Short VIX Strategy: $VXX, $UVXY, $TVIX

Appended below are the charts of the 5 year, 1 year, and 3 months for the VIX index, as well as the ETFs derived from it:

• $VXX
• $UVXY
• $TVIX

Just absolutely remarkable how predictable the long-term downward sloping trend is. Even though I don’t place too much emphasis on Technical Analysis, I don’t think it’s completely useless if proper understanding of its limitations are noted. Here, this trend is indicative on the very construction of these ETFs and the continuous rolling of the nearest expiration futures. Much is written elsewhere on these ETFs, for example, the persistent contango most of the time or the more hard-to-borrow nature of $TVIX.

The strategy I’m pursuing in addition to my core strategy of selling credit spreads on high implied volatility options is to maintain a long-term short on these positions and pile on a larger position on spikes up.

I plan to write more in depth about the overall strategy, the specific trading plan, and a bit on each ETF when I have more time. Currently, it’s a tradeoff between allocating limited time to knowledge acquisition and analysis, and writing here, and the writing is of secondary importance as of now.

$VXX appears to be the best candidate now since there are plenty of shares to borrow through my broker, Interactive Brokers, and the borrowing rate is short loan interest rate is currently pretty low ~3% for VXX vs. ~16% for $UVXY. $TVIX is around 7%, but the shares are not available and need to be located. Furthermore, margin requirements are more favorable for $VXX, there’s more room for the downside (with some assumptions of no reverse split and such), and I’d like to test the waters out with a less volatility version.

I view it as similar to a snowboarding race down an infinite huge mountain where you collect hundred dollar bills along the way. Although you can collect more bills faster by going faster (larger positions), there exist jagged rocks (up spikes) where any fall can injure you severely or could kill you (margin call), so you simply must not go too fast (position size vs. margin) at all times.

I’m very excited thinking of my future portfolio, which can be scaled nicely.

1. $XXII
   Maintain core position in $XXII as my long equity position where I’m currently up 90%.
2. Core Options Strategy
   Continue my current short term options trading across the most liquid S&P500 options displaying the highest implied volatility.
3. Short $VXX
   Maintain a continuous short position in $VXX, while shorting heavily on up spikes.

5 Year – $VIX

5 Year – $VXX

5 Year – $UVXY

5 Year – $TVIX

1 Year – $VIX

1 Year – $VXX

1 Year – $UVXY

1 Year – $TVIX

3 Month – $VIX

3 Month – $VXX

3 Month – $UVXY

3 Month – $TVIX

 

Consistency in Options Trading

I was looking at my stats for running on DailyMile and books I’ve finished (Google Sheet link), and was thinking that this also exactly what I will strive for in my options trading and portfolio growth.

Repeatable and consistent. Scalable and persistent. No crazy shots. The extraordinary emerges from the mundane and ordinary.

There is great power to consistency built into the plan, and it’s a perfect match for my options trading plan that’s so oriented in statistics, probability, and expectation over a large number of trials.

For example, I don’t question, ponder about, and certainly don’t like all the days I run, I just stick to the plan and I just do it. Another example concerns keeping good records, I know it’s been 1,987 days since August 13, 2012 and I’ve finished off 401 books since then for a rate of 4.96 days per book.

Trailing four weeks of running from DailyMile

List of books finished since August 13, 2012

 

M&A Options Trades

Reading through this 117-page paper about unusual options volumes prior to impending M&A announcements.

Informed Options Trading Prior to M&A Announcements: Insider Trading?
Patrick Augustin, Menachem Brenner, and Marti G. Subrahmanyam

Abstract
“We document pervasive informed trading activity in equity options before M&A announcements. About 25% of takeovers have positive abnormal volumes. These volume patterns indicate that informed traders are likely using bullish directional strategies for the target and volatility strategies for the acquirer. We show that this abnormal activity cannot be explained by deal predictability, speculation, news and rumors, trading of corporate insiders, or leakage in the stock market. While the SEC litigates only about 7% of deals in our sample, the characteristics of illegal option trades before M&A announcements they prosecute closely resemble the documented patterns of unusual options activity.”

