The Three Prime Assets sounds like something out of a religious text, or maybe the name of a covenant ship (y’know, from Halo?)… Anyways. You ever think about how the universe is fundamentally built around the ‘=’ sign? I mean, E=MC², the laws of thermodynamics, literally everything we understand about reality through physics, chemistry, etc all point to cause and effect, exchanges of one thing for another in order to balance the fundamental equations of reality. The omnipresent ‘=’ is inescapable. As humans, we live this way too. Constantly exchanging effort and time for value, exchanging value for more time (self-preservation) or for someone else’s effort. Gets exhausting after a while. Where were we?

Oh yeah, three prime assets. Already mentioned them; effort/skill, time and stored value. These are pretty much the variables in the human equation. The one thing we all seem to agree on is that our time is valuable, probably because our entire existence is defined by the inescapable knowledge that it will eventually come to an end. Since our time is limited, non-fungible and constantly experienced and spent whether we would like it to be or not, we tend to value our time based on the manner and environment in which we experience its passing, another subjective assignment of value.

Stored value can come in any number of arbitrary forms, but we tend to assign value to things which enhance our experience of time. Entertainment, our favorite foods, art, these are all things which enhance our subjective human experience and we collectively have assigned value to them. But being the fickle creatures that we are, our preferences for spending time changes often and with it, our estimation of the value of an object or experience changes too.

We’ve collectively decided to invent money because our collective delusion of it becomes a convenient method for exchanging value as mentioned in the last article. The reason it functions to store value is because we’ve created so many liquid markets for it that we are able to trust our money to be worth exchanging for.

Money is only as useful to store value as it is easy to exchange for assigned value. It is only as easy to exchange for assigned value as it is useful for storing value. And thus, the collective delusion.

So now we’ve established that the easiest way to measure how well a Thing Stores Value is whether people are consistently willing to exchange other assigned value for it. Money is worth owning because it’s easy to trade it for something you value. It’s easy to trade for something you value because it is worth owning.

Skill is a lever. If you have a skill which people value, you are able to spend less of your time in exchange for more money (or other form of value). Note that this has absolutely nothing to do with the difficulty of your skill, or the amount of effort required; only whether others are willing to give you something for it.

Skill is measured in value just like anything else; how much does it exchange for? To put it in 21st century terms; what’s the hourly rate? Just like any other market, that rate of exchange will be determined by supply and demand. Not measured by the difficulty of providing or acquiring it, only how much demand there is relative to the supply of that skill.

So. We’re all trading.. something.

All the time. You’re trading time reading this article for the chance to learn something and increase your skill, or perhaps because you prefer to experience your time by reading the ramblings of a schizophrenic smiley face on the internet. Either way, you have assigned enough value to the contents of this article that it is worth spending your time on. Let’s unpack the implications of that.

This article did not exist until I used my time (and arguably very little skill) to create it. I used a computer, a keyboard, an internet connection, this website, etc. All of which had to be conceptualized, constructed, maintained and all of which are agreed to have some value attached. This value did not come from an inherent quality of the materials used or their construction, this value was created. And upon creation, value was assigned to each of these things because enough people decided that these things enhance their lives, their time. Markets for these things were created, exchanges occurred and new value was added to the world.

But for new value to be created requires risk. The risk of lost time, wasted skill and resources in exchange for no value, no perceived enhancement to anyone’s life. Risk is uncomfortable and most would consider exposure to risk to be a negative effect on their experience of time. Often if a risk is known to exist, we will pay to avoid experiencing it. We have only to look as far as any sort of insurance policy to see this in action; we pay a regular premium to avoid the risk of suddenly having to pay a car repair bill. We exchange our skill and time with an employer for less than the value they generate in order to shield us from the risk of fluctuation in value for our skill. We talk ourselves out of decisions such as going back to school, taking a new job or trying a new restaurant because we don’t want to allow the uncomfortable possibility of regret.

And so we create the risk premium; those who go to school for the skill have fewer competitors and are able to charge a premium for their time. The person changing jobs can negotiate for better compensation with fewer qualified competing applicants. The new restaurant popping up is forced to provide an even better dining experience to attract customers, rewarding them for their curiosity. Value has been assigned to risk, which means that skillful risk-taking has perhaps more value-accretive potential than any other use of time.

Foundation of thought: markets are opinions about the value of both the asset and denominator, but both are measured arbitrarily by the degree to which they improve our experience of time. Comfort is consistently valued, risk reduces comfort and therefore the assumption of risk becomes a valuable service.

Now that we’ve tied it all together, we finally have a mental framework for how to think about markets as a concept. From here, we can build frameworks of thought about how to profit from them, but not before. In the same way that a child’s decision-making improves with his understanding of the world around him, we must understand the nature of markets before we can hope to participate wisely in them.

I wrote this article, there’s no financial advice in it or anything, insert usual disclaimers and follow me on twitter. Please clap.

Sup. For those of you interested in learning more in-depth about the highly technical, complex, competitive and very difficult world of market-making from an absolute fucking moron who may or may not be completely wrong about half of it, this one’s for you guys!

Warning, it’s not an easy read and you will have to do math with me

(oh god I probably messed some equation up somewhere huh)

But hopefully this article is a good jumping off place for guys who maybe read my first article and are interested in trying to get started market-making. I’ll talk you through the more important things to consider when designing a rudimentary system as we build one completely on the fly, then briefly explain how trying it manually can work (less than ideal) and point you towards some decent freeware for starters.

In the last article, we mostly focused on one imaginary scenario where we assumed a lot and things more or less worked out pretty well. In the real world though, it’s very different and there are a million dynamics at play to keep track of. But we’re still gonna assume a lot of shit. First off, let’s talk pricing.

Pick a price, any price (except that one)

Especially when the spread is multiple ticks wide, determining “fair price” isn’t obvious. When there’s not enough volume or the market is too volatile for anyone to quote tight spreads, if we don’t have a model for our understanding of fair price, we are at risk of someone else with a better understanding of fair price picking off our orders (aka, getting filled because we’re no longer pricing correctly), leaving us on the wrong side of the market with inventory that we can only close at a loss. There’s no single right answer, and unfortunately that’s the case for a lot of the problems we’ll encounter, especially in smaller crypto markets with weird quirks where the “textbook” answer hasn’t even been written down anywhere yet.

The upside is that if you can ad-hoc decent solutions, there’s a good chance you can make good money even if you’re miles off from the undiscovered textbook solution, because everyone else probably is too.

Back to the pricing problem, there are a few “standard” approaches you can try or tweak.

If you’re quoting on a pair that trades in multiple other places, you can use an index price from those other places, which ought to provide a reliable oracle most of the time. On the rare occasions the data-feed gets interrupted, or a component member of that index experiences abnormal behavior though, your understanding of fair price might become severely unreliable.

Using the mid price (the price point directly in the middle of the bb/o) as fair price is much easier but it does leave you reliant on whatever the other marketmakers are doing, since by default they will be deciding what you think is fair price. They may also try to exploit you if they notice it in very small markets, but being specifically noticed or consistently targeted is unlikely in a busy market with multiple marketmakers. If you’re a comparatively small fish in a large and active market with multiple other marketmakers, using mid price to determine what’s fair is often a great solution. As I recall seeing on twitter somewhere in the bell-curve iq meme format, “Quote around mid, the market is probably right”.

As an interesting aside, in very slow markets with wide spreads and only one or two other players, you may be able to rely on other marketmakers’ understanding of price and successfully quote continuously in front of them, so long as you keep your size small enough that your undercutting takes away less of their profit than it would cost them to tighten their spread to beat you. Eg, if they quote a $0.06 spread for $10k and the average taker order size is 5k with low variance, it’s cheaper for them to let you quote a $0.04 spread for $1k in front of them than it would be for them to match or beat you, since they’d be giving up at least 33% of their profit to do so, while you’re taking somewhat less than that (depending on the size variance of taker orders). Cat and mouse games are to be expected!

There are infinite other ways you could try and calculate or derive fair price, but remember that you always have to be able to unload your inventory. If your fair price model has your quotes skewed too much higher or lower than everyone else, you’re unlikely to get filled equally on both sides and your model will end up costing you money no matter how superior you believe it is.

Butter, Jam and Other Spreads

Deciding how wide to quote is one of the most challenging and multilayered questions I’ve encountered in my life, probably because I’m bad at math and staying focused. Let’s give it a shot.

