Microsoft beat and barely got rewarded for it, Alphabet sold off on a miss and its spending plans, Amazon showed $42.4 billion of negative free cash flow, and Intel’s guidance set off a chip rout on Friday. NBC put the damage at about $1 trillion of tech market value.

Oil had its week. A tanker got hit by limpet mines in the Strait of Hormuz; the Houthis kept firing at Saudi Arabia, oil settled at six-month highs, and Chevron and Exxon hit all-time highs.

Crypto got the biggest hack in its history, with Binance losing about $6 billion, suspending withdrawals. Midterms are on Tuesday, with prediction markets favoring the Democrats.

As a reminder, all data are coming from Tradingriot.com, those of you that decide to subscribe will get access to historical options and futures data for equities, commodities, rates, FX & digital assets. Distilled into daily signals, actionable strategies, and a pre-made portfolio to follow.

If you are interested in getting free market updates during the week, you can join a free read-only Discord.

Equities

Earlier this month there was a large divergence between SPX price action and the composite regime on Tradingriot.com which tracks VIX, credit, breadth, and momentum.

Some of that gap has closed, partly through price and partly because breadth has improved, but it’s still there. And while I don’t think chasing shorts makes much sense here, it also does not look too appealing to bid yet.

Looking at VIX, the market definitely looks nervous but not scared yet.

Credit was a main warning early sign at the end of September as it went risk-off and is nearing the March panic lows.

Friday’s sell-off came close to tapping the August lows, which is the first area of support I am looking at. If things are going to get worse between 7200 and 7300, it will be another clear support area with 200-DMA.

Sectors

As probably all of you know, under the surface this has been a rotation out of tech. XLE, XLP, and XLV are each up about 11% over that time, while XLK and XLC are down about 9% and SMH 15%.

This week nine of the eleven sector SPDRs were still up, and IWM gained while QQQ fell.

The damage was concentrated in the AI trade. Intel’s ~30% drop on Friday was reported as its worst day since the 1970s. This type of panic usually gives a good opportunity, and while I don’t expect this to bounce back to ATH in a straight line, the price is sitting at an interesting spot now, testing the prior ATH.

Earnings

Plenty of major names are reporting next week, which will surely continue to shake things up. Apple is priced well above its history, and Palantir is well below. AMD and Arm are priced about in line with theirs.

Systematic portfolio

The systematic book is down ~1% a month, which is pretty solid all things considered, while it underperformed QQQ last month; tables have definitely turned this time around.

This is mostly due to the well-time rotation out of AI stocks and small profit on tactical SPXL short.

Crypto

As it is the staple in crypto, the good news of ICE (exchange, not the ICE that deports people) partnering with Hyperliquid to bring perpetuals to US customers was followed by over six billion dollars lost during the Binance hack.

While Bitcoin sold off, it did not have the “$6bn was just hacked from Binance” type of sell-off and was mostly following the risk-off Friday move across the board.

Options showed this pretty clearly with increasing put demand as skew slid lower on all tenors; perps don’t seem to bulge yet.

As funding rates stay elevated and there are no visible pukes in open interest, I am not too eager to buy the dip on BTC.

I think next week will be pretty interesting for BTC, as it’s coming to the “loose this level and it’s light out” level at around 75k with 200-DMA also hanging around there; under that there isn’t much to support it back down to the 60s.

For altcoins, the main outliers besides Hyperliquid are Monero and Z-cash, with the privacy narrative still holding well, and XMR finally showing strength as the true privacy play.

Futures

Metals fill the bottom of the regime board. In the future this regime consists of options skew, COT positioning, slope of term structure, and momentum.

Gold’s regime is its weakest since October 2023, with gold itself reported at a one-year low. Platinum and silver are both at the bottom of their years as well.

Copper is the odd one out. Price barely moved this week, but its regime is the most extreme, with carry and skew what’s dragging it down, so the options and curve markets look a lot weaker than the price chart.

For the next week, I will be looking for some signs of mean reversion in metals; if that comes, I’d expect copper to lead.

Lean hog futures are also at an interesting spot as the market is experiencing a huge sell-off due to oversupply, which large speculators being heavily short.

I have traded European futures markets on Eurex before pivoting to crypto and traded very short-term strategies up until around 2022, when I decided that I probably don’t want to spend 14 hours a day glued to my monitor and started pivoting into systematic and longer-term trading.

These days while I trade crypto, equities, and futures, all strategies are almost fully systematic and held over weeks to months, which means I spend much less time watching charts. If you are interested in that, you can check out tradingriot.com, but without the early years of shorter-term speculation, which was often based much more on vibes than anything else, I would not be where I am today and have the experience I have.

I used to write a blog about day trading strategies back in the day and have some courses that were about the topic; these are no longer available since I have unfortunately become a “day trading customer support,” and I didn’t want to a) provide feedback on strategies I no longer trade and b) be a dick and ignore people.

This article takes everything I have shared over the years and puts it in one place. I will break down the key principles of discretionary trading and then share the exact price action and orderflow strategies I was using. While my trading changed a lot over the years, these are, in my opinion, evergreen concepts and apply to all styles of trading, and I use many of these principles to this day.

Fundamentals of discretionary trading

Discretionary trading describes a type of trading where you, a human being, take trades based on your discretion; this can come from technical analysis, fundamental analysis, tea leaves, or other esoteric ways like Elliot Waves.

While we might often think that we can predict the future using our unbelievable charting skills, the truth is that the outcome of each individual trade is a coin flip.

You can have an A+ setup at a perfect level with clean orderflow and it can go straight to the dumpster the minute you enter. And the trade you hesitated on, half-sized, almost skipped, runs to target without looking back.

This is what makes discretionary trading very hard. Looking at systematic trading for a second, in strategies like trend-following or harvesting variance risk premium, all trades follow the same rules and patterns, which can especially these days be effortlessly tested and looked at to see how they performed in the past, so all trades from signal, sizing, entry, and exit are fully automated.

In discretionary trading you need to create these rules yourself, and what is often the case, multiple-timeframe analysis, orderflow patterns, and price action patterns can be backtested easily.

This does not make discretionary trading impossible; I don’t think I would be able to make the returns I made in early years if I were trading a fully systematic way. Of course, these were made by a sequence of large oversized bets, which had the same chance of ruin to success, but especially if you are trading with smaller capital, this sort of risk-taking needs to be done. Nowadays I just don’t need to do it, as my capital grew, and I am happy to accept lower returns with lower volatility.

Discretionary traders can also adapt much faster than systems to any regime shift and can get much more profound understanding of individual markets, mechanics, and patterns driving them; this obviously comes with ungodly amount of screen time and dedication.

Once you spend enough time trading, journaling, and monitoring your setups, you will be able to create a rulebook and much better refine trades you take so they will almost look like an automated system is taking them. Once that is done, you will be able to put on much more confidence and scale your trading.

R-multiple

Once you trade with defined risk, you stop thinking in dollars and start thinking in R.

R is one unit of risk. If you have a $10,000 account and risk 1% per trade, 1R is $100. If you take a long on Bitcoin at $82,250 with a stop at $80,250 and a target at $86,250, you are risking $2,000 per coin to make $4,000. That is a 2R trade.

Thinking in R multiples is useful, as it makes trades comparable; if you trade more markets,markets a 5-point stop in ES and a $2,000 stop in Bitcoin become the same thing if both are 1R.

R multiples also take money out of your mind. When I used to daytrade, I knew there were plenty of traders who taped their account balance and P&L on their screen so they wouldn’t see the money value of them. I also used to have all my profit and loss settings on Sierra Chart in tick values instead of dollars.

Expected value

Expected value (EV) is the average result per trade over many trades. In R terms:

EV = (win rate × average win) - (loss rate × average loss)

With losses fixed at 1R:

• 50% win rate, 2R winners: 0.5 × 2 - 0.5 × 1 = +0.50R per trade

• 40% win rate, 2.7R winners: 0.4 × 2.7 - 0.6 × 1 = +0.48R per trade

• 20% win rate, 2.7R winners: 0.2 × 2.7 - 0.8 × 1 = -0.26R per trade

The first two strategies look different and make almost the same money. The third one looks fine on paper until you do the math.

This is why arguing about win rate alone is pointless. The win rate you need depends on what you make when you win. The breakeven win rate is 1 / (1 + R).

The chat above shows the breakeven required for each R multiple; in the real world this obviously won’t be so straightforward unless you take fixed R:R setups every single time.

Fees also matter a lot; if you scalp a whole day and pay 0.1R in fees on a round trip, for example, it can eat your profits quickly.

When optimizing and measuring R, you should also consider what the market did after you closed the trade; this is called maximum favorable excursion (MFE). On the opposite side, measuring how much the market went against you is also good, as it can tell you if your stops are too wide or narrow; this is called maximum adverse excursion (MAE).

Sample size

Now that you know how EV works, you should be able to answer this question: if we flip a coin two times in a row and heads is on both flips, I will pay you $3,000; if not, you will pay me $1,000.

Is this a good bet? To find that out, we should figure out what is a chance for this to happen.

If I flip a coin twice, the possible outcomes are following:

HH - HT - TT - TH; therefore, there is a 1/4 chance, or 25%, for both flips to be a head.

Since your win rate is 25%, the EV is following:

EV = (0.25 × 3,000) - (0.75 × 1,000) = 750 - 750 = 0

So is this a good bet? Well, not really; it’s a zero EV bet.

Over the 100 bets (one bet is two flips each) using the same payoff scenario, we can end up anywhere between up $36,000 or down $20,000 purely based on variance.

Now let’s change this to $3,500 paid against $1,000 lost.

With a positive EV of $125, you can notice that while several paths still end up in loss, the distribution is much skewed to the upside. Play the same game 1000 times, and only one path ends up in a loss.

Trading works the same way. Your results over a short stretch are noise. You will have down days, down weeks, and sometimes months, or quarters, or even years with a perfectly good strategy.

The systematic portfolio, which you can copy on tradingriot.com, had two losing years in the last decade; that’s not fun, but completely normal to happen.

Money in trading isn't made in one trade or one week. It's made by repeating something with positive EV enough times for the math to show up. This is why using EV is hard in discretionary trading.

In poker, a hand is a hand. Pocket Aces are pocket Aces every single time. The math behind them is known, and players track results over tens of thousands of hands. Within a few months of serious play, you have a big enough sample to know your win rate.

Discretionary trading has none of that. No two setups are the same, but many trades will follow a very similar pattern, and you will need a lot of them to see whether they are profitable or not.

With losers fixed at 1R, here’s roughly how many trades it takes before a real edge stands out from luck at roughly 95% confidence1.

Now translate that into time. At about 200 trades:

• Scalping, 10 trades a day: about a month.

• Day trading, 2 trades a day: about five months.

• Swing trading, 2 trades a week: about two years.

• Position trading, 2 trades a month: about eight years

That is for one setup; if you trade four different setups and lump them together in your journal, you don’t know which ones work. One great setup can hide two bad ones.

Setups can have many looks and forms; I will share some of them later in this article, but this is the hardest part of discretionary trading. Not reading charts but building setups that are defined tightly enough that you can count them and then taking enough of them to see your expected value.

While I am not the biggest fan of trading on demo accounts or using backtesting software to run a chart replay, if you are planning to trade on higher timeframes, this is definitely something worth exploring to shorten the path to confidence.

Should you day or swing trade?

If you trade on lower timeframes, you get a much quicker feedback loop. Scalpers and day traders take hundreds of trades a year. They can find out whether a setup works in weeks or months, change it, and test again. Their edge mostly comes from microstructure and positioning: trapped traders, stop runs, and absorption at levels.

If I am going to ask you what you will be doing in the next hour or in the next two days, you have a pretty decent idea about that. If I ask you what your plans are three months from now, that will be much harder to answer.

Trading is similar in a way, especially if you plan to use technicals solely for your trading; then this will be true: price jumping back from support because of mechanical flows and liquidations will be true on intraday moves, but you can’t really say that the market trended for a span of four months only because it hit some bigger support level and bounced.

Swing trading is much more complex; moves happen because of fundamentals, regime changes, or major positioning flows hitting multi-year extremes.

One of the strategies that I trade trades like this in CME and ICE futures markets is that it’s very slow, only finding a handful of trades each month across almost 50 markets.

While I find trades systematically, a lot of discretion comes into execution, and I still look for “setups” that combine price action and orderflow elements for me to help decide if I want to trade with or against trends, which then constructs positions using a mix of futures and options.

Since this is a strategy with very low frequency of trading (it does not use lower than daily timeframes), there is much more uncertainty evolving around it, it would probably take me a good year to realize the strategy might have fully stopped working, and I am very used to months of almost no returns compensated by occasional considerable winners (since I do not use fixed take profit levels).

I am fine with this, as I have spent the necessary time to build confidence in the system, and this is only one of many strategies I trade, as it does not require more than about 30-60 minutes per day to go through all markets and positions using tradingriot.com tools and screeners.

For starting out, shorter timeframe trading is, in my opinion, better; you will get much faster feedback from the market on things you are doing. Similar to poker, you want to be grinding small cash games first to build a track record and have data to analyze before moving into tournaments, which are known for long dry drags before a large single win.

Losing streaks

The pattern for countless people is that they learn a strategy, trade it for a week, get bad results, try to look for something else.

As you can see above with the 50% win rate, there is an over 80% chance you will see five losses in a row; if your trading frequency is not that high, this can feel like eternity.

For discretionary traders who focus on convex payoff curves (you win more than you lose), the win rates are often in the 30-50% range over the long run, so you better get used to some prolonged losing streak periods.

Risk per trade

Since you probably have no idea what your EV is yet or don’t have any other data collected about the strategy you trade, the conventional wisdom generally says not to risk more than 1-2% of your account per trade.

This is decent advice, but slightly too simplified.

The right amount for risk per trade is not only going to depend on your win rate, but also how much you trade.

Foremost, if you risk too much and hit a string of losses, it will suck a lot because, as you can see on the chart above, if you start trading with $100,000 and you lose 50%, or $50,000, to make it back to breakeven, you actually need to make 100% now.

As established already, you are playing a law of large numbers; you want your edge to play over a large sample size of trades. If you take 10 trades every day, there is no point to risk 1% on each trade, as you would rather not see your account fluctuate daily.

But what if you take 10 trades per month? If that is the case, risking 1% doesn’t sound that unreasonable considering you can stomach a 10% monthly drawdown.

If you have $100,000 and take three trades a month at 1% risk, a great year might be 20R. That's $20,000 and twelve months of sitting on your hands. If your data supports it, risking more on fewer trades is a rational choice. If you take 60 trades a week, risking 3% each will very likely lead to a blowup.

The key phrase here is if your data supports it. You don't get to pick 5% because you feel confident. You pick it because your data supports it; if you are truly new to this, start as conservative as possible. The goal here is to build the track record and data; the sizing and growth will naturally.

How much money you need to start trading?

Brokers made it easy for everyone to start trading. Most people think they need $100 and dream of making it big in trading space. People also tend to think they will return 50% every month trading; this unfortunately gets amplified by all paid trading groups that advertise 50R monthly returns for the cheap price of $99 per month.

Just so we are on the same page here, if you start with $1,000 and make 50% each month, you are the richest person in the world by year four, and by year five you have more money than than the whole US economy’s annual output.

The last time someone asked me how much money they need to go trading full time, I told them that I think $500,000 is reasonable and they shouldn’t expect to pull out much of that, at least for the first few years, so they better have some saving. They were not thrilled about the answer.

If you plan to focus on more active trading, $100,000 is doable but very tough, but if you trade a lot and risk 0.5% per trade, that’s $500; if you make 10% a month (which is an excellent month), that will be $10k. If that is your only income and, god forbid, you have family to support, that’s tough to sustain psychologically, as your account won’t grow much since you are constantly pulling money out and need to consider other fun stuff like taxes.

Of course there are many factors that influence this, and I can’t speak on this since everyone is in a different situation. Making $10,000 a month is extremely diverse in Warsaw compared to New York, the same as if you were 18 living with your parents or 35 with two kids.

So have I started with $100 or $500k? Of course not; I started with very low five figures in my account, but the idea of going full-time for the first few years did not even cross my mind.

Therefore, if you are in a position where you can support yourself by doing other work, you don’t need much money to trade with. Moreover, as you will soon find out, not depending fully on trading income is much easier, and you can build your account and track record gradually.

These days, you don’t even need to have your money, as concepts of prop trading firms got very popular. I am part of Breakout, where I work on a lot of trading and risk-related stuff, so this is the only prop firm you will see me ever recommend, but even with these you should 100% know what you are doing before even thinking of attempting to get funding; there is really no point to just buying evaluations without knowing what you are doing. You will also often have to adjust your strategy based on prop-firm rules, maybe becoming slightly less conservative in your risk to meet the loss rules, but if you manage to do that you can access a large outside capital which is well worth it.

