This article introduces the conepts you’ll need to understand my trading strategy in upcoming pieces.
Here’s a high-level overview of the core concepts you’ll need to apply my trading strategy. Each concept is explained in more detail in the sections that follow.
If you aren’t familiar with these concepts, this article may feel overwhelming. I’ve intentionally left out many details to help you focus on the key ideas. With a general understanding in place, I’ll be able to add more technical details as we walk through examples in the future articles.
The normal distribution, also called the Gaussian distribution, describes how values tend to cluster around a mean (average) in many natural and financial systems.
It’s often referred to as the “bell curve” because of its distinctive shape.
I used to dismiss statistics as “boring math” until I was faced with large-scale data analysis. Just learning the basics of the normal distribution turned out to be incredibly useful for everyday decision-making—especially once I understood the 68-95-99.7 rule.
HV30 stands for 30-day historical volatility. It estimates the potential price movement range based on how much the stock has moved over the last 30 trading days.
In my experience, HV30 has been both accurate and useful for setting expectations.
You can calculate this yourself or pull it directly from trading software like Fidelity Active Trader Pro.
Trading volume shows how active the market is. When combined with HV30, it helps identify where the real opportunities are—high volatility with high volume is where you want to be.
Dealing with high volatility and high volume requires sound judgment and strong emotional control. This isn’t where you want to start. However, the principles remain the same: you can’t make money if no one is exchanging money.
Stock trading softwares and online platforms provide this data.
Many believe trading options is difficult and risky—and that’s true if you take unreasonable risks. I haven’t mastered the entire world of options myself, but that turned out to be unnecessary. Conservatively trading covered calls has generated strong profits for me. I briefly cover the basics of covered calls in this article.
When combined with option deltas and HV30, it helps us assess whether a stock is overpriced or underpriced—and can guide us in setting smart entry and exit points.
Stock trading softwares and online platforms provide options greeks and prices.
In the stock market, there are several order types that determine how trades are executed. Each has its place, depending on your strategy. I stick to one: the limit order.
Limit orders help you maintain some control and avoid giving away money. My experience shows a “slow” trader like me can still generate good profit; I make ten or so trades in a day.
The normal distribution, also called the Gaussian distribution, describes how values tend to cluster around a mean (average)—denoted as \(\mu\) or “mu”—in many natural and financial systems.
Most financial models assume that stock prices follow a normal distribution. A great example is the Black-Scholes options pricing model.
Over the past two years, I’ve found that Black-Scholes works well—so I started making decisions assuming the market behaves this way. That shift has given me excellent results.
The market isn’t logical. It’s shaped by fear, greed, and human emotion. But as we say in computer science: “When you can’t get the exact number, estimate it.” For me, the normal distribution has been the best way to do that.
I won’t get into the math—you can find that on Wikipedia. But there’s one concept you must know: the 68-95-99.7 rule. Memorize it. It’s made me a lot of money.
This rule explains that most of the time, values that appear random tend to cluster around a known average. In other words, it helps answer the question: given a history of stock prices, what range captures most of the price fluctuations?
In a normal distribution, sigma (written as the Greek letter \(\sigma\)) represents the standard deviation. It measures how spread out the values are around the mean (written as the Greek letter \(\mu\)).
If the mean is the average, \(\sigma\) tells you how far data typically falls from that average:
Here is the same rule as a graph.
A small \(\sigma\) means the data points are tightly clustered around the mean. A large \(\sigma\) means they are more spread out.
There’s an example of this rule in the next section, where I discuss HV30.
I make decisions based on \(1\sigma\) — "one standard deviation."
An intuitive understanding will come as you study more, and as I explain my strategy.
HV30 stands for 30-day historical volatility. It measures how much a stock’s returns—and by extension, its price—have fluctuated over the past 30 trading days.
This value is annualized; and therefore, I convert it to daily volatility for my use.
