From ByteDance to Financial Freedom: How did "Byte Brother" Leto develop his investment judgment skills to achieve a turnaround of 30 million?

Compiled by: PANews

Background

This article is compiled from a live interview of the derivative program "Traditional Finance Crash Course" under the "Blockchain 100 People" column at Binance Square. The program preview used the tagline "Exclusive on the entire network, Byte Brother Leto speaks for the first time" to introduce the guest Leto (Twitter @leto_bao), who resigned from ByteDance and achieved financial freedom through investments in the US stock market. His story has previously attracted over 2 million viewers and has been questioned by many regarding its authenticity. The live broadcast was themed "Worker Leto speaks for the first time, reviewing the path of a $30 million comeback in the US stock market," and it aired on July 5, 2026, at 20:00 (UTC+8) on Binance Square. Before the broadcast, audience questions were publicly solicited, and three questions were randomly selected for live interaction during the program. (Related reading: ByteDance stock trading made a profit of $30 million; the protagonist: I bought hard drives on Pinduoduo and unexpectedly got into storage? How can ordinary people capture "information around them" to trade?)

Introduction

In this live broadcast, host Jenny invited Leto—a data engineer from ByteDance—who made several decisions over the past few years during a period of intense turbulence in technology and the economy that piqued the curiosity of those around him and yielded good results. More interesting than the results themselves is how he thinks, how he makes decisions, and where he has set his goals after achieving financial freedom.

Data-driven thinking comes from ByteDance

Leto's investment methodology can largely be traced back to his years as a data engineer at ByteDance. Working with data every day made him exceptionally sensitive to numbers—when he sees a number change, he instinctively questions the meaning behind it. This is an intuition left by professional training.

More importantly, it is the working style of ByteDance itself. As a globally tier one company, many of the technical stacks and product logic at ByteDance are at the forefront of the industry, and most of his colleagues are top talents from prestigious universities like Tsinghua, Peking University, and NUS. "Many people in our company are smarter than me," he described his former colleagues. The experience accumulated from being immersed in a high-density intellectual environment for a long time directly influenced his approach to data architecture and trading decisions.

However, a technical background can also be a double-edged sword. Leto observed that many investors with a programming background often fall into the trap of "overly trusting what they know"—because they are in the internet industry, they naturally assume that internet companies will always rise. This cognitive inertia has caused many to suffer losses in the past year or two: large tech companies have high capital expenditures and soaring storage costs, leading to a continuous decline in stock prices, yet many still cling to the belief that "I understand this industry, it will definitely rebound," resulting in continued losses.

In his view, "deep cultivation" and "exploration" are not contradictory. He has never stepped out of the field of data engineering but has transitioned between companies at different stages of development—some in growth phases, others in decline or stable periods—this experience of repeatedly switching scenarios within the same domain allows him to view the same problem from multiple angles.

Making investment decisions with A/B Test thinking

ByteDance is a company that heavily relies on data-driven decision-making; any product feature must first undergo A/B testing: users are divided into two groups, one using the new feature and the other not, letting the data speak rather than relying on the subjective judgment of the boss. This way of thinking was later directly applied by Leto to investment decisions.

His advice for ordinary investors is very straightforward: if you are a beginner and feel that index growth is too slow, you can split your position in half—invest half in index funds and select stocks with the other half. After one or two years, look back at the data—growth rate, drawdown, Sharpe ratio—if the selected stocks have not outperformed the index, it indicates a problem with the investment logic, and one should abandon stock picking, honestly invest in the index, or revisit where the logical flaws are. This method can almost be applied to any judgment of "am I suitable for doing this."

Signals read from a hard drive business: Reviewing the Pinduoduo hard drive case

In the program, Leto reviewed a trade that he believes best represents his judgment logic. The story begins in August of last year.

At that time, his position had shifted towards value investing, with major funds invested in the Nasdaq 100 index, leaving only a small portion for active stock picking. One day in August, he saw a book online discussing "tail hedging" strategies and wanted to replicate the methods in the book by building his own database for backtesting—so he bought two hard drives on Pinduoduo to set up a storage environment.

