More on Economics & Investing

Jan-Patrick Barnert
3 years ago
Wall Street's Bear Market May Stick Around
If history is any guide, this bear market might be long and severe.
This is the S&P 500 Index's fourth such incident in 20 years. The last bear market of 2020 was a "shock trade" caused by the Covid-19 pandemic, although earlier ones in 2000 and 2008 took longer to bottom out and recover.
Peter Garnry, head of equities strategy at Saxo Bank A/S, compares the current selloff to the dotcom bust of 2000 and the 1973-1974 bear market marked by soaring oil prices connected to an OPEC oil embargo. He blamed high tech valuations and the commodity crises.
"This drop might stretch over a year and reach 35%," Garnry wrote.
Here are six bear market charts.
Time/depth
The S&P 500 Index plummeted 51% between 2000 and 2002 and 58% during the global financial crisis; it took more than 1,000 trading days to recover. The former took 638 days to reach a bottom, while the latter took 352 days, suggesting the present selloff is young.
Valuations
Before the tech bubble burst in 2000, valuations were high. The S&P 500's forward P/E was 25 times then. Before the market fell this year, ahead values were near 24. Before the global financial crisis, stocks were relatively inexpensive, but valuations dropped more than 40%, compared to less than 30% now.
Earnings
Every stock crash, especially earlier bear markets, returned stocks to fundamentals. The S&P 500 decouples from earnings trends but eventually recouples.
Support
Central banks won't support equity investors just now. The end of massive monetary easing will terminate a two-year bull run that was among the strongest ever, and equities may struggle without cheap money. After years of "don't fight the Fed," investors must embrace a new strategy.
Bear Haunting Bear
If the past is any indication, rising government bond yields are bad news. After the financial crisis, skyrocketing rates and a falling euro pushed European stock markets back into bear territory in 2011.
Inflation/rates
The current monetary policy climate differs from past bear markets. This is the first time in a while that markets face significant inflation and rising rates.
This post is a summary. Read full article here
Sam Hickmann
3 years ago
What is this Fed interest rate everybody is talking about that makes or breaks the stock market?
The Federal Funds Rate (FFR) is the target interest rate set by the Federal Reserve System (Fed)'s policy-making body (FOMC). This target is the rate at which the Fed suggests commercial banks borrow and lend their excess reserves overnight to each other.
The FOMC meets 8 times a year to set the target FFR. This is supposed to promote economic growth. The overnight lending market sets the actual rate based on commercial banks' short-term reserves. If the market strays too far, the Fed intervenes.
Banks must keep a certain percentage of their deposits in a Federal Reserve account. A bank's reserve requirement is a percentage of its total deposits. End-of-day bank account balances averaged over two-week reserve maintenance periods are used to determine reserve requirements.
If a bank expects to have end-of-day balances above what's needed, it can lend the excess to another institution.
The FOMC adjusts interest rates based on economic indicators that show inflation, recession, or other issues that affect economic growth. Core inflation and durable goods orders are indicators.
In response to economic conditions, the FFR target has changed over time. In the early 1980s, inflation pushed it to 20%. During the Great Recession of 2007-2009, the rate was slashed to 0.15 percent to encourage growth.
Inflation picked up in May 2022 despite earlier rate hikes, prompting today's 0.75 percent point increase. The largest increase since 1994. It might rise to around 3.375% this year and 3.1% by the end of 2024.

Sofien Kaabar, CFA
3 years ago
How to Make a Trading Heatmap
Python Heatmap Technical Indicator
Heatmaps provide an instant overview. They can be used with correlations or to predict reactions or confirm the trend in trading. This article covers RSI heatmap creation.
The Market System
Market regime:
Bullish trend: The market tends to make higher highs, which indicates that the overall trend is upward.
Sideways: The market tends to fluctuate while staying within predetermined zones.
Bearish trend: The market has the propensity to make lower lows, indicating that the overall trend is downward.
Most tools detect the trend, but we cannot predict the next state. The best way to solve this problem is to assume the current state will continue and trade any reactions, preferably in the trend.
