01 FIRST SCREEN INFORMATION · A-SHARE DRAGON TIGER LIST
Enter a stock name or ticker to organize buying and selling seats, institutional and hot-money differences, and same-sector performance.
A stock has just appeared on the Dragon Tiger List. You want to know which seats bought, which sold, and whether the move aligned with its sector. lhb-analyzer works from disclosed records to organize hot-money seat clues, institutional and hot-money trading differences, and the stock's place in its sector context.
| The original review task | The analysis framework provided by the Skill |
|---|---|
| See a brokerage branch name and search for related clues one by one | Organize buying and selling direction by seat, separating institutions, hot-money clues, and unidentified seats |
| See one net-buy figure and struggle to understand divergence | Compare buying, selling, and concentration across seat types to explain the leading judgment |
| Focus on one stock without a horizontal context | Compare same-sector listing records to observe the stock's relative attention |
Disclosed records reflect completed trades, not real-time positions. The analysis is for research and review.
In an after-hours review, the list is often already in front of you while the interpretation has not yet taken shape. A single seat may buy and sell at the same time; a familiar branch name may not identify one trader; and activity in one stock may not mean the whole sector is strengthening. These clues need to be checked together in one analysis.
| User | Typical task | Useful result |
|---|---|---|
| Researcher | Organize the trading structure after a stock appears on the list | Seat details, institutional and hot-money differences, questions to verify |
| Creator | Write an after-hours review or list interpretation | Organize content around records, judgment, and limitations |
| Builder | Design a Dragon Tiger List research workflow | Use the input and output structure as a reference; integration capability requires separate validation |
| Marketing | Explain the product's purpose and research context | Show the analysis path without packaging seat clues as return promises |
Break “who is buying” into answerable questions: which seats were disclosed, how much each bought and sold, which identities are only clues, and whether the activity stands out in the sector. Once those questions are clear, a list can become research material that is discussable and reviewable.
Provide a stock name or ticker and specify the date or period of interest. First check the listing reason, disclosure basis, and buying and selling seats so a single-day list is not mixed with a multi-day cumulative list.
Use seat reference material to identify institutions and potentially related hot-money labels. Keep the original branch name in the output. When a match is uncertain, state it as a candidate clue rather than treating the label as a confirmed identity.
Read the buys, sells, and net amounts associated with institutional and hot-money clues together. A larger net buy indicates more active buying under that basis; it does not establish control of the share price or continued holding.
Within the same period and sector classification, compare other stocks' listing frequency and performance. Use the result to understand relative attention. Do not force a leader ranking when comparable data are unavailable.
Typical outputs include listing records, seat breakdowns, institutional and hot-money differences, same-sector comparisons, and the basis for conclusions. Full execution requires the corresponding data source and script dependencies.
The following are use cases and constructed analysis illustrations. They show how to ask and read results; they do not correspond to real stocks and are not observed outputs from this Skill run.
After the close, you see a stock on the list and want to understand what types of seats make up the buyers and sellers. After providing the ticker and the date of interest, you can describe the task like this:
“Analyze this stock's current Dragon Tiger List entry. List the listing reason, buying and selling seats and amounts. Summarize the net amount for institutions and hot-money clues separately, explain where the divergence lies, and keep unidentified seats separate.”
| Seat or category | Buy RMB 10k | Sell RMB 10k | Net RMB 10k |
|---|---|---|---|
| Institution-only A | 8000 | 1000 | +7000 |
| Institution-only B | 500 | 3500 | −3000 |
| Branch A Hot-money clue | 6000 | 500 | +5500 |
| Branch B Hot-money clue | 1000 | 4500 | −3500 |
| Other disclosed seats Unidentified | 2000 | 3000 | −1000 |
| Illustration total | 17500 | 12500 | +5000 |
This illustration assumes records have been deduplicated within the same disclosure period. Net amount = buying amount − selling amount; it does not represent market-wide net capital inflow.
Institutional seats have a combined net buy of RMB 40 million, hot-money clue seats have a combined net buy of RMB 20 million, and other disclosed seats have a net sell of RMB 10 million. In this illustration, institutions have the larger net buy, but there are both buys and sells within institutions, so it cannot be summarized as “institutions are unanimously bullish.”
Hot-money clue seats have total buys of RMB 70 million and total sells of RMB 50 million. Looking only at the buy list would miss the selling on the other side. Putting both sides together reveals the divergence.
A review can state that “within disclosed seats, institutional net buying was relatively higher while hot-money clue buying and selling coexisted.” Then check the background against announcements, price, and turnover changes; do not turn the list's net amount directly into a buy recommendation.
The list shows the Fuzhou Liuyi Zhonglu branch of China Merchants Securities, and you want to confirm which hot-money labels it may relate to. You can ask:
“Check the associated labels for this seat in the reference database and list the evidence and ambiguities. Do not identify the trader solely from the branch name or use it to predict next-day price movement.”
| Check | What to look for in the result |
|---|---|
| Reference clue | The local seat encyclopedia includes the related branch name under both the “Trading Ape” and “Liuyi Zhonglu” entries |
| Appropriate wording | There are multiple associated labels; the specific record and mapping point need further verification |
| What cannot be inferred | A name cannot uniquely confirm a particular hot-money participant, much less their current position |
The value of this case is retaining uncertainty. A familiar name can start verification, but identity attribution still requires more evidence. The overlap above comes from a local reference check, not an actual listing event.
Several stocks in the sector you follow have appeared on the list, and you want to judge which names current discussion is concentrating on. You can ask:
“Within the same observation period, compare this stock with listed stocks in the same sector. Show listing frequency, period return, and institutional and hot-money differences. State the sample scope and do not fill in missing data by guesswork.”
| Fictional stock | Times listed | Period return | Observation clue |
|---|---|---|---|
| A | 3 | +18% | More listings and a larger period gain |
| B | 2 | +9% | Activity falls between the other two |
| C | 1 | −4% | A listing can also coincide with a decline |
Constructed illustration: the three stocks belong to the same sector and use the same observation period; frequency is counted from deduplicated listing dates.
In this three-stock illustration, Stock A has more frequent listings and a larger price gain, so it can be placed on the list for further research. But “higher relative attention” does not mean it is the sector's fundamental leader or that future returns will be higher.
For researchers, this can help prioritize follow-up checks. For content creators, it puts a single-stock story back into sector context. More seat detail is still needed to describe capital structure further.
From listing reasons and seat trading to sector context, the analysis path fits A-share after-hours research. Readers receive clues organized around one stock rather than just a description of increased turnover.
Separating institutional and hot-money clues while keeping both buy and sell directions helps explain who contributed to net buying and whether similar seats diverged. A single total no longer hides the detail.
Same-sector comparison provides a reference for listing frequency and period performance. A single-stock review can then connect to sector observation, provided the data period, sample, and statistical basis remain consistent.
A useful Dragon Tiger List review should let the next reader see what was disclosed at the time, what the judgment was based on, and which identities remain unconfirmed. With the same analysis structure, research records are easier to accumulate and teams can more easily discuss why a judgment changed.
Dragon Tiger List disclosures are delayed. Seat records cannot directly reconstruct current positions, and labels associated with brokerage branches are not proof of a trader's identity. It is suitable for supporting research and is not an independent buy or sell signal.
lhb-analyzer Skill
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Enter a stock name or ticker and state the date or period of interest to organize buying and selling seats, institutional and hot-money differences, and same-sector performance.
You can ask: “Analyze the Dragon Tiger List for [stock name or ticker] during [date or period]. Explain seat buys and sells, institutional and hot-money differences, and same-sector performance. Mark the data date, uncertain identities, and missing data.”