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Bitcoin has hit new all-time highs quite a few times in the last week. At the time of writing, bitcoin has struck a high of $34,830. Clearly, bitcoin is in a bull run, especially with Coinbase striking, what could be, potential OTC deals pushing BTC out of exchanges.

With more BTC being bought up by hungry institutions or high-net-worth individuals, the scenario for bitcoin is getting more bullish by the second. While the retail FOMO plays a part in this rally, I think it’s time to take a step back and look at what’s happening in the market.

The Bigger Picture

Phase 1

In hindsight, these two events among others are what sparked the bull run that we see today.

Let’s look at what has happened since August.

MicroStrategy invested ~half of $1 billion in cash reserves in Bitcoin without moving the price of BTC.

Since this was the first major investment by a traditional finance company in bitcoin, it was paraded all over the news for bringing more credibility to bitcoin among retail.

CashApp and many companies invest in bitcoin to prevent their cash reserves from debasing due to inflation by the Fed.

Even with billions of dollars moving into bitcoin, the price seemed to stay put as it hovered around the previous all-time high. After two failed attempts, the price went above the 2023-high at $19,666.

Phase 2

Michael Saylor invested the other half of $1 billion in bitcoin despite what the critics had to say.

Major Bitcoin outflows from major exchanges such as Coinbase Pro, Binance, etc.

Drying up of the exchange reserves as a result of point 2 and retail pulling out their BTC from exchanges, signifying the strength of the rally.

Unlike 2023, this bull run showed that retail is more matured. Hence, the bull run this time around isn’t as volatile as it was in 2023.

More companies/institutions are actively looking to buy more BTC or are already buying it.

Point of inflection

Since 2023, things have been difficult, for both the front end of the bitcoin ecosystem which includes investors, exchanges, companies built around bitcoin, and the backend, which includes miners and related companies.

Let’s take a look at miners and what’s happening with them, especially since they are the major source of selling pressure in the entire bitcoin ecosystem.

After the March crash, the worst was behind for miners, and by the start of the 3rd quarter, things were already looking up for them. This is when bitcoin crossed $8,000 and eventually hit $10,000.

At this point miners were not profitable enough, hence, selling pressure was present. Considering the price now, miners will only have to sell a portion of their mined bitcoins to cover all expenses incurred due to mining.

This selling pressure has now reduced, which is the third reason why bitcoin is heading higher without stopping.


Together. these events in whatever order, have caused bitcoin to surge. As for what the future holds, bitcoin will keep surging, as more people keep depositing stablecoins to exchanges.

Perhaps, the best point for a local top would be at $40,200. From this point, we can expect bitcoin to start its retracement, but then again, the further one tries to predict the future, the more uncertain the conclusions are going to be.

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Bear Run Or Bull Run, Can Reinforcement Learning Help In Automated Trading?

This article was published as a part of the Data Science Blogathon.


Every human being wants to earn to their maximum potential in the stock market. It is very important to design a balanced and low-risk strategy that can benefit most people. One such approach talks about using reinforcement learning agents to provide us with automated trading strategies based on the basis of historical data.

Reinforcement Learning

Reinforcement learning is a type of machine learning where there are environments and agents. These agents take actions to maximize rewards. Reinforcement learning has a very huge potential when it is used for simulations for training an AI model. There is no label associated with any data, reinforcement learning can learn better with very few data points. All decisions, in this case, are taken sequentially. The best example would be found in Robotics and Gaming.

Q – Learning

Q-learning is a model-free reinforcement learning algorithm. It informs the agent what action to undertake according to the circumstances. It is a value-based method that is used to supply information to an agent for the impending action.  It is regarded as an off-policy algorithm as the q-learning function learns from actions that are outside the current policy, like taking random actions, and therefore a policy isn’t needed.

Q here stands for Quality. Quality refers to the action quality as to how beneficial that reward will be in accordance with the action taken. A Q-table is created with dimensions [state,action].An agent interacts with the environment in either of the two ways – exploit and explore. An exploit option suggests that all actions are considered and the one that gives maximum value to the environment is taken. An explore option is one where a random action is considered without considering the maximum future reward.

Q of st and at is represented by a formula that calculates the maximum discounted future reward when an action is performed in a state s.

The defined function will provide us with the maximum reward at the end of the n number of training cycles or iterations.

