So You Thought You Knew Gamma?

 A gamma squeeze is a market phenomenon that occurs when there is a rapid increase in the price of a stock due to the activities of options traders and market makers. It is closely related to the mechanics of options trading, specifically the hedging activities of market makers who sell options. Here's a detailed explanation:

Key Concepts:
Options and Delta:

Options: Financial derivatives that give the holder the right, but not the obligation, to buy or sell a stock at a predetermined price before a certain date.
Delta: A measure of how much the price of an option changes with respect to a $1 change in the price of the underlying stock. For example, a delta of 0.5 means the option price changes by $0.50 for every $1 change in the stock price.
Gamma:

Gamma: Measures the rate of change of delta with respect to the price of the underlying stock. High gamma means that delta changes rapidly as the stock price changes.
Market Makers and Hedging:

Market Makers: Entities that provide liquidity in the options market by buying and selling options. When they sell options, they often hedge their positions to manage risk.
Hedging: To hedge a short call position, market makers buy shares of the underlying stock to offset potential losses if the stock price rises. This is known as delta hedging.
Gamma Squeeze Mechanism:
Initial Buying Pressure:

The process often starts with significant buying of call options by investors. This buying can be driven by speculation, bullish sentiment, or other factors.
As call options are purchased, market makers who sold these options need to hedge their exposure. They do this by buying the underlying stock.
Increase in Stock Price:

The hedging activity (buying the stock) by market makers increases the demand for the stock, pushing its price higher.
As the stock price rises, the delta of the call options increases (because gamma is high), meaning the options become more sensitive to further changes in the stock price.
Further Hedging and Feedback Loop:

With the increased delta, market makers need to buy even more shares to maintain their hedges.
This creates a feedback loop: the stock price rises, delta increases, market makers buy more shares, and the stock price rises further.
Acceleration:

The process can accelerate rapidly, causing a sharp increase in the stock price over a short period. This rapid price increase due to the hedging activity of market makers is called a gamma squeeze.
Example:
Assume a stock is trading at $50, and there is significant buying of call options with a strike price of $55.
Market makers who sold these call options will start buying the stock to hedge their positions.
This buying pushes the stock price to $53, increasing the delta of the $55 call options.
As the delta increases, market makers buy even more stock, pushing the price to $55 and beyond.
The cycle continues, potentially leading to a very rapid increase in the stock price.
Risks and Considerations:
Volatility: A gamma squeeze can lead to extreme volatility in the stock price.
Sustainability: The price surge driven by a gamma squeeze may not be sustainable and can be followed by sharp declines once the squeeze pressure subsides.
Regulatory Scrutiny: Unusual market activities, including gamma squeezes, can attract regulatory attention.
In summary, a gamma squeeze is a powerful and rapid price movement driven by the feedback loop of options market makers hedging their positions. It highlights the complex interplay between options trading and the underlying stock market.

also elaborate on this comment
Me too, but very very small. For one, it hasn't triggered yet. But the tell tale sign of degens really getting involved and do the whole collusive gamma squeeze thing is when the tail end of the call side option chain has a ton of volume - they always go for the last strike, no matter how ridiculous it seems (it forces the dealers to buy). Not seeing that in GRPN yet.
ChatGPT
The comment you're referring to provides insights into the behavior and patterns of traders, particularly those involved in highly speculative or "degenerate" trading strategies, which often target gamma squeezes. Let's break down the comment and elaborate on each part:

Comment Breakdown and Elaboration:
"Me too, but very very small."

Meaning: The commenter agrees with the previous sentiment but indicates that their involvement or investment is very small.
Context: This suggests a cautious approach, likely due to the risks associated with speculative trading and the potential for high volatility in stocks that might be targeted for a gamma squeeze.
"For one, it hasn't triggered yet."

Meaning: The anticipated gamma squeeze has not yet begun.
Context: The trader is observing market conditions and waiting for specific signals or triggers that indicate a gamma squeeze is starting. This could involve watching for increased options activity or significant price movements in the stock.
"But the tell-tale sign of degens really getting involved and doing the whole collusive gamma squeeze thing is when the tail end of the call side option chain has a ton of volume."

