Metronome (MTN)

Metronome (“MTN”) is one of the crypto currencies that will be launched at the end of 2017. They define themselves as first cross-blockchain cryptocurrency, making decentralization possible and delivering institutional-class endurance.

Reading some articles from founder (Jeff Garzik) it enable the users to jump from one currency to others, so an individual can avoid the volatility of a currency moving the assets to other currency.

Applying the tragedy of commons principles, if everybody changes their positions from one currency to other, doesn’t it increase the volatility?

European Green Vehicles Initiative (EGVI)

I had a conversation with some friends that started with the following question:

Does it make sense to me to purchase a Diesel van or I should wait to the electric van?

To purchase a car/van is an investment to be analyzied, the disruption that is coming in terms of electric vehicles makes that some type of models do not make sense anymore.

This is specially important as the law is being adapted to fulfill the CO2 targets we have in the EU. Nobody wants to purchase a diesel van and 5 years later the valuation is zero because basically you cannot circulate in the city (there are local authorities applying this type of practices in big cities).

For cars the situation is different, the market already offers solutions and the trend is clear through the electric vehicles. The government is also promoting the procurement of electric vehicles through subventions, so these are things you can add to the analysis of cost.

The habits

are changing a lot, in my case, now we do not use the car at all during the week, and we only do it during weekends when we go out of city. I have jumped from doing 25.000 kms / year to drive just 11.000 kms / year.

In the city we live there are sharing car companies, so you can access to a car whenever you need.

We are asking ourselves if it makes sense to purchase a car or not.

European Green Vehicles Initiative,

EGVI is a is a contractual public-private partnership dedicated to delivering green vehicles and mobility system solutions which match the major societal, environmental and economic challenges ahead.

I have read the electrification roadmap defined for Europe which contains a lot of data related to:

  • Stakeholders implied on this revolution.
  • Forecast of readiness of the industry.
  • Level of CO2 targets estimated and how they can be achieved.
  • What are the legal aspects that need to change.
  • How the electric net needs to evolve.
  • What are the expected benefits of the changes.
  • Logistics challenges for the transport of energy, the peaks of compsumption…
  • Scenarios about the penetration of electric vehicles during next 15 years.
  • The importance of the research projects and the local investments in the different countries for the right deployment of the whole thing in the different regions.
  • How this initiative is founded by Horizon 2020 framework.

The document answers a lot of questions about where we are going to, and there are so many other challenges that right now are still not known.

In a nutshell, if you want to purchase a van now, you can do it, it will be useful for the next 10 – 15 years. The electrification roadmap document details that for vans there are still so many technical challenges that they need to be resolved, and they give a pesimistic forecast that the peaks of high presence of new sold vehicles will be happening around 2030.

Bitcoin, 21 million limit

The number of bitcoins has a limit,this constrains the market availability and the behavior of the value. Bitcoins are divisible in infinite number of parts, so this facilitates the situation on the market space.

There is a mathematical explanation for having around 21 million limit:

Calculate the number of blocks per 4 year cycle:

6 blocks per hour
* 24 hours per day
* 365 days per year
* 4 years per cycle
= 210,240
~= 210,000

Then you can sum all the block reward sizes (rewards are divided by 2 every 4 years):

50 + 25 + 12.5 + 6.25 + 3.125 + ... = 100

Finally, you multiply the two figures:

210,000 * 100 = 21 million.

The estimation is to reach this limit around 2033.

The question is that the reward to complete a block is decreasing with time. Could it be the case that one day to mine bitcoins is not profitable?

Legal aspects

The legal aspects of use in terms of virtual currencies are evolving differently. In Canada and US there are currently specific laws and taxation fees for these environments. Other countries are lagging from these areas. The EU is still not communicating a common pathforward about virtual currencies.

There is a lot of evolution on legal aspects and the number of transactions to be done

There are a good bunch of companies that are communicating that they will enable transactions with bitcoins by 2019.

