The Application Of Risk

Risk is an elusive concept to cover, and certainly a much misunderstood one. It is defined in different ways for different purposes but it is critical to fully understand what constitutes risk in order to find sustained success in any speculative venture.

Depending on the context, risk can mean the expectation of volatility and illiquidity: This market is one of great risk. It can also be an albeit abstract measurement of the likelihood of success or failure: I believe this is a low-risk proposition. Lastly, risk can be a calculation related to exposure and downside versus upside potential: I have £5,000 at risk here, though my returns could be as great as £15,000. This latter definition is the one most commonly used by traders, but I believe an understanding of them all is particularly useful for profitable speculation. Only by seeing the full picture of market volatility, exposure and risk versus reward can we then come to some sort of conclusion on the second definition; whether our current speculations will prove successful. In fact, part of the full picture of risk is illuminated by the price-history of the market, depicting where, in the past, similar scenarios to those we are presently expecting have seen success or failure.

This post will, I hope, serve as foundational material for those who are unfamiliar with applying the many aspects of risk to their speculative positions. I will run through the process for each of the three relevant definitions of risk and how they each relate to the full picture.

Firstly, however, I’d like to emphasise the most essential point concerning risk: Particularly when speculating in the cryptosphere, the thing that determines whether one masters risk management or not is whether one invests money they cannot afford to lose. If you start out with non-discretionary income, you’ve already lost the game. The other components of risk management are only relevant if your speculations are comprised of money you can afford to lose. If this is not the case, the likelihood is that no amount of searching out low-risk, high-reward opportunities is going to save you, as your emotions are inextricable from your positions.

For those that the above applies to, stop reading and reorganise your portfolio until it resembles something that you could lose the entirety of tomorrow and it would not affect your quality of life. For the rest of you, let’s crack on.

Risk, as in volatility and illiquidity:

When a market experiences high levels of volatility (as is the case with all cryptocurrencies), it is said to be risky. Similarly, when a market is highly illiquid, there is inherent risk in exposing your capital to said market, as there is a possibility that, once a position is entered, it would be very difficult or costly to exit until market conditions improve sufficiently.

These aspects of risk management are critical to one’s speculative positions, as they are very much linked to personality and thus the quality of the decisions one makes. If you are highly risk-averse by nature, entering a position in a more volatile and illiquid market is perhaps not the brightest idea; if you are risk-tolerant, an illiquid market may not bother you and high volatility may not affect your trading decisions. In either case, having a clear understanding of these aspects of risk prior to entering a new position is a contributing factor to the likelihood of success.

But how can one determine volatility and illiquidity as a component of risk management? Most commonly, these are abstract terms for the general market participant, relative to the more concrete calculations one makes for exposure and reward-to-risk. However, simple calculations can be made to make things clearer.

For volatility:

  1. I tend to first determine the duration that I’m expecting to hold a position for. In my case, this almost always tends to be over a month. We’ll use a month for the purposes of clarification.
  2. Given this trade duration, I collate (in a spreadsheet) 30 days of historical price data for the coin I’m interested in using Coinmarketcap’s Historical Data tab.
  3. I delete everything except the Close Price data.
  4. I then calculate the average Close Price for the 30 days. This is my benchmark figure.
  5. Using this average Close Price, I calculate the percentage change from it to the highest Close Price during the month.
  6. I do the same for the lowest Close Price, also.
  7. For example, if the average price was $1, the highest price was $1.50 and the lowest price was $0.30, this would give me figures of 50% and -70%.
  8. The final step is to multiply these figures together to find a volatility ratio for the given duration. In this case, it would be -0.35 over the past 30 days.
  9. The closer to 0, the less volatile the market during that period of time, and vice-versa.

This process is particularly useful for cross-comparing the volatility of altcoins over the same time-period. It is rudimentary in its methodology, but gives us some form of concrete figure to apply to our risk management. Those that are risk-averse may opt to only enter positions in coins that have volatility between 0 and -0.1, for example.