Meaning: A key indicator of highly speculative traders (often referred to as "degenerates" or "degens") initiating a gamma squeeze is when there is a large volume of trading in the far-out-of-the-money call options (those with very high strike prices).
Context: These traders often coordinate their actions to buy large amounts of call options with strike prices that are significantly higher than the current stock price. This forces market makers to hedge by buying the underlying stock, which can drive up the stock price and potentially trigger a gamma squeeze.
"They always go for the last strike, no matter how ridiculous it seems (it forces the dealers to buy)."

Meaning: Speculative traders typically target the highest strike prices available in the options chain, regardless of how unrealistic those strike prices may seem.
Context: This strategy is employed because buying these far-out-of-the-money call options forces market makers (dealers) to hedge their positions by buying the underlying stock. This can create upward pressure on the stock price, contributing to a gamma squeeze.
"Not seeing that in GRPN yet."

Meaning: The commenter is observing that Groupon (GRPN) has not yet exhibited the high volume in far-out-of-the-money call options that would indicate a coordinated gamma squeeze effort.
Context: The commenter is looking for this specific activity as a signal to get more involved but hasn't seen it yet, which suggests that the conditions for a gamma squeeze in Groupon are not present at the moment.
Key Points and Implications:
Speculative Behavior:

Traders referred to as "degens" engage in highly speculative and risky trading strategies, often attempting to trigger gamma squeezes.
These traders may coordinate their actions, buying far-out-of-the-money call options to force market makers to hedge by buying the underlying stock.
Gamma Squeeze Indicators:

A significant increase in volume at the far end of the call options chain (highest strike prices) is a common indicator of a potential gamma squeeze.
This activity suggests that speculative traders are attempting to create conditions that will force market makers to buy large quantities of the stock, driving up its price.
Market Dynamics:

When market makers sell call options, they often hedge their risk by buying the underlying stock. If speculative traders buy large amounts of call options, this can lead to substantial stock purchases by market makers, increasing the stock price and potentially triggering a gamma squeeze.
Observing the options market, especially the volume and open interest in far-out-of-the-money calls, can provide insights into potential gamma squeezes.
Caution and Risk:

The commenter indicates caution by keeping their involvement small and noting that the conditions for a gamma squeeze (in GRPN) have not yet been met.
This reflects an understanding of the high risk and volatility associated with gamma squeeze attempts, and the importance of waiting for clear signals before increasing exposure.
In summary, the comment provides a detailed look into the strategies and signals that speculative traders use to identify and potentially trigger gamma squeezes. It emphasizes the importance of observing specific market indicators, such as the volume of far-out-of-the-money call options, and highlights the cautious approach of waiting for these indicators to materialize before taking larger positions.

Data Analysis AI Tools

 To analyze the NAAIM Exposure Index data and pull out predictive movements, you can use various AI and machine learning resources. Here are some powerful tools and platforms that are well-suited for this kind of analysis:

1. Python with Machine Learning Libraries:

Python is a versatile programming language with a rich ecosystem of libraries for data analysis and machine learning. Here are some key libraries you might use:

Pandas: For data manipulation and analysis.
Scikit-Learn: For building and evaluating machine learning models.
Statsmodels: For statistical modeling and time series analysis.
TensorFlow/Keras: For deep learning models.
Prophet (by Facebook): For time series forecasting.
2. AutoML Platforms:
Automated Machine Learning (AutoML) platforms can simplify the process of building and tuning models:

H2O.ai: An open-source platform that provides tools for building machine learning models, including time series forecasting.
DataRobot: A commercial AutoML platform that can automate the process of building and deploying machine learning models.
Google Cloud AutoML: Google's platform for training high-quality custom machine learning models with minimal effort and machine learning expertise.
3. Jupyter Notebooks:
Jupyter Notebooks provide an interactive environment for exploring data and developing models. You can integrate various libraries mentioned above within Jupyter Notebooks.

4. BigQuery ML:
If your dataset is large and you prefer a cloud-based solution, Google BigQuery ML allows you to create and execute machine learning models using SQL queries.