Internet of Food & Farm 2020

Read, learn, make questions to myself and start again. So this week I learn the existence of this plan named: Internet of Food & Farm 2020.

The heart of the project is formed by 19 use cases grouped in 5 trials with end users from the Arable, Dairy, Fruits, Vegetables and Meat verticals and IoT integrators that demonstrate the business case of innovative IoT solutions for a large number of application areas.

This program in part of the IoT European Large Scale Pilots program.

The program is also linked to the Smart Spesialization Platform.

The different investment roadmaps are key to understand the direction that the high level investment done by the EU is going on.


Pirineos with the bike

I have been able to travel again a week to the Pirinees mountains with the mountain bike. This is a journey I love to do and I was able to do it after 2 years off.

The first thing is to prepare me physically. 7 weeks riding at least 3 times a week made the difference. This time I prepare the days of training combining climbing, long rides and specific workouts with high slopes.

The speed during the routes is very slow, and this helped me to be at the same pace of the group. It’s vacations, not a ride, so we stop to take pictures, talk to people and this type of things.

We have stayed in Castejón de Sos and Ainsa.

The routes we have done were:

  1. Day 1: Castejón de Sos –  Integral de Gallinero: 40 kms, 1600m positive high. 6 hours ride. Good feelings as I rested some days and the pace was slow. We extended the original ride going to Gabás, so we added an extra downhill. The fact that this week I rested help too.
  2. Day 2: Villanova- Rabaltueras: 20 kms, 1050, positive high, 3 hours. The slopes were more complicated, but the ascend was only 10kms. The downhill is in a forrest and it was very good to stop from time to time an enjoy the views. The cows were in the middle of the path when we were downhilling, so a technical stop was required.
  3. Day 3: Ainsa – Viaje al Inframundo: 55 kms, 1585 positive high, 7:30 hours. We wanted to do this one some years ago, but in summer is not suitable because of the heat. Even today it was a hot day. The travel to infraworld is a ride reaching some abandoned towns and old houses were there is nobody. Today we did not see anybody in almost 40 kms. It was a large day but it was a nice day, as the route description says: it’s a bad route for the body and good for the soul.
  4. Day 4: Ainsa, raining, so no bike today.. so the alternative was a gastronomic route with special meet of the region.
  5. Day 5: Ainsa, Portillo del Vallés: 39 kms, 1360 positive high, 5 hours. We picked this route due to a local rider told us there were less posibilities of mud. The worst part was the start of the downhill on the north face with a lot of water there. The other paths where fine. We started at 13:00 as the whole morning was raining. It was a good decision as finally we were lucky and no more rain falled. The path over a river with stones was fantastic.
  6. Day 6: Ainsa: Saravillo – Ainsa: 37 kms 960 positive high and 1400 negative high. We took the van till Saravillo, there we started going to Badaín, then we stopped to take lunch in Laspuña. After lunch we went to Araguás, Torrelisa and finally Ainsa. The final path over the badlands was amaizing.

You can find all available routes here:

  • For the 2 of Castejón de Sos: Puro Pirineo.
  • For the 3 of Ainsa: Zona zero . We have to thanks again to the guys of the Intersport store in Ainsa for the guidance on the routes and the election of the best ones taking into account our directions and the weather.

Only 2 falls without consequences and the bike without any incident, what else can I ask for?

I hope I can come back next year.

Python, Good morning

Good morning is the name of a library that enables you to download data from stocks related to fundamentals.

I have uploaded the GIT file on my eclipse and I have shown some basic data in a data frame. This was something initially easy.

My goal is to draw data in a monthly basis for these fundamentals and be able to compare with other datasets. By the moment I’m not able to show data by at least in a monthly basis.

The code of my basic test:

import good_morning as gm

kr = gm.KeyRatiosDownloader()
frames =‘T’)

count = 0
frame_size = len(frames)
while (count < frame_size):
         print( ‘The count is:’, count)

count = count + 1

print(“Good bye!”)