Trading can have the following calls – Buy, Sell or Hold

Q-learning will rate each and every action and the one with the maximum value will be selected further. Q-Learning is based on learning the values from the Q-table. It functions well without the reward functions and state transition probabilities.

Reinforcement Learning in Stock Trading

Reinforcement learning can solve various types of problems. Trading is a continuous task without any endpoint. Trading is also a partially observable Markov Decision Process as we do not have complete information about the traders in the market. Since we don’t know the reward function and transition probability, we use model-free reinforcement learning which is Q-Learning.

Steps to run an RL agent:

Install Libraries

Fetch the Data

Define the Q-Learning Agent

Train the Agent

Test the Agent

Plot the Signals

Install Libraries

Install and import the required NumPy, pandas, matplotlib, seaborn, and yahoo finance libraries.

import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns sns.set() !pip install yfinance --upgrade --no-cache-dir from pandas_datareader import data as pdr import fix_yahoo_finance as yf from collections import deque import random Fetch the Data

yf.pdr_override() df_full = pdr.get_data_yahoo("INFY", start="2023-01-01").reset_index() df_full.to_csv(‘INFY.csv',index=False) df_full.head()

This code will create a data frame called df_full that will contain the stock prices of INFY over the course of 2 years.

Define the Q-Learning Agent

The first function is the Agent class defines the state size, window size, batch size, deque which is the memory used, inventory as a list. It also defines some static variables like epsilon, decay, gamma, etc. Two neural network layers are defined for the buy, hold, and sell call. The GradientDescentOptimizer is also used.

The Agent has functions defined for buy and sell options. The get_state and act function makes use of the Neural network for generating the next state of the neural network. The rewards are subsequently calculated by adding or subtracting the value generated by executing the call option. The action taken at the next state is influenced by the action taken on the previous state. 1 refers to a Buy call while 2 refers to a Sell call. In every iteration, the state is determined on the basis of which an action is taken which will either buy or sell some stocks. The overall rewards are stored in the total profit variable.

df= df_full.copy() name = 'Q-learning agent' class Agent: def __init__(self, state_size, window_size, trend, skip, batch_size): self.state_size = state_size self.window_size = window_size self.half_window = window_size self.trend = trend chúng tôi = skip self.action_size = 3 self.batch_size = batch_size self.memory = deque(maxlen = 1000) self.inventory = [] self.gamma = 0.95 self.epsilon = 0.5 self.epsilon_min = 0.01 self.epsilon_decay = 0.999 tf.reset_default_graph() chúng tôi = tf.InteractiveSession() self.X = tf.placeholder(tf.float32, [None, self.state_size]) self.Y = tf.placeholder(tf.float32, [None, self.action_size]) feed = tf.layers.dense(self.X, 256, activation = tf.nn.relu) self.logits = tf.layers.dense(feed, self.action_size) chúng tôi = tf.reduce_mean(tf.square(self.Y - self.logits)) self.optimizer = tf.train.GradientDescentOptimizer(1e-5).minimize( self.cost ) def act(self, state): if random.random() <= self.epsilon: return random.randrange(self.action_size) return np.argmax(, feed_dict = {self.X: state})[0] ) def get_state(self, t): window_size = self.window_size + 1 d = t - window_size + 1 res = [] for i in range(window_size - 1): res.append(block[i + 1] - block[i]) return np.array([res]) def replay(self, batch_size): mini_batch = [] l = len(self.memory) for i in range(l - batch_size, l): mini_batch.append(self.memory[i]) replay_size = len(mini_batch) X = np.empty((replay_size, self.state_size)) Y = np.empty((replay_size, self.action_size)) states = np.array([a[0][0] for a in mini_batch]) new_states = np.array([a[3][0] for a in mini_batch]) Q =, feed_dict = {self.X: states}) Q_new =, feed_dict = {self.X: new_states}) for i in range(len(mini_batch)): state, action, reward, next_state, done = mini_batch[i] target = Q[i] target[action] = reward if not done: target[action] += self.gamma * np.amax(Q_new[i]) X[i] = state Y[i] = target cost, _ = [self.cost, self.optimizer], feed_dict = {self.X: X, self.Y: Y} ) self.epsilon *= self.epsilon_decay return cost def buy(self, initial_money): starting_money = initial_money states_sell = [] states_buy = [] inventory = [] state = self.get_state(0) for t in range(0, len(self.trend) - 1, self.skip): action = self.act(state) next_state = self.get_state(t + 1) inventory.append(self.trend[t]) initial_money -= self.trend[t] states_buy.append(t) print('day %d: buy 1 unit at price %f, total balance %f'% (t, self.trend[t], initial_money)) elif action == 2 and len(inventory): bought_price = inventory.pop(0) initial_money += self.trend[t] states_sell.append(t) try: invest = ((close[t] - bought_price) / bought_price) * 100 except: invest = 0 print( 'day %d, sell 1 unit at price %f, investment %f %%, total balance %f,' % (t, close[t], invest, initial_money) ) state = next_state invest = ((initial_money - starting_money) / starting_money) * 100 total_gains = initial_money - starting_money return states_buy, states_sell, total_gains, invest def train(self, iterations, checkpoint, initial_money): for i in range(iterations): total_profit = 0 inventory = [] state = self.get_state(0) starting_money = initial_money for t in range(0, len(self.trend) - 1, self.skip): action = self.act(state) next_state = self.get_state(t + 1) inventory.append(self.trend[t]) starting_money -= self.trend[t] bought_price = inventory.pop(0) total_profit += self.trend[t] - bought_price starting_money += self.trend[t] invest = ((starting_money - initial_money) / initial_money) self.memory.append((state, action, invest, next_state, starting_money < initial_money)) state = next_state batch_size = min(self.batch_size, len(self.memory)) cost = self.replay(batch_size) if (i+1) % checkpoint == 0: print('epoch: %d, total rewards: %f.3, cost: %f, total money: %f'%(i + 1, total_profit, cost, starting_money)) Train the Agent close = df.Close.values.tolist() initial_money = 10000 window_size = 30 skip = 1 batch_size = 32 agent = Agent(state_size = window_size, window_size = window_size, trend = close, skip = skip, batch_size = batch_size) agent.train(iterations = 200, checkpoint = 10, initial_money = initial_money)