For liquidity:

  1. This is even simpler than the calculations made for volatility. The first step is to calculate the buy support across listed exchanges for the coin you’re interested in, within 10% of the current price. Calculate this in BTC-denomination.
  2. Now divide this figure by the market cap of the coin (again, use the BTC figure).
  3. Multiply the result by 100 to get the buy support as a percentage of the market cap.
  4. Anything lower than 0.1% is highly illiquid. Anything higher than 1% is highly liquid. Most altcoins tend to be between these two figures.
  5. Do this once a day for a week and calculate the average to get a more reliable figure.

Now, the most important thing to do with this information is devise a benchmark that works for your personal relationship with risk. And stick to it. For some, this will be a commitment to only entering positions in coins with greater than 0.5% liquidity and between 0 and -0.05 volatility. Just make sure you know what works for you.

Risk, as in likelihood of success or failure:

This second definition of risk is, as mentioned earlier, more abstract than the other two. We often use low-risk synonymously with high-probability in everyday conversation, or high-risk synonymously with low-probability. The utility for speculators comes from finding historically similar scenarios to those we are expecting to profit from and evaluating their successes and failures. To make this clearer, let’s use a simplistic example:

First we must define the terms of the position we are considering. Let’s say I am considering an entry on X at 3000 satoshis. I am anticipating prices above 6000 satoshis, and would consider my trade idea incorrect below 2000 satoshis (which would be my soft stop-loss). I am willing to hold the position for 3 months.

Given these points-of-reference, we would simply backtest the trade using the coin’s price-history. Every time price reaches 3000 satoshis, we would enter an imaginary trade; does price reach 6000 satoshis? Does it reach it within 3 months? How many times would the position be stopped out? Ask all the relevant questions and compile an historical evaluation of your trade idea. If we’re looking at a trade that has been successful 80% of the time in the coin’s price-history, it gives us some degree of confidence that our own position will be successful, also. Of course, to be able to evaluate your idea to this degree, you first need to know all the critical information regarding exposure, entries, exits and risk versus reward. As such, the most important aspect of risk management outside of using capital you can afford to lose is found in the third definition.

Risk, as in exposure and returns:

Risk management for traders is mostly concerned with this third definition of risk that concerns all things quantitative, and for good reason. For me, calculating exposure is a prerequisite to entering a new position. It is the primary element upon which the rest of the trade is structured. Speculating without a clearly defined plan for capital exposure is a sure-fire way to wipe out your portfolio, and we don’t want that, if we can help it…

I will, at a later date, be writing an in-depth post on position sizing, which itself is a integral part of managing exposure, but for now let’s consider the basics. You could, of course, create an intricate plan of position sizing based on the volatility and liquidity calculations I mentioned earlier – almost as though you’re basing your risk on, well… risk itself. Riskception. But for the purposes of this post, let’s stick to the most common method of determining position size, which is focused on market cap or network value, however you like to refer to it:

  1. Firstly, you need to calculate the value of your portfolio. This is the base figure that you will use to calculate exposure for a new position. Let’s say it is 10 BTC, or ~$40,000 at current prices.
  2. Now, figure out whether the coin you are considering a position in is a microcap, lowcap, midcap, highcap or megacap. These are arbitrary terms, of course, but I can only offer my approach here. I categorise these using the following figures: microcap = 0-25 BTC; lowcap = 25-250 BTC; midcap = 250-2500 BTC; highcap = 2500-25,000 BTC; and megacap = 25,000 BTC or higher. These, again, are subjective numbers based on my own experiences in the space. If you wanted to use $ figures (though I advise against it, as these are heavily dependent upon the price of Bitcoin), then I’d opt for 0-$250k for a microcap; $250k-$2.5mn for a lowcap; $2.5mn-$25mn for a midcap; $25mn-$250mn for a highcap; and $250mn or higher for a megacap.
  3. Now, for each of these market cap-based groups, I have a different band of exposure based on the original value of my portfolio prior to entering the position: 0-1% for microcaps; 1-3% for lowcaps; 3-5% for midcaps; 5-10% for highcaps; and 10% or more for megacaps. I do not commit to the minimum percentage exposure within these bands, but I explicitly do not exceed the maximum for the given market cap. For example, I might choose to only allocate 5% of my capital to a megacap, but I would never allocate 5% of my capital to a microcap.
  4. Of course, there are some caveats here. Firstly, this approach is known as fixed-risk, wherein one allocates a fixed percentage of capital to a position often in lieue of setting a stop loss (but not always, as we’ll come to shortly). The position is then held until: it reaches its target price(s); it fails to reach its target within the predetermined duration of the trade, at which point it is exited; or, the coin dies. This is a common approach with microcaps and lowcaps, but makes less sense when one is concerned with the larger coins.
  5. When it is these larger coins that are being considered, the bands of exposure are still used, but a stop-loss (hard or soft) is added as a second risk-mitigator. My approach to stop-losses is that they should be based on technical factors rather than predetermined percentages, such as the break of long-term support or something similar, but it is often useful to have a maximum percentage stop-loss in place. For example, let’s say we were looking to enter a position in ABC. ABC is a highcap and so our capital exposure is a maximum of 10% of the value of our portfolio. Further, since it is a highcap, we choose to place a stop-loss. The maximum we are happy to lose is 25% of the initial capital, and thus a stop-loss is placed 25% below the average entry price. This equates to 2.5% of the value of our portfolio, which is our maximum capital loss.

