The 2026 Intern’s Guide to Trading

We recently updated our Intern’s Guide to the Market Structure Galaxy. Today, we graduate to how trading works.

In our guide to market structure, we talked about who trades and how quotes across the 16 different exchanges are aggregated. Let’s pick up from there and think more like a trader – starting with some basics.

What is an order?

Orders are the instructions that a trader sends. It will include the:

• Stock (ticker)
• Side (buy or sell)
• Size (number of shares)
• Price

What is a quote?

When we all try to buy (or sell) anything, sellers want to get the highest price possible - but buyers are always trying to get a discount or lower price.

In the markets:

• A seller offers to sell stock.
• A buyer bids to buy.

That’s why when you look at a trading screen, offers always have higher prices than bids.

The quotes you see on a screen all come from exchanges (although some venues have “hidden” orders). The benefit from exchanges of sharing prices publicly is that it should “advertise” where liquidity is and help other investors know which exchange to go to if they want to trade.

There is also a huge benefit to the public from those quotes as it helps everyone know what a good price is — even if they don’t trade on exchange.

Because of this benefit to the public, regulators require quotes be published on a series of centralized processors, called the SIPs, which computes the National Best Bid and Offer (NBBO) that is widely used to measure trade performance and protects investors from bad trades.

What is the spread?

The difference between the public bid and offer prices is called the spread. Sometimes people will talk about the spread in cents; sometimes they will convert it to basis points (bps), which are 1/100th of a percent.

Some market makers try to “capture” spread by quoting both a bid and an offer at the same time. Buying at the (lower) bid and selling at the (higher) offer.

But don’t confuse spreads and ticks.

• Ticks are the legal increment that stocks can be quoted in. In the U.S., for almost all stocks, all ticks are 1 cent (at least for now), but many stocks quote multiple cents (or ticks) wide.
• Spreads matter for a trader, as it is the economic return a trader captures (or pays when “crossing” the spread).

Many stocks have spreads that are many ticks wide, and typically, spreads in less liquid stocks are much larger (see Chart 1).

Chart 1: Spreads tend to be wider for less liquid stocks (and only the blue stocks trade with 1-tick spreads)

What is the difference between a quote and a trade?

Quotes are prices where trades could happen. They persist until the order is cancelled or the order quantity is completely traded. 

In contrast, trades occur when a bid and offer occur at the same price. They are completed in an instant and often mean prices change, so others have missed the opportunity to trade at those prices.

That’s also why trades and quotes are reported and rewarded separately by the SIP. 

Price moves are all about supply and demand (and news)

In the long term, stock valuations track expected earnings and news events. Good news makes stock prices rise, and bad news does the opposite.

However, in the short term, trading (and trading impact cost) is all about supply and demand.

In fact, when you look at the amount of stock available at higher and lower prices in the screen (the “depth of book”), you see an actual supply and demand curve for the stock (Chart 2): 

• As prices rise, there are more willing sellers, and the quantity for sale increases (supply).
• As prices fall, there are more willing buyers, and cumulative bid volume increases (demand).
• This creates the “V” shape in the chart below.
• Right in the middle is the “equilibrium” price, where buyers offset sellers and trades occur.

And just like in your economics courses, adding demand moves prices up while adding supply moves prices down.

Chart 2: Trading adds to supply or demand for a stock

Trading costs come from many places

If you want better returns on the stocks you hold, managing trading costs is important. Trading costs are a combination of:

• Explicit trading fees (commissions and exchange fees).
• Spread crossing costs.
• Shortfall, or market impact, caused by adding demand (or supply) to the market.
• Opportunity costs (from missing trades because you were too patient).

We can see from reported mutual fund trading costs that stocks with wider spreads tend to cost more to trade, but that shortfall caused by their trades is an even larger cost (we will talk more about how mutual funds trade later).

Chart 3: Trading costs are a combination of spread costs and liquidity or impact costs

Importantly, in a 2020 study, we estimated that mutual fund trading costs added up to around $70 billion each year, even though they average just 0.31% per trade. So, minimizing the costs of trading is important!

Different types of order types introduce different types of costs

One thing that helps traders minimize costs is different order types. Although, as Table 1 shows, each has its own costs and benefits.

The most basic orders are market and limit orders, but there are also non-display (hidden) orders where quotes are not advertised. Often, they help traders try to capture some spread without showing up as new supply and demand (reducing market impact).

Table 1: Traders’ choices and costs

1. Time is Relativity: What Physics Has to Say About Market Infrastructure

2. Lit Markets Provide Price Improvement Too

3. What Markouts Are and Why They Don't Always Matter

4. Are Large Queues a Big Problem?

5. Large Queues are a Small-Cap Problem

6. Routing 101: Identifying the Cost of Routing Decisions 

For professional traders, there are more complicated order types. Some let buyers automatically reduce their bid prices (fade the market) as sellers arrive at the market (or vice-versa).

Market makers and hedge funds also sometimes need to use short sell orders, which lets them sell shares of a stock that they don’t own (but will need to borrow from a holder to be able to settle the trade).

