Author: Zuo Ye I'm out of money, so I'll just have to keep your attention. I'll end the live stream at 6 PM, change out of my OKEx clothes, and go have a few beersAuthor: Zuo Ye I'm out of money, so I'll just have to keep your attention. I'll end the live stream at 6 PM, change out of my OKEx clothes, and go have a few beers

What's the most expensive thing in the 21st century? The traffic anxiety of stock exchanges.

2026/01/22 14:30

Author: Zuo Ye

I'm out of money, so I'll just have to keep your attention.

I'll end the live stream at 6 PM, change out of my OKEx clothes, and go have a few beers during the Binance liquidation.

Earn a commission by using your mom's KYC at the Meme counter's crazy Perp mall.

The opening act of 2026 witnessed the collapse of the exchange edifice. Unlike previous events that triggered direct shocks, such as large-scale liquidations or price spikes, the focus shifted away from exchanges, manifested in the inability of Binance Square and OKX Planet to attract new users.

When the inspirational stories of beautiful female dealers go unnoticed, the stock exchange has no choice but to go out and shout, "Look at the kids! You get paid to watch them for free!"

This article is dedicated to the first anniversary of Trump's inauguration. His presidential-level antics have given us a lot of emotional value.

Lie down and be money

Either erupt in silence or perish in silence.

Exchanges did nothing wrong in 2025, embracing Perp DEX and actively going on-chain, actively complying with regulations and cracking down on companies that offer rebates for hospitalized mothers. Just two days ago, Binance Wallet also held a Meme trading competition in response to OKX Wallet's smart account system.

In 2026, exchanges will not be able to produce any good products. Retail investors' attention will affect the flow of funds. BNB Chain's 2 million monthly active users are nothing more than a numbers game, like Twitter's million-dollar prize contest, which attracted success stories that lost their appeal in a day.

The wealth effect is changing its spokesperson; when people are focused on the $1,000 USD reward for creation, no one is bound to pay attention to the wealth story of $1,000x.

These facts all emphasize one thing: exchanges need people, not glamorous VIPs, but disheveled contract traders.

When you place an order to open a contract, you're a VIP; when you're liquidated and your account is wiped out, you're just a nobody.

A less precise calculation suggests that the lifespan of an outsider entering the crypto community is approximately 6 months, while the lifespan of a crypto KOL leading a single account is approximately 18 months. Regardless of the approach taken, the essence is the competition for the attention of individual users.

Unfortunately, exchanges' understanding of attention is still stuck in the pre-AI era, focused on KOLs. In the era of Vibe Coding, the value of code and media is declining exponentially. Not only are products worthless, but even ideas are worthless. Engineering trial and error can combine everything for A/B testing.

Dissemination capability is what gives content commercial value, but recommendation algorithms are not God. Musk's 100 million reposts are a liability for X. Only content that tames the algorithm can generate cash flow in the new era, which can both increase the platform's daily active users and attract advertisers to place orders.

In short, poetry cannot diminish Li Bai's value, and algorithms cannot iterate infinitely.

However, exchanges still treat retail investors as appendages of KOLs and media. My friend, times have changed. The era of retail investors making their own decisions has arrived.

Analyzing the role of content creators reveals that they are always caught between the exchange and retail investors, providing psychological support for retail investors' trading behavior, helping the exchange's brand department achieve its KPIs, and jointly slaughtering and exploiting batches of retail investors.

This doesn't mean that retail investors never have moments of awakening. The "smart investors" who remain in each cycle will remember the past and become beacons in the dark forest, evolving from the anti-fraud track to the troll track. No one can record the crypto world, but the crypto world grows its own memories.

Retail investors aren't refusing to spend money; rather, they've realized that their attention is the most valuable asset. This is a very simple principle. Domestic social media platforms like Xiaohongshu have been frantically blocking cryptocurrency content to make way for the digital yuan, and Twitter has significantly reduced its crypto weighting to reshape the user experience. Agencies, which only know how to swipe and spam, have already gone their separate ways with Kaito.

Image caption: CZ's response

Image source: @cz_binance

Amidst this bizarre double whammy of internal and external pressures, CZ can still console himself by claiming to be a "minor shareholder" of Twitter. This $500 million story is still used to comfort retail investors and create the illusion that Binance is very powerful, when in reality, Binance Square has been frantically recruiting new users.