Now, there are numerous other avenues one could go down when devising an approach to risk management, but I believe this approach will suffice for most. The issue is that stop-losses can and must (in my opinion) be linked to the risk versus reward of the trade. So let’s discuss the final aspect of risk for this post: returns.

The goal in investing is asymmetry – Howard Marks

Asymmetrical opportunities are the real secret to profitable speculation and proper risk management. Finding opportunities that present returns many multiples greater than the potential risk is what is so special about this space – they are ubiquitous.

Reward is almost always predicated on the price paid for the position, which is why buying low is so important. It allows for the low-risk (here meaning minimal amount of capital loss), high-reward opportunities. The most important thing to take away about risk versus reward is to exclusively go after opportunities that offer at least twice the reward against the risk. So, if, after calculating your capital exposure and your stop-loss, you have a maximum capital loss of 2.5% of the value of your portfolio (as in the earlier example), then your opportunity must present a reward equating to 5%. This is why the fixed-risk approach is suitable for the smaller altcoins; the potential rewards are so large that the trades are often asymmetrical in our favour despite the potential loss of the entire position.

Now, to conclude this post, how do we tie it all together? Well, what you can do is create a risk framework that all future trades must adhere to, and this would be based on your own level of risk tolerance. For example, you could decide to only enter positions in coins that have 0.5% liquidity, between 0-0.1 volatility over the past 90 days, at least one instance of success of a similar scenario in the coin’s price-history and at least 3:1 reward-to-risk. Play around with the numbers to see what works for you – the important thing is to have a consistent framework to which you always adhere.

I hope this post has proved useful. Feel free to leave any comments and questions below and I’ll get back to you!

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Orderbook Reading 101

Orderbook reading is a key component of my trading toolbox. It is a technique I developed myself, back in 2014, and one that there is little-to-no quality information on online. (Seriously, Google “orderbook reading” and you’ll be shocked by the lack of resources.) In my book, I dedicated an entire 5,000-word section to orderbook reading, and, given the lack of material readily available elsewhere, I figured that it might be useful for anyone interested in the technique to have a primer written; if you find orderbook reading compelling, you can take a look at the more advanced material in the book.

Of course, I don’t doubt that there were others who had dissected the orderbook in a similar way to myself prior to 2014, and I don’t take any credit for the concept of orderbook reading; but it is the one technique that I learnt entirely via my own experience, with no help from resources such as those one would look to when learning other aspects of technical analysis. Now, I say it is an aspect of technical analysis, though strictly that isn’t true. TA is exclusively concerned with the chart, but orderbook reading resides in that grey area between fundamentals and technicals. The technique itself feels more like technical analysis, intuitively, but given that it is not derived from the chart, the analytical grey area is where it will remain.

So, what is orderbook reading? In short, it is the study and subsequent analysis of the ledger of orders for any given market. Orderbooks contain a list of all the buy and sell orders currently placed within a market on a specific exchange, and it is this transparency on which I learnt to capitalise. By studying the orderbook, one can often find clues as to the plans of those manipulating price. This can add confluence to our technical targets for entries and exits, as well as boost confidence in the validity of our positions.

