steve joseph

steve joseph


Top new cryptocurrencies to invest in June – 2022

June – is the month of new beginnings, and what better way to start off fresh than by investing in one (or more) of the many new cryptocurrencies that have recently hit the market? In this article, we’ll give you our top picks for the best cryptocurrencies to invest in for June of 2022.


  1. Solana is a new cryptocurrency that is quickly gaining popularity. It is based on the Ethereum blockchain and utilizes Proof of Stake (PoS) to validate transactions. This makes it more energy-efficient than other PoW-based currencies.
  2. Solana has a very fast transaction speed. It can handle up to 65,000 transactions per second. This is much faster than Ethereum, which can only handle around 15 transactions per second.
  3. Solana has a strong team of developers behind it. The team includes former employees of Google, Facebook, and PayPal.
  4. Solana is already being used by some major companies, such as Coinbase and Binance.

Overall, Solana is a very promising new cryptocurrency. Its fast transaction speed, strong team of developers, and major company partnerships make it a good choice for investors looking for a new currency to invest in.


Hashpe is a new cryptocurrency that was created in June of this year. It is based on the Bitcoin protocol, but it has some unique features that make it different from Bitcoin.

For one, Hashpe uses a different mining algorithm than Bitcoin. This means that it is more resistant to ASIC miners, which are specialized hardware that is designed for mining Bitcoin. This makes Hashpe more accessible to regular people who want to mine cryptocurrency.

Hashpe also has a built-in privacy feature called “zk-SNARKs.” This allows transactions on the Hashpe network to be private and secure.

Overall, Hashpe is a promising new cryptocurrency with some unique features that make it worth investing in.

Shiba Inu:

Shiba Inu is a new cryptocurrency that was created in August 2020. It is an Ethereum token that was created as a parody of the popular Dogecoin. However, Shiba Inu has quickly gained popularity in its own right and is now one of the top 10 cryptocurrencies by market capitalization.

Investors are attracted to Shiba Inu because it has a very low supply of only 21 billion tokens. This compares to other popular cryptocurrencies like Bitcoin, which has a supply of 21 million. This limited supply means that Shiba Inu could potentially increase in value over time as demand increases.

Shiba Inu also has a very active community on social media. The coin has its own Twitter account with over 400,000 followers and an active Telegram group with over 8,000 members. This community helps to generate interest and awareness of the coin, which could lead to more people buying it.

Overall, Shiba Inu is a promising new cryptocurrency with a lot of potentials. Its low supply and active community could help it to increase in value over time.

Saitama Inu:

Saitama Inu is a new cryptocurrency that was created in June of 2020. The team behind Saitama Inu is anonymous, but they are based in Japan. The currency is named after the Japanese anime character Saitama, who is known for his strength and power.

Saitama Inu has a total supply of 10,000,000,000 tokens and a circulating supply of 1,000,000,000 tokens. The currency is currently trading at $0.000016 USD.

The Saitama Inu team has plans to use the currency to build a decentralized social media platform. The platform will be called Soar and it will allow users to earn rewards for creating and sharing content.

So far, the team behind Saitama Inu has been very active on social media and they seem to be very committed to their project. The currency has a lot of potentials and it will be interesting to see how it develops over time.


Tezos is a new cryptocurrency that offers several unique features that make it an attractive investment. For one, Tezos uses a proof-of-stake algorithm instead of proof-of-work. This means that Tezos is more energy-efficient than other cryptocurrencies.

Another key feature of Tezos is its on-chain governance system. This system allows holders of Tezos tokens to vote on changes to the protocol. This helps to keep the Tezos network decentralized and responsive to the needs of its users.

Finally, Tezos has a strong development team behind it. The team is led by Arthur Breitman, who has a background in mathematics and computer science. This gives Tezos a solid foundation on which to build and grow.

Overall, Tezos is a promising new cryptocurrency with several unique features that make it worth investing in.


Cronos is a new cryptocurrency that was launched in June of 2018. Cronos is designed to be a more stable and secure form of currency than Bitcoin. It uses a Proof-of-Stake system, which means that users earn rewards for holding Cronos coins in their wallets. This helps to keep the currency more stable than other cryptocurrencies that use a Proof-of-Work system.