If Binance does it, then OKX will do it too. If the leading platform does it, then Bitget and Gate will do it too. If KOLs don't join, they will directly pull content to pretend the ecosystem is thriving, and then report to the boss/leader to successfully poach them.

But all of this is fake. The attention of retail investors is an infinite mercy to KOLs, media, and exchanges. KOLs and media need this to obtain traffic to maintain their survival, and exchanges need the number of retail investors to obtain momentum to maintain a decent appearance. The park is a den of iniquity, and the platform is worth tens of thousands of dollars.

Image caption: Rebate programs end, live streaming begins.

Image source: @binancezh

Only on TikTok, outside of Twitter, can you find the public traffic that exchanges are so eager for. They were also among the first to realize this, with countless comments emphasizing that Viwers are not passive numbers, but living, breathing souls.

  • In the adorable cat comment section, users know that the number of views for cat videos is very important, as it determines the quality and appearance of the cat food for the night. They know to willingly lie down and become cat food.

  • In the comments section of "Overbearing Egg Boss," users appreciate the difficulty of meticulously crafting an egg, the successes and failures of several years of hard work in creating the account, and they know to willingly lie down and become the mother hen.

  • In the comments section for widows, widowers, and the lonely, users actively liked and promoted the live stream's charitable acts, creating a positive cycle that judges actions over intentions. They knew they were willing to lie down and become rice and noodles.

Under intense "internet literacy" education, everyone is an internet user, and the arena of our time is the creation and destruction of gods. Everyone knows that their attention can be turned into money by algorithms, and everyone knows that their attention to a token is real money.

In the crypto industry, exchanges have always treated retail investors as passive fools and even tried to price KOLs based on their followers and number of fans in tiers. This will no longer happen. The awakening of retail investors will completely change the operating model of the entire industry.

Psychoanalysis of ineffective behavior

The frenzy of exchanges is nothing new, but the anxiety they feel over "content" is unprecedented.

However, this anxiety only leaves behind a thirst for emotional value, which cannot be truly transformed into trading value. The problem now is that retail investors know the value of their own attention, KOLs need to adapt to the changes in Twitter's algorithm, but exchanges are still playing it safe.

After Musk announced the Feed algorithm as promised, the "human touch" became the most valuable move. Mechanical interactions and matrix-based feeds were temporarily restricted, and now the moderators have become tireless Grok Transformers.

Image caption: X Interaction Weight

Data source: @elonmusk

Then the exchange is still bidding for OKX Planet's promotion based on KOL's number of followers. To be honest, Xu Mingxing should do a good job of market training for new employees. Simple employee benefits cannot attract retail investors to trade. OKX Wallet is 10,000 times better than Binance Wallet, but retail investors have not surpassed Binance.

If the wealth effect cannot be improved, at least the signs of flaunting wealth should be reduced.

Retail investors are having such a tough time, while exchange owners are living a life of luxury. This battle is over before it even begins. OKEx can't catch up with Binance, and Square can't surpass Twitter. All that's left is a deserted planet singing "Wrong, wrong, wrong."

Binance is anxious about OKEx's poor performance, while Musk is attacking from both sides.

If exchanges cannot provide emotional value to retail investors, then retail investors will not provide trading value to exchanges. KOLs are simply one link in the value transmission chain, thinking what retail investors think and being anxious about what CEXs are anxious about.

This process is very simple. Everyone knows that KOLs who shoot videos and write articles can turn traffic into real money. The more people watch, the more comments they make, and the longer they stay on the platform, the stronger the creator's ability to monetize on the platform becomes. In turn, KOLs can provide users with a better reading experience.

Then the exchanges, sensing the opportunity, demanded that KOLs sell their traffic to them and dump retail investors on the market, which led to the complete collapse of the market's microstructure.

Binance claims 300 million users, even more than Twitter's monetized monthly active users (mDAU) in 2021 by 100 million, but this is meaningless. It can be clearly stated that this anxiety about traffic can be quantified.

  • Twitter's monthly active users are likely declining, with daily usage time on X/Twitter dropping from 34 minutes to 28 minutes. Nikita Bier's statement that X users read only 30 posts per day on Twitter confirms that there has been no significant growth.

