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NVDA

The trend for NVDA is predicted to go up tomorrow.

Headlines

The latest headline about NVIDIA Corporation (NVDA) is that the French competition authority has confirmed an investigation into NVIDIA. This investigation comes as NVIDIA continues to navigate competitive pressures and maintain its market position in the AI and semiconductor industries​.

Sentiment analysis

The impact of the investigation by the French competition authority on NVIDIA's stock price is uncertain and could depend on the investigation's findings and market perception.

QQQ

The trend for QQQ is predicted to go up tomorrow.

Headlines

The latest headline about the Invesco QQQ Trust (QQQ) highlights that the ETF has declared an increased quarterly dividend of $0.76 per share. This update represents a positive change from its previous quarterly dividend of $0.57 per share.

Sentiment analysis

Increasing dividends typically indicate strong financial health and can boost investor confidence in the short term.

TSLA

The trend for TSLA is predicted to go up tomorrow.

Headlines

The latest headline about Tesla (TSLA) stock indicates significant market movement. Tesla's stock has surged by around 7% as investors anticipate a key report on vehicle deliveries. Despite expected year-over-year declines in delivery numbers, investors are hopeful that Tesla might surpass these lowered expectations. Analysts have projected deliveries between 410,000 and 420,000 units for the second quarter, compared to 533,000 last year.

Sentiment analysis

Investor sentiment is mixed due to the anticipated year-over-year decline in deliveries despite the stock surge.

VOO

The trend for VOO is predicted to go up tomorrow.

Headlines

The latest headline regarding the Vanguard S&P 500 ETF (VOO) is that it reached a new 12-month high at $511.61. The fund has experienced consistent growth, reflecting its strong performance tracking the S&P 500 Index. Currently, VOO is trading at $514.55, marking a 0.62% increase​​​​. This upward trend is indicative of the overall bullish market sentiment and the continued popularity of low-cost, diversified investment options like VOO.

Sentiment analysis

The new 12-month high indicates strong performance and positive investor sentiment.

The Augmented Dickey—Fuller (ADF) Test for Stationarity

Stationarity is a fundamental concept in statistical analysis and machine learning, particularly when dealing with time series data. In simple terms, a time series is stationary if its statistical properties, such as mean and variance, remain constant over time. This constancy is crucial because many statistical models assume that the underlying data generating process does not change over time, simplifying analysis and prediction.

In real-world applications, such as finance, time series data often exhibit trends and varying volatility, making them non-stationary. Detecting and transforming non-stationary data into stationary data is therefore a critical step in time series analysis. One powerful tool for this purpose is the Augmented Dickey—Fuller (ADF) test.

What is the Augmented Dickey—Fuller (ADF) Test?

The ADF test is a statistical test used to determine whether a given time series is stationary or non-stationary. Specifically, it tests for the presence of a unit root in the data, which is indicative of non-stationarity. A unit root means that the time series has a stochastic trend, implying that its statistical properties change over time.

Hypothesis Testing in the ADF Test

The ADF test uses hypothesis testing to make inferences about the stationarity of a time series. Here’s a breakdown of the hypotheses involved:

  • Null Hypothesis (H0): The time series has a unit root, meaning it is non-stationary.
  • Alternative Hypothesis (H1): The time series does not have a unit root, meaning it is stationary.

To reject the null hypothesis and conclude that the time series is stationary, the p-value obtained from the ADF test must be less than a chosen significance level (commonly 5%).

Performing the ADF Test

Here’s how you can perform the ADF test in Python using the statsmodels library:

import pandas as pd
from statsmodels.tsa.stattools import adfuller

# Example time series data
data = pd.Series([your_time_series_data])

# Perform the ADF test
result = adfuller(data)

# Extract and display the results
adf_statistic = result[0]
p_value = result[1]
used_lag = result[2]
n_obs = result[3]
critical_values = result[4]

print(f'ADF Statistic: {adf_statistic}')
print(f'p-value: {p_value}')
print(f'Used Lag: {used_lag}')
print(f'Number of Observations: {n_obs}')
print('Critical Values:')
for key, value in critical_values.items():
    print(f'   {key}: {value}')

Interpreting the Results

  • ADF Statistic: A negative value, where more negative values indicate stronger evidence against the null hypothesis.
  • p-value: If the p-value is less than the significance level (e.g., 0.05), you reject the null hypothesis, indicating that the time series is stationary.
  • Critical Values: These values help to determine the threshold at different confidence levels (1%, 5%, 10%) to compare against the ADF statistic.

Example and Conclusion

Consider a financial time series data, such as daily stock prices. Applying the ADF test might reveal a p-value greater than 0.05, indicating non-stationarity. In such cases, data transformations like differencing or detrending might be necessary to achieve stationarity before applying further statistical models.