When he bought them, the hard drive prices were only over 2000 yuan, but just a few days later, prices began to rise continuously. He noticed two signals: first, reports indicated that AI demand was driving up memory prices; second, as a data engineer at ByteDance, he personally felt that the company was compressing the data lifecycle (TTL) due to tight storage resources—data that could originally be stored for two to three years was now required to be shortened to one year or even half a year. The combination of these two signals and some research reports outlined a trend, but it was not yet "conclusive."

What truly led him to fully invest was the 13F filings he later saw—institutions had been increasing their positions in storage-related assets for three consecutive quarters. He held this position until this year, yielding substantial profits, and he is still holding it.

The logic behind this case is actually consistent with his approach to product development: first, there is a hypothesis, then find signals to validate it, and only when a sufficiently strong confirmation signal appears does he make a significant bet.

What are options? Explaining clearly with a home-buying analogy

A significant portion of the program was dedicated to Leto using a simple analogy to explain options. (Related reading: Did a ByteDance employee turn 20,000 into 2 million? Revealing the truth behind the "end date options" that led to sudden wealth)

Assume there is a house priced at 1 million, and you hear that the developer is planning to offer demolition subsidies nearby, which might cause the house price to skyrocket by 50% in a year. If you happen to have 1 million cash, you can buy the house outright—this is like buying stocks normally. But if you only want to pay a 100,000 deposit to lock in the right to "buy at 1 million next year," regardless of the market price at that time, this right itself is a call option. The developer is willing to accept this money because the 100,000 is his regardless of whether you eventually buy or not. If you simultaneously pay 10 deposits (which is 1 million), and a year later each house rises to 1.5 million, your profit would be 4 million—using the same amount of capital, you earn 10 times more than buying a house outright, which is the leverage effect of options.

A put option, on the other hand, is the opposite—it is like buying insurance for an asset: you judge that the house will drop in value, pay an insurance premium, and lock in the right to sell the asset at a fixed price in the future. If a drop occurs, your loss is capped at the premium; if it doesn’t drop, you simply lose the premium.

Reflecting on his early experiences with options trading, Leto admitted that initially buying into stocks like UPS and Google was based on "logical but unproven" reasoning—the pandemic led people to shop online, increasing demand for delivery, which was his reason for buying UPS; the surge in daily active users from remote work was his logic for buying Google’s advertising business. These judgments were not entirely gambling, but they did carry a degree of luck, and fortunately, his initial capital was only 20,000 dollars, which kept the risk manageable.

Risks worth taking and those not to touch

Risk management is a topic Leto spent considerable time discussing during the interview. His core judgment criterion is simple: risks should match your life stage and cash flow situation.

If you are in your early twenties with little savings, your risk tolerance is naturally high—even if you lose everything in stock trading, a few months' salary can make up for it, and you can always find a new job without mortgage or car loan burdens; in this case, moderate risk-taking is completely acceptable. However, once you cross a certain threshold—having dependents, carrying a mortgage or car loan—the consequences of risk-taking are no longer borne solely by yourself but can affect the entire family.

He also mentioned a specific boundary: risking a few tens of thousands to start a business and failing, but being able to earn it back in a few months, is a risk worth taking; but borrowing hundreds of thousands to start a business, and if it fails dragging the whole family down, is a risk not to be taken.

The worst loss: Following Pelosi to buy Nvidia

In the program, Leto also shared the most painful experience of his investment career. In 2022, he saw Pelosi buy Nvidia and judged that she might have insider information, so he followed suit and went all in, with a purchase price of 180 (later Nvidia split 1:10, equivalent to 18 now). As a result, with the US interest rate hikes, the entire market fell, and Nvidia dropped from 180 to 120, a nearly 40% drawdown. During that time, he even uninstalled his trading software, not wanting to look at it anymore.

Fortunately, the AI wave triggered by ChatGPT later brought the stock price back up. This experience left him with the understanding that determining whether a company is worth investing in is only part of the equation; timing and the broader environment are equally important. Pelosi did not misjudge Nvidia as a company, but the timing of her purchase coincided with the interest rate hike cycle, which can simply be described as "buying too early." Later, Pelosi sold part of her Nvidia holdings in 2023 and bought back in before the OpenAI explosion, which ultimately earned her even more.