If the EURUSD is above its moving average and making higher highs, a trend-following strategy would be to wait for dips before buying and assuming the bullish trend will continue.
Indicator of Relative Strength
J. Welles Wilder Jr. introduced the RSI, a popular and versatile technical indicator. Used as a contrarian indicator to exploit extreme reactions. Calculating the default RSI usually involves these steps:
Determine the difference between the closing prices from the prior ones.
Distinguish between the positive and negative net changes.
Create a smoothed moving average for both the absolute values of the positive net changes and the negative net changes.
Take the difference between the smoothed positive and negative changes. The Relative Strength RS will be the name we use to describe this calculation.
To obtain the RSI, use the normalization formula shown below for each time step.
The 13-period RSI and black GBPUSD hourly values are shown above. RSI bounces near 25 and pauses around 75. Python requires a four-column OHLC array for RSI coding.
import numpy as np
def add_column(data, times):
for i in range(1, times + 1):
new = np.zeros((len(data), 1), dtype = float)
data = np.append(data, new, axis = 1)
return data
def delete_column(data, index, times):
for i in range(1, times + 1):
data = np.delete(data, index, axis = 1)
return data
def delete_row(data, number):
data = data[number:, ]
return data
def ma(data, lookback, close, position):
data = add_column(data, 1)
for i in range(len(data)):
try:
data[i, position] = (data[i - lookback + 1:i + 1, close].mean())
except IndexError:
pass
data = delete_row(data, lookback)
return data
def smoothed_ma(data, alpha, lookback, close, position):
lookback = (2 * lookback) - 1
alpha = alpha / (lookback + 1.0)
beta = 1 - alpha
data = ma(data, lookback, close, position)
data[lookback + 1, position] = (data[lookback + 1, close] * alpha) + (data[lookback, position] * beta)
for i in range(lookback + 2, len(data)):
try:
data[i, position] = (data[i, close] * alpha) + (data[i - 1, position] * beta)
except IndexError:
pass
return data
def rsi(data, lookback, close, position):
data = add_column(data, 5)
for i in range(len(data)):
data[i, position] = data[i, close] - data[i - 1, close]
for i in range(len(data)):
if data[i, position] > 0:
data[i, position + 1] = data[i, position]
elif data[i, position] < 0:
data[i, position + 2] = abs(data[i, position])
data = smoothed_ma(data, 2, lookback, position + 1, position + 3)
data = smoothed_ma(data, 2, lookback, position + 2, position + 4)
data[:, position + 5] = data[:, position + 3] / data[:, position + 4]
data[:, position + 6] = (100 - (100 / (1 + data[:, position + 5])))
data = delete_column(data, position, 6)
data = delete_row(data, lookback)
return dataMake sure to focus on the concepts and not the code. You can find the codes of most of my strategies in my books. The most important thing is to comprehend the techniques and strategies.
My weekly market sentiment report uses complex and simple models to understand the current positioning and predict the future direction of several major markets. Check out the report here:
Using the Heatmap to Find the Trend
RSI trend detection is easy but useless. Bullish and bearish regimes are in effect when the RSI is above or below 50, respectively. Tracing a vertical colored line creates the conditions below. How:
When the RSI is higher than 50, a green vertical line is drawn.
When the RSI is lower than 50, a red vertical line is drawn.
Zooming out yields a basic heatmap, as shown below.
Plot code:
def indicator_plot(data, second_panel, window = 250):
fig, ax = plt.subplots(2, figsize = (10, 5))
sample = data[-window:, ]
for i in range(len(sample)):
ax[0].vlines(x = i, ymin = sample[i, 2], ymax = sample[i, 1], color = 'black', linewidth = 1)
if sample[i, 3] > sample[i, 0]:
ax[0].vlines(x = i, ymin = sample[i, 0], ymax = sample[i, 3], color = 'black', linewidth = 1.5)
if sample[i, 3] < sample[i, 0]:
ax[0].vlines(x = i, ymin = sample[i, 3], ymax = sample[i, 0], color = 'black', linewidth = 1.5)
if sample[i, 3] == sample[i, 0]:
ax[0].vlines(x = i, ymin = sample[i, 3], ymax = sample[i, 0], color = 'black', linewidth = 1.5)
ax[0].grid()
for i in range(len(sample)):
if sample[i, second_panel] > 50:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'green', linewidth = 1.5)
if sample[i, second_panel] < 50:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'red', linewidth = 1.5)
ax[1].grid()
indicator_plot(my_data, 4, window = 500)Call RSI on your OHLC array's fifth column. 4. Adjusting lookback parameters reduces lag and false signals. Other indicators and conditions are possible.
