Tag Archives: Bitcoin

Intro to Cryprocurrency

This post is a bit of a quick rant on the basics of cryptocurrencies (crypto) and is just my opinions and not any advice on investing. It started as a response to someone that was interested in learning a bit of the basics of cryptocurrency. Then I got on a roll and covered a bit of ground. It does not contain many references to sources as I was working from memory of information that I have seen or heard.

Pros and Cons

One of the cons with cryptocurrencies is wading through the charlatans and hype out there. Scammers too, malware that tries to copy your crypto addresses as you cut and paste them, always visually confirm and never leak your private addresses in an unsecured environment, they are your money. hype and nonsense out there also applies to trading, investing and mining it. Lots of pros to it. But, it all really depends what you want to do with it. The options typically center around trading,investing,mining it and using it as a payment method. I’ll work through the list a bit and build my case for crypto as I move along. Mining a coin such as ETH ,is not profitable for most people at the moment, Currently electricity and equipment costs factor into this heavily. However this may change in the future as ETH will be moved from a proof of work to a proof of stake model (a bit much to explain in this intro, but if you go to the site name for the creator of Bitcoin, Nakamoto.com, there is a whole intro to cryptocurrency series of posts there), with ETH 2.0, along with other changes. Using it as a payment method is getting simpler every day, there are numerous apps for it, Cash App and Coinbase for example.  PayPal and Venmo will be integrating crypto purchases into there systems.

Fundamentals

What is going on that may cause prices to rise

People in Europe that work on the underpinnings of the banking system that allows money to move around under the hood, kind of like our ACH system here in the US are working with the folks who developed the crypto called LINK. This has leaked out, I saw the presentation where the guy giving it slipped and said the name that LINK uses in it’s code instead of the secret project name that they were using to cover up LINK’s involvement. These projects are typically secret with NDA’s and all but, human error and a slip of the tongue can happen. The US Gov’t, Treasury Dept. has hired the person that developed the stable coin called USDC for Coinbase. He worked for a bank prior to Coinbase, the name I can’t remember, but there is a picture of him with 2 other co workers that now work in the Treasury Dept. so he was most likely hired because he was the lead in developing the USDC stable coin for Coinbase. A stable coin is a coin that attempts to stay in lockstep with a fiat currency. USDC is always $1, some others ary a little bit around $1, like a range a few pennies up and down at most. Examples are Tether and DAI. On top the Gov’t reserach into an electronic coin, there was initial language in the draft for the stimulus package that was removed that reference disbursing payments using electronic coins developed by the US Gov’t. I saw copies of this, it was interesting to see a reference to an electronic coin in a gov’t doc. Also, large banks are starting to work to become custodians for crypto currencies as well, they are entering into agreements with exchanges to be the custodians of large amount of crypto. BTC futures are traded on the Chicago exchange (Chicago Mercantile Exchange), something the big boys in the investment banks are interested in. There is something called BAKKT that the investment banks are working on, I forgot the details but, it relates to cryptocurrencies Why does this matter? This is all a big pro. Because governments adopting blockchain technology, Investment banks are starting to use crypto as an investment vehicle and considering being custodians of it and integrating blockchain tech into transactions. All of this validates it’s existence, which means only more growth in the future as people (investors really and end users eventually) realize it is not some kind of fad and a toy for tech hobbyists. Remember the curve of other tech. The first home computers were also “toys” and now we can’t live without (several of them, think phones too) them, the same will happen with crypto currencies in time. As far as investing in it. A big pro is that it is a new asset class in the world. Every time a new asset class comes into being it makes for a great investment opportunity as new asset classes don’t some along often.