So, start small if you can; think about the journey rather than buying a Rolex next week, and don’t resign from your job after finishing this article because you think you know stuff.

Put enough money into your trading account where wins and losses make you feel something. If there is $100,000 in your bank account, putting $90,000 into your trading account is insane since every trade is basically a make or break for you, but putting something like $10,000 and risking $100 per trade might be enough to care and not do stupid things but also not want to end your existence after a small losing streak.

Sitting at the right table

Poker players have a saying: if you can’t spot the sucker at the table in the first half hour, you’re the sucker.

Good players obsess over table selection. The best player at a table full of professionals can easily lose money. An average player at a table full of tourists makes money all night. Where you sit matters as much as how well you play.

Most discretionary traders gravitate to the same few markets: ES and NQ futures and BTC perps. It makes sense on the surface. They’re liquid, they move, spreads are tight, and everyone on your timeline is talking about them.

But that’s precisely the problem. These are the most competitive markets in the world. You’re competing with someone at an HFT firm who probably graduated college at the age of 16, market makers with co-located servers, prop desks with decades of data and teams of quants, and every other discretionary trader. Every obvious edge there gets found and arbitraged away fast. You’re very likely the tourist at the high-stakes table.

When I was day-trading, I used to scalp a few ticks during European morning hours in Euro-stoxx and Bund which worked decently well when these markets were much thicker and slower than they are now. Then I moved to Bitcoin, which back in 2019 was much more inefficient with only two or three venues where you could trade perpetuals, so flows were not scattered across so many places, and that also went quite well as I could easily spot dumb flows getting trapped. Then I had a short period of time when I figured out I will be day-trading ES futures; it was extremely hard and awful.

It is up to you what you will decide to trade; I am not saying it’s impossible to trade ES or NQ futures, but in my opinion you will be playing on a hard difficulty while there is not much of a reason to do so.

Stop Losses and Take Profits

Before we get into the meat of the article covering different trading concepts, we should talk a little bit about stops and take profits.

Firstly, if you haven’t already, you should read the market microstructure article; this is basically a primer to trading, which will make the buying and selling things much less of a headache, as you will understand the underlying machinery.

You should always use a stop-loss, at least when you are starting out.

Stop loss is usually placed based on either technical levels (“If price is below xyz and I am out”) or based on volatility of the underlying asset.

The volatility-based stop is what I tend to use, and it is also slightly more complicated; there are two ways to measure volatility-based stop-loss.

The average size of a bar over the last N bars, usually 14, on your trading timeframe. It includes wicks and gaps, it’s in price units, and you will find it on every charting software. A typical rule is a stop at 1.5 to 3 times ATR from entry. If the 1-hour ATR on BTC is $600 and you use 2x, your stop is $1,200 away.

The realized volatility is the standard deviation of returns, usually quoted as an annualized percentage. It’s more work than ATR, but it’s comparable across assets and timeframes. To turn it into a stop distance, scale it to your holding period with the square root of time:

Expected move ≈ annualised volatility × √(holding period in days / 365)

Use 365 for crypto and 252 for markets that close over the weekend.

For example, BTC realized volatility is 45% annualized. That’s about 2.4% per day. You plan to hold a swing trade for around four days, so a normal one-standard deviation move is 2.4% × √4, about 4.7%.

Put the stop at 1.5 standard deviations, and it sits about 7% away. Risking $1,000, your position is roughly $14,000 of BTC.

Now volatility drops to 30%. The same math puts the stop about 4.7% away, and the same $1,000 of risk buys roughly $21,000 of BTC.

Your stop and your size adjust to the market automatically. When the market gets wild, your stop widens and your position shrinks. When it goes quiet, your stop tightens and your position grows. Your dollar risk never changes. It also makes trades comparable across assets.

If you want to use volatility-based stop-losses instead of price-action ones, the rule of thumb is that for short-term trading it’s probably much easier to stick with ATR-based stops since there are indicators that will automatically plot these for you, and for sizing into bigger swing positions, using realized volatility of the asset makes more sense. The advantage here is that your stop-loss is not placed at exactly the same level as every other retail trader is going to use above key highs and lows.

Once you have more experience, you can also experiment with hard and soft stops. The hard stop is the absolute end of the world scenario of the market just straight up going to zero, while the soft stop is more of a technical level where you are not going to automatically get stopped out but will rather wait if the price closes through that level, for example.

The hard stop frequently is risking slightly more, maybe 1.5-2R per trade, so it stings a little more if you get stopped out, but if you have the discipline, you can typically cut the trade early if a soft stop is violated, and you can save yourself from situations where you will get just wicked out of the trade because you placed your stop at the obvious level.

There is definitely a place in trading for not using a stop loss at all. There are numerous strategies that I trade that have no stop in the traditional sense of price hitting an XYZ level but exit rather on forecast changes or other indicators that signal changes in trends, volatility regimes, and so on. These are slightly beyond the scope of this article, and for you as a discretionary directional punter, I would highly recommend always having some sort of stop-loss in place.

Besides having to figure out what to do when you are wrong, you also need to come up with a plan for what to do when you are right.

When it comes to taking profits, you either set the exit price at a technical level, often called the first trouble area, or just try to run trades and trail your stop loss based on some technical indicator, etc.

There are pros and cons to both; no one ever went broke taking profits, but you might be letting a lot of money on the table if the price just decides to run.

I would say that this is somewhat correlated with your trading style and holding periods. If you trade intraday for a couple of minutes to hours, there is no real need to try to make each trade a generational runner, but if you wait for your setups several days to weeks, taking profits quickly can hurt you since moves from higher timeframes usually last longer.

This can be visible in the example above, where one strategy has 40% WR with a fixed 3R take profit, while the other one has only 30% WR with 2R (so not profitable), but every tenth trade catches a 9R trending move.

Comparing the two curves, we can notice that the latter strategy has much deeper drawdowns but also higher jumps in equity curves, as it catches big moves from time to time, while the former is just gradually grinding up.

The latter strategy is how most equity curves for trend-following strategies look; I cover these more in the article about risk premium.

Partial take profits

“Closed half here, stop to break even, letting the rest run risk-free. Thanks for playing.”

Every time I see this on X, something dies inside me, as there are not one but two issues with this.

First, the partial take profits; typically, partials just buy psychological comfort. And as soon as you get hit with a string of losses, you notice you've been trimming all your winners while taking every loser in full.

Again, you will find an answer if you should use partial take profits in your journal, but from my point of view, these are just used to make people calling the trades look good without the math backing the reason.

The breakeven stop

Most of you are technical traders. You use price action, order flow, levels, and indicators to pick our entries, our stops, and targets.

Then, as soon as the trade is a bit in profit, you throw all of that away and move the stop to the least technical price on the chart: your entry. A random number that has nothing to do with the market and everything to do with not wanting to lose money.

In my view, reakeven stop loss is very similar to psychological comfort as partial profits.

If you are using technical analysis, move your stop based on technical analysis. When the market makes a new swing low that holds, that’s new information. The stop goes under that. If the structure hasn’t changed, the stop doesn’t move.

Another dumb thing people say is that once their stop is at breakeven, it is free trade.

If you buy something at $100 and it goes to $200, you are up $100. Thereafter, you will move your stop-loss, which was originally at $50, to $100. If the price goes back to $100, you will be stopped out after you were $100 in profit. So you gave back $100; that doesn’t sound free to me at all.

Especially if your target for this trade was $250, your trade was close to take profit, but you just let it go all the way back for no reason.

Orderflow which we cover later,, will be useful here, but technical analysis can work as well; if you see price stalling and reversion against you, there is no reason to just be stubborn and watch all profits evaporate.

You should always be using the timeframe you have entered on; one of your favorite things to do is enter a trade from the daily level and then switch down to the 5-minute chart to question all life decisions and over-manage the positions. Take a look at the example below with some annotations.

Sizing up and sizing down

Another thing I see so often is people sizing down when they are on a losing streak.

See this trade sequence, and let’s say you risk $100 and win $200 if you are correct.

W W W L L L L W W W

If you run a math on this, it will be 200 + 200 + 200 - 100 - 100 - 100 - 100 + 200 + 200 + 200, which is 800.

Now let’s say after a third loss in a row you will cut your risk to half size, so it’s 200 + 200 + 200 - 100 - 100 - 100 - 50 + 100 + 100 + 100, which is $550.

You took the full hit from the losing streak and then cut your size right as the winners came back. Losing streaks are random. Changing size because of them just puts you on the wrong side of the randomness.

If you think I just made this example purposely to make the sizing down look bad (which I did), here is an outcome over 100 trades with even more favorable rules where, after three consecutive losses, the size is halved until two wins in a row.

Price action

Now with some fundamentals out of the way, we can talk about technicals. While the order flow that follows is applicable to short-term trading, these price action concepts are universal and will work across all timeframes and trading styles.

Every market, whether crypto, forex, stocks, or futures, needs a buyer for every seller.

That means “there were more buyers than sellers” is never true. Every trade has both. What changes is urgency. When buyers are willing to pay higher and higher prices to get filled, the price goes up. When sellers are willing to accept lower and lower prices, it goes down.

Price rises while demand overwhelms supply and stops rising when buyers run out or enough supply shows up to absorb them. Price movement is the result of an imbalance in supply and demand, and that imbalance comes from someone's urgency to transact.

I am a fan of this rather simple explanation of price going up or down instead of trying to attach magical reasonings to it. People don't move markets because of a technical or fundamental indicator. People move markets. Indicators just describe what they did.

Markets are either trending or ranging. In auction terms, imbalanced or in balance.

Newer traders hunt trends constantly. But markets spend most of their time in balance. Once a price finds a fair value, it takes time for someone with enough size to show up and push it somewhere else. You can see this on the Bitcoin chart, where it is pretty clear that over the last two years the price went mostly sideways, and when it trended, it was often during very short times.

Put a volume profile on the side of the chart, and the pattern is clear. In balance, lots of volume trades as buyers and sellers go back and forth. Those are high-volume nodes (HVN). In imbalance, price moves fast and little volume trades. Those are low-volume nodes (LVN).

When the market leaves balance, it tends to travel to the next area where a lot of volume was traded before, like BTC selling from all-time highs all the way to $60,000, which was the key balance area back in 2024. Markets look for fair value where they found it in the past.

Indicators, candlesticks, and chart patterns

This article will only really scratch the surface of technical analysis. MACD, RSI, moving averages, and stochastics are mostly averages of past prices, plotted different ways.

Trend indicators work in trends and get chopped up in ranges. Oscillators work in ranges and blow up in trends. The good news is that they're objective, so you can backtest them. From my personal perspective, indicators are great and can be useful, but you need to know in what market conditions you are in or what type of market you are trading.

This is the S&P500 chart over the last 70 years or so; obviously, using something like a moving average crossover would be great here since the price went up.

Overall, indicators are great for confluence; they can add conviction to trades or help you stay in trades longer, but I would not use them blindly. To demonstrate this better, we can take a look at BTC; it obviously went up a lot over the last decade, so something like a long if RSI is below 30 (oversold) and going flat at 70 (overbought) is going to suck, as during trending markets RSI will be way above 70.

On the other hand, using a 50-day moving average looks pretty good and actually outperforms buy and hold, as it rides the trends when Bitcoin rallies while sitting in cash once the trend is broken.

When it comes to candlestick patterns, there are hundreds of them; I have only ever found a few of them to be useful.

Directional candles, like a large engulfing bar or a long-wick rejection, tell you volatility expanded and one side took control. Indecision candles, like inside bars, tell you volatility is contracted and a move is coming.

It is always worth putting them into context. When the market is trying to break prior highs or lows and you see large rejection in the form of an engulfing or reversal candle, it is pretty clear that supply overwhelmed demand or vice versa, and there will be a decent amount of traders who were betting on breakouts stuck in their positions.

Inside bars are also interesting, as they signal possible incoming volatility. When highs and lows of inside bars are tested, they trigger a lot of automated trading strategies so we can either trade with or against them.

These candlestick patterns are worth observing on daily, weekly, and monthly charts; there are no algorithms waiting for a breakout of the 5-minute inside bar or trading 15-minute engulfing candles since those are too frequent and therefore noisy.

For chart patterns like head and shoulders, double tops, triangles, etc. They usually form in balance, during accumulation or distribution, which comes from Wyckoff and is a good description of how markets work.

While I would not trade head and shoulders and triangles per the textbook description, these Wyckoff accumulation and distributions are what you will often see happening at the end of the moves in a less textbook-looking way.

For your own sake, you should skip things like the Gann Theory, Elliott Waves, and all the other astrology for men approaches to trading.

How you should think about price action trading

The way most price action courses and education work is they will usually present you with several textbook examples so you will then open the charts and start hunting for exact patterns without much of a context.

I am not sure if this is the best way; price action can be very noisy, and I think using it alone without any meaningful context is pretty hard.

So how do you get the context? There are basically three ways: the regime modeling I mentioned for the swing trading approach, which we are not going to focus on now as it’s more complex, and the top-down analysis, or using indicators for your navigation.

The top-down analysis basically goes from higher to lower timeframes to find trading opportunities. If you want to be trading very short term, you might use D1 and H1 timeframes for your analysis and execute trades in 5 minutes. For slightly longer holding periods, weekly and daily timeframes can be used for analyzing charts and H1 for trade execution. Take a look at the chart above; what timeframe is it?

Unless you are chronically online and watching charts, you won’t be able to tell, which is fine; one of the key characteristics of price is that it is fractal, the patterns that happen on higher timeframes will exist on lower timeframes in the same way.

You can see on the chart above that on the left there is a daily chart of euro futures; the price went down, and then it made a higher high; thereafter, it retraced to the origin of the move and rallied higher. This whole thing took about 1-2 months on a daily timeframe. On the right side, the same thing happened on the 15-minute chart within this bigger price move over the span of just a few days.

This is why timeframe selection doesn’t really matter; there really is no difference between hourly and 45-minute charts, apart from thefact that the 45-minute chart will be more granular.

The main reason to stick to popular timeframes like daily, weekly, 4-hour, or 5-minute is that everyone is already using them so they trigger crowding reactions.

Support and resistance

Probably the simplest and most popular price action concept. You can probably teach a 5-year-old to find these; all you have to do is look left and look at prior areas where price has bounced from. Once these are broken, they are often tested from opposite sides, so support turns into resistance and resistance into support.

These work because of herding; people see these levels and want to participate around them. Every level has a certain amount of resting liquidity: limit orders from people who want to buy support or sell resistance. Each test fills some of those orders. Each test leaves fewer of them. Eventually aggressive traders on the other side don’t have much to push through, and the level breaks.

There are two ways to trade a level. I, you, and the trade support tested as support and resistance as resistance. Liquidity pools sit just beyond the level: stops and breakout orders. You can either wait for confirmation of the price reacting at the level, and if you see support being supported, you can jump in; a more brave approach would be placing your limit order blindly at the prior support and resistance levels.

While this will often present a better risk-to-reward ratio, the support might break completely or have a really deep swing into the level, which can easily shake you out before the move happens.

There is a pretty common misconception that the more time level is tested, the stronger it is; this is false since each test consumes residual liquidity and levels become weaker. Because of that, if you want to play a hero and buy these blindly, you should only do it on the first test of prior support or resistance level since the probability of holding is higher.

If you trade broken resistance retested as support, broken support retested as resistance. Again, untested levels will be the best, and choosing an exact place to place your limit will overlap with some supply and demand trading principles, which we will cover later.

Why false breaks happen

When resistance breaks, breakout traders buy with market orders. Shorts that were sitting at the level get stopped out, which is also market buying. That’s a wall of aggressive buying.

If you’re a large trader who wants to sell size. You can’t just hit the bid wherever you like. You’d move the price against yourself. You need someone to sell to. That burst of aggressive buying above resistance is the best liquidity you’ll see all day. So you fill your sell orders into it, passively, with limit orders.

The breakout runs out of buyers. There’s no follow-through. Everyone who bought is underwater, and when they exit, that’s more selling. Price drops back through the level. This happens in every market on every timeframe.

People love to dress this up as evil market makers hunting your stops. It’s not. Market makers make markets. They quote both sides, try to stay delta neutral, and earn the spread.

Markets do get manipulated, but nobody at a bank is running the 1-minute EURUSD chart to take out your 10 pips. Clustered orders at obvious levels are basic human behavior, and large orders need liquidity to get filled.

Liquidity pools

These areas that draw a lot of participation are often called liquidity pools; you will find them not only at support and resistance levels but also prior day/week/month highs, double tops and bottoms, clean swing points, and other areas that naturally stand out on the chart.

The chart above is slightly messy, but it shows a resting liquidity in the form of limit orders. If you look to the left, the dashed white chart represents the prior resistance areas; as the market overshoots above it, it hits a wall of limit orders (yellow line), which allowed many participants to fill the sell orders and price sold off.

