So what I figured I’d do, something that felt like a nice breath of fresh air, is share one of the strategies I actively trade. One that has actually been profitable, with the data and reasoning to back up why it works.

In this article, I will take you step by step through the whole process: why the strategy actually makes money, why it sucks, and what you can expect from running it.

While this article won’t be hidden behind any paywalls, I will be using data from my website at https://www.tradingriot.com/. There you will also find the exact screener to get markets to trade each week and a position size calculator for optimal betting. If you want to give it a try, you can use code “ANALYTICS” for 50% off your first month.

Risk Premium

Usually a lot of these articles start by giving you some insane hooks about how much alpha you will find in the text. This won’t be one of them.

The reason I am sharing this strategy in the first place is exactly that: there is no alpha in this.

While people who use the word alpha online usually don’t understand what alpha is in the first place, alpha strategies are extremely scarce. They don’t last long and everyone who has any actual alpha keeps it for themselves for a reason.

This strategy is simply a fairly basic risk premium.

I wrote a longer article about risk premium already, so go ahead and read that if you haven’t. I don’t want to spend too much time explaining myself here, but in short, risk premium is compensation for bearing risk others don’t want to hold.

This strategy in particular is harvesting two different risk premium at once.

Equities risk premium

TLDR here is stonks go up.

The longer version: stocks have a built-in positive drift over time. People who park their money in equities are sitting with real risk, and they expect to get paid for it.

Historically that payment has worked out to roughly 4-6% annually on top of whatever the risk-free rate is.

This is obviously not a secret. Academics have picked it apart for decades. Yet it keeps paying out. Why? Because the discomfort of owning something that can drop 30% in a month never goes away.

That fear is real and the reason why you are getting paid

Variance risk premium

This one is slightly more complicated but equally important for understanding why you’re getting paid.

Variance risk premium is the difference between implied and realized volatility. In other words, it’s what the market thinks is going to happen versus what actually happened.

As you can see on the chart above, variance risk premium (VRP) for SPY is mostly positive with some nasty spikes to the downside. Market participants consistently overestimate the amount of volatility that will occur over the next n-days. But when volatility does exceed the estimate, it usually overshoots by a lot.

This is the “staircase up, elevator down” feature markets have. Most up days are calm and slow while crashes are fast and violent.

So why is VRP consistently overstating what ends up happening? Are people dumb? Yes, generally, but that’s probably beside the point.

There are several reasons for it, and VRP probably deserves its own article sometime in the future. But long story short, implied volatility is historically elevated because sellers demand compensation due to concave payoff structure, buyers are buying protection, and gamblers are gambling. All of this creates a structural imbalance between option buyers and sellers.

Variance risk premium has been studied for a long time and similar to the directional risk premium, it is unlikely to disappear anytime soon.

Why This Sucks

I generally hate people who glorify trading. Holding stuff that can go down sucks. And while I continue to do this because my mom raised no pussy, for everything you trade it’s good to understand why it sucks.

We established that you are making money because stonks go up and because options are overpriced. So what you end up doing is selling options while taking an upside directional bet.

A lot of you probably know that selling options has a concave payoff, or in other words, negative skew. You get paid small amounts of money while risking much larger losses.

Don’t worry, this is not one of those covered call or wheel strategies you’ve probably seen on YouTube, where your risk is technically unlimited hidden behind one of the dumbest phrases in trading: selling options for “income.”

While our risk will be limited, the risk to reward will still be negative. When we lose, we lose more than when we win.

If you want to trade a strategy like that, you need a high win rate to compensate for the larger losses. This is probably a decent spot to share the full strategy backtest including commissions, drawdowns, and everything else.

If you continue reading after the disappointment that this is not a hidden alpha returning 500% per year, let’s quickly break down the chart above.

The strategy returned 18% CAGR with only 11.5% annualized volatility. For context, SPY gives you roughly 10% CAGR with around 15-16% volatility, so you’re getting almost double the return with less risk.

Sharpe ratio sits at 1.46, which is strong for anything that isn’t curve-fitted. The pessimism-adjusted Sharpe still comes in at 1.02, meaning even after penalizing for potential overfitting, the strategy holds up.

Win rate is 78% across 2,473 trades. That high win rate is exactly what you need to offset the negative skew we talked about. The strategy skew of -2.13 confirms what we already know: wins are frequent and small, losses are rare but painful.

Max drawdown hit 33.3%, which is not nothing. The Sortino ratio of 0.73 and Calmar of 0.54 reflect that. When this thing bleeds, it bleeds.

One detail worth noting: the backtest is split into in-sample and out-of-sample at March 2022. The in-sample Sharpe is 1.46, out-of-sample is 1.45. Almost no degradation. That’s rare and it matters, because most backtests fall apart the moment they hit data they weren’t optimized on.

Total costs came to $51,913 over the full period starting with $100,000 account. This is running on IBKR commissions and execution at mid-price which is realistic after learning more about instrument selection.

The Strategy

The sharper ones of you (those that can read) already saw from the backtest that we are going to be selling 30/10 delta put spreads on ETFs.

While there are many strategies that harvest these risk premiums, credit put spreads are the cleanest structure for it.

Straddles and strangles will get hurt during any move in either direction. Selling naked options receives more credit but can catch you off guard during crashes, especially since the strategy holds up to twenty positions at any given time.

Here are the exact strategy rules. I will expand on each step after.

1. Every Monday, look at all ETFs with more than 25,000 average options volume over the last 20 days.

2. Sell a 30/10 delta put spread on the top five with the highest VRP. Aim for the closest expiration to 30 days out.

3. Repeat every Monday, so you are holding up to 20 positions at any given time.

4. If VIX is above 40, do not enter new trades for that week but let existing positions run.

There is a screener on Tradingriot which will do all of that for you each week.

I would say that one of the good signs of strategy being actually being any good is if you can mess around with entry parameters to see if it holds true.

I would say that one of the good signs of a strategy actually being any good is if you can mess around with entry parameters and it still holds up. As you can see here, it does.

Panel 1 shows entry day sensitivity. Monday comes out on top with a 1.44 Sharpe and 17.8% CAGR, but Tuesday and Thursday aren’t far behind. Even Friday, the worst performer, still pulls a 1.06 Sharpe and 13.2% CAGR. The strategy isn’t fragile to when you enter. It works across the whole week, Monday just happens to be the best also kind of logical to know the trades out at start of the week and move on.

Panel 2 tests how many positions you open each week. Whether you trade the top 3, top 5, top 10, or even top 20, the Sharpe stays between 1.07 and 1.44. The CAGR ranges from 15.4% to 18.1%. More positions give you slightly more return but the risk-adjusted performance barely moves. Top 5 looks like a sweet spot where you are taking the trades with generally high VRP rather than trying to trade everything.

Panel 3 compares the 30/10 delta spread against a wider 30/5. The wider spread squeezes out more return at 23.2% CAGR, but the max drawdown jumps from 33% to 47%. The Sharpe barely changes, 1.38 versus 1.35. You’re getting paid more but you’re taking a significantly bigger hit when things go wrong. Its honestly up to you here if you are willing to try to get higher returns (as 5 delta options are much cheaper) or try to manage the drawdown better.

Overall, backtesting options turned out to be a little bit of dark magic (pain in the ass), so in reality you won't take perfect 30/10 delta trades. Which is fine. The key point is that the strategy performs well even when you mess around with the parameters, since the two risk premiums you are getting paid for remain the same.

Market Selection

As I already mentioned, you will be trading liquid ETFs. The backtest actually shows that the most liquid ETFs above 50,000 traded contracts on a 20-day average perform the best, but 25k is a decent sweet spot to include a few more markets so you avoid trading heavily correlated ones.

Going to less liquid ones is fine, but while the backtest includes costs and slippage, you will just have a generally harder time trading the less liquid ones, especially with a larger account.

One important thing is that all leveraged and inverse ETFs were removed. The main reason you want to be trading ETFs is to remove a lot of the idiosyncratic risk stocks have. There are no earnings, no surprise news, and none of those annoying things that make a stock gap 20% overnight.

As you can see from the chart below, all key sector ETFs had positive drift over the last few years, which makes them great candidates. You might be thinking that some stocks go up much more. This is true, but your upside is capped on the put spread, so you generally prefer markets that grind up slowly rather than something much more volatile.

The VIX above 40 filter is there to protect you during the most turbulent times. Obviously, if there are no ETFs with positive VRP on Monday you just don’t open new trades since there is no variance risk premium to harvest. Also if you for example opened put spread on SPY on Monday and week after SPY is still in top five markets based on VRP, you will open new one.

The trades are held until expiration. No management, no closing things early. This keeps things simple and also saves you on commissions since you’re not paying to close positions that will expire worthless anyway. On IBKR, options that expire out of the money have no closing commission, so letting winners expire is both the laziest and cheapest approach.

Position Sizing

So how much do you bet? In the backtest and in actual trading, instead of fixed risk per trade I am using volatility targeting. This is borrowed from systematic trading frameworks from Robert Carver and the idea is simple: every position should contribute roughly the same amount of risk to your portfolio, regardless of what you’re trading.

You pick a target portfolio volatility. In this backtest, it’s 12.5% annualized. That means you expect your account to swing about 12.5% per year, or roughly 0.8% on any given day. For negative skew strategies like this one, 12.5% is a good sweet spot.

For each trade, you calculate how many contracts to sell so that the position’s expected daily P&L swing matches your risk budget. Volatile markets get fewer contracts because each one moves more. Calm ones get more contracts because each one barely moves. Every position ends up contributing roughly the same dollar risk to the portfolio.

It also scales with your account. When you’re making money, positions grow. When you’re losing, they shrink automatically. Built-in compounding on the way up, built-in de-risking on the way down.

Since this strategy has a fixed number of positions it holds at any given time, I built a calculator on the website that spits out the exact size for each trade. You plug in your account size and it does the rest.

To achieve this volatility target I would recommend a starting balance of at least $100,000. If your account is smaller, you will struggle to trade large cap ETFs, so lowering the minimum volume threshold and selecting cheaper markets is reasonable. I understand that $100,000 is a lot of money but do not worry, most of the ETFs will be much smaller and cheaper, my point here is to be mindful of the proper sizing more than anything.

I also ran the backtest with fixed percentage max loss per trade. The 12.5% annualized volatility target translates to somewhere around 1.5-2% risk per trade. As you can see from the chart, the 1% risk version has the best Sharpe at 1.41 but only returns 11.6% CAGR. The 2% version hits 23.4% with a 1.32 Sharpe and drawdowns around 40%. The 3% version pushes 33.2% CAGR but drawdowns exceed 50%.

Drawdowns

I already mentioned that this strategy sucks, but before we wrap up I want to mention it one more time.

With the negative skew you are making less money than you are risking. Not sure about you, but with my luck I generally start trading something new and hit the worst case scenario within the first few weeks.

This can very easily happen here since a lot of the markets you will be trading are correlated. Negative months are absolute norm. Not only that, over the last 10 years you would have spent almost two years in drawdown from equity highs.

That’s not fun. You can obviously try to get cute with this by adding new rules and filters that might help you avoid bad trades. That’s completely up to you.

For me, when I can get paid for selling overpriced options and benefit from the fact that these markets go up and to the right over time, that’s more than enough.

I actually spent some time building a regime filter for the broad market and applied those findings to the backtest to see if avoiding trades when things get shaky does any good. It turned out it doesn't.

Which makes sense, because you are getting paid to hold risk others don't want. Sitting out during the rough patches means giving up the exact moments where that compensation is highest.

Conclusion

This strategy is not something that will make you a millionaire overnight, but it delivers the promise from the headline without any bait.

Executing five trades on Monday is not going to take you more than 15 minutes (although don’t try to be an ape and actually work on getting the best fills possible) and you can and actually should do exactly nothing after that.

This has been my go-to strategy for “passive investing.” Getting exposure to the broad market without having to hang on trying to study all the different narratives and timing sector rotations.

As I mentioned, all the data is available on my website which significantly eases the whole workflow, but obviously there are many other places where options data is available.

Hope you enjoyed the read, and if you end up blowing up your account, don’t @ me.

There is no shortage of people who turn options knowledge into 4,000-word essays packed with third-order Greeks charts, volatility surfaces, and enough jargon to make it feel like rocket science.

While these obviously have their time and place, I do not assume you plan to become a quant or options market maker.

For retail traders, naming all third-order Greeks and calculating the theoretical fair value of an option using the Black-Scholes Model in your head is more telling about your social life than giving you any trading edge.

Contrary to popular belief, understanding how options work is not an edge. Understanding what product you trade should be the bare minimum.

But you still need to know the mechanics before you can do anything useful with them. This article covers all the fundamentals: what options actually are, how they’re priced, what drives their value, and most importantly, when it makes sense to buy them versus sell them.

By the end, you’ll have a complete picture of how options trading works and enough context to start thinking about where real edges might exist.

Before we dive into the article, if you want analytics for over 1,000 markets across options, crypto, and futures while learning systematic trading strategies that harvest different risk premiums across multiple asset classes, check out tradingriot.com. Use code “ANALYTICS” for 50% off your first month.

What Is an Options Contract?

Before we go into any complex stuff, I figured it would be nice to give you an example outside of financial markets that completely covers options basics and Greeks.

Options give the holder the right, but not the obligation, to buy or sell the underlying asset at a specified price on a specified date.

The buyer of the option pays a premium. The seller of the option receives the premium.

To translate this into much simpler terms, let’s say you want to buy a house and find a seller.

You ask the seller if he can issue you an option that expires in 12 months and gives you the right to buy the house for $500,000. He will also receive a premium for this option, which equals $10,000.

If he agrees, you have the right to buy the house for $500,000 anytime in those twelve months, and you don’t need to care about housing market price fluctuations.

Let’s say the twelve months passed, and the current price of the house is only $450,000.

In this case, you are obviously not going to buy the house for 500k, so you will not exercise your right to buy it.

But you lost the $10,000 premium, which was the risk you put up.

In another example, let’s say that the housing market skyrocketed, and the current price of that house is now $700,000.

Of course, you are going to exercise your right to buy the house for $500,000, and you made a profit of $190,000 (final price (700k) - strike price (500k) - premium (10k)).

Let’s now flip the table and look at the situation from the perspective of the option seller.

In the first scenario, the option expired worthless as the price of the house was below the option price, and the seller ended up with the premium he was paid.

In the second scenario, the option ended up in the money, and the seller technically lost $190,000 as he now has to sell a house that has a $700,000 value for only $500,000 (minus the premium).

This principle is the same in options trading.

• If you are an option buyer, your risk is limited only to the premium paid, and your reward is technically unlimited.

• If you are an option seller, your reward is limited only to the premium you receive, and your risk is technically unlimited.

Although this might seem like selling options is pure stupid, you will find out than more often than not options selling is actually preferred, one of the cases was already covered in trading options around earnings.

What makes options complicated is that several factors influence the price of the option during those twelve months.

The most obvious factor is the price of the house itself. If the house goes up in value, your option to buy it at $500,000 becomes more valuable. If it drops, your option loses value.

But price isn’t the only thing at play. Time works against you as the option buyer. You paid $10,000 for twelve months of optionality, and every day that passes, a piece of that value disappears. Even if the house price doesn’t move at all, your option is worth less tomorrow than it is today.

Uncertainty also plays a role. If a rumor spreads that a tech company might build nearby, the house could go to $800,000 or crash to $400,000. That uncertainty alone makes your option more valuable, even without any actual price movement.

Interest rates matter too. For shorter-dated options, the impact is negligible, but for longer-dated ones or in fast-moving rate environments, it starts to add up.

All of these factors act on your option at the same time. On any given day, the house price might go up, but time passed and uncertainty dropped. The price you see is the net result of all these forces pulling in different directions, and they work opposite for option buyers and sellers. We will cover each of these in detail later in the article.

Options Basics

So now that you know how options work, it is a great time to translate this knowledge into the markets.

Although the example of buying a house was rather simple, if you open an option chain, it looks like everything but simple.

Reading an Options Chain

When you want to open an order on futures, it is usually very straightforward. You either buy or sell.

On options chains, there are many more elements to consider.

Calls and Puts

There are two types of options contracts: calls and puts.

A call option gives you the right to buy the underlying asset at the strike price. You buy calls when you think the price is going up.

A put option gives you the right to sell the underlying asset at the strike price. You buy puts when you think the price is going down.

Worth to keep in mind that this is slightly different from futures where you go long or short, in options buying a put is still speculating that price will go down despite you are buyer of options contract.

Looking at our SPY options chain, you’ll notice calls on the left side and puts on the right side, with strike prices running down the middle. This is the standard layout across most platforms.

Both sides of the chain work the same way mechanically. They have the same columns: bid, ask, delta, gamma, vega, theta. The difference is the directional bet. Calls profit when SPY goes up. Puts profit when SPY goes down.

Strike Price and Moneyness

The first thing you need to pick is a strike price. When choosing one, you’ll come across three terms that describe the relationship between the strike and the current price of the underlying asset. This relationship is called the “moneyness” of the option.

Looking at our SPY options chain with SPY trading at $657.44:

• In the money (ITM): the option has intrinsic value. For calls, this means the strike is below the current price. The 640 strike call is in the money because you’d have the right to buy SPY $17.44 below where it currently trades. For puts, it’s the opposite. The 670 strike put is in the money because you’d have the right to sell SPY $12.56 above where it currently trades.

• At the money (ATM): the option strike is closest to the current price of the underlying. The 657 strike is the nearest at-the-money option in our chain, for both calls and puts.

• Out of the money (OTM): the option has no intrinsic value. The 670 strike call is out of the money because SPY would need to rally above $670 before that call has any value at expiration. The 640 strike put is out of the money because SPY would need to drop below $640 for that put to have value at expiration.

