJaO-TcPEQxQdPNiw6bOpis7WvFyMg/viewform

https://docs.google.com/forms/d/e/1FAIpQLSfrwgo0icveoFA1MAq9GhOu0yXyZyK1oUHWPeoNbMS57lssiQ/viewform

Lite paper

https://xend.finance/XendDefiLitePaperV11.pdf
99 Red Balloons & Special Cards
By: Obelus Voto
Tags: Cryptocurrency, Fomo, Ethereum, Smart Contracts, Bitcoin

https://ItsJust.PiZZa/#/contract/0xb5cf15c8731f728ceeb6fc30695eaf12ecc78fd9

Spam Token Exclusives. Must be holding at least 1 BOND. Spam Exclusive cost 1 Spam and must meet the condition to obtain the card.

Snowman: Penguin is holding Red Balloon and is in the 7th or 8th Row

Jack-o-Lantern: the fox is next to the skull, spider and bat.

Black Swan: Skull is next to crane and crane is in the top two rows.

Haley’s Arrow: The Forest is the floating kingdom & the deer is holding the magic lamp in one of 4 center boxes.

Honey: The bee is next to a flower.

Other Cards. Spam Jerky (SpJ) is always purchased alongside other cards.

Third Eye: The eye is floating and the shroom is holding the magic lamp. Costs 100 MVT.

Hourglass Stardust: Shooting star is holding magic lamp. Cost 100 MDT.

Teleporter: Paw Prints are in 1 of the 4 corners. Cost 100 XYO.

Shadow Wolf: Wolf is holding Red Balloon and Moon is the floating kingdom. Cost 0.01 MOONDAY.

Vampire: Floating kingdom is bat and the red balloons left is 99, 88, 77, 66, 55, 44, 33, 22, 11 or 00. (Red Balloon count will be displayed soon). Cost 100 DRC.

Miracle Milk: Cattle is holding Red Balloon. Cost 1 BarnBond

Surf Board: RedBalloons at 54 or more. Water is in 4 center spaces and holding the red balloon. Costs 50 SURF

Canoe: RedBalloons at 22 to 53. Water is in 4 center spaces and holding the red balloon. Costs 50 SURF

Boat: RedBalloons at 7 to 21. Water is in 4 center spaces and holding the red balloon. Costs 50 SURF

Submarine: RedBalloons above 0 to 6. Water is in 4 center spaces and holding the red balloon. Costs 50 SURF

FOMO APE: Monkey is holding redballoon and is next to palm tree. Cost 10 BCO

Many Place: ( SUPER RARE ) Only on the first and last balloon and while the Goat is the floating kingdom. Cost 300 SENT.

Chimera Gargoyle: Snake, lion and goat in center 4 squares. Cost a special amount of CID.

PsyKyuubi: floating kingdom is the fox. Cost 1 RFOX

Iron Giant: Golem is holding the red balloon and the gold bars are the floating kingdom. Cost 10 GLM

Cake: Wheat is holding the red Balloon. Cost 12 BRD.

Bee Hive: Bee is next to a flower and in the center. Cost 10 BZZZZ.

Kraken: Water is the floating kingdom. Octopus is holding the balloon. Cost 0.1 OCTO

Pegasus: The Unicorn is floating kingdom and the red balloon is in the space it was before it became the floating kingdom. Cost 2 UNI

President of Kekistan: Frog is holding the red balloon is holding the magic lamp and is in the center. Cost 0.1 PPBLZ

Sniper Drone: For those of you who know, you know
Blockchains 101: Cardano
Tags: Proof Of Stake, Cardano, Smart Contracts, Dapps, Ada

Sleeping Giant

Introduction

Cardano (ADA) is a cryptocurrency and decentralized blockchain platform allowing programmable transfers of value via smart contracts.

Cardano was released in 2017 by the Blockchain development firm Input Output Hong Kong (IOHK) Founded by Charles Hoskinson and Jeremy Wood.

Cardano’s logo

Description

Cardano platform is built on a Proof of Stake algorithm (Ouroboros), determining how the consensus is achieved between the separate nodes of the network. To use the platform, participants must install the Deadelus deterministic wallet designed specifically for serving the Cardano ecosystem.