We can also explain this from the opposite standpoint.

This chart does not show a pending limit order, but the bubbles represent market buy orders. You can notice that there is a lot of buying represented in blue bubbles as the price tries to break the resistance. Why isn’t it going higher when there is so much buying? Because there is someone sitting on the opposite side getting their sell orders filled with size.

These are often called SFPs (swing failure patterns), false breaks, or excess. While you can trade them using only the price action chart, orderflow tools help heavily here, and we will look at them after this chapter.

Efficient and inefficient moves

When you start looking at charts, it is good to recognize the type of environment you are in. While conventional wisdom usually separates trends and ranges, I think it is well worth looking at how price behaves.

Efficient moves are two-sided. Buyers and sellers both get filled. On the chart it looks like overlapping candles, a grind with pullbacks, every bar trading back into part of the previous one. Plenty of volume traded at every price along the way.

Inefficient moves are one-sided. Someone needed to get filled urgently and lifted every offer in their way. On the chart it looks like big candles stacked on top of each other with very little overlap.

Why do these matter? Because inefficient areas tend to get revisited. The move happened because one side overwhelmed the other, which usually means the large trader behind it didn’t get their full size done.

On the retests, they’re often still there to defend it. And other participants, who missed the move, want in at those prices too. This is covered more in-depth in the market microstructure article.

When price moves fast through thin liquidity, the market makers and dealers who got run over are left holding inventory at bad prices. Price tends to get pulled back towards the least liquid side as the market looks for two-way trade again. The market doesn’t like leaving prices that were barely traded for too long.

From liquidity to liquidity

Price can go whatever it wants; the price of any underlying can go to zero tomorrow without really any reason. This is useful to understand because you will otherwise get into this mindset of “price has to do this now.”

While you can’t ever predict what price will do next, if you actively start to look at areas with potential resting liquidity, it can offer your trades with favorable risk-to-reward ratios. Equal highs and lows, clean swing points, the prior day and week extremes, session highs and lows, and retracements of inefficient moves into prior efficient consolidations. Being a good discretionary trader doesn’t mean you have to predict what price will do next; it means you need to be able to spot areas where it is worth for you to participate and do business.

Supply and Demand Clusters

I am sure you have heard about these supply and demand zones, clusters, bases, order blocks, and many other different names these were given over the years.

Essentially these are the efficient price ranges before the inefficient move happens. As I mentioned before, these areas of balance allow two-sided trading; thus, large participants can fill their orders there and eventually overwhelm one side. When these areas are revisited, they often have some residual liquidity present to cause reaction; moreover, herding and exit of underwater positions that traded in that consolidation on the wrong side take place.

When I look at these, I want the inefficient move to be significant, meaning it should have enough demand to break through the prior level of support or resistance.

• Demand zone: consolidation before an inefficient move up that breaks structure (closes above the previous swing high).

• Supply zone: consolidation before an inefficient move down that breaks structure.

Since the prior consolidations clearly establish the areas of these, you can trade them using limit orders with invalidation on the opposite side. The same as with support and resistance, the liquidity gets consumed with more touches it has, so your first touch is always the best one.

Liquidity gaps

When the move is directionally strong, price tends to revisit the supply and demand clusters any time soon.

What it tends to do instead is hold the inefficient move as support or resistance; this often happens as there is just too much urgency and not much of a counterparty pressure to fight the move.

Here you can see that after the initial leg up, the market did not pull back into the demand area but only tested the inefficient candle and acted as support there.

From my personal experience, when you see a large price move, you often won’t get the full retracement into the efficient price zone, as this is something everyone is betting on. Price rather uses the body of a large inefficient move as support/resistance before making the next leg up/down.

Over and Under

Another very common pattern that you will see paying out often is called over and under, sometimes quasiomodo, or maybe a completely different name.

Think of it as a head and shoulders where, instead of waiting for the neckline to break, you trade the right shoulder.

Price breaks above a level, can’t hold the momentum, and falls back hard enough to break market structure. Everyone who bought the breakout is now trapped above.

This is the only time when I would also use a market structure in the classical sense of looking at higher highs and higher lows since price does not behave as conventional wisdom teaches for most of the time.

Once you start looking for these, you will start seeing them everywhere, so be very picky about which ones you trade. These are reversal structures at key areas of interest, not something you blindly trade on a 1-minute chart.

Compressions

I am not a big fan of trendlines; horizontal support and resistance by themselves can be pretty subjective. When it comes to diagonals, it is even worse.

For that reason, I don’t really draw trendlines on the chart, but there is one pattern that I think is well worth paying attention to, which is called compression.

When markets trend in almost a textbook manner, making clean higher highs and higher lows going up, it brings in many flows from both price action traders and traders using things like moving averages and so on. As the price continues to grind higher, the stop-losses start to cluster behind obvious swing points.

Once this move starts to crumble and stops, it starts to get triggered; prices often cascade lower very fast as the move gets very illiquid. These can often be spotted any time you can draw a perfect trendline as support or resistance. If these break, you can find great trading opportunities targeting full retracement of the move.

AMT and Orderflow

Similar to efficient and inefficient price moves, auction market theory boils trading down to two jobs a market does: it facilitates trade, and it searches for fair value.

Foremost, profiling methods and order flow are two utterly different trading techniques and are not dependent on each other. Auction market theory and profiling tools can be used anywhere, but orderflow is strictly a lower timeframe trading tool. There is no point in using the things I will talk about on higher timeframes.

When people get into orderflow they immediately think it’s a holy grail, especially since it’s often presented as the way “professionals trade”. Orderflow tools need markets that trade on CLOB. Legacy and perpetual futures are the best.

There are also nuances within those; for example, spot forex is OTC with no real volume, and currency futures only show you a slice of the market, so the readings can mislead; commodity futures expire every month, and volume tends to be scattered all over the place; ES futures are the most liquid and traded ones, but there is an equal amount of volume traded in SPX and SPY options, and a lot of flows you might represent as directional can be just MM hedging their 0DTE exposure; Bitcoin and other cryptocurrencies can work, but nowadays they also trade on about twenty different exchanges, so orderflow can be all over the place.

The takeaway from this is that you shouldn’t use anything in isolation; order flow works as a great confluence for existing setups. These setups can be found at key areas using both price action and profiling methods.

Auction Market Theory

The Auction Market Theory breaks down the market’s primary purpose and how its participants interact to fulfill this purpose.

Trading is no different from any other auctions where there are only two main goals to be achieved.

• Facilitate trade and act as a place where buyers and sellers meet

• Seek for fair value

When prices are too high/low, and below/above fair value, the buyers/sellers are expected to push prices back to the fair value; this is called responsive activity.

Then market trades above/below fair value; buying/selling is not expected. It is called initiating activity, which is when we can see a market trend until they find a new fair value.

As in every auction, volume confirms or rejects new prices. Stock prices are a straightforward example. Let’s say that right now, Samsung stock is trading at $50. A new Samsung phone comes out, and it’s bad; the battery is not working, overheating, etc.

Because of this event, Samsung stock starts to drop in value until it finds new buyers at, let’s say, a $30 per share price. This is where the new value area was created.

After some time, phones get repaired, and the price of the stock starts to rise again. Where is the market likely to stop? The previous value was around $50. This is eventually what every market does as 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.

There are 3 key components of Auction Market Theory:

• Price—Advertise opportunity in the market

• Time—Regulate price opportunity

• Volume—Measure the success of the failure of the auction. Volume is variable and represents the interaction of market participants at different levels.

To sum up the auction theory, you always have to understand the context of a given market, as there are two types of auctions. When the market is trending up/down, we can see a fair value constantly moving in the direction of the trend.

Range represents balance between buyers and sellers that is represented by overlapping values. The general rule of thumb is once the market is inside the value, it will more likely stay in balance and explore inside the range of the value.

But if the market is an imbalance, it will often drift higher/lower until it stops, which is usually inside a previous value area.

The 5 Auction Market Theory Rules

When it comes to actual trading, these five rules are all you need. You can use them for both daily and composite profiles. These rules are what I follow and keep at the back of my head every day. Of course, for actual execution there are more factors that have to align, but these rules can make bread and butter setups for trading.

Market Profile

Market Profile, also known as TPO (time-price-opportunity), arranges separate trading sessions for the so-called profiles instead of classical chart representations. In one trading session, the time is not separated by the position of elements but only with a typographical separation for different trading periods. These are called TPOs, and one TPO usually represents a 30-minute period.

On the left picture is a typical 30-minute timeframe chart. Individual candles are replaced by letters of the English alphabet; in the above example, starting with a letter D (picture in the middle).

In the first period, Letter O highlights the opening price, and symbol # represents the price close in the last period. At the end of each session, all the letters will line up so there is no space left between them. This way, the Market Profile (last picture on the right) is finished.

From the TPO profile, we calculate POC (point of control)—a line with the most amount of numbers—and VA (value area)—70% of letters around POC. Market profile is not a trading strategy. It is only the way to organize data.

If you are daytrading, having a market profile chart open on one screen provides instant information on where the value is, where we are trading, and what anomalies there are on the chart.

To not make this article an extra 20 pages long, if you are eager to learn more about Market Profile, search books from Dalton or Steidlemayer, who are considered founders of Market Profile; there you will find much more detail.

I will briefly explain the market profile glossary and point out things worth looking at.

Initial Balance

The first two TPOs (first two 30-minute candles) of a day. Every market has a different initial balance.

CME products differ from different indices and commodities. ES, for example, opens at 9:30 EST. Eurex products at 6 AM UTC. Crypto at midnight UTC,

Forex can have just about any time, such as the first hours after London opens, New York opens, or the whole Asian range.

Initial balance can be used as basic S/R lines; generally, you want to be long above and short below IB to stick to the day’s trend. If the price is too far from IB, you can play a mean reversion to it. Initial balance is a factor of confluence, not a standalone entry decision, but many strategies evolve around this concept, such as the famous opening range break.

Day Types

Normal Day, Neutral Day, Double Distribution Day, Trending Day.

There are even more of them; should you learn about all of them? Maybe. Was I ever using them in my trading? No.

Trending and double distribution days are trend days, with trending days having the highest market conviction. You will often see one-time framing when the TPO doesn’t trade below the previous TPO’s low. In other words, the 30-minute candle doesn’t trade below the low of the previous 30-min candle in an uptrend. The opposite is true for the downtrend.

Double distribution has a lower conviction with a single print in the middle and two single distributions.

All the other days signal range days and balance, which built up for a bigger move in the future.

Opening Day Types

Open Drive, Open-test-drive, Open-rejection-reverse, open auction.

Again not that big of a reason to overfixate on these. They hold value when you can recognize them early on, but in my opinion, you don’t need to.

Poor highs / poor lows

Two or more TPOs at the same price point at the top or bottom of the profile.

On a 30-minute chart, these are the double tops and bottoms. These signal us the unfinished auction, which is often revisited to complete the auction.

These are great intraday areas of residual liquidity.

Excess

Excess generally indicates the end of the auction and the start of another. Excess is seen in the extremes of the profile with a strong pullback. On the candlestick chart, you see this as a candle with a long wick.

As you can see, on this day the market was trading lower until the U period, where it made excess to the low and the auction reversed.

Single Prints

These are the same as excess but in the middle of the profile, leaving low-volume nodes. On candlestick charts this is your inefficient move.

These show that the market might have been too quick to move in one direction. They often leave liquidity gaps, which tend to get filled, and because of them, you can use these single prints as both targets and entries.

Volume Profile

There are two ways to display an auction, with volume and price. Which one is better? It’s up to you to decide. They are essentially the same; the only difference is the market profile will use time, while the volume profile will use volume.

Value Area (VA) – 68% of the highest traded volume in the volume profile

Point of Control (POC) - Level of the most traded volume. Naked points of control bring the most significance, as they are untested.

On the chart, you can see a volume profile representing one trading day; besides the Value Area High, Value Area Low, and Point of Control, we also recognize the High-Volume Nodes and Low-Volume Nodes.

High-volume nodes (HVN)—areas where price found acceptance in the past, so they should act as support and resistance areas in the future; we want to use edges of these HVNs as our entries and exits.

Low-volume nodes (LVN)—areas where price moved quickly through. Hence, we can find liquidity gaps there.

To recap this, when the market is in HVN, we expect more of a range-bound environment with price bouncing from the edges. When the market enters into the LVN, we can look for more trending environments.

Composite Profiles

Composite profiles are all profiles that consist of more than one trading day. It can be days, weeks, or years.

Above you see the composite profile on Bitcoin, which only draws the composite profile from the start of the rally in 2023 until the top in October 2025, so it did not see any of the future price action.

Even without that, you can notice that the price moved quickly down until it hit high-volume nodes and made a bottom this summer in the biggest high-volume node it could find. As it rallied up, prior points of control were acting as resistance.

Another popular way of making a composite profile is by putting together multiple days with an overlapping value, like this example with ten days of range.

Some simple rules for this:

1. If the POC shift is outside the value area of the prior day, don’t merge profiles

2. Trend days that break straight through the value without building new value do not violate the structure.

3. More recent composite profiles are more important

4. The more days the composite profile is built from, the more important it is.

As you can see, here are 7 trading days. Which ones can we merge?

Day one and day two are a no-brainer, as they have overlapping POCs. The third day moved completely lower, so there is no merge there.

The fourth day can be merged with the third, although it was a big trend day; POC, and main value overlap the third day. The rest of the profiles are not a merge.

When the market is trading around these composite levels, we can put more weight on them compared to single-day profiles. As with everything in auction market theory, we are using the 5 concepts of value I described earlier.

As you can see from the price chart, there was an acceptance of the day 3 & 4 value and rotation to the opposite side.

VWAP

Volume-weighted average price is a name that suggests moving average weighted by volume.

It happens to be pretty important. for a lot of trading strategies, therefore looking at a daily, weekly, monthly, or yearly VWAP can provide levels of support and resistance.

You can also plot one standard deviation band from VWAP to get a dynamic version of the value area and use the same auction market theory principles as with market and volume profiles.

Accepted inside the bands, expect rotation to the other side. Accepted outside, use them as dynamic support and resistance for a trend. They aren’t precise, so don’t place orders on them, but they can give you a decent guide of where prices are likely to head next.

At this point of the article, you should probably know that on its own these are not that special, but stacking them on top of things you already know, you can find some decent trading opportunities.

Delta

There are two types of orderflow. Active (market orders) and passive (limit orders). The active Orderflow is usually called the aggressor, as they don’t want to wait, so they execute their orders at the bid or the offer.

Market orders are typically used by smaller players because they aren’t concerned about the spread that much. If you are, for example, open 1 lot in German Bund (FGBL) and get in at the market, the one tick you pay in the spread is worth €10.

Trading only that 1 lot, a 10 € spread is something you can live with, but if you want to execute 100 or 500 lots, you won’t be so happy to pay a few thousand euros just in the spread.

Once you hit a market order, you are taking liquidity from the market. During high-impact news, for example, markets tend to get driven by the market orders; that means high volumes and low liquidity cause high volatility.

Those traders that use market orders to get in the market are usually the weaker hand, as they are less patient ones.

Passive order flow is the advertisement in the book and the resting bids and offers. Large players typically use them, and they determine the market structure.

More often than not, they are a heavier hand in the market, making the liquidity. In a healthy trending market, you want to market and limit orders that come in hand, but they do not agree frequently, which is where you can find trading opportunities.

Cumulative Volume Delta (CVD) shows us a running total of finalized executions at the bid and ask. If the cumulative market volume delta is rising, buyers are more willing to trade into the offer than sellers hitting the bid. And vice versa, CVD is not the leading price but shows us where we can find out absorption areas, which will present as divergences in the chart.

CVD can work as a great confluence for price action setups I have shared in this article, as it can confirm numerous principles I spoke about.

Another is the delta attached to the daily volume profile. This shows us price levels where large buying and selling occurred.

As you can see in the example, we had two areas with very high selling pressure displayed with long red delta bars during the down day. In these areas, there are also supply levels we can use as entry levels.

Because there was high selling pressure in the past, there are likely to be stacked orders, which will add more selling pressure on a retest.

A lot of these are different visualizations of the same thing. The example I have shown you before with absorption above the resistance highlighting the market order bubbles will show the spike in CVD as market orders are heavily buying and will highlight the same buying delta on the volume profile. You obviously don’t need to use all of these at once.

Footprint charts

The footprint chart offers us a look inside the candle with volume traded at exact prices. You can watch a footprint chart on the monthly chart down to the tick chart, but I would not recommend going higher than something like 30- to 60-minute charts; thereafter, that many details can get lost, especially if you are looking at delta instead of volume.

Thanks to the footprint chart, we can see where sellers and buyers are interested and where they are trapped. You can see sellers hitting the bid on the left side and buyers lifting the offer on the right side on a footprint chart.