In summary, the ADF test is an essential tool for diagnosing the stationarity of a time series. By understanding and applying this test, analysts can better prepare their data for modeling, ensuring the validity and reliability of their results.

The Augmented Dickey—Fuller (ADF) Test for Stationarity

Welcome back to another episode of Continuous Improvement! I'm your host, Victor Leung, and today, we're diving into a crucial concept in statistical analysis and machine learning—stationarity, especially in the context of time series data. We'll explore what stationarity is, why it matters, and how we can test for it using the Augmented Dickey—Fuller (ADF) test. So, if you're dealing with financial data or any time series data, this episode is for you!

Stationarity is a key concept when working with time series data. Simply put, a time series is stationary if its statistical properties—like the mean and variance—do not change over time. This property is vital because many statistical models assume a stable underlying process, which makes analysis and predictions much simpler.

However, in real-world applications, especially in finance, data often shows trends and varying volatility, making it non-stationary. So, how do we deal with this? That's where the Augmented Dickey—Fuller, or ADF, test comes in.

The ADF test is a statistical tool used to determine whether a time series is stationary or not. Specifically, it tests for the presence of a unit root, a feature that indicates non-stationarity. A unit root implies that the series has a stochastic trend, meaning its statistical properties change over time.

The ADF test uses hypothesis testing to check for stationarity:

  • Null Hypothesis (H0): The time series has a unit root, which means it is non-stationary.
  • Alternative Hypothesis (H1): The time series does not have a unit root, indicating it is stationary.

To conclude that the series is stationary, the p-value obtained from the ADF test should be less than a chosen significance level, commonly set at 5%.

  • ADF Statistic: A more negative value indicates stronger evidence against the null hypothesis.
  • p-value: If this is less than 0.05, you reject the null hypothesis, indicating that the series is stationary.
  • Critical Values: These are thresholds for different confidence levels (1%, 5%, 10%) to compare against the ADF statistic.

In summary, the ADF test is a powerful tool for determining the stationarity of a time series. This step is crucial in preparing data for modeling, ensuring that your results are valid and reliable. Whether you're working with financial data, like daily stock prices, or any other time series, understanding and applying the ADF test can greatly enhance your analytical capabilities.

Thanks for tuning in to this episode of Continuous Improvement. Stay curious, keep learning, and join me next time as we explore more tools and techniques to enhance your data analysis skills. Until then, happy analyzing!

增廣迪基-富勒 (ADF) 站性檢定

站性是統計分析和機器學習中的基本概念,尤其是在處理時間序列數據時。簡單來說,一個時間序列若其統計屬性,例如均值和變異數,隨著時間保持常數,則該時間序列稱為站性。這種站性至關重要,因為許多統計模型假設生成數據的基礎過程不隨時間改變,這簡化了分析和預測。

在現實世界的應用中,例如金融,時間序列數據經常會呈現出趨勢和波動性,使它們非站性。因此,檢測並轉換非站性數據為站性數據是時間序列分析的關鍵步驟。增廣迪基—富勒(ADF)檢定是實現此目的的一項強大工具。

什麼是增廣迪基—富勒(ADF)檢定?

ADF檢定是一種統計檢定,用來確定給定的時間序列是站性還是非站性。特別地,它檢測數據中是否存在單根,這是非站性的指標。單根意味著時間序列有一個隨機趨勢,這意味著它的統計屬性會隨著時間改變。

ADF檢定中的假設檢定

ADF檢定使用假設檢定來對時間序列的站性進行推論。以下是這些假設的闡述:

  • 零假設 (H0):時間序列有單根,意即它為非站性。
  • 對立假設 (H1):時間序列沒有單根,意即它為站性。

為了拒絕零假設,並得出時間序列是站性的結論,從ADF檢定中獲得的 p 值必須小於所選的顯著性水平(通常為 5%)。

執行ADF檢定

以下是使用 statsmodels庫在Python中執行ADF檢定的方法:

import pandas as pd
from statsmodels.tsa.stattools import adfuller

# 示例時間序列數據
data = pd.Series([your_time_series_data])

# 執行ADF檢定
result = adfuller(data)

# 提取並顯示結果
adf_statistic = result[0]
p_value = result[1]
used_lag = result[2]
n_obs = result[3]
critical_values = result[4]

print(f'ADF Statistic: {adf_statistic}')
print(f'p-value: {p_value}')
print(f'Used Lag: {used_lag}')
print(f'Number of Observations: {n_obs}')
print('Critical Values:')
for key, value in critical_values.items():
    print(f'   {key}: {value}')