Notice that what counts as ITM for calls is OTM for puts, and vice versa. The 640 strike call is in the money, but the 640 strike put is out of the money. Same strike, opposite moneyness.

Intrinsic and Extrinsic Value

Every option premium consists of two components: intrinsic value and extrinsic value.

Intrinsic value is the real, tangible value of the option if it were exercised right now.

For calls: intrinsic value = underlying price - strike price (or zero, whichever is higher). For puts: intrinsic value = strike price - underlying price (or zero, whichever is higher).

The 640 strike call on SPY at $657.44 has an intrinsic value of $17.44. The 670 strike call has zero intrinsic value because SPY hasn’t reached that level yet. On the put side, the 670 strike put has $12.56 of intrinsic value, while the 640 strike put has none.

Extrinsic value is everything else baked into the premium on top of intrinsic value. It depends on time to expiration, volatility, dividends, and the risk-free interest rate.

If we compare two options with the same strike, one expiring next week and one expiring a month from now, the longer-dated option will have a higher extrinsic value (they are more expensive). More time means more opportunity for the underlying to move, and that optionality costs money.

At expiration, extrinsic value is always zero. The option is worth only its intrinsic value. This is why time works against option buyers. Every day, a piece of that extrinsic value evaporates.

Premium

The premium is what the option costs. Looking at our chain, the 657 strike call (closest to at the money) shows a bid of $13.52 and an ask of $13.57.

The bid is the highest price a buyer is willing to pay. The ask is the lowest price a seller is willing to accept. If you want to buy this call immediately, you pay the ask price of $13.57. Since SPY options have a contract multiplier of 100, your actual cost is $13.57 x 100 = $1,357 per contract.

The contract multiplier applies to most equity and ETF options. When you see a premium quoted as $13.57, that’s the per-share price. One contract controls 100 shares, so you always multiply by 100 to get the real dollar amount.

The difference between the bid and the ask is called the spread. On this 657 strike call, the spread is $0.05 ($13.57 - $13.52). That spread is a cost to you. The moment you buy at the ask for $13.57, you could only sell at the bid for $13.52, meaning you’re immediately down $0.05 per share, or $5 per contract.

Spreads vary across the chain. At-the-money options with high volume tend to have tight spreads, like the $0.05 we see here. Deep out-of-the-money options with less liquidity will often have much wider spreads. That wider spread is a hidden cost that eats into your potential profit, and it’s something many new options traders overlook.

Expiration and Exercise Style

Every option has an expiration date. In our chain, we can see multiple expirations available: April 8th (14 days), April 10th (16 days), April 17th (23 days), and April 24th (30 days). This is the date by which the option either has value or expires worthless.

There are two styles of options when it comes to how they can be exercised.

American-style options can be exercised at any point before expiration. Most equity and ETF options, including SPY options, are American-style.

European-style options can only be exercised at expiration. It doesn’t matter if the underlying moves in your favor during the life of the option. You have to wait until the expiration date. Most index options, like those on SPX, are European-style.

Exercise and Assignment

When you own an option and decide to use your right, that’s called exercising.

If you exercise a call, you buy 100 shares of the underlying at the strike price. If you exercise a put, you sell 100 shares at the strike price.

Most traders never exercise their options. They simply sell the option back to close the position and collect the profit. Exercising usually makes less money because you throw away any remaining extrinsic value. If your 640 call is worth $18.50 but only has $17.44 of intrinsic value, selling it gets you the full $18.50. Exercising only captures the $17.44.

The exception is deep in-the-money options very close to expiration, where extrinsic value is basically zero. In that case, exercise and selling are roughly the same.

Now here’s where it gets important if you’re an option seller. Assignment is the other side of exercise. When someone exercises their option, a seller on the other side gets assigned.

If you sold a call and get assigned, you must sell 100 shares at the strike price. If SPY is at $680 and you sold the 660 call, you’re selling shares at $660 regardless of the market price. If you don’t already own the shares, your broker buys them at $680 and sells them at $660, and that $20 per share loss (minus the premium you collected) is yours.

If you sold a put and get assigned, you must buy 100 shares at the strike price. If SPY dropped to $620 and you sold the 650 put, you’re buying shares at $650 when they’re worth $620. That’s a $30 per share loss, minus the premium.

For American-style options, assignment can happen at any time, not just at expiration. It’s most likely to happen when the option is deep in the money and has very little extrinsic value remaining, or right before an ex-dividend date. If you sold an in-the-money call on a stock that’s about to pay a dividend, the option holder might exercise early to capture that dividend, and you’ll wake up to an assignment notice.

This is why many option sellers prefer to trade European-style index options like SPX. No early assignment risk.

Put-Call Parity

There is a fundamental relationship between the prices of calls and puts at the same strike and expiration. This is called put-call parity, and the basic formula is:

Call + Strike = Put + Stock Price

Or rearranged: Call - Put = Stock Price - Strike

This means if you know three of the four values, you can calculate the fourth. More importantly, it tells you that calls and puts at the same strike always carry the same amount of extrinsic (time) value.

Let’s use our SPY chain to check this. SPY is at $657.44, and we’ll look at the 657 strike.

The 657 call mid-price is roughly $13.54 (averaging bid $13.52 and ask $13.57). The 657 put mid-price is roughly $13.20 (averaging bid $13.03 and ask $13.38).

Call - Put = $13.54 - $13.20 = $0.34 Stock - Strike = $657.44 - $657 = $0.44

These numbers are close but not identical. The small difference comes from interest rates, expected dividends, and the bid-ask spread. In textbook conditions, they’d match perfectly. In real markets, they’re always very close, and when they drift apart, market makers jump on the arbitrage and push them back in line.

Why does this matter for you? A couple of reasons.

First, it tells you that calls and puts are not independent instruments. They’re mathematically linked. You can’t have a situation where calls are “cheap” and puts are “expensive” at the same strike without someone arbitraging that away.

Second, it means you can create synthetic positions. Buying a call and selling a put at the same strike is synthetically the same as owning the stock. Buying a put and selling a call is the same as being short. This becomes useful when building more complex strategies.

Third, it’s a quick sanity check. If you’re looking at option prices and something feels off, run the parity formula. If the numbers don’t line up within a reasonable margin, something is wrong with the quotes, or there’s a dividend or hard-to-borrow situation affecting pricing.

Below are few examples on calculating put/call parity, just a small note In real markets, you may see a Rev/Con adjustment added to the formula. This accounts for ,Interest rates, dividends and borrow costs.

For learning purposes, we’ll use the basic formula. Rev/Con is typically small (a few cents to a dollar) and professionals use it for arbitrage calculations.

The Greeks

Now for the scary stuff: option Greeks. As I mentioned, Greeks represent variables that determine the option’s price, which is constantly changing.

There are first, second, and third-order Greeks.

If the image above looks insane to you, don’t worry. In reality, you only need to understand first-order Greeks (second-order are useful for day trading), and the math is really not important.

In my trading, I refuse to acknowledge things such as parmicharma, which sounds like Italian ham, or speed, that is solely reserved for 3am on a weekend when you want the night to keep going.

The Black-Scholes Model

The Black-Scholes Model (BSM) from 1973 is the foundation of modern options pricing. It provides a theoretical price for European options and serves as the basis for calculating the Greeks. No model perfectly captures reality, but Black-Scholes gives us a common language for pricing options. You don’t really need to know the formula since everything is calculated for you inside your broker, but it’s still a good exercise to understand what’s happening under the hood.

If this still looks scary, forget the math for a moment. Black-Scholes answers one question: “What should I pay for the right to buy a stock at a fixed price in the future?”

It does this in four steps.

First, start with intrinsic value. If the stock is at $105 and the strike is $100, there’s $5 of real value baked in.

Second, add time value. More time means more chance for the price to move in your favor. An option can be worth something even if it’s currently out of the money, simply because there’s still time left.

Third, figure out how much that time value should be. This is where volatility comes in. Higher volatility means bigger potential moves, which means more time value. Volatility is the single most important input in the model.

Fourth, discount for the time value of money. You’re paying now for something that settles later, so the model adjusts for interest rates.

The Greeks are just partial derivatives of this formula. Each Greek tells you how the option price changes when one of these inputs moves.

Black-Scholes makes several assumptions that don’t hold up perfectly in the real world.

It assumes volatility stays constant. In reality, volatility changes all the time. It assumes stock returns follow a log-normal distribution. In reality, markets have fat tails, meaning crashes happen more often than the model expects. It assumes no dividends, though adjusted versions exist for stocks that pay them. It assumes European-style exercise only, while most stock options are actually American-style.

Despite all of this, Black-Scholes remains the standard. When you hear “implied volatility,” that’s the volatility value that, when plugged into the Black-Scholes formula, produces the current market price. Implied volatility isn’t directly observable. It’s backed out from the model. The entire options market speaks in volatility terms because of this framework.

You will find free calculators on tradingriot.com, with a BSM calculator included, so you can see how the calculation works across a range of different options.

Five Primary Greeks

There are five Greeks you will care about: Delta, Gamma, Theta, Vega, and Rho.

These are often called first-order Greeks, although fun fact (not really) is that Gamma is actually a second-order Greek of Delta.

The Greeks measure how an option’s price changes relative to various factors. Understanding them is essential for managing risk and knowing what you’re actually betting on.

Delta

Delta measures how much an option’s price moves for a $1 change in the underlying. It ranges from 0 to 1 for calls and 0 to -1 for puts.

A deep in-the-money call has a delta close to 1.00, meaning it moves almost dollar for dollar with the stock. An at-the-money call sits around 0.50. A far out-of-the-money call might have a delta of 0.05, barely reacting to price changes at all. Puts work the same way but in reverse, with negative deltas.

Let’s say the stock is at $100 and you own a $100 call with a delta of 0.50. If the stock moves to $101, your call gains roughly $0.50. If the stock drops to $99, your call loses roughly $0.50.

Delta also has two practical interpretations beyond price sensitivity. First, it roughly approximates the probability of the option expiring in the money. A delta of 0.50 means roughly a 50% chance. This is less accurate the further out in time you go, more on that maybe sometimes in future. Second, it tells you your equivalent share exposure. One contract with a 0.50 delta gives you exposure equivalent to holding about 50 shares.

Above you can see a delta diagram for a long call. The more in the money your call is, the closer to 1.00 delta it gets. Once an option reaches a delta of 1.00, the position behaves the same as owning 100 shares of the underlying.

Position builder is free to use on tradingriot.com.

Gamma

Gamma measures how fast delta changes as the underlying moves. Think of delta as speed and gamma as acceleration.

At-the-money options have the highest gamma. Their delta is the most sensitive to price changes. Deep in-the-money and far out-of-the-money options have low gamma because their deltas are already close to their extremes and don’t move much.

Here’s a quick example. You hold an ATM call with a delta of 0.50 and gamma of 0.05. The stock moves up $1, and your delta increases to roughly 0.55. Another $1 up and it’s around 0.60. Your position gets increasingly bullish as the stock rises.

Gamma gets particularly intense near expiration for at-the-money options. With days left, a small move in the stock can flip delta from 0.30 to 0.70 in a hurry. This is why expiration week can feel chaotic, you can see how on Gamma diagram the highest gamma is ATM right before the expiration.

If you’re long options, gamma works in your favor. Your delta adjusts to catch profitable moves in either direction. If you’re short options, gamma works against you. The more the stock moves, the worse your position gets. This long gamma vs. short gamma dynamic is one of the most important concepts in options trading, and we’ll revisit it when we talk about strategies.

Theta

Theta measures how much value an option loses each day, all else being equal. It’s expressed as a negative number for option buyers because time is always working against you.

At-the-money options have the highest theta. They carry the most extrinsic value, so they have the most to lose. Deep in-the-money and far out-of-the-money options have lower theta because there’s less extrinsic value to decay.

Take an ATM call priced at $3.00 with a theta of -$0.08. If nothing changes overnight, that option is worth roughly $2.92 tomorrow. After five days of nothing happening, it’s down to about $2.60. You lost $0.40 just by holding it.

Theta decay is not linear. Early in the option’s life, decay is slow. You barely notice it. As expiration approaches, it accelerates. The last two weeks are where theta really starts to bite. This is why short-dated options lose value so fast and why option sellers love selling with 30 to 45 days left, capturing the steepest part of the decay curve.

The key insight: theta is the cost of holding long options and the profit of holding short options. Option sellers make money from time passing. Option buyers lose it.

Vega

Vega measures how much an option’s price changes for a 1% change in implied volatility. This is where options get interesting, because when you trade options, you’re often betting on volatility rather than direction.

At-the-money options have the highest vega. Longer-dated options have higher vega than shorter-dated ones because there’s more time for volatility to play out. All options have positive vega, meaning higher implied volatility increases every option’s price.

Here’s an example. You own an option priced at $5.00 with a vega of 0.15. Implied volatility rises from 25% to 26%, and your option goes to $5.15. If implied volatility drops from 25% to 20%, your option falls to $4.25. That’s a $0.75 loss from a volatility move alone, with the stock potentially not having moved at all.

This is why you can buy a call, have the stock go up, and still lose money. If implied volatility got crushed at the same time, the vega loss can outweigh the delta gain. It happens all the time around events like earnings, where implied volatility collapses after the announcement regardless of which direction the stock moves.

Understanding vega is what separates directional gamblers from actual options traders. Strategies like the variance risk premium, which we cover extensively on TradingRiot, exist specifically to exploit the tendency of implied volatility to overstate realized volatility. You’re selling high vega when IV is elevated and collecting the difference.

Rho

Rho measures how much an option’s price changes for a 1% change in interest rates. For most traders, it’s the least relevant Greek because interest rates move slowly and predictably compared to price and volatility.

Calls have positive rho, meaning higher rates make calls more valuable. Puts have negative rho, so higher rates make puts less valuable. Longer-dated options have higher rho because there’s more time for rates to matter.

For a LEAPS call with a one-year expiration and a rho of 0.25, a 1% rate increase adds about $0.25 to the option’s price. For a 30-day option, rho might be 0.02, which is basically nothing.

Rho starts to matter in a few specific situations: LEAPS options with a year or more to expiration, deep in-the-money options where you’re essentially financing a stock purchase, and high-rate environments or periods around Fed decisions. For short-term weekly or monthly options, you can safely ignore rho. Delta, gamma, theta, and vega will dominate your P&L.

Second and Third Order Greeks

Second-order Greeks measure how the primary Greeks change with respect to price, time, and volatility. Third-order Greeks measure how second-order Greeks change. These are the “Greeks of Greeks,” and they’re used primarily by market makers managing large books and systematic funds running precise hedging.

For most retail traders, you can safely ignore the majority of them. Understanding delta, gamma, theta, and vega covers 95% of what you need for informed trading decisions.

That said, two second-order Greeks are worth knowing about if you trade short-dated options.

Vanna measures how delta changes when IV changes, or equivalently, how vega changes when price moves. This matters because when price falls and IV rises simultaneously, vanna effects amplify the move. Market makers who are short options get hit on both sides at once, forcing aggressive hedging that pushes the market even further. This is the dealer hedging amplification effect you hear about during sharp selloffs. On 0DTE options, vanna can cause wild delta swings. A small move in the underlying combined with an IV spike can flip a position from barely directional to heavily exposed in minutes.

Charm measures how delta changes as time passes. OTM options lose delta over time while ITM options gain delta, even without any price movement. Dealers have to rehedge daily to account for this drift. In 0DTE trading, charm is on overdrive. With the entire time decay compressed into a single session, delta shifts rapidly throughout the day. A slightly OTM call at the open can see its delta melt away by lunch even if price hasn’t moved, and the dealer hedging flows created by this process contribute to the intraday pinning effects you see around large open interest strikes.

Everything beyond vanna and charm, things like vomma, speed, color, zomma, or ultima, is firmly in market maker and quant territory. Interesting to know they exist, but not something you need to lose sleep over.

Volatility

If you have made it this far in the article, you might be asking yourself a very simple and logical question, which most likely goes something like: “Why would I bother with all of this when I can just trade futures?”

That’s understandable. While there is definitely a use case for buying options as directional bets since they can have highly convex payouts and offer great risk-to-reward ratios, the truth is they often bring a large amount of complexity. You’re watching Greeks, timing entries, and racing against theta bleeding out your debit the entire time.

One of the main reasons for choosing options over other derivatives is being able to speculate on volatility.

What Is Volatility?

Volatility measures how much an asset’s price wiggles. High volatility means wiggles a lot. Low volatility means calm, steady movement.

In statistical terms, volatility is the annualized standard deviation of returns. If a stock has 20% volatility, about 68% of the time its annual returns will fall within plus or minus 20%. About 95% of the time, returns will fall within plus or minus 40%.

These are just rough expectations based on a normal distribution, and real markets don’t always behave normally. But it gives you a framework for thinking about what the market is pricing in.

The Rule of 16

There are approximately 252 trading days per year. The square root of 252 is roughly 16 (19 for crypto options as these trade 365 days in year).

This creates a quick conversion between annual and daily volatility.

Daily volatility = annual volatility / 16

Expected daily move = stock price x (IV / 16)

If a stock trades at $100 with an implied volatility of 32%, the expected daily move is $100 x (32% / 16) = $100 x 2% = plus or minus $2. The market expects this stock to move about $2 per day on average.

This is a useful sanity check. If an ATM straddle costs $4 and implied volatility suggests $2 daily moves, you need two days of maximum movement just to break even. Is that realistic? Probably not. That kind of quick math helps you spot overpriced or underpriced options before you even open a position.

