There are plenty of guides on building crypto trading strategies. Many are decent, none resonated with me. Here's my take. Five easy steps.
0. Get your expectations in check
1. Code a low-quality execution bot
2. Build an orchestration system around that execution
3. Add performance measurement system
4. Use performance data to improve steps 1 to 3 until you're not terrible
5. Start trying to actually make money
Let's walk through these.
To start with, I don't think automated crypto trading is a path to riches. I'm not even sure it's a particularly good path. I have done very well. With that said,
If you exclude the HL airdrop, I've made about as much as I would if I money kept on my previous path. I do get a lot of flexibility and I got some lottery tickets one of which happened to be a winner; but I also put my own money on the line and got more risk / stress / loneliness.
More generally, I don't think the crypto trading cottage industry has much of a future.
Given that, lets get going with the good stuff. Quoting a movie as I tend to, “If he [my son] really wants a cigarette. I'll buy him his first pack.”
Brilliant people have spent decades optimizing trade execution. Luckily, they haven't spent decades on crypto execution because crypto is so new.
Your first step on the path I have set is to build a basic execution engine. This is not a trading strategy. This is not going to make money. This is a placeholder you will use to build out measuring and monitoring capabilities. Once you have those in place, you will improve this into something passable, which you will then use one things that actually CAN make money.
I'd suggest you start with Hyperliquid. Why?
Make sure your bot can, say, take order directions from a REDIS DB and turn that into trades without crashing or losing too much money.
Now that you can place orders, you need scaffolding to direct that order placement and understand it. Build out your bot into.
Why bother with all this for a $500 HL account making $10 trades? Because launching a real-money bot without this stuff would be dumb. You're testing in prod with a small account.
The third step is measuring performance. This is incredibly important, because a lot of what you are going to be doing is iteratively improving 1 based on the metrics from this. An entire industry exists for this.
Big picture, focus on execution first.
Are the prices you get filled at good?
For all of those measures, but especially the long term measures, you want to strip away as much noise as possible. I tend to do this by controlling for movements in the Binance prices of either the crypto in question or BTC. If the Binance price was perfectly fair, how could be use it to reduce variance?
So instead of doing
Profit = Sell price - Buy price
You decompose your returns into…
Profit = Sell price - Price from Binance at sale - Venue adjustment + Medium term change in fair price on Binance due to that coin + Medium term change in fair price on Binance due to broad market moves - (Buy Price - Price on Binance - Venue adjustment)
Each of those can be measured separately, stripping away the noise created by broad market moves allows you to better measure the actual gains you create.
When you have an infra here, test the big MMs on HL. They typically make ~0.2-1 bp. If you see them making or losing huge sums, you are wrong.
Do other measures look good?
In medical trials, they often look at blood pressure rather than heart attack deaths. Why? Because blood pressure is easy to measure whereas heart attacks are mostly random and the result of long term factors. Blood pressure is a surrogate endpoint for the heart attacks we actually care about.
Tick-to-trade is the primary surrogate endpoint for my execution. You want to measure the time from a Binance web socket post to a cancel order. You measure this not because it is inherently important, but because you can measure this extremely accurately.
You will measure it and you will work to shorten it. But always keep in mind that your local deli accepts cash, not ns. You are using tick to trade ONLY because it is easy to measure and allows you to iterate fast.
Implementation tips:
You won't get good but you can suck less. Once you have measurement and risk checks in place, you should be losing only a handful of basis points per trade. Set your bot to do a thousand trades a day (trade against the stuff that is cheap or expensive vs Binance) and measure what comes out. If your bot is doing dumb stuff, stop it from doing that dumb stuff.
Tune your bot to improve your metrics until you get to a reasonable level. What is reasonable? I'd say your execution costs on small trades executed passively should be less than 2 basis points plus the difference between your fee tier and the top MM fee tier. That should be easy to hit. Keep in mind, you aren't trying to make money (yet), you are just getting to a position where you can move risk around without losing too much money.