I want a baseline of profitable trades, and allocate something like 20% of my options-trading portfolio to discrete events such as earnings trades and M&A. In addition to my main bread-and-butter strategy of selling options spreads, I’m thinking of earnings announcements as secondary strategy that last for 1-2 days each, and M&A-based options strategies like this as opportunistic one-offs. It’s nice to think of how a low probability trade may return some amount $X such that the expectation is greater than what it is now with the rest of my baseline options trading. The original impetus was this news article about how a trader made $2.4 million in 28 minutes.

Many things to consider, but the task at hand is to absorb this paper and an arbitrary number of 10 related papers to ramp up, and learn a bit more about unusual options activity in general and what they mean. Next step after that is to write code that detects unusual activity activity in the options chains of a watchlist of stocks, enhanced by a general awareness of potential big acquisitions.

Closing trades: $UNH and $XXII

Two closing trades planned for tomorrow, assuming the market doesn’t move against me on open. Quick preliminary and general thoughts on optimization vs. maximization with an analogy to poker, then my thought process for these specific trades.

It’s definitely possible to achieve an excellent performance X of rate of return on capital by sticking to a baseline game plan that optimizes across “all” trades, but thinking things through and trying to maximize for the specific context can potentially achieve 2X to 10X. It’s sorta like playing a tight-aggressive bread-and-butter game in poker can fetch the money at rate Y, but tailoring your strategy to a more loose-aggressive game accounting for specific opponent personalities can enhance your win rate massively to 2*Y or 10*Y. What’s important is the “meta”-strategy of choosing between discretionary vs. systematic strategies. I suspect though that at the highest levels, there’s an element of discretion in systematicness, and vice versa.

1. $UNH March 230/220 Put Credit spread for $400

$UNH Put Credit Spread: 2018 March 220/210

Started only 3 trading days ago(!) on Tuesday, 1/16 after a good earnings announcement and the trade already reached the usual 50% profit target. Will be closing this out and re-opening another same March expiration closer to the money for more premium. Absolutely no-brainer trade. The plan is to repeat this same trade trailing beneath the stock price for as long as it goes up, and it’s been going up for over a year now. If I could do this every week, it’ll be $20,000 just on this single Put credit spread position, lol. If there’s no clear trend, I’ll simply stop this trade and wait for a better opportunity.

Some thoughts on maximization to try to get an even larger profit even faster is to veer from the usual bread-and-butter optimization via the following.
A. Sell closer to the current stock price at like 50 Delta (50% probability of success according to pricing model) instead of 30 Delta, the usual target 70% probability of winning is is similar to the probability of “not” hitting the flow with any two non-paired cards in no limit Texas hold’em
B. Sell a Put credit spread “above” the market, since I’m super bullish on it.
C. Buy a Call Debit spread
D. Can also larger position size.
Ultimately, I should just stick with my standard plan for now before being more creative for a few dozen more trades. This is already printing money as it is, since 13.3% return of $400 on $3000 of “max” risk in 3 days is a pretty good trade… but always be thinking of improving the reward to risk and the rate of capital accumulation.

2. $XXII April $3 20 naked Puts

$XXII Naked Put: April $3 Strike

Obviously an easy winner, but I’m breaking the 50% target profit rule since I believe I can do vastly better, even accounting for a margin of error in my assessment, because of the following reasons.

The margin collateral here is about $4,400 to make $1,100 in 3 months, which is $250 per $1000, or only $83.33 per $1000 per month. Still an annualized ~100% return, but compared to the $UNH trade above, it appears to be a very bad use of capital indeed. Even if it can get 10% the $UNH capital efficiency performance for that $4,400 margin collateral, it would still make this current trade really bad.