Implied vs. Realized Volatility

Implied volatility (IV) is forward-looking. It represents how much of wiggling markets expects to happen. It’s derived from option prices: higher option prices mean higher IV. Think of IV as a measure of uncertainty priced into options.

Realized volatility (RV), also called historical volatility, is backward-looking. It measures what actually happened. It’s calculated from historical price changes over a specific lookback period.

When IV is higher than RV, options are “expensive” relative to what actually materializes, and sellers have an edge. When IV is lower than RV, options are “cheap,” and buyers have an edge.

This sounds simple, but it’s the foundation of the entire variance risk premium, which is one of the most well-documented edges in options markets. We cover it extensively on TradingRiot.

Volatility Characteristics

Volatility has a few key properties that matter for trading.

First, it’s mean-reverting. Volatility tends to return to its long-term average. Extreme high vol eventually calms down. Extreme low vol eventually spikes. This is why selling vol at highs and buying at lows has an edge over time.

Second, it clusters. High-vol days tend to follow high-vol days. Quiet days tend to follow quiet days. Volatility comes in regimes, trending up or trending down before reverting. This means timing matters more than most people think.

Third, the relationship between price and volatility varies by asset class. In equities, there’s a strong negative correlation: vol rises when prices fall and drops when prices rise, this is the classic “staircase up, elevator down” effect, for most equities when prices go up, they go up slowly, but when they drop it tend to be very fast.

This creates the “cascade effect” and is the reason put skew exists in equity options.

Commodities behave differently. Some, like oil and grains, can have positive correlation where prices and vol rise together during supply shocks. Gold often has weak or mixed correlation. Crypto is generally negative like equities but weaker, and it can flip positive during euphoric rallies when vol expands alongside price. FX is mixed and depends on the specific pair and the broader risk-on/risk-off regime. Don’t assume equity vol behavior applies everywhere. Check the asset’s historical correlation.

Fourth, volatility has a term structure. Short-term IV often differs from long-term IV. Normally, longer-dated options have higher IV than shorter-dated ones. This is called contango. During crises, the term structure inverts: near-term IV spikes above long-term IV as the market panics about immediate risk. This is called backwardation or inversion.

IV Percentile and IV Rank

Raw IV numbers are hard to interpret without context. Is 30% IV high or low? It depends entirely on the stock. A 30% IV on a utility stock is extreme. A 30% IV on a meme stock is a quiet day. Percentile metrics provide context.

IV Percentile tells you what percentage of days in the past year had lower IV than the current level. If IV is at the 90th percentile, that means current IV is higher than 90% of all observations over the past year. It considers every data point, making it more robust.

IV Rank tells you where current IV sits relative to its 52-week range. The formula is (current IV - 52-week low) / (52-week high - 52-week low). It’s simpler but can be skewed by a single outlier spike that stretches the range.

High IV percentile (above 70%) generally favors premium selling. Low IV percentile (below 30%) generally favors premium buying. Our platform’s screeners surface these opportunities across over 1,000 markets.

That said, there are two common misconceptions worth addressing.

The first is “always sell when IV percentile is high.” Many strategies recommend selling options when IV percentile is above 50% or 70%. The problem is that high IV tends to cluster and persist longer than expected. If IV is at the 90th percentile, it often stays elevated for days or even months. Selling into rising vol can lead to significant losses as your short options gain value. It’s better to wait for signs of mean reversion, meaning IV that has started to decline from its peak, rather than selling just because it’s “high.”

The second is “low IV percentile means options are cheap, so it’s a good time to sell.” When IV percentile is very low (below 10-20%), premiums are small. Selling cheap options gives you poor risk-to-reward. You collect minimal premium but still face full downside risk. A vol spike from 15% to 30% can devastate short positions even if you correctly predicted the direction. Low IV environments are generally better for buying options or staying flat, not selling.

Term Structure

Implied volatility is not a single number. It varies across expirations, and this relationship is called the term structure.

As mentioned, in normal market conditions, longer-dated options have higher IV than shorter-dated ones. This makes intuitive sense. More time means more uncertainty, and more uncertainty means higher volatility priced in. This upward-sloping shape is called contango.

During crises, the term structure flips. Near-term IV spikes above long-term IV as the market panics about immediate risk while assuming things will eventually calm down. This is called backwardation or inversion.

You can see this clearly in something like the VIX term structure. In a calm market, the VIX might sit at 15 with 3-month futures at 17 and 6-month futures at 19. That’s contango: calm and complacent. In a crisis, the VIX might spike to 35 while 3-month futures sit at 28 and 6-month futures at 24. That’s backwardation: fear and panic concentrated in the short term.

For example, as writing of this article, current conflict in Iran caused near-term VIX to be above the further expirations.

The shape of the term structure tells you a lot about market sentiment, and shifts between contango and backwardation often signal turning points worth paying attention to.

There’s also the concept of forward implied volatility, which lets you extract the vol the market is pricing for a specific future window by comparing two points on the term structure. It’s useful for calendar spreads, detecting events priced into specific windows, and comparing forward vol against historical realized vol for relative value trades. We’ll cover forward volatility in depth in a dedicated article.

Skew

If term structure describes how IV varies across expirations, skew describes how IV varies across strike prices for the same expiration.

In theory, Black-Scholes assumes all strikes should have the same IV. In reality, they never do. That deviation tells you about market expectations and positioning.

In equities, OTM puts typically have 3-8% higher IV than ATM options. OTM calls usually have slightly lower IV. This creates an asymmetric shape called the “smirk” rather than a symmetric smile.

Skew also varies by expiration. Combine strike-axis skew with time-axis term structure and you get the volatility surface, a 3D representation of IV across all strikes and expirations. Short-dated options often have steeper skew with a more pronounced smirk. Longer-dated options have flatter skew as immediate crash fears fade.

Why Skew Exists

Skew is fundamentally a supply and demand imbalance. Options are priced by market makers who must hedge. When one side has more natural flow, prices adjust.

On the put side, there’s massive structural demand. Pension funds hedge equity portfolios because they’re mandated to. Insurance companies buy puts to meet regulatory requirements. Retail investors buy crash protection. Hedge funds buy tail hedges. On the other side, market makers sell these puts reluctantly and at premium prices, while some yield-seeking investors sell puts for income. More buyers than sellers means put prices get bid up, which shows up as higher IV for OTM puts.

On the call side, the dynamic reverses. Buyers are mostly speculators betting on rallies and retail traders buying lottery tickets. Sellers include a massive supply of covered call writers and market makers who are more willing to offer fair prices. More sellers than buyers means call prices stay lower, which means lower IV for OTM calls.

Beyond supply and demand, there are a few other forces that reinforce skew. The leverage effect plays a role: when stocks fall, company debt-to-equity rises, making equity riskier. This self-reinforcing cycle justifies higher put prices. There’s also asymmetric jump risk. Markets can gap down overnight in a crash, but they rarely gap up by the same magnitude. Puts need to price in that asymmetry. Finally, dealer hedging matters. Market makers who sell puts must buy stock as the price falls to stay delta-hedged. This buying pressure during crashes is limited by capital, so they charge more upfront, which means higher IV.

Measuring Skew

The most common way to measure skew is the 25-delta put-call skew, which is the difference between 25-delta put IV and 25-delta call IV.

To make skew actionable, you can normalize it using a z-score, which compares current skew to its historical average. The chart above shows the SPY 25-delta skew z-score from our platform.

Negative z-scores mean puts are expensive relative to calls. You can see the sharp negative spikes lining up with selloffs in SPY, which makes sense. When the market drops, put demand surges and skew steepens. The more extreme the spike, the more panicked the demand for downside protection.

Positive z-scores mean calls are expensive relative to puts. This tends to happen during strong rallies or momentum-driven grinds higher, when upside speculation heats up and call demand pushes OTM call IV above its normal range.

A z-score beyond 2 or below -2 signals an extreme. Both can present opportunities. When skew is extremely negative and puts are expensive, there’s potential to sell put spreads and collect that elevated premium. When skew is flat or positive and puts are cheap, it might be a good time to buy protection before the market reprices risk.

Skew is not static. It shifts with market conditions. Sharp selloffs steepen skew as put demand surges. Rallies flatten skew as put demand fades. Slow grinds higher can sometimes push call skew into positive territory as momentum traders pile in.

You can also use skew as a research input by plotting it against forward returns. The chart above shows SPY skew z-score on the x-axis against 60-day forward returns on the y-axis across 2,437 observations.

The R-squared is low at 0.020, which tells you skew alone is not a standalone predictor of direction. But look at the distribution. When skew is extremely negative (far left of the chart), forward returns tend to skew positive as the market bounces from panic. When skew is positive (right side), the spread of outcomes is more balanced. It's not a signal you trade blindly, but it adds context when combined with other factors like IV percentile and term structure. This scatter plot along with skew z-scores and term structure data are available for every stock and ETF on tradingriot.com.

Options Strategies

Now that you understand all of the important options dynamics, we can talk about strategies. Although these should be called payoff diagrams rather than strategies, since they are just structures you can use to speculate on prices.

As I already mentioned, when trading options, you will notice the lack of classic “Long” and “Short” buttons you might be used to from other derivatives. You trade calls and puts, and you can go both long and short on them.

When you are trading options, you can be either bullish or bearish, make a profit when markets are not moving, or profit on a significant move without being correct on the direction.

Let’s start with the simple things.

Single-Leg Trades

• When you buy a call, you are bullish. Your risk is the premium you paid, and your profit potential is unlimited when the market goes up.

• When you buy a put, you are bearish. Your risk is the premium you paid, and your profit potential is unlimited when the market goes down.

• When you sell a call, you are bearish. Your risk is unlimited, and your maximum gain is the premium you received.

• When you sell a put, you are bullish. Your risk is unlimited, and your maximum gain is the premium you received.

The chart above shows the payoff at expiration for all basic positions. The solid lines are options. The dashed lines are futures (buying and selling an ATM call and put at the same strike creates synthetic futures exposure with a linear payoff).

Notice the key difference. Futures payoffs are straight diagonal lines. You make or lose money linearly with every price move. Long futures profits when price goes up, short futures profits when price goes down. Simple.

Options payoffs have a kink. That kink is the strike price, and it’s what makes options fundamentally different from futures.

The long call (purple) loses only the premium paid if the price stays below the strike, then starts profiting once price moves above the strike plus the premium. Risk is capped, upside is not.

The long put (green) is the mirror image. It loses only the premium if price stays above the strike, then profits as price falls below the strike minus the premium.

The short call (orange) and short put (pink) are the exact opposite. They collect the premium upfront and keep it if price stays on the right side of the strike. But if price moves against them, losses grow with every tick. Capped reward, uncapped risk.

Compare the dashed futures lines to the solid options lines. Futures give you dollar-for-dollar exposure in both directions. Options let you choose your risk profile: pay a premium to cap your downside, or collect a premium and accept open-ended risk.

The thing about options is that you can buy or sell more of them simultaneously to create multi-leg positions.

There is a plethora of these “strategies” using multi-leg options. Some of them have insane names like jade lizard (who came up with that?). I personally think this is a little far-fetched, and instead of knowing all the different names, you should focus on understanding how the payouts on these work.

Vertical Spreads

The simplest multi-leg structure is a vertical spread. You buy one option and sell another at a different strike within the same expiration.

Debit spreads are the directional version. A bull call spread means you buy a lower strike call and sell a higher strike call against it. The short call partially finances your long call, reducing the premium you pay. The trade-off is that you cap your upside at the short strike. Bear put spreads work the same way in reverse: buy a higher strike put, sell a lower strike put.

Debit spreads are particularly useful when skew is extreme. If puts are expensive due to steep skew and you think the move has further to go, buying a put spread instead of a naked put drastically improves your risk-to-reward. You're selling the expensive wing back to the market and keeping the directional exposure. The same applies on the call side during momentum rallies when call skew gets bid up.

Credit spreads flip the logic. Instead of paying for a directional bet, you collect premium and profit if the market stays away from your short strike. A bull put spread means you sell a higher strike put and buy a lower strike put as protection. A bear call spread means you sell a lower strike call and buy a higher strike call above it.

Credit spreads typically have negative risk-to-reward. Your max loss is larger than your max profit. But the trade-off is that you don’t need the market to move in your favor. You just need it to stay relatively flat or not move against you past the short strike. Debit spreads are the opposite. They offer positive risk-to-reward but require momentum to continue in your direction to pay off.

Credit spreads work best in high IV environments where premiums are inflated. You collect more credit upfront, which gives you a wider breakeven and better probability of profit. The long option you buy defines your max loss, so unlike naked short options, your risk is capped on both sides.

Both debit and credit spreads define your max profit and max loss at entry. Max profit on a debit spread is the width between strikes minus the debit paid. Max loss is the debit. For credit spreads, max profit is the credit received. Max loss is the width between strikes minus the credit.

Ratio Spreads

Ratio spreads add an extra dimension. Instead of buying and selling the same number of contracts, you use uneven legs. The most common structure is a 1x2: buy one ATM or ITM option and sell two OTM options against it.

A 1x2 call ratio spread is for a moderately bullish view. You buy one call closer to the money and sell two further out. The extra short call finances the trade, often bringing it to a net credit. Max profit hits at the short strike where both short calls expire worthless and your long call has full value. The catch is that above the upper breakeven, you have a naked short call, so risk becomes unlimited to the upside.

The put ratio spread is the mirror image. Buy one put closer to the money, sell two further out. This one is particularly interesting in steep skew environments. You’re selling the expensive OTM puts that are inflated by skew demand and using them to finance your long put. If the market drops moderately to your short strike, you make maximum profit. If it crashes through the lower breakeven, the naked short put exposure kicks in and losses grow.

Ratio spreads are great for trading skew. When the options you’re selling have elevated IV relative to the ones you’re buying, you collect significantly more premium on the short legs, which widens your breakevens and can turn the entire structure into a net credit. You’re essentially monetizing the skew while still maintaining directional exposure.

The important thing with ratio spreads is knowing when to get out. Since the naked leg creates technically unlimited risk if price blows through the short strike, managing the position and exiting at the right time is the difference between a clever trade and a disaster. But for traders who have a view on both direction and magnitude, they offer a payoff profile you can't replicate with simple verticals.

Risk Reversal

A risk reversal, also called a collar, combines a short option on one side with a long option on the other. The bullish version sells an OTM put and buys an OTM call. The bearish version buys an OTM put and sells an OTM call.

These are popular as hedging structures, but they’re also one of the best ways to trade skew. The idea is simple. When put skew is steep, OTM puts are expensive and OTM calls are relatively cheap. A bullish risk reversal lets you sell the expensive put and use that premium to finance the cheap call, often for zero cost or even a net credit.

Compare this to buying and selling an ATM call and put at the same strike, which creates synthetic futures exposure with a linear payoff. A risk reversal gives you similar directional exposure but benefits from the skew differential. You're getting paid for the asymmetry in implied volatility across strikes rather than paying fair value on both sides.

This makes risk reversals particularly effective when skew is elevated and you think the move is done. If the market just rallied hard and call skew spiked, selling that inflated cal premium and buying cheap put lets you position for a reversal while collecting the skew normalization as a bonus.

You can profit from both the directional move and the skew compressing back to normal levels.

These are often my preferred structures for directional reversal trades. That said, you need to be careful. The short option leg carries unlimited loss potential. If you sell a put and the market keeps crashing through your strike, losses are substantial. Position sizing and exits matter just as much as the thesis.

Volatility Strategies

This is where options really separate themselves from every other instrument. The strategies above were all directional to some degree. You had a view on where the market was going. Volatility strategies don’t care about direction. They care about how much the market moves, or doesn’t.

The concept behind this is delta neutrality. When you buy both a call and a put at the same strike, the positive delta from the call and the negative delta from the put roughly cancel each other out. You’re not long or short the market. You’re long or short volatility itself.

Straddles and Strangles

A long straddle means buying an ATM call and an ATM put at the same strike. You profit if the market makes a big move in either direction. You don’t care which way, you just need it to move far enough to cover the premium you paid on both legs. Long straddles are long gamma, long vega, and short theta. You need realized volatility to exceed what implied volatility priced in. Every day that passes without a big move, theta eats into your position.

A long strangle is the cheaper version. Instead of buying ATM options, you buy OTM calls and OTM puts. The premium is lower, but the breakevens are wider. You need an even bigger move to profit.

Flip both of these around and you get short straddles and short strangles. A short straddle means selling both the ATM call and ATM put. You collect the combined premium and profit if the market stays flat. You’re short gamma, short vega, and long theta. Time decay works in your favor, and you need implied volatility to overstate what actually happens. The risk is that a big move in either direction creates unlimited losses.

A short strangle sells OTM options on both sides instead of ATM. The profit zone is wider since the market has to move past either short strike before you start losing. Higher probability of profit, but still unlimited risk on both sides.

Delta Hedging

Here’s how it works. You sell an ATM straddle on SPY at the 657 strike. At entry, the position is roughly delta neutral. SPY then rallies to $662, and your short call gains delta while your short put loses it. Your position is now net short delta, meaning you’re losing money on the move up.

To neutralize this, you buy SPY shares (or futures) to offset the delta exposure. Now you’re delta neutral again. If SPY then drops back to $655, your position becomes net long delta from the shares you bought. You sell some shares to flatten out again.

Every time you rehedge, you’re locking in small losses. You bought shares higher and sold them lower. These rehedging costs are the realized volatility component of the trade. If the total cost of all your delta hedges (realized vol) is less than the premium you collected (implied vol), you profit. If the market whips around more than implied vol predicted, those hedging costs add up and you lose.

This is the core of volatility trading. You’re not betting on direction. You’re betting that the premium you collected overcompensates for the actual movement that materializes. The entire variance risk premium concept is built on this dynamic.