Trading is a trade-off between cost and time

As we see, trading is often a trade-off between speed and cost to trade.

Some of our own research shows that different order types can be used to trade-off spread capture and trade speed at a very granular level.

Chart 4: Buyers’ choices and consequences

How retail trades

Data suggests that the average retail trade is small – less than $10,000. That fact is important because usually the NBBO size is much larger than the size of a retail investor’s whole trade.

That means retail market orders should be able to trade instantly without any residual market impact. It also means retail trades should rarely cost more than the spread to complete.

Because of that, retail investors usually choose between market and limit orders.

There are also rules in Reg NMS to protect retail from bad trades, such as:

• NMS Rule 605 keeps track of all the trades executed worse than the NBBO, as well as all of the price improvement wholesalers pay.
• NMS Rule 606 tracks all the payments for order flow (PFOF) paid for retail flow.

Chart 5: Rules to keep track of retail execution quality

It turns out that, using 605 data, you can see that retail usually beats the NBBO spread, which is called price improvement, often with trades occurring at sub-tick levels that venues aren’t allowed to accept limit orders for. That’s because almost all retail orders actually trade off-exchange, with a retail market maker (also called a wholesaler).

The reason price improvement works for a wholesaler is that retail buying and selling is usually pretty random, something academics call “less informed.” That makes it easier for market makers to capture spread (or avoid adverse selection) trading with just retail.

Interestingly, a recent academic study found that retail customers receive  consistently different  fill prices, depending on which retail broker they use. That highlights how wholesalers know how “informed” each of their customers is on average, and that they set a different spread that results in no losses on that customer alone.

The important takeaway for traders is to appreciate that the market has evolved to service retail traders very differently than everyone else.

How mutual funds trade

Mutual funds and pension funds (so-called “institutional” traders) represent professionally managed pools of thousands of investors. That means their portfolios and trades are usually much larger.

For example, Vanguard has one mutual fund with over $1.3 trillion invested in just 500 companies (the S&P 500). A while ago we estimated that mutual and pension funds trade around $70 billion each day, which includes a lot of daily cash flows. That added up to around $17 trillion over a year – a number that is surely much larger today.

Consequently, institutional trades are usually significantly larger than retail orders, which means they can’t use simple market orders or even complete a trade at the NBBO. That also means spreads are often a fraction of the total costs of trading.

To deal with this, institutional brokers use additional techniques to keep trade costs as low as possible, including:

• Algorithm working orders: Brokers will usually “work” orders for mutual funds over a number of minutes or hours. That means they split larger “parent” orders into smaller (child) orders. That way, each child has a smaller impact on supply and demand and, therefore, price. Typically, this is done using algorithms that automate trading.
• Dark pools: Others in the market are always looking for signs that a stock will rally or fall (to save money trading). Posting orders in dark pools or using non-display order types on exchange allows investors to be in the market without advertising they are there. Many intuitional brokers have their own “dark pools,” which allows them to match trades off exchange without advertising orders.
• Smartly routing: Different stocks have wider spreads, longer queues and more depth, and some venues have different trading costs, too. An algo and smart router can choose different paths and prices for each child order throughout the day to improve the price and speed of trading, including using dark pools to sometimes trade with less informed flow.

Brokers are constantly tuning and refining each component — many have execution cost and router analysis experts.

In fact, there is even evidence to show that brokers tune algorithms to account for very small differences in exchange fees.

Chart 6: Exchange fees are a fraction of most spreads

Some exchanges charge liquidity takers (so called maker-taker venues), while others pay takers (inverted venues). Economically, those crossing a spread (takers) prefer to be paid, so that makes orders go to inverted venues first, which makes those exchanges NBBO queues move faster. That, in turn, makes inverted venues more likely to capture spread and lowers opportunity costs – although it results in higher explicit trading costs (fees).

Interestingly, a study with buy-side trade data found that net realized spreads (all in trading costs, including fees and opportunity costs) are statistically identical regardless of whether trades happen on maker-taker or inverted exchanges.

Importantly, mutual fund execution experts (or smart interns) can do this for themselves, using FIX tag information on each trade that shows which venues each “child” order traded in and public exchange fee sheets.

From there, they can determine how their brokers are routing flows, as well as estimate the net fees being paid (or rebates earned) by each broker. They can use that to determine if one broker’s net execution costs (commission plus shortfall) seem out of line with others.

They can also use institutional 606 reports that the SEC created, showing high level disclosures on this activity, too.

Where do stocks trade?

Routers are especially important for our very fragmented U.S. market. Recall that most U.S. stocks trade in many different venues, including:

• Sixteen different exchanges (and counting), regardless of where a stock’s “primary listing” is.
• Over 30 ATSs (dark pools).
• As well as bilaterally with a number of wholesalers or proprietary firms (single dealer platforms or SDPs).

However, routers can’t address the kind of fragmentation we mentioned above, where retail trade directly with wholesalers. That is known as segmentation, and in that instance, retail flow is considered “inaccessible” to mutual funds.