Cronos is also a very fast cryptocurrency. Transactions take only seconds to confirm. This makes it ideal for people who want to use cryptocurrency for everyday purchases.

Investors are bullish on Cronos, and the currency has already seen significant growth since its launch. If you’re looking for a new cryptocurrency to invest in, Cronos is a good option.


If you’re ready to invest in cryptocurrency, exchanges are the most secure and user-friendly way to purchase, sell, and trade digital assets.

Koinbazar is the best leading cryptocurrency exchange in the world because it offers a number of unique features compared to other exchanges.

Koinbazar offers a wide variety of cryptocurrencies to invest in, including Bitcoin, Ethereum, Litecoin, and more. They also offer a variety of different payment methods, so you can choose the one that best suits your needs.

For one, Koinbazar allows users to trade in Indian rupees. This is important because it means that users do not have to convert their currency into another currency (such as US dollars) before trading. This makes the trading process simpler and more convenient.

Another unique feature of Koinbazar is that it offers a mobile Android and iOS app. This is convenient for users who want to trade on the go. The app is also very user-friendly and easy to navigate.

In addition, Koinbazar has low trading fees. It also offers a referral program, which gives users a 10K Shiba Inu (SHIB) if they refer new users to the platform.

#crypto #cryptos #cryptocurrency #invest #investments #investment #investing #cryptotrading 

Top new cryptocurrencies to invest in June – 2022
Anand FMC

Anand FMC


It’s a tough one! And we can only speak for ourselves here.

Let’s first define what we mean by best - An ideal cryptocurrency trading platform is SIMPLE, SECURE, and thinks Beyond Business.

FidoMeta Coin was designed with the idea to build the simplest crypto trading platform ever. We have removed all the intricacies of crypto trading and built a go-to solution for cryptocurrency trading simplistically.

For More info:
#cryptocurrency #forexmarket #onlineforextrading #forexbusiness #forextrading #crypto
#investing #crypto #cryptotrading


Anand FMC

Anand FMC


Is it worth it to invest in cryptocurrencies?

Let’s first understand what makes cryptocurrencies so different and unique compared to traditional investment options.
Cryptocurrencies are based on blockchain technology, changing the way we do commerce, store information, and connect.
Blockchain technology is the main catalyst behind the growth of cryptocurrencies in the market.
Kindly Fill out This Form:
For More info:
#cryptocurrency #forexmarket #onlineforextrading #forexbusiness #forextrading #crypto
#investing #crypto #cryptotrading

Is it worth it to invest in cryptocurrencies?
Anand FMC

Anand FMC


Best Cryptocurrencies To Invest In

Based on the market and crypto which have a roadmap, completed the coin audit, and has listed in exchange listing, which has high demand future and has also completed the coin audit. Such cryptocurrencies tend to be the best cryptocurrencies to invest in. 
For More info:
#cryptocurrency #forexmarket #onlineforextrading #forexbusiness #forextrading #crypto

Best Cryptocurrencies To Invest In

INVESTING in GERMAN stocks-Overview of the stock market & Indices [DAX]&Index CHANGES 2021

Disclaimer: This is not an investment advice. The video is for educational and entertainment purposes only. I might be invested in the mentioned stocks.

In this video I am introducing the German stock market and giving the most important information to get started invested in Germany. Having German stocks is essential for an international portfolio.

I am also talking about the upcoming change in the German equity market in September 2021.

Let me know what you think in the comments below.

The historical performance was taken via Python using the Yahoo Finance API (check out videos on my channel how to do that). The chart was visualized with Matplotlib.

Facts about Frankfurt Stock Exchange

German shareholder share: 2013_Factbook_08_6_Aktionaersstruktur_Laendervergleich.pdf

Largest stock exchanges worldwide:

Index change:

Fast entry, regular entry, fast exit regular exit further information:

Unfortunately only in German but just pull it into a translation platform. Let me know if you need support for that.

#Investing #DAX #Germanstocks #Indexchange

#investing #indexchange

INVESTING in GERMAN stocks-Overview of the stock market & Indices [DAX]&Index CHANGES 2021
Mike doctor

Mike doctor


How to Retire at Age 30 (Investing the SMART Way). DO NOT MISS!!!