Example Workflow in Python:
Data Preprocessing: Load your Excel data using Pandas and preprocess it (handling missing values, normalizing, etc.).
Exploratory Data Analysis (EDA): Use data visualization libraries like Matplotlib or Seaborn to identify trends and patterns.
Feature Engineering: Create new features that might help in predicting movements (e.g., moving averages, volatility measures).
Model Building: Use Scikit-Learn or other libraries to build and evaluate machine learning models.
Time Series Analysis: Use libraries like Statsmodels or Prophet for forecasting future movements.
Here’s a brief example of how you might start with Pandas and Scikit-Learn:

python
Copy code
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error

# Load data
data = pd.read_excel('NAIIM_data.xlsx')

# Preprocess data
data.fillna(method='ffill', inplace=True)  # Fill missing values

# Feature Engineering (example)
data['lag_1'] = data['NAIIM'].shift(1)
data['lag_2'] = data['NAIIM'].shift(2)
data.dropna(inplace=True)

# Train-test split
X = data[['lag_1', 'lag_2']]
y = data['NAIIM']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Model Building
model = RandomForestRegressor()
model.fit(X_train, y_train)

# Predictions
y_pred = model.predict(X_test)

# Evaluation
mse = mean_squared_error(y_test, y_pred)
print(f'Mean Squared Error: {mse}')
This example is a basic starting point. Depending on the complexity and characteristics of your data, you might need more sophisticated models and preprocessing techniques.

Demand Zones and Supply Zones and NAIIM

 

Education

Posted by keyhole7 on 25th of Jul 2024 at 08:45 am

Attached are the posts related to Demand/Supply zones

https://breakpointtrades.com/blog/post/426504/

https://breakpointtrades.com/blog/post/426511/

See April 10, 2024 newsletter also.

Chatty

The NAAIM Exposure Index refers to the National Association of Active Investment Managers' (NAAIM) measurement of the average exposure to U.S. equity markets by its members. The index is derived from weekly surveys of NAAIM member firms, which report their overall equity exposure as a percentage of assets under management. A significant drop in the NAAIM Exposure Index indicates that active investment managers are reducing their exposure to the U.S. stock market, reflecting a more cautious or bearish outlook.

Key Points About the NAAIM Exposure Index:
Purpose: The index tracks the sentiment and market exposure levels of active investment managers, providing insights into their collective stance on the equity market.

Range: The exposure can range from -200% (leveraged short positions) to +200% (leveraged long positions), with 0% indicating no exposure to U.S. equities.

Interpretation:

High Exposure: Indicates that managers are bullish and heavily invested in the equity market.
Low Exposure: Suggests a bearish outlook, with managers holding more cash or short positions.
Sharp Drops: A significant drop in the index signals that managers are rapidly decreasing their equity exposure, potentially due to market volatility, economic concerns, or other risk factors.
Usage: Investors and analysts use the NAAIM Exposure Index to gauge market sentiment and potential turning points. A sharp decline can be a warning sign of increasing caution among professional investors, which might precede broader market downturns.

Example:
If the NAAIM Exposure Index drops significantly, it might indicate that active managers are concerned about potential market risks and are moving to reduce their exposure to equities. This could be driven by various factors, such as economic data, geopolitical events, or changes in monetary policy.

In conclusion, a very large drop in the NAAIM Exposure Index suggests a shift towards a more defensive or cautious positioning among active investment managers, reflecting a less optimistic view of the stock market's near-term prospects.

Visible Range Volume Profile --- A Deep Dive

 

Visible Range Volume Profile: Deep Dive

Overview

The Visible Range Volume Profile (VRVP) is a technical analysis tool used by traders to identify price levels with significant trading volume over a specified period. It displays the distribution of volume across different price levels on the chart. This information helps traders understand where significant buying and selling activity has occurred, which can indicate potential support and resistance levels.

Here is an example from BPT.....foody518

Components of VRVP

1. Volume Profile: A histogram that shows the amount of volume traded at each price level over the visible range on the chart. The profile is typically plotted horizontally along the price axis.
2. Point of Control (POC): The price level with the highest traded volume within the visible range. It represents the most traded price and is often considered a strong area of support or resistance.
3. Value Area (VA): The range of prices where a specified percentage of the total volume was traded, usually set at 70%. The Value Area consists of the Value Area High (VAH) and Value Area Low (VAL).
   • Value Area High (VAH): The upper boundary of the Value Area.
   • Value Area Low (VAL): The lower boundary of the Value Area.
4. High Volume Nodes (HVNs): Price levels with relatively high volume compared to surrounding areas. These levels often act as strong support or resistance.
5. Low Volume Nodes (LVNs): Price levels with relatively low volume compared to surrounding areas. These levels can act as areas of potential price movement, as there is less traded volume to act as support or resistance.