Another suggestion is to develop an RSI Heatmap for Extreme Conditions.
Contrarian indicator RSI. The following rules apply:
Whenever the RSI is approaching the upper values, the color approaches red.
The color tends toward green whenever the RSI is getting close to the lower values.
Zooming out yields a basic heatmap, as shown below.
Plot code:
import matplotlib.pyplot as plt
def indicator_plot(data, second_panel, window = 250):
fig, ax = plt.subplots(2, figsize = (10, 5))
sample = data[-window:, ]
for i in range(len(sample)):
ax[0].vlines(x = i, ymin = sample[i, 2], ymax = sample[i, 1], color = 'black', linewidth = 1)
if sample[i, 3] > sample[i, 0]:
ax[0].vlines(x = i, ymin = sample[i, 0], ymax = sample[i, 3], color = 'black', linewidth = 1.5)
if sample[i, 3] < sample[i, 0]:
ax[0].vlines(x = i, ymin = sample[i, 3], ymax = sample[i, 0], color = 'black', linewidth = 1.5)
if sample[i, 3] == sample[i, 0]:
ax[0].vlines(x = i, ymin = sample[i, 3], ymax = sample[i, 0], color = 'black', linewidth = 1.5)
ax[0].grid()
for i in range(len(sample)):
if sample[i, second_panel] > 90:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'red', linewidth = 1.5)
if sample[i, second_panel] > 80 and sample[i, second_panel] < 90:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'darkred', linewidth = 1.5)
if sample[i, second_panel] > 70 and sample[i, second_panel] < 80:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'maroon', linewidth = 1.5)
if sample[i, second_panel] > 60 and sample[i, second_panel] < 70:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'firebrick', linewidth = 1.5)
if sample[i, second_panel] > 50 and sample[i, second_panel] < 60:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'grey', linewidth = 1.5)
if sample[i, second_panel] > 40 and sample[i, second_panel] < 50:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'grey', linewidth = 1.5)
if sample[i, second_panel] > 30 and sample[i, second_panel] < 40:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'lightgreen', linewidth = 1.5)
if sample[i, second_panel] > 20 and sample[i, second_panel] < 30:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'limegreen', linewidth = 1.5)
if sample[i, second_panel] > 10 and sample[i, second_panel] < 20:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'seagreen', linewidth = 1.5)
if sample[i, second_panel] > 0 and sample[i, second_panel] < 10:
ax[1].vlines(x = i, ymin = 0, ymax = 100, color = 'green', linewidth = 1.5)
ax[1].grid()
indicator_plot(my_data, 4, window = 500)Dark green and red areas indicate imminent bullish and bearish reactions, respectively. RSI around 50 is grey.
Summary
To conclude, my goal is to contribute to objective technical analysis, which promotes more transparent methods and strategies that must be back-tested before implementation.
Technical analysis will lose its reputation as subjective and unscientific.
When you find a trading strategy or technique, follow these steps:
Put emotions aside and adopt a critical mindset.
Test it in the past under conditions and simulations taken from real life.
Try optimizing it and performing a forward test if you find any potential.
Transaction costs and any slippage simulation should always be included in your tests.
Risk management and position sizing should always be considered in your tests.
After checking the above, monitor the strategy because market dynamics may change and make it unprofitable.