In this video, I’m going to show you how you can retire early, using a combination of strategic investments, savings, and money building hacks. I’m 28 years old right now (almost 29) and my plan is to be able to retire at age 30 if I want. Watch all the way through and you’ll have a actionable steps you can take today to start building wealth and retiring early.
id you know that 36% of workers and retirees have under $1,000 in their saving? This crazy stat shows us that not enough people are prioritizing saving money and investing it the smart way. Not only that, but they are working more years and getting fewer years to relax in retirement. I know YOU are going to beat that statistic :)

My goal is to show you that we don’t need to live the traditional 9-5 career, where you graduate school, spend 30+ years working 5 days a week, and finally get to enjoy your life after retiring. You can start living now :) But it will take getting out of your comfort zone and doing things that most people won’t want to do.

We’ll talk about:
The FIRE movement and how you can utilize it to build more wealth and retire early
The effects of building your income and how drastically an extra $1,000 passive income can have on your path to retirement. Serious, watch the video for a breakdown of how crazy it is!
Some investing strategies you can use right now to increase your wealth
The 4% rule and how to interpret this
Examples of different incomes and savings rates
Relocation as a key strategy in retiring early
📺 The video in this post was made by Charlie Chang
The origin of the article:
🔺 DISCLAIMER: The article is for information sharing. The content of this video is solely the opinions of the speaker who is not a licensed financial advisor or registered investment advisor. Not investment advice or legal advice.
Cryptocurrency trading is VERY risky. Make sure you understand these risks and that you are responsible for what you do with your money
🔥 If you’re a beginner. I believe the article below will be useful to you ☞ What You Should Know Before Investing in Cryptocurrency - For Beginner
⭐ ⭐ ⭐The project is of interest to the community. Join to Get free ‘GEEK coin’ (GEEKCASH coin)!
☞ **-----CLICK HERE-----**⭐ ⭐ ⭐
Thanks for visiting and watching! Please don’t forget to leave a like, comment and share!

#bitcoin #blockchain #how to retire at age 30 #investing the smart way #investing #how to retire at age 30 (investing the smart way)

How to Retire at Age 30 (Investing the SMART Way). DO NOT MISS!!!

How to calculate Heikin Ashi candles in Python for trading

Note from Towards Data Science’s editors:_ While we allow independent authors to publish articles in accordance with our rules and guidelines, we do not endorse each author’s contribution. You should not rely on an author’s works without seeking professional advice. See our Reader Terms for details._

Quantitative traders know that they need to extract as much information as possible from price charts. Price action is a very useful technique to spot trading opportunities, but it’s sometimes difficult to read because candlestick charts might be messy or noisy. So, traders need a tool to smooth price action and remove noise. Heikin Ashi candles can help us. Let’s see how.

What are Heikin Ashi candles?

Heikin Ashi candles (sometimes called Heiken Ashi) are a particular kind of candlestick chart that tries to remove noise from price action. Particularly, in this kind of chart, there are no gaps.

Let’s see a chart of S&P 500 with plain candlesticks.

S&P 500 chart. Image by the author.

As you can see, there are a lot of daily gaps and the shadows of the candles are sometimes wide and make it difficult to decide what to do.

Heikin Ashi candles are calculated this way:

  • Open: (Open (previous candle) + Close (previous candle))/2
  • Close: (Open + Low + Close + High)/4
  • High: the same of the actual candle
  • Low: the same of the actual candle

As you can see, the calculation of today’s HA candle uses data from both today’s and yesterday’s candles. This way, HA candles smooth price action.

The result, applied to the same chart is:

S&P 500 chart with Heikin Ashi candles. Image by the author

As you can see, there are no gaps and the chart is easier to read.

How to use Heikin Ashi charts?

Using HA charts is very simple. First of all, we can see that before an important reversal we can detect some spinning top patterns, that are candles with very small real body and large shadows of almost the same size. This is an important way to detect a trend reversal.

Then, we can see that when the trend is strong, the opposite shadow is almost absent (i.e. in a strong bullish trend, lower shadows are invisible and in a strong bearish trend, upper shadows are invisible). This gives us a clear overview of the strength of a trend. Finally, a color change is very often used as a confirmation that the trend has changed direction and may be used as an operative signal.

These are all useful items for building a trading strategy and we don’t need to be able to catch several types of patterns like in candlestick charts, but we only need to spot spinning tops and color changes.