How often should you delta hedge? What delta should you actually hedge? Should you delta hedge at all or just take the loss on the chin?

These are all good questions. The main thing you need to think about is that delta hedging is trading. Trading costs money in spreads and commissions. Losing money on spreads and commissions sucks.

Because of that, you shouldn’t hedge sporadically but rather be strategic, setting delta bands where you hedge your delta back to zero. For any longer-term options, hedging more than once per day doesn’t make much sense.

You can use a free calculator on tradingriot.com that calculates hedging bands automatically for you, or you can just trade iron condors and butterflies with fixed loss.

Iron Condors and Iron Butterflies

If unlimited risk on short straddles and strangles makes you uncomfortable, iron condors and iron butterflies are the defined-risk versions.

An iron condor is a bull put spread combined with a bear call spread. You sell options on both sides of the market and buy further OTM options as protection. Max profit is the net credit received, and you keep it if the market stays between your two short strikes. Max loss is the width of the wider wing minus the credit. It’s essentially a short strangle with wings that cap your risk.

An iron butterfly is the same idea but centered at one strike. You sell an ATM straddle and buy OTM options on both sides as protection. It’s a defined-risk version of the short straddle. Max profit is higher than an iron condor because ATM options collect more premium, but it only hits max profit if the stock pins exactly at the short strike. In practice, you’re usually taking it off before expiration once you’ve captured enough of the decay.

The key advantage of both structures is that the wings act as insurance against tail moves. Unlike naked short straddles and strangles, you know your worst-case loss at entry.

When buying wings for protection, I see people often trying to get comfortable and create a good risk-to-reward ratio. This is the wrong approach. Spending too much on wings eats a lot of your premium, and it doesn’t really make sense.

When buying wings, you should only do it for the catastrophic case of the underlying teleporting overnight, the kind of move that would send you two tax brackets lower automatically.

I often go for 1-10 delta wings and try not to spend more than 5% of the credit I’m receiving.

To end this chapter, here is return profile per structure (I left out calendar spreads since they tie to forward volatility that I decided to not cover in this article).

Conclusion

Was I able to cover everything you need to know about options trading?

I hope so, but I have likely missed something. At the end of the day, there are whole books written about this stuff. Option Volatility and Pricing by Sheldon Natenberg or Options, Futures, and Other Derivatives by John Hull naturally come to mind.

That being said, I think this gives you a very solid primer to understand options. In the next articles, I will dive deeper into different trading strategies using options.

Price action always differs, and there’s a separate phenomenon called PEAD (Post-Earnings Announcement Drift) that falls outside the scope of this article. But what stays almost constant is the massive repricing of options.

This article covers three distinct approaches to trading around these events.

First, we look at why earnings matter so much and how they dominate stock price movement throughout the year. Then we get into trading volatility at the earnings event itself. Finally, we explore the weeks before earnings, where implied volatility ramps up in ways that create opportunities for both buyers and sellers.

Each approach has real edge and real ways to blow up if you don’t respect the risks.

All analytics in this article are powered by TradingRiot Analytics. Sign up to access data for 1,000+ futures, crypto, stocks, and ETFs, plus education on turning data into strategies. You can use code “ANALYTICS“ to get 50% off your first month.

Why Earnings Dominate Stock Price Movement

Stocks move every day. Usually up a small amount, sometimes down a lot. But a disproportionate share of a stock’s annual volatility gets concentrated into just a handful of days: the earnings announcements.

On any random day, a stock might drift 0.5% on no news. On earnings day, that same stock gaps 8% before the market even opens. These events compress months of uncertainty into a single overnight repricing.

Stocks scheduled to report earnings historically earned an annualized abnormal return of roughly 9.9% just from holding through their announcement windows. More recent research suggests this premium has shrunk as companies now disclose material events more frequently throughout the year. But the core point holds: earnings days produce outsized moves, and that volatility gets priced into options.

You can see on this Nike chart, all those big ugly gaps are caused by earnings (besides one).

This “earnings announcement premium” was first identified back in 1968 and keeps showing up across different time periods and countries. Some researchers attribute it to systematic risk, since earnings reveal information about the broader economy, not just the reporting company. Others blame limited investor attention, since earnings grab eyeballs and trigger retail buying. The explanations vary, but the fact holds: earnings days are where the action is.

For any trader, the massive uncertainty these events create gets priced into options well before the announcement. That pricing is where the real opportunities live.

How Implied Volatility Behaves Around Earnings

The pattern has been studied since the late 1970s and it’s straightforward: implied volatility increases in the weeks leading up to an earnings announcement, peaks just before the event, and drops sharply once the numbers drop.

This is the famous “IV crush.”

Below you can see a chart of the NVDA IV crush.

During the last earnings, pre-earnings IV sat at 6.1% while post-earnings IV came in at 4.8%.

That means before earnings, buying an ATM straddle would cost 6.1% of the stock price. After the announcement, that same straddle was only worth 4.8%.

The -20.8% IV crush means the straddle lost roughly 21% of its value overnight as the earnings uncertainty got removed.

It makes intuitive sense when you think about it in terms of variance.

An option expiring after an earnings announcement has to price in two things: the “normal” day-to-day volatility of the stock (call it ambient volatility) and the one-day jump that earnings can produce (event volatility).

As each trading day passes and the earnings date gets closer, that one-day event becomes a larger percentage of the option’s remaining life. The event’s contribution to total variance stays fixed, but you’re dividing by fewer remaining days, so annualized implied volatility keeps climbing.

Here’s the nuance that trips up most traders: the “IV ramp” heading into earnings is not free money for straddle buyers. The rise in annualized IV is largely mechanical.

It reflects the increasing weight of a fixed event variance as ambient days get consumed. Total variance in the option can actually decline even as the quoted IV number rises. This distinction matters enormously for anyone trying to trade the pre-earnings period. We’ll get into specifics later.

What matters for the volatility trader is how the market prices this event risk, and whether that pricing is systematically too high or too low.

Selling Earnings Volatility

There’s solid empirical evidence that the market consistently overprices the uncertainty around earnings announcements. Implied volatility before earnings typically exceeds the actual move that shows up after the numbers drop.

You can harvest that gap by selling straddles or strangles on stocks reporting earnings. You’re not predicting whether earnings will be a beat or a miss. You’re betting that the market’s expectation exceeds reality.

Why the Premium Exists

Several groups bid up options before earnings. Institutional hedgers need to protect concentrated positions through the event. Portfolio managers with single-stock exposure are often mandated to hedge. Retail traders pile into protective or speculative positions. Market makers widen spreads to account for jump risk. This combination creates systematically inflated premiums.

The premium is compensation for bearing a real risk. Earnings can produce gap moves that no amount of delta hedging can cover. That’s also why this trade sucks a lot (remember all those gaps on the Nike chart).

You as a seller are providing a genuine service: absorbing the uncertainty that others don’t want to hold. The edge exists because the market, on average, overestimates how bad things will be.

Euan Sinclair covers this well in Positional Option Trading, framing it as one of several harvestable risk premiums available to options traders. His key distinction is useful here: this is not a market inefficiency. It’s a risk premium.

People aren’t stupid for buying these options. They’re paying for insurance they genuinely need. But the insurance is, on average, priced too expensively. The seller captures that spread over many trades.

The implied-versus-realized gap shows up clearly in the data. When you compare the implied move priced by options to the actual move that shows up after earnings, the implied tends to overshoot. Not every time. Not even most times by a huge margin. But consistently enough that selling premium across a large sample of earnings events has positive expected value.

The key predictor of earnings straddle returns is simple: the gap between historical realized moves and the current implied move. When the implied move significantly exceeds what the stock has historically done on earnings, short straddles tend to profit. When the implied move is low relative to historical moves, long straddles tend to profit. Intuitive, and confirmed repeatedly.

Why Buying Vol Into Earnings Sucks

I will explain why selling vol sucks in a moment, but before doing that let me cover why buying vol also sucks.

This is one of the most common mistakes in options trading. Buying straddles right before earnings sounds great in theory: “The stock is going to move big, so I should own options!”

The problem is that implied volatility before earnings is already extremely elevated. The market knows the event is coming and prices it aggressively. For a long straddle to profit, the stock needs to move significantly more than the implied move, which is already inflated.

LUNR, for example, is one of the stocks with earnings coming up soon. You can see that 20-day IV, which includes the earnings event, sits at about 130%. But if we exclude earnings, IV drops to 114%. That means buying those options already carries the earnings premium baked in.

Run the math. If a stock has a 10% implied move and you buy the straddle, the stock needs to move well beyond 10% just to break even. Most stocks don’t deliver moves that far exceed what’s implied. The rare ones that do are impossible to predict in advance. Over a large sample, the systematic overpricing of earnings events means selling premium has the statistical edge.

As Sinclair puts it in Volatility Trading, the entire concept of the volatility risk premium comes down to implied volatility being, on average, higher than realized volatility. This phenomenon is amplified around earnings because event risk gets priced at a premium. Buying that premium right at its peak is fighting the strongest headwind in options.

Selling Options is a Concave Strategy

Selling earnings straddles has a specific payoff profile that you need to internalize before trading it. Your profit is capped at the credit received. Your loss is theoretically unlimited if the stock makes a massive gap. You win small and often. You occasionally lose big. Very big.

This is the classic short gamma payoff. Small payouts against occasional large losses. The same dynamic as writing insurance. An insurance company collects small premiums repeatedly and occasionally pays out on catastrophic claims. The edge comes from diversification across hundreds of uncorrelated events, not from any individual policy.

Expect individual trades losing 3-5x the premium collected on moderate gaps. Expect occasional trades losing 10x or more on extreme moves. Expect multi-day winning streaks followed by a single trade that wipes them all out. This will happen. It is the cost of doing business.

The edge is real, but it only shows up over a large number of trades. This is the law of large numbers at work. Any small sample will look random and painful. You need dozens of events each quarter, repeated over multiple quarters, for the statistical advantage to compound. Single quarters can and will be losers.

Improving the results

While some literature frames this trade purely as a law of large numbers where you should sell earnings vol on every single stock, I don’t think that’s 100% true. The outcome of any single earnings event is random, but for some stocks the implied moves are consistently way higher than the average realized moves.

This can of course mean there is information we don’t know about. But selecting stocks that historically show higher implied than realized moves and tend to be profitable to short on earnings beats the approach of just selling volatility on every stock with earnings coming up.

As you can see in the backtest below, trading only stocks where the implied move is larger than the historical average realized move produced better returns with fewer trades.

One thing worth pointing out is that this is still not a free lunch. Blowouts are inevitable. You can notice on the equity curve (the thicker one) that it went straight into a drawdown at the start, while selling volatility on all stocks actually didn’t.

On the TradingRiot website, you can find a screener showing all stocks that meet the criteria used for the backtest with upcoming earnings, allowing you to focus on liquid markets with a more favorable outcome.

Trade Mechanics

The core trade is selling ATM straddles or strangles at the implied move levels on stocks reporting earnings between today’s close and tomorrow’s open. Enter in the last 30-60 minutes before close when IV peaks. Exit 15-30 minutes after the open the next morning, once spreads tighten.

The important detail when choosing between a straddle and a strangle is positioning your breakevens at the implied move. This is the entire thesis behind the trade: the market will underperform its implied move. A straddle collects more premium but has tighter breakevens. A strangle placed at the implied move levels gives the stock room to move within the implied range and you still keep the full credit. Both express the same bet.

Target the closest weekly expiration to maximize event exposure and minimize exposure to non-event days. Size at roughly 0.5-2% of account per trade as credit received. Check the maximum historical move for each stock and make sure to prepare for worst case scenario happening again, this is inevitable pain, but it should wipe out half of your account so don’t be stupid.

Buying protection using condors might seem like a smart idea, but adding wings to create a favorable risk-to-reward profile takes away a lot of edge. You are getting paid to hold tail risk. Hedging it away removes the reason you’re getting paid, in other words, your mom raised no pussy.

Traders can buy 1-5 delta wings to prevent truly catastrophic events, but these should not take away more than 5-10% of credit you are getting and they will also make it harder to exit the position as a spread on the next day.

Being smart with sizing and using historical data to see what the maximum moves were, then accepting that those moves can happen again, is the better approach.

The critical management rule: never hold a losing position hoping it recovers. Post-Earnings Announcement Drift (PEAD) is one of the most robust findings in finance. Stocks that gap on earnings tend to continue drifting in the same direction, not reverse. Hoping for mean reversion after a big gap is gambling.

Pre-Earnings Volatility: Trading the Ramp

The second way to trade earnings volatility is to position before the event, during the period when implied volatility is building. This approach targets the 1-3 weeks before earnings, buying or selling straddles to capture the repricing of event uncertainty as the announcement approaches.

The Myth and the Reality

The “pre-earnings IV ramp” is misunderstood phenomena in options. The popular story goes: “IV rises into earnings, so buy straddles early and sell when IV peaks.” It sounds like free money. It isn’t.

Option implied volatility before earnings is a blend of ambient volatility (normal daily movement) and event volatility (the anticipated earnings jump). As each trading day passes, you’re “consuming” an ambient variance day. The event’s fixed chunk of variance stays the same, but it now sits over fewer remaining days. In variance space, total variance actually decreases. But because IV is quoted as an annualized number, the annualization math makes IV appear to rise.

The visible “ramp” in IV is largely mechanical. Simply buying a straddle and watching IV tick higher does not automatically produce profits. Your theta decay can easily eat the gains from the apparent IV increase, because total option value might not be changing much at all.

Sinclair addresses this concept in Volatility Trading when discussing the decomposition of implied variance into ambient and event components. Jeff Augen covers the same mechanical ramp in Trading Options at Expiration. The core point from both: you cannot trade the ramp blindly. The edge, if it exists, has to come from somewhere else.

Where the Real Edge Lives

The actual opportunity is not in the mechanical ramp. It’s in the repricing of event volatility itself.

Sometimes the market underprices a specific earnings event relative to the stock’s historical norms. When the current implied move is cheap compared to past events, there’s room for the market to reprice that event higher as the announcement approaches. That repricing generates genuine vega profits for straddle holders.

The data analysis behind our Pre-Earnings screener examined this systematically. The key predictors of pre-earnings straddle returns all center on one question: how does the current implied move compare to historical benchmarks?

The strongest signals include the current implied move versus the average realized move across all past earnings, the current implied move versus the average historical implied move, and the current implied move versus the last event’s realized move. The direction is consistent: when current implied is low relative to history, long straddles perform better. When it’s high, they perform worse.

Buying options before earnings is a convex trade, so expect a lot of small losses with occasional large payouts. Keep in mind this kind of sucks because you are buying expensive options before earnings, and you want to exit before the event since that is not what you are betting on.

Because of that, you should focus on stocks with strong momentum already in place coming into earnings and very low VRP, ideally negative, but that will be quite a challenge to find.

As an example, I took a trade in Palantir recently before earnings. This is the perfect candidate: the implied move is way lower than average, and while the last few earnings ended up within the expected boundaries, the blowouts are significant.

This might be a very unpopular thing to say among all the true vol-trading fans who believe in the efficient market hypothesis and the idea that you can’t predict movements in any way.

When I am buying volatility, I am not the biggest fan of buying both puts and calls. I much prefer using momentum and simple technical analysis to bet on direction. The reasoning is straightforward: a long ATM straddle costs double what a single put or call does. If you already have a directional view, paying for the other leg is dead weight that just bleeds theta. Since these are convex trades where most attempts lose, keeping the cost of each attempt low matters, but obviously you do you.

The Short Vol Variant Before Earnings

The pre-earnings window works both ways. When the current implied move is expensive relative to historical norms, the opposite trade makes sense: selling straddles 1-3 weeks before earnings.

The same model’s “Short Vol” signal identifies stocks where event volatility is significantly overpriced. The thesis: IV will not expand further, or the expansion is already priced in. You collect premium from elevated IV and close before the actual announcement.

This is where VRP (Variance Risk Premium) becomes especially critical. For the short vol variant, you want IV extended well above realized volatility, giving you a cushion. For the long vol variant, you ideally want low or negative VRP, where the stock is actually moving but options haven’t caught up yet.

When historical data shows that a stock has consistently overpriced its implied move across multiple quarters, the short pre-earnings straddle can even be held through the announcement itself. You’re combining the pre-event premium capture with the post-event vol crush on a name where the market systematically overestimates earnings risk. This is higher risk, but also brings the additional benefit of post-earnings IV-crush.

In Conclusion

Earnings events bring distinct trading opportunity. Selling straddles at the event captures the short-term overpricing of jump risk. Buying straddles weeks earlier captures the repricing of undervalued event uncertainty. Selling straddles weeks earlier captures the overpricing of event uncertainty that won’t expand further.

These are different strategies with different risk profiles. The earnings event trade is concave (small wins, occasional big losses). The pre-earnings short vol trade is also concave, but compared to the earnings event trade, the further dated options are less sensitive to gamma. That means a single overnight gap won’t hit as hard. The risk shifts instead to the stock trending against you over multiple days before the event, whether through a sustained rally or a slow bleed lower. The pre-earnings long vol trade is convex (many small losses, occasional big wins).

What they share: the edge comes from volume and diversification, not from any single trade. But every one of them requires discipline, proper sizing, and the emotional resilience to sit through inevitable drawdown periods.

Pick good markets to trade and let the law of large numbers do the work.

I started with day trading because that’s what you see everywhere online. The dopamine hits and watching the DOM for 12 hours were fun at first, but I quickly realized staring at charts all day isn’t how I want to spend the next decade.

Day trading involves a lot of discretion. That’s not a bad thing, but the longer I traded, the more I got drawn to building systematic strategies. Trades where I can clearly explain why they make money and have a proven track record of doing so.

