The deep research logic is organized as follows: 

Okay, let's tackle the user's question: "What stock ready for squeeze." The user is asking about stocks that are potentially ready for a short squeeze. From the references provided, I need to find stocks with high short interest and other indicators that could lead to a squeeze.

First, I'll check the references. Reference 2 has a query that matches the criteria for a short squeeze. The query is: Short Interest > 10%; Beta > 1; Closing Price > 5-Day Moving Average; 5-Day Moving Average > 10-Day Moving Average; 10-Day Moving Average > 20-Day Moving Average. The results from this query show 1056 stocks, but the user is asking for specific stocks ready for a squeeze. However, the results in Reference 2 don't have any stocks meeting the criteria because the query in Reference 1 (Short Interest > 10%; Beta > 1.5; ...) found 0 results. Wait, no, Reference 2's query has Short Interest > 10% and Beta > 1, and it found 1056 results. But the user's question is about stocks ready for a squeeze, which typically involves high short interest and favorable technical conditions.

Looking at the data in Reference 2, the stocks listed have Short Interest (Total Liabilities / Total Assets) but not the exact short interest percentage. However, the user's question is about short interest, so I need to check if any of these stocks have high short interest. The data in Reference 2 includes "Total Liabilities (MRQ)" and "Total Assets (MRQ)", but the ratio given is "Total Liabilities (MRQ) / Total Assets (MRQ)", which is a leverage ratio, not short interest. So, the short interest percentage isn't directly provided here. That's a problem because the user is asking about short interest.

Wait, the user's question is about stocks ready for a squeeze, which typically involves high short interest. The references provided don't have a direct list of stocks with high short interest. However, Reference 3 and 4 mention "Most Shorted Stocks" and "Top Most Shorted Stocks List" but don't provide specific data. Reference 5 and 6 discuss short interest and short squeeze models but don't list specific stocks. 

Looking back at the user's question, they might be referring to the stocks listed in Reference 2, which have Beta > 1 and other moving average conditions. The user's previous questions were about penny stocks and AI stocks, but this time it's about short squeeze candidates. The user's profile mentions interest in NASDAQ Composite and blue-chip stocks, so maybe the answer should focus on those.

In Reference 2, the stocks listed include BAC, WFC, PRU, USB, PNC, TFC, COF, PFG, AAPL, AXP. These are all large-cap stocks, some of which are financials. The user's profile shows interest in NASDAQ, but these stocks are on NYSE or other exchanges. However, the user's previous questions didn't specify exchange, so it's okay.

The user's question is about stocks ready for a squeeze. The criteria from the query in Reference 2 include Beta > 1, which indicates higher volatility. The moving averages are all in an uptrend (Closing Price > 5-Day > 10-Day > 20-Day), which is a bullish sign. High short interest is another key factor, but the data in Reference 2 doesn't provide the exact short interest percentage. However, the user's question might be based on the assumption that these stocks have high short interest because they're in the query results. 

Wait, the query in Reference 2 is Short Interest > 10%, but the results don't show the short interest percentage. The columns in Reference 2 include "Total Liabilities (MRQ)" and "Total Assets (MRQ)", but the ratio is "Total Liabilities / Total Assets", which is a leverage ratio, not short interest. So, the short interest percentage isn't provided here. That's a problem because the user is asking about short interest. 

But the user's question is about stocks ready for a squeeze, which requires high short interest. Since the data in Reference 2 doesn't provide short