If your are making money after step 4, you screwed up. Revisit step 3.
When you're confident you are in okay shape, then you pick your role. Broadly, I see a few paths to success:
If something is trading at $10 on one venue and $11 on the other, there is a good chance one of those is wrong. The obvious trade is to buy the cheap one and sell the expensive one. Take that logic and scale it.
Your first step should be to download both CCXT and Hummingbot. You want to carefully note every venue they support. Then you should not only DELETE both CCXT and Hummingbot, you should avoid EVERY SINGLE VENUE they support. If a venue is supported there, don't bother, you won't make money. No old venues.
Why not? Well, for starters I'm there and even worse maybe Wintermute or goblin or loris or someone else better than me is there. I've been running that bot for years, I've tuned it, I've worked out the API quirks, it will be hard for you to compete. But even if your bot is BETTER than mine, you still won't win. Why? I'm willing to run my bots for basically zero profit. Even if I'm not making money today, keeping them going keeps my optionality, supports a platform I have a bunch of points on (or relationship with the team etc.), and gives me the ability to build up exposures I like or hedge risk from another venue. Even if you are a better coder, you can't compete with that.
So you go to NEW PLACES. The API may be broken, there isn't that much volume, and you are slightly worried you will lose all your money. Those are bad, obviously, but the pros aren't there and even if they are entering, both you and the pros are starting from square one.
Beyond the competition aspect, crypto is a game of lottery tickets. Old venues are not winning tickets. New venues may well be. I won with Hyperliquid, I lost with a DOZEN other venues. If you want to win, you need to be going to places you CAN win.
On that new venue, you will absorb risk. Your goal is NOT to make money on every trade - your goal is to get into a position where you would make money if you perfectly hedged every trade on Binance at no cost. You don't HAVE to hedge, this is just a reference. The governing assumption is that if you are trading on some degen den, then they aren't adversely selecting against you and changes in the Binance price are just random noise. So if you get filled for better than Binance mids every day, the times the degens lose will more than balance out the times degens win.
This risk absorption could be high frequency. For that, you likely want to specialize in a few venues, build up good relationships, learn the API quirks. You will optimize for those (few) venues, try to take 30-40% of their orderflow for a decent return.
It could be low frequency, e.g., funding rate arbitrage. For this, you model the paths of funding rates and how much leverage you can safely take. While higher frequency was about depth, this is more about breath - you want to have more opportunities to pick from, so you want to have coverage on many futures venues.
A lot of people start with modeling prices using prices. It seems so tempting. You download the download Binance data, backtest, and are amazed at the excellent returns you found.
Unfortunately, you found excellent returns because you screwed up. Rule of thumb: if you think you found a 3 Sharpe ratio trading on Binance using price data, you found it because you screwed up.
Somewhat informally, I view the returns here as being of two types: alphas and betas
Alphas are the price being wrong. The price of LUNC gets thrown out of whack because of hedging done by a weird instrument. The contract specs of a weird venue mean funding is paid based on an idiosyncratic formula, the market maker is dumb and hasn't realized this. A Korean venue posts listing announcements on its site early if you can guess the URLs. These alphas are great but they are short lived and have limited capacity - markets tend toward efficiency. People tend not to talk about alpha because if you leak them they get traded away.
Betas are risk premia ' momentum, reversal, funding rate patterns, shit coins going to zero. These are well known, big, people trade on them. They exist because there isn't enough smart money in crypto to absorb them.
You can tell by the examples I gave that alphas usually aren't coming from prices alone (they can, of course). Very likely, the prices give you a clue something is going on. You will then keep pulling the thread until you find out the cause of the distortion. Then you will trade on that.
Most of what you see in the candles will be betas. Betas are still pretty good in crypto, but they are there because taking the other side is unpleasant. I think ScottPh77711570 gives a solid feel for the action technical analysis returns give. macrocephalopod is of course an all time great.
If you like betas, you should try to get a bunch of somewhat different ones so the winners balance the losers (and keep in mind that when things go bad, everyone who had that genius idea will be unwinding at the same time and they will all be correlated).
