PANews has published a market analysis article that frames one of investing’s hardest problems in practical terms: staying restrained when markets are hot, then still having both cash and conviction when markets turn ugly enough to present real opportunities. The piece asks a set of questions many investors struggle with in a selloff. Is this actual panic? Is the asset being unfairly punished, or has something fundamental broken? If you buy, how much more downside can you live with, and how long can you wait?
The author, identified as jiayi, says the idea was sparked by a video on using AI to monitor market sentiment. What stood out was not a one-off trade signal, but the possibility of turning the search for so-called "blood-in-the-streets" opportunities into a standing process. In that setup, AI handles routine data gathering and change tracking, while the investor steps in when something genuinely worth studying appears. The article says that kind of workflow is especially useful for people with a primary job who still want exposure to crypto and U.S. equities.
Step one: define what you would actually want to own
The first step is simple but easy to skip: tell AI what kinds of assets you are willing to buy. The article argues that asking "what is worth buying today" usually produces a list driven by whatever theme is getting attention at the moment. The author prefers building a watchlist that is understandable on the investor’s own terms.
For crypto, the checklist includes whether there is real demand, whether the project has reached product-market fit, whether revenue is sustained by subsidies, whether the team relies on token sales to survive, whether token holders can actually benefit, and what future catalysts may exist.
For U.S. stocks, the screening lens is different: business competitiveness, cash flow, debt, valuation, and whether expected growth has already been fully reflected in the share price. The article’s point is that investors should decide in advance which assets they are willing to research and buy at the right price. Once that is clear, a drawdown becomes easier to interpret.
Step two: use AI to monitor whether the market is starting to offer discounts
The second step is monitoring. The article says there is no need to start by asking AI to invent an elaborate formula.
For equities, it suggests CNN Fear & Greed as a reference point. For crypto, it points to the Alternative.me or CMC fear-and-greed indexes, then recommends combining those readings with price declines, trading volume, and fund-flow data across the watchlist. The author stresses that these sentiment indicators use different methodologies, so their scores should not be treated as directly comparable. They are useful as clues about market mood, not as stand-alone verdicts on a single asset.
What the author wants AI to answer at this stage is more specific:
- Is the decline happening across the entire market, or is it concentrated in one sector or one asset?
- Which explanations are tied to confirmed events, and which are still speculation?
- When placed in historical context, does the current stress look like ordinary volatility or something less common?
If investors want to create their own scoring model, the article advises testing it against historical data first and then keeping the rules fixed. In the author’s example, investors should not move a buying threshold from 20 to 40 just because waiting for a score below 20 feels too hard.
Step three: once panic appears, check whether the asset is worth holding
This is described as the most important part of the process. Panic means someone is in a hurry to sell, but the real issue is why they are selling and whether the original investment thesis still stands.
For each candidate, the author says AI should be asked to present both the case for buying and the strongest case against it, then work through four questions:
- Upside: if the thesis is right, how much room is there on the upside?
- Downside: if the thesis is wrong, how much could be lost?
- Time: how long might it take for the thesis to play out?
- Volatility: how much price movement must be endured while waiting?
The article makes a pointed distinction in crypto. A project having revenue does not automatically mean the token captures that value. A large drop from a previous high does not automatically mean the token is cheap either. The author’s standard is to assess quality, price, and risk together before deciding whether an asset is the kind of distressed opportunity worth pursuing.
Step four: turn the process into a live recurring task
The fourth step is operational. Investors should choose a tool that supports internet-connected data access and scheduled tasks, then load in the watchlist, monitoring rules, and reporting times.
The article gives a sample cadence: scan crypto every 12 hours and review U.S. stocks after each trading day closes. Under normal conditions, the system can issue a brief update. If preset conditions are triggered, it should produce a list of assets that deserve deeper work.
The author also warns that investors need to verify that the task was actually created and that at least one report has run successfully. Saying "remind me every day" in a chat window does not mean there is a live background process. The article adds another practical limitation: a 12-hour scan can only detect anomalies at the time of each scan, so it should not be mistaken for real-time monitoring.
The prompt the author suggests using as a starting point
The piece includes a prompt that can be used as the foundation for a crypto and U.S. stock opportunity-monitoring workflow. It tells AI to start with the investor’s preferences and watchlist, check data sources and historical coverage, and then use public sentiment indexes, price data, and trading volume to identify unusual pressure. It also asks for separate analysis at the market, sector, and individual-asset levels, with timestamps, sources, and missing items clearly labeled.
If a sharp decline appears, the prompt instructs AI to determine whether the move is a short-term shock or a deterioration in fundamentals. For each candidate asset, it should list quality, valuation, catalysts, the strongest bearish case, upside potential, downside risk, time to thesis realization, and expected volatility. The prompt explicitly says not to invent target prices or probabilities without evidence.
It also draws a line around the use of sentiment scores: panic readings should only trigger further research, while actual purchases must still meet the investor’s asset-selection rules and risk budget. Any threshold that has not been tested on historical data should be treated only as an observation alert.
If the selected tool supports scheduled tasks, the article says it should create them at the requested frequency and confirm the next run time. If it does not, it should state that plainly. In reporting, the system should prioritize three questions: what changed, which assets deserve a closer look, and which situations still call for patience.
AI lowers the cost of monitoring, but it does not replace judgment
The author’s closing point is that the workflow should first be built, then judged through historical testing and actual operating records. Even formulas organized by AI still need that validation step.
The article says many periods in investing simply do not offer a suitable action. AI’s value lies in lowering the cost of continuous research and market monitoring, helping investors avoid being pushed around by emotion or missing opportunities because they are busy. The money to be made, in the author’s view, still comes from buying good assets at reasonable prices and then waiting for value repair and long-term growth. The process ends not with a bold forecast, but with a routine: study carefully, set price conditions, define position limits, and leave room for life outside the market.