In reality, institutional orders have their own market segmentation. Most brokers offer dark pools, and most dark pools create customer tiers that allow traders to be categorized based on their likely spread capture and (in theory) help reduce costs.

The result is shown in Chart 7 below, which has circles sized by actual volumes. We see that retail and institutional trade flows are directed to quite a different group of brokers, shown by the green and blue arrows. In total, around 44% of all trading occurs before it even reaches exchanges, with the roughly 30 broker dark pools adding around a quarter of that flow.

Chart 7: Where stocks trade

For any interns looking into this data, note that it comes from a variety of sources:

• Exchanges all send their trades to the SIP, with attribution about which exchange did the trade.
• All of the other trades, which are considered “off-exchange,” print to the SIP anonymously via one of two Trade Reporting Facility (TRFs).
• In order to see the breakdown of the dark pool trades, FINRA reports aggregated flows that show trades for each trading venue by ticker, but on a delayed basis.
• FINRA also publishes “non-ATS” trading data. Although that is “mostly retail,” there are other trades reported that way, too.

Another trade-off: Off-exchange spread capture vs. a good NBBO

This has important market structure implications, too, as it means that limit orders and market makers that are trying to capture spreads, often don’t. Instead, spread crossing orders trade in dark pools and with wholesalers first. This is known by academics as “cream skimming.” And, in theory, it should make public markets “more toxic,” which should ultimately make spreads worse.

In order to combat this, some exchanges use rebates are to offset some of the adverse selection. That helps improve spread capture on those exchanges and keep spreads tight. That’s especially important for many small companies with wider spreads and less liquidity.

Chart 8: Exchanges that create more competitive quotes across more (especially illiquid) stocks help reduce trade costs and costs of capital for companies

How fast should you trade?

In most cases, the data shows that trading is a trade-off between how fast a trader can trade and how much their trade costs.

Which brings us to an important question: How fast should you trade?

In reality, the optimal trading speed depends a lot on what you and other investors know.

There is a mathematical way to optimize this problem, which we discussed in How Fast Should You Trade? This shows that you need to understand the trading trade-offs:

• Market impact is created when you add more demand to the market, so prices rise to attract more sellers. The faster you do that, the faster prices rise – adding to trade costs.
• Alpha in the trade. For a portfolio manager, alpha is good as it represents the amount a stock outperforms the market. But trading alpha measures how fast the stock goes up when you want to buy it, even if you don’t trade – so it’s an opportunity cost.
• Trade size reflects how much your order changes the normal supply and demand. Generally, larger trades cost more.
• Liquidity in the stock determines the minimum time a trade size should take to finish. Smaller-cap stocks typically have less liquidity, which limits how fast you can build a large holding in those stocks.
• Spread costs add up. Generally, the wider the spread, the more expensive a trade will be (Chart 3). That’s because investors typically need to cross more spreads than they can capture.
• Risk is a factor, too. All other things being equal – why wait if the costs are roughly the same, if for no other reason than loss aversion. Behavioral science shows that individuals feel the pain of losing is around twice as bad as the pleasure of gaining!

Once you know all this, you can (theoretically) estimate how trading costs, opportunity costs and risk change over time. After doing that, you can see what trading speed will minimize all the different trading costs – weighing the alpha (opportunity costs) of trading slower against the market impact (cost) of trading faster. In the diagram below, for example, “X” marks the spot!

Chart 9: Optimal speed to trade-off impact and opportunity cost can be mathematically determined

People trade at different speeds throughout the day

Complicating the problem above is the fact that trading dynamics change throughout the day. For example:

• Spreads are usually wider in the morning.
• Volatility is typically higher in the morning, and around events and news.
• Trading activity is higher in the mornings and afternoons – and slower around lunchtime – forming what’s known as a VWAP curve, or smile.

Chart 10: Trading speeds change over the day

The close is usually the most liquid part of the day. But the open and close work differently than trading during the day. Rather than a bid and an offer creating spread costs, the market open and close are auctions. In these auctions, buyers and sellers add orders, and the “clearing” price is found – where supply equals demand – literally a single price where buy shares equal sell shares.

On specific days in the year, like when index funds all need to trade or futures or options expire, closes are even larger.

One of the latest trends is pre-market trading increasing, driven by non-U.S. investors.In fact, although “regular hours” will most likely stay 9:30 a.m. – 4 p.m., U.S. markets are looking to extend available trading hours to close to 24 hours, five days a week.

Don’t stress — computers do most of the work for investors

Although this all sounds complicated, the reality is that computers (trading algorithms and market maker models) do most of the trading these days, and they can be optimized with data and programmed to fix much of the complexity that human traders face. Some likely even incorporate machine learning and artificial intelligence.

It’s also important to remember that most of the market is also interconnected and automated. The SIP and NMS rules (still, for now) require it.

So, the biggest input required from most investors is to decide what stocks they want to buy, tell the algorithm how fast they need to trade, and sit back and watch as fills come in.