The drawback of Heikin Ashi candles is that, like any other kind of moving average, they catch the trend reversal with some delay. So, their signals are always a confirmation of something that has already happened. Thus, they are useful only then you have to work with long-term strategies because the delay in the signal will always make you enter and exit late. However, HA candles are a very useful tool, especially when you work with scalping and need to smooth very noisy price action. Just like any other indicator, it’s useful to use it with another signal, for example with moving averages.

Let’s now see how to calculate Heikin Ashi candles in Python.

#algorithmic-trading #trading #algorithms #investing #python

How to calculate Heikin Ashi candles in Python for trading
Ray  Patel

Ray Patel


Algorithmic Trading with the Disparity Index in Python

Coding and Backtesting a powerful indicator in Python

There are an extensive amount of technical indicators out there for trading purposes but traders being so picky will end up choosing only a handful of them and the indicator we are going to discuss today definitely adds to this list. Behold, the Disparity Index. In this article, we will first discuss what the Disparity Index is all about, the math behind the indicator, and then, we will move on to the coding part where we will use Python to first build the indicator from scratch, construct a simple trading strategy based on it, backtest the strategy on Google stocks and compare the returns to those of the SPY ETF (an ETF designed to track the movements of the S&P 500 market index).

#python #investing #education #finance #technology

Algorithmic Trading with the Disparity Index in Python

Algorithmic Trading with Stochastic Oscillator in Python

Learn to implement and backtest one of the most popular trading indicators with python

There are a bunch of technical indicators that can be considered for research and analysis but the one we are going to discuss today is one of the most popular indicators used among traders for trading purposes. It’s none other than the Stochastic Oscillator technical indicator. In this article, we will use python to create a Stochastic Oscillator-based trading strategy and backtest the strategy to see how well it performs in the real-world market. Additionally, we will also compare our trading results to the SPY ETF (an ETF specifically designed to track the S&P 500 market index) as a method to validate our strategy. Without further ado, let’s jump into the article.

#finance #investing #programming #data-science #python

Algorithmic Trading with Stochastic Oscillator in Python
Archie  Powell

Archie Powell


10+ Questions you Should ask Yourself Before Developing an AI Solution

So, you’d like to spice things up in your current business strategy and add an extra layer of high technology with a sophisticated AI solution. Let me just ask you one simple question first:

Are you sure it is the best move?

If your eyes sparkle with excitement as you think “Yes!”, you can skip this article and go find your perfect AI vendor.

If, however, you have even a shade of doubt — stick around. I promise that you won’t regret it!

What I have for you is 10 questions answered by Miquido’s Head of Innovation that should help anyone understand whether adopting AI technology is the right move or financial suicide for your business. Ok, no more waiting — here are the questions:

  1. Can you state your problem clearly?
  2. Can you solve your challenge without AI?
  3. What do you understand by “doing your task right”?
  4. Do you accept that your solution will never be perfect?
  5. Do you possess enough data to get started?
  6. Is your** data relevant**?
  7. Will you have continuous access to new data?
  8. Can you label your data correctly?
  9. Do you need an interpretable model or is accuracy enough?
  10. Do you have enough resources to keep the project going?

Sound simple enough? Well, it gets a bit more complicated. So we’ll look into each of them in more detail in just a second.

Get comfy, grab something to take notes with, and go along with this article, or simply save it for later.

Let’s dive in!

1. Can you state the problem clearly?

A clear vision of what you want to achieve is a must when it comes to applying AI in business. You need to have a specific problem that requires high-tech solutions that can be solved with Artificial Intelligence and/or Machine Learning.

Introducing AI for the sake of bragging about having it won’t fly here.

To help you answer this question, try thinking about specific challenges you want to address and always have your target audience in mind. Ask yourself:

  • Which repetitive tasks are you trying to automate?
  • Who will benefit from this automation?
  • Will it be worth the investment?

Pro tip

_If you’re not sure where to start — start with data mining. _Its ultimate goal is to help you get inspiration and once your idea is stated, you can come back to Machine Learning. Focus on what you can have right there and right now — the cornerstone of Artificial Intelligence, data.