Value Area Up and Value Area Down

• Value Area Up: This indicates the upper part of the Value Area, where the prices are more likely to find resistance if the price is moving upwards.
• Value Area Down: This indicates the lower part of the Value Area, where the prices are more likely to find support if the price is moving downwards.

Analyzing the Combined Width of Volume Bars

The combined width of the volume bars within the Value Area gives traders insights into the overall trading activity and interest at different price levels. Wider bars indicate higher volume and can signal strong interest and potential support or resistance.

Using VRVP in Trading

1. Identify Support and Resistance Levels: Traders use the POC, VAH, and VAL to identify key levels where the price might find support or resistance.
2. Trend Analysis: By observing the distribution of volume, traders can identify whether the market is trending or consolidating. A well-distributed profile may indicate consolidation, while a skewed profile may suggest a trending market.
3. Entry and Exit Points: Traders often look for entry points near the VAL in an uptrend or near the VAH in a downtrend, aiming to trade towards the POC.
4. Volume Gaps: Areas with low volume (LVNs) can indicate potential price gaps where the price might move quickly.

Advantages of VRVP

• Visual Clarity: Provides a clear visual representation of volume distribution across price levels.
• Support and Resistance: Helps identify strong support and resistance levels based on actual trading activity.
• Market Sentiment: Offers insights into market sentiment by showing where traders are most active.

Limitations of VRVP

• Complexity: Can be complex to interpret for beginners.
• Historical Data: Relies on historical volume data, which may not always predict future movements.
• Market Conditions: May be less effective in highly volatile or low-volume markets.

Practical Example

Consider a stock trading within a range of $50 to $60. The VRVP shows that the POC is at $55, with a Value Area spanning from $52 to $58. Traders can interpret this as follows:

• If the price is approaching $52 (VAL), it might find support and be a good entry point for a long position.
• If the price is nearing $58 (VAH), it might face resistance, making it a potential exit point or an entry for a short position.
• If the price is around $55 (POC), it indicates a high interest level where a lot of trading activity has occurred.

Conclusion

The Visible Range Volume Profile is a powerful tool for technical traders, providing essential insights into volume distribution across price levels. By understanding the key components and how to interpret the combined width of the volume bars, traders can make more informed decisions and identify potential trading opportunities.

Feel free to ask if you need more detailed examples or further explanation on any specific aspect of the Visible Range Volume Profile!

----------------------------- I am not that impressed with ChatGPT image example

True Range and Average True Range

 This Technical Indicator, invented in 1978 before the personal computer boom.

It is used by Breakpoint Trades in their KISS system, which now model 770 stocks.

So let's Grok it.

Volatility for Individual Stocks

 $vxapl.x   $vxgs.x     $vxazn.x   

Apple, ??, Amazon

Volatility Symbols from Tradestation

Stock Market Actions After Multiple Gap Up Days, Analysis Using SPT ETF Since 1993

 stock here:

After this Face Ripping launch out of the recent up channel, I noted 2 big Gap Ups from the closing price to the opening price....aka markets moved most easily when the markets are closed and less money is needed to move them.    There were many bearish people at the beginning of the week, and they got their faces ripped off.   I went to cash on many positions, missing out on large gains, but had some Anti-USD bets like COIN, as well as Gold and Silver.    So it was a net positive week. 

But after 2 fairly large Gap up Days, I decided to so a bit of analysis using Excel.   What happens next?   So I got the entire data set of SPY since 1993 and proceeded to spend far too much time with it.   

If you want my spreadsheet, here it is on Box

https://app.box.com/s/ix5a0fdvq5b4t3ph5rhwjs88xznefhan

I started my blogging "life" with stock trading, lurking for years before making my first comment even, often on Slope Of Hope with Tim Knight.    Then came the Gulf Oil Crisis, and then.....Fukushima, after which I ran down many rabbit holes only to discover that the real difference between Conspiracy Theory and Conspiracy Fact, was usually just a matter of time.

Even in this Analysis of Gap Action, I uncovered a real conspiracy....wait for it.   LOL

If the third day is a down day that closes one of the gaps, it would be a "Upside Gap Three Methods" which traditionally was thought of as a bullish continuation pattern, but the patternsite lists it as 59% being a bearish reversal.   

https://www.thepatternsite.com/UpGap3Methods.html

Here are the numbers of consecutive large gap up days since SPY started in 1993 

For the case when there are JUST 2 large gap up days, and the third day is not also a large  gap up day (0.55%), there are quite a few negative gap down days, 72, versus 40 of the "smallish" gap up days.    