Output –

Test the Agent

The buy function will return the buy, sell, profit, and investment figures.

states_buy, states_sell, total_gains, invest = = initial_money) Plot the calls

Plot the total gains vs the invested figures. All buy and sell calls have been appropriately marked according to the buy/sell options as suggested by the neural network.

fig = plt.figure(figsize = (15,5)) plt.plot(close, color='r', lw=2.) plt.plot(close, '^', markersize=10, color='m', label = 'buying signal', markevery = states_buy) plt.plot(close, 'v', markersize=10, color='k', label = 'selling signal', markevery = states_sell) plt.title('total gains %f, total investment %f%%'%(total_gains, invest)) plt.legend() plt.savefig(name+'.png')

Output –

End Notes

Q-Learning is such a technique that helps you develop an automated trading strategy. It can be used to experiment with the buy or sell options. There are a lot more Reinforcement Learning trading agents that can be experimented with. Try playing around with the different kinds of RL agents with different stocks.

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Understanding The Basics Of Cloud Computing

This article was published as a part of the Data Science Blogathon.

Table of contents


What is Cloud Computing?

Service Models

Deployment Models

On-Premise vs Cloud Computing



In this article, we will learn about the basics of cloud computing. We will see what are different service models, what are different deployment models and finally we will understand the difference between on-premise architecture and cloud computing.

Cloud Computing refers to the delivery of computing services including storage, servers, databases, networking, software over the internet. To offer faster innovation flexible resources. Here you have to pay only for those cloud services which you use. So that you can help with your operator cost.

Nowadays many leading companies like Amazon are investing billion dollars in cloud services.

What is Cloud Computing?

First, we will understand the situation that existed before cloud computing came into existence.

In order to host a website, people used to face these problems.

1. Buying a stack of servers which are very costly.

2. With more number of servers usually there is high traffic.

3. Troubleshooting problems.

4. Monitoring and Maintaining servers is also very difficult.

In those days the data that is being generated is not an issue to store. But nowadays data is very huge. Everything is online these days. To play music, to watch movies, Business matters, to shop online, e-books, Applications for everything data is being generated and to store this is a huge task.

All these problems which I have mentioned so far are taken care of by the cloud.

Now let’s understand the cloud in detail.