For further context, the vastly decreased price of these puts came from the sharp anomalous $0.50 rise in $XXII stock price today, so the current return may be much worse if the underlying $XXII price drops and and these options prices decays at Theta rates instead of a today’s sharp rate from Delta and Gamma.

Trading Thesis Formulation: Earnings Announcements

Just finished two very illuminating books on trading around corporate earnings events:

• John Shon’s – Trading on Corporate Earnings News
• John Shon, Ping Zhou – Options Strategies for Earnings Announcements

There’s a seemingly endless amount to research and to write ranging the theory and core rationales to the practical trading and nuanced points, and I certainly plan to treat each of those much more deeply in future posts. The purpose of this post is to start to articulate to myself as I formulate my thesis on trading options spreads and combos around corporate earnings events. This constitutes my first shot as I prepare for next quarter’s earnings trade.  All ideas here are subject to refinement or outright discarding as I gain more experience and knowledge.

1. No directional assumptions

Interestingly, predicting a positive or negative earnings surprise is only half the battle, the other half is in predicting the market’s reaction to that surprise. A large body of research has shown that even if you can perfectly predict a surprise or even the magnitude of the surprise, the market’s reaction to that surprise is at best a 60%/40% probability proposition, as in positive surprises lead to upward stock movement and negative surprises lead to downward stock movement. As a trader it’s the market’s reaction that’ll determine your profit and loss. Rather than try to expend much time and energy on either prediction or reaction to the earnings, I’m simply going to take a the view that it’s unknown, and even unknowable.

2. Net short positions post-announcement

Although it is indeed possible to profit via taking a long position, either by an outright buy of a call or put for directional assumptions or a strangle or straddle for a non-directional play, it’s difficult to consistently profit since the heightened levels of implied volatility embedded into the options makes their prices inordinately expensive, at least for the magnitude and rate of movement of the underlying movement.

In effect, you’re fighting both the priced enhanced volatility in the trade entry, as well as the implied volatility collapse. Better to take the other side of the trade so that you benefit from the intake of the credit and the implied volatility collapse. Furthermore, in the rare occasion the underlying goes far beyond the breakeven points of a net short strangle/straddle (or their risk-defined cousins, the iron condor or butterfly). you can always roll the untested side closer to the money for an additional credit.

3. Net long positions pre-announcement

There may certainly be ways to profit from the increase of implied volatility and options prices leading up to the announcement, but I’ll need to do much more research and am avoiding these for now. I must admit that I’ve been pondering how cool it’ll be to benefit from the rise in implied volatility via a long position pre-announcement of earnings via a calendar spread or a long non-directional combo (long straddle/strangle/condor/butterfly), and then immediately take the other side in a short position post-announcement on those non-directional combos.

Furthermore, I there’s the issue of Theta time decay being an effect of relatively similar magnitude, especially when options are traded closer to expiration for these earnings trades. There’s even an additional tradeoff here that options traded further away in expirations that don’t see as much theta decay also won’t see the increase in implied volatility. These optimizations and competing rates seem to be too difficult at best and perhaps even impossible to precisely account for, WRT to real world trading realities like transaction costs and bid-ask spreads.

4. Liquidity: Volume and Bid-Ask Spreads

Only target highly liquid options chains with high volume and tight bid-ask spreads, which are inversely correlated. Nothing necessarily new here, just that it’s extra important to be mindful since these earnings announcement trades probably work best on the order of 1-3 days only and so the trade will incur the rountrip entry and exit pricing of the bid-ask spread very quickly and it may constitute a large part of the tactics of the trade.

5. Additional Issues and Questions

Will need to do research on the following miscellaneous issues and questions to further refine my gameplan.