Cryptocurrencies as Technology

Trading and Investing

Cryptocurrencies behave a lot like a new technology in that there is an exponential growth curve which is reflected in the price. ETH could be bought for around $10 in 2016-17 and today it is worth over $300 with recent highs above $400. Weekly time-frame Ichimoku chart has a target at $750 within the next year most likely. Trading and investing are similar but, two different animals. A con with trading in general that does not occur as easy with investing is that it is easy to lose money trading for a lot of reasons, as a beginner. Most of them have less to do with proficiency and the mechanics of trading and more to do with emotional factors, especially how we view money itself. The market it a brutal but honest teacher at times and it will teach you how to look at your belief system. And, it is always your fault when you lose, the market never lies, only we can lie to ourselves. In the beginning I wish I would have spent a bit more time learning this versus spending most of my allotted learning time on technical analysis. I have found quite a few good time tested resources on the trading topic if you are interested. Always start with a few hundred $ and work up slowly until the charts stop looking like just a bunch of lines and start to intuitively make sense and your heart no longer drops every time the prices go red. It’s as if you have to develop a Zen or Taoist attitude to the market. Start with something really simple for a trading rule and stick to it. The more simple and boring the better until you get experience. And, on a longer time frame, daily and weekly charts, not less than hourly charts to start. In the limit as time –> 0 the charts become just noise which can cause confusion at best. Crypto has much more volatility than many other asset classes, so more noise there too. Especially the small cap coins, up 100% one day, down the next and as usual it takes double the gain to make up for a loss of half, so losses mount fast in downward price shocks. BTC and ETH, being large cap are less volatile than the rest, about 3000 of them, some of which are a joke (literally DOGE and Siraj coin are memes) and really have no future at all, Big pro. Investing for the long haul is one of the easiest easy options to start that can even be combined with trading/active investing. One of the best methods to invest in an asset with exponential growth is to accumulate the asset when the price is below the 1 year to 2 year average price and then distribute out at a price that is a multiple (2,3,5) of the 1-2 year moving average. Golden Ratio Multiplier Chart. This is an almost foolproof way to actively invest and could be used to dollar cost average in at the best times. Feel free to reach out to me for any more and research. I would highly recommend listening the Lex Fridman podcast where he interviews Vitalik Buterin the co-creator of ETH to get a flavor of the mind of who is behind it.

BTC Gains

BTC versus Stock Bull Run

Stock Bull Run
Stock Bull Run to Bust, 2009-2020

This post is an addition to the series on DMAC algorithm trading, which is simple algo trading example. I was wondering now that the S&P500 and etc, legacy market has dropped out of it’s long bull run, how would Bitcoin gains compare against the stock market?

First Post in Series to Learn More Background

Code to run this model on Github

BTC Buy and Hold -v- S&P 500 Buy and Hold

The plan: Comparing  Bitcoin (BTC) gains from January 1,2010 to December 31,2019 against the bottom for stocks on March 9, 2009 to Feb 19,2020, the 400% bull run for the S&P 500. But, for some reason the Yahoo data for Bitcoin is only going back to 2014-09-16. So we are basically getting a bit short changed here on BTC and giving the S&P a long head start for nearly 5 years. (I am sure at one point in the past, I think this used to work and go back all the way to 2010 but Yahoo must have cutoff the data since I first tried this code.)

So with 5 years head start for S&P

S&P500 4x gain across history shown in the above chart 03-09-2009 to 2-19-2020

BTC 16.96x gain from 9-16-2014 to 2-19-2020

Now for a true side by side comparison

S&P 500 on 9-16-2014 closed at 1998.98

S&P 500 on 2-19-2020 closed at 3386.15

S&P 500 gain 1.69x from 9-16-2014 to 2-19-2020

So Bitcoin beats the S&P using by and hold across the same time frame by 10x.

Buy and Hold -v- DMAC Algo Trading on BTC

OK, now what about BTC HODL (Buy and Hold) gains versus optimized DMAC algorithm trading from 9-16-2014 to 2-19-2020?

HODL Gains: 16.96393901373665

Algo gains for a 1 day short and 4 day long average. This is wicked tight and it will trade like mad, so in real life, the transaction fees and slippage would eat away at this number after 10 years.

Short AVG,Long AVG,Best Bank 1 4 2244962.564086914 which is 22.4x

which is 22.4x gains. But, with fees/slippage  and HODL at ~17x , HODL is really great. Beats the 4x or 1.69x depending on the time frame of the S&P 500!

It could do better

This is a simple algorithm, one of the simplest ones that can be thought of. Also, remember that the algorithm only tuned once across the entire time period. If the time  period was cut up into smaller periods and the algorithm learned the market as it changed over time instead of trying to get a best fit across 5 years it would have produced more gains. The example the initial post which has it trade from Jan 1 to Dec 31 of 2019 shows in making 900K alone in one year. So an algorithm that could in theory re-optimize periodically as the market conditions change would easily beat the results of this simple algorithm. The best thing that I can think of would be a machine learning algorithm that would use differential evolution to change the parameters as time goes on. It would learn on an ensemble of random sections of random lengths of data from the past and tune the algorithm’s parameters based on learning from the data ensembles. That is just one way that comes to mind.