The platform relies on a layered structure with a (Cardano) Settlement Layer (CSL), (Cardano) computation Layer (CCL) and additionally the integration of Sidechains and cross-chain transactions (KMZ Framework).

The Cardano Foundation is the supervisory and educational entity of Cardano in charge of protecting, maintaining and promoting the network as well as keeping track of the regulatory environment and maintaining the community.

Smart contracts

Cardano is expected to integrate smart contracts functionalities by end 2020, transiting from Shelley to GoGuen era and opening the door for more on-chain functionalities (dApps, DeFi).

Cardano’s daedalus official wallet logo

Storage and wallets

Official cardano wallet: Deadelus

Technical specificities:

Type: Native Coin

Native Coin Total Supply: 45,000,000,000

45,000,000,000 Subunit: NA

NA Consensus: Proof of Stake (PoS)

Proof of Stake (PoS) algorithm: Ouroboros

Ouroboros Encryption: HMAC-SHA512

HMAC-SHA512 Privacy: Pseudonymous (Low)

Pseudonymous (Low) Blocktime: 10 minutes

10 minutes Coding languages: Haskell, Plutus

Haskell, Plutus Open source: Yes

Yes Current version: Shelley

Shelley Past version: Byron

Byron Next version: Goguen

Documentation:

TradingBull.io

Telegram community: https://t.me/TradingBull_DA

Twitter: @_Digital_Assets
Time Travel to Right Before Error Occurs
Tags: Debugging, Scrypt, Smart Contracts, Bitcoin Sv, Bitcoin

Time Travel to Right Before Error Occurs

sCrypt IDE v0.4.13 lets you launch a debugger that stops right at where sCrypt test fails. This will help developers to locate contract bugs tremendously.

Previously, when a test fails using the sCrypt testing framework, error message only shows where it fails in JavaScript/TypeScript code. If a user has to find out where it fails inside the sCrypt code, he has to print out debug arguments, copy and paste them into launch.json, which is a laborious and error-prone process.

Launch Debugger Right From a Failed sCrypt Test

In the latest IDE, when a test fails, there are two additional lines in the error message.

Go to Source : it shows which line fails inside sCrypt code and also the last opcode executed. Right clicking on the link will take you directly to the offending line.

: it shows which line fails inside sCrypt code and also the last opcode executed. Right clicking on the link will take you directly to the offending line. Launch Debugger: Right clicking on the link will directly launch a debugger session, based on arguments in the test. The debugger will pause right before the error about to occur, allowing inspection of what caused the failure.

Debug Info in Output

Happy debugging!
Function Island: The Christmas Match Competition (2020)
By: Function Island
Tags: Smart Contract, Defi, Competition, Blockchain, Tron

Settlers of Function Island, we are excited to announce another event led by the one and only DabsGalore — it’s time to get those D1VS and share those screenshots in! Show your riches! 🤑

How do I participate?

All you have to do, is either buy new D1VS or reinvest, and send the

transaction link and a screenshot as proof to DabsGalore! You can find him in the Function Island telegram chats, and via his handle (DabsGalore)

What’s the prize?

The prize will be a randomly-selected amount of D1VS, issued to the winner by DabsGalore, after the event concludes. As a bonus for the whole D1VS contract, Dabs is also going to MATCH the winning buy/reinvest to his own bag, ready for the next event (whenever that is)! 🏆

What are the details?

— Entries for this event open on Monday 21st of December 2020 and close on the eve of the 26th of December 2020.

— New D1VS purchases and Reinvests both count as valid Tx types, and you can enter more than once — your entries will be stacked!

— DabsGalore will be the final caller on winners, prizes and any other factor of the event — his word is final.

How will I know if I’ve won?

Once the event is concluded, DabsGalore will declare the winner via the Function Island telegram chat and channels! We’ll also share the winning entry on Twitter, for the world to see the awesome that is the winner’s D1VS!

We hope everyone on Function Island enjoys this event — let’s see some D1VS!

Function Island Team would also like to take this opportunity to wish every Islander a happy, safe and Merry Christmas! We can’t wait to see what the new year brings (and Santa, too!)