If you want alphas, you are more likely to find them by looking deeply into the oddities of market structure than you are to find them by running ML models on Binance price data.
The low tech approach here is to just do whatever you did as a degen gambler, but slightly better because you have less slippage using your bot. I do some of this - I type in my target allocation and my bots tilt my trading to move toward that allocation. E.g., I short MSTR and rely on my bot to hedge out the BTC risk.
For me at least, the higher tech approaches often emerged from the lower tech approaches. Whenever you see something weird, you should be thinking - does this weirdness create an opportunity? If so, can I write code that captures this weirdness and trades against it every time it happens.
Again, you want to focus on things that Wintermute ISN'T DOING. Sadly, this means you probably want to focus on things that DO NOT scale. Why? Because if it could scale, someone better than you (are now) is doing it.
Buying Bitcoin on Binance based on a DataBento CME subscription? That isn't going to work.
Buying HL memes to front run TWAPs or large deposits? That might work.
I repeatedly tell people not to start trading, and to quit if they are ahead. Very few people have the skills required to do this well. Trading is not a zero-sum game; once you account for fees, spreads, slippage, and outright scams, it is a negative-sum game.
The ratio of people who actually know what they are doing to people who are effectively doomed is extremely low. I personally make a lot of money trading, but that fact is not especially informative. I have also used all sorts of drugs without developing an addiction. If a teenager outside a liquor store asked me to buy them alcohol, I would not tell them that I never had a problem with opiates and that insulin needles are easy to buy. I would tell them to stay in school.
Even if you do have the skills, trading your own account is usually a bad idea. If you are genuinely good enough to make money trading, you should either be doing something else entirely, or trading other people's money in an environment that allows you to scale, learn, and be challenged by a high-skill peer group. Trenching garbage capital does not count, and neither do most paid trading groups. The kind of trading that makes money in crypto, in particular, is often a dead end.
Most retail traders -- whether in crypto, Robinhood equities, FX, or elsewhere -- look a lot like gambling addicts. In crypto especially:
I am genuinely mystified by the behavior. How can Coinbase charge 2 percent fees and still have enormous volume? Why do people trade on venues like MEXC? Why are they trading at all?
From the outside, it looks less like investing or skill acquisition and more like a mix of entertainment, lottery-ticket thinking, and addiction. For most people, the dominant strategy is not to find a better exchange, a better indicator, or a better strategy. It is simply not to play.
If there is one category of trading retail should especially avoid, it is traditional finance options and structured products.
Robinhood makes a huge amount of money from options trading. Wealth management firms make a huge amount of money selling options and exotic structures. That fact alone should tell you who is winning. The flip side is simple: you, the buyer, are getting hosed.
At a fundamental level, any edge available to retail traders in TradFi is going to be small. It usually comes from taking on some hard-to-hold risk or trading in a niche market that institutions do not care enough about. If you believe you are routinely finding large, clean edges in listed options as a retail trader, you are wrong and should stop trading.
Option markets are dominated by wide spreads, adverse selection, and professional counterparties. The bid-ask alone is often large enough to swamp any edge a retail trader could plausibly have. Whatever you think you have discovered is almost certainly already priced, and priced aggressively.
Many people believe options reduce risk. They do not reduce risk; they transfer it. When you buy options, you are paying someone else -- usually a hedge fund or a market maker -- to take risk you do not want. As a retail investor, you are not going to win that trade on average.
This is not an argument for covered calls either. Covered calls are just the mirror image: you are selling risk back to the same institutional players, either directly or at prices set by a market maker whose business is trading against much more sophisticated participants. You are still the weak side of the trade.
I am posting this because I see crypto traders -- some of whom I believe actually have alpha -- buying TradFi options. In my view, they are lighting money on fire. There is a reason some surgeons refer to motorcyclists as “organ donors.” That is roughly how many TradFi professionals think about retail options and structured product traders.