Think of the cloud as a huge space that is available online. Cloud is like a collection of data centers where u host your websites and store your files. To understand better, there were a group of organizations that went and bought these servers, these compute services, storage places, and all. And all these have their own network. What you have to do is just go and rent these services. That too the amount of it you want and to what extent you need it. You have to rent and use what you want. So you ended up paying for what you used.

Cloud Computing is storing data on remote servers, processing data from servers, and Accessing data via the internet. You can actually access it from anywhere in the world.

Source: Author

Service Models in Cloud Computing

Basically, there were different types of people like there were some people who just uses the cloud to use only one particular resource and there were some who will create their own applications, create infrastructure, and all those things. So Cloud service providers came up with some models which can satisfy different people’s needs.

Let us try to understand these models.

There were three types of service models.

 Let us start with SaaS.

SaaS(Software as a Service)

What happens here is you just use the software that is already being created and maintained by someone else. To understand it, think of Gmail where you can actually send and receive emails. But you neither created nor being maintained. Google will take care of everything. Similarly here also you just consume the service.

Example: chúng tôi provides CRM which is Customer Relation Manager on cloud infrastructure to its client and charges for it but the software is owned by salesforce company only.

PaaS(Platform as a Service)

Here cloud provider gives the ability to deploy customer-created apps using programming languages, tools, etc that are provided by the Cloud Provider. To give an example, we have a Google App engine where you can create your own applications. Here you are using the Platform. Similarly, PaaS also provides a platform for creating your own applications.

IaaS(Infrastructure as a Service)

  IaaS provides the whole infrastructure to you so that you can create your own application. The whole underlying infrastructure is provided to you so that you can choose whatever Operating system you want to use, technologies you want to use, and application you want to build.

Source: Author

With this, I think you can understand better. Here you can see in SaaS only Data is being managed by you. Everything else like applications, runtime, middleware, operating system, virtualization, servers, storage, and networking are being managed by the vendor. Coming to Paas, Data and applications are managed by you and everything else is managed by the vendor. Now finally for IaaS, Data, applications, runtime, middleware, operating system are managed by you and basic things like virtualization, servers, storage, and networking are managed by the vendor.

Deployment Models in Cloud Computing

Cloud deployment models are divided based on the security control, who has access to data, and also based on whether the resources are shared or not. It represents the particular category of the cloud environment.

Basically, there are three main types of deployment models.

1. Public Cloud

2. private Cloud

3. Hybrid Cloud

Let us understand one by one.

Public Cloud: As the name suggests it is available to the general public over the internet. Here provider makes all resources and makes them available publicly. It is very easy and inexpensive because all the hardware, application, and bandwidth costs are covered by the provider. Resources are also not being wasted because you will pay for what you use.

Private Cloud: Here actually you can create your own applications and you are protected by a firewall so that security issues are being minimized.

For example, HP data centers, Ubuntu, Elastra-private cloud, Microsoft.

Hybrid Cloud: Hybrid cloud is the combination of both private cloud and public cloud. You can build your own application and access it. And whenever you feel like there is traffic you can move it to the public cloud and use it.

The top 5 Hybrid Cloud Providers are Amazon, Microsoft, Google, Cisco, Netapp.

On-Premise vs Cloud Computing

To understand it first, we will see what is on-premise architecture.

On-Premise Approach

This is like the traditional approach. You will write your own piece of code and you will own your own server. As a company, employees are maintaining them and making sure the software is deployed properly. Remaining everything like monitoring these applications, having servers, maintaining them all those you should take care of. There is a high up-front cost and maintenance cost. Here owner manages security.  When it comes to on-premise, you have more control.

Cloud Computing Approach

It is a huge space available online with a stack of servers that are orchestrated to provide you with various services like databases, storage, computation, security, and many more. There is a cheaper up-front cost. When it comes to security, it is vendor dependence. Owners are expected to give up control. Here you have less control over it. You have to be a little bit dependent on your vendor.

In this article, we have learned the basics of Cloud Computing like what exactly cloud computing means, how this actually helps us, different service models, different Deployment models, and comparing On-Premise and Cloud computing.

Hope you guys found it useful.

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Understanding The Importance Of Windows Task Manager

Understanding The Importance of Windows Task Manager What is a Window Task Manager?

A Windows task manager is a monitoring tool in Windows that contains details of running programs on your computer. This tool displays process graphs, resource usage, memory consumption streaming in the background.