The biggest shift from day trading to swing trading was the number of markets I had to cover. Day trading kept me focused on European bonds, indices, and Bitcoin.

Swing trading suddenly had me looking at forex, commodities, ETFs, stocks, and more.

Over time, I figured out what I actually want to build: a portfolio of uncorrelated strategies that harvest different risk premiums across a range of assets, using both futures and options.

Eventually developed several strategies trading momentum or mean reversion using dark pool volumes, options skew, crypto derivatives data, and futures COT positioning.

As I got deeper into options, I started harvesting the variance risk premium by selling what I believed were overpriced contracts, trading volatility around earnings events, and expressing views on forward volatility through calendar spreads.

This quickly resulted in a growing stack of active subscriptions across a range of platforms, costing thousands of dollars a month, while still missing features I actually wanted.

So around six months ago, I decided to consolidate as much of my process as I could. What started as simple Python scripts for custom indicators and local data storage evolved into a fully functional multi-asset end-of-the day analytics platform covering 1,000+ futures, crypto, stocks, and ETFs with deep historical data and a range of proprietary indicators which is now live.

I figured I’d put this article together as a deep dive into all the features you can find on the platform.

Before we get into it, you can check it out at tradingriot.com. Registering a free account gives you access to a range of tools and actual data for selected markets. For those who want the full experience, there’s a premium tier that unlocks unlimited access to all analytics and tools, which I’ll cover throughout the rest of the article.

Those interested trying it out can use code "ANALYTICS" to get 50% off their first month.

Cryptocurrencies

Inside crypto section you will find analytics for over 150 coins using aggregated data from 8 different centralized exchanges and 5 years of historical data.

All markets display perpetuals data like open interest, funding rates, and liquidations with Z-score functions that quickly flag any elevated positioning relative to historical levels.

Besides the perp data, you also get access to spot orderbook delta to clearly see supply or demand coming from spot markets. For Bitcoin and Ethereum, options data is also available, giving you clear insight into volatility and skew positioning.

Bitcoin and ETH have composite regime indicators available. This is my own model that gauges sentiment by combining data from different derivatives. It can be used in several ways, from spotting regime flips to identifying historical extremes of peak fear and euphoria.

One of my main goals with this project was not only to build the analytics but also to distill them into actionable signals. That's why each asset class has its own screener that highlights all the extremes and finds tradeable instruments.

Equities

For equities, analytics across 900+ markets are available: IV term structure, volatility risk premium, skew, dark pool activity, and earnings performance history, all with 10 years of historical data. You also get 12 quarters of earnings history with implied moves and accuracy tracking, plus a news and press releases feed for every symbol.

Most of my trading these days is in equities using options. Selling volatility where it’s overpriced, buying where it’s cheap, trading around earnings, expressing views on skew through risk reversals, or spotting early momentum shifts using dark pool volumes.

In the equities section, you’ll find straddle backtests for both earnings and longer-term VRP. For any dataset that can translate into directional moves, like dark pool volumes or skew, you can see the historical effect on price when extremes were reached.

You also get access to additional analytics dashboards focused on global market performance across sector ETFs, international markets, and the largest single-name stocks.

While crypto and futures have single rule-based strategies, equities can be approached in many different ways. All of these are covered in depth inside the platform's educational section. That's why you'll also find different screeners that surface the best markets for each strategy based on preset criteria.

SPX and Macro Events

Speaking of equities, besides the options analytics for SPY and futures data for ES, SPX also has its own dashboard with a composite regime index built from indicators like market breadth, credit spreads, and VIX regime.

The goal of this section is to give you look of overall strength of stock market, and make more educated decision when to go risk-on and risk-off even for more passive investing if you are trying to capture the positive drift in equities.

On top of that, you can access the Events page, which is free for anyone with a registered account. There you can keep up with all daily events, the economic calendar, and weekly summaries posted on weekends.

Futures

Inside the platform you will also get analytics to the most liquid futures, you can find data such as CFTC Commitment of Traders positioning, seasonality patterns, valuation models, and CME and ICE options on futures data for 35+ contracts across indices, bonds, currencies, metals, energies, grains, meats, and softs.

The options market especially is something not many traders pay attention to, but it can reveal very strong positioning clues. While a lot of other platforms only use ETF equivalents, the data I use comes directly from CME and ICE futures, so you get analytics on a range of different contracts.

The futures scanner then uses a predetermined set of rules for each indicator to provide a directional bias for each market on a weekly basis.

Momentum

One thing which is universal for all markets is the momentum section.

Inside that you will find proprietary indicator designed for both trend following and mean reversion strategies. Directional intensity and volatility regime detection across all asset classes with a proprietary price distribution model and cross-asset screener.

The momentum section can be used as confluence for any strategy, but each asset class also has its own momentum screener. These strip away all other indicators and simply compare momentum using a variety of metrics: performance within sectors, relative volume, and more.

Another tools and features

Besides the main analytics, you'll also find a range of tools and features across the website. Premium subscribers get a daily outlook on their homepage that highlights the top trading opportunities for each day.

If you've followed me in the past, you'll know I've shared plenty of educational articles and content.

I've tried to do the same on the website. All free accounts get access to an educational primer that explains futures and options dynamics in depth. Premium accounts get access to twelve additional educational chapters breaking down each strategy, with systematic rules for trade selection and execution.

Beyond that, anyone with an account can use different calculators, the options strategy position builder and earnings calendar. Premium members also get access to relative value, portfolio, and trade intelligence tools.

In Conclusion

This has been a great personal project over the last few months and has allowed me to create a one-stop place for all the strategies I trade. The website is live at tradingriot.com. Whether you consider joining premium or not, I'd be glad for any feedback.

Feel free to reach out any time with questions, either via email or on X.

One thing that I can comfortably say is that the longer I trade, the less of a fan I am of technical analysis.

Social media is always filled with a bunch of different indicators, price action techniques, trend lines and so on, breaking down usually the hindsight situations that already played out.

I am not saying that all technical analysis is bad, I still use some things to this day, but what I see online most of the time truly reminds me of astrology for the unemployed rather than an actual useful trading approach.

One of the things I learned early on and that stuck with me throughout the years is Auction Market Theory. It is not because it is the best way to view the market, or because there are any hidden secrets behind it, but mostly because it makes sense.

And this is exactly what I cover in this article. We will start with the fundamentals of why markets move, break down Auction Market Theory itself, and then look at the tools used to apply it: Market Profile, Volume Profile, and VWAP.

Before we do so I will leave a supplementary reading list here for those who want to dig deeper into the topic:

• Options, Futures, and Other Derivatives does not cover Auction Market Theory, but it is useful book to understand how markets function.

• Steidlmayer on Markets: Trading with Market Profile, the 2nd Edition

• Markets in Profile: Profiting from the Auction Process

• Mind Over Markets: Power Trading with Market Generated Information, Updated Edition

• CBOT A Six-Part Study Guide To Market Profile

All analytics in this article are powered by TradingRiot Analytics. Sign up to access data for 1,000+ futures, crypto, stocks, and ETFs, plus education on turning data into strategies.

Orderflow and Why Markets Move

A lot of people have no idea why prices actually go up or down.

There are always many theories behind every move, with the word manipulation usually flying around, but fundamentally things are much more simple.

Sometimes you hear that price went up because "there were more buyers than sellers." This is actually also not correct.

Prices move because of the dynamics between market and limit orders, and since for every buyer there has to be a seller and vice versa, prices will move when there is aggression on either side.

What you see above is an example of a trading DOM (Depth of Market).

This particular DOM shows the depth of market for Euro stoxx futures. In trading, there are only two types of orders that matter for understanding price movement.

Market Orders

Market orders are orders to buy or sell at the best available price right now. They are executed immediately against the resting limit orders in the order book. Market orders are the aggressive side of the equation.

They are the ones that move prices, because every time a market buy order takes out the best available ask, the price ticks up.

Every time a market sell order takes out the best available bid, the price ticks down. This is how and why prices move.

It is not because of an RSI crossing a level or a candle closing above a moving average. Prices move because aggressive participants are willing to pay the spread to get filled immediately.

Limit Orders

Limit orders are resting orders placed on the order book at a specific price.

They advertise the intention to buy or sell and sit there waiting to be executed against incoming market orders.

Limit orders are often called the heavier hand in the market, because large players cannot afford to use market orders.

If they did, they would incur considerable slippage trying to fill large positions.

Instead, they use various advanced order types such as icebergs to hide their intentions and avoid getting front-run.

If you are placing a limit order on the order book, you are making liquidity. Because limit orders are the heavier hand, they are the ones that stop markets in trending environments from advancing or declining further.

Aggressive market participants firing off market orders often hit these limit walls and get absorbed.

This dynamic between aggressive market orders and passive limit orders is the foundation of everything that follows.

What is Auction Market Theory?

J. Peter Steidlmayer developed the concept of Auction Market Theory. Jim Dalton was one of the first to recognize the value of Steidlmayer’s ideas and contributed to them significantly in his book Mind Over Markets. If you want to go deeper on this topic, those are the two names to look up.

Auction Market Theory breaks down the market’s primary purpose and how market participants interact to fulfil that purpose.

The main idea lies in the fact that financial markets are no different from any other auction where buyers and sellers meet daily. There are two major things every market is trying to achieve:

1. Facilitate trade in a two-way auction process

2. Seek the fair value of the asset

A two-way auction means that the end of the “up” auction is followed by the beginning of the “down” auction and vice versa.

Auction Market Theory translates this process through supply and demand dynamics and price discovery. This process is then represented by tools such as Market Profile or Volume Profile, which produce bell-shaped curves representing 68% of activity as one standard deviation from the mean.

A Practical Example

To make this tangible, let's look at a hypothetical example in the stock market. Say that one share of Samsung stock trades at $100 (which is actually not that far off from the price in Korean won where it actually trades).

A new Samsung phone comes out and it is terrible: the battery is not working, overheating, the whole mess.

Because of this event, Samsung stock starts dropping in value until it finds new buyers at, let's say, $50 per share. This is where a new value area is created.

After some time, the phones get repaired and the price of the stock starts to rise again. Where is the market likely to stop?

The previous value area around $100. This is eventually what every market does. Market participants constantly negotiate prices between balanced and imbalanced values.

Here you have a chart of Samsung.

While I have absolutely zero clue what caused the price movements from 2019 to 2023, it reflects our scenario quite well.

Value built at the lows, followed by a move to the upside where new fair value was established, and once price sold off, it found support at the prior value area.

When it comes to trading, there is always a narrative. Trump said this, war here or there, Powell did that, someone ate a bat in China and so on.

I earlier mentioned that the longer I trade, the less I care about technical analysis. If there is something I care about even less, it is all of these narratives.

What every market does is facilitate trade, and market participants negotiate prices between balanced and imbalanced values.

Auction Market Theory defines an area where 68% of the volume has traded as a Value Area. Inside the Value Area is also a Point of Control; this is the level where the market traded the most volume or spent the most time.

• A Market Profile represents the Value Area and Point of Control based on time.

• A Volume Profile represents the Value Area and Point of Control based on volume.

This advertising mechanism and price always seeking value can be represented in the image below.

Value Area and Point of Control

Auction Market Theory defines an area where 68% of the volume has traded as a Value Area. Inside the Value Area is also a Point of Control (POC), the level where the market traded the most volume or spent the most time.

• A Market Profile represents the Value Area and Point of Control based on time.

• A Volume Profile represents the Value Area and Point of Control based on volume.

Both are tools that present us with value areas in a non-subjective way, which is one of their greatest advantages over hand-drawn support and resistance levels.

Key Components of AMT

There are three key components of Auction Market Theory:

Price advertises opportunity in the market. When price moves away from value, it is advertising to potential participants that there may be an opportunity. If a underlying drops significantly below its established fair value, that low price is essentially advertising to buyers that there might be a bargain available.

Time regulates price opportunity. The longer the market spends at a certain price level, the more accepted that price becomes. Time validates or invalidates the price discovery process.

Volume measures the success or failure of the auction. Volume represents the interaction of market participants at different levels. From these three components, volume is arguably the most important in the current age of electronic trading. By looking at volume, we can see where other market participants were interested in doing business, which can significantly help us make more educated trading decisions.

Balance and Imbalance

To fully understand how markets behave, we have to understand the context of a given market, because there are two types of auction environments.

Balanced Markets

In a balanced market, buyers and sellers agree on prices and they are willing to buy and sell at current levels. They perceive these prices as fair value. A balanced market is usually represented by lower volatility.

Prices remain more or less the same, and markets are ranging. Thanks to Market Profile, Volume Profile or VWAP, we can easily recognize fair value. It looks like the Gaussian bell-shaped curve.

Imbalanced Markets

With new information coming to the market, whether fundamental or technical drivers, markets leave fair value and shift to an imbalanced state.

The imbalance is the exact opposite of balance. There is a disagreement about fair value, one side of market participants is more aggressive, and the market trends.

Despite popular belief that markets always absolutely have to trend, markets are mostly balanced. This tendency can be especially observed in the options market by the fact that implied volatility (the market’s expectation of future volatility) overstates realized volatility.

This is why selling structures like straddles or strangles is often a profitable thing to do, until it is not and you get run over by the market being imbalanced.

While you obviously cannot predict the future, using tools to help you establish fair value can be extremely beneficial for having an idea of future market behavior.

Auction Market Theory Rules

Although Auction Market Theory is not a trading strategy by itself, there are some rules that can be applied.

Rule 1: If the price accepts into the balance area, it is likely to revert to the other side. Price often retests the edge of the balance area before travelling to the opposite side.

Rule 2: Price inside a balance is expected to reject the edges and behavior is choppier. This is where most traders get chopped up trying to trade breakouts that never materialize.

Rule 3: Once price accepts outside the balance, it is likely to become imbalanced and seek new value, often the POC of an older balance area.

Rule 4: If the price reacts strongly from POC, it can disrupt Rule 1. A strong rejection from POC can prevent the full rotation to the other side of the balance.

Rule 5: If time and volume build at the edge of a balance, price is likely to push through. When you see the market spending time at the boundary of a balance area and volume is increasing, that is often the setup for a genuine breakout rather than a failed auction.

Market Profile: The Original AMT Tool

Market Profile was created in the early 1980s at the Chicago Board of Trade (CBOT) by Peter Steidlmayer.

Using the statistical bell curve, Steidlmayer developed a system that displays trading data and shows the activity happening during a trading session.

In 1985, he published the CBOT Market Profile guide. Although Market Profile has evolved since the 80s, the core principles have remained the same.

Market Profile is not a trading strategy. It is a tool for organizing data in a way that gives us a different, and I would argue better, view of the market.

How Market Profile Works

The building block of a Market Profile chart is the TPO, or Time Price Opportunity. The default and most common setting uses 30-minute time intervals.

Instead of candlesticks, you see strings of letters. Each string of letters represents one 30-minute candle. After the trading session is done, you can collapse all letters to form a bell-shaped distribution.

In the Market Profile, the Value Area represents 68% of data within one standard deviation of the mean. In other words, it is the area that contains 68% of the trades that occurred during a single session, measured by time.

Because this is where most of the activity happened, we can say that buyers and sellers agreed on prices in this area. In the center of the Value Area sits the Point of Control, the price closest to the center where the most TPOs printed.

The great advantage of using Market Profile is that you end up with market-generated information instead of subjective levels.

Volume Profile

If you understand Auction Market Theory and Market Profile, Volume Profile becomes very easy.

Volume represents the number of contracts or shares traded between buyers and sellers for a given period.

In stocks, volume is the number of shares traded. In futures and options, it is based on the number of contracts exchanged.

From the three components of Auction Market Theory, volume is the most important in the current age of trading.

By looking at volume, we can see where other market participants were interested in doing business.

Market Profile vs Volume Profile

The key difference is simple.

Market Profile measures how much time was spent at each price level. Volume Profile measures how much volume was executed at each price level.

Both produce a similar bell-curve shape, but the levels they generate can differ.

It is honestly completely up to you which one you prefer. There is a strong argument for using volume since in the current day and age volume is what matters, but it can also be easily skewed.

For example, in futures the most volume during a trading day happens in the last hour as many traders are simply forced to close positions. This does not necessarily represent the market's fair value.

Composite Values

While Market Profile and Volume Profile are often displayed on a day-to-day basis, you might have noticed that in previous examples I showed you the value areas over many days, sometimes weeks or months.

These are called composite values. Composite values are created when you overlay one or more days where the value areas overlap.

This can be done with both Volume and Market Profiles. Volume composites are usually used to highlight bigger ranges while Market Profile composites serve day trading ranges.

These composite can be either created manually by eye-balling the overlapping values, or used on fixed look backs such as weekly, monthly, quarterly or yearly.

Volume Weighted Average Price

Volume Weighted Average Price (VWAP) is calculated by taking the cumulative total of price multiplied by volume, divided by the cumulative total of volume. In simple terms, it gives you the average price of an asset weighted by how much volume was traded at each price level.

The key difference between VWAP and a simple moving average is that VWAP puts more weight on price points where more volume was traded. On a 30-minute chart, you might see a $0.30 difference between a 24-hour simple moving average and the VWAP. That difference comes entirely from the volume weighting.

VWAP is most often plotted on fixed time horizons such as daily, weekly, monthly, or yearly, but you might also experiment with anchored or rolling VWAPs.

The Institutional Use Argument

If you have ever watched a video or read an article about VWAP, one of the first things you hear is that VWAP is used by large players and therefore you must use it too.

The reality is more nuanced. Institutional traders often use VWAP as a benchmark for execution quality, not necessarily as a directional signal. Their algorithms are designed to execute large orders at or near VWAP to minimize market impact. This does not mean that VWAP acts as some magic level of support and resistance because institutions are “defending” it.

