Overview

I'm happy to provide this important update. We've worked on this for a year and a half, so I'm glad to finally write it. We named the application AESIR because we’ve love Norse Mythology. AESIR automates and runs trading strategies.

• Volatility, technical analysis, oscillators, and other signals are currently supported by AESIR.

• Additionally, we enhanced AESIR's ability to create distinctive bespoke signals by allowing it to analyze many indicators and produce a single signal.

• AESIR has a significant social component that allows you to copy the best-performing public setups and use them right away.

Enter your email here to be notified when AEISR launches.

Views on algorithmic trading

First, let me clarify. Anyone who claims algorithmic trading platforms are money-printing plug-and-play devices is a liar. Algorithmic trading platforms are a collection of tools.

A trading algorithm won't make you a competent trader if you lack a trading strategy and yolo your funds without testing. It may hurt your trade. Test and alter your plans to account for market swings, but comprehend market signals and trends.

Status Report

Throughout closed beta testing, we've communicated closely with users to design a platform they want to use.

To celebrate, we're giving you free Aesir Viking NFTs and we cover gas fees.

Why use a trading Algorithm?

• Automating a successful manual approach

• experimenting with and developing solutions that are impossible to execute manually

One AESIR strategy lets you buy any cryptocurrency that rose by more than x% in y seconds.

AESIR can scan an exchange for coins that have gained more than 3% in 5 minutes. It's impossible to manually analyze over 1000 trading pairings every 5 minutes. Auto buy dips or DCA around a Dip

Sneak Preview

Here's the Leaderboard, where you can clone the best public settings.

As a tiny, self-funded team, we're excited to unveil our product. It's a beta release, so there's still more to accomplish, but we know where we stand.

If this sounds like a project that you might want to learn more about, you can sign up to our newsletter and be notified when AESIR launches.

Useful Links:

Join the Discord | Join our subreddit | Newsletter | Mint Free NFT

WRITE FOR THE WEB3

Why web3’s messaging is lost and how crypto winter is growing growth seeds

People interested in crypto, blockchain, and web3 typically read Bitcoin and Ethereum's white papers. It's a good idea. Documents produced for developers and academia aren't always the ideal resource for beginners.

Given the surge of extremely technical material and the number of fly-by-nights, rug pulls, and other scams, it's little wonder mainstream audiences regard the blockchain sector as an expensive sideshow act.

What's the solution?

Web3 needs more than just builders.

After joining TikTok, I followed Amy Suto of SutoScience. Amy switched from TV scriptwriting to IT copywriting years ago. She concentrates on web3 now. Decentralized autonomous organizations (DAOs) are seeking skilled copywriters for web3.

Amy has found that web3's basics are easy to grasp; you don't need technical knowledge. There's a paradigm shift in knowing the basics; be persistent and patient.

Apple is positioning itself as a data privacy advocate, leveraging web3's zero-trust ethos on data ownership.

Finn Lobsien, who writes about web3 copywriting for the Mirror and Twitter, agrees: acronyms and abstractions won't do.

Web3 preached to the choir. Curious newcomers have only found whitepapers and scams when trying to learn why the community loves it. No wonder people resist education and buy-in.

Due to the gender gap in crypto (Crypto Bro is not just a stereotype), it attracts people singing to the choir or trying to cash in on the next big thing.

Last year, the industry was booming, so writing wasn't necessary. Now that the bear market has returned (for everyone, but especially web3), holding readers' attention is a valuable skill.

White papers and the Web3

Why does web3 rely so much on non-growth content?

Businesses must polish and improve their messaging moving into the 2022 recession. The 2021 tech boom provided such a sense of affluence and (unsustainable) growth that no one needed great marketing material. The market found them.

This was especially true for web3 and the first-time crypto believers. Obviously. If they knew which was good.

White papers help. White papers are highly technical texts that walk a reader through a product's details. How Does a White Paper Help Your Business and That White Paper Guy discuss them.