  • The US and Japan have the largest user base on Twitter, with around 100 million users in the US and around 20 million users in Hong Kong and Singapore. Considering that mainland Chinese users cannot access Twitter directly and need to use a proxy, it can be estimated that the number of Chinese Twitter users in the cryptocurrency community is at most in the millions.

So why do Binance and OKEx need to grow their user base from millions of users, and why does the conversion rate to their own platforms decrease exponentially? We can conclude that the attention of tens of thousands or hundreds of thousands of people can support Binance's total user base of 300 million.

In interpersonal relationship theory, the six degrees of separation theory refers to the difficulty of information dissemination. The information network can only transmit to a maximum of 6 people. There is also a three-degree influence index, which refers to how each person can influence the behavior of "friends of friends". Assuming a KOL has 20 friends, their maximum influence is 20 x 20 x 20 = 8000 people. This is not the upper limit of dissemination power, but it is the upper limit of the ability to generate sales.

Unfortunately, Dunbar's number still limits the extent of its influence. 150 people is the contact circle we can reach because there is a high degree of overlap among cryptocurrency users. You can see similar KOLs and exchange operators in every group. Believe me, the retail investors who are just there to entertain you in each group are roughly the same group of people.

Image caption: Across the vast mountains and fields, you are the joy I hide in the gentle breeze.

Under repeated cross-influences, OKX Planet can at most attract third-rate wed3 KOLs who can't even get into Binance Square, while at least Dragon Mom goes to BNB Chain.

Do you remember the lifecycle of retail investors and KOLs? At least the NFT groups I joined are completely inactive now.

Conclusion

Humility is an attitude towards life, while ascension is a choice in life.

Broadcasting to the entire universe, every bit of attention we give to exchanges is a blessing, especially during these difficult times for the industry. This is not a way for exchanges to exploit users.

Exchanges now need to consider one thing: whether to focus their remaining attention on monetization, letting employees flaunt their wealth to satisfy bosses and shareholders, or to treat retail investors with a "serving God" attitude, focusing on creating high-quality content and letting traffic grow naturally.

Market Opportunity
Notcoin Logo
Notcoin Price(NOT)
$0.000457
$0.000457$0.000457
-2.14%
USD
Notcoin (NOT) Live Price Chart
Disclaimer: The articles reposted on this site are sourced from public platforms and are provided for informational purposes only. They do not necessarily reflect the views of MEXC. All rights remain with the original authors. If you believe any content infringes on third-party rights, please contact service@support.mexc.com for removal. MEXC makes no guarantees regarding the accuracy, completeness, or timeliness of the content and is not responsible for any actions taken based on the information provided. The content does not constitute financial, legal, or other professional advice, nor should it be considered a recommendation or endorsement by MEXC.

You May Also Like

21Shares Launches JitoSOL Staking ETP on Euronext for European Investors

21Shares Launches JitoSOL Staking ETP on Euronext for European Investors

21Shares launches JitoSOL staking ETP on Euronext, offering European investors regulated access to Solana staking rewards with additional yield opportunities.Read
Share
Coinstats2026/01/30 12:53
Digital Asset Infrastructure Firm Talos Raises $45M, Valuation Hits $1.5 Billion

Digital Asset Infrastructure Firm Talos Raises $45M, Valuation Hits $1.5 Billion

Robinhood, Sony and trading firms back Series B extension as institutional crypto trading platform expands into traditional asset tokenization
Share
Blockhead2026/01/30 13:30
Summarize Any Stock’s Earnings Call in Seconds Using FMP API