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Ashraful Islam
4 years ago
Clean API Call With React Hooks
| Photo by Juanjo Jaramillo on Unsplash |
Calling APIs is the most common thing to do in any modern web application. When it comes to talking with an API then most of the time we need to do a lot of repetitive things like getting data from an API call, handling the success or error case, and so on.
When calling tens of hundreds of API calls we always have to do those tedious tasks. We can handle those things efficiently by putting a higher level of abstraction over those barebone API calls, whereas in some small applications, sometimes we don’t even care.
The problem comes when we start adding new features on top of the existing features without handling the API calls in an efficient and reusable manner. In that case for all of those API calls related repetitions, we end up with a lot of repetitive code across the whole application.
In React, we have different approaches for calling an API. Nowadays mostly we use React hooks. With React hooks, it’s possible to handle API calls in a very clean and consistent way throughout the application in spite of whatever the application size is. So let’s see how we can make a clean and reusable API calling layer using React hooks for a simple web application.
I’m using a code sandbox for this blog which you can get here.
import "./styles.css";
import React, { useEffect, useState } from "react";
import axios from "axios";
export default function App() {
const [posts, setPosts] = useState(null);
const [error, setError] = useState("");
const [loading, setLoading] = useState(false);
useEffect(() => {
handlePosts();
}, []);
const handlePosts = async () => {
setLoading(true);
try {
const result = await axios.get(
"https://jsonplaceholder.typicode.com/posts"
);
setPosts(result.data);
} catch (err) {
setError(err.message || "Unexpected Error!");
} finally {
setLoading(false);
}
};
return (
<div className="App">
<div>
<h1>Posts</h1>
{loading && <p>Posts are loading!</p>}
{error && <p>{error}</p>}
<ul>
{posts?.map((post) => (
<li key={post.id}>{post.title}</li>
))}
</ul>
</div>
</div>
);
}
I know the example above isn’t the best code but at least it’s working and it’s valid code. I will try to improve that later. For now, we can just focus on the bare minimum things for calling an API.
Here, you can try to get posts data from JsonPlaceholer. Those are the most common steps we follow for calling an API like requesting data, handling loading, success, and error cases.
If we try to call another API from the same component then how that would gonna look? Let’s see.
500: Internal Server Error
Now it’s going insane! For calling two simple APIs we’ve done a lot of duplication. On a top-level view, the component is doing nothing but just making two GET requests and handling the success and error cases. For each request, it’s maintaining three states which will periodically increase later if we’ve more calls.
Let’s refactor to make the code more reusable with fewer repetitions.
Step 1: Create a Hook for the Redundant API Request Codes
Most of the repetitions we have done so far are about requesting data, handing the async things, handling errors, success, and loading states. How about encapsulating those things inside a hook?
The only unique things we are doing inside handleComments and handlePosts are calling different endpoints. The rest of the things are pretty much the same. So we can create a hook that will handle the redundant works for us and from outside we’ll let it know which API to call.
500: Internal Server Error
Here, this request function is identical to what we were doing on the handlePosts and handleComments. The only difference is, it’s calling an async function apiFunc which we will provide as a parameter with this hook. This apiFunc is the only independent thing among any of the API calls we need.
With hooks in action, let’s change our old codes in App component, like this:
500: Internal Server Error
How about the current code? Isn’t it beautiful without any repetitions and duplicate API call handling things?
Let’s continue our journey from the current code. We can make App component more elegant. Now it knows a lot of details about the underlying library for the API call. It shouldn’t know that. So, here’s the next step…
Step 2: One Component Should Take Just One Responsibility
Our App component knows too much about the API calling mechanism. Its responsibility should just request the data. How the data will be requested under the hood, it shouldn’t care about that.