There are various ways to open Windows task manager. You must be familiar with many old school methods which include keyboard shortcuts too.

The easiest method to open this tool is to press

Ctrl + Shift + Esc

keys on your keyboard together.

Another easy way to start the task manager is by pressing

Ctrl + Alt + Del

together on your keyboard. A window will appear with different options. Choose Task Manager among them.

Let’s catch on the various tabs of Task Manager-

1. The Processes tab

2. The Performance

The actual time charts show the overall usage of memory, network, disk, CPU resources of your system. At the bottom left of this tab, there is a link of Open Resource Monitor which can guide to look at the report that Task Manager does not give. 

3. The App History

This tab reveals the complete history of Windows applications that have run on your computer. You can also remove the usage history of applications at any time from this tab but if you are not using any application and that app is consuming system resources, you can easily stop it from happening.   

4. The Startup

5. The Users

This tab will tell you the currently logged users into Windows 10. If the system has a single user, then it will display the rank with their functioning processes. You can also find out whether the specific account is creating the system work slowly.

6. The Details

This tab allows a variety of knowledge on running processes on your PC that processes and performance tabs can’t show. You can end the task and restore system resources with this tab.

7. The Services-

This tab is the last part of the task manager and it enables all the running functionality in your PC. If you are facing any issue with running service, then you can easily disable the functionality. You can stop/start/restart services.

What To Do If The Task Manager Is Unable To Launch?

Many of us have reported that we had an issue while launching the task manager. It was either unable to respond or maybe we couldn’t find any other way, to begin with.

Find the number of resolutions that can help you out with this issue.

1. Start with creating another account in Windows 10.

There may be possibilities of error in system profile or the administrator hasn’t given you access to perform some activities on the system. To cover up this issue, you can try and check to make a local account with the following steps.

After signing in, check whether the task manager is working on this account or not.

2. Check the latest windows updates

Sometimes there are a lot of OS updates that keep on waiting to get updated. These out-of-date updates may disrupt in launching the task manager.

To find out the pending upgrade-

Once you update your system, restart your PC and check for the task manager.

3. Activating Task Manager From Different Methods.

Start With CMD Command

Press Windows + R simultaneously and type Regedit.

A new window will appear. Locate the below path on the left pane.

HKEY_CURRENT_USERSoftwareMicrosoftWindowsCurrent VersionPoliciesSystem

Restart your system and check the issue again it’s solved or not.

By using the Group Policy Editor enabling The Task Manager.

The Group Policy Editor is a powerful utility tool which helps you to regulate local policy settings. If the task manager is disabled, you can enable this tool.

Press down

Windows + R

together and type


and enter.

Find this path on the left pane in a new appeared window. 

We chose Enabled and applied settings, so the group policy can defeat any settings made by any outer application or malware. Press OK for desired changes. Restart your computer for the reforms to take place.

5. Checking by System File Checker

The System File Checker is used to find and diagnose corrupt files present on your computer.

Search command prompt on the search dialogue box and Run as Administrator.

Enter the following command –  Dism.exe /online /Cleanup-image /StartComponentCleanup dism / online / cleanup-image /restorehealth sfc /scannow

After finishing this process, restart your computer and check for task manager.

You can check some other quick fixes on your system such as-

System Restoration

Inspection of malware

Uninstalling third-party applications and anti-virus software.

Windows reinstallation

Final Word Quick Reaction:

About the author

Harshita Singh

Analyzing How Bitcoin May Shape The Future According To This Expert

The performance of the King coin hasn’t been in the best of spirits lately, especially given its position of extreme volatility in the last few weeks. However, despite the constant state of war that Bitcoin (BTC) has been in, Dan Morehead, CEO of Pantera Capital, felt otherwise.

In a recent episode of the Bankless podcast, he stated,

“I think we’re done with the bear market. The next six to 12 months are likely to see massive rallies investors flee stocks, bonds, and real estate for blockchain.”

Is the bear cycle becoming weak?

At the time of writing, BTC was trading at a value of $38,015 as per data from CoinGecko. The token was -1.6% down in the last 24 hours and was approximately lower by -3.9% in the last seven days. At press time, the Relative Strength Index (RSI) was fluctuating below neutral 50 at a score of 39.19. The Awesome Oscillator (AO) further substantiated the bearish movement of the token at press time.