解讀結果

  • ADF 統計量:一個負值,其中更負的值表示對零假設的證據更強。
  • p 值: 若 p 值低於顯著性水平(例如,0.05),則您拒絕零假設,認定時間序列為站性。
  • 臨界值:這些值幫助確定不同信任等級(1%,5%,10%)的閾值,用來與 ADF 統計量進行比較。

範例和結論

考慮一個金融時間序列數據,像是每日股價。應用 ADF 檢定可能會得出 p 值大於0.05,表明非站性。在此情況下,可能需要進行數據轉換建如差分或去趨勢以達到站性,然後再應用進一步的統計模型。

總結來說,ADF 檢定是檢測時間序列站性的重要工具。通過了解並應用此檢定,分析師能更好地為建模做好數據準備,從而確保他們結果的有效性和可靠性。

Running npm install on a Server with 1GB Memory using Swap

Running npm install on a server with only 1GB of memory can be challenging due to limited RAM. However, by enabling swap space, you can extend the virtual memory and ensure smooth operation. This blog post will guide you through the process of creating and enabling a swap partition on your server.

What is Swap?

Swap space is a designated area on a hard disk used to temporarily hold inactive memory pages. It acts as a virtual extension of your physical memory (RAM), allowing the system to manage memory more efficiently. When the system runs out of physical memory, it moves inactive pages to the swap space, freeing up RAM for active processes. Although swap is slower than physical memory, it can prevent out-of-memory errors and improve system stability.

Step-by-Step Guide to Enable Swap Space
  1. Check Existing Swap Information

Before creating swap space, check if any swap is already configured:

sudo swapon --show
  1. Check Disk Partition Availability

Ensure you have enough disk space for the swap file. Use the df command:

df -h
  1. Create a Swap File

Allocate a 1GB swap file in the root directory using the fallocate program:

sudo fallocate -l 1G /swapfile
  1. Enable the Swap File

Secure the swap file by setting appropriate permissions:

sudo chmod 600 /swapfile

Format the file as swap space:

sudo mkswap /swapfile

Enable the swap file:

sudo swapon /swapfile
  1. Make the Swap File Permanent

To ensure the swap file is used after a reboot, add it to the /etc/fstab file:

sudo cp /etc/fstab /etc/fstab.bak

Edit /etc/fstab to include the swap file:

echo '/swapfile none swap sw 0 0' | sudo tee -a /etc/fstab
  1. Optimize Swap Settings

Adjust the swappiness value to control how often the system uses swap space. A lower value reduces swap usage, enhancing performance. Check the current value:

cat /proc/sys/vm/swappiness

Set the swappiness to 15:

sudo sysctl vm.swappiness=15

Make this change permanent by adding it to /etc/sysctl.conf:

echo 'vm.swappiness=15' | sudo tee -a /etc/sysctl.conf

Adjust the vfs_cache_pressure value to balance cache retention and swap usage. Check the current value:

cat /proc/sys/vm/vfs_cache_pressure

Set it to 60:

sudo sysctl vm.vfs_cache_pressure=60

Make this change permanent:

echo 'vm.vfs_cache_pressure=60' | sudo tee -a /etc/sysctl.conf

Conclusion

Creating and enabling swap space allows your server to handle memory-intensive operations, such as npm install, more efficiently. While swap is not a substitute for physical RAM, it can provide a temporary solution to memory limitations, ensuring smoother performance and preventing out-of-memory errors. By following the steps outlined above, you can optimize your server's memory management and enhance its overall stability.

Running npm install on a Server with 1GB Memory using Swap

Hello and welcome back to "Continuous Improvement," the podcast where we dive into the intricacies of optimizing performance, whether it's in life, work, or tech. I'm your host, Victor Leung, and today, we're tackling a common challenge for those working with limited server resources: running npm install on a server with just 1GB of memory. Yes, it can be done smoothly, and swap space is our savior here.

So, what exactly is swap space, and how can it help? Think of swap space as an overflow area for your RAM. When your server's physical memory gets filled up, the system can move some of the inactive data into this swap space on your hard disk, freeing up RAM for more critical tasks. It’s slower than RAM, but it can prevent those dreaded out-of-memory errors that can crash your operations.

Let's walk through how to set up and optimize swap space on your server.

First, you'll want to see if swap space is already configured. You can do this with the command:

sudo swapon --show

This command will display any active swap areas. If there's none, or if it's too small, you'll want to create or resize your swap space.

Next, ensure you have enough disk space to create a swap file. The command df -h gives you a human-readable output of your disk usage. Ideally, you want to have at least 1GB of free space.

Assuming you have the space, let’s create a swap file. You can allocate a 1GB swap file with:

sudo fallocate -l 1G /swapfile

If fallocate isn’t available, you can use dd as an alternative method to create a swap file.