We will extract the API client-related codes from the App component. Also, we will group all the API request-related codes based on the API resource. Now, this is our API client:
import axios from "axios";
const apiClient = axios.create({
// Later read this URL from an environment variable
baseURL: "https://jsonplaceholder.typicode.com"
});
export default apiClient;
All API calls for comments resource will be in the following file:
import client from "./client";
const getComments = () => client.get("/comments");
export default {
getComments
};
All API calls for posts resource are placed in the following file:
import client from "./client";
const getPosts = () => client.get("/posts");
export default {
getPosts
};
Finally, the App component looks like the following:
import "./styles.css";
import React, { useEffect } from "react";
import commentsApi from "./api/comments";
import postsApi from "./api/posts";
import useApi from "./hooks/useApi";
export default function App() {
const getPostsApi = useApi(postsApi.getPosts);
const getCommentsApi = useApi(commentsApi.getComments);
useEffect(() => {
getPostsApi.request();
getCommentsApi.request();
}, []);
return (
<div className="App">
{/* Post List */}
<div>
<h1>Posts</h1>
{getPostsApi.loading && <p>Posts are loading!</p>}
{getPostsApi.error && <p>{getPostsApi.error}</p>}
<ul>
{getPostsApi.data?.map((post) => (
<li key={post.id}>{post.title}</li>
))}
</ul>
</div>
{/* Comment List */}
<div>
<h1>Comments</h1>
{getCommentsApi.loading && <p>Comments are loading!</p>}
{getCommentsApi.error && <p>{getCommentsApi.error}</p>}
<ul>
{getCommentsApi.data?.map((comment) => (
<li key={comment.id}>{comment.name}</li>
))}
</ul>
</div>
</div>
);
}
Now it doesn’t know anything about how the APIs get called. Tomorrow if we want to change the API calling library from axios to fetch or anything else, our App component code will not get affected. We can just change the codes form client.js This is the beauty of abstraction.
Apart from the abstraction of API calls, Appcomponent isn’t right the place to show the list of the posts and comments. It’s a high-level component. It shouldn’t handle such low-level data interpolation things.
So we should move this data display-related things to another low-level component. Here I placed those directly in the App component just for the demonstration purpose and not to distract with component composition-related things.
Final Thoughts
The React library gives the flexibility for using any kind of third-party library based on the application’s needs. As it doesn’t have any predefined architecture so different teams/developers adopted different approaches to developing applications with React. There’s nothing good or bad. We choose the development practice based on our needs/choices. One thing that is there beyond any choices is writing clean and maintainable codes.

Jano le Roux
3 years ago
My Top 11 Tools For Building A Modern Startup, With A Free Plan
The best free tools are probably unknown to you.
Modern startups are easy to build.
Start with free tools.
Let’s go.
Web development — Webflow
Code-free HTML, CSS, and JS.
Webflow isn't like Squarespace, Wix, or Shopify.
It's a super-fast no-code tool for professionals to construct complex, highly-responsive websites and landing pages.
Webflow can help you add animations like those on Apple's website to your own site.
I made the jump from WordPress a few years ago and it changed my life.
No damn plugins. No damn errors. No damn updates.
The best, you can get started on Webflow for free.
Data tracking — Airtable
Spreadsheet wings.
Airtable combines spreadsheet flexibility with database power without code.
Airtable is modern.
Airtable has modularity.
Scaling Airtable is simple.
Airtable, one of the most adaptable solutions on this list, is perfect for client data management.
Clients choose customized service packages. Airtable consolidates data so you can automate procedures like invoice management and focus on your strengths.
Airtable connects with so many tools that rarely creates headaches. Airtable scales when you do.
Airtable's flexibility makes it a potential backend database.
Design — Figma
Better, faster, easier user interface design.
Figma rocks!
It’s fast.
It's free.
It's adaptable
First, design in Figma.
Iterate.
Export development assets.
Figma lets you add more team members as your company grows to work on each iteration simultaneously.
Figma is web-based, so you don't need a powerful PC or Mac to start.
Task management — Trello
Unclock jobs.
Tacky and terrifying task management products abound. Trello isn’t.
Those that follow Marie Kondo will appreciate Trello.
Everything is clean.
Nothing is complicated.
Everything has a place.
Compared to other task management solutions, Trello is limited. And that’s good. Too many buttons lead to too many decisions lead to too many hours wasted.
Trello is a must for teamwork.
Domain email — Zoho
Free domain email hosting.
Professional email is essential for startups. People relied on monthly payments for too long. Nope.