Besides, footprint charts look pretty cool, which is probably the main reason everyone tries to use them; they can often do more harm than good, as you can get easily overfixated on every print you see.

While there are some strategies like stacked imbalances and finished/unfinished auctions, from my experience the main use case is to confirm absorptions, when you want to fade the moves.

Putting things together

This was quite a lot; if you are really new, you should take things step by step, starting with price action and building slowly upon that.

Since over the years I have traded many markets and many trading styles, here is a breakdown of how I would approach things and what tools I would use for different trading styles.

Swing/Positional Trading

If you have time to trade intraday and want to only focus on holding positions for days, weeks, and occasionally a month, you can confidently skip the whole orderflow and auction market theory section.

For all swing traders, I would recommend visiting tradingriot.com, where you can follow my systematic portfolio and also trade several low-frequency strategies in futures and options that won’t require much time and will help you build a portfolio of non-correlated returns.

If you would rather not do that, just use price action concepts I have covered. Use monthly and weekly timeframes to mark out key levels on your chart and daily timeframes for trade execution and management.

It will be a slow process to build confidence and a track record of only trading price action setups, so be sure to commit to that.

Intra-week trading

Those of you that don’t necessarily want to go into trading 1-minute charts can structure your trades around finding levels on the weekly and daily timeframes with execution on H1, which provides an ideal amount of granularity.

While for swing and positional trading you will have to track loads of markets to find trade opportunities, this is not as drastic for intraweek trading, although you should still look at more than one market to be moderately active.

Use price action and some composite profiling methods or monthly/quarterly/yearly VWAPs to analyze your charts. For execution while H1 is on the edge of “orderflow-usefulness”, you can still gain additional confluence from footprint absorptions and looking at how the market behaves once large orders go through. In this timeframe I would probably not use CVD, as that is most useful in intraday moves.

Day-trading/Scalping

When it comes to active day trading, here you want to experiment with all the tools I went through in this article.

While not everything might stick, they are worth testing out to see if they can improve your trading.

For timeframe selection there isn’t much of a reason to look above the daily chart, as you shorter timeframe looking at the 30-minute chart is beneficial as it lines up with the market profile that gets constructed from 30-minute periods.

For trading execution you will end up anywhere between 1-5 minute charts; you can also experiment with non-time-based charts, which I was highly fond of when I used to day trade and was using exclusively tick charts. You won’t really have time to trade more markets than one, so choose wisely, and think about not only your competition but also trading hours, capital requirements, and so on.

Conclusion

There’s no right or wrong way to make money in the markets. You can find traders who only use indicators, trade price action, or trade fully algorithmically and be successful.

Discretionary trading is definitely sometimes on the edge of being astrology for men, but I believe you can make it work if you put the time in, find a repeatable process, have proper risk management, and have the discipline to keep showing up through the boring and painful parts.

Trading doesn’t really have a best rep these days; if you look online, you are most likely going to find some broccolihead driving a rented car claiming you can make millions by drawing a bunch of lines on a chart.

Trading is often presented as something extremely simple that you can do from anywhere in the world from your mobile device while making six figures every year. All of this simplification of financial markets also pushes people toward different ideas about why markets move in the first place.

In most cases, trading is also presented as a game of retail traders versus predatory market makers and “smart money” constantly running stops of small traders, creating liquidity in the market to initiate moves.

But is this true? Are markets this black and white, with market makers playing a hidden hand that moves the market all the time?

This is what we are going to explore in this article. Moreover, we will also dive into several concepts that I think everyone who wants to take trading seriously should understand.

It is worth mentioning that markets are definitely manipulated, and there are many ill practices happening in financial markets regardless of what market you decide to trade.

But not every single price move hides behind market manipulation. Most of them are just orders meeting other orders, which certainly does sound way less cool.

What is market microstructure?

Market microstructure is the study of the inner workings of financial markets. How they operate, how prices are formed, how traders behave, and how exchanges are structured.

Understanding it matters because you will properly understand the supply and demand dynamics behind every market movement instead of blindly buying and selling without paying attention.

Every so often you execute a trade and get filled at an entirely different price than you wanted. This is common in illiquid markets such as various altcoins or options, and it happens in liquid markets during high volatility events.

Market microstructure is a complex topic. It is lectured at universities and involves a lot of math.

Most of that math is beyond what you need, but you should understand a handful of ideas underneath it and the numbers that show those ideas at work: how these mechanics change your execution, how liquidity dries up during news, and how order book depth differs across assets.

Okay, so what is market microstructure? If you go to any exchange, it doesn't matter if it is crypto, forex, or stocks; placing a trade takes a couple of seconds. Your trade gets executed, and you are in a position.

Although this is a swift process, every market works on a system of open auctions. This means there has to be a seller for every buyer and vice versa.

Your counterparty is either another trader, an institution, an algorithm, or a market maker. Market makers especially are often presented as an evil entity that always runs the stops of retail traders.

This is not true. The market maker’s job is to stay delta neutral and capture the bid-ask spread. Delta represents directional exposure to the underlying. If you buy stocks, crypto, or options, you have a delta one position: you are directly affected when the underlying moves. If you are delta neutral, you are not.

Similar to options traders, who are often delta neutral because they trade volatility, or traders who trade carry trades, market makers are delta neutral to capture spreads.

We will come back to market makers later. First, what moves the market in the first place?

What truly moves the markets?

Markets move based on supply and demand. When buyers and sellers agree on fair prices, markets are in equilibrium. In other words, they range. When there is an imbalance, markets trend.

You frequently hear that a market went somewhere because there were more buyers than sellers, or vice versa.

This is not true, because of the open auction I mentioned. For every trade executed, there has to be a counterparty. For every buyer, there has to be a seller.

What changes is the balance between two kinds of orders.

Liquidity is the number of orders resting in the order book, waiting. Volume is the number of orders that got executed. Markets move when aggressive orders (market orders) meet resting orders (limit orders), and how far they move depends on how much is resting.

Some markets move a lot; some don’t move at all.

Imagine you bought Bitcoin and your position is up 5% on the day. That is wonderful, but you skipped buying Solana, which ended up 15% off on the day, but why?

Volatility is a function of available liquidity. Less liquid markets, with fewer orders resting in the book, get pushed around much more easily than markets with well-populated books.

If Bitcoin has $10m resting on each side within 1% of the price and Solana has $2m, the same $5m of aggressive buying moves Solana a lot more.

To see it in real data, I took a snapshot of the order books for BTC, ETH, SOL, XRP, and HYPE perps on Hyperliquid and pushed the same market buy through each one.

The left chart shows the price impact of that order as it grows. Bitcoin absorbs it with the smallest impact, and ETH comes next. Sol and XRP move noticeably more for the same size, and HYPE moves the most. The ranking follows how much is resting near the price: Bitcoin has by far the deepest book, and each coin down the list has less.

The right chart puts that depth next to volatility. The thinner the book, the more volatile the coin has been over the last six months.

Since correlation in crypto is very high, most coins move together a lot. When macro news or something else moves BTC, the same flow hits all of them at once. The buying that lifts Bitcoin a few percent runs into a much thinner book in SOL and pushes it further. That is why altcoins tend to move more than Bitcoin in both directions.

This is also why during the fun events like the 10th of October 2025, most altcoin holders were sent four tax brackets lower since the liquidity basically disappeared when it was needed the most.

This same relationship exists in equity indices, stocks, commodities, and FX.

In general, markets can be separated into four categories based on their volume and liquidity.

High volume, low liquidity

These markets move a lot. There is a lot of aggression coming into a market that doesn’t have enough resting orders to take the other side. This leads to gaps and slippage. If liquidity is provided only by HFT algorithms, you get a flash crash. In crypto, you get a liquidation cascade: the only “liquidity” is ongoing forced liquidations with no real buyers on the other side.

For some real market examples, here is a chart from Nanex showing the 10 year treasury futures during the high-impact news.

Low volume, high liquidity

The opposite extreme. Very thick markets such as Treasury futures. The book is so well populated that it takes a large amount of buying or selling to move the price in any significant way, and little aggression arrives to try. Each order takes a small bite out of a deep book, and the book refills before the next one comes.

The result is prolonged, low-volatility movement. On the chart the price line is almost flat. These markets are cheap to trade size in and challenging to make money in on direction, because nothing moves far enough to pay for a trade.

High volume, high liquidity

The healthy, busy market: ES during US cash hours, Bitcoin on a weekday. A lot trades, and there is plenty resting to absorb it. Market and limit orders arrive in roughly equal measure, so price moves continuously in small steps without gaps.

The chart shows the key idea of this section. Volume alone does not set volatility. This market trades the same flow as the high volume, low liquidity case, and it moves a small fraction as much. What matters is the ratio between the aggression coming in and the depth waiting for it.

Low volume, low liquidity

Quiet but fragile. Think small caps at lunch, long-dated options, or most altcoins on a weekend. Little trades, and little rests in the book. Most of the time nothing happens, then a single order arrives, takes out a few thin levels, and the price jumps to a new level where it sits again.

On the chart this shows as flat stretches broken by sudden steps. Over a full session it can move about as much as a busy, deep market, because the ratio of volume to depth is similar.

Another Nanex chart shows multiple of these states at once, as these can exist in one market during different parts of each day.

Auction theory

If you spend your time on X or YouTube, you are going to meet many traders who like to express their opinions on the market.

“The market is at this level of support. Therefore, we are heading higher.”

“We just retraced to this level, signalled by this indicator, so we are heading this way.”

The truth is that most retail traders don’t understand why the markets they trade are actually moving. All the overbought/oversold RSIs, supports and resistances, or moving averages look cool on the chart, but 99% of traders lack the understanding of orderflow dynamics.

Auction Market Theory breaks down the market’s primary purpose and how market participants interact to fulfill this purpose. The main idea is that financial markets are no different from any other auction where buyers and sellers meet daily.

There are two things to be achieved: facilitate trade in the two-way auction process and seek the fair value of the asset. Two-way auction means that the end of the “up” auction is followed by the beginning of the “down” auction and vice versa.

This process is represented by tools such as market profile or volume profile. These bell-shaped curves represent 68% of the trading as one standard deviation from the mean. This is what we know as the value area. Inside the value area is also the point of control, the level where the market traded the most volume or spent the most time. Market profile builds this from time; volume profile builds it from volume.

A hypothetical example. One share of Samsung trades at $50. A new Samsung phone comes out, and it’s terrible; the battery is not working, and it’s overheating. The stock starts dropping until it finds new buyers at, let’s say, $30. This is where the new value area is created. After some time, phones get repaired, and the stock starts to rise again. Where is the market likely to stop? The previous value was around $50.

This is eventually what every market does. Participants negotiate prices between balanced and imbalanced values.

Auction Market Theory is not a trading strategy, and you don’t necessarily have to use a market or volume profile in your trading, but understanding these concepts is useful. It is visible on every timeframe in the form of trends and consolidations.

Is your size size?

There has been a lot of research done 1 on what happens to price when a large order is worked through the market. The result is the same across stocks, futures, options, and crypto; impact grows with the square root of order size instead of going up in a straight line.

In plain terms, the expected price move caused by your order is roughly 0.5 to 1 times the daily volatility times the square root of your order as a fraction of daily volume. The exponent comes out between 0.5 and 0.6 in every dataset anyone has looked at.

This won’t be an issue for a lot of you, especially when you are starting out, but you will be surprised how easy it is to end up in some illiquid market where the execution matters. If you end up in these deep parts, remember that your size is relative to volume, not your size in dollars.

Say you buy $30,000 of BTC. Daily volume is around $30bn, so your order is 0.0001% of the day. Run the formula with Y of 0.6 and 2.5% daily vol, and you get an expected impact of about 0.15 basis points. Since that is basically part of the spread, you won’t notice that at all.

Now take the same $30,000 into a small-cap altcoin that does $3m a day and moves 8% on a normal day. Your order is now 1% of daily volume. Same formula: 0.6 × 0.08 × √0.01 = 0.48%, or 48 basis points. You will quickly find out this sucks, as you need to figure out how to execute your trade well, and it will also suck even more if you want out rushing.

While all this might sound too complex and something that probably won’t really matter to you, it will; being able to execute trades well can be the make or break of the range of different trading strategies.

Liquidity

The main buzzword you see constantly circulating online when it comes to trading, often tied to resting stop-losses and the market constantly hunting them.

This is not what liquidity actually is, liquidity much much less exciting terms represents resting orders you see inside the orderbook, stop orders like stop losses are not included in this view.

The chart above shows the depth-of-market (DOM) of S&P 500 futures ES. In the middle you have the current price; on its left there are pending buy orders sitting at the bid, and on the right side there are sell orders waiting on the ask.

As you can notice, there is also a price difference between them, in this case 7,791.50. Despite buyers sitting at 91.25, if I would rather not wait and want to immediately buy the ES futures and I click the market order, I will have to cross the spread, and my buy order will open at 91.75.

The same goes for selling, while sellers waiting to sell at 91.75, if I want to be short immediately, I will be crossing the spread and getting filled at 91.25.

Can you trade directly from DOM without looking at anything else? This is how I actually started trading years ago and was doing it mostly in European bonds. Back then all markets were much thicker and slower, so it was much easier. A lot has changed since then; markets got faster, and high-frequency trading got more popular and advanced, so these days it will be much much harder.

But even if we are not going to use DOM for trading, we probably won’t have access to any advanced DOM like you see above. I look at the depth of the market before every single trade I am about to place. I watch for three main factors.

The spread is arguably the most important, as it tells you the cost of crossing the bid-ask. We all know we want to be in trades as soon as possible, but as I mentioned, crossing the spread can make or break many trading strategies.

While it will probably be a non-issue if you only trade ES, Bitcoin, or other very liquid assets, it can cost you a lot for any less liquid market, especially if you are trading a lot.

For that reason, it is always well advised that even if you want to get into trade right away, start working on your orders from bid to mid if you would like to buy. Or ask down to mid if you would like to sell. Especially if spread exceeds one tick.

Depth will tell you information about how much size you can get; you might be looking at a market that has a very tight spread, thinking that you can trade whatever size you want. But only by looking at the full depth will you know where your order lands and how much slippage you will get if you execute the desired size.

This is the order book of ZROUSDT on Binance. The spread is only one tick, but if I want to short 100k ZRO tokens ($200,000 notional) at market, the order consumes every bid all the way down to approximately 1.9895. My average fill ends up around 1.9927, which is 0.0078 below the best bid, so I am down roughly $800 before the price even does anything. This is obviously not ideal, especially since I decide 5 minutes later that I don’t like the trade anymore and want out.

If you are observing lower timeframe charts, these can pop up from time to time, like this Bitcoin 1-second candle, which moved $1,000 as someone presumably fat-fingered a large market order.

The resilience is not that important and is probably something you won’t really observe unless you really decide to go deep into trading using the order book. It shows how orders are getting reloaded, which can provide some insights about underlying supply and demand.

In general, don’t get too hung up on watching order books the whole day. Most resting orders cancel before they are traded, and for any sophisticated market participant who wants to execute a large size, there are many ways to do that without showing their hand to everyone; I will come back to these later.

And beyond the book entirely, there is latent liquidity, the traders who would sell if the price got 1% higher but have not bothered to post an order. They show up as price moves. This is the real reason the impact is square-root-shaped rather than linear as your order executes.

Depth also changes throughout the day; while most markets trade 24/5 or even 24/7 these days, the liquidity will change as the day goes on.

The issue with available liquidity is that it often disappears when you need it the most. This does not only happen intraday during the high-impact market news but also during the increased volatility due to panic selling or buying.

There has been research published last year by CME monitoring the liquidity in the S&P 500 over the several weeks during the tariff selloff.

As you can see on the chart above, the liquidity does not only dry up intraday at high-impact news but also gets progressively lower when volatility increases.

The same goes for other markets, especially crypto, where the liquidation cascades are extremely common due to the high leverage perpetual swaps offered. There is a great article by @ltrd_ on X that breaks down the 10/10 liquidation cascade.

Why is there no depth when you need it the most? It is mostly because the liquidity is provided by market makers who pull quotes when uncertainty rises, and whatever aggressive flow shows up next hits an empty book and directs the move.

Where Does the Spread Come From?

As already mentioned, market makers make money from the spread, but why is there a spread in the first place?

Three separate costs make a spread that leaves the mark on a price. The first is plain order processing: exchange fees, clearing, technology, the market maker’s overhead, and whatever margin they can squeeze out on top.

Your buy nudges the price a tick or two; the next sell undoes it, which provides no information about where the market might be heading.

The second is inventory risk. Once they fill your buy, the market maker is short and wants to get flat again, so they shade both quotes down a little to tempt the next seller. Over the next few minutes they work their way back to neutral, and the price that got pushed around drifts back to where it was.