1. What were average signed (positive and negatives) and unsigned (magnitude only) earnings surprise in previous quarters?
   • To what extent could this be a first filter?
2. What was the market reaction in terms of average price move for previous announcements?
   • And how does this correlated with the premiums that can be received in the selling of the spread?
3. What is the correlation between earnings in terms of positive-to-positive, negative-to-positive, and switched?
   • Could I benefit from a slight directional tilt, if not an outright directional assumption provided strong evidence
4. Which options chains saw the best run up in implied volatility?
   • Must be balanced by liquidity concerns
5. What is the risk-reward profile of the slightly out-of-the-money iron condors vs. at-the-money butterflies?
   • Really a balance between premium received and probability of success
   • Also very important: margin requirement vs. premium received for most efficient capital usage
6. To what extent should I consider undefined risk spreads: strangles and straddles?
   • Unless there’s a violent move, can always roll the untested side closer to the money. However, always a possibility of a black swan event to wipe out profits from lots of trades.
7. Is the time and effort better spent on a greater number of earnings trades with smaller position sizes vs. a fewer number with larger position sizes?
   • Lots of effort to scan, analyze, enter, manage, and exit a trade, but highly amenable to writing code for that
8. Are there any advantages between weeklies or front month options?
   • Probably liquidity?
9. When is the best time to enter and exit?
   • Base assumption is the day before the announcement for entry and the end of the day of or day after for exit.
10. How will this interfere with the current options spreads I have on the couple of dozen securities?
    • Should this focus more on underlyings and options chains that aren’t in my core positions? Probably doesn’t matter if the core positions are at a much different timeframe such as 30-60 days, while these earnings trades utilize weeklies or close-to-expiration options
11. To what extent will this replace or enhance the trading of the core monthly to bi-monthly timeframe
    • Consider return on investment and capital efficiency – these earnings trades last on a very small timeframe with very high proportional returns, and so capital can be recycled through at a much higher rate: 1 day vs. 60 days!

…So this represents a good start. I’ll plan to refine each of these issues in the future.

January 2017 Core Positions

My core options positions are based on the movement of the following underlyings: $BA, $UNH, $UPS, $SPY, $LRCX, $JNJ, $CAT, $ABBV, $VXX. I have a few range bound positions, but since they take a while to realize a profit due to collapse from the core strategy of collapse of implied volatility and Theta (time decay), the plan for the coming weeks is to stick to these clear directional plays since options tied to them realize profit faster and are more capital efficient as I close positions at the 50% profit target level and re-open others closer to the money for the current month or the next month.

$BA

$UNH

$SPY

$UPS

$LRCX

$JNJ

$CAT

$ABBV

$VXX

Two Risk Mistakes: Relative and Absolute

Two trades I’m planning to close this week for a profit provide a great lesson in risk management and two big mistakes I made: one relative and one absolute.

1. Relative mistake: Reward vs risk profile

$UNH (United Healthcare) and $BA (Boeing Aircraft) have similar reward profiles (stand to gain half of about $675 premium) but $UNH has 2x as much risk since strike price width of $10 in the credit spreads mean I can lose $1,000/spread vs half that at $500 for the $BA spread.

2. Absolute mistake: Total risk

I sold 3 Bull Put spreads of both $BA and $UNH. This means that I could lose up to $1,500 for $BA and $3,000 for $UNH for these positions. $UNH is pushing the limits of the risk I feel comfortable with since it is more than the target 1%-2% risk of my margin.

I will close out $UNH very soon even though it’s not quite past the standard 50% target profit level, for the specific purpose of risk mitigation.

$UNH Feb 220/210 Bull Put Credit Spread

$BA Feb 320/315 Bull Put Credit Spread

Winning is great, but it’s dangerous too since it may blind you to the risk that’s always present. Losing $10k in winnings is the same as losing $10k in starting money. In general, it’s crucial to always be super critical in self-assessment for the “long-term” success. There have been far greater sums made than me that were all lost in due to overconfidence and ignorance of or lack of respect for risk.

Keep Trading Simple!

Just finished this Rhoads book – Trading VIX derivatives. It’s a great academic and descriptive presentation, but I have a feeling it’s too much nuance and complexity for actual trrading.