To start with, we need to ask a million questions all at the same time. How much volume is the market doing on average, and how much does that number vary? What’s the average taker order size, and how much does it vary? For each tick away from fair price, how much will be on the book on average, and how much does that vary?

Spoiler, you can’t answer that one because you and the other marketmakers are destined to spend eternity trapped in an ever-repeating dance of constant reaction to each other’s behavior, perpetually making changes to how you quote in order to get ahead of each other.

We also need to know what the low time frame volatility and its variance are, what we’ll be paying or receiving in maker fees, whether our competitors are paying the same amount (if the exchange has fee tiers), and then consider other sub-factors like our API ratelimit, how how fast we can place and remove orders and the list goes on.

But let’s think through this. If you quote at the top of the book on each side to maximize the number of spreads, you’re setting the mid price and you’re guaranteed to get picked off when price changes. So that’s not a great idea, unless the market doesn’t move often or by much (so you’ll get picked off infrequently and at low cost), but still does enough volume that you’ll make more from all those spreads than you lose when it moves against you. But if that’s the case, won’t the other marketmakers just re-jump ahead of you? Eventually this continuous leapfrogging would result in an equilibrium where everybody’s quoting at the same level because any tighter would result in net loss over time, except that then everyone’s sharing the taker order volume so they’re making fewer spreads, which means less money, which means they’re no longer profitable because they get picked off for more than they make again. It’s a Bitmexican standoff!

To some degree, this is indeed what happens, but calculating the ever-changing perfect bid/ask placement and taking all of these variables into account properly and before anyone else can beat you to it is incredibly difficult and relies on near perfect understanding of the market with no room for error… and marketmaking like this certainly can’t account for that one guy on Binance who suddenly decides that he has 3,000 too many Bitcoin and must immediately sell them at market, completely destroying that “average loss when picked off” stat that we were relying on. So we need to adapt our strategy a bit.

There are academic papers on this topic, formulas to follow and all sorts of highly technical and mathematical models for the ideal marketmaking models, but I never learned any of them because I am relatively dumb. So here is where we part ways with the academically correct solutions and gigabrain quant models so we can instead talk about rough estimations and subpar heuristic solutions because that is what I’m familiar with. Ironically, this next part is gonna be the one that gives you a headache. Tone shift.

Everything in this article going forward is highly likely to be less-than-ideal reasoning at best and entirely based on my own anecdotal experience and logic. Neither of these are terribly reliable or extensive. You’ve been warned.

Clearly, there’s a lot of variables at play and we’re heavily reliant on averages for determining our ideal behavior. But reality diverges from averages often and by large amounts. We can and will compensate somewhat by using variance and standard deviations of behavior, always opting to act more cautiously than the averages suggest, but it is important to remember that market behavior and therefore most of our data we rely on DOES NOT FOLLOW A STANDARD DISTRIBUTION OF BEHAVIOR AND IT IS NOT RANDOM. Every other participant in the market is trying to take our money. Every taker order is accepting our quote ONLY when they believe we are already or will soon be offsides, unless the order is forced. So any time we are reliant on an average, or an assumption of evenly distributed random behavior, we must be extra cautious and ensure that it pays significantly more than what we estimate our risk to be.

So let’s get back to the original question: how tight should we quote? I’m going to assume that we are quoting in a market that is busy and has multiple other makers. The following model will be of middling complexity in order to highlight design considerations and it will be full of flaws… but it will give you some ideas to play with while presenting most of the important questions that every model needs to answer.

We’ll start with volatility (formula here, I suggest you get very familiar). We want to know how volatile the pair is on a very granular level. We can use either a tick/number of ticks, elapsed volume, or a time period for our data sample point size. The goal is to find a baseline that tells us “how much does this asset bounce around its (very low timeframe/recent) average performance per [data sample] on average”. Now remember, this formula assumes a standard distribution of market movements, which means the numbers on paper are unlikely to match reality. Since we want to price based on the assumption that mid is fair, but we want to know how far price actually trades from it, we’ll calculate based on relative variance between the two instead of variance.

Math time

If we use 1 second as our sampling period with the last 60 seconds as our sample size and determine that the mean mid price is equal to the price 60 seconds ago with a $0.16 relative variance of mark price, this results in a standard deviation in variance of mark price equal to $0.04 relative to the mean mid price. This gives us the basis for a formulaic assumption of the future, which is that ~68% of the time, mark price will close somewhere between +$0.04 and -$0.04 cents from the mean mid price and ~99.7% of the time, mark price will close somewhere between +/- $0.12 from the mean mid price, which remains unchanged. If I got the math right.

Given this information, we can make an assumption that if we quote a spread of $0.08 ($0.04 cents away from mid on each side), we need price to trade one standard deviation from the median mid price or more to get filled if we quote around it. This happens in around 32% of the periods. Then crossing the entire 8 cent spread would require a move 4 cents or more past median to the other side, which happens in around 16% of those periods. Assuming continuous complete fills at each extreme, including loading exposure to the opposite side for the next spread, the average profit per period rounds out to somewhere around 1.28 cents per unit per period.

Already we are highly inexact, since we haven’t accounted for what happens during our selected periods (only what they close at) to see how much and how often price extends beyond the opening and closing values, and therefore can’t calculate the “accurate” probability of a fill. However even if we did, that value would still be based on assuming random market behavior, which we already know is also wrong. If you use individual ticks as your sampling period that calculation becomes redundant, but we’re going to move on and continue with bad assumptions for the sake of brevity, just make sure you understand that these numbers are very unreliable and only designed to serve as example solutions for actual questions. I know it’s a lot, bear with me.

Using the same math, we can estimate that offering 2 cent spreads will return .8 cents per unit/period, 4 cent spreads return 1.22 per unit/period and 6 cent spreads return 1.35 per unit/period. These numbers assume zero fees or rebates but we would account for those in our spread size numbers before calculating average returns per period.

As you can see, the best returns come from the 6 cent spread, with orders placed 3 cents aka .75 standard deviations from the mean before factoring in any risk.

But Wait, There’s More!

Now let’s try to think about risk. If price is moving against us while we hold inventory on the opposite side by an average rate per period that is equal or greater than our average income rate, we are losing money. But unfortunately when this occurs, the assumptions that determined our income rate cease to hold even remotely.

In our example, the average market return per period was 0, since the mean remained unchanged throughout. But if the mean price changes over time, the sample set of selected periods is not following a random distribution because it is weighted to one side, otherwise there could have been no shift in the mean. This means that each result on the opposite side of the mean is less probable than we anticipated and we’re much less likely to fill orders on that side. Not only are we losing money to a shifting mean value when we hold inventory, our average profit per period is revealed to be lower than we expected.

Or is it? What if we expected the mean to move because we calculated that 1m periods were expected to follow a standard distribution of returns (!warning!) around a 60m mean?

If we calculate that 1m periods have a standard deviation of variance of $0.20 around their mean, it would stand to reason that the 1s periods must regularly shift their mean by quite a bit. So what if we simply quoted a 6 cent spread, but stacked orders every tick and then averaged in and out as the 1m periods floated around their mean? Estimating that the 1s periods hold their volatility values on average relative to their shifting mean, we know that that mean is also likely to revert according to the 1m period data, which means we can simply spread out our exposure across the orderbook for the same estimated profit per unit per period. We just won’t have nearly as many units at each level.

For our example, we’ll place orders at every price tick that lies within 3 1m period standard deviations of the 1m mean (this translates to +/- $0.60) and size them evenly to split up our max inventory size. We’ll open long from below the mean and open short from above it. Now 99.7% of the time, we will be averaging that profit rate of 1.35 cents per unit per second (assuming that the 1s variance numbers remain the same on average).

If the average performance of the 1m period shifts to -$0.006 (-$0.36 performance on the hour), well, that’s only an average drawdown of $0.0001 per unit held per second. We are holding more inventory for longer though and that indicates an imbalance in the 1m chart, meaning our probabilities are once again proven to be estimated at best despite all our fancy math. However, with our current setup, we only hold 1/3 of our maximum inventory per standard deviation from median 1m price, meaning that the drawdown impact is massively less severe. We’re only holding more than 1/3 exposure approximately 32% of the time, and only holding it on the wrong side (a flawed but approximate estimate of)16% of the time.

If our maximum inventory size happened to be 60 units, the estimate says that would mean we are earning $0.0135 per second 99.7% of the time, while losing $0.002 or more per second 16% of the time, with a maximum loss per second of $0.006 at that rate of decline if our inventory was full. Since the income is not as sensitive to mean price changes any more, this looks comfortably profitable even though we don’t entirely trust our own math.