➡ Function Island (Main URL)

➡ Function Island (backup URL)

➡ Function Island Telegram (EN)

➡ Follow Function Island on Twitter

➡ Function Island on VibraVid
Uniris Weekly Update N°51 — EN. News of the week of December 14, 2020.
Tags: Blockchain, Authentication, Technology, Smart Contracts, Biometrics

The original condition: single sign-on and tenfold security

Whether you are in a subway station, an airport or a polling station, the device that authenticates you must be secure and infallible! Uniris has integrated a technical solution that guarantees authenticity for all devices, allowing tamper-proof identification and payment.

In other words, it is possible in the PoW to select a category of device to retrieve the associated cryptographic key. The addition of an additional “biometric” condition ensures optimal transaction security.

Example — electronic voting: this development guarantees that a person’s biometric identity is generated only by a recognized device, and not by any random or malicious device!

A major step forward for our Smart-contracts

We have successfully implemented the ‘smart contracts interpreter’. This improves the capacity of our smart-contracts at different levels:

Code detection ;

Transaction generation based on Smart Contracts triggers : minute by minute, hour by hour, etc… (see the weekly update n°50) ;

Utility function calls : regular expression, hash…

An in-depth guide to smart-contract generation and its deployment will be published very soon.

Uniris innovation: « Any address mechanism »

The Any Address Mechanism allows the use of the address of any transaction in the same chain as the destination address. In other words, it is not necessary to specify the last transaction in the chain to execute a new one.

More details on this advance in our Yellow Paper (Remark 1) p9.
API3 is hiring!
Tags: Hiring, API, Dao, Smart Contracts, Blockchain

The API3 DAO has two types of activities:

Building of monoliths Continuous operations

It executes these in two ways:

One-off grants (e.g., the authoritative DAO being built by dOrg) Employing teams (what this post is about)

In general, employing teams is preferred for the building of mission-critical monoliths (e.g., the ones related to the development of core solutions such as Airnode) and the execution of mission-critical operations (e.g., signing up first-party oracles). The API3 founders have already formed core teams that would be able to execute the API3 vision and work is going on in full swing. However, the point of the API3 DAO raising funds was to scale these efforts up to deliver better results faster. To this end, we are looking for teammates that will extend these core teams, while preserving their surgical nature.

We are looking for two types of teammates. Type 1 are experienced people with highly relevant qualifications that allow them to start contributing to the core teams at the highest level, and in a largely autonomous way. Type 2 are contributors that follow guidelines and help with scaling the operations up. Type 1 contributors are expected to work at least part-time (20 hrs/week), and will receive compensation. Both the workload and compensation will be more flexible for Type 2 contributors.

Please see the new Open Positions section of our website and apply to the Type 1 positions that fit you. If the positions don’t fit you or a Type 2 arrangement will work better for you—for example, because you don’t want to leave your current job — then you can join our builder Discord to find out how exactly you can get involved.

We are now at the point where the DAO needs to scale, and it will scale by having people working for it. If you think what API3 has set out to do is worthwhile, come join us on this journey!
Uniris Weekly Update N°51
Tags: Tech, Biometrie, Blockchain, Smart Contracts

We are disrupting the way people make transactions with unlimited frictionless protocol. #Blockchain #Marketplace #Biometrics
ACryptoS DAO Vote: Should we publish the source code of our Venus Vaults’ Strategies?
By: X
Tags: Defi, Binance Smart Chain, Yield Farming, Yield Optimization, Smart Contracts

We were the first to launch Vaults farming on Venus Protocol, and remained the only ones for 3 weeks after, accumulating a TVL of more than $2M in our Venus Vaults alone. We have published all our smart contract code before, but we did not publish the source code of our Venus Vaults’ Strategies at first, as I was curious to see how much of a competitive edge it would give us. Now that Beefy has released their Venus Vaults, we should now review if we would like to publish our source codes.

Considerations

Beefy’s Venus implementation is a relatively poorer one. We can see this before reviewing their source codes:

This means we can harvest and compound our assets 5 times more often than them at the same cost, and their users pay many times more in gas fees compared to ours.

More importantly, they are achieving a leverage (ratio of total supply + borrow on Venus, to the principal) of ~3X compared to ~3.67X on our Vaults, which means our Vaults have a yield advantage of about 23.3% above theirs.