But, at the end of the day this is just an example. It is not always possible to predict future results from past data. It is also well known and Covid-19, The Great Recession and Black Monday in 1987 serve as examples, there will be price shocks that can upend any model in practice. The statistics of price action do not fall neatly under a bell curve but have fat tails that lead to excursions far away from the mean on occasion. One only has to look to history for this lesson and one example is the firm Long-Term Capital Management (LTCM) led by a brilliant team of folks including Nobel prize winners Merton and Scholes, two out of the men that invented the Black-Scholes option pricing formula. Fischer Black, the other man was not alive at the time that the prize was awarded. Some credit for  the formula rests with Ed Thorp as well, at least a way in the backstory. Ed Thorp had basically the same kind of formula running on convertible bonds, not a far jump to options pricing. This is all outlined in the great book The Quants by Scott Patterson. Quants was one of the seeds that led me to pursue trading, specifically algorithmic trading a few years after I read it.

Lessons To Be Learned

So there were brilliant guys were at the heart of LTCM but, due to high leverage and a divergence of their models from reality when the Russian government defaulted on their debt, the firm crashed and burned financially. It happened fast as they and others holding similar positions in the market all simultaneously unwound those positions. LTCM was stuck holding a lot of positions trying to offload into a market without many buyers, low liquidity, and the worst part of it, being leveraged heavily 30-40:1 was the real problem. They were effectively running backwards selling off, what normally one would not want to be forced to sell off, being highly leveraged it quickly ran out of margin and went effectively bankrupt. The pieces of LTCM were bought up by 13 banks and this averted a disaster that  could have been equal to 2008 if the banks had not carved up the LTCM carcass among themselves. It could have easily had a domino effect on the rest of the banks and the global market much like what happened in 2008. LTCM as brilliant as it began, ended as a good lesson for the future, if only heeded and rocked the markets for a while and the shock cascaded. Then the lesson seems to get lost in general, although I imagine some took it to heart, in general  history repeated itself about a decade later in terms of the next financial crisis which was the same thing only an order of magnitude larger. 2008 was not containable by some short term heroics between the Fed and 13 banks as in the case of LTCM.

https://www.pbs.org/wgbh/nova/transcripts/2704stockmarket.html

https://www.pbs.org/wgbh/nova/stockmarket/

https://www.pbs.org/wgbh/pages/frontline/warning/themes/ltcm.html

 

BTC Algo Traded Using DMAC algo
BTC Algo Traded Using DMAC algo.
Bitcoin Buy and Hold: How much was made?
Bitcoin Buy and Hold: How much $ was made? 100K to 2.2M
Maximum draw-down for the algo for the time period.
Maximum draw-down for the algo for the time period.