The last and arguably most important is the adverse selection. When someone lifts their offer, they have no way of knowing whether they know something they don’t, and some people trading with them do.

So they price their ask at what the asset is worth given that someone just bought, which is a little higher than what it was worth a moment earlier, and do the same on the bid in reverse.

The spread is their insurance against informed traders, and they would need it even if trading were free and they didn’t mind risk at all. It also leaves the only mark that lasts: the informed flow snaps back almost immediately, similar to inventory fluctuations, but when an informed buyer moves the price, it stays moved.

This is the mechanical reason your market order tends to move the quote against you. The market maker's pricing treats every aggressive order as partly informed, so the quote steps away from you whether anyone noticed your order.

Because most of the liquidity in any market comes from market makers (their job is literally to make a market), during high-impact news and uncertain events you see the books get much thinner.

They pull their orders to avoid carrying inventory into a move they can’t price. This is why your stop loss during these events might not fill where you wanted.

Can you make markets? Proably not. Market making these days is done by high-frequency trading firms and is extremely competitive and infrastructure dependent. You can try some very niche markets where competition is low, such as stablecoins to stablecoin markers or some shitter altcoins no one wants to touch with a ten-foot pole.

But I hope this helped debunk the “market makers running stops” myth, since in reality they don’t really need to.

Orders and the Book

As already established, traders willing to buy and sell an asset place orders in the order book. These resting orders are what we call liquidity or limit orders.

In crypto exchanges you often see maker and taker fees. You are a liquidity maker if you put orders into the book (a limit order). You are a liquidity taker if you take liquidity out of the book (a market order). Since most crypto exchanges have their own CLOB (central limit order book), they offer drastically lower maker fees compared to taker fees, so traders add liquidity into their book.

The resting bids and asks are not horizontally next to each other but diagonally. The gap is the spread. For the market to move one tick higher, someone has to buy everything resting at the best ask. For it to move one tick lower, someone has to sell into everything at the best bid.

In the ladder above, the 2 BTC buy takes a small slice of the 12 BTC resting at 100,010 and fills at the best ask, so it pays only the spread. The 40 BTC buy clears 100,010, 20, 30, and 40 in one go. Its average fill is 100,023.5, 1.3 basis points above the best ask, and the next buyer finds the cheapest offer at 100,050.

Limit Orders

Anyone can place a limit order in the book. For that reason, they are sometimes called advertisements or passive orderflow. Buy limits sit below the market; sell limits above.

Limits are the patient and cheaper approach and therefore are often used by larger players. Paying the spread might not matter for a small retail trader, but any large player would rather wait than use a market order that costs much more.

The downside is not only that you might not get filled, but also that you might get picked off.

If you were around during the FTX days, there was this very popular saying that “if your limit order gets filled on FTX, you know it’s a losing trade.”

A resting limit order is an option you have written to whoever has information. If the price moves through your level because something changed, you get filled right before the move continues against you. The academic name is the winner's curse. Traders call it getting picked off.

The closer you rest to the mid, the more often you get filled, and the larger the share of those fills that are bad. This is very relevant, especially if the spread is wide; often in options or less liquid markets, you should always work the entry and try to get the best fills you can, despite the possible option of missing out on the trade completely.

Market Orders

The so-called impatient approach, as you aren’t concerned about the price and want to be in (or out) now.

This comes at the cost of paying the spread, usually higher fees, and if you trade a thin market with too much size, a fill is nowhere near the price you see on your screen.

In crypto this is especially costly, where taker fees compound it. On Hyperliquid the base schedule is 0.045% taker versus 0.015% maker. On Binance futures it is 0.05% versus 0.02%. For an active trader doing 50 round trips a week, always taking instead of mostly making costs about 3% of the traded notional every week. While you might not think about it at the moment of taking the trades, this makes or breaks many short-term strategies.

When comparing market and limit orders, you often hear the term “delta,” which is the difference between finalized executions at the bid and the offer.

Stop orders

Finally, the infamous stop hunting: first, as I previously mentioned, stop orders are not visible in the orderbook, they are trigger market orders that activate above the price for buying and below for selling. Traders use them for stop losses, and breakout traders use them to enter.

So why do markets reverse below the key highs and lows?

Everyone is looking at the same chart; for this example we will use a Bitcoin chart where the white lines above highlight key support and resistance areas, so you know I am not just making this up. For the sake of the example, here is the zoomed-out chart that shows support and resistance lines someone with very little experience could identify.

I am going to zoom in slightly more into the support case that happened very recently to break this down a little further.

Traders see the same charts, which increases participation at obvious levels such as higher-timeframe highs and lows. Above a visible high, sit two kinds of buy orders: breakout entries from people (and algos) betting on continuation and buy stops from people who are short and need to cover. Both are market orders firing at around the same price.

If a large trader wants to get a sell order filled, this is the perfect area. There is a lot of aggressive buying coming in, so he has an easy time filling his passive sell into it.

The chart above shows the behavior around the support area; the delta bars oscillator represents that delta I just mentioned, which is different between finalized executions at the bid and the offer. In this case you can see there is a lot of aggressive selling coming into the lows, which is a mix of traders betting on breakdown and stops triggering from long orders (since the stop loss on long is market sell).

We can zoom in even further using the footprint chart, where you see the pickup in selling below the level. You can also see that price snapped back immediately after breaching the support.

Why? Because someone is taking the other side. This is called absorption. Aggressive buyers got absorbed by passive sellers who were the heavier hand.

Think about this logically: if you are a larger trader and want to get a filled big position, the best place to do that is in the area where a lot of other market participants are forced to trade or at the key levels, which will draw a lot of attention. This is why support and resistance, key swing points, moving averages, and 26- or 52-week highs and lows make price react; they are not magic, they are just areas where a lot of market participation happens naturally.

So the reason prices react at different levels is due to herding and order splitting; if you trade with a large size, it comes with certain problems. Use a market order, and you get slippage. Rest one massive limit, and your intention is visible to everyone, and you get front-run. Therefore, large traders split orders into areas of interest and fill slowly. These areas then often provide reaction as there is residual orderflow resting.

How do you find these areas? If you have a large order to execute, you want to do this during the times when the market is rather calm so there is enough counterparty for you to trade. You have probably heard about orderblocks or supply and demand zones. If you ever looked into some price action trading concepts, this is how these are created, as once the supply overwhelms demand or vice versa, the market tends to react when these zones are revisited, as there are still some residual orders pending.

While technical analysis can be subjective, volume and the market profile show a clear picture. Looking at where the most volume was executed and where the market “picked up the pace” can be useful to use.

Other than that, obvious levels and indicators like prior week, month, and year highs and lows, daily moving averages, and other give reactions. Like I said, there is nothing magical behind them, but there are so many strategies and algorithms evolving around these that they will naturally bring a lot of trading activity.

This is why higher timeframe levels and indicators play a bigger role. Prices are fractals; the 10th of October in crypto is a liquidation event that happens maybe once per few years and plays out over hours to days, but smaller liquidation events play on intraday charts multiple times per month.

Without me telling you, you won’t be able to guess what timeframe this is or even what market it is, as what we covered today is fundamental to every single market. I am a big proponent of timeframes not really being relevant; it doesn’t matter if you look at an hourly, 30-minute, or 47-minute timeframe, but again, because of herding, a 200-day MA is much more likely to give a reaction and bring important trading activity compared to 18 moving averages on a 90-minute chart.

Advanced order types

These days, a lot of more sophisticated trading platforms and terminals offer so-called advanced order types. While there are not new order types, it is always a limit, market, or stop order you are putting in; the way you do it matters, as it can improve the execution efficiency.

Iceberg orders only show a small size inside the order book but automatically keep reloading every time the market hits the same price. On the big US exchanges, non-displayed orders were about 20% of volume in 2025. On Nasdaq, hidden liquidity has been found sitting at the best quote about 72% of the time.2

Chase orders follow price and keep re-pegging a tick away as it moves. Useful when you want to get into the market immediately but don’t want to use a market order.

VWAP is following the daily volume curve to execute trades trying to adapt to actual trading activity, TWAP executes equal slices over a set time instead of all at once.

Pov, or percent of volume, trades based on a pre-set participation rate, say 10%, and it trades 10% of whatever the market trades, minute by minute.

Types of venues

These days you can trade stocks, crypto, forex, or futures on entirely different venues, while the asset you are trading will be the same; the matching mechanism will be thoroughly different.

Lit order books

So far all the examples in this article, have shown trading using central limit order books. Futures (CME, Eurex), stocks (NYSE, Nasdaq, LSE), and crypto (Binance, Bybit, Hyperliquid, and many others) run central limit order books with price-time priority.

Everyone sees the same quotes without knowing who posted them, the executed volume is transparent, and trading works in the form of a queue.

If I want to long 0.01 BTC here and want to trade at the best bid, after placing my order, it jumps to 9.23 and then to 9.24. Since I was the last one to put this order in, the 9.23 BTC has to get executed first on this level before my order will go through.

Some futures (short-term interest rates, the 2-year Treasury) use pro-rata matching, or a blend of FIFO and pro-rata, where orders at a price share each fill proportionally in size, which rewards posting big.

Futures that are traded on CME, Eurex etc. are probably the most transparent and easy to understand since they only trade on one venue. There might be different contracts since the futures expire, but most volume usually happens inside the front month; therefore, you will generally have a pretty easy time navigating these.

Crypto is a fragmented market. The same coin trades on dozens of venues: spot, perpetual swaps, options, dated futures, and so on.

While during most normal conditions prices are tightly close to each other, the fragmentation cracks during price discovery and stress. On 10 October, BTC traded close to $1,000 apart between Binance, OKX, and Bybit for several minutes.

Off-exchange Trading

In 2025, for the first time, more than half of US equity share volume was executed off-exchange3.

So if trading is not happening in the CLOB, where is it going? The vast majority of volume is either internalized or happens in dark pools. Internalizing the volume is done by firms such as Citadel Securities, Virtu, or Jane Street, which buy retail order flow from brokers and fill it against their book.

This is, for example, the case of Robinhood, which has been running on the PFOF (pay for order flow) model. This recently also touched crypto with Lighter Exchange, which is using something similar on-chain.

While this immediately might sound horrible since the market-making firm sees your orders and will hunt your $20 stop-loss, it often is not that bad. Price improves most retail orders versus the public quote, because these firms know retail flow is mostly uninformed and they can afford to. Fees also tend to be lower in the cost of increased latency, like in the example of Lighter.

It is adverse selection in reverse: if the counterparty is known to be noise, the spread charged is smaller. But it does mean your order never sees the lit book, your limit order sits in a queue most flow never reaches, and the “volume” you see on a chart is mostly not where your order went.

Dark pools are something different and not something you will most likely reach; they are private financial forums or alternative trading systems (ATS) where securities, derivatives, and other instruments are traded away from public exchanges.

They are run by major investment banks, high-frequency trading firms, and so on. Trading in a dark pool is beneficial for the participants since they can heavily reduce the market impact at better execution costs. They are less transparent and open to abuse.

While we can’t trade in dark -pools, we can track the volume executed, which can be predictive of future price moves, like this dark pool buying on MO at the beginning of October.

This can indicate informed flow demand for the underlying asset; this data can be found on tradingriot.com alongside other indicators for futures, stocks, and cryptocurrencies.

Over-the-counter and request for quote

The PFOF model is only one way of how off-exchange trading works; while wholesalers that operate PFOF models with exchanges get offers routed automatically, there are other types of off-exchange trades you can encounter.

If you trade over-the-counter, you trade directly with a counterparty, usually a dealer who takes the other side from their inventory. This model is popular in crypto and retail forex brokerages. In crypto, OTC is heavily utilized for large trades and also trading low-liquid assets for higher size. OTC firms simply give you a quote, and you either accept it or not.

RFQs are similar, but instead of one counterparty like in an OTC trade, you get offers from multiple dealers at once. This model has been recently popularized in newer decentralized exchanges since they can provide better trading conditions for users instead of using the CLOB model.

Forex and CFDs

Forex is traded over the counter between banks. It is the most liquid market in the world, with trillions of dollars traded every day with no central orderbook to look at.

Retail traders never touch the interbank market. They trade solely against their brokers; this is what made the forex and CFD space extremely popular, as brokers often reside in exotic jurisdictions, offering traders massive leverage under countless shady circumstances.

There are two different models in this space: A-book or B-book. In A-book broker passes your trade to liquidity providers and earns a markup or commission. A B-book broker takes the other side of your trade and earns your losses. Most brokers run both models, and you never really know where your orders end.

Since the majority of retail traders lose money over the long run, B-booking is beneficial most of the time. Since this space lacks a lot of transparency, you can face some shady conditions, such as widening spreads during less liquid hours, news, etc.

Orderflow tools

Now that you have the framework, here are the tools traders use to see orders in real time when trading markets with CLOBs, such as futures or crypto perpetuals.

These are orderflow tools. All of them are best used for short-term trading. If you only swing trade, there is still some use in them, but it is more nuanced and mostly for narrowing down the high-timeframe ideas.

Depth of market

The DOM shows the orders resting at each price. Bids are in the left column, and asks on the right. On an advanced DOM, the middle columns show what got executed at each price, and the left side carries a volume profile.

Is there an edge in actively using the DOM? There is, but it is not easy, and it is hardest in crypto and other thin markets.

The best use of the DOM is in liquid markets such as bonds or index futures. Limit orders are advertisements. Anyone can add and pull them at any time. This often creates the illusion of supply and demand that isn’t real.

That is where the edge in the DOM comes from. Skilled DOM readers can tell whether the orders near the price are real or spoofs. It’s very difficult to trade using only DOM, and it is getting progressively harder as markets become thinner and more HFT firms dominate all the flows.

If you are an intraday trader (or any trader), the DOM is also a good execution tool. Placing orders on the ladder is faster and more precise than clicking on a chart. Most platforms let you bind different order types to keys and work orders with a click or two.

Tape reading

The tape shows market orders as they execute. Most platforms let you build a reconstructed tape that only shows prints above a threshold, which is the fastest way to see large executions in any market.

You use the tape to see where traders are interested in buying and selling and, most importantly, how the market responds to that effort. If you see a resistance level where a lot of market buys come in and the price does not follow through, it can be a strong signal of absorption from the limit orders.

Footprint charts

Footprint charts allow you to see historical reconstruction of the tape. It shows the same market orders traded at bid and ask, inside a candlestick chart, so you can go back and look at what already printed.

The common bid-ask footprint shows sells on the left and buys on the right. A delta footprint nets the two and shows just the imbalance per price.

Footprints can be plotted on any timeframe, but they are most useful for immediate action intra-day. On a higher timeframe the balance between buyers and sellers washes out, but in short intraday rotations you can see inefficiencies that offer short-term trades.

Heatmaps

Heatmaps plot resting limit orders onto the price chart over time. While many traders are using large orders as support and resistance areas, coming back to the spoofing example and the overall downsides of showing everyone your incentives, I would say it is more complicated than that.

Not every order is a spoof. Genuine buyers and sellers do rest sizes. In the example above, a large order rested and got filled, and the market went higher from there.

For crypto, looking at the spot order book can be more indicative of genuine supply and demand, and it is also useful to look at the overall delta between bids and asks to get a broader idea. Instead of watching single levels, like on the chart below, which shows delta on BTC spot within 2.5% of the price, you can notice when this gets a really high or low price and tends to find short-term bottoms and tops.

In Conclusion

I think that one of the reasons why so many people lose money in trading is because they start wrong right from the beginning. Similar to any other profession, having a profound understanding of many moving parts and intricacies will make you much better prepared for a range of situations and will make your journey much easier no matter if you decide to day trade futures, swing trade using options, or develop algorithms.

While there is nothing wrong with that, the issue with trading space and education evolving around trading is that it is always focusing on specific strategies. It uses this or that indicator, price action concept, or any other thing to miraculously predict future returns.

I am the first to admit that I genuinely used to believe that markets are moving because they touched the horizontal line I draw on the chart. My line is the correct line to be drawn, while all other lines suck. Despite never getting too delusional to believe in Elliott Waves, this is a common issue among the trades. They draw levels like they are in art school or watching countless indicators to give them a magical signal to buy and sell.

Let’s think about this. I will offer you a coin flip. If you win, you will get $110; if you lose, you will lose $100.

Would you take it? Considering most people reading this article know about expected value, I suspect the answer is yes, as it should be since this game has an expected value of $5 (0.5 × 110) - (0.5 × 100), but there are many people who would pass on this.

Losing $100 hurts psychologically more than winning $110, as loss aversion research has studied. If we were to play this game only once, the return is completely random, but playing the same game a thousand times, the odds are highly in your favor.

The goal of this article is to show you how you can play similar games in the financial markets; they are not fancy, you won’t make riches overnight, or you will not be anything smart or sophisticated. You will be making money because you will be willing to take risks others don’t, similar to the coin flip game where, while the vast majority of the paths ended in profits, some barely broke even.