Looking at their source code, they can adjust the leverage somewhat in a coarse fashion, but any increase in leverage would result in an exponential increase in their already expensive gas costs.

It is my assessment that to match the efficiency and efficacy of our Vaults, Beefy would need to almost completely rewrite their Strategy, essentially taking a fundamentally different approach. I feel it is likely they would not be able to achieve this without outside help, if we do not release our source codes.

OTOH, if we do release our source codes, it will almost surely be immediately copied, and we would lose any competitive advantage from it. However, we may attract more funds from investors who were previously unwilling to deposit into unpublished contracts, and may increase overall trust and transparency in our protocol.

Background

We’ve had a few requests to publish the source codes of our Venus Vaults’ Strategies since they went live, including somewhat aggressive ones from Beefy devs and admins, accompanied by disparaging comments belittling us for believing our strategies gave us an edge.

I did not know Roman was a Beefy dev at this time, and he did not disclose this when asking us to publish our source. I do agree somewhat with his argument here though.

Wait what

“Roman” is a Beefy dev? Interesting…

You’re welcome.

We’ve also had some feedback from the community who felt we should keep the source unpublished. Although it did make me feel a little uneasy.

We continued to get pressure to publish the source shortly after Beefy’s first Venus Vault went live, so I promised to put this to a vote.

Vote here: https://vote.acryptos.com/#/acryptos/proposal/QmbVJoGoiGLU6e6cNyHeDR6WJYQmQdrBNiFEbGaYEb6SK6
PySpark
Tags: Pandas, Pyspark, Python

Pandas is powerful analytic tools, but took long time running on extensive datasets.

PySpark is an python API for Spark architecture computing.

Enable python users to write pandas analytic functions while benefit from Spark speed up computing.
Hey Alexey, I will have a look over your suggestion and come back to you.
Tags: For Loop, Machine Learning, Python Programming, Pandas, Data Science

Hey Alexey, I will have a look over your suggestion and come back to you. Have you tried that before? 👨‍💻
Create AWS Lambda Layers for Python packages
By: Amirhs. Imani
Tags: Lambda, Python, AWS, Cloud9, Pandas

Check/Install Python

You can check to see if your desired Python version is installed on your Cloud9 environment.

which python3.8

If the required version doesn’t exists, install it following these steps

# install prerequisites for Python before installing it.

sudo yum install gcc openssl-devel bzip2-devel libffi-devel

sudo wget

sudo tar xzf Python-3.8.6.tgz # Download the Python from the Python official website.sudo wget https://www.python.org/ftp/python/3.8.6/Python-3.8.6.tgz sudo tar xzf Python-3.8.6.tgz cd Python-3.8.6 # compile Python 3.8 from the source code and install

sudo ./configure --enable-optimizations # make altinstall is used to prevent replacing the default python binary file /usr/bin/python.

sudo make altinstall # move downloaded source archive file from your system

sudo rm -f /opt/Python-3.8.6.tgz # check the version

python3.8 -V

Create Lambda Layer

now install the packages you want. Here I am installing pandas. You can use requirements.txt as well.

Put the correct version of python where you see python3.x.

mkdir folder

cd folder

virtualenv v-env -p python3.x

source ./v-env/bin/activate

pip install pandas

deactivate

Then type the following code line by line to create your layer. Please note that I have two version of python for the compatible runtimes.

mkdir python

cd python

cp -r ../venv/lib/python3.8/site-packages/* .

cd ..

zip -r pandas_layer.zip python

aws lambda publish-layer-version --layer-name pandas --zip-file fileb://pandas_layer.zip --compatible-runtimes python3.7 python3.8

AWS Cloud9

If you don’t have AWS CLI setup on your machine, you can use AWS Cloud9 to run the commands in the cloud.
Christmas Crisis!
By: Jack Lindsay
Tags: Christmas, GIS, Pandas, Data Science, Projects

First and foremost, I must make it abundantly clear that there are a million ways to open a present, just like there are a million ways to solve this project. If you come up with a better / more efficient way to solve these steps (especially the GeoPandas section) please leave a comment below to help out your fellow scientists!