But what’s the process? Below, I’ve outlined a breakdown of what I tend to look for when studying an orderbook:

  • There are three key components to orderbook reading: Order Depth, Order Patterns and Time. In this post, we will be looking at the first two.
Order Depth:

This is the simplest form of orderbook reading, and is predicated on studying the value of the orders in the bid and ask (buy and sell) sides of the orderbook.

  1. Pick any given market on any given exchange. Ideally, you’ll want to use exchanges that provide the most transparency with their orderbooks. Bittrex and Cryptopia are two exchanges that I like to use.
  2. Calculate the total value of orders in both the altcoin itself and Bitcoin for the bid-side and ask-side of the orderbook. For example, Vertcoin on Bittrex currently has ~20 BTC-worth of orders in the bid-side, totalling ~28,800,000 VTC. It has ~9.5 BTC-worth of orders in the ask-side, totalling ~100,000 VTC.
  3. At this point, you want to calculate the average order size in both sides of the orderbook. Using the above values, the average buy order is 69 satoshis. This doesn’t make a lot of sense, at surface-level, and we’ll come back to it in a second. The average sell order, however, is 9500 satoshis, or ~30% above current prices.
  4. A quick thing to note before we continue is that, from this point, one can also make another surface-level analysis based on the total Bitcoin values of the bid and ask sides: given 20 BTC-worth of buys and 9.5 BTC-worth of sells, it is evident that there is greater demand than supply. This, as mentioned, is surface-level, and does not account for orderbook manipulation, which I won’t go into here.
  5. Returning to our average order values, why is it that the average buy order for Vertcoin is so low relative to current prices? If we inspect the latter pages of the orderbook, we find a buy order worth 1.13 BTC at 4 satoshis, totalling ~28,200,000 VTC. This explains everything. At your discretion, you can then discount this order from the calculations. Doing so would give us ~600,000 VTC remaining in the buy side at a total of ~18.87 BTC, which equates to an average buy order of 3145 satoshis. Far more insightful a figure.
  6. Now, the ask-side calculation also comes with a caveat. Despite there being less than 10 BTC-worth of orders listed, we can see, if we look to the last page shown, that we are only being shown 20 pages of data – in this case, orders up to ~11,000 satoshis. Undoubtedly, there will be orders above that point, but this is a disadvantage of Bittrex; it only shows 20 pages of orderbook data for either side. Cryptopia, and many other exchanges, offer full transparency of the orderbook.
  7. Another point to note is that these values are dynamic, as orderbooks are dynamic. Consistent monitoring and study is required in order to garner a more accurate understanding of order depth.
Order Patterns:

Order patterns are perhaps the most complicated aspect of orderbook reading, as they illuminate much of the manipulation that occurs in altcoin markets. Whilst I won’t go into the more advanced stuff here, it is significant to highlight the four types of order pattern: clean orders, bot orders, walls and, for lack of a better term, non-clean orders or messy orders.

Clean Orders: a clean order is simply an order that is unusually perfect, mathematically. These are often orders comprising of multiples of 5s or 10s that occur at regular intervals in the orderbook. For example, buy orders of 50,000 Vertcoin at 5000 satoshis, totalling 2.5 BTC, with corresponding orders at 1000-satoshi intervals. These are indicative of significant price-levels for the market-maker; perhaps levels at which accumulation is taking place. On the ask-side, you may find similar patterns of clean orders that are set up as future targets for price-action. Again, this is simplistic, and not taking into account orderbook manipulation, but it is obvious to see how this can be useful when looking for entries and exits.

Bot Orders: a bot order is one that defies human ability in its execution. Often, algorithms are in place to bid up a buy order or push down a sell order. This is easily noticeable and you have likely experienced it yourself. Recall a time when you’d place a buy order at the top of the orderbook, only for it to be displaced within milliseconds by another buy order; this is a bot order. The purpose of these orders can be two-fold: either to drive you to make irrational decisions (as we’ll discuss in the next section) or to beat you out for the purposes of active and immediate accumulation or distribution.

Walls: Most are familiar with walls. A wall is an unusually large order in the orderbook. However, most tend to react in the most irrational manner when it comes to these orders. Large buy orders being pushed and pulled near current prices are often viewed by the masses as the perfect time to enter, lest one miss out on what must be an imminent bullish move. Large sell orders are viewed as the perfect time to exit, lest we lose all our money when the market crashes and burns. This is orderbook manipulation at its finest. Buy walls are an attempt at causing irrational market participants to buy up the orderbook, and vice-versa  for sell walls. This allows for better pricing to be had for the puppet-master for both accumulation and distribution purposes.