It is completely useless to make huge assumptions based on VWAP and justify trading decisions with this “institutional trading approach,” simply because you never know what someone on the other side of the screen is about to do. With that said, VWAP is a valuable tool when used properly and without the mythology.

VWAP Standard Deviation Bands

This is where VWAP and Auction Market Theory connect.

By adding standard deviation bands around VWAP, we create a framework that directly mirrors AMT concepts: the VWAP line represents fair value, and the bands represent areas where price is statistically extended from that fair value.

As you can see on the chart of Canadian dollar futures, the yearly VWAP is used, therefore each VWAP represents one trading year.

After price re-accepted into the prior year’s value area, it traveled to the prior year’s VWAP which acted as a point of control.

After breaking down, the -1 standard deviation served as resistance before rotation through the current value area.

These levels are never going to be perfect, but they This is where you can very loosely follow the Auction Market Theory rules.

I generally expect prices to range when they hit a key Point of Control or VWAP. When the reaction is strong, this can break that rule.

I prefer to look for trending moves once price breaks outside of the value, simply because there is going to be less volume resisting price from going in the desired direction.

Putting It All Together

Auction Market Theory is a framework, not a strategy.

It tells you how to think about markets: as a continuous two-way auction process where participants negotiate prices between balance and imbalance.

Market Profile gives you the time-based structure of that auction. Volume Profile gives you the volume-based structure. VWAP with standard deviation bands gives you a dynamic, representation of fair value and its boundaries.

None of these tools work in isolation, and none of them are a magic.

What they provide is context. Knowing whether you are inside a value area or outside it, whether the market is balanced or imbalanced, whether new prices are being accepted or rejected, these are the questions that actually matter.

Whether you are trading directionally or running non-directional strategies like selling options premium, knowing where fair value sits and whether the market is balanced or imbalanced gives you a significant edge.

Even simple technical analysis such as price action can be beneficial when used as confluence to spot acceptance into value or breaks outside of it.

At the end of the day, no single tool or concept will give you all the answers, but understanding the auction process behind every market will make everything else you use more effective.

We’re seeing a lot of “rotation from metals to crypto, coming any day” posts on the timeline. Unfortunately, people who post such things are well-known mouthbreathers whose only profitable PnL comes in the form of monthly X engagement checks.

I want to spend some time analyzing if this "rotation from metals" actually has any substance (spoiler alert: it doesn't), and then share some historical turning points for crypto and how you could spot them.

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Bitcoin and Gold relationship

The first obvious issue with finding a relationship between gold tops and Bitcoin performance is that gold didn't really have many tops in the last decade.

As much as hopium is awesome, when making claims on the internet, it’s good to have data backing them up so we don’t sound like complete morons. In the last 10 years, gold had three decently sized corrections: 2018, 2020, and 2022. Three data points. That alone would be enough for me to not bother further, but for the sake of this article, let’s dig in anyway.

If you look at the chart above, two out of three gold tops actually preceded a BTC downtrend, 2018 and 2022. The only time Bitcoin rallied was during the risk-on craze of 2020.

The all-time correlation over about 10 years of data is 0.8, which makes sense since both markets went up over time. But correlation doesn't tell you what you actually want to know here. When you want to explore if two assets have this desired relationship of reverting when one outperforms the other, you need to look at cointegration.

Cointegration

Correlation measures whether two assets move together day-to-day. Cointegration is different, it asks whether two assets maintain a stable long-term relationship that snaps back when it gets stretched. Think of it like two drunk friends walking home: they might wander all over the place individually (non-stationary), but if they’re tied together with a rope, they can’t drift too far apart. That’s cointegration.

For the “rotation” narrative to have any substance, you’d need BTC and gold to be cointegrated, meaning when gold rips and BTC lags, there’s some actual force pulling them back together.

Looking at the chart above, here’s what the data actually says:

The Engle-Granger cointegration test finds no cointegration. Full sample p-value is 0.44, nowhere near the 0.05 threshold you’d need. Rolling 2-year windows show 0 out of 31 periods with cointegration at 5% significance. The spread residual is non-stationary.

The simple BTC/Gold ratio tells a slightly more optimistic story, but not by much. The ADF test on the ratio is borderline stationary (p = 0.034), suggesting the ratio may weakly mean-revert. But the half-life is around 216 days, roughly 7 months. That’s glacially slow and buried in noise.

Currently, BTC is priced at about 16 ounces of gold, which is 11% above the historical mean of 14.4. The z-score sits at -2.62, meaning BTC looks “cheap” relative to gold by historical standards.

But here’s the thing: that reading is largely driven by gold’s recent parabolic move, not some reliable mean-reverting relationship pulling them together. There’s no robust cointegration. They’re fundamentally different assets, gold is a mature safe haven, BTC is a volatile risk asset that happened to trend upward over the same period.

For those of you that have zero clue what any of that means, here is a quick stats primer.

The Engle-Granger test is the standard way to check for cointegration. It works by fitting a regression between two assets, then testing whether the residuals (the “spread” between them) are stationary, meaning they fluctuate around a stable mean rather than wandering off forever. If the residuals are stationary, the assets are cointegrated.

The ADF test (Augmented Dickey-Fuller) is what actually checks for stationarity. It tests whether a time series has a unit root, fancy way of saying “does this thing trend or mean-revert?” A low p-value (below 0.05) means you can reject the unit root hypothesis and conclude the series is stationary, i.e., it mean-reverts.

Half-life tells you how fast that mean reversion happens. If a spread has a half-life of 30 days, it means when the spread gets stretched, half of that deviation will correct in about a month. Short half-life = tradeable. Long half-life = basically useless for anything except buy-and-hold prayers.

Anyway, I’ve always thought that trying to tie Bitcoin to any tradfi asset is silly. People use this mostly to fit whatever narrative currently matches their bias—one day Bitcoin is digital gold, next day it’s Nasdaq on crack.

The stock market correlation is honestly much more accurate. Bitcoin has seen both tops and bottoms in line with SPX over the last 5 years—up until now, where SPX is sitting at all-time highs while BTC is down 40% from its peak.

Because of that, you should view Bitcoin as its own thing. It’s not a metal, nobody is going to treat an asset with 50%+ annualized volatility as a safe haven (for context, gold sits around 15%, and even that’s considered volatile for a “store of value”). It’s also not a stock index, there are no components in Bitcoin, it’s just code. It’s had a rotating cast of narratives over the years: payments, store of value, digital gold, global reserve asset, and so on.

While all of them sound nice, this is still a fairly young market and it’s very hard to say if it has any actual use case besides being a speculative asset. Which is completely fine, but it’s good to be realistic about these things.

The Bottom

Reliably timing the bottom in Bitcoin is extremely hard, not that it’s easy in any other market, but Bitcoin has changed so much over the years that historical patterns become even less useful.

While gold and SPX were more or less the same a decade ago, the main reason to own Bitcoin in 2015 was to buy heroin online.

This has obviously changed dramatically. We have much more serious participants now, especially after the massive open interest influx into CME futures and options in 2023, and the introduction of ETFs in 2024.

Bitcoin is a very volatile market, and if we can say one thing with decent confidence, it's that bottoms are often marked by large overreactions and blow-offs across different derivatives. This includes crypto-native metrics like open interest or funding rates, as well as more institutional signals like options skew and ETF flows.

I've personally built an indicator that tracks these things in a composite regime, but you can see the red areas marking a more depressed state of the market: decreasing open interest, negative funding rates, traders overpaying for puts, and realized volatility outpacing implied.

The spot-vol correlation in Bitcoin, while a little more all over the place, is increasingly resembling equity indices.

In conclusion

If you came here hoping for “Entry, SL, TP,” I’m sorry to disappoint (not really).

The point of this exercise was to almost state the obvious: Bitcoin is its own market. While it sometimes resembles gold or stocks in some ways, there is no fundamental reason for these to move in tandem.

If you’re watching prices go down right now and want a better understanding of when the bottom is going to happen, look at data that’s actually important for the market you’re trying to trade. Look at positioning, as this always tells the best story. And remember that most bottoms are made during the time when everyone has already given up.

Jokes aside, we are seeing a major shift in perpetual futures markets. Binance just launched gold and silver perpetual contracts settled in USDT.

Their API suggests stock perpetuals are next.

Decentralized exchanges like Hyperliquid, Ostium, or Lighter already offer perpetual contracts on equities, commodities, and forex. Traditional players are watching closely, with TD Securities publishing research on how perpetuals could reshape tokenized equity markets.

This creates new opportunities. It gives crypto-native traders access to new markets and TradFi natives access to new derivatives.

It also creates new ways to lose money if you do not understand what you are trading.

Perpetual futures are not complicated, but they have mechanics that differ from spot markets and traditional futures.

This guide covers everything you need to know, from the basic structure to the data that actually matters for trading decisions.

The knowledge shared applies to all perpetual futures.

One thing I would note is the liquidity issues of newly listed TradFi markets. You should be careful trading those, as this is still very fresh innovation.

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What Are Perpetual Futures

Perpetual futures are derivative contracts that let you trade an asset with leverage without ever taking delivery.

Unlike traditional futures, they have no expiration date. You can hold a position indefinitely, as long as you maintain sufficient margin.

They function similarly to Contracts for Difference (CFDs), which are popular in forex and equity markets outside the US. The key difference is that perpetual futures trade on exchanges with standardized contracts, rather than directly with a broker who sets their own terms.

Because there is no expiration, perpetual futures need a mechanism to keep their price aligned with the underlying spot market. This is where the funding rate comes in, which I will cover in detail below.

A Brief History

Many people think perpetual futures are a crypto invention. They are not.

The concept was proposed by economist Robert Shiller in 1992. His idea was to enable derivatives markets for illiquid assets like real estate, where traditional futures with expiration dates would not work well.

The problem was that nobody actually built them for those markets.

In 2011, Alexey Bragin developed an early version for ICBIT, a Bitcoin derivatives platform. But perpetual futures truly became popular in 2016 when BitMEX launched them for Bitcoin.

BitMEX’s innovation was combining three things: inverse contracts denominated in Bitcoin, a funding rate mechanism to anchor prices, and high leverage accessible to anyone. This combination turned out to be extremely popular with retail traders.

By 2020, most volume had shifted from inverse contracts to linear contracts denominated in stablecoins. Binance and FTX (rest in piss) drove this transition.

Today, perpetual futures account for roughly 75% of all crypto volume, with over 90% of that activity happening on offshore exchanges.

Now we are seeing the model expand beyond crypto. The same structure that works for Bitcoin is being applied to gold, silver, oil, equities, and forex.

The appeal is straightforward: 24/7 trading, high leverage, and no expiration dates.

Linear Contracts

Linear contracts are the simpler version and where most volume sits today.

They are both settled and quoted in the same currency, typically a stablecoin like USDT. If you have $100,000 USDT in your account and want to go long 1 BTC at $100,000, you provide $100,000 as margin.

Your profit and loss is calculated in USDT.

If Bitcoin rises to $101,000, you make $1,000 USDT. If it falls to $99,000, you lose $1,000 USDT.

Inverse Contracts

Inverse contracts are more confusing but were actually the original design.

They are settled in the underlying coin but quoted in USD. For an inverse BTCUSD contract, your collateral is Bitcoin, but the chart shows prices in dollars.

This creates non-linear P&L. If you are long, you make Bitcoin when the dollar price goes up and lose Bitcoin when it goes down.

But because Bitcoin itself is changing in dollar value, the math gets complicated.

if you hold 1 BTC and open a 1x short on an inverse BTCUSD contract, you are effectively hedged in dollar terms.

If Bitcoin falls from $100,000 to $90,000, you lose $10,000 of value on your BTC holding but gain roughly $10,000 equivalent on your short position (denominated in BTC).

Inverse contracts used to dominate when BitMEX and early Bybit were the main venues.

They have fallen out of favor because linear contracts are easier to reason about, and stablecoin availability removed the need to keep all your capital in volatile assets.

There are also Quanto contracts, but nobody really trades them, and they are confusing enough that we can pretend they do not exist.

Nowadays, inverse contracts (also often called coin-margined) are still traded here and there, but are mostly used by those who want to denominate their whole portfolio in a chosen coin. BTC is the most popular, obviously.

The Funding Rate Mechanism

The funding rate is what makes perpetual futures work. Without it, the price of a perpetual contract would drift away from the spot price over time, since there is no expiration forcing convergence.

The mechanism is straightforward: every eight hours (on most exchanges), one side of the market pays the other.

• If the perpetual price is trading above spot, funding is positive. Longs pay shorts.

• If the perpetual price is trading below spot, funding is negative. Shorts pay longs.

This creates an incentive. If funding is very positive, there is money to be made by shorting the perpetual and buying spot (this is the cash-and-carry or basis trade).

Arbitrageurs doing this push the perpetual price back down toward spot.

How Funding Rate Is Calculated

The funding rate formula varies slightly between exchanges, but the core structure is the same:

Funding Rate = Interest Rate + Premium Index

The Interest Rate is typically a fixed component, usually 0.01% per 8-hour period on most exchanges (equivalent to roughly 10.95% annualized) which exists as crypto-natives prefer long side over the short.

This represents the cost difference between holding the base currency (e.g., BTC) versus the quote currency (e.g., USD).

The Premium Index measures how far the perpetual price deviates from the spot price:

Premium Index = (Perpetual Price - Spot Price) / Spot Price

Most exchanges apply a dampener or clamp to prevent extreme funding rates.

Binance, for example, caps the premium component and uses a time-weighted average price (TWAP) over the funding interval rather than a single snapshot.

The final funding payment you pay or receive is:

Funding Payment = Position Size × Funding Rate

If you are long 10 BTC and the funding rate is 0.01%, you pay 0.001 BTC to shorts. If funding is -0.01%, you receive 0.001 BTC from shorts.

Funding Rates as a Directional Trading Signal

Many traders treat funding rates as a contrarian indicator. Funding is very positive? Everyone must be long. Time to short.

This is too simple.

High funding can persist for extended periods during strong trends. During crypto rallies, funding often stays positive for weeks or months. Traders who shorted just because funding was high got run over.

Think of funding as the cost of doing business. If I believe the market will rise significantly, I do not mind paying funding every eight hours to maintain my position. The expected gain from the move exceeds the funding cost.

Funding becomes more useful as a contrarian signal when it reaches extremes and persists there. Historically, we saw extreme funding rates during all major tops and bottoms in Bitcoin over the years.

But with the introduction of ETFs and more institutional players coming in, funding rates are not what they used to be. Yield has gotten much lower than it was just few years ago.

This can be an issue for those who expect markets to top on 2021 levels of euphoric funding rates or bottom on 2022 FTX crash levels of yield.

Funding Rate Arbitrage

The basis trade (also called cash-and-carry) is one of the most common strategies in crypto. The setup is simple: when funding is positive, you buy spot and short the perpetual. You collect funding payments while being delta neutral.

Here is a concrete example:

You notice that ETH funding on Binance is running at 0.05% per 8 hours (about 55% annualized). You buy 10 ETH spot on Binance at $3,000 and simultaneously open a 10 ETH short perpetual position.

Your net exposure to ETH price is zero. Every 8 hours, you collect 0.05% on your short position, paid by the longs.

If funding stays at this level for a week, you collect roughly 1.05% return on a delta-neutral position. Scale this up and it becomes real money.

Some traders run this across exchanges. For example, you might hold spot ETH on Coinbase (where you can earn staking yield) and short perpetuals on Bybit (where funding is highest). This adds complexity but can improve returns.

Huge rise of decentralized exchanges also popularized the cross-exchange funding rate arbitrage.

As you might notice on screenshot above, the same perpetual contract often has different funding rates across exchanges.

This creates arbitrage opportunities where you can collect funding on one exchange while paying less (or receiving funding) on another.

These opportunities often provide much higher yield, but also much greater risks.

For example, 雪球 (Snowball token) shows +10.9% on Aster and +15,604.8% on Gate.io.

If those numbers are accurate, you could short the perpetual on Gate.io (collecting massive funding) and long the perpetual on Aster (paying much smaller funding).

Your net position is delta neutral, but you pocket the funding differential.

The math on an extreme case like this:

You short 1,000 USDT notional on Gate.io at +15,604% annualized funding. You long 1,000 USDT notional on Aster at +10.9% annualized funding. Your net funding is roughly (15,604% - 10.9%) / 365 = ~42% per day.

These funding rates obviously do not last for the whole year, but can still yield nice returns as the spreads are massive.

Why These Spreads Exist

Funding rates reflect supply and demand on each specific exchange. A token might have heavy short interest from hedgers on one venue and heavy long interest from speculators on another.

Low liquidity altcoins show the widest differentials because arbitrageurs cannot easily move size between venues to close the gap.

Additional Risks of Cross-Exchange Arbitrage

Beyond the standard basis trade risks, cross-exchange funding arbitrage adds:

Capital inefficiency. You need margin deposited on both exchanges. Your capital is split, reducing overall returns and creating rebalancing headaches.

Transfer delays. If one leg gets liquidated or funding flips, you cannot instantly move funds to rebalance. Blockchain transfers take time, and some exchanges have withdrawal delays.

Price divergence. The perpetual price itself can differ between exchanges. You might enter at slightly different prices, creating unintended directional exposure. During volatile moves, this divergence can widen significantly.

Counterparty concentration. Many of the exchanges showing extreme funding rates are smaller, less established venues. The highest yields often come with the highest counterparty risk.

Liquidity on exit. Getting into a position is easy. Exiting a large position on a low-liquidity exchange during a volatile move is where the trade falls apart. Slippage on exit can erase weeks of funding.