With that out of the way, let’s dive in!

You can view the entire GITHUB REPO with code

Step 1: This step is pretty straight forward, access the ReadMe file and you will find Santa’s coordinates

Photo by Author

Step 2: You will find the list of Starbucks addresses in the file labeled ‘Santas Starbucks List’. In order to check the data, simply read it into Pandas and check for Nan or Null values. If any rows have Nan or Null, you can decide to drop them or keep them after looking over them.

Step 3: For step 3, there are quite a few ways to go about it. You can just read the data from the zipped file, you can extract the data and read in the shapefile, or you can use the parquet file and convert it as needed. I chose to use the parquet because it read in and processed much quicker than just the shapefile.

Photo by Author

Step 4: You will need to get an API key from Placekey.io (don’t worry it is free and possibly one of the most useful tools you will invest in learning). Once you have your key, you can plug your address in to assign a Placekey to each address that will allow you to merge to other datasets without having to worry about correcting for things like Street, St, st, street, str, etc. You will want to get a Placekey for both your Starbucks data and SafeGraph POI data to then merge on Placekey. This will allow you to get the Lat, Lng of each Starbucks.

Photo by Author

Step 5: This step will probably be the most challenging for most users. You will need to convert your newly created POI merge file to a shapefile in order to check for Lat, Lng within the polygons of the Ookla dataset. You can find the full code HERE. Once you have the master Dataframe with Starbucks addresses and their respective Upload and Download speeds, you can choose the one with the fastest Upload speed for Santa to go to

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Step 6: Comment below with your optimal Starbucks location for Santa to go to

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Step 7 (BONUS): For this one, you will need to go into the bonus folder and find the patterns file that will give you all of NYC’s foot-traffic data for the past week — courtesy of SafeGraph. Merge the data to your master DF on SafeGraph Place ID to find the lowest foot-traffic that still meets your requirement for over 80mbps download speed.
Read Data from Google Sheets into Pandas without the Google Sheets API
By: Merlin Schäfer
Tags: Python, Google Sheets, Data, Pandas, Data Science

Read Data from Google Sheets into Pandas without the Google Sheets API

Photo by Myriam Jessier on Unsplash

Google Sheets is a common alternative to Microsoft Excel and is especially useful if you wish to work collaboratively on your spreadsheet or share your data. I want to quickly share two ways of reading Google Sheets data into a pandas DataFrame through the .read_csv() method without having to safe the sheet locally first or having to use the GoogleAPI.

Setup

All you need is a Google Sheets file with one or more sheets and of course some data. The file needs to be set to the sharing option which allows everyone with the link to view the data.

Image by Author

Option One — if you have multiple sheets

This option allows you to read in a specific sheet from a file containing multiple sheets. You can use the url-structure of google sheets in combination with the unique id of your file and a given sheet name to read in the data.

All you need to do is create a f-string for the url which takes the sheet id and sheet name and formats them into a url pandas can read.

import pandas as pd

sheet_name = “sample_1”

url = f” sheet_id = “1XqOtPkiE_Q0dfGSoyxrH730RkwrTczcRbDeJJpqRByQ”sheet_name = “sample_1”url = f” https://docs.google.com/spreadsheets/d/{sheet_id}/gviz/tq?tqx=out:csv&sheet={sheet_name }"

You can find the sheet id in the url of your file behind “d/”, copy it from your browser and paste it into your code. The sheet name is the name you gave your sheet.

After you’ve specified the id and name you can simply use the appropriately formatted url string to read the data into a pandas DataFrame (see below).

Image by Author

Option Two — if your file only has one sheet

If your file is simply one sheet with data you can directly copy and paste the url into your code and just replace one part by a set expression. This expression changes the url, so that it ouputs a csv file when called.

It’s as simple as this:

The only thing you have to replace in this code snippet is the sheet_url. After part of the string has been replaced you can use .read_csv() as usual.

Image by Author

I hope this helps you and allows you to quickly get data from Google Sheets without having to manually download it and read it in from a local csv -file.

Please note that the composition of the GoogleSheets urls might change in the future, which might break the code.