Non-Clean Orders: much like clean orders, non-clean orders are about determining symmetry and patterns in the orderbook. However, they are more difficult to notice, as, unlike their clean counterparts, they are comprised of seemingly random numerical values. For example, an order of 24879.284733 Vertcoin at 6435 satoshis might appear in the orderbook. This order would seem irrelevant to most, but perhaps you notice another order of that exact same amount at 5733 satoshis… and another at 5210 satoshis. Coincidence? I think not. Non-clean orders, such as these, are simply a more discrete way for market manipulators to mark out significant levels for future reference.

Note these various orders down as and when you come across them – and you will come across them – and you’ll begin to piece together a trail of footsteps that must be left by those manipulating price.

I hope this post has proved somewhat insightful and piqued your interest in orderbook reading. For anyone compelled to learn more, there is an advanced section in my book that goes into far more detail.

If you’ve enjoyed this post and want to receive new posts straight to your inbox, I’ve set up a RSS-to-Email feed that will be sent out weekly; every Monday, 12pm. Just submit your email and I’ll make sure you’re included in the list. Cheers.

The Speculator’s Guide To Masternodes and Masternode Network Value

Given the fervour that began with DASH announcing the release of its initial masternode system and that continues to surround the plethora of projects now offering their own versions of one, what exactly is the incentive for a speculator to run a masternode, and how can one navigate the oversaturated space to find the most promising opportunities? One brief glance at a masternode directory, such as, will suffice to show just how daunting a task this can seem to those unfamiliar with the territory; with well over 300 masternode coins listed on that website alone, what should you really be looking for?

Masternode Network Value (MNV), as I like to call it, is the calculation that I consider the key to unlocking the most dependable opportunities in the masternode space. Note that I do not say ‘most profitable’, but rather ‘most dependable’. There is a critical difference between the two, but we will get into that a little later. By the end of this post, you will have a comprehensive take on my approach to masternodes: how I research and analyse them; the distinction between profitability and dependability; why I believe that MNV is the most informative calculation one can make regarding masternode speculation; and how the common pitfalls can be avoided.

But first, we must first define what a masternode is and why they can be a profitable addition to an altcoin portfolio. Forgive me if I butcher the definition, though I write from the perspective of a speculator, and, as such, much of the (irrelevant) technical information has been omitted:

A masternode is simply collateral, in the form of a predetermined amount of a given coin, that fulfils certain tasks on the blockchain and is rewarded for these tasks, often with a fixed portion of the block reward.

Thus, running a masternode is financially incentivised, and one can begin to accrue a steady passive income given an appropriate strategy. Needless to say, it is a space rife with opportunity but also pitfalls, and rarely is it ever as easy as simply selecting a masternode coin, buying the collateral and watching the income pour in. I first began to utilise masternodes in my own altcoin portfolio around twelve months ago, being aware of the potential rewards but anxious about the technology and all that goes with it prior to that. Since then, they have become integral to my strategy, and some of the greatest returns-on-investment that I have gained have stemmed from such projects.

Without further ado, let’s get stuck in.

Tools and Resources:

To begin with, we need to identify the tools and resources available to us to search out – with some luck – promising masternode projects. As far as I am aware, there are five useful masternode directories or ranking websites:

These should suffice for the research process, though I often also scour the Bitcointalk threads – using a simple search for ‘masternode’ – to bolster the list of potentials.

Masternode Network Value:

Now, we must define what I deem to be the most important calculation (for a speculator) concerning masternodes: Masternode Network Value, or MNV. Masternode Network Value is as follows:

(cost of one masternode x number of masternodes online) / circulating market cap

To clarify, let’s take DASH as an example. The MNV of DASH would be:

(39.04BTC x 4656) / 318,233BTC = 0.571 or 57.1%

For comparison, let’s take MANO as an example. The MNV of MANO would be:

(1.8BTC x 186) / 937BTC = 0.357 or 35.7%

And finally, let’s take MEDIC as an example. The MNV of MEDIC would be:

(2.31BTC x 144) / 2202BTC = 0.151 or 15.1%

This is essentially just an equivalent calculation to that of the Coins Locked figure that can be found on some masternode directories, which shows the supply that is currently locked in masternodes as a percentage of the circulating supply, except that I prefer working it out as an MNV figure because I get to know the strength of the underlying masternode network. This leads us on to the next question; what exactly am I looking at with this calculation, and why is it helpful?