Summarize Any Stock’s Earnings Call in Seconds Using FMP API

Turn lengthy earnings call transcripts into one-page insights using the Financial Modeling Prep APIPhoto by Bich Tran Earnings calls are packed with insights. They tell you how a company performed, what management expects in the future, and what analysts are worried about. The challenge is that these transcripts often stretch across dozens of pages, making it tough to separate the key takeaways from the noise. With the right tools, you don’t need to spend hours reading every line. By combining the Financial Modeling Prep (FMP) API with Groq’s lightning-fast LLMs, you can transform any earnings call into a concise summary in seconds. The FMP API provides reliable access to complete transcripts, while Groq handles the heavy lifting of distilling them into clear, actionable highlights. In this article, we’ll build a Python workflow that brings these two together. You’ll see how to fetch transcripts for any stock, prepare the text, and instantly generate a one-page summary. Whether you’re tracking Apple, NVIDIA, or your favorite growth stock, the process works the same — fast, accurate, and ready whenever you are. Fetching Earnings Transcripts with FMP API The first step is to pull the raw transcript data. FMP makes this simple with dedicated endpoints for earnings calls. If you want the latest transcripts across the market, you can use the stable endpoint /stable/earning-call-transcript-latest. For a specific stock, the v3 endpoint lets you request transcripts by symbol, quarter, and year using the pattern: https://financialmodelingprep.com/api/v3/earning_call_transcript/{symbol}?quarter={q}&year={y}&apikey=YOUR_API_KEY here’s how you can fetch NVIDIA’s transcript for a given quarter: import requestsAPI_KEY = "your_api_key"symbol = "NVDA"quarter = 2year = 2024url = f"https://financialmodelingprep.com/api/v3/earning_call_transcript/{symbol}?quarter={quarter}&year={year}&apikey={API_KEY}"response = requests.get(url)data = response.json()# Inspect the keysprint(data.keys())# Access transcript contentif "content" in data[0]: transcript_text = data[0]["content"] print(transcript_text[:500]) # preview first 500 characters The response typically includes details like the company symbol, quarter, year, and the full transcript text. If you aren’t sure which quarter to query, the “latest transcripts” endpoint is the quickest way to always stay up to date. Cleaning and Preparing Transcript Data Raw transcripts from the API often include long paragraphs, speaker tags, and formatting artifacts. Before sending them to an LLM, it helps to organize the text into a cleaner structure. Most transcripts follow a pattern: prepared remarks from executives first, followed by a Q&A session with analysts. Separating these sections gives better control when prompting the model. In Python, you can parse the transcript and strip out unnecessary characters. A simple way is to split by markers such as “Operator” or “Question-and-Answer.” Once separated, you can create two blocks — Prepared Remarks and Q&A — that will later be summarized independently. This ensures the model handles each section within context and avoids missing important details. Here’s a small example of how you might start preparing the data: import re# Example: using the transcript_text we fetched earliertext = transcript_text# Remove extra spaces and line breaksclean_text = re.sub(r'\s+', ' ', text).strip()# Split sections (this is a heuristic; real-world transcripts vary slightly)if "Question-and-Answer" in clean_text: prepared, qna = clean_text.split("Question-and-Answer", 1)else: prepared, qna = clean_text, ""print("Prepared Remarks Preview:\n", prepared[:500])print("\nQ&A Preview:\n", qna[:500]) With the transcript cleaned and divided, you’re ready to feed it into Groq’s LLM. Chunking may be necessary if the text is very long. A good approach is to break it into segments of a few thousand tokens, summarize each part, and then merge the summaries in a final pass. Summarizing with Groq LLM Now that the transcript is clean and split into Prepared Remarks and Q&A, we’ll use Groq to generate a crisp one-pager. The idea is simple: summarize each section separately (for focus and accuracy), then synthesize a final brief. Prompt design (concise and factual) Use a short, repeatable template that pushes for neutral, investor-ready language: You are an equity research analyst. Summarize the following earnings call sectionfor {symbol} ({quarter} {year}). Be factual and concise.Return:1) TL;DR (3–5 bullets)2) Results vs. guidance (what improved/worsened)3) Forward outlook (specific statements)4) Risks / watch-outs5) Q&A takeaways (if present)Text:<<<{section_text}>>> Python: calling Groq and getting a clean summary Groq provides an OpenAI-compatible API. Set your GROQ_API_KEY and pick a fast, high-quality model (e.g., a Llama-3.1 70B variant). We’ll write a helper to summarize any text block, then run it for both sections and merge. import osimport textwrapimport requestsGROQ_API_KEY = os.environ.get("GROQ_API_KEY") or "your_groq_api_key"GROQ_BASE_URL = "https://api.groq.com/openai/v1" # OpenAI-compatibleMODEL = "llama-3.1-70b" # choose your preferred Groq modeldef call_groq(prompt, temperature=0.2, max_tokens=1200): url = f"{GROQ_BASE_URL}/chat/completions" headers = { "Authorization": f"Bearer {GROQ_API_KEY}", "Content-Type": "application/json", } payload = { "model": MODEL, "messages": [ {"role": "system", "content": "You are a precise, neutral equity research analyst."