CryptoCurrency Analysis #2

Earlier this week I released CryptoCurrency Analysis #1 (02/14/2018), during which I sold all my Litecoin (LTC). Considering I hate being out of the market – this morning I was deciding where to enter.

Bitcoin (BTC) – 02/17/2018

Today (compared to 02/14/2018), Bitcoin is a bit on the up trend. Overall, I believe it’s a wise decision to hold the coin for the time being. However, I’d be very hesitant to buy it at the moment, with recommending a hold pattern:

With that in mind, I’ll not be tossing any of my money that way.

Bitcoin Cash (BCH) – 02/17/2018

Bitcoin Cash on the other hand has moved to a strong buy position (according to

Personally, I feel it’s an alright time to buy Bitcoin Cash, as it is on the uptrend and is recommending. On the other hand, I think there is another better opportunity out there which potentially has more upside. Particularly, because there appears to be more development…

Litecoin (LTC) – 02/17/2018

Today, Litecoin is recommended as a sell / hold. It’s shot up a lot, but the Experts’ Opinion on has not followed. In a sense, this means the price has likely outpaced its perceived value: has recommended a sell for the last several days, which had you been following the trend would have made significant gains. In fact, we sold right around $210, so we missed some of the gains, but had bought much lower (so we were up ~30%).

Ethereum (ETH) – 02/17/2018

Today, we are putting our money into  Ethereum. For better or worse, we view it as the current best opportunity over the next few days / week.

Part of the reason we favor Ethereum over Bitcoin Cash is, is there is simply more discussion and particularly more positive promotional score:

Ethereum is consistently more positive and thus we believe it’ll have a higher chance of being stable.

Overall Holdings – 02/17/2018

Today, I’m sitting at roughly 54% into Ethereum. While still holding the Bitcoin Cash I’ve had for some time, and a little bit of Bitcoin:

From here, we’ll see how long I hold these assets and when I’ll either switch to cash or move to another currency.

CryptoCurrency Analysis #1

From here on out I’ve decided to do an investment post every week. I’ll spend my time doing an analysis of what I’m going to invest in and go over how it has been determined. Particularly, I’m going to be focusing on cryptocurrencies a stand alone investment (i.e. not mix with Stocks, although Project Piglet supports that).

Bitcoin (BTC) – 02/14/2018

First, I’ll review Bitcoin. It’s been a rough month watching it’s value drop from ~$20,000 to right around $9,200 at the time of this post. That being said, having been watching / holding bitcoin since 2009 – it’s nothing new.

Today, Bitcoin is at a “sell” state:

Meaning, I should probably sell the Bitcoin (if I have any). There will likely be another downturn soon, so it’s worth selling now, and buying again later.

Bitcoin Cash (BCH) – 02/14/2018

Bitcoin Cash has similarly had a wild ride (perhaps even more than Bitcoin). Today it’s sitting at a “hold”, on, I somewhat agree with that sentiment. There really isn’t much discussion about it, and thus the buy / sell information is somewhat foggy. Honestly, I wouldn’t buy Bitcoin Cash any time soon, and I’d cash out on the next up day.

Litecoin (LTC) – 02/14/2018

When I personally saw the Litecoin price, I cashed out immediately. Even if it goes up further, I’ve secured my ~30% gain (bought around ~$160 January 17th, sold at ~$210) in about one month.

It’s likely I’ll be putting money back in shortly, however – today I’m just going to sit on my earnings. The discussion in a previous article: Causality in CryptoMarkets, suggests that time should be roughly a week.

Ethereum (ETH) – 02/14/2018

Following the trend for the day, Ethereum is a sell as well. If you had bought around February 5th as Piglet suggested you would have made money (between 5% – 50% depending on the day). For the most part though, Ethereum has been a solid sell and I still recommend that.

Personally, I have not held Ethereum since December, and I’d recommend waiting until a new period to buy (if you’re interested in holding Ethereum).

Overall Holdings – 02/14/2018

Today, I’m sitting at roughly 58% cash, with some other minor holdings I’m sitting on simply because I don’t want to pay the taxes (plan to hold for twelve months, so I only pay capital gains):

Essentially, I’m completely in cash for short term investments. We’ll see what the next few days hold to see if we’ll buy any more.

Causality in CryptoMarkets

In this article: We discuss causality in crypto markets. Specifically, when is a good time to buy / sell a cryptocurrency; this analysis will be conducted from (at the time) live data from

Intro to Trends on

For this article, we will be applying Granger Causality to a few cryptocurrencies for the past few months. We’ll use live trend and pricing data from, between April 1, 2017 and December 1, 2017.

The goal of is to provide insights from experts to the masses; bubble up the knowledge, if you will. In the case of crypto-markets, it’s often the reverse, where the masses tell the experts how they feel. Specifically, (crypto)currencies are massively impacted by how confident masses feel. This is part of the reason for the federal reserve in the U.S., as it stabilizes the U.S. Dollar. In contrast, cryptocurrencies have no such system, so a boom-bust cycle is inevitable.