One important caveat: this logic applies much less to crypto. Crypto options are often mispriced, sometimes badly. In crypto, strategies that would be a punchline in TradFi can actually work. Python HFT is a good example. The market structure, participants, and level of sophistication are simply very different.
But in TradFi options and structured products, the conclusion is straightforward: do not trade them.
First, you should understand why you made money (if you did) or how you could hope to make money.
If you think you make money through some way that isn't above, that's a bad sign.
More practically, what should a retail trader do if they want to get better?
Benchmark
If you want any hope of winning, you need to identify what works and do more of it. To do that, you need benchmarking. Buying GOONCOIN because you thought it was a great meme and then winning because of an inflation print doesn't mean you have skill – it's just luck. You need to try to figure out whether the decision-making process behind that bet had positive expected value.
Two simple ways to improve this immediately:
Stop using strategies that imply you can predict the broad market
Here's an analogy. Suppose a friend tells you they've figured out how to turn lead into gold using household chemicals. If you know even a little chemistry or history, you know they're wrong. You don't need to see the formula. People have tried this for millennia and failed, and we understand why.
Predicting short-term movements in the broad stock market is similar. It's not impossible, but it is extremely hard. People get paid millions of dollars a year to predict it slightly better than chance. If your strategy implies you can do it reliably, you are wrong.
Stop obsessing over cost basis
Whether a position is good or bad has nothing to do with what you paid for it or how much you've already lost. Cost basis is sunk. It is irrelevant.
People hate realizing losses, so they let losers grow. Then they blow up.
For individuals engaged in active trading, establishing a corporate structure can offer meaningful advantages in both tax treatment and legal risk management. In many jurisdictions, traders operate through an operating company owned by a holding company, a structure that is fairly common and well understood by professionals. The primary benefits are twofold. First, corporate entities may allow for tax deferral or modest reductions in the effective tax rate. Second, losses or negative balances incurred by the operating company are typically ring-fenced, meaning they cannot be charged against the holding company or the trader personally, provided the structure is set up and maintained correctly.
That said, which structures work - and which do not - is highly region-specific. Tax law, securities regulation, and corporate liability rules vary widely across countries and even across sub-national jurisdictions. A sensible approach is to use tools like ChatGPT to sketch out the broad pros and cons of potential structures and to identify the types of professionals who are best suited to advise on them. From there, one can ask their personal or professional network to help identify reputable experts with relevant experience.
It is also important to speak with multiple professionals on at least an introductory basis. Comparing advice helps reveal both consensus views and potential red flags. Once those relationships are established, general questions can again be routed through ChatGPT, reserving paid professional time for issues that truly require bespoke legal or tax advice.
Finally, expectations should be kept firmly in check. Proper structuring can reduce legal liability, allow for tax deferral, and modestly lower the overall tax burden. It is not a magic solution. Professionals operating in offshore financial centers often promise far more aggressive outcomes, but such arrangements frequently carry substantial legal and regulatory risk, including the possibility of serious criminal penalties. Conservative, well-documented structures may be less exciting, but they are far more likely to achieve the intended benefits without creating new and far more severe problems.
Many builders end up in a position where they want active liquidity on their platform. They need someone who will quote prices for the thing they've dreamed up, keep inventory, and stand ready to buy and sell. What should they do?
I'm one of the people you may be targeting, so I'll try to give you what attracts me to the platforms. I've been one of the first active liquidity providers for at least a dozen projects.
Given that, **this is what I want**. Note that EVERYTHING on this list is negotiable. But the more costs you put onto me, the more I'm going to demand.
Many builders assume pointing to an API spec is enough, but that's a mistake. Figuring out signing by yourself is always a massive pain. Because of that, I am very reluctant to integrate with a platform that lacks a proper SDK.
Making an SDK is a version of dogfooding. If someone on your team can't quickly put together an SDK, how on earth do you expect me to?
In the age of chat GPT, the language of the SDK doesn't really matter. I'd suggest either Python with type hints or TS to hit the widest possible audience.