From my experience in poker and trading so far, theory is usually overcomplicated, and reality only requires that you do a few simple correctly and well. If you do those core things correctly, the extra complexity is irrelevant since you’ve covered like 90% and everything else is just optimizing. If you don’t do those core things correctly, any slick fancy stuff will not permit you to overcome the deficit in the core game plan. In other words, it’s not the theory or elaborate plan, it’s simply the execution. Granted, knowing what to do can be very hard, especially when real money and emotions are involved.

In poker, I’ve read some detailed game theory analyses about delayed semi-bluff check-raises on the turn or river, based on carefully observing your opponent, even what he thinks you think he thinks you think, LOL! In reality, you don’t need any of that stuff. Just play tight, play more hands when in later position, don’t bluff too much, and don’t play poker when tired/sleepy/angry/hungry/drunk/lonely. Just try to maintain your B-game. No A-game is required to print cash.

Similarly, I’m taking this approach to trading options. There are crazy spreads like Diagonal spreads where you’re doing a spread across different strike prices on different months, using all sorts of sophisticated indicators. Everything has to be analyzed and worked out perfect according to your plan for such complexity to work. It’s much more important (and profitable I’m finding) to have a simple and robust plan that works in all conditions that doesn’t require you to analyze perfect or think hard, and has enormous room for error.

In reality, I take the view that I should just simply do the following. The plan is simple, but the execution is difficult since it requires a bit of “feel” on the one hand, but strict adherence to trading rules on the other hand. Here are my rules:

1. Position-sizing and risk management
Keep position size small to about 2%-3% of your trading capital, and never go above a max 5% in any single position. (I have a lot more to write here about the Kelly Criterion in mathematical gambling theory and information theory about how to get the most explosive growth for a given edge and odds, about how I believe it’s too academic since it ignores human emotion.)

2. Keep credit spreads simple. 
I use 3 credit spreads: Bull Put credit spread for when I have a bullish belief, Bear Call credit spread when I have a bearish belief, and those a combination of both when I believe it’s range-bound. When I look at the chart, if I can see a very obvious trend within 0.5 seconds that is trending up or down, I sell a Bull Put credit or Bear Call credit spread, respectively. If there is even any doubt or it takes longer than 0.5 seconds, I take an agnostic view towards the future direction.

3. Keep indicators simple
I’m currently using only a single indicator: the ranking of Implied Volatility over the trailing year to pick filter candidates. I think there are many enhancements to indicator and much deeper analyses, but I think this extreme simplicity may be enough to make create a satisfactory return in the near term. Once I have more free time and capital, I can strategize how to maximize earnings even more.

4. Liquidity
On top of all this, which is probably the hardest part, are proper trade mechanics and processes. Trade liquid markets. Lac liquidity can annihilate even the best laid out plans.

…I suppose this plan is not too simple, but it’s as simple as it can be. There are many refinements for optimization, but this is good enough for now.

Paradoxically, Perfect Market Efficiency Implies Its Very Opposite

“The opposite of a true statement is a false statement. The opposite of a deep truth is another deep truth.” – Niels Bohr

The debate rages on concerning whether markets are efficient or not. Exploring the academic complexities and nuances of Market Efficiency would be a major research effort in itself, and even to create well-posed testable hypotheses leading to statistical significant conclusions or even to define what terms mean may be difficult. For example, what constitutes exploitable profitable trading strategies at various timeframes in markets that act according to the weak/semi-strong/strong form of the Efficient Markets Hypothesis?

For the purposes of this post, I will define perfect Market Efficiency as simply a state where all market participants can perfectly price securities using quantitative models of the world based on qualitative assumptions congruent with reality, and are furthermore those market participants have equal capability to intake, process, and act on information to arbitrage and capture market inefficiencies, without such practical disadvantages as transaction costs. Perfect market efficiency is usually associated with the belief that a trader cannot gain a net profitable advantage, but I believe these are distinct independent ideas. I will focus on the applicability to options, in particular the time and volatility premium as priced into options.