Raw Dump

(base) erick@OptiPlex-790 ~/python/simple-strat $ python simple-strat-loop-backtest-2010-2019.py
Get Data
Run Model
Short AVG,Long AVG,Best Bank 1 1 100000.0
Short AVG,Long AVG,Best Bank 1 2 1495120.7946777344
Short AVG,Long AVG,Best Bank 1 3 1845316.8991088867
Short AVG,Long AVG,Best Bank 1 4 2244962.564086914
 signal short_mavg long_mavg positions
Date 
2014-09-16 0.0 457.334015 457.334015 NaN
2014-09-17 0.0 424.440002 440.887009 0.0
2014-09-18 0.0 394.795990 425.523336 0.0
2014-09-19 0.0 408.903992 421.368500 0.0
2014-09-20 0.0 398.821014 406.740250 0.0
2014-09-21 1.0 402.152008 401.168251 1.0
2014-09-22 1.0 435.790985 411.417000 0.0
2014-09-23 1.0 423.204987 414.992249 0.0
2014-09-24 0.0 411.574005 418.180496 -1.0
2014-09-25 0.0 404.424988 418.748741 0.0
2014-09-26 0.0 399.519989 409.680992 0.0
2014-09-27 0.0 377.181000 398.174995 0.0
2014-09-28 0.0 375.467010 389.148247 0.0
2014-09-29 1.0 386.944000 384.778000 1.0
2014-09-30 1.0 383.614990 380.801750 0.0
2014-10-01 0.0 375.071991 380.274498 -1.0
2014-10-02 0.0 359.511993 376.285744 0.0
2014-10-03 0.0 328.865997 361.766243 0.0
2014-10-04 0.0 320.510010 345.989998 0.0
2014-10-05 0.0 330.079010 334.741753 0.0
2014-10-06 1.0 336.187012 328.910507 1.0
2014-10-07 1.0 352.940002 334.929008 0.0
2014-10-08 1.0 365.026001 346.058006 0.0
2014-10-09 1.0 361.562012 353.928757 0.0
2014-10-10 1.0 362.299011 360.456757 0.0
2014-10-11 1.0 378.549011 366.859009 0.0
2014-10-12 1.0 390.414001 373.206009 0.0
2014-10-13 1.0 400.869995 383.033005 0.0
2014-10-14 1.0 394.773010 391.151505 0.0
2014-10-15 0.0 382.556000 392.153252 -1.0
... ... ... ... ...
2019-12-03 0.0 7320.145508 7409.014038 0.0
2019-12-04 0.0 7252.034668 7329.615234 0.0
2019-12-05 1.0 7448.307617 7335.619019 1.0
2019-12-06 1.0 7546.996582 7391.871094 0.0
2019-12-07 1.0 7556.237793 7450.894165 0.0
2019-12-08 1.0 7564.345215 7528.971802 0.0
2019-12-09 0.0 7400.899414 7517.119751 -1.0
2019-12-10 0.0 7278.119629 7449.900513 0.0
2019-12-11 0.0 7217.427246 7365.197876 0.0
2019-12-12 0.0 7243.134277 7284.895142 0.0
2019-12-13 1.0 7269.684570 7252.091431 1.0
2019-12-14 0.0 7124.673828 7213.729980 -1.0
2019-12-15 0.0 7152.301758 7197.448608 0.0
2019-12-16 0.0 6932.480469 7119.785156 0.0
2019-12-17 0.0 6640.515137 6962.492798 0.0
2019-12-18 1.0 7276.802734 7000.525024 1.0
2019-12-19 1.0 7202.844238 7013.160645 0.0
2019-12-20 1.0 7218.816406 7084.744629 0.0
2019-12-21 0.0 7191.158691 7222.405518 -1.0
2019-12-22 1.0 7511.588867 7281.102051 1.0
2019-12-23 1.0 7355.628418 7319.298096 0.0
2019-12-24 0.0 7322.532227 7345.227051 -1.0
2019-12-25 0.0 7275.155762 7366.226318 0.0
2019-12-26 0.0 7238.966797 7298.070801 0.0
2019-12-27 1.0 7290.088379 7281.685791 1.0
2019-12-28 1.0 7317.990234 7280.550293 0.0
2019-12-29 1.0 7422.652832 7317.424561 0.0
2019-12-30 0.0 7292.995117 7330.931641 -1.0
2019-12-31 0.0 7193.599121 7306.809326 0.0
2020-01-01 0.0 7200.174316 7277.355347 0.0

[1933 rows x 4 columns]
 BTC-USD holdings cash total returns
Date 
2014-09-16 0.0 0.0 100000.0 100000.0 NaN
2014-09-17 0.0 0.0 100000.0 100000.0 0.0
2014-09-18 0.0 0.0 100000.0 100000.0 0.0
2014-09-19 0.0 0.0 100000.0 100000.0 0.0
2014-09-20 0.0 0.0 100000.0 100000.0 0.0
 BTC-USD holdings cash total returns
Date 
2019-12-28 731799.023438 731799.023438 1.515663e+06 2.247462e+06 0.001243
2019-12-29 742265.283203 742265.283203 1.515663e+06 2.257928e+06 0.004657
2019-12-30 0.000000 0.000000 2.244963e+06 2.244963e+06 -0.005742
2019-12-31 0.000000 0.000000 2.244963e+06 2.244963e+06 0.000000
2020-01-01 0.000000 0.000000 2.244963e+06 2.244963e+06 0.000000
Portfolio Plot
Sharpe Ratio 1.5767602439593071
Compound Annual Growth Rate (CAGR) 0.7067353770154898
HODL Gains: 16.96393901373665