2. Can you solve your challenge without AI?

Artificial Intelligence is great because it offers seemingly simple solutions to complex problems. The only issue is that, in fact, it’s far more demanding than it appears. So the ugly truth about the development of AI technology is that if you can get on without it — just do it.

Consider these questions:

  • Is your problem complex enough to engage machine learning?
  • Could it be represented by a mathematical equation?
  • Can you think of a step-by-step recipe for an output?

If you answered “yes” to the last two questions — congratulations, you **don’t **need artificial intelligence!

If, however, you couldn’t quite wrap your mind around the correct patterns, advanced machine learning algorithms might be your only chance to succeed.

Pro tip

It’s quite simple, really: if you can recognise the pattern yourself, you don’t need Machine Learning to do that for you.

By now, you should have a pretty good understanding of whether you actually need AI in your strategy or if you simply want to follow the trends. Let’s see if Artificial Intelligence actually can help you out.

3. What does it mean to do your task right?

First of all, you’ll have to define what “right” and “wrong” mean to you and your business. The world of technology is still pretty binary, and if something isn’t “true”, it is false by definition.

So, before investing in any solution, make sure you understand what it is you hope to gain from it.

Artificial intelligence is, first and foremost, a complicated algorithm. And in order for it to learn, you need to be able to evaluate its performance. Think along the lines of:

  • How will you evaluate that a task is done correctly?
  • Which mistakes will be more harmful than others?
  • How many mistakes per 1000 results can you afford to have?

That’s another big revelation about working with AI: mistakes are unavoidable.

Pro tip

Be careful not to aim too high with accuracy. Placing an excessively high bar may result in you missing some profitable opportunities.

That brings us straight to question #4:

4. Do you accept that your solution will never be perfect?

If your reply to this question is “no”, I’ve got some bad news for you: you’re not ready to work with machine learning. **There will be mistakes. **Sometimes more, sometimes less, but there’s no chance in the world for your solution to run error-free. Even if you have Elon Musk on your team.

At this point, think about yourself, your mental health, and the daily struggle you’ll have to accept from now on in your business:

  • Can you live with occasional mistakes in your model?
  • What are the actual consequences of such mistakes?
  • What does it all mean from an ethical standpoint?

Once again, there are two potential outcomes here:

  • You’ve evaluated the risks correctly, talked yourself into accepting them, and are on your merry way to hiring an AI software development company.
  • You’ve reached the conclusion that the stakes are too high, so you can either give up on the idea of working with machine learning altogether.

Pro tip

To minimise the chance of costly and dangerous mistakes, simply have someone who will double-check the results. This is known as a “human-in-the-loop approach” and can save you some headaches.

With that in mind, you must have a pretty good idea of whether or not the AI onion is worth peeling. Now it’s time to see if you’ll be able to actually build your model.

5. Do you have enough data?

The most common and broadly discussed limitation of Artificial Intelligence is its heavy dependency on datasets. There is simply no machine learning without data.

To put it bluntly: if you don’t have the data to keep your project running, your chances of launching it in the first place are slim. Some questions that might help you think are:

Do potentially useful inputs even exist? Can you gain access to them (e.g. build them, buy them, etc.)?Do you have enough examples?

Pro tip

It usually takes at least 10 thousand samples when training the model from scratch. Yet, the more examples you have the more reliable your AI model will be, and don’t we all thrive for perfection?

In data science, however, it’s both quality _and _quantity that matter, so if you do have enough materials to proceed with, let’s check if they hold any value.

6. Is your data relevant?

As you could’ve guessed from the previous point, without having a well-thought-through plan for data collection, you’ll stumble across multiple serious issues with your AI solution pretty quickly.

The best way to ensure it doesn’t happen is to double-check the actual relevance of your data.

There’s no point in having 1000+ features with no practical use, they’ll only eat up your precious storage space. The important questions to keep in mind to ensure that you’ll only work with the highest quality data points are:

  • Are your data points significant?
  • Is all the data clean enough?
  • Is the data you have relevant to your target audience?
  • Is it free of bias?

Pro tip

To learn from examples, AI needs good examples to learn from. In order for everything to work in an orderly fashion, you need to ensure your sample data is well-balanced, clean and free of inconsistencies.

And if you’ve got that covered, it’s time to think about the further evolution of your AI solution.