To ensure we aren’t gradually caught more and more offsides by the drift though, when the mean 1m price moves, we re-center our orders so that we are still only opening below mean and closing above it. We will also immediately add an additional closing order at the top of book for each unit of inventory that we should no longer be holding based on our new mean price, or a new opening order for one that we should. Eg; if the 1m mean price has dropped by a total of $0.04 since we began and price is currently below it, we are now long one more unit than we ought to be at current price. We will immediately send a closing order for that unit. If price is trading above mean when this happens, we are now short one less unit than we ought to be, and so we shall send an opening order.

We’re going to add one last period to look at, using 1h periods and a 24h average. This one will tell us what variance to expect on the mean hourly returns. We can use it as a gut check to see whether the lower timeframes are behaving and calibrated as they should be, or we could build in additional parameters around it.

If the 1h standard deviation of variance from the 24h mean is $1 and the mean 24h performance is -$1, we would expect hourly performance to be between -$2 and + $0 (flat) 68% of the time, while 5% of the time it could be anywhere between -$3 and +$1.

This would indicate that our lower timeframe mean performances are each expected to average much larger and more negative changes than they currently do by quite a wide margin, and this would result in much lower and likely negative returns if our current setup remains as it is. Fortunately, as our average low timeframe values shift, we adjust our strategy to account for the new variable values. When the 1s standard deviation from 60s mean increases, so too does our spread, therefore profit. As the 1m standard deviation from 60m mean increases, we decrease order size but increase total orders so that we are not sizing too heavily for the volatility when near the 60m mean.

Given the discrepancy, we could choose to manually interfere with some of the variables, decrease the sizing, pause the bot, anything we want really. An external check like this is really more of a comfort item but can be very useful.

And with that, we’ve finished the logic design.

System Complete (Said No One Ever)

So that’s it, that’s a system. Is it any good? Fuck no, we relied on a million different assumptions and it’s not nearly risk-averse enough even if the assumptions were correct, along with countless other problems. But it’s not meant to be a perfect system, it’s meant to show you the sort of things you’ll need to consider. Every variable and decision above can and should be tested, optimized and theorized about, most of them were completely arbitrary. There are many many alternatives to every single decision made above.

But is it profitable? In the long run, no. It’ll go bust when the market does something far enough outside of its poorly padded expectations of “normal”. But it might make a few bucks in between. That’s the trap of assuming a correct understanding of actual probability. It’s easy to get comfy and assume things will always work because they always have, until something that’s never happened before… does.

Made it this far? Impressive

If you’re still interested in all this shit but don’t know how to actually get started after brainstorming, try doing it manually to start. Most people seem surprised to hear anyone’s tried it manually, but after reading this article it should be clear that marketmaking does not require you to be at the top of the book. “Pure” marketmaking is all about mean reversion, and although doing it at very rapid pace for small spreads is usually the most profitable, the concepts are broadly applicable at any distance from mid.

You really do want to be fast though.

To get started, just open your favorite exchange, start a test subaccount and place some buy and sell orders at equal spacing as close to price as you can put them while still replacing them when they fill. Maybe try implementing some of the logic from the example system, all of the parameters can be re-created as indictors in TradingView. If you have no idea how to choose any logic, start by grid trading and then add parameters.

Create rules for yourself, see if they work or not and why. Manual is slow and capital inefficient, but you’ll start to see and understand some of the ephemeral math concepts we talked about earlier. How far out should you stack orders? Is mean reverting a good idea past a certain point of drawdown, or should you dump it? Start testing those averages and questions, then once you think you have answers, start assuming you’ll be wrong at some point and design rules to minimize loss when it happens.

If you can make a system that works consistently and has hard risk limits, you’re doing well.

For those interested in publicly-available software, Hummingbot is free and open-source, which allows you to modify it as you please. It’s an excellent place to start and connects to a wide variety of exchanges. Mango Markets also offers an open-sourced bot, though it’s designed to work specifically for their DEX on Solana. Of course, you can always code or commission your own as well, but if you’re ready for that, you probably don’t need to be reading this. Just be careful and redundant, software mistakes can cost a lot of money.

Summary

If you got through this, congrats. Seriously. If you actually enjoyed it, you’re a fkn weirdo but you’ll probably end up picking me off in the orderbook soon.

If you’re wondering, my first model was a gridbot with periodically updated logic for application that ended up behaving a little like the example model. My latest is less different than you might think, but the small changes add up to massive differences. So keep trying new shit!

If any of my smart marketmaker friends are reading, would love any feedback. This entire article is based completely on limited understanding built on trial and error and is far from comprehensive.

If this was useful, claps, likes, shares, blowjobs and follows are always appreciated, thanks for reading!

*Edit 11/09/21, corrected erroneous use of term “covariance” when referring to relative variance between variables. Simplified portions of the mathematic operation descriptions.

Sup fuckers, this would be a tweet thread but an article is easier. Less of you will probably read it, but whatever. Title is self-explanatory, I drew some shit in Microsoft paint and maybe it’ll help you understand what I’m talking about (press x to doubt).

First picture up there is supposed to be an orderbook for our example trading pair, let’s call it ASSET1!/USD. For now, we’re gonna assume a couple things:

• This is a futures contract.
• We’re the only market-maker on this pair on this exchange (and nobody else is placing limit orders).
• There is an available market for the underlying spot asset and there is no premium on our futures.
• We are independent, which means we haven’t signed an agreement with the exchange and can provide liquidity (or choose not to) any way we like.
• The minimum tick size is $0.01.
• All taker orders pay 2.5 bps (0.025%) in fees, all maker orders receive a 1bp (0.01%) rebate.

So. The last trade to occur (denoted by the arrow) was a market sell into a limit buy for a price of $100.00, which is now the mark price. We have an order to buy one more contract at $100.00, then another bid for 1 contract at $99.99 and yet one more contract at $99.98. We also have mirrored sells, with one contract offered at each tick up to $100.03.

The difference between the best bid price and best offer price (collectively called the bb/o) is called the spread, right now it is $0.01. As market-makers, our goal is to capture spreads by buying and selling in equal amounts. Since we’re the only market-maker, if someone were to sell one contract at market, they would hit our bid of $100. We’re now long 1 contract at a price of $100, right?

Wrong. Because of the fee structure, when the seller accepted our bid of $100, they had to pay 0.025% * $100 in fees ($0.025), out of which $0.01 is paid to us. Essentially, even though we bid at $100.00, when our order was hit, the final price that we paid after our rebate was only $99.99, while the seller only received $99.975 of our $100 bid after fees.

Let’s take a look at the updated orderbook:

Mark price is still $100, because that’s the last price at which a trade happened. But now the spread is $0.02 and the book is imbalanced because we haven’t changed anything yet. In reality, one side of the book being 50% larger than the other ought to be corrected, but let’s keep it simple for now and ignore that.

If someone comes along and buys at market, great! We’ll have made a spread of the $0.01 displayed, but we’ll also have earned the rebate on both the buy and sell orders. Our spread would actually end up being worth about $0.03, even though we’re buying at $100 and selling at $100.01. Then we’d replace those orders, balancing the book and our exposure again.

If we can do that once every couple seconds or faster, that $0.03 each time will turn into a pretty decent return on capital! But of course price isn’t going to bounce between $100 and $100.01 forever, so what happens when it moves?

Let’s say the spot market (which our market tracks identically, for example’s sake) drops from $100 to $99.80. We’ve just bought one contract at $100 (aka $99.99), but we’re quick and pull our 99.99 and 99.98 bids before someone can arbitrage against us. THIS is where market-making starts getting tricky. There’s a couple approaches to handling scenarios like this. You could just sell your contract at market (if there were other market-makers here) for a loss of around $0.22 and hope to make it back on future spreads. But since that’s not an option for us (and it’s rarely the best one), we can choose to drop our best offer down to $99.81, which would net us a realized loss of around $0.17 if filled. This is a bit better than dumping on the market, but still not great. We have the advantage of being the only market-maker here though.

So we place our offer at $99.98, which is roughly our breakeven price, and we place our bids at $99.80 and $99.79. Let’s take a look at the book:

Our spread is now blown out much wider than it was, but we aren’t losing money if our offer is accepted. In fact, since we’re the only market-maker, we could decide to not even lower our asking price at all! If someone decides the offer is fine? Great! Mark price would jump back up, and the whole drop would have been a quick deviation. But even though we set the spreads in this scenario, that doesn’t mean that anyone will want to transact with us if we only offer bad prices. So more realistically, our bid price likely looks more attractive than our ask price to the rest of the market. Let’s say our bid at $99.80 gets filled, which translates roughly to a cost basis of $99.79. Now, we’re long two contracts at an average cost basis of $99.89 each, so we drop our offer price to $99.89. All of a sudden, that massive spread has been cut in half AND we’ll still make a little money from the rebate if we manage to sell both contracts. As we averaged down, we were able to tighten our spread back up and continue normal operations.