However, there are many large Gap Up Days on the Third Day, and Nine of the 27 have been since the Pandemic, just 18 of these between 1993 and 2019.   

A quick look at all the Gaps since 1993

And a look at SPY volume over the years, Curious how it is gently sloping downward after the 2008 Debacle.

AI Provides a Framework on Correlating US Dollar and S&P 500, Here It Is

 

Correlating the US Dollar Index ($DXY) with the valuation of the S&P 500 index or the SPY ETF over a 15-year period can provide insights into the relationship between the strength of the US dollar and the performance of US equities. You can use historical data and statistical analysis to assess the correlation between these two variables. Here are the steps to do this:

1. Gather Data: Start by collecting historical data for both the US Dollar Index ($DXY) and the S&P 500 index or the SPY ETF for the past 15 years. You can typically find this data from financial websites, data providers, or trading platforms.

2. Organize the Data: Organize the data into a spreadsheet or a data analysis tool like Excel or Google Sheets. Make sure you have both the daily or monthly values for $DXY and the S&P 500 or SPY ETF for the entire period you want to analyze.

3. Calculate Returns: Calculate the returns for both variables. Returns can be calculated by taking the percentage change in value over each time period. For example, you can calculate daily or monthly returns.

4. Compute Correlation: Use a statistical software or spreadsheet program to compute the correlation coefficient between $DXY returns and S&P 500 or SPY ETF returns. The correlation coefficient will help you determine the strength and direction of the relationship between these two variables.

5. Interpret the Results: The correlation coefficient will fall between -1 and 1.

   • A positive correlation (close to 1) suggests that the two variables move in the same direction.
   • A negative correlation (close to -1) suggests that the two variables move in opposite directions.
   • A correlation close to 0 indicates a weak or no correlation.

6. Consider Additional Factors: Keep in mind that correlation doesn't imply causation. Other factors may influence the performance of the S&P 500 or the US dollar. You should consider economic, geopolitical, and market-specific events that could affect these indices.

7. Analyze Trends: Look for trends or patterns in the data. Are there specific periods when the correlation is stronger or weaker? Consider what might have influenced these trends.

8. Monitor Over Time: It's important to remember that the relationship between the US dollar and the S&P 500 or SPY ETF can change over time. Continuously monitor and update your analysis as new data becomes available.

Analyzing the correlation between the US Dollar Index ($DXY) and the S&P 500 or SPY ETF over a 15-year period can provide valuable insights for investors and traders. However, it's essential to approach this analysis with a holistic view of the financial markets and the potential drivers of these movements.

Germantown WI, Suspect Dead On School Roof, Shot by 3 Police After Acting Erractically, Threatening A Groundskeeper

I'll add more, but for now keypoints: School door was locked, officers pursued aggressively and had good aim.   These are good cops. 

----------------------------------------------------------

An odd story, with 4 days later, still no information about the dead suspect, except that his car had out of state plates.

 https://www.msn.com/en-us/news/crime/what-we-know-about-the-active-shooter-shot-and-killed-at-kennedy-middle-school-in-germantown/ar-AA1iL4GW 

He was behaving erratically at the shop and eventually walked to the school. He tried to get in the school doors, but they were locked, and he was seen again behaving erratically in the parking lot. 

 Police responded about 6:30 p.m. to Kennedy Middle School after a 911 call that the man was removing signs reserving parking spots for people with disabilities in the parking lot, according to dispatch radio traffic from Broadcastify. 

The man told a groundskeeper who approached him to "get back or something might happen," according to the dispatch audio. He had a large ratchet tool to take the signs off the posts, the dispatcher said. When police arrived, the man climbed to the roof of the school and fired at officers, the Department of Justice said.

Divergence Between SPX and High Yield Bonds, Tim Kinight From 2021 and BPT now 2023

Here is Knights Article https://www.investing.com/analysis/hyg-and-sp-500-no-longer-bosom-buddies-200608456

Finviz Daily Scan Resource on Relative Volume over 1.5, Then Hover Over Tickers for a Quick Chart

 https://finviz.com/screener.ashx?v=111&f=sh_curvol_o750,sh_price_o1,sh_relvol_o1.5&o=-change