**Actionable Suggestion:** Post an SDK that covers the basic methods of your protocol.
**Role Model:** I'd put BitMEX as the role model here. BitMEX not only provided an SDK, they provided a basic MM bot. I think that's part of the platform's (past) success.
Anything that isn't your core innovation should be as close to established players as possible.
If I'm quoting on your platform, I'm going to be taking existing stuff (e.g., HL) and making whatever changes are needed. Every deviation from the norm adds a cost. Minimize unnecessary changes.
**Actionable Suggestion:** Follow existing players unless you have a good reason not to.
**Role Model:** Hyperliquid did a good job here - USDT indexes, a similar funding formula to Binacne, and an AWS tokyo location made it easy to port Binance quotes over. Something like Kraken Futures is an anti-role model: 24-hour factor futures with unclear indices quoted in Europe is a nightmare for no good reason.
Slowing down taker orders by 100ms (e.g., Hyperliquid) makes it a lot easier to set up a market making bot because you do not need to worry about being picked off by sniping bots.
If you want me to provide reasonable price quotes, you need to provide an exact and detailed description of the cash flows of the assets being traded.
**Actionable Suggestion:** Take two similar products, review their documentation, and ensure yours covers everything they do. Information buried in Discord chats is not sufficient.
**Role Model:** Skimming through platforms, Hyperliquid's docs are okay. Kraken Futures is poor. Whales Market is horrible.
Everyone needs confidence that their funds are safe. Personally, I look for backers I know (VCs and ideally community members), a doxxed team, and a track record.
In order of priority:
Everyone wants growth, so this isn't really actionable. But realize that a lot of the integration costs are a one-time cost. If I'm earning that dividend over a long time, it's a much easier sell.
The returns to integration increase as the platform grows. If your growth is stagnant or uncertain, the cost of integration becomes harder to justify.
The liquidity provider's profitability is of course the most important thing. I'm not saying much here because I don't want to leak too much alpha to other MM.
My suggestion here is just to provide information easily. Don't tell people to enquire about the MM program on Discord and then reply three days later. Put it in the docs.
EG: If you want better funding rates... make sure you have a funding rate history end point to make life easy for funding arbers. Even better, put together a funding rate comparison page (e.g., https://app.hyperliquid.xyz/fundingComparison).
I've seen a few posts recently about Lighter, the hot zero-fee perpetuals DEX, and its future business model. One post that triggered me (and this essay) argued that good market makers earn 10 basis points per trade—so the platform can just 'charge them' and call it a business model. That logic doesn't hold up. Let's unpack why.
Before anything else: I like Lighter. I've ranked it in the tier just below Binance, Hyperliquid (HL), and Bybit in my perp exchange rankings here. It isn't a primary venue for me, but it is one of literally two new venues I trade on and I currently have millions in notional open interest there and a few thousand dollars' worth of points. I prefer Hyperliquid, but I admit my biases.
My goal here isn't to FUD Lighter. It's to lay out what I see as economic realities of the sector.
If a platform promises free trades for users, someone still has to pay the bills. The obvious candidate is market makers (MMs). Hence the argument: just charge them.
The problem? 'Good' market makers make maybe 1bp before cost, plus a bit from rebates. You can check this yourself: take the largest MMs on Hyperliquid, divide their P&L by their trading volume, subtract the maker rebates, and you'll see something around 1bp. (Make sure to exclude spot, since the HYPE airdrop and HYPE holding profit distorts the math.)
That 1bp is gross profit. It doesn't include the cost of actually running a market-making operation:
None of that is free.
Taxes on Market Makers Go Straight to SpreadsIf MMs are barely breaking even, what happens when you charge them? They widen their spreads. A 10bp maker fee becomes a 20bp bid–ask spread. You could see this clearly on Kraken, which historically had high maker fees and correspondingly wide spreads.
That's why most exchanges do the opposite: they pay maker rebates. Hyperliquid pays 0.3bp, and many venues pay even more. Charging MMs is fighting gravity—it pushes spreads up, liquidity down, and retail away.