Let’s start first with an interesting paradox. How do securities prices move? Are those prices predictable or not? Let’s assume that they indeed are not predictable and move due to randomness. Interestingly, it is this very characteristic of unpredictability and randomness that permits the modeling of the prices according to geometric Brownian-motion, most relevantly as we’ll explore in this post, the Black-Scholes options pricing model. If price movements were indeed “not” random, they would not be able to be modeled, forget “accurately” modeled to such precision with a mathematical model that assumes mathematical idea as geometric Brownian motion.

Let’s assume now that the Black-Scholes options pricing model and its modifications for various market realities like dividends, stock splits, interest rates, discontinuous price jumps, and even more subtle things like constant volatility, are indeed capable of modeling reality perfectly. We’ll also assume that the model can correctly account for extreme events at the tails, which is effectively impossible since there will always be one-off unique unpredictable events in the real world (e.g. CEO gets incapacitated by an escaped chimpanzee from the zoo), but let’s just assume this is not an issue and the markets are behaved for this discussion. For practical context, let’s assume options pricing models are sufficiently robust to model options prices and that outlier events do not happen, at least in the timeframe of a typical options expiration cycle.

The core issue I see is that, although mathematical/statistical models rightfully assume that time is continuous and securities prices evolve in continuous time intervals, this is not the case practically speaking in the real world trading environment.

Paradoxically, the power of an accurate and robust pricing model of the real world is the very reason for trading opportunities, due to the fact that trading timeframes practically are discontinuous and the implications of this fact. For a normal trading week, options trade between 09:30 and 16:00 for 6.5 hours per trading day for 5 days, and do not trading for the other 17.5 hours. Weekends introduce a a 65.5-hour discontinuity from Friday  at 16:00 to Monday at 09:30. Despite the pervasiveness and utility of pricing models that use continuous mathematical models, trading for only 32.5 hours (19.3%) of a 168-hour week, and do not trade for the other 135.5 hours (80.7%) of that week.

This leads us to the curious situation where either the following may occur

1. Pricing models must work unimpededly during the market off hours
2. Off-hours get compressed into the trading hours, with a transitional period to account for an effective jump during the transition from the close of one trading session to the open of the subsequent trading session

How would a savvy trader exploit this? One way would be to always be net short options at closing time on Friday, and take advantage of the extra couple of days of free time decay of the options positions. Note that the prices of options have embedded in them a time premium that erodes, and at an accelerating pace, as time goes on.

Technically, this would also assume that the extra risks of shorting relative to buying options are negligible since, for example, if a large geopolitical event were to cause market underlying prices to crash, the options shortseller would assume extra margin risks as the underlying moves against them, whereas a buyer is only subjected to the risk of the capital already spent for buying), but for the sake of argument let’s assume these are negligible.

However, since we’re assuming perfect models and perfect markets with aware participants, those on the other side of the trade would know that there’d be this extra time decay. To protect themselves, they would adjust accordingly by dialing back options prices so that they do not suffer from the extra time decay over the weekend, and thereby pricing as if there were effectively no weekend.

Paradoxically, this very market efficiency and accuracy of pricing models lead to an efficiency and trading opportunity. How?

Implied volatility values as expressed by options prices will be artificially low at the close each day as it deflates to account for the anticipated off-time trading, and will be artificially high the next trading day for the same off-time. This effect will be accentuated on weekends relative to intraday weekdays since there is more time involved, and the orderly artificial deflation towards the end of the day and furthermore, the orderly artificial inflation as the next trading day opens will proceed at an increased rate!

Practically, how would a trader structure trades around this phenomenon? There can be two stages:

1. Deflation of implied volatility
During the deflation phase, options prices will decrease from towards the end of the trading day. In this case, you’d want to tend to short options and capture a much faster rate of artificial implied volatility collapse. This effect would be more pronounced on Fridays compared to the rest of the weekdays since there will be more off-time.