Not only that, at the end of the article I will break down my own core portfolio that is harvesting different risk premiums and has been consistently beating the S&P500 since 2016.

This portfolio is part of Tradingriot.com, which provides deep historical data for equities, futures, or cryptocurrencies distilled into actionable signals and this exact portfolio to copy trade.

Efficient Market Hypothesis

If there is one definition of someone who “must be fun at parties,” it will be Eugene Fama, who is closely associated with the Efficient Market Hypothesis (EMH).

EMH says that prices already reflect all available information as thousands of smart people compete to find any mispriced assets, so any obvious edge gets traded away immediatelly.

So you should basically just give up since you can’t beat the market with publicly known information.

While this might sound like you should just give up, it’s not the correct takeaway. EMH says you can’t earn extra return without taking extra risk, not that returns are zero.

There is this weird belief that “equities should return 7-10% per year,” like there is some kind of law of physics that magically moves them higher year over year, or as if the market owes you these returns for showing up.

This is understandable if you look at the S&P 500 performance over the last fifty years, but it doesn’t work this way.

The historical positive drift in equities is compensation for taking the risk not everyone wants to take. If you buy equities and hold them, 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 and wars happen. The whole thing feels terrible every so often.

If holding stocks felt safe, they'd return the same as treasury bills. Why would anyone accept equity risk for the same return as risk-free government bonds? They wouldn't. So equity investors demand extra compensation for the uncertainty, and historically that's been around 4-6% per year above risk-free rates.

Interestingly enough, the equity risk premium sticks around even though everyone knows about it. It's been documented across over a century of data, in dozens of different countries, through world wars and 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.

Alpha

Every time I open X or watch trading YouTube content, I see the word “alpha” thrown around all the time.

This usually comes together with some kind of technical analysis setup or strategy, which makes me die inside a little every time I see it.

In trading, alpha is excess return from skill or information advantage. It decays once discovered because others can replicate it. Examples of that would be strategies evolving around speed, such as high-frequency trading or market making, where infrastructure matters, or getting access to alternative data that gives you an advantage, but it disappears once more traders find out about them.

For most retail traders, alpha is not really achievable, but inefficiencies can be.

Inefficiency is a mispricing that should not exist; the same as alpha, it is highly competitive but more achievable for you and I.

The examples of inefficiencies are the index inclusion effects where stocks are added to the S&P 500 and used to jump on the announcement because index funds must buy them. Something that has now shrunk sharply since it is getting front-run, similar to statistical arbitrage, exchange-pricing gaps, or post-earnings announcement drifts (market slowly digesting earning jumps might as well be risk premium) and so on.

These do exist and happen repeatedly, but their returns are also shrinking as more traders find out about them.

A risk premium is not like this; it is what you earn for bearing a risk that can’t be diversified away. Insurance companies are not a secret. Everyone knows they exist, and everyone knows they profit. They keep profiting because the demand for getting rid of risk never goes away.

Skew

If you are buying insurance year after year, there is a high likelihood nothing is going to happen. Your car won’t break, your house is not going to burn down, you won’t need surgery, and so on.

For an insurance company, this means they will collect a small payoff but every once in a while are obligated to take a considerable loss when someone’s house burns down.

This is very similar to several risk premium strategies; they have what we call a “negative skew.

It is not pleasant, and every time there is a crash, spike in volatility, or any other market stress, you are very likely going to hear how you are picking up pennies in front of a steamroller. This is fair but, if done correctly, can still be profitable despite losing a few fingers to the steamroller every once in a while.

Besides trend following, negative skew shows up in most risk premium strategies. So you need to be ready for long drawdowns and size carefully. Markets also have fat tails, which means extreme moves happen far more often than a bell curve says, which is is called high kurtosis.

Momentum

The “trend is your friend” saying is one of the biggest clichés you can hear in trading, but not many people actually ask why that is.

Momentum describes the fact that prices might as well move in the direction they were already moving, and you usually hear about two different types of momentum.

The cross-sectional momentum ranks many assets against each other, stocks in the same sectors, correlated commodities, altcoins from similar categories, and so on. You buy the winners and either avoid or short the lossers.

Time-series momentum, or trend-following, looks at the performance of an individual market against its own past, typically using trending indicators such as moving averages or channel breakouts to go long or short.

I will give you a full breakdown on how I, personally, run these at the end of the article in the systematic portfolio section; for now what is important to know is that these are again forms of risk premium, although exact reasoning can be up to speculation.

Cross-asset momentum is slightly clearer; by buying individual assets, you are accepting the possibility of the crash risk which is much larger than in index funds or other less volatile assets. Because you are not the only genius who figured out to buy stocks, a lot of the movement can be explained by behavioral factors such as investors underreacting to news. Money chasing recent winners and forced flows from funds are adding to it.

An example of this can be as simple as instead of buying SPY, you will be buying the top 10 performers from SPY with monthly rebalancing.

For time-series momentum, the explanation of the risk premium also comes from behavioral biases, but there are also some other factors.

Using futures as an example, where going long and short is much simpler compared to equities, there are many participants who are not actually trying to make money. Central banks smooth rates and currencies, which slows the speed at which information reaches prices and creates trends. Hedgers are removing the directional risk using futures against physical commodities and so on.

When a commercial hedger sells futures to offset the directional risk, trend-followers such as CTAs (commodity trading advisors, also called managed futures funds) offset that risk from them.

While this might sound as easy as “long when CTAs are long,” it unfortunately doesn’t work like that.

Classic trend-following in futures used to do very well in the last few decades but deteriorated heavily once it got too crowded and more capital started chasing the same signals. You can see this on the chart below, which is using some basic CTA-style trend-following rules.

So is trend-following dead? No, it isn’t; it just got harder and more competitive over the years, so we have to be more creative and look into less competitive spaces, such as crypto, for example.

When it comes to trend following, the case for this being a pure risk premium is thinner, especially since trend following has the opposite skew profile. For trend-following strategies, the vast majority of your trades are going to end up being small losers, and the vast majority of your returns will chop sideways until you end up riding a massive trend.

As visible on the chart above, this is a fairly simple system in crypto trading for the most liquid coins. Over the years it did quite well with sharpe ratio similar to prior century of futures trading, but you can also eyeball on the equity chart how much of a sideways chop will you have to endure. Usually several months or more of just nothing before the next big trend move arrives.

A strict risk premium pays you for bearing a risk that others want to offset. Momentum fits that as you face the crash risk, similar to simple buy and hold. Trend following, not so much since it is often using a stop-loss that quickly gets you out if trends have no follow-through. This and the fact that it tends to do very well during strong unexpected moves gives it a positive skew. It's unusual to get a hedge and a positive expected return at once, and that may be why it's been competed down as money piled in.

I will return to both trend-following and momentum later on when I will go through the systematic portfolio as both are large part of it.

Variance Risk Premium

There are not that many reasons to cover the variance risk premium in detail since it has a standalone article on the blog. However, for those of you that have no desire to trade options, the main premise is that implied volatility tends to sit above the realized volatility. There are several reasons why this happens, but one of the biggest ones is that traders overpay for protection, which makes it a perfect example of a risk premium trade.

SPY’s 30-day implied vol was about 13.4, against a 20-day realized vol of about 10.5. That's a gap of roughly 3 vol points. Over the past year the mean was around 2-3 points, so you can see it is persistent.

This gap is the risk premium; options sellers earn it on average, but they face occasional massive losses during market crashes.

While the above mentioned above article goes into much detail on structuring trades and avoiding large losses, in the simplest form of continuously selling 30-day straddle, the negative skew is clearly visible on SLV which is ETF tracking performance of silver. Blindly selling volatility performed well up until the middle of last year, when silver went on a massive rally.

But because of these large unexpected moves that lead to massive losses, VRP is persistent. More sellers, more products, and more systematic vol funds have narrowed it in places, and it generally goes through cycles of thin vs. rich premiums, but it hasn't disappeared, because the demand for protection hasn't either.

Skew Premium

When I spoke about skew earlier in this article, I spoke about it in relation to payoff profile; this is a different skew now. In options, skew is the difference in implied volatility across strikes.

In equities, the out-of-the-money puts usually cost more than out-of-the-money calls. Since the markets tend to showcase the “staircase up, elevator down” behavior where usual up days are slow and steady while down days feel like the world is about to end tomorrow.

Over the last 5 years, the skew on average was -5.42 points in QQQ. In simple terms this means that buying OTM puts will cost you more than OTM calls since portfolio managers buy puts as crash insurance, regardless of price, driving this demand up.

Compared to VRP, where we would sell a delta neutral structure like straddle or strangle, to harvest skew premium, we will be selling only the out-of-the-money options.

The risk profile is the same as selling straddles or strangles, with the difference that a short put collects a lower premium but benefits from positive drift, which stocks tend to have.

Either way you are getting paid for bearing a crash risk others are trying to hedge away; this has been persistent for decades but carries one of the ugliest tails from the group.

Term Premium

Futures usually sit in contango, meaning longer-dated futures cost more than spot. This has a pretty simple explanation since we are way more certain about near events compared to what is going to happen weeks or months from now. Volatility mean reverts, so when spot VIX is low, futures price in a drift back up. And hedgers pay a premium for protection since long volatility works as a hedge for equity portfolios.

Looking at VIX futures here, which is the most popular product for this trade, we can see that futures expiring a month from now are trading about 3% higher compared to spot. This means that if we short the front-month contract, we can earn the roll-down as futures converge to the spot price.

While this type of trade got crowded again over the years with many ETFs and ETNs flooding the space, the underlying risk remained the same, and there are many participants trying to offset them.

This trade can be done in a range of futures markets from currencies to commodities, although it is most popular in the VIX since it does not have any directional drift.

Bonds are another example, but this is more of an institutional-grade premium. Long-dated bonds pay more than short-dated ones because you carry interest rate risk. For US bonds, it now sits around 0.5 to 1%. That's a thin reward for the risk. Also worth mentioning that treasuries performed worse than many altcoins and lost roughly half their value in the past 5 years, so trading the roll-down in VIX sounds like a better use of your time.

Speaking of some other more nuanced premiums you probably won’t be harvesting but are worth mentioning in this article would be credit risk premium, where corporate bonds yield more than government bonds because companies can default. Or a liquidity premium, where illiquid assets pay more because you can’t sell them quickly.

These are the oldest and most durable premia here. Their size varies across cycles, but they’ve shown up consistently across many regimes. Nobody has arbitraged away the fact that lending to companies is riskier than lending to governments.

Carry

The original premise of a carry trade is simple: borrow where money is cheap and lend where it's expensive. You're paid for holding the higher-yielding asset, as long as prices don't move against you.

The classic carry trade works in currency markets. You borrow money in a low-interest-rate currency (e.g., Japanese yen at 0.5%) and invest it in a high-interest-rate currency (e.g., Australian dollars at 5%). You pocket the 4.5% difference. This is the currency carry trade, and it's been profitable historically.

When risk appetite collapses globally, high-yielding currencies tend to crash while safe-haven currencies like the 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 arbitrage.

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.

Luckily, similar to momentum, crypto still offers great opportunities not only in the delta-one space, like trend-following, but also in delta-neutral trades.

Because perpetual futures do not expire, to balance supply and demand, they pay a funding rate between longs and shorts. When traders crowd into longs (futures above spot price), longs pay shorts. Buy spot, short the perp, and you collect funding while staying market neutral.

This is again a classic example of risk premium, since demand for leveraged long exposure is persistent, and the people supplying the other side get compensated.

In the majors it compressed a lot over the years as more sophisticated capital arrived. But the huge yields are not fully gone; they are just more risky. To find them, you have to scout less liquid symbols on often shady venues that sit on-chain where you have to constantly stress about the exchange going under. But even for BTC the annualized basis has been around 5-10%, which is higher than any traditional futures assets.

Mean Reversion

When you research risk premium harvesting, mean reversion is not brought up despite having several clear cases when you can get paid to hold risk others are trying to avoid.

When markets are in stress, they are usually not in stress for very long; liquidation cascades in crypto, deeply backward-dated term structures, and blowouts of spreads tend to revert to the longer to mean.

During these extreme conditions no one really want to to trade se stepping in and providing liquidity in thin markets is harvesting a risk premium, of course the risk here is that you are stepping in too early and markets will remain irrational longer than you can remain solvent.

Building a Systematic Portfolio

When it comes to trading, theory only gets you so far. While there are a lot of different risk premiums you can harvest, I will show you which one I am running and how you can run them as well.

Why would I share this? This portfolio is only one smaller part of strategies I trade, and I felt like it is nice to be able to offer something that actually has systematically backtested results, is simple, and I have run myself for years at this point. Even if you decide not to copy my exact portfolio, use these strategies as a showcase of the concepts I shared in the article.

To access this portfolio, you will have to subscribe on tradingriot.com. After you do that, besides the access to derivatives data for thousands of equities, cryptocurrencies, or futures, you will be able to set up your trading balance inside the systematic cockpit, where you will be automatically prompted to set up the trades based on the strategies. You will also get rebalance notifications inside the private Discord.

The portfolio is harvesting risk premiums, which were tested over decades and are very likely to stay for more decades to come.

Core Allocation

This can be viewed as sort of a main leg of the portfolio, which works on the simple premise that assets go up over time. While stonks tend to go up the most, during some market cycles commodities go up, the dollar goes up, or bonds go up.

This is why core allocation of the portfolio allocates long-only into main ETFs using a set of trend-following rules. Namely, this “all-weather” portfolio trades ETFs for bonds, commodities, dollars, equities, and gold during different market cycles rebalanced once per week.

This is nothing too fancy; we are obviously still exposed to the unexpected market crashes, which is the risk we are compensated for, but trend-following rules soften these blows, which has been proven over time.

VIX Term Premium

A short-volatility strategy that harvests the term premium embedded in equity hedging demand while the volatility environment stays stable. Because markets tend to go crazy sporadically, strategy tracks the term structure slope and only shorts VIX ETN VXX when markets are in contango and goes flat during backwardation.

The same as the core allocation strategy rebalances once per week, and it is built to earn steady carry in calm markets while limiting participation in volatility spikes.

Equity momentum

Stonks go up, so I want to own stonks, but I also don’t want to spend 10 hours a day stock picking and trying to find the next best performer. In the momentum section I have spoken about cross-sectional momentum; this strategy is a pretty classic example of one.

It scans the whole stock market once per month using several filters such as liquidity and market cap to find the best performers across the whole stock market; it is also using some trend-following filters to again avoid the massive tail risks.

While VIX and core allocation trade once per week, this only rebalances once per month, so there is really not much you need to do here. Below you will find the strategy performance since 2006, which was tested on survivorship bias-free data.

SPX Tactical Long / Short

The last strategy in the portfolio is mean reversion; it shorts SPX during the extreme complacency and longs during the panic. To get these signals, it is using the data from credit markets, VIX term structure slope, and overall market breadth.

The strategy does not trade much; the signals trigger every few months or so, but historically this has worked very well, especially on the long side where you are the other side of forced selling, with again filters in place so you are not turned into forced sellers just a few days after if the reversion does not happen.

These four strategies put together achieved about a 1.5 Sharpe ratio over the last decade with a 30% compound annual growth rate and a 24% maximum drawdown targeted at 20% volatility.

Conclusion

Risk premium harvesting is nothing fancy; you are not going to make millions overnight. But in my opinion these strategies should be a backbone of every robust portfolio since they are benefiting from profit mechanisms that were documented over many decades and are consistently persistent in the market.

You are getting paid because you are doing something useful (for once); it ain’t much but it’s honest work.

Shortly after the video started getting traction online, I got curious how he actually blew up and learned that he managed to do so by selling naked options on natural gas and crude oil.

This theme of massive blowouts of short vol strategies kept repeating over and over throughout the history but also during COVID, which came shortly after.

So why do people do it? Why, if you search “options strategies” on YouTube, are there thousands of videos promoting “selling options for income”?

I would like to explore that in today’s article, but not only that.

Throughout the years I have developed a short volatility strategy that consistently works. Considering the fact I still have a roof over my head after many of the recent market stresses, I decided to share it step by step with all the rules, data, and backtests.

All the data you will see in this article comes from tradingriot.com. There you can find all relevant charts, screeners, and calculators, which allow you to start trading this strategy yourself.

Why selling volatility makes money

If you are coming from the simpler side of trading space and you spend your days drawing lines on charts and convincing yourself that the twenty-something-year-old trader you are watching on YouTube really made his money by trading 1-minute charts of Nasdaq futures for 15 minutes a day. You might have no idea what I am actually talking about.

I don’t want this article to be a 101 explainer on options. I have a whole course about trading basics on my website, which is completely free, so make sure you read that if you have some gaps in options trading.

In short, trading futures gives traders linear types of payoff structure: if you have a long future and the market goes up, you make money; if it goes down, you will lose money.

