I've just wrapped up an insightful data analysis that might change the way we view economic indicators.

I delved into the correlation between the Consumer Price Index (CPI) and oil prices, revealing a fascinating interplay over time. My study involved calculating z-scores for both CPI and oil data. The highlight was a visualization that revealed a clear trend: an increase in oil prices typically precedes a rise in inflation within less than a year. This finding prompts an intriguing question - could oil prices, along with other indicators, serve as a predictive tool for inflation?

But here's the twist: as we move towards a future of electric vehicles and alternative energy, could this correlation shift? It's a fascinating area for further exploration and research.

The data for this analysis was sourced from the Federal Reserve Bank of St. Louis, underscoring the reliability of my findings.

This study emphasizes the importance of understanding the dynamics between different economic factors. Stay tuned for more insights as I continue to delve into the world of data analysis!

Language models have revolutionized the field of natural language processing, enabling machines to generate coherent and contextually relevant text. While base language models (LLMs) excel at word prediction, the true power of these models can be unleashed through prompt engineering. In this article, we will explore the essentials of prompt engineering and how it can optimize the performance of Instruction Tuned LLMs (IT LLMs) by providing clear instructions and leveraging reinforcement learning from human feedback (RLHF).

To effectively optimize IT LLMs, it is crucial to be clear and specific in the prompts provided. Ambiguity or vagueness can lead to undesired results. By using precise language and clearly defining the desired output, we can guide the model towards generating more accurate and desired responses.

Delimiters play a vital role in prompt engineering. They help in structuring the input and output of the model. By using appropriate delimiters, such as special tokens or markers, we can instruct the model to pay attention to specific parts of the prompt and generate responses accordingly. This enables fine-grained control over the output and ensures that the model understands the intended context.

When formulating prompts, it is beneficial to ask for structured output whenever possible. This means specifying the desired format or structure of the response. For example, if we want the model to generate a list or a paragraph, we can explicitly mention it in the prompt. This helps in obtaining more organized and coherent outputs.

Checking conditions within the prompts is another important aspect of prompt engineering. By incorporating conditional statements or constraints, we can guide the model to generate responses that meet specific requirements. These conditions act as guidelines for the model and can help avoid outputs that might be irrelevant or incorrect.

Employing few-shot prompting techniques is a powerful strategy in prompt engineering. Instead of relying solely on large amounts of training data, few-shot prompting allows us to fine-tune the model using a limited number of carefully crafted examples. This approach enables the model to generalize and adapt to new tasks or domains quickly.

Patience is key when working with language models. Giving the model sufficient time to think and process the prompt can significantly enhance the quality of the generated output. Language models are complex systems, and allowing them a moment to deliberate ensures that they produce more thoughtful and coherent responses.

However, it's essential to be cautious of hallucinations. While language models are incredibly powerful, they can sometimes generate outputs that may seem plausible but are entirely fictional or inaccurate. Vigilance and critical evaluation of the generated text are necessary to ensure the reliability and credibility of the model's responses.

For those interested in diving deeper into prompt engineering, Deep Learning.ai offers a short course specifically focused on this subject. The course provides comprehensive insights, techniques, and practical examples to master the art of prompt engineering. You can find more information about the course and enroll using the following link: [Link: https://lnkd.in/gFtjq8Pp].

In conclusion, prompt engineering is a crucial skill for harnessing the full potential of language models. By being clear and specific in our instructions, utilizing delimiters, asking for structured output, checking conditions, employing few-shot prompting, giving the model time to think, and remaining cautious of hallucinations, we can optimize the performance of language models and unlock their power in various applications. So, let's embark on this journey of leveraging prompt engineering to propel language models to new horizons of creativity and utility!

As the United States approaches its debt ceiling, the question of whether Congress will raise it once again has come to the forefront of political discussions. One key indicator to consider when analyzing the government's debt situation is the debt-to-GDP ratio. As this ratio has more than doubled in the past 20 years, it's clear that the government's debt burden relative to the size of the economy has increased significantly. This has implications for the government's ability to finance its debt and make investments in other areas. The looming debt ceiling debate highlights the need for the government to address its growing debt and find long-term solutions to manage it effectively. 

Data: Fred API (Federal Reserve Bank of St. Louis)  

The housing market is experiencing a surge in prices, making it increasingly difficult for middle-class families to afford a home of their own. The median home price in the US has once reached an all-time high of $479,500, while the median household income is only $88,590. This has resulted in a widening affordability gap that is making it harder for many Americans to achieve homeownership.