According to the data chart given below, the “Net Transfer Volume to/from Exchanges” stands at -3,012.95 BTC at the time of writing. The negative volume indicates that token investors are willing to hold onto their investments and not get pressurized by the bear cycle just yet.

The future is “Bull”

Commenting on the performance of BTC and the overall cryptocurrency market, Dan Morehead expressed his astonishment at the ongoing state of all the cryptocurrencies. He also addressed the reasons for the ongoing bear market and the correlation between macro news and the cryptocurrency market.

“Bear markets are half as long as bull markets. With the Russian invasion of Ukraine and all of the policy responses, it’s hard to know how everything is going to play out but when the dust settles, it’s going to make a lot of people use crypto”, he stated.

Amid the ongoing bear run of the king token, Willy Woo, a BTC analyst, also shared a tweet supporting the bullish outlook of the market.

BTC price holding up well while equities tank and USD Index moons is testament to the unprecedented spot buying happening right now.

In other words: Investors already see BTC as a safehaven, it will take time for price to reflect. Wait for the futures sells to run out of ammo.

— Willy Woo (@woonomic) April 30, 2023

Is BTC the future then? Most likely not…

The Berkshire Hathaway Annual Shareholder meeting took place on 30 April 2023, where Warren Buffet, yet again, expressed his views on how cryptocurrencies are of no value to him. Commenting on the volatility of the current market, he stated,

“Whether it goes up or down in the next year, or five or 10 years, I don’t know. But the one thing I’m pretty sure of is that it doesn’t produce anything.”

Holding a $20 bill in his hand, he also stated,

“Assets, to have value, have to deliver something to somebody. We can put up Berkshire coins… but in the end, this is money. And there’s no reason in the world why the United States government… is going to let Berkshire money replace theirs.”

Run The Xperia Xz Premium In 4K All The Time With This Trick

Run the Xperia XZ Premium in 4K all the time with this trick

The Sony Xperia XZ Premium is easily one of if not the most expensive flagships this year, at least for now. But if, for a moment, you ignore that price tag, you’re actually left with a rather competitive high end smartphone with a very high-end screen. One of the very few, perhaps only two, 4K smartphone screens in the market. But for performance and battery life reasons, Sony doesn’t make full use of that UHD screen all the time. But if you think it’s well worth the consequences, you can easily force the Xperia XZ Premium to use that high density setting all the time with these relatively simple steps.

Two years ago, Sony unveiled what was perhaps the first 4K smartphone in the market, followed by the Xperia XZ Premium this year, with a dash of HDR support. The idea was to allow users to enjoy 4K and HDR content on their smartphones, despite such content being on the slim side. That, of course, never stopped Sony from trying, though it did make some concessions to appease worried users.

The 4K resolution only really kicks in when watching 4K content and nothing else. Everything runs at 1080p Full HD, save perhaps for some 2K (QHD) content. The UI itself is stuck at 1080p, however. This is to prevent the screen from eating all that precious battery minutes. Some, however, think that’s a premature optimization and that running the phone in 4K all the time has minimal impact but has all the perks of having a crisper display.

Fortunately for these people, it’s actually not that difficult to flip a switch. Actually two switches. It’s not as simple as going to the settings app and pushing a slider like on the WQHD+ Samsung Galaxy S8 and LG G6. But it’s not as difficult as having to root your device or tinker around with files. At least not much. The most difficult part of the process is installing the Android SDK. Even that is actually now easier since Google now provides the relevant ADB tool as a separate download. Once installed and your phone connected to your computer, you need to run ADB in your OS’ shell (Command Prompt on Windows, Terminal on macOS and Linux) and enter this commands to change the resolution to 4K:

adb shell

wm size 2160×3840

You will notice everything suddenly look smaller which is easily fixed by setting the correct pixel density for the new resolution:

wm density 820

That’s pretty much all that’s needed. Android Kosmos, who broke news of the trick, says that the setting is preserved across reboots. Going back to the normal Full HD setting simply involves setting the wm size to 1080×1920 (Full HD) the density back to the default 420.

Damir Franc, who gives the video tutorial below, says that the battery drain by having 4K all the time is almost non-existent. Of course, your mileage might vary but what’s more important to note is that some apps might not display correct or not work at all. Hopefully someone will soon come up with an app that lets you change all these on the fly without having to go the ADB route every time.

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