A beginner's checklist

For complete beginners who have never engaged with US stocks, Leto's advice is very direct: the first step is not to pick stocks but to buy index funds. If you really want to practice, you can start with a simulated account, but the results of a simulated account cannot be taken too seriously—because it is not your real money, being able to hold on to it is different from not being able to. The truly reliable testing method is still that A/B test thinking: invest a portion of your own money, and after one or two years, compare the returns with the index; if you outperform, it indicates you are truly suited for stock picking; if not, then honestly invest in the index.

As for how much to invest initially, the answer depends on risk tolerance—if you are young, have no burdens, and losing this money won't affect your life, you can afford to be more aggressive.

Should you understand the terms that appear daily in the news?

CPI, non-farm payrolls, the Federal Reserve, earnings season—these high-frequency terms in financial news are not difficult to understand according to Leto:

• CPI (Consumer Price Index): A core indicator closely monitored by the Federal Reserve; a rising CPI indicates inflation, while a falling CPI indicates deflation. The long-term goal of the Federal Reserve is to stabilize the CPI around 2%—moderate inflation is a positive signal, indicating healthy overall economic activity.

• Non-farm payroll data: There is a certain correlation with inflation (strong employment can easily push up inflation), but it is not a strictly linear relationship.

• The Federal Reserve: An independent institution from the US government that adjusts the economy through interest rate-related policies.

He also admitted that he does not believe these macroeconomic pieces of information are purely "noise"—it was precisely because he ignored the broader environment of interest rate hikes that he suffered significant losses in that Nvidia trade.

An information system for a data engineer

Leto's method of information acquisition essentially involves transferring the data architecture experience he accumulated at ByteDance into his personal investment system. He has built an AI agent workflow that connects to IBKR's MCP service, which is then linked to Claude Code—pushing out a morning and evening report daily, summarizing position changes and noteworthy market dynamics, automatically organized into Notion documents. Trading data (tick, trades, minute-level data) is synchronized daily to local mechanical hard drives and solid-state drives for cold and hot storage layering.

He also uses Rust API to access real-time data for backtesting, primarily to predict risks rather than for high-frequency quantitative trading. An automated trading bot is specifically responsible for hedging with options, but the buying and selling decisions for stocks are always made by him—bots do not participate in stock trading. This "human makes decisions, machine assists" division of labor also runs through his review habits: he conducts a brief review daily and a systematic review weekly to check whether each trade truly has logical support or is influenced by emotions.

Reading list: From beginner to advanced

Leto's recommended reading list is categorized by investment level:

• Must-reads for beginners: "Lifecycle Investing" and "Constant Dollar Investing," both of which he read thoroughly.

• Advanced (for those who have crossed the index investment stage and want to pick stocks): Peter Lynch's "Beating the Street."

• For those wanting to understand options: "Options as a Strategic Investment," a systematic book on options strategies.

• Advanced options practice: a book on tail hedging that teaches how to protect positions with options before a significant drop; and a book not yet available in Chinese, roughly translated as "The Second Leg," which discusses what to do when the market has already dropped by half, and you judge it will continue to drop, but puts have become expensive.

He also mentioned "Laughing at Wall Street," a modern version of "Beating the Street": the author Chris captures consumer trend signals that Wall Street analysts miss from user comments (UGC) on TikTok and Reddit, such as the trend of people turning to outdoor cycling during the pandemic, which led to the discovery of a bicycle brand stock opportunity that ultimately rose by 60%-80%.

Know yourself: Four types of risk profiles

Leto proposed a simple framework that categorizes ordinary people into four types: those with high debt and no savings; those with savings but no interest in studying investments; those with savings who love to research (and an implied fourth type, those with low debt but also little savings). He believes that the most vulnerable group in the market is the one with high debt and the weakest risk tolerance—because once losses occur, the impact is not just on themselves but also on their families.

As for why ordinary people find stock picking difficult, his explanation is straightforward: people, like AI large language models, rely too much on their existing knowledge base and do not actively incorporate new information to correct their judgments—these "logics" based on personal experience often do not reflect the true laws of the market. The simplest way to judge whether one is suited for stock picking remains that A/B test: take a small amount of real money to compare against index returns; if you do not outperform, admit that you are not suited.