-Merlin

References:
Pandas Date Lookups
By: Edward Brown
Tags: Pandas, Python

Q.E.D

52x Speed Up! (293 vs 5.57)

If you can pre-compute the pandas date time object, you are much better off using that than a string.
Data Science 101: Build your first Machine Learning Model with Pandas, Scikit-Learn, and Google Colab
By: Pierre-Louis Bescond
Tags: Data Science, Machine Learning, Towards Data Science, Pandas, Scikit Learn

Data Science 101: Build your first Machine Learning Model with Pandas, Scikit-Learn, and Google Colab

Photo by Lorenzo Herrera on Unsplash

Lately, I had to design a simple coding exercise to help pure beginners take their first steps on Python and feel the “Machine Learning fever”. Today, I am happy to share it with anyone interested to start exploring this field.

And if you ever find yourself stuck with the code, the complete notebook is stored here.

Coding environment

Like for every journey, you will need a proper vehicle to navigate towards your first model.

My advice would be to start with an on-line Python environment like Google Colab.

This will save you from installing Python on your computer or managing libraries (deciding to switch to Pandas 1.0 or the last Scikit-Learn release is probably not your main concern at this time !).

Once connected to Google Colab, just create a new Python notebook:

Like for every on-line platform, Google Colab requires a Google account

(Image by Author)

Note: each notebook you create will be stored in a dedicated folder on your Google Drive: Colab Notebooks.

Let’s rename your first notebook by clicking on its title:
Summary of CSV Data Loading using Pandas and Numpy
Tags: Pandas, Numpy

Data loading methods are very various and sometimes confusing owing to different modules existing. Particularly, those loaded data may be called or operated by different modules, such as numpy, pandas, at the same time. Therefore, it is important to know how to load data and transform the type of variables between different modules.

Here, two examples are given and shown how to load the data using Pandas and Numpy. And then show how to convert the input (also called as regressor, independent variables, predictor, covariates, features)[1] and output (also called as response, dependent variables, response, labels)[1] type for Numpy and Pandas.

To load data using Pandas

import pandas as pd

csv_file = “file.csv”

# use read_csv to read the csv_file

data = pd.read_csv(csv_file)

# or use read_table to read the csv_file

data = pd.read_table(csv_file,delimiter= “,”)

#read input variables (also called as regressor, independent variables, predictor, covariates, features). m indicates rows number : -1, 0, 1, … max(rows), n denotes columns number: -1, 0, 1, …, max(columns). Here, -1 in m or n indicates unknown row and/or column number

X = data[“X_col_Name”].values.reshape(m,n)

# read output variables (also called as response, dependent variables, response, labels)

y = data[“Y_col_name”].values #usually, predictor only has one value

To load data using Numpy

import numpy as np

from numpy import array

csv_file = “file.csv”

#read data. Below [, skiprows=k] is used to skip the headers and/or first k lines

data = np.loadtxt(csv_file, delimiter= “,” [, skiprows=k])

# read input variables, here, numpy read the data as matrix

# below matrix X, the first colon “ : ” without any numbers denotes read all rows; the second colon “ :-1” indicates read from all columns except for the last column. e.g. X=data[0:5, 3:5] means read the first 5 rows and the column 4 and 5 (note: index starts from 0)

X = data[:,:-1]

# read output variables

y = data[:, -1] # read all rows and the last column

# Transform X and y for Pandas

# below -1 denotes unknown rows, 1 denotes the first column.

X = array(X).reshape(-1,1)

y = array(y)

When loading data in machine or deep learning, we have to set train and test datasets, sometimes we may need to set verification data. We can use same way to load those data.

I will update this post continuously.

References:
Helping to Control Defaults with Exploratory Data Analysis in Python
By: Jose Alexander
Tags: Python, Finance, Default, Eda, Pandas

Using Python to Answer Business Questions

For the business is critical to predict the default of customers over future periods, that way the business can keep the default under control and improve their approval policies.

In general it would be interesting to find in a Exploratory Data Analysis (EDA) phase variables related to the target variable (Default). With this in mind I’ll be interested in answering the next questions:

Does men have higher default rates compared to women? (Just for analysis, this variable isn’t allowed to use according to multiple regulatory conventions) The default rates are lower for customers with higher level of education? The delays in payments from previous months are related to default over next menth?