2. Re-inflation of implied volatility
The other stage is the re-inflation phase at the beginning of the next trading day.
In this case, you’d want to be net long options and capture a much faster rate of artificial implied volatility re-inflation.

So, paradoxically, it is the very utility of the options pricing models in particular and the efficiency of the markets in general that provides this trading opportunity! Whether markets are efficient or not is an ill-defined theoretical issue at best and is usually irrelevant practically. Here, these trading opportunities involving artificial deflation and re-inflation of options prices are only possible because of the predictable phenomenon that arises from efficient markets. Whereas if the markets were not efficient, these trading opportunities would not exist.

It seems then that the issue of market inefficiency is one that is irrelevant to whether it’s possible to profit from a particular trading strategy or not. Practically speaking, there are many other issues such as risk management and position sizing or selection of specific markets or instruments that may take precedence in importance relative to a particular trading strategy.

My practical tasks now are to research which underlying securities have an options chain that exhibit this phenomenon most clearly and have the liquidity for it to be practical tradeable so as to overcome the liquidity and transaction costs in the form of bid-ask spreads, slippage, and commissions. My core trading strategy will still be at the timeframe of 45-60 days holding for roughly 2/3 to 1/2 of that time to get to the 50% profit target, but I’d like to use the ideas here to enhance returns at the weekly level around going into and emerging from the weekends.

Crypto Markets: Some thoughts before entering

I plan to enter the cryptocurrency market in some capacity, but need to figure out how I should allocate my capital, time, and knowledge acquisition in the following for the best long-term outcomes.

1. Local mining
Buying some hardware and plugging it into my shared office space where I get free electricity (no variable costs), but high fixed cost risk in buying the hardware miner(s). 

2. Cloud mining 
Fixed costs and varying costs are bundled and benefit from economies of scale.
You can buy some fixed amount of hashes worth of computing power, usually measured in terms of terahashes, via fiat or crypto. Terahashes allow you to get some return depending on crypto mined (aka: algo used) via a pool. You can cash out via crypto or fiat, but you chose to reinvest into more terahashes at rate at that time

3. Direct trading
Liquidity risk is primarily what I’m worried about since I don’t feel comfortable with a high 5-figure or 6-figure USD position in some shady exchange/broker. In a normal broker and exchange, I could exit instantly. There are other issues of secondary importance such as trading fees, regulation, outright crashing, etc.

I could just trade crypto directly using the shady exchanges, but there’s a lot of risk there. The extraordinary returns now are the compensation for extra risk and uncertainty, in this case the liquidity premium and overall future of cryptos respectively.

I’m thinking of this long-term as part of my portfolio while working as a software developer and starting up my automated options trading business. Pure speculation is great and necessary, but I take the view that you need either a steady job or have it be a small part of your overall portfolio.

If I can reduce risk by a lot while diminishing return by just a little, I’m OK with that.

4. Derivatives on cryptos
Trading financial derivatives in regulated markets: CME Bitcoin futures (my broker has them) right away, and soon, options on futures (my main competency in the markets is options trading), ETFs (stock-like), or options on ETFs

…

After doing a bit more research during the course of this blog post, my first impression is that being an individual miner is not sustainable unless:
1. you can benefit from massive economies of scale – in which case you become a data center yourself
2. the price of cryptos relentlessly increase without end

There are massive fixed costs in the latest computing equipment and the varying costs in electricity to run the computations and cool it all down.

It seems the main issue is that the fraction of your hashing rate (aka: mining power) compared to the entire system’s decreases exponentially since computing equipment increases exponentially in capability for a given dollar (fiat currency) value.

One interesting way to view this is that since computing hardware increases exponentially in power and the costs of electricity stays relatively stable, fixed costs (hardware) and varying costs (electricity) are actually switched now since now electricity acts more like a fixed cost and you need to buy new hardware just to keep up, and that acts more like a varying cost.