But how common are moves like that in this market? What if the market had dropped 5 or 10% instead of .2%? Following the above approach of averaging down, we’d likely still have to sell at a loss because otherwise our spreads would simply be too gigantic to attract a counterparty.

Market volatility happens, and it’s always got the potential to cost us money. So how do we avoid or mitigate it?

We’re gonna need to figure out a few things:

• How volatile is the market across various timeframes?
• How much volume does the market do?

Volatility is a measure of how much the market bounces around compared to its average performance. It’s measured by comparing the average distance over time between price and where price would be if it was performing exactly the same as it’s performance average (see full calculation here). It’s important to note that volatility measured over different timeframes tells us very different things about how we should react to it.

Volume on the other hand, simply tells us how often we might be able to get our orders filled, hence how many spreads and rebates we could earn.

If volatility is low across the board, large size and tight spreads are the way to go. You’re just trying to maximize volume. If price moves against you though, just cut the loss as soon as possible or you could eat some hefty drawdown.

If daily volatility is low, but intraday volatility is high (meaning that price moves a lot more without really getting anywhere), you can widen those spreads and use larger orders. You might not make as many spreads, but they’ll pay better! In this environment, you can protect yourself by averaging in with a bit more confidence when price moves against you.

If daily volatility is high but intraday volatility is low (meaning that price trends up and down hard during the day but at a consistent rate), you’ll likely want to make tight spreads with small size, aiming for more spreads instead of bigger ones and then cut your losses quickly when price moves against you.

If volatility is high across the board, you can widen your spreads but use smaller size, averaging in very carefully to protect yourself. This is the riskiest sort of market to make in my opinion, but often scares away other market-makers, leaving more opportunity.

Final notes and stuff

Market-making is a game of averages. You’re measuring the average number and average size of your spreads against the average volume and the average momentum and and average volatility in the market, all while trying to compete with any number of other participants who could throw the averages out the window at any point in time they want to. How well you can do at it is usually a function of how well you react when things don’t match the averages. Most of the time you make a steady stream of money, and then every once in a while the market only hits one side of the book, chews through your orders and you’re forced to unload inventory at a loss. (“Quick Bob, what’s the average on that?”)

But I like it, and I don’t think enough people talk about it in plain English, most of the other shit I’ve read on the topic is either written in hieroglyphics by the math guys at Stanford or it’s poorly-typed on Reddit by your neighbor Steve who thinks the market-makers are why his 1d TSLA calls are losing. Figured I’d try and straddle that line a bit.

There’s infinitely more to say about market-making but I don’t know most of it and I said I’d publish this article like three days ago. Since I haven’t yet, I’m just gonna abruptly stop this here. Might do a part two on this at some indeterminate point in the future, might not, we’ll see! Also might do more “Poorly illustrated” stuff with Microsoft paint but as always, no commitments because I don’t feel like obligating myself. To any more practiced market-makers reading this, please feel free to tell me why I’m wrong about everything, unless you’d rather just laugh and keep picking me off (fuckers).

If you enjoyed the read, gimme claps (up to 50), subscribe here, follow me on twitter and retweet this article post, call me a cunt, whatever, see you next time.

Welcome to crypto! Self-sovereign money, NFTs, DeFi, cross-border transactions and new volatile markets await!

Now get your head out of the clouds and start paying some fucking attention to what you’re actually doing. I’m writing this article because a lot of the newer entrants to crypto seem to have very little understanding of the responsibilities they are assuming by default when they first dip their feet in.

Everybody in crypto wants to take your coins. Some of us will steal it if possible, some of us have a moral compass. Don’t rely on it. Unless you’re cool with handing over all of your shit with no way to get it back, follow these security measures (and think of ways to improve on them where possible). I’m holding your hand here, if you fail to follow these fairly basic instructions, I’m going to laugh and call you a moron when you inevitably lose everything. The responsibility is entirely on you. Don’t like it? Fuck off and play somewhere else, this article is for adults. You don’t get a choice.

On-Chain Security Measures

Gonna keep it real basic. There’s a lot of on-chain shit to do these days, most of this applies to all chains, wallets, browsers, etc.

Seed Phrases

When you create a new wallet address (whether it’s using a browser extension like MetaMask or a hardware wallet like a Ledger), you receive a “seed phrase”. This seed phrase gives total and absolute control over the wallet and all of its contents to anyone who knows it.

YOU SHOULD NEVER EVER EVER HAVE TO USE THIS.

It is for emergency use only (like if your computer or hardware wallet completely bricks out), and represents complete, total and irreversible access to everything stored in that wallet. It cannot be changed or reset, if someone else at any point in time discovers that seed phrase, they now own everything you thought was yours.

Keep it physically secure and offline. Do not store it as a text file. Do not store it as a picture. Do not keep it anywhere you can even get to it easily, because if you can get to it easily, so can someone else. Store it in a safe or bank vault, engraved in metal or on a piece of paper. Redundant copies are ideal.

Do not (for the love of God and all things holy) type it in to any web browser. You should not even be able to access it easily, mine are split into two halves, physically secured and stored across state lines. There is absolutely no fucking excuse for you to ever be typing that phrase into a website or giving it to “support staff”.

The ONLY use for a seed phrase is to hand over complete control of the wallet and everything in it.

So if you think you want to do that, go ahead! But don’t come crying about it later when your stupid ass decided that the guy pretending to be support staff needed ownership of your jpegs and illiquid meme coins.

All of the above also applies to your private key.

Wallet Management

Hot new NFT dropping? Sketchy new DEX the only place to farm PotatoYamSolPunkNFTCoin? Cool, make a new wallet.

Every. Fucking. Time.

The reason is simple: if you don’t know exactly what the transaction you’re signing does (aka, if you can’t read and verify the code being executed), approving that transaction could very well be approving instructions to drain your entire wallet and send the contents off to Vitalik-knows-where.

While your philanthropic urge to feed second and third-world scammers is admirable, consider sending the funds you intend to use into a wallet freshly generated for the purpose, that way any malicious code only receives access to the funds in that specific wallet, not your entire net worth.

If you decide that your funds aren’t worth protecting properly, at least go to https://etherscan.io/tokenapprovalchecker and regularly (read, after every time you do something on chain) remove approvals for different dApps you may have interacted with. There’s no reason to allow continued permission for these apps to move your tokens around any longer than absolutely necessary.

If gas fees are too expensive, use a better blockchain. Or at least realize that you’re gambling with your wallet contents and if you keep it up, eventually you’ll be playing the part of Santa Claus for some dude in Nigeria who learned how to ctrl+c / ctrl+v some Solidity code.

Hardware Wallets

Use one. Use multiple and split balances between them. If your portfolio is worth more than the cost of a hardware wallet and you aren’t using one, you fucked up.

My favorite is Ledger (works with MetaMask most of the time and also supports Solana). I’m too lazy to set up a reflink so just go buy one from their site https://www.ledger.com/. Don’t ever buy a wallet from a third party, it could easily have been tampered with.

General Common Sense

Don’t sign transactions you don’t understand.

Don’t google for sites you want to visit, find the project twitter handle and visit the site from their official links. Bookmark that link, then only visit using your bookmarks. This prevents accidental use of fake phishing websites.

If someone DMs you on Discord or Telegram claiming to be support staff, or hosting a giveaway from anywhere, for any reason, ignore them. I have never seen support staff contact people in this way. Stop giving them your money/seed phrases/private keys.

Google forms are a no-no. Don’t put any wallet information into them.

If something looks sketchy, it is. If something doesn’t look sketchy, assume it is anyways. Be a paranoid motherfucker or be a broke motherfucker, there isn’t a third option.

Off-Chain Security Measures

This is a bit simpler because it’s largely synonymous with general online security. Nevertheless, the bounds of human stupidity know no limits. Onward!

Email Management

Use a different email for every exchange account. Never tie a personal email to a financial account login.

Using multiple email accounts is a free and easy way to ensure that if there is a compromise somewhere, you’re only partially fucked. Much better than being completely fucked. This is negated if you keep all your shit in one place, so don’t do that.