7. Will you be able to access new data continuously?

We are almost done with data questions, I promise! But while we’re on the subject, let’s think about the scalability of your project.

Having an accurate initial model might suffice for obtaining reliable predictions for a relatively short period of time. Yet, you’ll soon notice there are numerous factors that may negatively affect its performance. These threats include social events, seasonal changes, shifts in demographics, the geographical location of your users, etc.

When there are factors that can influence your target audience over time, your AI model needs to be constantly retrained.

Here, you will have to face new challenges and try to answer questions like:

  • How sensitive is your dataset to changes?
  • Do or will you have access to new data continuously in order to update it?

A great example of such unexpected threats is Covid-19. It has pretty much rendered a massive amount of data obsolete, as people’s behaviour has changed drastically.

Pro tip

Make sure that your AI solution will be weather-proof._ Or, in case of a disastrous setback, like a worldwide pandemic, at least can be easily updated._

That brings us to the final question about data.

8. Can you label your data properly?

We’ve covered some paramount issues like obtaining, updating, and navigating Big Data, so now it’s time to talk about data management.

In order for your AI solution to correctly understand the data, it requires proper labelling. Some datasets, such as image recognition systems, inherently contain labels based on the logged user actions. However, if your plan is to build a classification from the ground up, you’ll need to come up with a system for correct data labelling.

The important questions to think about here are:

  • Do you need to label your dataset?
  • Can you get a human to do it?
  • How much time and money will that process require?

Pro tip

_When working with advanced data, such as ECG signals or medical images, __hire experts to correctly classify each case _before your model could learn from examples.

With that, we’re ready to move on to the next question!

9. Does your model have to be interpretable?

Interpretability is one of the primary issues with machine learning. But what does it even mean?

**In the simplest terms, the higher the **interpretability of an AI training model, the easier it makes it for a human to understand the processes behind the algorithm’s decision making.

It is crucial for some businesses to fully understand the flow due to external policies and regulations. However, more often than not, having a model that is accurate yet not entirely interpretable, is enough to get your AI project up and running.

So, the two major questions that arise here are:

  • Are there any regulations governing your model’s interpretability?
  • What is your trade-off between accuracy and interpretability?

Pro tip

_Testing helps to launch many projects regardless of their complexity. _If you can’t explain how something works, run as many tests as needed to prove that it does work.

Looks like it’s time for the final and the most important question you should ask yourself before committing to an AI project:

10. Do you have enough resources to implement & maintain your AI solution?

As you may already know, machine learning projects aren’t particularly cheap, are not that easy to implement, and require an experienced team behind the wheel. So before you jump into your next idea headfirst, try to evaluate with great care whether you have enough resources to really pull this off.

Some of the questions that might help you at this stage are:

  • How much does building and updating the database cost?
  • Do you have access to enough processing power?
  • Are you aware of the costs of training and retraining your model?
  • Are the expected benefits higher than the estimated costs?

Depending on the complexity of your model, all these numbers can vary drastically.

#ai #future-of-ai #ai-technology #investing #technology #tech

10+ Questions you Should ask Yourself Before Developing an AI Solution
Jamison  Fisher

Jamison Fisher


Active Investing with Python: Part 2

Automating reports with python and a little cloud compute.

In part one, I introduced the concept of active investing and highlighted one particular strategy, the asset rotation model (ARM), that I recently began implementing with the help of some code and my server. The ARM bucket of my portfolio rotates between two securities: QQQX, a closed-end fund indexed to the Nasdaq 100, and TLT, an index of long-term US treasury bonds.

In this article, I focus on how to automate the “rotate” alert with code. With the help of python, I analyze the two stocks and email a mini report. The python script is then automated with Linux’s task scheduler, cron.

If you are unfamiliar with active investing or the asset rotation model, I encourage you to read part one for added context. If you’d rather dig into the source code directly, you can find it at my github repo.

Disclaimer: This article is in no way constitutes financial advice. It is incumbent upon you to develop a financial plan that best suits your needs and adhere to the behavioral discipline necessary to execute that plan in a principled way. If an active approach is attractive to you, then continue reading.

#automation #python #investing #cloud

Active Investing with Python: Part 2

Algorithmic Trading with Aroon Indicator in Python


The Portable Document Format (PDF) is not a WYSIWYG (What You See is What You Get) format. It was developed to be platform-agnostic, independent of the underlying operating system and rendering engines.