They're meant for knowledgeable readers. Investors and the technical (academic/developer) community read web3 white papers. White papers are used when a product is extremely technical or difficult to assist an informed reader to a conclusion. Web3 uses them most often for ICOs (initial coin offerings).

White papers for web3 education help newcomers learn about the web3 industry's components. It's like sending a first-grader to the Annotated Oxford English Dictionary to learn to read. It's a reference, not a learning tool, for words.

Newcomers can use platforms that teach the basics. These included Coinbase's Crypto Basics tutorials or Cryptochicks Academy, founded by the mother of Ethereum's inventor to get more women utilizing and working in crypto.

Discord and Web3 communities

Discord communities are web3's opposite. Discord communities involve personal communications and group involvement.

Online audience growth begins with community building. User personas prefer 1000 dedicated admirers over 1 million lukewarm followers, and the language is much more easygoing. Discord groups are renowned for phishing scams, compromised wallets, and incorrect information, especially since the crypto crisis.

White papers and Discord increase industry insularity. White papers are complicated, and Discord has a high risk threshold.

Web3 and writing ads

Copywriting is emotional, but white papers are logical. It uses the brain's quick-decision centers. It's meant to make the reader invest immediately.

Not bad. People think sales are sleazy, but they can spot the poor things.

Ethical copywriting helps you reach the correct audience. People who gain a following on Medium are likely to have copywriting training and a readership (or three) in mind when they publish. Tim Denning and Sinem Günel know how to identify a target audience and make them want to learn more.

In a fast-moving market, copywriting is less about long-form content like sales pages or blogs, but many organizations do. Instead, the copy is concise, individualized, and high-value. Tweets, email marketing, and IM apps (Discord, Telegram, Slack to a lesser extent) keep engagement high.

What does web3's messaging lack? As DAOs add stricter copyrighting, narrative and connecting tales seem to be missing.

Web3 is passionate about constructing the next internet. Now, they can connect their passion to a specific audience so newcomers understand why.

A completely operating application requires many processes and technical challenges. Implementing coding standards can make apps right, work, and faster.

With years of experience working in software houses. Many client apps are scarcely maintained.

Why are these programs "barely maintainable"? If we're used to coding concepts, we can probably tell if an app is awful or good from its codebase.

This is how I coded much of my app.

Make It Work

Before adopting any concept, make sure the apps are completely functional. Why have a fully maintained codebase if the app can't be used?

The user doesn't care if the app is created on a super server or uses the greatest coding practices. The user just cares if the program helps them.

After the application is working, we may implement coding principles.

You Aren’t Gonna Need It

As a junior software engineer, I kept unneeded code, components, comments, etc., thinking I'd need them later.

In reality, I never use that code for weeks or months.

First, we must remove useless code from our primary codebase. If you insist on keeping it because "you'll need it later," employ version control.

If we remove code from our codebase, we can quickly roll back or copy-paste the previous code without preserving it permanently.

The larger the codebase, the more maintenance required.

Keep It Simple Stupid

Indeed. Keep things simple.

Why complicate something if we can make it simpler?

Our code improvements should lessen the server load and be manageable by others.

If our code didn't pass those benchmarks, it's too convoluted and needs restructuring. Using an open-source code critic or code smell library, we can quickly rewrite the code.

Simpler codebases and processes utilize fewer server resources.

Don't Repeat Yourself

Have you ever needed an action or process before every action, such as ensuring the user is logged in before accessing user pages?

As you can see from the above code, I try to call is user login? in every controller action, and it should be optimized, because if we need to rename the method or change the logic, etc. We can improve this method's efficiency.

We can write a constructor/middleware/before action that calls is_user_login?

The code is more maintainable and readable after refactoring.

Each programming language or framework handles this issue differently, so be adaptable.

Clean Code

Clean code is a broad notion that you've probably heard of before.