Make sure that your email provider supports…

Two-Factor Authentication

Use it. Use it everywhere. Email and exchange accounts, you MUST have it on both.

Dedicate a device for your 2FA. This means a not-your-cellphone-device. Old cellphones work just fine, so does a tablet or iPod touch. You don’t want your 2FA device to be stolen, so don’t use a device you regularly leave the house with. Ideally, don’t connect the device to the internet at all, ever.

Never use text message-based 2FA, sim swap attacks are incredibly easy to perform. Use Google 2FA, Authy or a Yubikey.

Password Management

Do: use a random password generator, with different passwords on every single login.

Don’t: store passwords in text files, on your computer’s clipboard or anywhere connected to the internet. Memorize or write them down, then make a copy. Physically secure the copy.

Conclusion

This is a really basic starting point to security, and we haven’t even talked about privacy (because it’s getting late and fuck it, article’s getting long).

All of this shit is a massive pain in the ass. No two ways about it. But you know what’s an even bigger pain in the ass? Not having any money because you’re a lazy fuck who assumed it would never happen to you.

I know most of you aren’t going to implement most/all of this advice, but that’s not my problem, it’s yours. If you want to play in crypto, be your own bank, make outsized returns in DeFi or NFTs, you have directly chosen to assume responsibility of your own financial security.

If you fail and get hacked/exploited/drained, it’s because you chose to suck at providing your own security. So don’t do that. Follow these steps, eliminate all single points of failure (also applies to trading, but that’s a whole different set of articles, go read them).

Be paranoid, or go broke when somebody steals all your crap. Not my problem either way, but at least now you know what you gotta do.

If you liked the article or learned something, gimme claps and share with your friends.

But most importantly, don’t wait until it’s too late to protect your money.

Don’t worry, I’m not going to jump in to talking about market-making, volatility, arbitrage and volume flows in this article series just yet.

Actually, I lied. That’s exactly what I’m going to talk about. Like, starting right now. For all the new people. Article number two, let’s fucking go! But fret not, we’re gonna start with the absolute basics and I promise you’ll be able to follow along.

So the primary question here is quite literally, what makes a market… a market? How does a market come to exist?

We all know you gotta have a buyer and a seller, blah blah old news. But going back to the previous article, let’s tie in the concept of value. In the first part of that post, I talked about valuing my car at a price of one hundred live chickens. Let’s stick with that scenario, because it makes me giggle. If that’s what I decide my car is valued at, it’s an opinion. Just like your opinion that my car is worth more chickens, less chickens, or ought to be valued against something else entirely. Maybe bushels of honeycrisp apples. Like I said in the last article, the car can’t actually considered to be worth anything at all to anyone but me, unless there’s someone willing to agree with me and make the swap. But how is anyone to know that I wish to exchange my car for chickens?

Ah, right. I need to announce my offer to make the exchange. Preferably in a public place, where plenty of people will see it. Maybe someone will be interested in this sweet deal! I’m going to post my offer, my asking price, on Facebook. And craigslist. And eBay (does anybody even use eBay anymore? idk). You get the point, I’ve made my offer, now to wait until someone decides to accept it. Now here comes my buddy Rick, from Facebook. Not the smartest chap, he didn’t read the listing very closely. He messages with his bid: 25 live goats in exchange for my 1995 automatic v6 Ford Mustang (a real panty-dropper when I was 18, trust me.) Now, goats are not nearly as good as chickens in my opinion, because goats are assholes. But he wants my car! After leaving Rick on read and thinking for a moment, I have a solution. I’ll simply have Rick post an offer to exchange live 25 goats for 100 live chickens. He can make the exchange, then he and I can make our exchange for my car.

Rick, (again, not the sharpest lightbulb in the toolshed) agrees to find a buyer for his goats. But now you step in; you want Rick’s goats, but you don’t have any chickens, only 50 bushels of delicious honeycrisp apples! You make a bid to trade with Rick and he’d love to accept it, but you see he really needs a car more than he needs 50 bushels of apples.

Now after arguing a bit, we’re all getting a little irritated, Rick is very confused and you’re tired of the goats trying to eat your apples. (I told you, goats are assholes). If only there was a way we could standardize the exchange for all of our various commodities. Maybe we could have someone print pieces of paper, or make little gold circles or something, and then just agree to use those to trade with, instead of herding goats, lugging apples and getting 16 miles to the gallon in a Mustang with a missing catalytic converter.

If you’ve successfully followed along with my weird-ass metaphors and mildly nightmarish hypothetical scenarios (like owning a piece of shit mustang with the catalytic converter cut out, who in their right mind would do that at 18 hahahahaha), you may have realized that you, Rick and I have just invented the concept of money. Turns out, bartering is a pretty tedious way to exchange things and severely limits your options. But if all of us decide that these pieces of paper, or this chunk of rock, or that string of code is worth using in order to avoid the inconvenience of herding goats around to trade for chickens to trade for cars, then… well, it is! Remember, value isn’t a fact, it’s completely subjective! But now that we’re all measuring using the same thing, it’s a lot easier for me to figure out just how badly you want Rick’s goats. And how badly he wants my car. Maybe I can charge him more than 15k! It is a pretty sweet car, after all.

So now I change the terms of my offer, my asking price. I tell Rick that he can have this dope-ass ride (and the 1/8th a tank of gas in it) for $16k. I value those $16k pieces of paper at one beat-up piece-of-shit Mustang, because I know that I can exchange them later for 100 live chickens and even have some cash left over to buy food for them. Now Rick, a true paragon of intellect, determines that this is a hell of a deal. He accepts my asking price, the cash and keys change hands and we’re on our separate ways. Don’t ask me how he got all those goats in the Mustang.

All in a good day’s work. But this negotiation business has gotten me hungry and I’m starting to think those apples of yours look pretty good! How much do you want for one? Oh, you weren’t planning on selling them individually? I’ll give you 10 bucks for one if you do, I’m recently rich! 16k in my pocket baby!

Well, that’s not a bad deal… but you wouldn’t be able to sell your last bushel because the grocery store you deal with only buys in increments of complete bushels. But wait! Your neighbor sells apples individually! And he only charges a dollar each. You grin, pocket my ten dollars and I munch away. On your way back home, you stop at your neighbor’s fruit stand. Only one apple left! You buy it for a dollar, and laugh at how stupid I was to overpay by nine dollars. Easy money for you! Good thing you didn’t sell me two though, there wouldn’t have been enough apples for sale to make your profit, and you’d have lost out on a whole bushel sale. It all worked out in the end though, what a fantastic day!

…And they all lived happily ever after, except Rick. He’s still stuck with his goats, who are assholes.

Now, if you were paying attention in that story, we saw a relatively large number of market dynamics in play. We had bids and asks placed across various (highly exotic) markets, we had a mark price set for MUSTANG/USD with one unit of volume in the market, and two different prices on a volume of 1 at each price point on APPLE/USD, which you were smart enough to arbitrage out. Oh, and we invented money. Seems handy.

A lot of these little interactions seem obvious in a simple story like this one. Most people already know all this, or at least understand immediately what is going on. You might think a tiny, local market like apples or one specific car are completely different than the digitized, high-speed financial markets of today but I disagree. Every market on the planet requires a minimum of two things: a bid or ask to be placed in a location visible to at least one other participant, and a participant willing to accept that bid or ask. The moment someone accepts a “resting” bid or ask for the first time, a market is created. There is now a specific value assigned to that asset, measured in whatever it was exchanged for.

In any heavily-traded market, there will be thousands of bids and asks set at a wide array of price points, and large amounts of people and money accepting them on both sides of the orderbook (the ledger of active bids and asks). The number of units changing hands is recorded as volume. If the volume of market buyers willing to accept the resting asks reaches the number of units listed for sale at a given price point, the buyers can choose either to accept the next highest offer to sell (and get a worse price), or they can choose to stop buying. If they accept the next highest offer, a new price is printed for the asset in question. It is now worth slightly more, according to the market. The same is true of market sellers, except they rely on pre-existing offers to buy and if those run out, the price will go down.

This works exactly the same as the sale of my car to Rick, except… well, most markets are faster. Thousands of assets can change hands in milliseconds and just as many bids or asks can be placed, moved to new prices or removed from the market entirely.

Boiled down to the base level (one could say a… foundation for thought?), all markets function exactly the same way: two entities find a price they agree on and make a transaction. Once there are no more people willing to exchange at that price point, participants must choose between not participating, or taking a worse price than previously available. Once an order at the new level is accepted, that level becomes the new market (or mark) price. This is how price fluctuations occur.