 

 

 

Crypto Compares Big Gains Q1 2019

Trading Resource Videos

There are many thousands of people who buy and sell stocks speculatively but the number of those who speculate profitably is small. As the public always is “in” the market to some extent, it follows that there are losses by the public all the time. The speculator’s deadly enemies are: Ignorance, greed, fear and hope. All the statute books in the world and all the rules of all the Exchanges on earth cannot eliminate these from the human animal. Accidents which knock carefully conceived plans skyhigh also are beyond regulation by bodies of coldblooded economists or warm-hearted philanthropists.– Reminiscences of a Stock Operator

 

Trading Resource Videos

The following post contains video resources. Some of this material especially the material from Francis Hunt and David Paul on the psychology and mindset of trading is quite important. It is best to learn about the psychology and mindset of trading while learning other typical trading topics such as technical analysis and fundamentals. It is just as important in the long run and having discipline and the right mindset goes a long way to preventing losses and maximizing gains over the long haul.

Channels

Josh Olszewicz: Crypto + Technical Analysis, Building your trading toolbelt

Josh Olszewicz produces market updates on a regular basis cover BTC and ETH primarily. Plenty to learn from and in my opinion, reliable insights.

https://www.youtube.com/channel/UC587BAG9cLTYtJ7Q4CqcOnw

Francis Hunt: The Crypto Sniper

Francis Hunt produces periodic crypto market updates and actually trades in legacy markets as well and provides useful insights from both realms and also currencies plus gold and silver.

https://www.youtube.com/channel/UCdC4a2KquFV1F4O21mW3K7g

Aswath Damodaran: Fundamentals and Value Investing in Stocks.

He is a professor, so get ready to study. Many videos on his channel. A few updates here and there but , most of it is in depth college level training on topics such as valuation and analyzing the fundamentals of companies. Good information for a background on the stock market. Plenty of videos to pick and choose from as you want to dive into a specific topic.

https://www.youtube.com/channel/UCLvnJL8htRR1T9cbSccaoVw

 

UKspreadbetting

Basics. Well done videos, most of them nice and short and covering specific topics. Everything that applies to spreadbetting can be applied to trading as well so don’t let the name fool you.

https://www.youtube.com/channel/UCnKPQUoCRb1Vu-qWwWituGQ

Trading 212

Basics. Watched a few videos off of this channel a bit of overlap with UKspreadbetting but short concise videos. Worth looking at.

https://www.youtube.com/channel/UCfWQuGYMfhZk9qq5x00sb9w


Series Worth Watching

Francis Hunt Interview Series on UK Spread Betting

Especially these two in the series are very important…
Trading Secrets of the Mind Master the Emotional Side of Trading
Improving the Mindset Game in our Trading

David Paul on UK Spread Betting

More David Paul Videos

 

https://www.youtube.com/watch?v=MGglyvc8d58&list=WL&index=113&t=0s

Subject: David Paul Tips from video

Asset above 89 day MA. Market above 21 day MA And rising , will get hit rate to 80 Probability matrix Pattern to finesse entry . 1-2 pct at risk per trade. Plan and repeated perfect execution. Build neural pathways after 8-30 trades…….. William O’Neil How to make money in stocks……from The Psychology of trading and investing

Audio book: Trading For a Living, Psychology,Trading Tactics, Money Management

 

Jim Simons

Not trading information specifically but, he is a legend. Mathematician, code breaker  turned trading genius. He co-founded Renaissance Technologies in East Setauket, New York, a town away from where I grew up. Renaissance Technologies, Ren Tech, makes huge yearly gains in it’s Medallion Fund. The company does a lot of quant work and hires PhD scientists, mathematicians and etc. Working for this company would be amazing to say the least. So much to learn there working with the talented people he has helped to pick and shape over the years.

Renaissance’s flagship Medallion fund, which is run mostly for fund employees,[8] is famed for the best track record on Wall Street, returning more than 66 percent annualized before fees and 39 percent after fees over a 30-year span from 1988 to 2018.[9][10]