I get this value from Fidelity Active Trader Pro. If you’re curious about how it’s calculated, I recommend checking resources online. For our purposes, it’s enough to understand how to calculate daily volatility from HV30.
\[ \text{Daily Volatility} = \frac{\text{HV30}}{\sqrt{252}} \]
Since HV30 is expressed as an annualized percentage, dividing by \(\sqrt{252}\) (the approximate number of trading days in a year) gives you the expected daily price movement as a percentage.I use HV30 to estimate daily volatility. It helps me decide when to trade.
For example, Ford is trading at $10. HV30 is 22%, so the daily move is about 14 cents. Most days, the price stays within \(\pm 1\sigma\), or 68% of the time.
If I buy at $10.00 and want to sell at $10.14 or higher, that’s \(1\sigma\) above average. The chance of hitting that price is about 16% or the "upper tail."
Put simply: the stock probably won’t hit $10.14 or higher. It usually stays closer to the average. Only 16% of the time does it go above \(1\sigma\).
Daily volatility is a useful way to judge whether a stock is cheap or expensive for the day.
For example, if Ford opens at $10 and drops to $9.50, that’s a clear discount—well beyond typical daily movement given HV30 of 22%.
But if it’s trading at $10.10, it’s already near \(1\sigma\), meaning it’s approaching the high end of expected fluctuation and is relatively expensive.
Of course, this isn’t the whole picture. To get a more accurate view, you also need to consider option delta. We’ll cover that later in the article.
In my system, generating profit depends on setting a target price based on \(\sigma\). This controls your order execution rate.
For example, setting a sell price at \(0.5\sigma\) above the mean makes an order more likely to fill than aiming for a full \(\sigma\) with a smaller profit.
Understanding this relationship has been a key to daily profit that compound returns every day.
To understand volatility, you need to know how stock prices are formed. There are three key prices:
For prices to move, there must be active trading between buyers and sellers. In other words, no trades, no volatility.
That’s why real opportunities show up in stocks with both high volatility and high volume.
Some consider a large difference between bid and ask prices—known as the bid-ask spread—to be an opportunity. But I haven’t found it useful. To take advantage of these small price gaps, you’d need high-scale automation or constant attention to the market to capture fractional differences. That’s the domain of high-frequency traders, not us.
In the U.S., there are several stock exchanges, and the “last price” you see depends on how your broker routes orders—sometimes through their own system. This can cause small price differences between platforms.
It’s good to expect some inaccuracy in on-screen prices, though I haven’t had issues when using limit orders.
You don’t need to trade options to benefit from them. Even if you never place an options order, understanding how they work can improve your trading decisions.
Option prices are just as emotional and unpredictable as stock prices. If you stay calm and disciplined, you can consistently profit from traders who don’t.
An option is a contract that gives you the right, but not the obligation, to buy or sell a stock at a specific strike price by a certain expiration date.
The strike price is the price at which you agree to buy or sell the stock. This matters because options are all about predicting whether the stock will hit or cross that price by expiration.
How an option is exercised depends on the type of option you hold. Here, we’ll focus on covered calls.
A covered call is when you sell a call option while owning the stock. You agree to sell your shares at the strike price if the option is exercised.
Weekly covered calls expire on Friday.
You own 100 shares of Ford at $10. You sell a call at $0.20 per share with a strike price of $10.50 that expires in one week. You collect a premium of $20 since per-share premium is $0.20.
You can buy and sell covered calls as often as you like. They’re highly sensitive to small market changes, which makes them ideal for capturing short-term gains.
If you know what to watch for, even small intraday moves can become profitable trades.
When trading covered calls, deep understanding of the greeks (delta, gamma, theta, and rho) aren’t necessary because covered calls are quite conservative and carry low risks. For short-term trades spanning a week or month, most option greeks don’t matter much.
The most important greek in my strategy is delta. Professional traders use delta to estimate the likelihood that an option will be exercised.
Daily volatility gives me a reasonable range of expected price movement. Delta shows where traders believe the stock will land within that range—and with what probability. Since delta updates constantly as stock price changes, it gives us a real-time view of how likely we are to win a trade.