Luckily, with we can use this to our advantage, particularly by reviewing the Trend Data / Chart (as it provides insights about the masses):


If you’re interested in the specifics regarding the chart, feel free to checkout our explanation. In summary, trend’s are the normalized volume of discussions between 0 and 1, as tracked by

Code for Granger Causality

Now, lets get to the fun part – coding!

First, we create a function that takes in trend and pricing data, normalizes the data using zscore, and formats the data for the granger causality function we will be using (from the python package statsmodels):

From there it’s relatively easy to ingest into the Granger Causality function (from the python package statsmodels). The only part we have left to do is defining the “max lag”, or how far back in time should be assessed by the function. In this case, 60 days was chosen as we are only looking at a few months of data. Just make sure it is less than one third of the data’s size (I recommend choosing <10% of the data set size):

With the output from that function it is possible to pull out the F-test and p-value from the “gc” variable (Granger Causality). Both the F-test and p-value are used for determining correlations, however I wont dive into that at the moment. The full documentation on the “grangercausalitytest” function is here, and the full code (in the github, below) will provide further insights.

In addition to the F-test and p-value, the Granger Causality function will provide us with a list of “lags”, which is let us know the offset to look for. For this example, the “lag” is how many days prior a change in trend will impact the price.

With this code it is now possible to run our Granger Causality assessment!

Trend and Price Causality in CryptoCurrencies

Using trend data from, we will review the following cryptocurrencies from April 1, 2017 to December 1, 2017 for “probable causality” (i.e. Granger Causality) based on trend and price data:

  • Bitcoin (BTC)
  • Ethereum (ETH)
  • Litecoin (LTC)
  • Bitcoin Cash (BTC)
  • Ripple (XRP)
  • Monero (XRM)

When we run a modified version of code above; we end up with the following chart representing the number of days lag in price after a trend event, for the six cryptocurrencies above:

Correlations between Trend and Price (based on offset of days)

For reference, a p-value < 0.05 is typically considered the threshold for the correlation, between datasets. In this case, there’s also what we are calling a “Strong Correlation” which is a p-value < 0.01. The strange part is the uniform band of “Strong Correlations” at the bottom.

Upon inspection, it appears that one cryptocurrency in particular is the culprit Bitcoin Cash (BCH):

Zscore BCH Trend & Price

The large amount of no data leading to a high amount of correlation. This is one of the common pitfalls we mentioned Limits of Granger Causality, and is an excellent example of why you always verify your data sets! Once we remove BCH from our dataset we now have the following cryptocurrencies we are searching for Granger Causality:

  • Bitcoin (BTC)
  • Ethereum (ETH)
  • Litecoin (LTC)
  • Bitcoin Cash (BTC)
  • Ripple (XRP)
  • Monero (XRM)

We then appear with the following chart representing the number of days lag in price after a trend event:

Correlations between Trend and Price (based on offset of days)

I’ve personally verified each of the remaining datasets. I believe there is indeed a probable causality (where the trend impacts the price). You’re welcome to do the same with the code / data provided below.

Causality in Crypto Markets

The more specific outputs (i.e. which currencies have causality) from our code are as follows:

Output of Granger Causality

Importantly, it appears there is a pattern – i.e. something we can trade off of. Most of the currencies show a strong movement roughly five days after there is a trend event; averaging major cryptomarket movements eleven days after a trend event and with standard deviation of 7 days.

Further, the one outlier Ripple (XRP) is mostly owned by a single entity. Meaning it might make sense there is no correlation between the price of Ripple (XRP) and the number of people discussing Ripple (XRP); where as the other currencies are mostly owned by various groups / the masses.

Verification of Granger Causality

For safe measure, I typically will do an additional assessments to verify theories and confirm Granger Causality is working. Searching for correlations between trends for each of the cryptocurrencies and a “random walk” (i.e. random data), which outputted the following chart:

Trend of CryptoCurrencies compared to Random Walk

For comparison purposes, it’s clear there is no pattern at all; where as the live data has a clear pattern we can follow. In addition, it’s highly recommended to review the output of the individual timeseries datasets being compared. It’s extremely common to get results such as we did with Bitcoin Cash (BCH).


Our goal in sharing our analysis using Granger Causality was two fold:

  1. Help explain Granger Correlations and how to use them
  2. Intice you to try! (Use coupon code: pigletblog2018 – 25% off for 12 months)

We hope we’ve done both!

The github repo with all the code and example data is here. Feel free to leave comments, tweet, emails, or leave issues on the repo itself.

We’d love feedback and/or to help if you need it.

Beginning a Dev & Investment Diary

Per the suggestion of remirezg on, we’ve decided to start a “Dev Diary” and “Investment Diary”! In our case, it’ll be a bit unique, as we’ll be exploring the development of not only our system, but the accuracy and usage of our algorithms.