Note that in this entire article, I never mentioned why I wanted chickens. Or why Rick wanted my Mustang, or why you wanted his goats. Lastly, I never mentioned why someone might accept enough resting bids to lower price, or enough resting asks to raise it. That was intentional; the end result is always the same, as is the process by which prices moves. Price goes up when people accept resting asks until there are none left at the original price, only more expensive prices. It doesn’t matter in the slightest why people accept those asks! I didn’t ask Rick why he accepted my offer, because it didn’t matter to me. I just wanted that amount of cash more than I wanted my car. I valued that quantity of cash more. In the same way, it doesn’t matter why other market participants are buying or selling, only whether they are. Their participation is a reflection of their sense of asset value. You may agree with their valuation or you may not, but remember that the mark price is the only standard we have, and it is subject to every participant’s whims to some degree. Remember that all value is subjective!

So often, traders (particularly amateurs) work very hard to find “reasons why” the market went up, down or sideways. And there are reasons! But not the ones you think. The reason the market moves is because there are enough participants accepting resting orders to exhaust them at a particular level and move the market. That is the reason, the only reason. If you truly want to know for certain the logic for a market move, you’ll have to ask every single participant who filled, moved or removed an order during it! A difficult task, I don’t recommend it.

You get two foundations for thought today, I’ll leave you with this one.

Market action trumps market explanation; people lie but prices do not.

I wrote this article, there’s no financial advice in it or anything, insert usual disclaimers and follow me on twitter. Please clap.

I spend a lot of time thinking, and by that I mean doing nothing else. Maybe it’s a sort of meditation, maybe it’s a sort of mental illness. Either way, I’m not very good at it but most of the time I’m thinking about the market and associated ideas because it’s the thing that fascinates me most in life. It’s made me a decent amount of money over the years and I like sharing things I find useful, so I’m going to start writing down the thought processes I think might be able to help some people make money, or more accurately to make money-making systems.

I’m not good at complex shit, so I try to boil down everything I do to its most basic reasoning. For that reason, most of these thought processes aren’t directly market “alpha” as much as my mental foundation for generating it. These foundational pieces overlap and intermingle with each other, but as a whole they are my best distillation of everything that I believe makes me capable of trading successfully.

This particular “article” isn’t so much an article as it is an index of articles on these foundations that I want to share. Maybe they’ll be useful, maybe they’ll be bullshit! Probably a mixture of both. I’m writing these in my typical twitter style; kinda shitpost-y mixed with some stuff that (if I’m doing this right) might make you sit back and think. Please take the time to do so, then come tell me why I’m wrong! Twitter DMs are always open, or you can leave a response here.

I haven’t started writing most of these yet and the list is going to be pretty fluid. I’ll update it with new links and ideas for future articles when I have them. Think of this as a rough outline, no timeline, no schedule, just for whenever I come up with shit that might be worth someone’s time to read. Maybe I’ll never finish it, who knows! But anyways, without further ado…

Small Brain: What is Value?

Can’t identify, exchange or capture value without having an idea on how to judge it. Turns out you kinda don’t get to judge it either.

Small Brain: What Makes a Market?

Literally, what is a market? At the most basic level, how does it work? Also, why is money?

Small Brain: The Three Prime Assets

The relationships between time, skill, stored value and the equivalent forms of risk. We all exchange varying levels of each every day, but I’m pretty sure we’re bad at it.

Small Brain: Probability vs Risk

Probability for dummies, by a dummy. Probably inaccurate (see what I did there?)

Small Brain: Relationships (Mostly Non-Romantic)

You probably won’t get a girlfriend, but at least you’ll get the difference between correlation and causation. If you do get a girlfriend though, it’s definitely because of this article.

More to come. Probably.

“Value” in my mind is one of the most ambiguous and useless words in the English language. It’s also one of the most interesting. It’s such a weird concept! To value something (i.e., to use the word “value” as a verb), means to attach a worth to it. For example if I am willing to exchange my car for $15,000, I have attached a value to my car. Maybe someone will pay that much, maybe they will not. How does anyone know whether that valuation adequately reflects the worth of the car? Let’s make it even weirder and say that I would not exchange my car for any number of dollars. I want 100 live chickens instead. Is my car now worth 100 live chickens? Does my car no longer have a USD value? By whose standard, and why?

Hopefully the absurdity of that first paragraph is enough to make you laugh a little, but it might also make you think a bit. It’s a good question isn’t it? What the actual fuck is “value”, and why are some forms of valuation considered reasonable and others aren’t? Some of you might also have done the math and wondered why I’m indirectly valuing a live chicken at $150 each and to be honest, I have no idea dude. Do any of us? I mean, how much is a dollar worth? What do we value it against?

Lots of questions. Feel free to puzzle through them and come to your own conclusions, but here’s mine: value isn’t real.

Here’s what I mean. It’s completely subjective! Value is an opinion, it’s ephemeral and useless. It changes constantly based on who you ask, and there’s no oracle of truth! Nobody is forcing you to agree with my idea of value, any more than I am forced to agree with yours (well, most of the time and the exceptions are pretty fucked up). So how in the hell can we possibly be certain in the value of anything? If there’s no value, no standard, what is the point of ownership or production?

Very mushy, vague sort of world we’re imagining (or waking up to?) and maybe your brain hurts a bit now. Mine does. Tends to happen when you start to really think about a lot of our base assumptions on Important Things and Ideas. Fortunately, there are actually two objective methods of valuation that we can use. The first is human necessity. Water has an intrinsic value (i.e., it cannot ever be entirely value-less), because humans require it to survive and humans are the only beings capable of understanding the concept of and therefore assigning persisting value. The standard of human necessity imparts a value, but does not impart an intrinsic quantity of value. There is still no standard against which to measure the value of water according purely to the rule of necessity, but there is an un-quantified imbuement of value.

The second method of valuation is the standard of a free market. A free market implies two things, without which it becomes nothing more than a disguised tool of force and a sham. Those defining qualities are property rights and free volitional participation. Everyone who wishes to participate must be permitted to do so, must own (read, retain full control, access and use of) their assets and must not be coerced by threat of force to exchange them for anything else, whether at a fixed rate or otherwise. If these things are not true, the market cannot adequately reflect any level of quantifiable value because the valuation is set by an arbitrary imposed force. This would make true valuation impossible beyond the intrinsic.

So. Big words, small brain. Let’s take a second to digest and recap. Value is an opinion. Necessities are intrinsically (but un-countably, by default) valuable. A free market provides a valuation standard.

Now let’s combine those ideas into a foundation for thought (the whole point of these articles).

Value is an opinion freely expressed by the act of buying or selling an asset for any reason (including necessity) on an open market, so long as that market remains free.

Note that this foundation does not mean that everyone agrees on value for… well, anything. A starving man will value a carrot much more highly against a gold bar than a farmer who grows thousands of carrots a year and only eats a very small fraction of them. So how is valuation standardized? The answer is market value, or mark price. If I value an asset lower than it is currently being exchanged for, I’m free to sell it (or short-sell it, which I’ll talk about in a much later article, maybe). Conversely, if I believe an asset is underpriced, I’m free to purchase as many as I’m able or willing. I am free to express my opinion of valuation via participation on either side of the market and doing so affects the mark price to some degree or another.

Regardless of whether I choose to participate or not, the mark price of any asset is the arbiter of value. I’m free to hold a differing opinion, but it either aligns with the market or it is wrong.

What this means is that even if the market later aligns to my opinion of value, until or unless it does so, my valuation is wrong no matter how well-reasoned I might believe it to be. The market price is a participation-weighted average of the opinion on the value of an asset and it’s the best and only way we have to truly quantify value on anything.

I wrote this article, there’s no financial advice in it or anything, insert usual disclaimers and follow me on twitter. Please clap.

Trading is simple in concept and highly challenging in execution. It is a professional skill-set which requires years to develop and master, often without the aid of a teacher or mentor. This compilation of reading materials has been hand-selected in an attempt to provide a comprehensive starting point for the discretionary trader in their journey of understanding. These resources will not make anyone successful (only experience can do that), but they will create a solid baseline of knowledge from which a trader can draw throughout the rest of their career in the financial markets.

Practical Literature

These books offer practical advice and understanding for analysis and trading across a variety of markets and trading styles. Each offers different insights which can be taken at face value or used in combination to create a personalized trading strategy.

Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications

- John J. Murphy

One of the best-selling trading manuals since its release in 1999, this book provides a solid guide for traders of all skill levels in understanding price as displayed on a chart, with a specific focus on the futures market.

Encyclopedia of Chart Patterns

-Thomas N. Bulkowski

The bible of technical analysis, this book provides detailed examples of 53 actionable chart patterns with a focus on public equity markets. The author also provides additional insight on technical chart patterns on his free website, thepatternsite.com.

The Intelligent Investor: The Definitive Book on Value Investing

-Benjamin Graham

Hailed by Warren Buffet as “the best book on investing ever written”, this book teaches the wisdom of choosing long-term investments in equity markets and stresses the importance of creating viable strategies with a proven track record. While focused on low time preference investing, there is plenty of knowledge for a position trader to draw from, in addition to understanding the principles of other participants in the markets they trade.

Trader Vic: Methods of a Wall Street Master

-Victor Sperandeo

With a focus on macroeconomic trends and environments, this book illustrates a top-down approach to determining broad trends and the ripple effects they cause, allowing for positional opportunities across global markets.

Trader Vic: Principles of a Professional Speculator

-Victor Sperandeo

The follow-up to the authors original book, the sequel is packed with even more techniques for trend identification and capitalization.

Trading in the Zone

-Mark Douglas

Mark Douglas does an excellent job of explaining common mental blocks in trading and how to avoid them. This book serves as a good primer in thinking in a probabilistic fashion and becoming comfortable with the uncertainty a trader is confronted with in the market every day.

Reminiscences of a Stock Operator

-Edward Lefèvre

A fictionalized account based on the life of Jesse Livermore, one of the greatest traders in history, this book is both an entertaining read and packed with dozens of useful heuristics for any aspiring trader.

How to Trade in Stocks

-Jesse Livermore

From one of the greatest traders in history comes a detailed account of his own strategies and methods in choosing winners for his portfolio as well as various tips on risk and emotional management.

The Alchemy of Finance

-George Soros

From the man who made a fortune betting on the downfall of the British Pound in one of the greatest trades of all time, this book describes his trading methodology and mindset, as well as outlining some of his predictions. Not the easiest read, but well-worth the effort.

What I Learned Losing a Million Dollars

-Jim Paul and Brendan Moynihan

Written by the former governor of the Chicago Mercantile Exchange, this book explores the psychology behind human loss aversion and tolerance for risk, as well as how to combat our innate fears as traders.

The Complete Turtle Trader: How 23 Novice Investors Became Overnight Millionaires

-Michael W. Covel

A recreated historical account of Richard Dennis, a famed trader who turned a group of novices into professional traders with the help of his breakout and trend-following trading system. Known as the Turtles, this book outlines the major points of the strategy and tells an inspiring tale of rags to riches for those disciplined enough to manage themselves and their money.

Studies in Tape Reading

-Richard D. Wyckoff

Wyckoff provides deep insights on the way to view markets, both in trending and ranging environments. With a focus on mid-term positional trading, the lessons from this book are as poignant as ever.

Psychology and Decision-Making Literature

These books are not specific to trading, but can provide insight on a healthy mindset, aid in decision-making and promote the understanding of the human psyche.

Thinking in Bets: Making Smarter Decisions When You Don’t Have All the Facts

-Annie Duke

One of the more relevant psychology books to trading, Annie writes from the perspective of an ex-professional gambler and decision strategist. She writes on the wisdom of collective over individual results for decision-making over large sample sizes in the midst of uncertainty.

Incerto

-Nassim Nicholas Taleb

A collection of Nassim’s five books (Antifragile, Fooled by Randomness, Skin in the Game, The Black Swan and The Bed of Procrustes), he dives deep into decision-making, probability in mathematics and life and the human reaction to circumstance. His previous work as an options trader informs many of the philosophies described in his writing. His books are surprisingly readable for the heavy-hitting topics he discusses.

Thinking, Fast and Slow

-Daniel Kahneman

Nobel Prize winner Daniel Kahneman outlines the two forms of thinking which our minds utilize throughout our daily lives, as well as the strengths and pitfalls of each. He also writes on fallacies in our thinking, and when to expect and correct them.

Principles: Life and Work

-Ray Dalio

From one of the most successful hedge fund managers in recent years, this books is broad in scope but has quite a bit to offer in terms of setting plans, making decisions and setting goals. While not specific to trading, it will provide you insights on how one of the most successful men in the field of finance organizes his life, business and decisions.

Anecdotal and Investigative Literature

These books are finance-adjacent, often telling stories of successful traders or large downfalls inside the industry. While they may not directly help your trading, they may provide insight on various financial goings-on and history, in addition to providing entertainment value.

Flash Crash: A Trading Savant, a Global Manhunt and the Most Mysterious Market Crash in History

-Liam Vaughan

An enjoyable tale well-told of Navinder Sarao, the trader often blamed for the flash-crash which occurred on May 6th, 2010. Non-fiction.

The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution

-Gregory Zuckerman

The account of Jim Simons, manager of the legendary Medallion fund, the most successful quantitative trading fund in history.

Flash Boys

-Michael Lewis

The story of the spectacular rise of high-frequency trading, and its shortcomings. An easily accessible tale of a very inaccessible portion of the financial markets.

The Big Short: Inside the Doomsday Machine

-Michael Lewis

A gripping behind-the-scenes look at the story of the men who bet against the American economy leading up to and during the 2008 financial crisis.

When Genius Failed: The Rise and Fall of Long-Term Capital Management

-Roger Lowenstein

A story of John Meriwether, who founded LCTM and built an impressively profitable system seemingly without regard to leverage, until the liquidity dried up.

Summary

These books are intended as a starting point for any potential trader or investor to learn about the financial markets and speculation. They will not guarantee a profit, nor will absorbing information from them create a winning trader. You must learn from them and apply the lessons yourself in the market, just as the people in these books had to do. Reading is wonderful, but only time and practice in the market can make you money.

I hope you’ve enjoyed this list. If you have, please share the article and consider leaving a few “claps” (up to 50!)

Disclaimers

This piece represents the honest opinion of this author, and does not necessarily reflect any positions I hold.

I am not a financial advisor, and nothing in this release should be construed as financial advice. I may hold speculative positions across any of the markets discussed. This is not a solicitation to buy, sell or otherwise transact in any financial or speculative market, currency, commodity, asset or investment.

You should consult a licensed financial advisor before making investment decisions. I am not responsible for any loss of funds due to any decisions made as a result of this article.

In this article, we’ll be creating a very rough trading system which may or may not be profitable to start with. We’ll be covering:

• Indicator and trigger selection
• Momentum vs mean reversion strategy basics
• Backtesting

If you haven’t read the first part in this article series, I recommend doing so before continuing further.

Indicator and trigger selection

As mentioned in the first part of this series, a discretionary trading system can be built from pattern recognition, or from indicators. To make things a little easier for beginners, we’re going to start with indicator-based systems and get to the pattern-based ones further down the line. Our entry and exit criteria will be based on signals from our chosen indicator(s).

When you’re new to trading, or just looking to try something you aren’t familiar with, it can be a pretty daunting task picking an indicator but it doesn’t have to be: pick one at random. Most indicators can be profitable with the correct set of rules to guide them, so don’t be afraid to grab one that sounds cool and play around with it. It probably won’t be ideal, but it doesn’t have to be. It just needs to be consistent.

For our first strategy, we’re going to use an indicator most people are familiar with; the Moving Averaged Convergence-Divergence oscillator, or MACD for short. This is an indicator that measures the relationship between two moving averages on an asset’s chart. It is unbounded, which means it does not have a limit to how high above zero or how low below zero it can go. If you’re unfamiliar, it looks like this:

It is a trend-based lagging indicator, which means that it indicates recent past momentum in the market; the wider the gap between the two lines, the stronger the momentum. It lags because it does not attempt to predict price, only shows the relationship of moving averages which are calculated live.

We’ll be using the default TradingView settings on the indicator to start (fast MA = 12 bars, slow MA = 26 bars and a signal length of 9).

Mean reversion vs momentum

Before we continue, it’s important to clarify the difference between a momentum strategy and a mean-reversion strategy.

A momentum strategy is based on trend-following, expecting to ride momentum in one direction or the other until the momentum slows or reverses.

A mean-reversion strategy on the other hand expects markets to remain relatively stable (although this can apply to a slow, steady trend as well), and looks for abnormal behavior to take positions betting on a return to the average.

