Arizona real estate has been pretty flat for a while so I haven’t posted any updates. But there are a few interesting things I noticed this week.
Median prices have already exceeded their highs of 2023. This is interesting because this usually occurs a little later in the year.
However, average sales prices have not done the same.
Price cuts should be reducing this time of year but they are not. They are going up showing sellers are more willing to negotiate.
Days of inventory are now higher than they were in 2023 and are about to surpass 2022’s highs if they continue.
Which is also listed in the average weekly listing counts. These are accelerating into the new year when they should be moving in the opposite direction.
And finally, annual sales rates are at their lowest levels since 2014. Which show no signs of letting up until interest rates go down.
The question of when the Federal Reserve will cut interest rates is at the forefront of many investors’ and minds. Given the current economic indicators and futures market trends, there seems to be little chance the FED cuts rates anytime soon. By analyzing the FOMC rate probabilities and market expectations, it’s evident that optimism for a rate cut may be premature.
Futures traders exhibit a strong belief that the Federal Reserve will not lower rates in their next meeting on March 20th, with a 97.5% probability against a rate cut. This sentiment extends to the subsequent meeting on May 1st, 2024, where there’s a 76.3% likelihood of rates remaining unchanged. Looking even further ahead to the June 12th, 2024 meeting, the probability of the Fed maintaining the current rates stands at 36.2%, with a slight majority of 51.1% betting on a modest quarter-point reduction. Given the economic uncertainty around the globe, my guess is these numbers largely reflect the fact that the future is too hard to predict and something bad happening in the next 3-4 months is “likely”.
The Federal Reserve’s decision-making process is heavily influenced by its dual mandate to ensure price stability and maximum employment. Current inflation trends, a key determinant in this process, show an alarming annualized rate of 6.537% based on last month’s annualized—a figure significantly above the Fed’s 2% target.
This uptick in inflation, rather than showing signs of abatement, has unexpectedly surged, underscoring the unpredictability of economic conditions. Core inflation, which excludes the volatile food and energy sectors, also presents a high annualized figure of 4.709%, further indicating that the Fed may not be inclined to lower interest rates in the foreseeable future.
Given that unemployment rates have remained relatively stable, there’s little pressure from the job market to prompt a rate cut. However, the specter of inflation, now more than ever, looms large over the Federal Reserve’s policy decisions. The fear of inflation spiraling out of control is likely a significant factor restraining the Fed from reducing rates.
Despite the current data and market sentiments suggesting that a rate cut is not on the horizon, it’s crucial to approach the future with caution. Economic conditions can shift rapidly, and recessions, when they occur, often strike with little warning. The data points to a cautious approach from the Fed, prioritizing the containment of inflation over stimulating economic growth through rate reductions. Yet, the unpredictable nature of economic transitions means that vigilance and adaptability are essential, both for policymakers and market participants alike.
Interactive data on this article is available on my FED dashboard.
If you’ve ever downloaded a scanned PDF and tried to search it you’ll quickly realize this isn’t possible. Here is how you can use Python to extract the text from and PDF file and make it searchable.
One of my favorite books for quantitative investing is Machine Learning for Algorithmic Trading. However, a lot of these concepts might be difficult for someone who getting started in programming strategies. So I created this Machine Learning for Algorithmic Trading GPT which can answer any questions related to this book. While these aren’t perfect responses they will typically put you on the right path with a little input.
Sample Excerpt:
You:
Based on the references in this book where can I download stock data?
Machine Learning for Trading Tutor:
Based on the references in the book, here are the sources where you can download stock data:
Quandl: Quandl provides stock price datasets covering the US equity markets for the years 2000-2018. The book mentions loading these datasets using pandas for analysis.
yfinance: yfinance offers a way to download historical market data from Yahoo! Finance. This library, initially named fix-yahoo-finance, allows for the downloading of various data points including end-of-day and intraday prices. The book describes the process of using yfinance to download historical prices for various periods, at different frequencies, and adjusted OHLCV (Open, High, Low, Close, Volume) data at a one-minute frequency along with corporate actions.
AlgoSeek: For more detailed intraday data, AlgoSeek provides adjustment factors to correct pricing and volumes for stock splits, dividends, and other corporate actions. The book suggests AlgoSeek as a source for such data, emphasizing its use for accurate market data analysis.
SEC (U.S. Securities and Exchange Commission): While not directly a source of stock prices, the SEC’s EDGAR database can be used to download financial statements and related information, which can be valuable for fundamental analysis. The book provides guidance on using Python to extract data from EDGAR.
Pandas DataReader: For additional fundamental data sources, the book mentions that many are accessible using the pandas_datareader module, which facilitates the download of financial data from a variety of sources including Yahoo Finance, Google Finance, and others.
International equity data and macro fundamental data: Besides U.S. stock data, the book also references sources for international equity data like Stooq and various macro fundamental and benchmark data from the Federal Reserve, indicating the broad scope of data sources available for trading and analysis.
These sources provide a comprehensive toolkit for accessing both historical and real-time market data, fundamental data, and other financial information crucial for trading and investment analysis.
You:
Can you write me a Python function for each of these with sample code to download data from this source.
Machine Learning for Trading Tutor
Sure, I’ll provide you with Python functions for downloading stock data from each of the mentioned sources. Please note that these examples require you to have the respective libraries installed and, in some cases, to register for API keys where necessary.
1. Quandl
For Quandl, you’ll need to install the quandl Python package and obtain an API key from the Quandl website.
pythonCopy code
import quandl
def download_quandl_data(symbol, start_date, end_date, api_key):
quandl.ApiConfig.api_key = api_key
data = quandl.get(symbol, start_date=start_date, end_date=end_date)
return data
# Example usage
# Replace 'YOUR_API_KEY' with your actual Quandl API key
# data = download_quandl_data('WIKI/AAPL', '2020-01-01', '2020-12-31', 'YOUR_API_KEY')
2. Yahoo! Finance
For Yahoo! Finance, use the yfinance package. This doesn’t require an API key.
pythonCopy code
import yfinance as yf
def download_yfinance_data(symbol, start_date, end_date):
data = yf.download(symbol, start=start_date, end=end_date)
return data
# Example usage
# data = download_yfinance_data('AAPL', '2020-01-01', '2020-12-31')
3. AlgoSeek
Downloading data from AlgoSeek would typically require accessing their proprietary database or API, for which you might need an account or subscription. Here’s a generic approach assuming you have access:
# Placeholder for AlgoSeek data downloading
# Please refer to AlgoSeek's documentation and your subscription details for specific access methods
# Placeholder for AlgoSeek data downloading # Please refer to AlgoSeek's documentation and your subscription details for specific access methods
4. pandas_datareader
To use pandas_datareader for downloading stock data, ensure you have this package installed.
import pandas_datareader.data as web
def download_pandas_datareader_data(symbol, start_date, end_date):
data = web.DataReader(symbol, 'yahoo', start_date, end_date)
return data
# Example usage
# data = download_pandas_datareader_data('AAPL', '2020-01-01', '2020-12-31')
5. SEC Filings (EDGAR)
Downloading data from the SEC’s EDGAR system for fundamental analysis typically involves parsing XBRL filings. This is more complex and requires specific libraries (e.g., python-edgar or similar). Here’s a basic structure:
pythonCopy code
# Placeholder for EDGAR data downloading
# This operation is complex and requires parsing XBRL files; refer to specialized libraries and SEC's documentation
These examples are meant to get you started with downloading stock data from various sources. Depending on your specific needs and the details of your project, you might need to adjust parameters, handle API limits, or process the data further after downloading it.