}, {"role": "user", "content": prompt}, ], "temperature": temperature, "max_tokens": max_tokens, } r = requests.post(url, headers=headers, json=payload, timeout=60) r.raise_for_status() return r.json()["choices"][0]["message"]["content"].strip()def build_prompt(section_text, symbol, quarter, year): template = """ You are an equity research analyst. Summarize the following earnings call section for {symbol} ({quarter} {year}). Be factual and concise. Return: 1) TL;DR (3–5 bullets) 2) Results vs. guidance (what improved/worsened) 3) Forward outlook (specific statements) 4) Risks / watch-outs 5) Q&A takeaways (if present) Text: <<< {section_text} >>> """ return textwrap.dedent(template).format( symbol=symbol, quarter=quarter, year=year, section_text=section_text )def summarize_section(section_text, symbol="NVDA", quarter="Q2", year="2024"): if not section_text or section_text.strip() == "": return "(No content found for this section.)" prompt = build_prompt(section_text, symbol, quarter, year) return call_groq(prompt)# Example usage with the cleaned splits from Section 3prepared_summary = summarize_section(prepared, symbol="NVDA", quarter="Q2", year="2024")qna_summary = summarize_section(qna, symbol="NVDA", quarter="Q2", year="2024")final_one_pager = f"""# {symbol} Earnings One-Pager — {quarter} {year}## Prepared Remarks — Key Points{prepared_summary}## Q&A Highlights{qna_summary}""".strip()print(final_one_pager[:1200]) # preview Tips that keep quality high: Keep temperature low (≈0.2) for factual tone. If a section is extremely long, chunk at ~5–8k tokens, summarize each chunk with the same prompt, then ask the model to merge chunk summaries into one section summary before producing the final one-pager. If you also fetched headline numbers (EPS/revenue, guidance) earlier, prepend them to the prompt as brief context to help the model anchor on the right outcomes. Building the End-to-End Pipeline At this point, we have all the building blocks: the FMP API to fetch transcripts, a cleaning step to structure the data, and Groq LLM to generate concise summaries. The final step is to connect everything into a single workflow that can take any ticker and return a one-page earnings call summary. The flow looks like this: Input a stock ticker (for example, NVDA). Use FMP to fetch the latest transcript. Clean and split the text into Prepared Remarks and Q&A. Send each section to Groq for summarization. Merge the outputs into a neatly formatted earnings one-pager. Here’s how it comes together in Python: def summarize_earnings_call(symbol, quarter, year, api_key, groq_key): # Step 1: Fetch transcript from FMP url = f"https://financialmodelingprep.com/api/v3/earning_call_transcript/{symbol}?quarter={quarter}&year={year}&apikey={api_key}" resp = requests.get(url) resp.raise_for_status() data = resp.json() if not data or "content" not in data[0]: return f"No transcript found for {symbol} {quarter} {year}" text = data[0]["content"] # Step 2: Clean and split clean_text = re.sub(r'\s+', ' ', text).strip() if "Question-and-Answer" in clean_text: prepared, qna = clean_text.split("Question-and-Answer", 1) else: prepared, qna = clean_text, "" # Step 3: Summarize with Groq prepared_summary = summarize_section(prepared, symbol, quarter, year) qna_summary = summarize_section(qna, symbol, quarter, year) # Step 4: Merge into final one-pager return f"""# {symbol} Earnings One-Pager — {quarter} {year}## Prepared Remarks{prepared_summary}## Q&A Highlights{qna_summary}""".strip()# Example runprint(summarize_earnings_call("NVDA", 2, 2024, API_KEY, GROQ_API_KEY)) With this setup, generating a summary becomes as simple as calling one function with a ticker and date. You can run it inside a notebook, integrate it into a research workflow, or even schedule it to trigger after each new earnings release. Free Stock Market API and Financial Statements API... Conclusion Earnings calls no longer need to feel overwhelming. With the Financial Modeling Prep API, you can instantly access any company’s transcript, and with Groq LLM, you can turn that raw text into a sharp, actionable summary in seconds. This pipeline saves hours of reading and ensures you never miss the key results, guidance, or risks hidden in lengthy remarks. Whether you track tech giants like NVIDIA or smaller growth stocks, the process is the same — fast, reliable, and powered by the flexibility of FMP’s data. Summarize Any Stock’s Earnings Call in Seconds Using FMP API was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story
Share
Medium2025/09/18 14:40