You might challenge what I'm saying and point out that Robinhood makes money on equity trades: If they make money on zero-fee TSLA trades, why can't Lighter make money on BTC perps?
But actually PFOF on equity isn't a core part of Robinhood's model. Their profits come from:
Don't take my word for it—check the filings. Roughly 40% of Robinhood's trading volume is equities, yet equities produce only about 6% of revenue. Within that, large-cap names like TSLA contribute disproportionatelylittle.
PFOF works when you can segment traders and share part of a wide spread.
In TradFi, suppose a stock has a $100 / $101 bid–ask. That $1 spread covers market makers' risk of being run over by smarter traders. They make money when uninformed ('dumb') traders buy at $101 and lose money when informed ones buy before news hits $102.
If you can separate the dumb flow, you can offer it a better price—say $100.8 instead of $101—while still earning a profit. The leftover margin can be shared between the broker and the market maker.
No spread, no PFOF.
Crypto perps don't have that room. Spreads are microscopic. If BTC trades at 100,000.12 / 100,000.13, there's simply no economic space to pay for order flow.
That's why, again, Robinhood earns little from equities. Equity trading dominates volume but not profits. Within equities, tiny small caps with wide spreads generate most of the PFOF revenue (e.g., here: https://brokerchooser.com/education/news/data-dashboard/payment-for-order-flow). In crypto perps, there's no equivalent—spreads are too tight to extract meaningful value.
Segmenting users does help, even if you aren't giving them price improvement like PFOF. 'Bucket shop' CEXs like M*** or B***** do this aggressively—by banning or throttling skilled traders to protect the flow of the people dumb enough to use their platforms. That's one way to keep spreads profitable: kick out the smart money.
DEXs can't do that. One thing they can do is play around with the microstructure. Hyperliquid was one of the first to add taker speed bumps and many venues (incl. Lighter I believe) have followed their leads. A short delay blunts toxic taker strategies without hurting normal users much, allowing tighter spreads for everyone else.
That's fairer than banning good traders—but there's a ceiling and I think current speedbumps are already at that. DEXs can't stop anonymous addresses or shady on-chain behavior. Toxic flow is part of the game. I've personally been carried out by insider flow on DEXs; it happens. Unless Lighter has a new way to handle that, these constraints remain.
You can go beyond speed bumps and build hidden subsidies into the system—essentially transferring a few extra bp from takers to makers.
That sounds appealing. But, I think Hyperliquid's microstructure is about as 'screwy' as users will take - and Hyperliquid's MMs are earning a 1.3bp profit including a maker rebate. Cranking the knobs further risks turning your venue into what feels like a 'rigged game.'
And it's important to distinguish this from PFOF. PFOF gives users better prices through segmentation. Microstructure gimmicks (beyond segmentation) usually do the opposite and gives worse fills despite zero fees.
It's possible Lighter currently charges MMs modest fees and they're happily paying. That doesn't make it sustainable.
I've been there. Years ago, I was the #2 or 3 MM on a platform that launched with zero fees and point rewards. I happily ran tight spreads and farmed the point farmers. Then points ended, a small fee was added, and the flow turned toxic. Spreads widened, volume cratered, and the venue faded.
Lot's of stuff works when you are pumping money into the system.
More generally, I'm not convinced that 'zero fees' are a strong selling point for perp traders. My impression: they care more about UI, reputation, farming, liquidity, leverage, and market access, with fees being less important. That's actually GOOD news for Lighter in a way: it suggests their success is driven by things other than simply being cheap.
Crypto just doesn't have the wide spreads or clean flow segmentation that make PFOF viable elsewhere. Unless Lighter invents a genuinely new way to extract value—beyond rebates, points, or stealth microstructure tweaks—it's hard to see how the math works out.
None of this means Lighter will fail. They have a good product and a good team. I just don't see PFOF or zero fees more generally as viable paths forward, unless Lighter invents a genuinely new way to extract value—beyond rebates, points, or stealth microstructure tweaks.