When creating a function, method, module, or variable name, the first rule of clean code is to be precise and simple.

The name should express its value or logic as a whole, and follow code rules because every programming language is distinct.

If you want to learn more about this topic, I recommend reading https://www.amazon.com/Clean-Code-Handbook-Software-Craftsmanship/dp/0132350882.

Standing On The Shoulder of Giants

Use industry standards and mature technologies, not your own(s).

There are several resources that explain how to build boilerplate code with tools, how to code with best practices, etc.

I propose following current conventions, best practices, and standardization since we shouldn't innovate on top of them until it gives us a competitive edge.

Boy Scout Rule

What reduces programmers' productivity?

When we have to maintain or build a project with messy code, our productivity decreases.

Having to cope with sloppy code will slow us down (shame of us).

How to cope? Uncle Bob's book says, "Always leave the campground cleaner than you found it."

When developing new features or maintaining current ones, we must improve our codebase. We can fix minor issues too. Renaming variables, deleting whitespace, standardizing indentation, etc.

Make It Fast

After making our code more maintainable, efficient, and understandable, we can speed up our app.

Whether it's database indexing, architecture, caching, etc.

A smart craftsman understands that refactoring takes time and it's preferable to balance all the principles simultaneously. Don't YAGNI phase 1.

Using these ideas in each iteration/milestone, while giving the bottom items less time/care.

You can check one of my articles for further information. https://medium.com/life-at-mekari/why-does-my-website-run-very-slowly-and-how-do-i-optimize-it-for-free-b21f8a2f0162

https://medium.com/life-at-mekari/what-you-need-to-make-your-app-a-high-availability-system-tackling-the-technical-challenges-8896abec363f

Non-technical talents can benefit data scientists in addition to statistics and programming.

As our article 5 Most In-Demand Skills for Data Scientists shows, being business-minded is useful. How can you get such a diverse skill set? We've compiled a list of helpful resources.

Data science, data analysis, programming, and business are covered. Even a few of these books will make you a better data scientist.

Ready? Let’s dive in.

Best books for data scientists

1. The Black Swan

Author: Nassim Taleb

First, a less obvious title. Nassim Nicholas Taleb's seminal series examines uncertainty, probability, risk, and decision-making.

Three characteristics define a black swan event:

• It is erratic.

• It has a significant impact.

• Many times, people try to come up with an explanation that makes it seem more predictable than it actually was.

People formerly believed all swans were white because they'd never seen otherwise. A black swan in Australia shattered their belief.

Taleb uses this incident to illustrate how human thinking mistakes affect decision-making. The book teaches readers to be aware of unpredictability in the ever-changing IT business.

Try multiple tactics and models because you may find the answer.

2. High Output Management

Author: Andrew Grove

Intel's former chairman and CEO provides his insights on developing a global firm in this business book. We think Grove would choose “management” to describe the talent needed to start and run a business.

That's a skill for CEOs, techies, and data scientists. Grove writes on developing productive teams, motivation, real-life business scenarios, and revolutionizing work.

Five lessons:

• Every action is a procedure.

• Meetings are a medium of work

• Manage short-term goals in accordance with long-term strategies.

• Mission-oriented teams accelerate while functional teams increase leverage.

• Utilize performance evaluations to enhance output.

So — if the above captures your imagination, it’s well worth getting stuck in.

3. The Hard Thing About Hard Things: Building a Business When There Are No Easy Answers

Author: Ben Horowitz

Few realize how difficult it is to run a business, even though many see it as a tremendous opportunity.

Business schools don't teach managers how to handle the toughest difficulties; they're usually on their own. So Ben Horowitz wrote this book.

It gives tips on creating and maintaining a new firm and analyzes the hurdles CEOs face.

Find suggestions on:

• create software

• Run a business.

• Promote a product

• Obtain resources

• Smart investment

• oversee daily operations

This book will help you cope with tough times.