To secure your swap file, change its permissions to prevent access from unauthorized users:

sudo chmod 600 /swapfile

Then, format it as swap space:

sudo mkswap /swapfile

And enable it:

sudo swapon /swapfile

Your server now has additional virtual memory to use, but we’re not done yet.

To make sure your server uses the swap file even after a reboot, add it to your /etc/fstab file:

echo '/swapfile none swap sw 0 0' | sudo tee -a /etc/fstab

For a balanced system, you’ll want to adjust how often the system uses swap space. This is controlled by the swappiness value. Check the current setting with:

cat /proc/sys/vm/swappiness

Setting it to 15 is a good starting point:

sudo sysctl vm.swappiness=15

To make this change permanent, add it to /etc/sysctl.conf:

echo 'vm.swappiness=15' | sudo tee -a /etc/sysctl.conf

Similarly, for vfs_cache_pressure, which controls how aggressively the system reclaims memory used for caching, a setting of 60 can be beneficial:

sudo sysctl vm.vfs_cache_pressure=60

And again, make this permanent:

echo 'vm.vfs_cache_pressure=60' | sudo tee -a /etc/sysctl.conf

By now, your server should be better equipped to handle memory-intensive operations like npm install. Remember, swap is a temporary workaround for insufficient RAM. If you find yourself needing it often, consider upgrading your server's physical memory.

Thank you for tuning in to this episode of "Continuous Improvement." I hope these tips help you optimize your server’s performance. If you enjoyed this episode, don't forget to subscribe and leave a review. I'm Victor Leung, and until next time, keep improving!

在只有1GB記憶體的伺服器上使用Swap來運行npm install

在只有1GB記憶體的伺服器上運行npm install可能會因為RAM有限而面臨挑戰。但是,通過啟用swap空間,您可以擴展虛擬記憶體並確保操作順暢。這篇文章將引導您如何在伺服器上創建和啟用swap分區。

Swap是什麼?

Swap空間是硬盤上指定的區域,用於暫時保存不活躍的記憶體頁面。它作為物理記憶體(RAM)的虛擬擴展,使系統能更有效地管理記憶體。當系統用盡物理記憶體時,它會將不活躍的頁面移動到Swap空間,為活躍進程釋放RAM。雖然Swap比物理記憶體慢,但可以防止記憶體不足的錯誤並提高系統穩定性。

啟用Swap空間的步驟指南
  1. 查看現有Swap資訊

在創建Swap空間之前,先檢查是否已有配置Swap:

sudo swapon --show
  1. 檢查磁盤分區可用性

確保您有足夠的磁盤空間來放置Swap檔案。使用df指令:

df -h
  1. 創建Swap檔案

使用fallocate程式在根目錄中配置1GB的Swap檔案:

sudo fallocate -l 1G /swapfile
  1. 啟用Swap檔案

透過設置適當的權限來確保Swap檔案的安全性:

sudo chmod 600 /swapfile

將檔案格式化為Swap空間:

sudo mkswap /swapfile

啟用Swap檔案:

sudo swapon /swapfile
  1. 將Swap檔案設置為永久

為了確保伺服器重啟後繼續使用Swap檔案,請將其添加到 /etc/fstab 檔案中:

sudo cp /etc/fstab /etc/fstab.bak

編輯 /etc/fstab 以包含 Swap檔案:

echo '/swapfile none swap sw 0 0' | sudo tee -a /etc/fstab
  1. 優化Swap設定

調整 swappiness 值以控制系統使用Swap空間的頻率。較低的值可減少Swap的使用,提高性能。查看當前的值:

cat /proc/sys/vm/swappiness

swappiness 設為 15:

sudo sysctl vm.swappiness=15

透過將其添加到 /etc/sysctl.conf 中,使此變更永久:

echo 'vm.swappiness=15' | sudo tee -a /etc/sysctl.conf

調整 vfs_cache_pressure 值以平衡快取保留與使用Swap的平衡。查看當前的值:

cat /proc/sys/vm/vfs_cache_pressure

將其設定為60:

sudo sysctl vm.vfs_cache_pressure=60

使此變更永久:

echo 'vm.vfs_cache_pressure=60' | sudo tee -a /etc/sysctl.conf

總結

創建和啟用Swap空間可以讓您的伺服器更有效地處理記憶體密集型操作,比如說 npm install。雖然Swap不能替代物理RAM,但它可以為記憶體限制提供臨時解決方案,確保更順暢的性能並防止記憶體不足的錯誤。通過跟隨上述重述的步驟,您可以優化伺服器的記憶體管理並提高其整體穩定性。