One of the hardest things in investing is to hold your nerve when the market is hot, and when things are bad, still have the money and the judgment to back yourself up—so you dare to buy.
In Buffett’s saying, “Be fearful when others are greedy, and greedy when others are fearful.” In terms of results, it means waiting for “bloody chips” and then striking—only when the risk-to-reward ratio becomes attractive enough, and with time acting as a multiplier, to achieve extraordinary outcomes.
Once it lands on you, the problem comes:
Is this really panic now? When an asset drops, is it being unfairly punished, or is there actually something wrong with it? If you buy, how much further decline can you withstand—and how long will you have to wait?
Recently I watched a video about using AI to monitor market sentiment. I think this direction is very worth doing: turning the search for “bleeding chips” into a long-running process.
In normal times, let the AI organize the data and track changes. When there’s an opportunity worth researching, then put your attention on it.
This is especially meaningful for people who have their own main job and also want to participate in Crypto and US stock investing.
Step one: first tell the AI exactly what you want to buy.
If every day you only ask, “What’s worth buying today?”, you’ll likely end up with a list that changes with the hot topics.
I’m more inclined to first build a watchlist that I can understand.
For example, in Crypto, I would focus on: is there real demand, is there actual PMF, is revenue being propped up by subsidies, does the team rely on selling tokens to survive, can token holders actually benefit in practice, and what future catalysts are there.
For US stocks, look at: business competitiveness, cash flow, debt, valuation, and whether growth expectations have already been sufficiently priced in.
Think ahead: which assets am I willing to research, and at what price would I buy them?
First you think about the assets you want to buy, and only then do you know what to look at when prices drop.
Step two: let the AI monitor whether the market has started to “discount.”
You don’t need to have AI invent a very complex formula right away.
For US stocks, you can refer to CNN Fear & Greed; for Crypto, you can refer to Alternative.me or CMC’s Fear & Greed index, and combine that with observed drawdowns, trading volume, and fund flow data.
These indices have different methodologies, so their scores can’t be mixed directly. They can provide clues about market sentiment, but judging a single asset still requires more information.
What I want the AI to tell me is:
Is this market-wide decline, or is it just a particular sector or a single asset falling on its own?
What confirmed events does the drop correspond to? Which explanations are still just speculation?
At this point, is the pressure within what’s normal compared to history, or is it relatively abnormal?
If you score it yourself, first validate with historical data, then fix the rules. You can’t just move the buy trigger line to 40 because “waiting is too hard below 20.”
Step three: when fear shows up, then check whether the “chips” are worth holding.
This is the step I think is the most important.
Fear means someone is selling in a rush, but then you need to judge: why are they selling? Is the original investment thesis still intact?
For each candidate, I’ll have the AI provide both the reasons to buy and the strongest reasons against. Then it will answer four questions:
Upside: if you’re right, how much upward potential is there?
Downside: if you’re wrong, how much could you lose?
Time: how long might it take for the thesis to be realized?
Volatility: while waiting, how much fluctuation do I need to withstand?
Especially in Crypto: having revenue doesn’t mean the Token has returns; if the price has fallen a lot from its peak, it doesn’t automatically mean it’s cheap.
Only by looking at quality, price, and risk together do you have a chance to judge whether this is the “bleeding chip” you’re looking for.
Step four: set it as a truly running task.
Choose a tool that supports联网 data and scheduled tasks, and enter your watchlist, monitoring rules, and report timing.
For example, in Crypto you scan once every 12 hours; for US stocks, you do a post-close recap after each trading day. In ordinary times, provide a brief report. When preset conditions are triggered, list the assets that need focused research.
You need to confirm that the task is indeed created successfully and that a report run goes through. Saying “remind me every day going forward” in the chat box doesn’t mean the backend is already running.
Scanning every 12 hours also means you can only spot anomalies during the scan—you can’t treat it as real-time screen-watching.
The prompt below can be used directly as a starting point:
Based on my investment preferences and watchlist, build a Crypto/US stocks opportunity monitoring process. First check the data sources and historical coverage, then use publicly available sentiment indices, along with information like price and trading volume, to identify abnormal pressures. Analyze the market, sectors, and individual assets separately, and label the data timestamps, sources, and missing items. When a clear drop occurs, verify whether it’s a short-term shock or a deterioration in fundamentals. For candidate assets, list the quality, valuation, catalysts, and the strongest arguments against, as well as upside potential, downside risks, waiting time, and volatility. Don’t make up target prices and probabilities without evidence. Fear scores should only trigger further research. Buying also needs to meet asset selection criteria and risk budget; thresholds that haven’t been validated by history are only for observational reminders. If you support scheduled tasks, create them at the frequency I specify and confirm the next run time; if you don’t support them, state clearly. In the report, prioritize telling me: what has changed, which assets are worth looking at further, and which situations should be continued to wait on.
My idea is to build this whole process first, and then use historical validation and real running records to judge whether it works. The formulas the AI organizes also need to go through this step.
In investing, there are many times when there’s just no suitable setup for yourself to act on.
The value of AI is to reduce the cost of continuous research and monitoring—so you’re less carried away by emotions, and you don’t miss opportunities worth looking at just because you’re busy.
What you ultimately want to earn is the money from value restoration and long-term growth, after a good asset is bought at a reasonable price.
Research seriously in normal times: set price conditions, set position boundaries, then make time for your own life.