We realized when we started this, trust is everything. With that in mind, by providing insights to our successes and our failures, we hope to gain your trust.

In addition, or rather – to start us off. We will share of where we are at in our Litecoin | LTC investments, which we wrote about previously.

Litecoin Investing Continued

Around January 16th we purchased our first Litecoin using the new short-term advising system (just after release).


This enabled us to make some pretty substantial gains… or at least would have enabled us. Turns out Capital One, no longer accepts credit card purchases on Which lead to me purchasing the litecoin via a bank account.

Now, typically – I’d go, what the hell sounds great. Unfortunately, the next sell date suggested by was only a few days after – prior to my funds clearing.


Now, luckily it was only a soft sell, and I’m actually still holding the Litecoin (LTC), but this really shows a weakness in our “Trade Potential” algorithm. The algorithm does indeed work quite well for short term trades, however I’m really interested in the long term. Everyone, wants to know when the next 100x gains are coming, and right now does not make it clear.

That isn’t to say we don’t have plans in the works, we do. We’ve even had something in testing for the past six months. The fact is though, it’s a hard problem. I can show individual profits (or reduction of losses), beating a buy and hold strategy nearly every time. However, what we’ve been working towards first is which asset to pick. That’s a hard question, which will make the largest gains (or least losses).

And for that, we still have a while to go.

Closing Remarks

In the mean time, I’ll try to do this as often as I can. My goal is to check and move money as I see fit – even if it’s against the algorithm. My wife is likely giving birth this week, so I’m wondering if my gains could outpace my babies growth…

My last investment was $500 and lets see how far we can grow it by the end of the year.


Happy Investing!

If you’re interested in using, use the coupon code: pigletblog2018

It’s 25% off for 6 months!

30%+ weekly returns using Piglet

One of the features added to this is called Trade Potential**. Trade Potential is a value ranging from negative one to positive one, provided on Piglet’s tracked assets (cryptocurrencies, stocks, etc.). A score of negative one is a very strong sell signal; where a score of positive one is a very strong buy signal. It’s essentially a tool which suggests short term market movements.

When to Buy (Using Trade Potential)

Over the past week, during the brief cryptocurrency “crash”, it became clear there was an opprotunity.

During the crash, notified the customers of the experts’ opinion change. When visiting the site and reviewing the Litecoin (LTC) asset, they were greeted with the following chart (photo taken a few days after).


Between January 15 and January 17, 2018 Litecoin (LTC) dropped roughly 25%. During the same period the experts felt it was unwarranted and were speaking positively about Litecoin (LTC). Thus, identified a positive Trade Potential of 1 or a very strong buy. This enabled the users following Litecoin (LTC) to buy at the low price of ~$150 – $170. Below is a chart off during the same period.


The initial buy notification went out on at 4pm, and continued through January 17th. Now, three to four days later, the price is fluctuating between $200 and $215, making it a clean 25% to 33% profit. Not bad for a few day investment. As usual, as a developer on Piglet, I like to put my money where my mouth is.


Which brings us to the next point…

When to Sell (Using Trade Potential)

One nice feature of using Trade Potential to invest, is that it has a bias towards selling – minimizing losses. As you may have noticed, I also sold my Litecoin(s) January 6th. At the time Litecoin (LTC) was right around $285 – and has been more-or-less declining ever since. The reason I sold, was the Trade Potential was at -1 or a very strong sell.

Note, that was right at the peak. The Expert Opinion line was falling below price (very quickly), and thus the Trade Potential recommended selling the assets, in this case Litecoin (LTC). In addition, the number of people speaking about Litecoin (LTC) dropped significantly (light orange/yellow bar), while at the same time there appears to have been a massive sell-off (blue bar).

For reference, the peak being discussed is in the below chart from


As you can see, since the most recent peak (January 6th) there has been a ~29% reduction in asset worth[1].

Closing Remarks

Using Trade Potential over the past two weeks has not only saved me from losing roughly 30% of my earnings, but also has made me an additional roughly 33% profit. Putting me ahead of those following the buy-and-hold strategy, by a whopping ~53% – ~62% in just two weeks! Which goes to show, the price tag of $50 / month is quite minimal, when you can grow your net assets at this exceptional pace!

Now, that isn’t to say you can always grow at such a rate. The opportunity still needs to be there (i.e. volitility in the market). However, when it is there, is there to help! The only challenge personally, has been deciding to sell even when it looks like we are going to the moon (even though it’s about to come crashing down)!


If you’re interested in using, use the coupon code: pigletblog2018

It’s 25% off for 6 months!

Calculations & Notes

[1] ((1.0 – ($207 / $291)) * 100% = 28.9%)

[2] ((1.33 – ($207 / $291)) * 100% = 61.86%)

** Note: “Trade Potential” is still being tested during the beta and is not guranteed to be accurate by any means. It is a tool to help you determine whether or not to buy, and is not a fully functioning advisor.