To achieve this, PDF was constructed to be interacted with via something more like a programming language, and relies on a series of instructions and operations to achieve a result. In fact, PDF is based on a scripting language - PostScript, which was the first device-independent Page Description Language.

In this guide, we’ll be using pText - a Python library dedicated to reading, manipulating and generating PDF documents. It offers both a low-level model (allowing you access to the exact coordinates and layout if you choose to use those) and a high-level model (where you can delegate the precise calculations of margins, positions, etc to a layout manager).

We’ll take a look at how to create a PDF invoice in Python using pText.

#investing #data-science #python #programming #finance

Algorithmic Trading with Aroon Indicator in Python

Using Python For Finance: How To Analyze Profitability Margin

Profitability ratios are financial metrics offering insights on how good a company is able to generate earnings from revenues, assets and equity. In this post, we will perform a profitability margin analysis with Python by comparing profitability ratios across peer companies. Below are the 5 different related profitability ratios that we will calculate and analyse:

  1. Net Profit Margin
  2. Gross Profit Margin
  3. Operating Profit Margin
  4. Return on Assets
  5. Return on Equity

How to Calculate Profitability Ratios

Before starting with the Python code to identify peer companies and analyse profitability margins, we will introduce each of the ratios in this section.

#python #finance #financial-analysis #python-for-finance #data-science #investing #backend #math

Using Python For Finance: How To Analyze Profitability Margin
Otho  Hagenes

Otho Hagenes


Will Artificial Intelligence Replace Portfolio Managers in The Financial Industry?

Artificial Intelligence (AI) chess gamers and poker players have already proven they could beat human masters. What’s to stop AI from doing the same with financial markets? What happens when AI becomes a portfolio player?

To some extent, it already has, even though investment success relies strongly on human interactions. In fact, very few industries depend on employees’ decisions as much as the financial markets. With AI, are these human decisions being overwritten by machine learning?

The reality is that “algorithm trading” has already impinged the market, exacerbating the exclusive Down Jones plummet of 700 points in 20 minutes back in February 2018. Traders and analysts agreed that the growing speed of algorithm trading models and automated sell orders impacted the collapse that day.

There are many positive roles that AI can play in the financial industry. AI algorithms can reduce risk, detect and manage fraud, improve operational efficiencies, and deliver improved customer services

#artificial-intelligence #machine-learning #finance #technology #ai #finance-and-banking #investing #ai-investing

Will Artificial Intelligence Replace Portfolio Managers in The Financial Industry?
Temesgen Kifle

Temesgen Kifle


A Fundamental Analysis of the Nasdaq 100 (NDX) with Python

Is the Nasdaq 100 index overpriced from a value investor’s point of view? That is a legitimate question to ask yourself before considering buying a Nasdaq 100 ETF or a similar asset.

This article aims to provide a tentative answer to the aforementioned question and, more importantly, to expose a methodology in Python that you might want to reuse for other indices.

If you are only interested in the results, skip to section 4!


The stock data is scraped from Yahoo! Finance using the _yahooquery _library and is considered to be correct and accurate without any further verification.

The data was downloaded on February 16, 2021, and it has likely evolved a lot when you are reading this article.

This story is not investment advice.

1. Download stock data

Nasdaq 100 tickers can be scraped from websites like and saved into a csv file.

Should you need help to scrape data from the internet, step 1 of the following story provides a methodology:

Once the tickers are saved, they are loaded into a Pandas data frame and the available information about the companies are downloaded from Yahoo! Finance:

import pandas as pd
from yahooquery import Ticker

#Load tickers and drop duplicated companies
path = "/your/path"
tickers = pd.read_csv(path + "ndx_tickers.csv")
tickers = tickers.set_index("Ticker")
tickers = tickers.drop(index=["GOOGL","FOXA"])

#download companies data from yahoo finance
infos_tickers = Ticker(tickers.index.values)
infos_tickers = infos_tickers.all_modules

Note: Some companies like Alphabet issue shares of different classes that have their own tickers but only one ticker per company is required.

#programming #investing #python #data-science

A Fundamental Analysis of the Nasdaq 100 (NDX) with Python