Zoho offers 5 free professional emails.
It doesn't have Google's UI, but it works.
VPN — Proton VPN
Fast Swiss VPN protects your data and privacy.
Proton VPN is secure.
Proton doesn't record any data.
Proton is based in Switzerland.
Swiss privacy regulation is among the most strict in the world, therefore user data are protected. Switzerland isn't a 14 eye country.
Journalists and activists trust Proton to secure their identities while accessing and sharing information authoritarian governments don't want them to access.
Web host — Netlify
Free fast web hosting.
Netlify is a scalable platform that combines your favorite tools and APIs to develop high-performance sites, stores, and apps through GitHub.
Serverless functions and environment variables preserve API keys.
Netlify's free tier is unmissable.
100GB of free monthly bandwidth.
Free 125k serverless operations per website each month.
Database — MongoDB
Create a fast, scalable database.
MongoDB is for small and large databases. It's a fast and inexpensive database.
Free for the first million reads.
Then, for each million reads, you must pay $0.10.
MongoDB's free plan has:
Encryption from end to end
Continual authentication
field-level client-side encryption
If you have a large database, you can easily connect MongoDB to Webflow to bypass CMS limits.
Automation — Zapier
Time-saving tip: automate repetitive chores.
Zapier simplifies life.
Zapier syncs and connects your favorite apps to do impossibly awesome things.
If your online store is connected to Zapier, a customer's purchase can trigger a number of automated actions, such as:
The customer is being added to an email chain.
Put the information in your Airtable.
Send a pre-programmed postcard to the customer.
Alexa, set the color of your smart lights to purple.
Zapier scales when you do.
Email & SMS marketing — Omnisend
Email and SMS marketing campaigns.
This is an excellent Mailchimp option for magical emails. Omnisend's processes simplify email automation.
I love the interface's cleanliness.
Omnisend's free tier includes web push notifications.
Send up to:
500 emails per month
60 maximum SMSs
500 Web Push Maximum
Forms and surveys — Tally
Create flexible forms that people enjoy.
Typeform is clean but restricting. Sometimes you need to add many questions. Tally's needed sometimes.
Tally is flexible and cheaper than Typeform.
99% of Tally's features are free and unrestricted, including:
Unlimited forms
Countless submissions
Collect payments
File upload
Tally lets you examine what individuals contributed to forms before submitting them to see where they get stuck.
Airtable and Zapier connectors automate things further. If you pay, you can apply custom CSS to fit your brand.
See.
Free tools are the greatest.
Let's use them to launch a startup.

Juxtathinka
3 years ago
Why Is Blockchain So Popular?
What is Bitcoin?
The blockchain is a shared, immutable ledger that helps businesses record transactions and track assets. The blockchain can track tangible assets like cars, houses, and land. Tangible assets like intellectual property can also be tracked on the blockchain.
Imagine a blockchain as a distributed database split among computer nodes. A blockchain stores data in blocks. When a block is full, it is closed and linked to the next. As a result, all subsequent information is compiled into a new block that will be added to the chain once it is filled.
The blockchain is designed so that adding a transaction requires consensus. That means a majority of network nodes must approve a transaction. No single authority can control transactions on the blockchain. The network nodes use cryptographic keys and passwords to validate each other's transactions.
Blockchain History
The blockchain was not as popular in 1991 when Stuart Haber and W. Scott Stornetta worked on it. The blocks were designed to prevent tampering with document timestamps. Stuart Haber and W. Scott Stornetta improved their work in 1992 by using Merkle trees to increase efficiency and collect more documents on a single block.
In 2004, he developed Reusable Proof of Work. This system allows users to verify token transfers in real time. Satoshi Nakamoto invented distributed blockchains in 2008. He improved the blockchain design so that new blocks could be added to the chain without being signed by trusted parties.