Recent market: Is the adjustment in the storage sector a clearing or a problem?

When asked about the recent market, Leto candidly stated that the storage sector has recently seen significant adjustments. He distinguishes between "normal clearing" and "real problems" with a simple standard: whether the logic behind the initial purchase has changed. He analyzed that the recent decline in storage stocks is mainly due to selling pressure from institutional quarterly rebalancing—for example, pension funds adjusting their portfolios according to fixed stock-bond allocation ratios (80/20 or 70/30 rules) due to storage stocks having risen too sharply, triggering rebalancing. This type of volatility is considered short-term noise, and the overall investment logic in storage has not changed. He had anticipated this risk in advance and bought some puts for protection, so this round of declines had limited impact on him.

After achieving financial freedom, what to invest in

When discussing the state after achieving financial freedom, Leto's answer transcended investment itself—he now values health the most, with fitness and "clean eating" becoming daily standards, and he even plans to buy the most expensive insurance to hedge against health risks. He applies this logic within the options framework: "Buying insurance for oneself is essentially buying puts—just in case health takes a significant downturn, having a put can provide protection."

Currently, his new goal is an AI-related startup he is preparing—writing business plans and meeting venture capitalists are his main focus lately. When discussing the impact of AI on personal investment and knowledge management, he believes the greatest value lies in the "compounding effect": in the past, research required manually collecting and organizing information, but now it only takes writing a skill for AI to execute, and in the future, when new demands arise, this skill can be iterated without repetitive labor.

When asked what he hopes people will think of first when they mention his name in five years, his answer was straightforward: "I hope it will be the company I create in the future, rather than that I made money from stock trading."

Audience Questions: Volatility management, barbell strategy, and undervalued sectors

In the final segment of the program, Leto answered three questions from the audience:

1. Are you more of a buyer or seller in options trading?

From a profit expectation perspective, sellers (the party collecting premiums) generally make more money, but Leto indicated that he primarily plays the role of a buyer—because options are not his main source of profit, but rather an auxiliary tool to smooth out volatility in his underlying stock positions. He often uses LEAPS (Long-term Equity Anticipation Securities) to leverage, holding them long-term until expiration, while also buying OTM puts for risk management.

2. What is your most trusted trading strategy?

The answer is Universa-style "barbell strategy," which he has modified based on his situation: placing the vast majority of his positions in stable assets like bonds or indices, while only using a small portion (5%-10%) to seize high-risk, high-volatility opportunities. Even if this portion goes to zero, it will not drag down the overall portfolio; but once it hits, the returns can multiply several times or even tens of times, significantly boosting overall returns—limited drawdown with considerable upside potential, which is the essence of the barbell strategy.

3. What is the current position of popular sectors like robotics, aerospace, and nuclear energy?

Leto's judgments are as follows:

• Robotics: He believes this is currently the most promising direction among these sectors, but admits he has limited knowledge in this field;

• Aerospace: More driven by sentiment in the short term, he is optimistic long-term but believes current valuations are high; he previously invested in SpaceX's early shares and profited by exiting when it rose to over 200 dollars;

• Traditional nuclear power: The construction cycle lasts over a decade, compliance approvals are complex, and the return cycle is too long, making it difficult to become mainstream;

• Small modular reactors (SMR): Due to the need to adapt to all environments, safety standards are raised to the highest level, resulting in unit costs far exceeding those of large nuclear power plants, making them not cost-effective;

• Natural gas power generation: He believes this is the truly mainstream direction in the short term—like Musk's data center directly deploying natural gas generators, as they can be quickly deployed, have no capacity limits, and are highly efficient, leading to the best performance among related equipment manufacturers (such as GEV, CAT) in the entire power sector.

Conclusion

Throughout the interview, what impressed host Jenny the most was that Leto never regarded his experience at ByteDance as a starting point he wanted to escape from, but rather as a place that made him stronger—each subsequent decision is, in a sense, a continuation of that experience. From data engineer to options trader, and then to entrepreneurship and health management after achieving financial freedom, the underlying methodology remains the same: let data and signals speak, use A/B tests to validate judgments, and seek asymmetric returns with limited risk exposure.