4. Obviously Awesome: How to Nail Product Positioning

Author: April Dunford

Your job as a data scientist is a product. You should be able to sell what you do to clients. Even if your product is great, you must convince them.

How to? April Dunford's advice: Her book explains how to connect with customers by making your offering seem like a secret sauce.

You'll learn:

• Select the ideal market for your products.

• Connect an audience to the value of your goods right away.

• Take use of three positioning philosophies.

• Utilize market trends to aid purchasers

5. The Mom test

Author: Rob Fitzpatrick

The Mom Test improves communication. Client conversations are rarely predictable. The book emphasizes one of the most important communication rules: enquire about specific prior behaviors.

Both ways work. If a client has suggestions or demands, listen carefully and ensure everyone understands. The book is packed with client-speaking tips.

6. Introduction to Machine Learning with Python: A Guide for Data Scientists

Authors: Andreas C. Müller, Sarah Guido

Now, technical documents.

This book is for Python-savvy data scientists who wish to learn machine learning. Authors explain how to use algorithms instead of math theory.

Their technique is ideal for developers who wish to study machine learning basics and use cases. Sci-kit-learn, NumPy, SciPy, pandas, and Jupyter Notebook are covered beyond Python.

If you know machine learning or artificial neural networks, skip this.

7. Python Data Science Handbook: Essential Tools for Working with Data

Author: Jake VanderPlas

Data work isn't easy. Data manipulation, transformation, cleansing, and visualization must be exact.

Python is a popular tool. The Python Data Science Handbook explains everything. The book describes how to utilize Pandas, Numpy, Matplotlib, Scikit-Learn, and Jupyter for beginners.

The only thing missing is a way to apply your learnings.

8. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

Author: Wes McKinney

The author leads you through manipulating, processing, cleaning, and analyzing Python datasets using NumPy, Pandas, and IPython.

The book's realistic case studies make it a great resource for Python or scientific computing beginners. Once accomplished, you'll uncover online analytics, finance, social science, and economics solutions.

9. Data Science from Scratch

Author: Joel Grus

Here's a title for data scientists with Python, stats, maths, and algebra skills (alongside a grasp of algorithms and machine learning). You'll learn data science's essential libraries, frameworks, modules, and toolkits.

The author works through all the key principles, providing you with the practical abilities to develop simple code. The book is appropriate for intermediate programmers interested in data science and machine learning.

Not that prior knowledge is required. The writing style matches all experience levels, but understanding will help you absorb more.

10. Machine Learning Yearning

Author: Andrew Ng

Andrew Ng is a machine learning expert. Co-founded and teaches at Stanford. This free book shows you how to structure an ML project, including recognizing mistakes and building in complex contexts.

The book delivers knowledge and teaches how to apply it, so you'll know how to:

• Determine the optimal course of action for your ML project.

• Create software that is more effective than people.

• Recognize when to use end-to-end, transfer, and multi-task learning, and how to do so.

• Identifying machine learning system flaws

Ng writes easy-to-read books. No rigorous math theory; just a terrific approach to understanding how to make technical machine learning decisions.

11. Deep Learning with PyTorch Step-by-Step

Author: Daniel Voigt Godoy

The last title is also the most recent. The book was revised on 23 January 2022 to discuss Deep Learning and PyTorch, a Python coding tool.

It comprises four parts:

1. Fundamentals (gradient descent, training linear and logistic regressions in PyTorch)

2. Machine Learning (deeper models and activation functions, convolutions, transfer learning, initialization schemes)

3. Sequences (RNN, GRU, LSTM, seq2seq models, attention, self-attention, transformers)

4. Automatic Language Recognition (tokenization, embeddings, contextual word embeddings, ELMo, BERT, GPT-2)

We admire the book's readability. The author avoids difficult mathematical concepts, making the material feel like a conversation.

Is every data scientist a humanist?

Even as a technological professional, you can't escape human interaction, especially with clients.

We hope these books will help you develop interpersonal skills.