Full analysis code and raw data processing:
Google Colab Notebook:
https://colab.research.google.com/drive/15dBsIpnSFBJzyruTeddNcWyrrCyVENL9?usp=sharing
Buying Logic
Looking back at 2025, most of my important buys can be summarized by one idea: Bet the Dip — buying when market sentiment was weak and prices had pulled back meaningfully.
My main entries were concentrated in a few periods. The first was the major pullback in April, which was largely related to Trump’s tariff policies. The second was in September, when the market started to worry about AI and SaaS. The third was in November, when investors began questioning the profitability of AI-related companies, leading to a broader pullback across the AI sector.
What these moments had in common was that short-term sentiment was poor, and the market was more cautious toward risk assets. But I have continued to believe in the broader narrative of AI driving U.S. equities, so I was more willing to buy during these periods of negative sentiment.
By the end of the year, my total realized PnL was $2,171, with a win rate of 77.78%, an expectancy of +$60.31 per trade, and an average holding period of 36.7 days.
These numbers are not extraordinary, but they are meaningful to me. They suggest that most of this year’s returns came from timing my entries reasonably well. It was not about one particularly lucky trade, but rather several entries made when sentiment was weak, which eventually led to relatively stable results.
Trades That Worked Well
The trades I was most satisfied with this year were mainly Rocket Lab (RKLB) and NVIDIA (NVDA).
RKLB was a typical example. After entering at a relatively low level, I did not sell too quickly and instead gave the position enough time to play out. The final return on that trade was around 95%–99%, with a holding period of roughly 100 days.
NVDA was similar. I did not capture the entire move, but I managed to stay patient once the trend started to develop, and the final return was around 37%.
These two trades made one thing clearer to me: when the direction is right, sometimes the most important thing is not to trade frequently, but to give the thesis enough time.
Losses and Issues
Of course, there were also clear mistakes this year. The most obvious one was the GME put trade.
This trade was essentially a short-term, high-risk speculation. The result was straightforward: a single loss of around $584, almost a 100% loss on that position.
The main problem was not simply that I got the direction wrong. Being wrong is normal in trading. The real issue was that the position size was not controlled well. When a speculative trade is too large, one mistake can have a disproportionate impact on the overall result.
Aside from GME, there were also some other losing trades, such as CRCL and AAPL. CRCL lost around 18.7%, while AAPL lost around 10.8%. These felt more like normal trading errors rather than something out of control. Still, they reminded me that when a trend has not fully confirmed itself, position sizing should remain more conservative.
But the issue this year was not only about losses.
I was able to buy when market sentiment was poor, but my execution was sometimes too fragmented. For example, once a position reached a certain profit target, I would take profits directly. As a result, some positions that could have been held for longer did not fully benefit from the larger trend.
On the other hand, I took too much risk in some speculative trades. The GME put was the clearest example. It was only meant to be a short-term attempt, but because the position size was too large, the loss had a much bigger impact than it should have.
AI View and Next Steps
My long-term view on AI has not changed much.
In the short term, 2C use cases may still have many limitations. Many products are interesting and imaginative, but large-scale, stable, and sustainable commercialization will still take time.
However, I still think the 2B opportunity is very large. Whether it is compute, infrastructure, or enterprise applications, AI is still in a relatively early stage of adoption.
Since I also work in AI applications, I can directly see how AI is already helping improve efficiency in SaaS products. I believe this will become an important long-term trend. Because of that, I tend to think more systematic commercial realization of AI may gradually happen after 2026.
That is also why I was willing to take some volatility and build positions during pullbacks this year. For me, short-term market sentiment did not change my long-term view on this direction.
For the next year, what I want to improve is not necessarily “what to invest in,” but rather “how to invest.”
First, I need to separate core holdings from speculative trades more clearly.
Core holdings are positions based on medium- to long-term views, such as AI infrastructure and enterprise applications. For these positions, I should give them more time instead of exiting too early because of short-term volatility or a partial profit target.
Speculative trades are different. They are short-term opportunities by nature, so the position size must be small enough. This is especially true for options, where the position can go to zero quickly. Before entering such trades, I need to assume the worst-case outcome first and decide whether I can accept that loss.
Second, I want to reduce the number of decisions I make.
Looking back at this year, I feel that truly valuable trades do not need to be frequent. A few trades with the right direction, reasonable sizing, and enough holding time can already contribute most of the returns.
On the contrary, some impulsive trades consume attention and can easily lead to unnecessary losses.
So going forward, I want to make fewer but higher-quality decisions. For medium- to long-term ideas that I truly believe in, I want to give them enough time and room to develop. For short-term ideas, I need to control the size and make sure they do not affect the overall portfolio too much.
Overall, the biggest lesson from 2025 is that I saw both the effective part of my judgment and the weaknesses in my execution and position sizing.
Buying AI-related assets when sentiment was weak generally worked well. But I also need to avoid two problems: taking profits too early and failing to fully benefit from good ideas, and taking too much risk in speculative trades, which can make mistakes disproportionately painful.
Next year, I want to focus less on simply judging direction and more on building a more stable trading structure — one that allows good judgments to compound over time, while keeping mistakes within a range I can actually tolerate.