Both strategies have their pros and cons, but for the time being we’ll focus on momentum strategies since the cryptocurrency market is usually highly momentum-driven, with brief periods of mean-reversion interspersed.

Our triggers

For this strategy, our basic framework is simple: we will take a long position whenever the MACD lines cross upward (which turns the histogram in the middle green), and short when they turn downward with the histogram red. Our triggers will only apply once the candle has closed, thereby cementing the position of the MACD. Is this profitable? Let’s find out!

Backtesting

Everything sounds pretty simple, the rules we have in place so far are easy to follow. But at the moment, while everything looks good on first glance, we have no idea whether this strategy is going to make or lose money. We need to backtest it.

While backtesting can be automated in TradingView with a bit of pinescript knowledge, I prefer to do it live using the replay tool. This allows me to spot patterns which I may find useful in honing my trade criteria. In essence, this method involves choosing a specific date in the past, applying my indicator to the chart and noting down each result in a spreadsheet (entry, exit, $ change and % change).

Throughout this series, I will be using January 1st, 2018 through June 1st, 2019 as our backtest date range when testing strategies on the 1D and higher timeframes. Tradingview has a limitation on past data on lower timeframes, and so backtest date ranges on those timeframes will be specified individually.

In the next article I’ll cover backtesting in a bit more depth because there is quite a bit to it in order to ensure that your data is reliable, but for now let’s take a look at our results (Google Sheets link here) using this strategy on the 1D timeframe. (When we get more in depth next week, I’ll include a graph.)

A quick look tells us that this strategy does seem to perform well with a total profit of $8,941, assuming a flat 1BTC position size was used on each trade. At 17 wins and 22 losses, this puts the strategy at a 43.6% strike rate with an average 2.71% gain per trade: not bad, considering all we did was slap an indicator on the chart.

So far we have some promising numbers and the strategy seems to work right out of the box but if you remember the last article, there are multiple parts required for a complete trading system and what we have so far only covers our entries and exits.

So how are we going to determine risk?

We’re going to need a lot more data, and that means more backtesting.

Join me next week!

This is the second in a series of articles on building your trading system. If you learned anything or found this article valuable, please leave it some claps (up to 50!), share it on Twitter and other social media and leave a comment! It really helps with visibility. For more like this, follow me here and on twitter!

By now most of you have probably heard of FTX, the new cryptocurrency derivatives exchange launched earlier this year. They’ve launched a whole bunch of new and interesting indices, contracts and features that would be considered commonplace in traditional markets, but are still novel to the nascent crypto trading community. In this article, I’ll be covering the Bull, Bear, Moon and Doom leveraged tokens in the hope that you’ll be able to understand a bit better how they function both from a technical perspective, and as a potential addition to your portfolio.

So what’s a leveraged token?

In this article, I’ll be using the example of a 3X leveraged long token on Bitcoin (ticker symbol BULL) for ease of reference. So in this case, the token is an ERC20 token issued by a smart contract on the Ethereum blockchain, which represents a 3X long position on Bitcoin.

While this sounds simple, there’s a bit more to it than that. In order to understand how the token differs from an actual 3X long position (and what some of the benefits/drawbacks are), we’ll have to dig into the technical side on how the token is managed.

Creation and redemption

To create a BULL token, a customer has to visit the FTX page and deposit the current value of the token. The exchange would then work some magic on the backend, and the smart contract would issue one BULL token to your exchange wallet.

The same process may be used in reverse to redeem your token, which helps ensure that the price of the token aligns properly with the price of the underlying asset.

Algorithm management

That magic on the back end I mentioned involves an algorithm which manages funds to automatically keep the price of the token in line with the percentage gain (or loss) on Bitcoin, multiplied by three on a daily basis. I emphasize daily basis, because the algorithm’s job is quite difficult and it has to maintain some parameters in order to function properly. One of those parameters is choosing a timeframe over which to emulate performance.

A 3X long token which aims to replicate the effect of a 3X long over the period of a year functions differently than one aiming to replicate the effect of the same over one day. The FTX tokens are all designed to emulate performance over a 24 hour period, otherwise known as the rebalancing period.

Let’s use an example before this gets too complicated. Let’s say both BULL and Bitcoin are trading at $10,000. If the price of Bitcoin increases to $11,000 in the period of a day (+10%), the BULL token should reflect that increase, magnified by 3X, meaning it should trade at $13,000 (+30%). So far so good. But what happens if price drops by the same amount?

Using our previous example, if BTC price drops by 20% instead of gaining, the price of BULL should drop by sixty percent to $4,000. But this causes an issue, since the BULL token is a leveraged token. We are now much closer to the point of liquidation with only another 10% drop in spot before the token’s collateral should be liquidated. In order to handle this, the algorithm reduces leverage and exposure to the market. This can happen intraday (in between rebalancing periods), and the conditions for this are a bit complicated. You can read the official documentation here, but for now the important part is that the algorithm will do its best to ensure that the position is never liquidated, and continues to hold some level of value (even if it’s much lower than before).

Again, not too difficult to understand. The primary differences between a leveraged token and a leveraged long position are beginning to show though. Since the algorithm is managing margin, the token itself is highly unlikely to ever be liquidated (this would only happen in market conditions which move so suddenly and violently that the algorithm does not have time to deleverage). So even if spot price drops by more than 33% (the usual liquidation point for a 3X long position), the token will maintain value as long as the algorithm can keep up. But now we come to the largest and most misunderstood difference between a leveraged long and a leveraged token.

Intra-day decay

We know that the tokens rebalance every 24 hours (at 02:00 UTC to be precise). So what happens when we get a large downswing in spot price at the beginning of the day, say -10%, and then finish the day up 5%?

This is where the leveraged token may not perform the same way that a leveraged long would. Whereas a leveraged long would maintain a linear correlation (3:1) with the spot price, a leveraged token may differ because of the auto-rebalancing. If the spot price drops by 10% and the algorithm is forced to reduce leverage in order to maintain token value, when the upswing comes later in the day, it will not be able to fully capitalize on that upswing because the token is “under-funded”. Again, this is necessary in order to avoid liquidation, but may catch some traders off-guard. The token will re-balance at 02:00 UTC in order to capitalize on moves for the next day (or if the token becomes 33% over-leveraged, which is rare), but will not have performed as well as a 3X leveraged long on spot or futures for that day only.

Compounding positions

To counteract this though, there are some very helpful benefits to trading the token as opposed to a typical long position. We’ve already covered the fact that the token is incredibly unlikely to be liquidated, but a second advantage is that due to the daily rebalancing, as long as price trends in your favor for multiple days, your position will automatically compound.

In our earlier bullish scenario, the token would end the day up $13,000 from $10,000. However on the next day, let’s say spot price increases by another 10% from now $11,00 (+$1,100) to a total of $12,100.

While a 3X long would simply be up 60%, or $6,000 (3X a total of a 20% move), the leveraged token has rebalanced. So on day one it is up $3,000 just like the margin long, but on day two, it is up 30% from the beginning of that day, not the day previous. So on day two, the token gains 30% on $11,000, or $3,300 for a total gain over two days of $6,300. This of course out-performs the margin long position by $300.

So how does this help me as a trader?

Leveraged tokens are a fantastic tool in several aspects. If you don’t want to have to worry about liquidation on your positions, these tokens can provide some peace of mind. They are also much better during strong trending periods, due to the daily rebalancing and compounding effect, which would otherwise need to be done manually in order to achieve the same result. Another advantage is that as ERC20 tokens, they can be stored offline in your cold wallet, giving you full control over your funds, though it’s best to avoid holding them for long during a period of mean-reversion or ranging action due to the possibility of deleveraging.

Though based on spot pricing, some systemic or algorithmic traders may also find these tokens to outperform on low timescales as trading instruments, simply due to the artificially increased momentum which these tokens can display.

Final thoughts

While I’ve only used the BULL token as an example in this article, each of the BULL, BEAR, MOON, DOOM and HEDGE tokens function in the same way, with varying directional bias and leverage applied. For more detail on how these tokens work, I encourage users to read the full documentation found here.

Thanks for reading!

If you learned anything or found this article valuable, please leave it some claps (up to 50!), share it on Twitter and other social media and leave a comment! It really helps with visibility. For more like this, follow me here and on twitter!

Disclaimer: I was not solicited or paid in any format for this article. I hold no exposure to the FTT token, as an American citizen I am prohibited from trading on FTX and all information contained in this article was provided via official product documentation. This article is not financial advice or an endorsement of any of the financial products discussed within.