MNV is a figure that shows you how much of the market cap of any given masternode coin has actually been bought up and locked into running masternodes. Therein lies its utility; market caps can be useless, artificial figures, but MNV cannot feasibly be faked. It is indicative of true demand and value for a masternode project, as it illuminates the amount of buying that has taken place to accrue those masternodes. The first part of the calculation is itself the masternode network value, as it is a sum of the cost of all masternodes currently online, and we use the circulating market cap to assess how much of a coin’s perceived value is actually in use. It’s all well and good having a billion-dollar market cap for a masternode coin, but if only 10% of that is in operation – running masternodes – it speaks volumes as to the dependability of that masternode network.

With regards to how this calculation becomes useful in our analysis, it differs based on the following section.

Masternode Selection:

The selection process from this point is dependent upon one’s aim; is it to find the most profitable masternodes or the most dependable? As I mentioned prior, there is a critical difference here, and this is where Masternode Network Value comes in. Before elaborating on the most dependable, I’ll first run through the most profitable.

I must preface this by mentioning the more imminent danger to high-profitability: inflation. With a swift glance at the masternode directories I have provided, you will see an abundance of coins seemingly offering upwards of 1000% annual ROI, and at least a handful offering upwards of ten times this. This is a death trap. Do not fall for the false glisten of such rewards; with immense profitability comes immense inflation. The cause of such rewards is as follows, and it is simply a marketing trick:

The Masternode Trick:

Project Z announces its launch and states that its masternodes will be providing 1000% annual ROI by offering the vast majority of their block rewards to masternode holders → This attracts a large number of speculators and miners → The collateral requirement is high and the project has a premine that allows for a handful of masternodes to be set up by the developers to ‘get the masternode network running and stable’ → The developers use part of the premine as a listing fee to index their projects on the popular masternode directories → Meanwhile, the early masternodes are reaping far greater than the specified 1000% annual ROI as there are so few up and running, so block rewards are disproportionately being accrued to those early few → The masternode directories now show the project to currently offer far greater than 1000% annual ROI, which in turn attracts more speculators and miners → This creates artificial demand for the coin, as the collateral amount is often so excessive that it is impractical to simply mine the amount in short enough a time-period to reap the current rewards → The early masternode holders can sell their rewards because of such high demand → More and more masternodes come online and the annual ROI greatly decreases to accommodate this but supply emission does not change as the block rewards remain the same → The price of the coin decreases as demand is no longer sufficient to maintain the supply emission, to the woe of anyone who bought their masternode collateral after the early few.

This may be a simple trick, but it has been used and re-used by so many projects that there is now a graveyard of tens, if not hundreds, of coins that will never recover. The allure of high profitability is too great for the trick to stop working. It is partly the reason for my hack, back in October 2017, as I was scouring the crevices of the cryptosphere for early entry into the most profitable masternodes; thereby unwittingly downloading an unsafe wallet with a hidden RAT that later devoured the majority of my altcoin portfolio. There can only be a few winners (at least at the highest levels of profitability) for such coins, and these are all too often the developers and the very earliest (and luckiest) miners. I myself was lucky enough to be perhaps one of the first five miners on Magnet, securing myself three or four of the first twenty or so masternodes, and, as such, reaping since-unparalleled profitability. But this is a rarity and not one I suggest seeking out. (Saying that, I may find someone with more experience in speculative mining to write a guest post on this, at some point.)