The tokens showing -2,000% annualized funding exist for a reason.

The market is paying you that rate because holding long exposure is painful, whether from constant price decline, upcoming unlocks, or structural selling pressure.

Funding arbitrage is a risk premia which works until the moment it does not.

Risks of the Basis Trade

Even if you only focus on “safer” basis trades, they are still not risk-free, despite being delta neutral.

Funding rate reversal. Funding can flip negative. If you are short perpetual and funding turns negative, you start paying instead of collecting. Unwinding the position during a funding reversal often means taking a loss.

Liquidation risk. Your short perpetual requires margin. If the spot price spikes hard and fast, your short can get liquidated before you can add margin, even though your spot position gained value.

This is especially dangerous if you are running the trade across exchanges, since your spot collateral on one exchange cannot cover your margin on another.

Exchange risk. Running the trade across venues means you have counterparty exposure to multiple exchanges. If one exchange freezes withdrawals or goes down (we have seen this happen), you are stuck with a one-sided position.

Execution risk. You need to enter and exit both legs simultaneously. In volatile markets, slippage on one leg can eat into your profits or create unintended directional exposure.

What Drives Extreme Funding Rates

Extreme positive funding happens during euphoric rallies when everyone wants to be long. The demand for long exposure exceeds the available supply of shorts, so longs pay a premium to maintain their positions.

Extreme negative funding is more interesting and happens frequently in altcoins.

A few dynamics drive this:

Market makers hedging. When market makers sell tokens to retail buyers in spot markets (during token unlocks, airdrops, or just normal selling), they often hedge by shorting perpetuals. This creates selling pressure on the perpetual side, pushing funding negative.

Anticipated unlocks or sell pressure. If a large token unlock is approaching, traders front-run the expected selling by shorting perpetuals. This drives funding deeply negative even before the actual selling happens.

Lack of organic long demand. Many altcoins simply do not have enough buyers who want leveraged long exposure. The short side is dominated by hedgers and speculators betting on the project failing, while the long side is thin. This structural imbalance keeps funding persistently negative.

Spot premium dynamics. Sometimes altcoins trade at a premium on spot markets (due to low float, exchange listing differences, or withdrawal issues) while perpetuals trade closer to fair value. This creates persistent negative funding.

You will often see altcoins with funding rates of -0.1% to -0.5% per 8 hours (that is -100% to -500% annualized).

This is the market paying you to be long, which sounds attractive until you realize the token might drop 80% anyway.

The funding income rarely compensates for the directional risk, but these trades are still worth exploring.

If I am being honest, I do not really trade low market cap altcoins anymore.

But what I used to do a lot was look at these extremely negative funding rate flips very early on to try to find the next “scam pump”.

At the end of the day, it does not really matter what causes funding to go extremely negative.

What matters is seeing large open interest rise alongside negative funding.

This tells you that while perpetual traders are opening positions, the discount against spot persists.

Someone is aggressively shorting perpetuals while spot buying happens elsewhere, often market makers hedging spot accumulation or insiders positioning before a move.

You will often see this at the very early stages of these pumps, with price action still looking muted.

The chart of RIVER shows this pattern clearly: open interest spiked while funding went deeply negative, all while price barely moved. What followed was a multi-hundred percent rally.

While funding often stays negative even at the top, by the time prices are trading at several multiples of their daily ATR, you are already too late to enter.

The edge is in spotting the setup early, when the data looks unusual but the price chart looks boring.

You can obviously also try to short the top, as these almost always dump back down.

But I would argue that trade is extremely hard to time.

At the highs the trade is already very crowded, you are paying funding on the short side, and the market can stay silly longer than you can stay solvent.

Open Interest

Open interest measures the total number of outstanding contracts that have not been closed. It tells you how much money is committed to positions in a given market.

For every buyer, there has to be a seller.

This is important to understand. When people say “more buyers than sellers,” they are not speaking precisely. In derivatives markets, the number of long contracts always equals the number of short contracts.

What changes is the composition of who is taking those positions.

Consider three traders in a new market with zero open interest:

Trader A thinks the market will rise and places a limit buy at $100. Trader B thinks it will fall and sells into that bid. Open interest is now 1.

The market rises to $150. Trader C wants to go long. Trader A decides to exit by selling his position. Trader C buys it. Did open interest change? No.

One long exited, one long entered. Still 1 contract open.

At $200, Trader B has had enough and buys to close his short. Trader C sells to close his long. Both sides exit. Open interest goes back to 0.

The three scenarios:

1. Both sides open new positions → Open interest increases

2. Both sides close positions → Open interest decreases

3. One side opens while the other closes → Open interest stays flat

So why do prices move if every buyer needs a seller?

Market makers. They provide liquidity by quoting both sides of the market and collecting the spread. They want to stay delta neutral, meaning they do not care which direction the market goes.

When directional traders (people who have an opinion) want to get long, they buy from market makers who take the short side.

The market makers then hedge by buying spot or other venues. This buying pressure moves prices up.

If many directional traders want to go long on rising prices, open interest increases alongside price. This tells you new demand is entering the market.

If open interest decreases while prices move, it means traders are closing positions rather than opening new ones.

Large decreases in open interest often coincide with liquidation cascades.

The areas where significant open interest builds often act as important price levels.

Think of it as money committed at specific prices that traders will defend or be forced out of.

Open interest is obviously not a unique crypto concept.

What is unique, though, is that changes in open interest are shown live compared to venues like CME, which updates them once per day with open interest scattered between different expirations.

I would say that this feature alone gives you a large advantage in trading perpetuals instead of any other derivative.

The directional flow, especially in altcoins, is still very much retail driven, and there are many platforms that aggregate data across multiple venues. It is very easy to see what is happening across exchanges in real time.

As you can see in example above, price dropping down while open interest rising in Hype with consistently negative premium (funding rates stripped off interest rate), followed by a classic short squeeze.

Open Interest as a Positioning Indicator

Open interest can work well as a sentiment indicator on larger lookbacks, considering that most participants are long and rarely short.

This is why open interest is almost always elevated at highs and depressed at lows.

Knowing this, we can turn open interest into a standard deviation indicator to identify extreme levels of positioning. This has historically worked well for finding tops and bottoms across the board.

Here you can see a price overlap of every time open interest crossed above or below two standard deviations using a 30-day lookback.

Another interesting relationship exists between Bitcoin and altcoin open interest.

Open interest often flows to altcoins during euphoria and concentrates in Bitcoin during fear.

Plotting this on a chart of Bitcoin (which will have the inverse effect of the indicator above), you can see that at all recent highs open interest was dominated by altcoins, while at all recent bottoms open interest was dominant in Bitcoin.

Liquidations

Perpetual futures are traded on leverage, which means you do not need the full notional value to open a position.

If Bitcoin is at $100,000 and you want to go long 1 BTC but only have $10,000, you use 10x leverage. The exchange provides the rest. Your $10,000 margin is at risk.

If Bitcoin falls to $90,000, you are down $10,000 on a $100,000 position. That is your entire margin. The exchange will not let you lose their money, so you get liquidated.

This is conceptually similar to a margin call in traditional futures, but the execution differs significantly.

On CME, if your account falls below maintenance margin, you receive a margin call and typically have until the next business day to deposit additional funds.

Crypto exchanges often send margin call notifications via email and app when your margin ratio deteriorates, but in volatile markets, price can move so fast that you hit the liquidation threshold immediately after (or simultaneously with) the margin call.

For example, Binance explicitly states they have “no obligation” to guarantee timely delivery of these notifications, and that liquidation can occur before you have any chance to react.

In practice, during sharp moves, the margin call and liquidation happen nearly simultaneously. There is no grace period like in traditional markets.

The exact liquidation price depends on your leverage, initial margin, and the exchange’s maintenance margin requirements.

Exchanges do not liquidate you based on the last traded price.

Instead, they use a mark price derived from a spot index across multiple exchanges. This prevents manipulation where someone could wick the price on a single low-liquidity venue to trigger liquidations.

If you are watching your position and the last traded price hits your liquidation level but you do not get liquidated, this is why.

The mark price smooths out anomalies and protects traders from artificial price spikes.

Most platforms show both prices, and your liquidation is always calculated against the mark.

Most platforms show you the liquidation price when you open a position.

It goes without saying that many retail traders use high leverage.

At 100x, a 1% move against you triggers liquidation. At 10x, it takes a 10% move. Crypto can easily move 10% in a day, and even Bitcoin regularly sees 3-5% intraday swings.

When the market moves sharply in one direction, leveraged traders on the wrong side get liquidated.

Liquidation is a forced market order to close your position.

If many liquidations happen at once, this creates additional selling (for liquidated longs) or buying (for liquidated shorts) pressure.

Liquidation Cascades

This can cascade. Liquidations push price further in the same direction, triggering more liquidations, which pushes price even more. These liquidation cascades are responsible for some of the sharpest moves in crypto.

The mechanics work like this: imagine Bitcoin drops from $100,000 to $97,000. This 3% move liquidates traders using 30x+ leverage. Their forced selling pushes price to $95,000.

Now 20x leveraged longs get liquidated, pushing price to $92,000.

The 10x longs start getting hit. Each wave of liquidations feeds the next, and price can move 10-15% in minutes with no fundamental news driving it.

The same works in reverse. A sharp move up liquidates shorts, their forced buying pushes price higher, more shorts get liquidated, and you get a short squeeze that sends price parabolic.

These cascades are why crypto volatility is structurally higher than traditional markets.

The leverage available, combined with automatic liquidations and 24/7 trading, creates feedback loops that do not exist in regulated futures markets with circuit breakers and actual margin call grace periods.

But there is another layer to this that most traders do not understand until it hits them: Auto-Deleveraging (ADL).

When liquidations overwhelm the system and the exchange’s insurance fund is depleted, exchanges activate ADL.

This mechanism forcibly closes profitable positions to cover the losses of bankrupt traders. You read that correctly.

You can have the right trade, be massively in profit, and still have your position closed against your will because the exchange needs your winnings to stay solvent.

October 10th 2025

On October 10, 2025, the crypto market experienced its largest single-day liquidation event in history when roughly $19 billion in leveraged positions were liquidated within 24 hours.

Bitcoin dropped from around $124,000 to below $100,000. Altcoins fell even harder.

But what made this event particularly painful was what happened to traders who got the direction right.

Many traders were short with high leverage, perfectly positioned for the crash. They should have made massive profits.

Instead, they watched their positions get forcibly closed by ADL. Hyperliquid alone liquidated $10.3 billion that day, more than Binance ($2.4 billion) and Bybit ($4.6 billion) combined.

It was Hyperliquid’s first cross-margin ADL activation in over two years of operation.

When long positions got liquidated en masse, exchanges needed counterparties to close those positions.

Normally, the insurance fund covers this. But when losses exceeded the fund’s capacity, exchanges turned to profitable shorts.

ADL ranked traders by profitability and leverage, then forcibly closed their winning positions to balance the books.

Approximately 35,000 positions across 20,000 traders on Hyperliquid were affected by ADL.

Some traders who had timed the crash saw half or more of their profitable positions closed without consent.

Binance burned through roughly $188 million of its insurance fund in a single day before resorting to ADL.

The October 10 crash also revealed another risk: exchange infrastructure failures.

Multiple platforms experienced frozen interfaces during the crash, preventing traders from managing positions or buying the dip.

Some traders could not add margin to avoid liquidation.

Others could not close positions manually. The combination of cascading liquidations, ADL activation, and platform outages created a perfect storm.

Market makers were also hit hard.

Many run delta-neutral strategies with long spot positions hedged by short perpetuals. When ADL forcibly closed their short futures, they were left with unhedged long exposure at exactly the wrong moment.

According to BitMEX research, this left market makers “stuffed with coins,” contributing to reduced liquidity and slower trading for weeks afterward.

ADL is another mechanism unique to perpetual futures that traders need to understand.

Although it is rare to see it triggered, when it does happen it can be devastating, even to traders who got the direction right.

The October 2025 crash was a stark reminder that in perpetual futures markets, being correct is not always enough.

Exchange solvency comes first, and your profitable position can be sacrificed to maintain it. This is a risk that simply does not exist in spot markets or traditional futures with clearinghouse guarantees.

Trading Into Liquidations

Liquidations can act as mean reversion signals.

If you see a flush that takes out a large number of leveraged traders, the forced selling or buying may be exhausted, creating an opportunity to trade the other direction.

There is genuine risk premium in taking these trades.

You are providing liquidity when others are being forced out.

The discomfort of buying into a sharp drop or selling into a squeeze is exactly why the edge exists. Markets pay you for being willing to take the other side of forced flows.

In trending markets, you can wait for over-leveraged late entrants to get liquidated before entering in the direction of the trend.

Fees and Liquidity

Trading perpetual futures involves two types of fees: trading fees and funding payments.

We have covered funding extensively.

Trading fees deserve their own attention because they work differently than what most traders expect from traditional markets.

Maker and Taker Fees

Crypto exchanges use a maker-taker fee model. The distinction is simple: makers (limit orders) add liquidity to the order book, takers (market orders) remove it.

If you place a limit order that does not immediately execute, it sits on the book and provides liquidity for others.

You are a maker. If you place a market order or a limit order that executes immediately against resting orders, you are a taker.

Taker fees are always higher than maker fees. On most major exchanges, taker fees range from 0.04% to 0.06% while maker fees range from 0.01% to 0.02% at base tier.

Some exchanges offer negative maker fees at higher tiers, meaning they pay you to provide liquidity.

This is how market makers and high-frequency traders operate profitably even on tight spreads.

Fee Tiers

Exchanges use volume-based fee tiers. The more you trade, the lower your fees.

A retail trader paying 0.05% taker fees competes against professionals paying 0.01% or less.

This creates a structural disadvantage for smaller traders, especially on high-frequency strategies where fees compound quickly.

A strategy that looks profitable at institutional fee tiers can be a loser at retail tiers.

Most exchanges also offer fee discounts for holding their native tokens or using specific collateral types.

Binance reduces fees for BNB holders, Bybit for holding their token, and so on. These discounts can be meaningful, often 10-25% off base rates.

Zero-Fee Models

Not all exchanges follow the traditional fee structure.

Lighter, a decentralized perpetuals exchange, has implemented a zero-fee model similar to what Robinhood did for equities.

The logic is the same, remove the visible fee to attract volume, then monetize order flow differently.

Robinhood sells order flow to market makers who pay for the right to execute against retail traders.

The market makers profit from the spread and informational edge, giving Robinhood a cut.

The customer pays no commission but often receives slightly worse execution.

Lighter and similar zero-fee venues work on comparable principles.

They may route flow to preferred liquidity providers, capture spread through integrated market making, or monetize through other mechanisms like token incentives or cross-subsidization from other products.

The trade-off is the same as in traditional markets: you are not paying an explicit fee, but you might be paying implicitly through wider spreads or worse fills.

For retail size, this often works out favorably. For larger traders, the hidden costs can exceed what they would pay in explicit fees on a transparent venue.

Zero-fee models also raise questions about sustainability.

Robinhood’s model works because equity market makers have massive edge against uninformed retail flow.

In crypto, where information asymmetry is different, the economics are less proven.

Whether these models persist or evolve remains to be seen, but they represent an interesting experiment in exchange business models.

Adverse Selection Risk

Zero-fee models create another problem: adverse selection against passive limit orders.

When you place a limit order on a traditional exchange, you pay lower fees (or receive rebates) as compensation for providing liquidity.

This rebate exists because limit orders carry risk. You get filled when the market moves against you and your order becomes attractive to someone with better information.

On a zero-fee venue, there is no rebate to compensate for this risk.

Market makers and faster participants can pick off stale limit orders when prices move, leaving passive traders holding losing positions.

You wanted to buy at $100, the market gaps to $99, and your order gets filled right before it continues to $95.

Professional market makers manage this by constantly adjusting quotes and using sophisticated infrastructure to avoid being picked off.

Retail traders placing limit orders do not have these tools.

Some view this as fundamentally unfair.

The zero-fee promise attracts traders who think they are getting a better deal, but the lack of maker rebates means passive liquidity providers subsidize the system through adverse fills.

The exchange and its preferred participants extract value that used to flow to anyone willing to post limit orders.

Whether the zero-fee trade-off works in your favor depends on how you trade.

If you are primarily taking liquidity with market orders at small size, zero fees probably benefit you. If you are posting limit orders and trying to capture spread, you may be giving up more than you realize.

Why This Matters for Trading

Fee structure influences which strategies are viable. Scalping works at 0.01% fees but bleeds money at 0.05%.

Longer-term directional trades are less sensitive to fees since you are not churning positions constantly.

If you are running any kind of arbitrage or high-frequency strategy, negotiating fee tiers or qualifying for VIP rates is not optional.

The difference between profitable and unprofitable often comes down to a few basis points.

Liquidity Considerations

Not all perpetual markets are created equal.

Bitcoin and Ethereum perpetuals on major exchanges have deep liquidity with tight spreads. You can move size without significant slippage.

Once you move to smaller altcoins, liquidity deteriorates rapidly.

Spreads widen, order book depth thins, and slippage becomes a real cost.

A 0.05% fee looks irrelevant when you are paying 0.5% in slippage to enter and exit.

This is especially pronounced in the newly listed TradFi perpetuals.

Gold, silver or equity perpetuals on decentralized exchanges often have thin books.

The product exists, but the liquidity to trade it efficiently might not.

The same applies to low-cap altcoin perpetuals.

The funding rate might look attractive, but if you cannot get in and out at reasonable prices, the funding income gets eaten by execution costs.

Before trading any perpetual market, check the order book depth and typical spreads.

Look at how much size you can execute at the quoted price versus how much you actually want to trade.