Question 1: Does Men Have Higher Default Rates Compared to Women?

With a very simple line in Python it’s possible to start to note some patterns in the data and the target variable.

print(df.groupby(“SEX”).mean()[“DEFAULT”])

Percentage of Default by Gender

It looks like men tend to have a higher level of default in this dataset!

Question 2: The default rates are lower for customers with higher level of education?

Customers could have different educational levels and that could be related (or not!) to default rates of customers.

Here is where Python comes to help: grouping the data, making summaries and plotting results it’s much easier with Pandas and Seaborn

A simple summary shows that customers with the higher level of education (graduate school) have the lowest default rate compared to customers with university or high school level of education. The “other” level of education have a minimal level of default but that group is little compared to graduate, university and highschool (1.5%)

Percentage of Default by Educational Level

Question 3: The delays in payments from previous months are related to default over next menth?

Another cool technique of EDA is the correlation matrix, that helps to visualize correlations between multiple numerical variables in the dataset.

Using Seaborn’s heatmap and the function corr()

The plot shows that that delays have a high correlation between each other but the correlation with Defaults, although is positive, seems lower.

Heatmap of the Correlation Matrix Between Payment Delays and Default Variables

Conclusion

Python with Pandas and Seaborn are very useful for analyzing customers defaults and getting quick insights of the default differences between sex, education level and other variables like delay in payments.

The Exploratory Data Analysus results are very powerful for business users because they can quickly understand patterns and improve their approval policies when they find something suspicious.

Here I analyzed the variables Sex, Education and Delay in Payments for analyzing the defauts, what other variables would you add to the analysis?

In this link you cand find the Github link to the jupyter notebook with all the code and the results showed here.
Pandas: How to get started.. How to start with Pandas.
By: Akshay Chavan
Tags: Pandas, Python

Photo by Serghei Trofimov on Unsplash

Pandas library in python provides a lot of features that are useful not only in data science projects but also for quick data manipulations or conversions.

Here I will list a few things that I used in my first Pandas project.

Import pandas

First and foremost you would need to import pandas into your script.

import pandas as pd

Read a comma separated file

Next you can load a data set saved as a comma separated file (.csv) using

df = pd.read_csv(‘datafile.csv’)

I prefer CSV format as it is the vanilla format and if need arises you use a simple text editor to peruse it.

Pandas uses data frame to store the data in memory, similar to R.

In this blog df will be used to denote the variable is a data frame.

Display stats

Displays stats of the data.

matdf.describe()

Print all the column names

Prints all the variables in the data frame

df.columns.values

Display the start and end of the data frame

Displays the first and the last ’n’ rows in a data frame respectively.

’n’ equals 5 by default.

matdf.head(n)<br>

matdf.tail(n)

Concatenate two data frames

Concatenates 2 data frames along the required axis.

When axis equals 0, more observations are added to the resulting data frame.

When axis equals 1, more variables (dimensions) are added to the resulting data frame.

A good explanation can be found here.

df = pd.concat([df1, df2], axis=1)

Unique values in a column

To display the unique values in a column.

Good to see different values of an ordinal variable.

df.columnName.unique()

To make a data frame from arrays

The Simplest way to create a data frame

pd.DataFrame([[1.0,2.0],[3.0,4.0]], columns=[‘a’, ‘b’])

Drop a column

To delete a column

df.drop(‘columnName’, axis = 1)

Set a column as index

Allows you to label rows using an already present column.

matdf = df.set_index(‘columnName’)

Select conditionally

loc command allows you to select rows (indices) from column2 and substitute them with value2, that equals a condition to the values in column1.

Note: columnName1 and columnName2 can be the same column name.

df.loc[df[‘columnName1’] == value1,’columnName2'] = value2

Get a subset of data frame

Get stats from a column for only those rows (indices) where the conditions are satisfied in one or more columns.

df.columnName3[(df.columnName1 == value1) & (df.columnName2 == value2 )].mean()

Write a data frame to CSV

After you preprocess the data you might want to save it to the disk.

to_csv function comes in handy to save a data frame as a CSV file,

matdf.to_csv(‘matYearCountry.csv’)

That's all, go fire up your first pandas project.