The Process (High-Profitability Masternodes):

For the inconvincibles, there is still a process for picking profitable masternodes that won’t have you weeping after a week:

  • All of the selection criteria highlighted in the book and in my post on Picking Out Microcaps remains valid, and should be the first port-of-call for separating the wheat from the chaff.
  • After that, ignore any masternode offering over 1000% annual ROI. This is too great a level of inflation for it to be worth the additional stress.
  • It is at this point that MNV comes into play. For higher profitability masternodes, you want a higher-than-usual MNV. This is because higher profitability means greater supply emission, thus likely devaluing the coin and thereby our masternodes. High MNV assures that a high percentage of the coin supply is locked up in masternodes. (Note: this figure is dynamic rather than static. We must monitor it closely to assure there is a constant demand for masternodes on this network.)
  • For masternodes with greater than 100% and less than 1000% annual ROI (the range I consider ‘high profitability’), I would suggest filtering for at least a 0.66 or higher MNV (66%). Two-thirds or more of the market cap in operation to run masternodes is rare but I believe it is a necessary filter to prevent falling into the liquidity trap, wherein demand for masternodes is too low to accommodate the immense supply emission.
  • From here, the usual technical analysis comes into play, where I make sure that the chart aligns well with MNV. What I mean by this is simply that the ideal scenario is one in which low price complements high MNV. This ensures that:
    • High profitability masternodes are still in heavy demand despite price being low.
    • One hedges against the likelihood of the value of the masternode itself decreasing by much; it is already cheap, relative to its price-history.
  • Then, once you’ve filtered for all of the above, the accumulation process can begin. You are free to set up your high profitability masternode. One word of final warning would be to consider making high profitability masternodes a shorter-term component of your strategy. The longer you run them, the longer you run the risk of supply emission beating out demand and your masternodes (and their rewards) becoming relatively worthless.
The Process (Long-Term Masternodes):

Now, you might be more like me. You might prefer dependable, longer-term masternodes to add to your portfolio. In this case, the bar is considerably lower for MNV and the range narrower for profitability. (This is where the sole distinction lies in the selection process, as the rest of the above outline works across-the-board.) Dependable masternodes are so because of their low inflation, historically sustained demand and stable networks. This kind of masternode is most suitable for passive income purposes.

How do we find them? Well, aside from the filtering process above:

  • Look for coins with an annual ROI between 10-100%. This is a stable range of inflation and still very profitable for a passive income model.
  • Filter for a MNV of 0.25 (25%) or greater. The reason the required MNV is lower for these is due to far less supply emission. Of course, the higher the MNV, the better, regardless of the profitability of the coin. We want more masternodes online.
  • Whilst you are scouring the masternode directories, keep in mind that a few of them – and come to mind – display graphs that depict the history of masternode cost, masternode count and other useful data. The masternode count data is especially useful, as it allows us to visualise how the network has grown over time. Ideally, we want to see steady, linear growth in the number of masternodes online. This ties in to the idea of historically sustained demand; long-term demand indicates that a potential passive income model may be sustainable.

Common Mistakes and Pitfalls:

In conclusion of this post, I’d like to run through some common mistakes that inexperienced (or experienced) speculators may make:

  1. Buying high: The most common pitfall is buying a masternode when it is extremely costly to do so. This is often due to the allure of high rewards but not exclusively so. Technical analysis remains paramount; by neglecting to cross-compare other analysis with the price-chart, one can fall into the all-too-common trap of buying an overvalued masternode. The closer to the lows you buy your masternode, the more effectively you hedge against potential devaluation, which protects your initial investment.
  2. Ignoring inflation: It might seem lucrative to disregard basic economics in the pursuit of high rewards, but five-digit percentage annual ROI is almost always likely to end up a loser, despite any early gains. The masternodes are usually incredibly expensive and devalue exponentially due to the immense, incessant increase in supply.
  3. Downloading unsafe wallets: This one hits home, as it is a mistake of mine; made in the pursuit of high profitability, to my despair. There will be times when new or less-established coins will come into your cross-hairs. Those unfamiliar with security procedures will unwittingly download potentially dangerous wallets. Refrain from this as much as you can. If you must download a wallet for a lesser-known project, scan the file for viruses prior to downloading and post-download. Further, run them in sandboxes or on Virtual Machines where possible. For further security advice, consult @notsofast’s article here.

I hope this post has been somewhat insightful, and I am happy to answer any questions in the Comments section. Further, what has worked for you in your own masternode strategies? I’d love to hear any thoughts and ideas.

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Disclaimer: This post references an opinion and is for information purposes only. It is not intended to be investment advice. Seek a duly licensed professional for investment advice.