Things are slightly more complicated with options; when you buy or sell an option, the change in the underlying asset is only one part of the equation.

Besides that, time, volatility, and interest rates (no one cares about those) play a role as well.

This volatility component is what matters here, as with options you create positions that make or lose money based on markets’ realized volatility, no matter the move in the underlying asset.

Volatility

When we are looking at volatility for each market, you often see two different names, implied and realized volatility.

Realized volatility is pretty simple; it is the annualized standard deviation of the returns of underlying assets over some past window.

I don’t want you to confuse yourself too much, but there are a bunch of different models and calculations, so the realized volatility number you see on different platforms can differ even on the same look back.

One of the simpler measures, and the one I also use, is close-to-close realized volatility.

You take a series of daily closing prices, compute daily log returns, compute the standard deviation of those returns over your chosen window, and annualize by multiplying by the square root of 252 (trading days per year; for crypto options, use 365):

sigma_annual = sigma_daily x sqrt(252)

If you are considering closing this article now, here is a much simpler explainer:

You figure out how big a typical daily wiggle has been, then scale it up to a yearly number so it will match the implied volatility quotes since those are always annualized.

The square root is doing the scaling because variances add across independent periods while standard deviations do not. Uncertainty over a whole year works out to roughly 16 times the daily uncertainty, since sqrt(252) is 15.87 and 16 is close enough.

This 16 number is good to remember since your square root of 252 is 15.87, which is close enough to 16.

When you see a stock with 20-day realized volatility of 32, you can divide 32 by 16; the typical daily move over those last 20 days is 2%. To be more precise, “typical” is actually the daily standard deviation. If daily returns were normally distributed, roughly two thirds (68%) would close within 2%.

The shorter look-back windows, such as 20 days, will be more affected by shorter-term wiggles, while the larger look-back windows will be more smoothed but can also react late to market changes. For this article and strategy that follows, we will use the realized volatility lookback, which matches the implied volatility of the option.

This number does not only include the future price movement; it also includes risk compensation, hedging costs, and supply and demand.

While the realized volatility is backward-looking, implied volatility is different; it tells us what the market thinks future volatility will be. Implied volatility is a forecast over the life of the option, so a 30-day IV is a statement about the next 30 days.

When you are selling volatility, the premium you actually collected is today’s implied against the volatility realized over the following 30 days; assuming you do not have a crystal ball, you will only know once those 30 days are gone. But this doesn’t make realized volatility useless. The trailing comparison tells you whether options look rich right now relative to how the market has been behaving, which is a reasonable starting point.

Implied volatility carries more than a forecast of future price movement. It also carries risk compensation, hedging costs, and plain supply and demand, all of which push the quoted number above what tends to get realized. That persistent gap between what options price in and what the market delivers is called the variance risk premium.

Variance risk premium

Variance risk premium (also called volatility risk premium, which is actually more correct since variance premium is defined on squared terms) is one of the most documented phenomena in options markets.

This is why I am also sharing this whole strategy and concept with you. There is no alpha here, as this is something that has been documented over decades and is very unlikely to go away despite more people trading it.

Variance risk premium describes the tendency of implied volatility to sit above the volatility that ends up being realized.

This does not necessarily mean that all options are expensive, this is the common fallacy of the selling options for the income crowd which might be topic for some other times.

The concept of insurance companies is often used when talking about options selling strategies. We will come back to this when we are talking about the payoff distribution. Besides insurance companies being known to collect small payoffs and face large losses occasionally, they are also very selective when selecting those whom they will insure.

If you are dying, scrambling for your last breath, I guess it will be pretty tough to get good health insurance. Similar to if you live in a wildfire epicenter, insuring your house might be either impossible or costly.

So while options have a tendency to be overpriced, we need to be very selective about which options to sell, as losses are typically much higher than possible gains.

The volatility risk premium is not mispricing waiting to be corrected; it is a risk transfer paid for the same reason insurance premiums exceed actuarially expected losses

Nobody looks at profitable insurance companies and concludes the market for insurance is broken.

Going back to markets and trading, there are several reasons why implied volatility is often higher than realized.

Large numbers of investors overpay for protection and are frequently willing to overpay for it. Out of the money put in, indices are prime examples. If you look at the options chain, you will be able to see that out-of-the-money put options in SPY have much higher IV than calls on the opposite side of the chain. This concept of shadow delta, we will revisit later on.

On the opposite side of this insurance-like behavior are your favorite WallStreetBets heroes buying lotto-like OTM calls and puts for speculation, as these offer huge convex payoffs and require low margin.

We have only spoken about the option buyers so far, but one of the reasons this premium exists also sits with option sellers. If you’ve ever sold options, you know you take the risk of large losses, so obviously you want to get paid for it.

As we also learned at the beginning of the article about James Cordier, the market periodically sends vol sellers into lower tax brackets year after year. During calm stretches, sellers pile in premium compresses and short vol products for retail, making selling vol sound like free money right before a massive spike. After yet another traumatizing event, sellers come back and require more premium for crash risk.

This also makes selling options hard, because the premium over the window, which consists of no disaster, is very difficult to measure. Think about 2019: how nice and easy a time you had before someone decided to eat a bat one day in China.

Overall there is an imbalance in options markets; buying options has fixed risk, requires only the premium to be paid, and is accessible to many traders. Selling options, on the other hand, needs you to put out a large amount of margin so you are well capitalized for any tail moves.

This supply and demand imbalance is also what drives option implied volatilities higher.

So selling volatility is not a free lunch, and for us to be profitable, we need to create a systematic approach while consistently monitoring the associated risks.

Variance risk premium in different assets

Because options are widely available across many markets, a good starting point is deciding what we are actually going to trade.

I mentioned that options tend to price volatility above what the market ends up delivering. That is something you can measure directly by looking at the IV/RV ratio over time.

Using data from 2018 on single stocks, ETFs, CME and ICE futures, and crypto, the ratio sits above 1 in every one of them.

As I mentioned before, realized volatility is backward-looking. So knowing that IV sits above RV is useful, but to actually make money, we need the market to realize less than implied over the period ahead.

That is something we can test too, by taking implied volatility against the realized volatility over the next 20 sessions.

While options on futures markets and ETFs are not the same, they cover the same underlying. ETFs are often cheaper to trade and offer deep liquidity, and you are removing some additional headaches like different contract sizes, roll mechanics, and so on. ETFs also cover crypto these days when documented VRP is rich over time.

This is why the choice can boil down to single-name stocks and ETFs.

Single stocks are tradeable, and I sell volatility on them regularly, but they carry much higher idiosyncratic risk from earnings, company-specific events, and random news.

You can clearly see this on the chart above; stocks are moving more on average and gapping more on average, which is opposite of what we want. This is why I am also excluding these going forward; the premium is there, but similar to single-name stocks, they carry a massive tail, and they didn’t have an impactful improvement on the final backtest, which will be shared towards the end of the article.

Stocks with earnings pay more, which makes earnings a tradable event, but it is better to trade earnings volatility in isolation; this might be a topic for another article.

What is the most important here and why we should mostly focus on ETFs is that stocks have much higher tails compared to ETFs.

Options structures and days to expiration

We have established by now that we want to sell volatility and to do it on ETFs.

Now we need to figure out what structures we are going to trade and how many days to expiration they will have.

If you are short options, you are short vega, short gamma, and long theta. You want the market not to move, volatility not to increase, and profits to arrive as time passes.

Gamma sensitivity is highest around the expiration; because of that, selling options with very short days to expiration can be quite tricky.

Above is a straddle on SPY expiring tomorrow. The market needs to stay inside about a half percent range for you to make money. Gamma, which is the rate of change in delta, is high here, so a one percent move already puts you outside the profit zone.

You can trade one-day options. What you cannot do is leave your desk while the position is open, and you will not be able to manage many of them at once. This hurts because diversification and the law of large numbers drive a lot of the returns in this business.

Options expiring in about 40 days from now look much more sensible; you are collecting about seven times the premium, and compared to the change in the underlying, it is more sensitive to what we are actually trading, which is vega—the change in implied volatility.

30- to 60-day options are the sweet spot for this as they have a meaningful decay without extreme gamma from day one. There is enough credit worth your time, and using monthly cycles lets you target the most liquid options.

The structure you see on screenshots above is a straddle. You are selling a call and a put at the same strike at the money, nearest to 50 delta. This way, you are going to collect the most premium that is available on the chain since that always sits in the at-the-money options.

Another structure you can go with is a strangle; the idea is the same, but you are selling a call and a put that are out of the money.

The max profit is much lower compared to a straddle. In exchange, your max profit area is much wider compared to a straddle, where you collect the max profit only if the straddle expires at the strike you originally sold.

Same as with the straddle chart above, I have run historical returns on a 16 delta strangle. Why 16 delta? Because it is representing roughly a one-standard-deviation move.

If the option market is pricing 20% annualized volatility over the next 30 days, the expected move is around 5.7%, so a 16 delta sits around there on both sides; therefore, you are selling strikes on what the market is pricing.

The second reason for choosing wings around a 15-25 delta is that the vega profile is close to the variance swap.

A variance swap pays the difference between realized variance and a fixed strike without any opinion on direction, and its exposure to volatility stays the same size wherever the underlying happens to be trading.

Straddle does not have it, as it all vega is sitting in one strike, which means that the more drift in the underlying, the less volatility exposure you have. Using strangles, you have roughly the same volatility exposure across the range the underlying is going to visit within the one-sigma move.

While you can’t really trade the variance swaps since they are over-the-counter instruments between dealers and institutions, they are the cleanest expression of the trade this article is about.

Pure exposure to realized against implied with direction stripped out entirely. They are a useful benchmark for what you are trying to approximate with listed options. A 16 delta strangle is pretty close to that.

While this is all nice, if you looked properly on historical returns of both straddle and strangle, you can see that while mean returns are positive, so selling these consistently is a profitable endeavor, they both have fat tails.

No matter how much smart talk and analysis you could have made around these, the COVID crash would find you lying in a fetal position. If you simply sold these structures because implied volatility is over-realized.

Luckily, we can increase our odds of not blowing out.

Increasing the odds

Now we know that selling straddles and strangles works and also sucks.

This is a good thing; the core principle of risk premium strategies is that you are taking a risk others are trying to avoid. That’s where your compensation comes from.

Not everyone wants to be short volatility, facing losses that are magnitudes higher.

That being said, you also shouldn’t strive to be a masochist; I like to sleep at night knowing that if broad markets don’t have five limit-down sessions in a row, I won’t have to send my resume to McDonald’s next week.

Hence, we can try to come up with conditions where we will be selling volatility and conditions where we are going to stay away.

In case you forgot, the core principle is that you want the market to stay calm during the lifetime of your option, so our goal is actually simple; we just need to be able to predict the future—I’m joking, at least partly. We don’t need to predict the future, but we should take a look at filters that will give us a better idea of what subsequent realized volatility will be.

Starting off with what it is: the core of volatility risk premia, which is implied volatility divided by realized.

What we find out in the next several tests is how much markets are going to wiggle over the next month.

When implied volatility is significantly higher than realized, markets are expecting outsized moves, and as you can see, the higher the ratio, the lower the actual realized move ends up being.

This can come back to reasons why this premium exists in the first place, but more importantly, what this test tells us is that the higher the ratio is, the better the potential returns are. Since markets are going to see fewer movements.

You might think now that all you have to do is to sell volatility when the implied is rich compared to realized, but that’s not exactly the case.

A lot of education about selling options promotes that selling volatility is best when the volatility is high; the IV percentile and rank are often used as indicators.

The chart above shows IV percentile, which is a trailing one-year lookback that spits out a percentage number, so IV percentile at 80% means that IV is higher than 80% of the prior 252 days.

As it turns out, the conventional wisdom is not really correct (shocking, right?); the higher the percentile, the more volatility ends up being realized over the next thirty days, which is not what we are looking for.

In general, the lower the IV percentile, the better it is to sell options. From the chart, you can see that the acceleration really starts after about the seventh decile.

On the other hand, we want to sell options that are rich, as the premiums are more attractive; hence, I only skip those that are above the eighth decile and will use that number going forward.

The last one is slightly more complicated, so bear with me. Different days to expiration have different implied volatilities (even different strikes have different implied volatilities, but more on that later).

A thirty-day option is pricing the thirty-day volatility, and a sixty-day option is pricing the sixty-day volatility (duh), but the sixty-day volatility is not a separate forecast; it contains the first one.

This is what forward volatility is all about; in this case it is what the market is pricing for days thirty to sixty.

If the thirty-day implied is 18 and the sixty-day implied is 20, the market is not saying that the second month has implied volatility of 20; it is saying that the whole months are going to average 20, but the first one only 18, so the second month must be doing some extra work.

You might be thinking why this is actually relevant; if we are selling an option that expires in thirty days, all that matters is that markets will be calm for that period and what happens after is not our problem.

Well, that’s not really the case, and it is because one of the main characteristics of volatility is that it clusters. If the market is pricing the shaky next month, it’s usually already quite shaky right now, and the front-month premium we are collecting might not have just caught up yet.

If you are interested in how this is actually calculated, I will be very brief here since I have another strategy which is solely dedicated to trading forward volatility, so to keep things brief, you can’t just add up two volatilities.

But you can add up variance since variance adds up over time and variance is volatility squared multiplied by the time it cover.

Therefore, the 30-60 forward volatility is calculated:

IV60² × 60 = IV30² × 30 + forward² × 30

Divide through by 30 and rearrange:

forward = √( 2 × IV60² − IV30² )

Calculating this number for all ETFs and turning it again into a percentile, we can see that the lower the percentile is, the lower subsequent volatility is realized.

To put simply, if markets are pricing calmer markets between two expirations, we are likely to see lower realized volatility during the lifespan of our option trade.

Do the filters work?

Now that we have established some rules, we need to make sure that they are doing the work wanted.

Looking at the chart above, the number of trades on straddles dropped from about 8,400 to 1,150, and the mean per trade improved, but the worst trade is still there.

This is still good. The original blind straddle was trading far too much, around 500 trades a year. Now it is closer to 70.

Returns on strangles also improved quite well on way fewer trades, but the worst trade is still there.

While the returns are great, I am not sure about you, but I do not personally feel like taking a trade with negative 9000% return potential. This is where we need to be careful, though; there’s a huge difference between calculated protection and being a pussy looking for favorable risk to reward.

Here is a clear example of what trade with “favorable” risk/reward looks like: the 50/25 butterfly on SPY showing a max profit of $1.6k against a $600 loss.

I see structures like these often when I look online at volatility selling content on YouTube, but here is how this actually performs through time.

While the worst trade is not capped at -125% (which is slightly better than -9000%), the mean also collapsed to about 0.23%. This clearly tells us that selling butterflies with 25-delta wings won’t make enough money to compensate for the risk protection.

Here is the butterfly with 5 delta wings. I also added a rule that the credit cost can’t be more than 10% of ATM sales; this is why the number of trades dropped slightly.

Compared to straddle, the worst trade drops significantly, but at the same time, the mean drops as well. We will see in later on if this is enough to survive.

For reference, as of writing, the 5 delta wings on SPY expiring in 36 days sit at 680 and 810 with the spot at 765. The put is over 10% below the market, and the call wing is about 5% higher, because put skew means a 5 delta put is priced at a much higher implied volatility and therefore sits much further out for the same delta. The downside wing is in large crash territory, and the upside would need an aggressive drift to be breached at all, either of those is not something that happens every month.

So wings are really protecting you from something like two standard deviation moves, and if you try to buy wings closer to the price, you are buying options with inflated implied volatility because of the skew, so you are paying a variance risk premium to someone else.

How to survive

We went through a lot at this point; we set up our filters, tested different structures, and tested buying protection, so now it’s worthwhile to actually see how actual trading would look.

Pretty sweet, you can see I was right. Strangles are best; buying wings sucks. It did pretty well during the 2022 bear market; you should just run it and call it a day, right?

Well, unfortunately, thanks to Wuhan, five years of data are not really enough to be confident.

Before I talk more about this abomination, I’d like you to know that this backtest covers just over 16 years, January 2010 to August 2026, selling volatility on 114 plain ETFs whenever the three conditions are met alongside a volume filter of 10,000 contracts traded on a 20-day average (dropped to 5,000 contracts during the early years).

Tested on actual bid-ask data with entry is priced a quarter of the way in from the ask price, which is better than the midpoint, so the fill assumption is on the generous but doable on liquid ETFs, with trading commissions also included.

Trades target 30 days to expiry on standard monthlies and are accepted between 21 and 45 days. Each one is held to expiration and settles at intrinsic, so no spread is crossed on the way out.

While a position is open, no new trade is taken in the same instrument. Every series is scaled to a 12.5% annualized volatility target, which is a sensible level for a negatively skewed strategy.