Satoshi Nakomoto mined the first Bitcoin block in 2009, earning 50 Bitcoins. Then, in 2013, Vitalik Buterin stated that Bitcoin needed a scripting language for building decentralized applications. He then created Ethereum, a new blockchain-based platform for decentralized apps. Since the Ethereum launch in 2015, different blockchain platforms have been launched: from Hyperledger by Linux Foundation, EOS.IO by block.one, IOTA, NEO and Monero dash blockchain. The block chain industry is still growing, and so are the businesses built on them.
Blockchain Components
The Blockchain is made up of many parts:
1. Node: The node is split into two parts: full and partial. The full node has the authority to validate, accept, or reject any transaction. Partial nodes or lightweight nodes only keep the transaction's hash value. It doesn't keep a full copy of the blockchain, so it has limited storage and processing power.
2. Ledger: A public database of information. A ledger can be public, decentralized, or distributed. Anyone on the blockchain can access the public ledger and add data to it. It allows each node to participate in every transaction. The distributed ledger copies the database to all nodes. A group of nodes can verify transactions or add data blocks to the blockchain.
3. Wallet: A blockchain wallet allows users to send, receive, store, and exchange digital assets, as well as monitor and manage their value. Wallets come in two flavors: hardware and software. Online or offline wallets exist. Online or hot wallets are used when online. Without an internet connection, offline wallets like paper and hardware wallets can store private keys and sign transactions. Wallets generally secure transactions with a private key and wallet address.
4. Nonce: A nonce is a short term for a "number used once''. It describes a unique random number. Nonces are frequently generated to modify cryptographic results. A nonce is a number that changes over time and is used to prevent value reuse. To prevent document reproduction, it can be a timestamp. A cryptographic hash function can also use it to vary input. Nonces can be used for authentication, hashing, or even electronic signatures.
5. Hash: A hash is a mathematical function that converts inputs of arbitrary length to outputs of fixed length. That is, regardless of file size, the hash will remain unique. A hash cannot generate input from hashed output, but it can identify a file. Hashes can be used to verify message integrity and authenticate data. Cryptographic hash functions add security to standard hash functions, making it difficult to decipher message contents or track senders.
Blockchain: Pros and Cons
The blockchain provides a trustworthy, secure, and trackable platform for business transactions quickly and affordably. The blockchain reduces paperwork, documentation errors, and the need for third parties to verify transactions.
Blockchain security relies on a system of unaltered transaction records with end-to-end encryption, reducing fraud and unauthorized activity. The blockchain also helps verify the authenticity of items like farm food, medicines, and even employee certification. The ability to control data gives users a level of privacy that no other platform can match.
In the case of Bitcoin, the blockchain can only handle seven transactions per second. Unlike Hyperledger and Visa, which can handle ten thousand transactions per second. Also, each participant node must verify and approve transactions, slowing down exchanges and limiting scalability.
The blockchain requires a lot of energy to run. In addition, the blockchain is not a hugely distributable system and it is destructible. The security of the block chain can be compromised by hackers; it is not completely foolproof. Also, since blockchain entries are immutable, data cannot be removed. The blockchain's high energy consumption and limited scalability reduce its efficiency.
Why Is Blockchain So Popular?
The blockchain is a technology giant. In 2018, 90% of US and European banks began exploring blockchain's potential. In 2021, 24% of companies are expected to invest $5 million to $10 million in blockchain. By the end of 2024, it is expected that corporations will spend $20 billion annually on blockchain technical services.
Blockchain is used in cryptocurrency, medical records storage, identity verification, election voting, security, agriculture, business, and many other fields. The blockchain offers a more secure, decentralized, and less corrupt system of making global payments, which cryptocurrency enthusiasts love. Users who want to save time and energy prefer it because it is faster and less bureaucratic than banking and healthcare systems.
Most organizations have jumped on the blockchain bandwagon, and for good reason: the blockchain industry has never had more potential. The launch of IBM's Blockchain Wire, Paystack, Aza Finance and Bloom are visible proof of the wonders that the blockchain has done. The blockchain's cryptocurrency segment may not be as popular in the future as the blockchain's other segments, as evidenced by the various industries where it is used. The blockchain is here to stay, and it will be discussed for a long time, not just in tech, but in many industries.
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