Using delta with daily volatility is useful even when you’re trading just a few shares. When you buy into a position, you can select an exit price—the price at which you want to sell—by checking a strike price and its corresponding delta.
This, too, will become clearer with an example in the next few articles.
A limit order lets you control your price:
Your order sits in the order book until someone agrees to your price. If no match happens, it stays unfilled. This gives you control and protects you from bad fills and price slippage.
Other order types—like stop-loss orders—aim to automate exits or limit losses. I find them unnecessary.
Here’s the golden rule: NEVER trade at market price.
High-frequency traders profit off people who use market orders, slight differences in information propagation through all exchanges.
You may wonder if day trading is possible with this strategy, since my \(1\sigma\) rule almost guarantees that not all orders will execute in a day. It’s not. I’m not interested in quick gains. I want to build assets that last a lifetime. That means adopting Warren Buffett’s wisdom.
Building an asset doesn’t mean I don’t buy and sell stocks or options every day. I hope to clearly illustrate this through example in the future articles.
The rule is simple: buy good businesses and ignore the noise.
Warren Buffett doesn’t react to daily price swings—he buys assets that create long-term value and holds them until it makes sense to sell. I take the same approach, and most of my trades only execute at a profit. I’m willing to hold unrealized losses, especially near year-end, to offset capital gains tax.
That’s why it’s important to buy with the intention to hold until profitable and tune out short-term movement. It’s also why I stress learning how to use debt wisely, since building assets with margin has been highly profitable.
Making large profits from day trading is only realistic if you’re trading millions of dollars.
That’s how firms exploit fractional price differences at scale. To do that, you need more than “good internet”—you need infrastructure, co-location, and high-speed machines. That’s out of reach for most people.
As I’ve mentioned before, I control my trade rate using a mix of tools—HV30, weekly options, and a strict process. Frequent trading adds up in commissions and fees. It’s costly if done without precision.
That’s why I recommend focusing on long-term planning, while watching for short-term opportunities to take profit.
In my engineering career, I’ve seen failure after failure caused by people blindly following rules without understanding the reasoning behind them. It’s possible to follow every instruction and still create a disaster. Take the simple instruction “click the OK button”—what if the message changed to “delete all files”?
If you’re willing to risk money, I believe you should be so familiar with the theory that you can recite it in your sleep. Even then, I still make bad trades. I just focus on making good trades superb and bad trades minimal.
Continuously building a strong foundation of core principles leads to intuition—and intuition creates real opportunities and decisive execution.
One of the best quotes I’ve heard is: “Poor people have all the answers, and rich people have none.” This isn’t about money—it’s about mindset. If you stop learning, you stay stuck.
I was born in one of the poorest areas of my hometown. Many of my friends did too. Today, they are self-made millionaires.
Why? Because they never stopped learning. If this feels hard at first, keep going. That’s how it starts. Knowledge is power. Always has been.
If you’re worried about your intelligence or where you are in life, I highly recommend reading Discrimination and Disparities by Thomas Sowell. History has shown time and again that a doctorate degree won’t save you. I know several medical doctors who’ve worked 12-hour shifts well into their 60s, unsure of when—or if—they can retire.
The popular wisdom is true: success depends on a combination of many factors. If intelligence guaranteed wealth, why don’t all lawyers retire early with financial independence?
Many people enter the stock market chasing quick gains. But most price movement is driven by emotion—fear, panic, and hope—not logic. This isn’t unique to stocks. It’s true across all types of investing. Everyone wants easy money. But in reality, most are just gambling.
Tesla can swing $20 per share in a single day. Do you really think its value changes that much in an hour? Unless something extreme happens—like a war—those moves aren’t based on real changes. They’re driven by how people feel. And that’s where we find opportunity.
If you’ve made it this far, you’re likely ready to see real examples and try a few trades for yourself. I recommend waiting until you see my examples in the next few articles.
As always, thanks for being a part of this journey. Let me know your thoughts and questions—your feedback helps guide the content for future articles.