While this does not include some edge cases of assignment, I would say it doesn’t matter anyway because you would still probably be licking your wounds from 2020 anyway.

I have mocked James Cordier through out this article only to ended in same position like him, this is the whole problem with selling volatility in markets that are often correlated and when shit hits the fan it often hits acrross the board.

But we are not giving up here, as I mentioned at the beginning of the article. I have a consistently profitable strategy, which I still intend to share, but before you are ready for that, you need to be able to see some pains and understand why certain things don’t work.

Delta hedging

We only looked at two scenarios, buying protection for catastrophic events or not buying it at all, but most trades will end up somewhere in the middle with prices just wiggling around through out the time of the option.

When you buy an at-the-money call, you are buying an option with a 50 delta; in other words, the option behaves like being long 50 shares of the underlying. For buying an ATM put, it’s about being short 50 shares.

Since we are selling options, this flips with ATM calls having a -50 and puts a +50 delta. If the underlying is trading at 500 and you sell both ATM call and put, you have a straddle, and at the open these two deltas cancel each other out and your position starts with a delta of zero.

This is precisely what we want since we are not speculating on price movements but on volatility, but unfortunately for us, markets are moving, and the next day the underlying is trading at 515.

The call you sold yesterday is worth more and has, let’s say, a -65 delta, while the put you sold is worth less, a 35 delta. You are now short 30 delta, which is something you don’t want; you want to avoid exposure to the underlying; you only care about the fact volatility is rich.

So you buy 30 shares to flatten out your delta. The day after, the underlying drops back to 500 again; your delta is zero, but you now have 30 shares; therefore, you have to sell them for a small loss.

Delta hedging must be pretty familiar to most of you since it is evolving to often buy high and sell low for a loss, but this is a necessary process to have no directional exposure.

Hedging is not always loss-making if the underlying rallies to 535 and expires there. Assume you sold a straddle for $20, but the call expired at 35, so you lost 15 ($1,500) on one straddle, but the 30 shares you had bought on 515 are now worth 20 points per share, so $600 is reduced from the straddle loss, and had you kept hedging as the delta grew past 30, you would have recovered more than that.

So we can just hedge nonstop right to fully remove the directional risk and live happily ever after? No, not really; trading costs money.

If you over-hedge, you can easily spend a large part of your premium on trading costs, making the whole strategy unprofitable, but hedging too little is also not the best since you are exposing yourself to large moves.

Some traders hedge once per day or per week on longer positions, or what I think is more reasonable is to set fixed delta bands and re-hedge to zero once these are breached.

From the example above, let’s say your delta bands would be at 30 delta; therefore, you would hedge only if the underlying moved to 515 and do nothing if it wiggled between 486 and 514.

The issue is that this number is arbitrary. Is the 30 delta band better than the 20 delta band, or maybe the 40 delta band is the best one?

Luckily for us, someone much smarter looked at this problem before; in 2006 Valeri Zakamouline published a paper called Optimal Hedging of Options with Transaction Costs, which I read about in Euan Sinclair’s book.

Using some simple elementary school math, we can calculate the band:

band = H0 + H1

H0 = λ / (γ · S · σ² · T)

H1 = 1.12 · λ^0.31 · T^0.05 · (e^(−rT) / σ)^0.25 · √(|Γ| / γ)

S is the spot, σ the implied vol of the position, T the time to expiry in years, r the risk-free rate, Γ the position gamma. The two you choose are λ, the cost of trading the underlying as a fraction of notional, and γ, how averse you are to risk.

H0 is the pure cost term. Cheap execution or a big, fast-moving, long-dated position makes it small, so it hardly matters most of the time. It blows up in the last few days before expiry, when T in the denominator goes to nearly nothing.

H1 is the term that does the work, and the only input that really moves it is gamma. Bigger gamma, wider band.

That might sound weird, as you would probably expect hedging more with large gamma, but the delta is moving so fast that any level you hedge to is stale within hours, so tightening the band just buys you more trades at worse prices for the same exposure.

Zakamouline’s answer is to stop being a pussy chasing and carry the drift instead.

If we use it in an actual example of a 30-day ATM straddle on our 500 underlying with 20% vol:

Γ (position): 0.0278 per share, 2.78 per contract

H0: 0.0061

H1: 0.1043

band: 0.110 = 11 shares per contract

So you do nothing while the position’s delta sits inside plus or minus 11 shares a contract, and when it breaks out, you flatten.

That is roughly a third as wide as the 0.30 fixed band most people use in this position, at this vol.

30DTE, Γ 0.028, band 0.11, 11 shares per contract.

14DTE, Γ 0.041, band 0.135, 14 shares per contract.

5DTE, Γ 0.068, band 0.186, 19 shares per contract.

1DTE, Γ 0.108, band 0.389, 39 shares per contract.

Vol pulls the other way, because a higher implied vol on the same strike means less gamma. At 12% the band is 17 shares, at 20% it is 11, at 40% it is 6. A high-vol name gets hedged tighter than a quiet one, which is the opposite of the instinct.

No need for a panic attack now; I have built this as a calculator on my website inside the Position Builder and calculator section so all the math is done for you.

There is one nuance worth knowing: Zakamouline’s original paper is hedging back to the band edge as it is for intraday trading. Since I trade long-term positions at EOD rebalancing, I am hedging back to zero delta instead.

It’s better, alright?!

Unfortunately though, delta hedging would not save you from COVID either; hedging is going to save you from the drift, but when the market is crashing 10% overnight and going limit-down, no amount of EOD delta hedging wont help.

Using a more convenient data set from 2021, you can see that delta hedging beats the naked straddle, and while it doesn’t beat the naked strangle on raw returns, it provides slightly lower drawdown and a smoother ride.

Understanding how delta hedging works and being able to utilize it is generally a useful tool; also, if you for whatever reason want to sell a straddle or walk your entry from 16 delta closer to ATM, your returns will improve if you delta hedge your positions (and there won’t be an end-of-world scenario in the following thirty days).

When music stops

I think it is pretty clear at this point that the only way to save ourselves is to exit trades early.

One of the first things that we have done is finding rules to predict the when subsequent realized volatility will be muted, we have also noticed that when those filters were at opposite extremes the volatility heavily incresed.

The forward volatility rules look at what the market pricing is for the month after the expiration and take trades only when calm conditions are expected. If, during the trade, the thirty-day rises above the sixty-day, the curve has inverted.

This is what the term structure slope measures: you take the 60-day IV minus the 30-day IV. If the number is positive, the curve is in contango; if negative, it is in backwardation.

On SPY, the curve went to backwardation on February 19th, right before the selloff happened.

Therefore, our new rule is simple: enter trade only when the ETFs’ 30-60 day term structure slope is in contango (above 0) and exit the trade if it gets backwardated (below 0).

The early exit is what saves us; it is worth more than buying wings and more than hedging. You can see their downside by looking at 2021 onwards.

They are not free; from the whole backtest, they only ever saved you during COVID. In other market conditions, hedging or just holding a naked strangle was better.

The whole strategy changes a lot; about 75% of trades are exited before the expiration as markets often go backwardated during small signs of stress. If you hold to expiration, you are not crossing the spread on the way out since settlement is not a trade; now every early exit costs money.

This was modeled into a backtest, but the same as the entry at 25% from the ask, exits should be started at the bid and walked to the mid. Since most of the signals are not leading to the end of the world, I believe this is doable with liquid ETFs, but you should be extremely mindful of trading costs when dealing with options.

The real world

While I kept the backtest as true to the real world as possible, there is always more variance when it comes to actual trading.

There might not be an actual 16 delta strike available, you won’t be able to afford the position due to high margin, and so on.

This is the type of strategy that I prefer to trade manually after it has been verified in a backtest rather than trying to fully automate it. This allows me to use some discretion when it comes to execution and the number of markets concurrently opened and so on.

One of the other reasons why we prefer to sell strangles is because of skew. As I mentioned previously in the article, not only do different expirations have different IVs, but different strikes also have different IVs; this is called skew.

Equities have persistent put skew driven by demand for hedging; commodities, on the other hand, might have positive call skew due to supply shocks.

While you are never going to get the same credit as for straddle, elevated skew richens the wings, so your credit gives you a decent premium considering wider breakevens.

We also need to understand the spot-vol correlation. Plenty of markets we are going to trade have high spot-vol correlation, mostly negative.

This is what is called “shadow delta” when price drops, IV tends to rise and vice versa. When this happens, options delta is not only changing because of price change but also because of IV change.

Equity markets especially have positive drift; if you are going to sell straddles, you can place short strikes not ATM but slightly above; therefore, you are not going to have to delta hedge when the price drifts upwards, and you will also benefit from a drop in IV if the market rises.

If you are trading strangles, you can also shift your strike based on skew to position slightly better for a positive drift.

For example, instead of selling 16 delta strangles on QQQ, you can shift your strikes slightly higher so the structure is more 20/15 delta, and you are entering a trade with slightly positive delta so you lean to the tendency for IV to fall on rallies.

Looking for trades

We are coming to the end. We established all rules and turned something that has a massive tail risk into a decent strategy, which beats the benchmark and deserves the place in a broader portfolio.

If you want to start the strategy yourself, you can use the Tradingriot analytics website to look for trades.

The short volatility screener is already pre-populated with filters we have established.

We can look at IWM as an example (while we ignore MUU, which is leveraged and slipped into the screener).

Volume conditions are not a concern here since IWM is trading 1.4m contracts on a 20-day average compared to a 10,000 limit; IV percentile is on the 13th percentile, well under the 80th limit.

Variance risk premium is present, with the premium being 5.1% and the IV/RV ratio at 1.4.

While short-term 10-day IV is elevated, the term structure slope condition is also met when looking at the difference between 30- and 60-day implied volatility.

The last condition is also met with forward volatility percentile at 16, so we can now look to structure the trade.

Now it comes to some discretion; you might have noticed that the screener showed SPY, IWM, KWEB, and FXI, among some other markets.

SPY and IWM are highly correlated, the same as KWEB and FXI. I tested correlation and how it affects performance, and having a book with lower correlation around 0.5 led to lower drawdowns. For that reason, I would not hold more than two highly correlated markets; for discretionary trading, I generally aim to have about 5 positions, ideally using uncorrelated assets.

Another thing you want is to give strangles some width; when IV is extremely low, usually under 10% in absolute terms is giving you very narrow strangles.

This is often an an issue on bonds where your breakevens are very narrow so any shock moves can lead to large losses

So if IV is under 10%, that translates to about 5-6% total width.

From this example, you can calculate width easily by (call strike - put strike) / spot, which in this case equals 11.8%, so comfortably above the 5%.

By the way, if you scroll through the position builder, you will see the exact percentage from the spot, so you can just eyeball the width by doing that.

When I trade these, instead of going for fixed 16 delta strangles, I am picking strikes by looking at the implied distribution.

Above you can see two models, markets implied, which is simply based on implied volatility, and my proprietary model that considers skew, momentum, and some other components.

For IWM these two are pretty close, with mine being slightly more narrow, which makes sense since IWM has been fairly trendless lately.

You can find this model not only on the momentum page on the website for each optionable market, but it is also built straight into the position builder, where it is adjusted to the exact DTE.

For IWM expiring in 35 days, 1 sigma moves based on my model are between 275 and 304.5 give or take, so I am selling the 275 put and 304 call. For this trade this put sits at 21 delta while the call is exactly at 16. You can also see that our delta starts very slightly positive, so we are better positioned for any positive drift.

Now for sizing, the backtest was sized to a 12.5% vol target while taking every single trade at a very fixed setting, so this is not really something we can do.

The first approach to sizing these trades, and arguably the more simple one, is trying to predict the worst-case scenario. Remember that we are using manual exit if term slope inverst so unless aliens are going to attack the earth tomorrow and everything opens at zero, we will exit trades ahead of a massive selloff like during the COVID or any other market stresses since term slope inverts fairly early on.

In the position builder, take a look at a two-sigma move and imagine the market closing there tomorrow; at that point term structure would be sure to be inverted, and you would see a signal to exit the trade. This should represent about 2-4% of your account, depending on how many positions you plan to open and how many positions in correlated markets you have opened.

Another option is to simulate a realized volatility path. Going back to the analytics section, you can take a look at the term structure chart to see current IV and RV readings if you hover over the slope. Presently we are selling approximately 18% against 14% realized volatilty.

Under the position builder there is a short volatility block where you can import both of these numbers. Foremost, effective fill IV is essential; this represents the implied volatility based on your actual fill versus the market. I already spoke about the importance of the execution, but you should never overpay for these trades where your effective fill IV would be worse than the market.

Underneath you also enter the forecast realized volatility; if we put 14 there, it represents the current RV, the platform simulates 20,000 terminal prices at the inserted forecast realized volatility, prices them off your fill, and reports a Kelly fraction as mean return over its variance.

Kelly shows 47%; we are never going to use full Kelly, but rather a fractional number like 1/10th of the Kelly fraction. 4.7% in credit received is a lot, while realized volatility is very suppressed; this would mean at 1/10 Kelly on a $100,000 account, we are selling about 13 strangles, and if the market gaps tomorrow to that two-sigma move, we will lose approximately 15% of our account.

Back in the analytics section, you will find a volatility forecast window. Heterogeneous Autoregressive Volatility (HAR). The HAR model is a popular model for forecasting future realized volatility using, again, some very primitive mathematics:

𝑅⁢𝑉𝑡+1=𝛽0+𝛽1⁢𝑅⁢𝑉𝑑𝑡+𝛽2⁢𝑅⁢𝑉𝑤𝑡+𝛽3⁢𝑅⁢𝑉𝑚𝑡+𝑢𝑡+1

This article is already way too long for me to give any more in-depth explanation, and while the model is not perfect, it can be used as a good benchmark for future volatility since it also reports a confidence score.

For IWM, you can see that the realized volatility over the next 30 days is actually expected to rise with about 91% confidence, and the forecast puts it at 18.3% with p10 at 12% and p90 at 24.5%.

The final number I put into forecast RV calculator varies based on what is IV thus VRP expected to do in upcoming 30 days as well.

In this case they are expected to fall, which can also be eyeballed on a long-term IV / RV chart that shows RV in historical lows.

Because RV looks depressed and the model is showing high likelyhood of realized going up while implied going down, I will stick to 18.3% as my forecast realized volatility.

This 18.3% realized volatility is now on-par with current IV, and the Kelly Fraction dropped to 1.2%. Sticking to 1/10th of Kelly’s, is this telling us to risk $120 on a $100k account? That is not even one contract.

If you think about this, it makes sense. The current implied volatility at what we are getting filled at is 18.7%, and HAR forecast is telling us that in the next 30 days the volatility will be about 18.3%, so is this trade dead?

No, not really. Remember why we are trading this; we are trading this because markets often overstate the implied volatility for a plethora of reasons I have mentioned at the beginning of this article.

Going back to some testing, using about 10,000 historical measurements of ETFs when IV/RV > 1, the mean implied volatility overstated realized volatility by approximately 1.92 points.

For that reason, you can calculate

fcstRV = 0.90 x fillIV

For this trade that turns out to be 10.4%, you are risking 1.04% per trade in credit received, and your loss on a two-sigma event tomorrow would be about 3.5%. If fcstRV from the HAR model were lower, you could use that number, but using multiplying your fill IV by 0.9 was well tested; therefore, you can use that while looking at the forecast from the HAR model as your volatility expectation.

After all is set and done, you should log your position and revisit it once per day. First and foremost, look at the term structure slope chart and see if it is inverted; if so, you will exit your trade (working your way out from bid to mid). The median holding period from the backtest was about a week, so you will be exiting many trades early, but this will also save you from end-of-the-world scenarios.

If you don’t think the world will end in the next month and you don’t fancy using the exit filter, you should at least delta hedge your trades. For that, use the delta hedging calculator on the website.

As proven by backtest, doing both doesn’t make sense, so just use one and stick to it. You will make more money by hedging but will also end up homeless in a COVID-like and probably some other scenarios like this February oil rally where an inverted curve would get you out right before the move, while delta hedging here probably wouldn’t help that much.

Conclusion

Short volatility strategies are not fun; you are accepting small payments in compensation for often account-wrecking risks.

If you are not careful, you can sell volatility for years and then give back everything and some within just a couple of days.

But as you could see in this article, by deploying a few clever and logical rules, we can turn this into a solid part of our portfolio, which doesn’t take much time to trade and track each day.

Two main things I would like you to take from this exercise are not trying to be a hero, especially if you are starting out with options, and making sure to start small and always be prepared to limit up or down.

Another thing is the execution: despite filtering for markets with liquid options and using only monthly contracts for expiration, options, especially at the wings, often lack liquidity. For anyone coming from the futures space, this can be a shock: how much each position needs to be worked and executed without overpaying. This strategy, like many others, can make or break based on the execution costs.