Concerns about transparency in electronic markets, especially on venues like Hyperliquid, often center on fears of predation. Traders worry that their positions, stop-losses, or liquidation levels are visible and actively exploited. Based on what I have seen, these concerns are frequently overstated. I expected to find clear evidence that detailed trader positioning or liquidation data was being systematically incorporated into the order book. Instead, I found very little. Many liquidations that get attributed to being “hunted” tend to coincide with broad market events, such as a weak NYSE open or an inflation print, rather than targeted behavior.
That said, transparency should create winners and losers. This is not a bug; it is the point. The question is who loses. The main groups are traders with extremely toxic information, such as insiders trying to quietly unload positions; traders with very large positions, where direction and size themselves convey information; and traders whose edge is so obvious that it can be easily copied once revealed.
For the first two groups, the real cost of transparency shows up during the accumulation phase. If others can infer that you are building a position, they can move the price against you before you finish. For the third group, the intuition is that transparency should be damaging, but in practice it often is not. High-frequency strategies are extremely hard to reverse-engineer from order flow alone. Discretionary skill is also difficult to identify from trading history, and when others do attempt to copy successful traders, they often end up pushing prices in the original trader's favor. The uncomfortable corollary is that traders without edge sometimes attribute losses to transparency rather than to the absence of skill.
Most traders, however, benefit from transparency. Giving worse prices to toxic or highly informative flow necessarily improves prices for everyone else. This is the same logic that underpins payment for order flow. Algorithmic traders should generally not worry about being copied, and discretionary traders with real skill should not worry much about information leakage. Those concerns are usually overstated.
The popularity of dark trading venues is also widely misunderstood. Much of the migration off-exchange is not about privacy itself. Payment for order flow is mostly about the nature of the orders, not concealing information. Dark pools are attractive largely because they offer lower fees and midpoint execution, which is partly regulatory arbitrage. These structural features explain far more than secrecy alone.
Importantly, transparency does not mean that all execution must be public. There are straightforward ways to keep TWAPs, stop-losses, and conditional orders private. Hyperliquid does not currently emphasize this, likely because the team believes this functionality should live in third-party front ends rather than in the core protocol.
More broadly, Hyperliquid has room to further mitigate transparency concerns through structural changes such as transferable positions. If implemented well, this could materially improve execution quality, even for very large accounts, and potentially offer a better experience than centralized exchanges.
There is one transparency issue that is more specific to Hyperliquid. Once mempools become public, the interaction between visible taker orders and the taker speed bump starts to resemble MEV on an order book. For example, makers may pull top-of-book liquidity when they see a large taker order coming. This is solvable, but it is genuinely more difficult than the other issues.
In many ways, Hyperliquid already looks more like off-exchange trading than a traditional exchange. Its microstructure reduces toxicity, and participants have some ability to know who they are trading with, similar to dark pools, private rooms, or payment-for-order-flow arrangements. Contrary to common narratives, much off-exchange trading exists not because traders want to hide who they are, but because they want to reveal it. Robinhood users get good execution precisely because their flow signals low toxicity. ETFs rebalance via RFQs because their trades are predictable and non-informative. Traders choose venues to avoid being picked off by snipers and HFTs.
Seen this way, transparency is not the enemy of good execution. With thoughtful market design, it is often the mechanism that makes good execution possible in the first place.
Crypto makes it very easy to win big, and even easier to gamble it all away. I've been in some bad places myself, but I was able to make it a lot better with time and effort. Even if you think it's all over, talk to someone and wait a week. If you are right and if it's all over, what's a week? But maybe you are wrong.
No matter how bleak it seems, financial losses can always be repaired. If you feel like it's all over - don't drink or do drugs. Talk to someone in your life or findahelpline.com
More generally, if you are struggling with mental health, you should go through the process on https://lorienpsych.com/2021/06/05/depression/. And recognize it is a process.