For newer markets, whether TradFi perpetuals or freshly listed altcoins, assume liquidity is worse than it looks.

The displayed order book often disappears the moment volatility picks up.

Market makers pull quotes, spreads blow out, and you are left trying to exit into a vacuum.

This is not a reason to avoid these markets entirely.

You should see these as features, not a bug.

Illiquid markets can offer better opportunities precisely because fewer participants are willing to deal with the friction.

Leaving resting orders for crazy wicks that only happen on illiquid perps, benefiting from elevated funding rates, and many other opportunities can be found by those willing to seek them.

But you need to size positions accordingly and factor execution costs into your expected returns.

Conclusion

Perpetual futures have become the dominant instrument in crypto derivatives and are now expanding into traditional asset classes.

Understanding their mechanics doesn’t give you the edge, but it is mandatory for anyone who want to take trading seriously.

The core concepts are not complicated: leveraged exposure without expiration, funding rates to anchor prices, and the interplay between underlying and derivatives markets.

What matters is applying these concepts. Watch how open interest builds and unwinds at key levels. Pay attention to liquidation cascades.

None of this is a magic formula.

Trading is hard, and most traders lose money regardless of what indicators they use.

But understanding the mechanics of the instruments you trade is the foundation everything else builds on.

With TradFi perpetuals expanding rapidly, these concepts will become relevant far beyond crypto.

The traders who understand perpetual futures today will have a head start as these products proliferate across asset classes.

I hate to disappoint, but while edges and alpha definitely exist, the things you often see on YouTube or Twitter couldn’t be further from it.

The strategies often presented online are not alpha; they are more fundamental, and most people overlook them entirely, barely understanding why they actually work.

Most people think that to make money in trading, you need to be smarter than the market, but in fact, you just need to be willing to hold risk that other people find uncomfortable.

That’s what a risk premium is. And once you understand it, you not only understand why the things you do make money, but also can structure strategies with a logical approach that should last you for a long time.

All analytics in this article are powered by TradingRiot Analytics. Sign up to access data for 1,000+ futures, crypto, stocks, and ETFs, plus education on turning data into strategies.

What is Risk Premium

Before we get into trading, let me explain this with something simpler.

Insurance companies are profitable businesses. When you buy fire insurance on your $200,000 home, you pay maybe $300 a year.

The insurance company has actuarial tables. They know the probability of your house burning down is roughly 1 in 5,000 (unless you live in California).

That means their expected payout is about $40 per year per policy.

So they collect $300, expect to pay $40, and keep the $260 difference.

Here’s the thing, though. You also know those odds. It’s not like the insurance company has secret information about fire probabilities. Both parties understand the math. So why do you pay?

Because you’re transferring risk. You’d rather pay $300 with certainty than face even a tiny chance of losing $200,000.

The discomfort of that uncertainty is worth $260 a year to avoid.

The insurance company takes that discomfort off your hands and gets paid for it.

That $260 is the risk premium.

It exists not because of information asymmetry or skill, but because bearing risk is inherently unpleasant and someone needs to be compensated for doing it.

Stonks Go Up

The simplest, and probably an “OG,” risk premium is the equity risk premium, so let’s talk about equities, as some people still get confused by this.

There’s this weird belief that stocks “should” return 7-10% per year, like it’s some law of physics. As if the market owes you returns just for showing up.

It doesn’t work that way.

The historical positive drift in equities is compensation.

If you buy SPY and hold it, you’re signing up for a ride. Your portfolio can get cut in half during a crisis. It can go sideways for an entire decade, like it did from 2000 to 2010. Individual companies go bankrupt.

Recessions happen. Wars happen. The whole thing feels terrible sometimes, and you might find yourself crying in a fetal position under a cold shower after looking at your portfolio.

That’s exactly why there’s a premium.

If holding stocks felt safe, they’d return the same as treasury bills.

If META had the same volatility and drawdown characteristics as bonds, everyone would buy it.

But it doesn’t. Despite being up gorillion percent since 2013, it also had a 40% correction last year and an 80% correction in 2021.

So why would anyone accept equity risk for the same return as risk-free government bonds?

They wouldn’t.

Equity investors demand extra compensation for the uncertainty, and historically, that’s been around 4-6% per year above risk-free rates.

What makes this interesting is that the equity risk premium doesn’t disappear even though everyone knows about it.

It’s been documented across a century of data, in dozens of countries, through world wars, depressions, and technological revolutions.

The premium persists because the underlying risk persists. Someone has to hold the stocks. And whoever holds them wants to be paid for the discomfort.

This is fundamentally different from a trading edge that is arbitraged away once it is discovered. The risk doesn’t go away just because you understand it.

Bitcoin: Risk Premium or Something Else?

This brings up Bitcoin, which is an interesting case.

BTC has experienced extraordinary positive price action since its inception.

Way higher than equities. If you bought and held through it all, the drawdowns were brutal, but the returns were enormous.

The honest question is: is this risk premium, or something else entirely?

I don’t think we can answer that definitively yet.

For equities, we have over 100 years of data across multiple countries and economic regimes.

That’s enough to be fairly confident the premium is structural.

For Bitcoin, we have about 15 years as a single asset during a specific technological and monetary environment, and Bitcoin has changed extremely since COVID, the flows are now coming from ETFs, options markets are extremely popular and so on.

The positive drift could be a risk premium.

Holding something that drops 80% multiple times is genuinely uncomfortable, and if the asset keeps appreciating, part of that return is probably compensation for sitting through the volatility.

But it could also be early adoption dynamics, speculative momentum, or just a bubble that hasn’t fully unwound. Probably some mix of all of these.

What I’d say is this: the speculative element in crypto is higher than in traditional markets. That’s not necessarily bad; it just means your uncertainty about future returns should be higher, too.

Anyone claiming they know BTC will return X% per year is extrapolating from very limited data. Be honest about what you don’t know.

For altcoins, I am sure we don't even have to go there, as the majority are just down bad and are only good for shorter-term speculation.

Carry Premium: Traditional and Modern

Let me explain carry trades because they’re one of the cleaner examples of risk premium, and they exist in both traditional finance and crypto.

The classic carry trade works in currency markets.

You borrow money in a low interest rate currency (say, Japanese yen at 0.5%) and invest it in a high interest rate currency (say, Australian dollars at 5%). You pocket the 4.5% difference. This is the currency carry trade, and it’s been profitable historically.

Why does it work? Because you’re taking risk.

When risk appetite collapses globally, high-yielding currencies tend to crash while safe-haven currencies like yen and Swiss franc appreciate.

Your carry profit gets wiped out in a few days of chaos. The steady income is compensation for the occasional violent reversal.

In futures markets, the same concept applies through basis trading.

When futures trade at a premium to spot (contango), you can buy spot and short the future, locking in the difference as profit.

This is called cash-and-carry arbitrage.

Traditional futures on things like Treasury bonds or equity indices often have a basis that sophisticated players can arbitrage.

Here’s the reality, though: in traditional markets, these opportunities have been compressed to almost nothing for retail traders.

Hedge funds run Treasury basis trades with 30-50x leverage through repo financing, earning tiny spreads that only make sense at massive scale.

The big players have picked these markets clean.

Crypto is different, at least for now.

Perpetual swap funding rates are basically crypto’s version of the carry trade.

When funding is positive (which is often the case, as most retail in crypto is long and exchanges charge interest rates on the long side), longs pay shorts every 8 hours.

So what you can do is go long spot BTC and short the perpetual, staying delta-neutral while collecting funding.

Why does positive funding persist? Because retail wants leveraged long exposure.

They’re willing to pay for it. Market makers and arbitrageurs take the other side, providing the liquidity that retail demand requires, and they are compensated through funding.

The catch is the same as currency carry.

Funding can flip violently negative during crashes, exactly when you’re most exposed.

The BIS actually published research on this, showing severe drawdowns in crypto carry strategies and frequent liquidations of the futures leg during volatile periods.

You can read it here.

You’re collecting small, consistent payments most of the time, then giving back months of profit in a single event. Same negative skew structure as selling insurance.

The funding premium still exists, though it is less obvious and profitable in large caps than it used to be several years ago. Altcoins still exhibit extremely elevated funding rates consistently, especially outside large centralized exchanges.

But this adds another layer of risk, the counterparty risk, as smaller and often decentralized exchanges are much more likely to get hacked, go bust, and so on.

The Volatility Risk Premium

If there’s one risk premium that’s both well-documented and accessible to trade, it’s the volatility risk premium.

Implied volatility runs higher than subsequent realized volatility about 85% of the time in S&P 500 options and consistently in most ETFs, Stocks, futures, and other products.

That gap exists because investors systematically overpay for protection.

Institutions need to hedge their portfolios.

Retail traders buy puts when they’re scared. All this demand pushes option prices above fair value.

The VRP is compensation for selling that insurance.

This is why selling options is often a better idea than actually buying them.

Selling strangles, straddles, or running covered calls. These strategies all harvest the same underlying premium.

The problem is the return profile. You make money steadily in most environments, then give a lot back during crashes.

Selling puts into COVID in March 2020, or the GFC in 2008, was devastating.

This is why I am also not a big fan of all those “income strategies” that sell options on baskets of stocks and often get hurt the most during crashes, more on this maybe sometimes later.

But overall, this risk profile is not a flaw in the strategy. That’s why the premium exists.

Here you can see the returns of selling a 30 DTE short straddle in SPY, a lot of consistent gains often wiped by large vol event.

If it felt comfortable, everyone would do it, and the premium would disappear.

You can see similar dynamics in skew, another form of harvesting the risk premium.

As you can see from the 25-delta skew chart, on SPY, it is always below 0, meaning put IV is higher than call IV for 25 delta options.

Out-of-the-money puts are “expensive” relative to ATM options and relative to calls.

The volatility smile tilts because everyone wants downside protection.

Selling put spreads or risk reversals harvests this skew premium. Same return profile: steady gains punctuated by occasional large losses.

This is due to the common “staircase up, elevator down” pattern, where crashes are often violent compared to slow-up days.

Momentum Premium: The One With Positive Skew

Most risk premia have negative skew. You collect small gains and occasionally take big hits. Momentum is different.

Historically, assets that have been rising tend to keep rising, and assets that have been falling tend to keep falling.

This trend-following premium has been documented across stocks, bonds, commodities, and currencies over very long time periods.

The explanations are mostly behavioral.

Investors underreact to news at first, creating trends that persist. Or there’s herding behavior, where success attracts more buyers, pushing prices further in the same direction.

What’s interesting about momentum is the return profile.

You lose small amounts during choppy, trendless markets.

Your signals whipsaw, you get stopped out, you bleed transaction costs. But when big trends develop, you make large gains.

This is positive skew, the mirror image of short vol strategies.

That’s why momentum tends to perform well during crises. When markets crash, trend followers get short and ride the move down.

When vol sellers are getting crushed, trend followers are often doing fine.

The bad news: momentum has weakened over the past couple of decades.

There’s a lot more capital chasing these signals now. When everyone discovers an edge, competition erodes returns.

The premium hasn’t disappeared entirely, but the magnitude is lower than historical backtests suggest.

The recent move in commodities is a perfect example of the return profile you can expect with this type of strategy: months to years of sideways, small returns, compensated once the big move finally happens.

How About Mean-Reversion?

When positioning gets extremely one-sided, mean reversion tends to work. This applies in both crypto and traditional markets.

In crypto, you see this with funding rates and open interest. When funding is deeply negative, futures trade below the spot price, everyone is max short, and no one wants to bid.

Things look terrible, which is exactly why everyone is positioned that way. Fading that extreme by going long tends to be profitable on average.

Why is this risk premium rather than just a pattern? Because you’re taking the other side of a crowded trade during maximum uncertainty.

There’s a reason everyone is short. The news is bad. Buying into that is uncomfortable.

You are doing something useful, you’re providing liquidity when nobody else wants to, and the expected profit is your compensation.

The same logic applies to liquidation cascades. When longs get liquidated, they’re not selling because they want to. They’re selling because they have to. Forced selling pushes prices below fair value.

Arguably, the same can be said on the other side.

Stepping in to buy during that chaos requires conviction and exposes you to genuine risk. The expected profit from mean reversion is payment for that service.

Traditional markets have similar dynamics.

Futures term structures are typically in contango (futures prices above spot prices) due to storage and financing costs.

But occasionally they flip to steep backwardation when there’s a supply squeeze or panic. Backwardation tends to revert to contango over time because contango is the equilibrium state.

If you fade extreme backwardation by going long the futures, you’re betting on normalization.

Historically, this works more often than not. But the risk is real.

Term structures can stay inverted longer than you can stay solvent.

If there’s a genuine supply crisis, backwardation might intensify before it normalizes. You get paid for taking that risk, but you can absolutely get run over.

I have a whole strategy for trading the “bumps” in the term structure, mostly via options, which I might cover later.

Professional-Grade Risk Premia

So far, I’ve covered things that are at least somewhat accessible.

But there are risk premia that mostly exist in institutional markets, worth understanding even if you can’t trade them directly.

Term Premium in Bonds

When you buy a 10-year Treasury instead of rolling 3-month bills for 10 years, you’re taking duration risk. Interest rates might move against you. The term premium is the extra yield you earn for accepting that uncertainty.

This premium has fluctuated significantly over time. During the QE era, it went negative in some models, meaning investors were actually paying for the privilege of holding long duration. Since 2022, it’s been positive again as rate uncertainty increased. The NY Fed publishes estimates if you want to track them.

For most retail traders, this is background context rather than something tradeable.

But it matters whether you’re allocating to bonds or considering duration exposure.

Credit Risk Premium

Corporate bonds yield more than Treasuries of the same maturity.

That spread is compensation for default risk. If you buy corporate debt instead of government debt, you’re taking on the possibility that the company can’t pay you back.

This premium widens dramatically during crises. In 2008-2009, credit spreads blew out to levels that implied mass defaults that never happened. Investors who stepped in to buy corporate bonds during the panic earned enormous returns as spreads normalized.

The catch is the same as always. You’re being paid to bear a risk that materializes exactly when everything else is going wrong, too.

Defaults spike during recessions, when your job might be at risk, when equities are down, and when you least want additional losses.

Liquidity Premium

Less liquid assets tend to offer higher returns than liquid ones. Private equity, real estate, small-cap stocks, and off-the-run bonds.

All else equal, illiquidity earns a premium.

Why? Because liquidity is valuable. Being able to exit a position when you need to is worth something. If you give that up, you should be compensated.

The challenge is that liquidity dries up exactly when you need it most. You might earn extra yield for years, then find yourself stuck in a position during a crisis when you desperately want out.

The premium exists because of that risk.

The Key Distinction: Risk Premium vs. Alpha

Every day I log in to X, and I see people sharing “alpha, which is often some technical indicator or just lines drawn on a chart. This is obviously not alpha at all.

Alpha is the excess return from skill or information advantage. It decays once discovered because others can replicate it. Low capacity. Temporary.

The risk premium is the compensation for bearing systematic risk. It persists even when everyone knows about it, because the underlying demand for risk transfer doesn’t go away. High capacity. Permanent.

The practical test: if your strategy works because you’re smarter than other participants, that’s alpha.

If it works because you’re willing to do something uncomfortable that others avoid, that’s a risk premium.

Insurance companies earn a risk premium.

Everyone knows how they make money, but they still make money, because someone needs to bear the risk and collect the premium.

Card counters at blackjack earn alpha. Once the casino figures out what they’re doing, they get banned. The edge disappears.

Most retail traders would be better off understanding which risk premia they’re exposed to rather than chasing alpha.

The premia are more reliable and less likely to disappear; chasing alpha is extremely competitive and hard.

Understanding you are probably too dumb to compete might suck, but it will be beneficial in the long run.

Why Most Premia Have Negative Skew

Something that’s easy to miss: almost every risk premium has negative skew.

Frequent small gains, occasional large losses.

This isn’t a design flaw. It’s why the premium exists.

If harvesting a premium felt good all the time, everyone would do it, and the premium would disappear.

The occasional large losses are what keep most people away.

They’re the barrier to entry that preserves the profit for those willing to endure.

Equity risk premium: You earn steady returns most years, then lose 30-50% in a crash.

VRP: You collect premium month after month, then give back a year’s gains in a week.

Carry: You earn income steadily, then get crushed in a risk-off event.

Credit: You clip coupons, then take default losses during recessions.

Understanding this pattern matters for sizing.

The premium might be positive in expectation, but you need to survive the drawdowns to collect it. Going too big means ruin before the long run arrives.

Diversification across premia with different skew profiles helps.

Combining short vol (negative skew) with momentum (positive skew) smooths your overall return profile.

Building a portfolio of premia rather than concentrating in any single one is generally the smarter approach.

Correlations are extremely important; if you are short VIX futures to capture roll-yield while being long 10 different stocks in the S&P500, you will be in a whole world of pain if the market drops 5% tomorrow.

What This Means Practically

Risk premium is getting paid to do what others don’t want to do: holding uncertainty.

The equity market rises over time because someone has to bear the risk of it going down.

Implied vol exceeds realized because someone has to sell insurance.

Funding rates stay positive because someone has to provide leveraged exposure.

Markets continue to trend because you are trying to short it other people are systematically buying.

Extreme positioning reverts because someone has to step in when liquidity dries up.

These returns persist even when everyone knows about them. That’s what makes them more reliable than alpha, which erodes once discovered.

The catch is the return profile. You get paid for the discomfort of occasional large losses.

Size appropriately. Diversify across premia. And accept that the premium exists precisely because it feels terrible sometimes.

You don’t have to be the smartest person in the market.

Sometimes it’s enough to be the one willing to bear risk that others find unpleasant.