A comparison between the rise in median home prices and median household income highlights a significant disparity as shown in the graph. At the highest median price even with a 20% down payment of $95,900, financing the remaining $383,600 at the current average 30-year fixed mortgage rate of 6.29% for an average credit score results in a monthly payment of $2,300 and that is excluding the taxes and other fees. This places a significant financial burden on many families, particularly those with lower incomes or living in high-cost areas.

To address this challenge, policymakers and industry leaders must collaborate to find ways to make housing more accessible and affordable for all Americans. By analyzing the data and understanding the trends, we can work towards creating a more equitable and sustainable housing market that benefits everyone.

Data obtained from the Federal Reserve Economic Data (FRED) API, Zillow 

The Bear Market Macro Index (BMMI) is a useful tool for investors to get a quick snapshot of the financial markets during a bear market. This indicator measures the spread between the 12-month and 24-month moving averages of the S&P 500, which can provide insight into the market's health and direction.

By comparing the BMMI spread with S&P 500 data over the past 5 years, it becomes evident that the BMMI spread can signal a trend reversal earlier than the S&P 500 data alone. A positive BMMI spread indicates that the market is in an uptrend, while a negative spread indicates that the market is in a downtrend.

While the BMMI is a helpful tool, it's important not to rely on a single indicator when making investment decisions. Instead, it's recommended to use multiple indicators and analysis methods to get a more comprehensive understanding of the market trend.

In conclusion, the BMMI can be a valuable tool for investors to monitor the market's health and direction during a bear market. However, it's important to use it in conjunction with other indicators and analysis methods to make sound investment decisions. As always, this is not financial advice and happy investing!

Tracking key economic indicators is essential in assessing the health of the U.S. economy. Among the indicators worth monitoring are M2 money supply, personal consumption expenditures (PCE), and the average sales price of houses sold.

The recent inflation of the M2 money supply, caused by the Federal Reserve's money printing during the COVID-19 pandemic, has stimulated economic growth and contributed to increased home sale prices. However, this also led to a rise in inflation, making it important to analyze the correlation between these indicators.

To better understand the interplay between these indicators, I have charted them (using FRED) and their correlation. The chart is worth examining as it provides valuable insights into the current state and future trajectory of the U.S. economy.

Analyzing these indicators and their correlation can help us make informed decisions about investments, business strategies, and economic policies. It is crucial to stay up-to-date with the latest developments in these indicators, especially in uncertain economic times.

Please follow me for more posts like this as we navigate through these uncertain economic times together. As a disclaimer, this is not financial advice, and it's essential to seek professional advice before making any investment decisions.

With the Federal Reserve announcing yet another interest rate hike, many are beginning to question whether we are headed for another financial crisis. To gain some perspective, let's take a look at the highest interest rate for each year since 2005.

According to data from @MacroTrends, the highest federal funds rate in 2005 was 4.25%. It reached 5.34% in 2006, and in 2007, it peaked again at 5.41%.The 2008 financial crisis led to a dramatic decrease in rates as the Fed sought to counter the crisis. By the end of that year, the highest rate was nearly at 0.09%.In the subsequent years, the Fed gradually increased rates. In 2019, the highest rate reached 2.45%, the highest since the crisis.

Today's rate hike brings the highest rate up to 5.0%, which is very close to the pre-crisis peak. It's uncertain whether history will repeat itself and another crisis will occur, but it is undoubtedly a possibility. However, according to the Fed, the economy is relatively robust compared to the crisis, and there is a strong job market. It's important to note that interest rates are just one factor among many that can contribute to a financial crisis. Nevertheless, keeping an eye on them can provide us with a sense of the overall economic landscape.

In conclusion, the recent Federal Reserve interest rate hike has prompted concerns about another financial crisis. By examining the highest interest rate for each year since 2005, we can understand how it's evolved over time. While it's unclear if we're headed for another crisis, monitoring interest rates, along with other economic indicators, can help us make informed investment decisions. As always, this is not financial advice, and investors should seek professional guidance before making any investment decisions.

Macrotrends LLC. (2015). Federal Funds Rate - Historical Chart. Retrieved from: https://www.macrotrends.net/2015/fed-funds-rate-historical-chart