Simple Moving Average, basis

I have read a couple of texts related to the simple moving average or rolling average, this one explains the basis and the main values used. I have done some tests and I have compared them to understand the use better.

The different articles always have some tips about this analysis:

  • Do not operate based in these signals, use them as control checks.
  • During lateral behavior of the market the signs can be false. They work better when there are major changes.

The test below shows the Amazon trend plus rolling average trends with:

  • short rolling = 10 days and long rolling 50 days
  • short rolling = 20 days and long rolling 100 days
  • short rolling = 5 days and long rolling 20 days

There is a concept where 3 lines are drawn, that is called the triple crossing, where:

  • Short rolling 4 days,
  • Medium rolling 18 days
  • Long rolling 40 days.

This typically happens when a lateral behavior has finished and this is making a lot of investors to go with the flow.

For general understanding of which moving average you should use as guide:

  • 5 days: For the hyper trader. This shorter the SMA the more signals you will receive when trading. The best way to use a 5-SMA is as a trade trigger in conjunction with a longer SMA period.
  • 10 days: popular with the short-term traders; great for swing traders and day traders.
  • 20 days: the last stop on the bus for short-term traders. Beyond 20-SMA you are basically looking at primary trends.
  • 50 days: used by traders to gauge mid-term trends.
  • 200 days: welcome to the world of long-term trend followers. Most investors will look for a cross above or below this average to represent if the stock is in a bullish or bearish trend.

Quantitative trading strategy: Pandas and matplotlib

Start from basis is important to me to understand how to handle basic data and start having real contact with the data and the code.

I found this tutorial very useful for these purposes. The use of Pandas for reading data from yahoo, google… and matplotlib to build an easy chart is key to take the first steps. The best of the tutorial are the comments about each step.

Problems I have found

Yahoo has closed the API that enabled Pandas to retrieve data. My colleague has found a workaround to continue using the API. The solution consists on:

  1. Add with pip the library: fix_yahoo_finance.
  2. Add these 2 lines to the code:

import fix_yahoo_finance as yf

yf.pdr_override() # <== that’s all it takes 🙂

This video is a must see to me as Jev Kuznetsov explains it from scratch.

The first code I performed was this one:

from pandas_datareader import data
import pandas as pd
import matplotlib.pyplot as plt

# Define the instruments to download. We would like to see Apple, Microsoft and the S&P500 index.
tickers = [‘T’, ‘VZ’, ‘SPY’]

# Define which online source one should use
data_source = ‘google’

# We would like all available data from 01/01/2000 until 12/31/2016.
start_date = ‘2015-01-01’
end_date = ‘2017-10-10’

# User to load the desired data. As simple as that.
panel_data = data.DataReader(tickers, data_source, start_date, end_date)

# Getting just the adjusted closing prices. This will return a Pandas DataFrame
# The index in this DataFrame is the major index of the panel_data.
close = panel_data.ix[‘Close’]

# Getting all weekdays between 01/01/2000 and 12/31/2016
all_weekdays = pd.date_range(start=start_date, end=end_date, freq=’B’)

# How do we align the existing prices in adj_close with our new set of dates?
# All we need to do is reindex close using all_weekdays as the new index
close = close.reindex(all_weekdays)
close = close.fillna(method=’ffill’)
# Get the MSFT time series. This now returns a Pandas Series object indexed by date.
vz = close.ix[:, ‘VZ’]
spy = close.ix[:, ‘SPY’]
# Calculate the 20 and 100 days moving averages of the closing prices
short_rolling_vz = vz.rolling(window=20).mean()
long_rolling_vz = vz.rolling(window=100).mean()

# Plot everything by leveraging the very powerful matplotlib package
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
ax.plot(vz.index, vz, label=’VZ’)
#ax.plot(spy.index, spy, label=’SPY’)
ax.plot(short_rolling_vz.index, short_rolling_vz, label=’20 days rolling’)
ax.plot(long_rolling_vz.index, long_rolling_vz, label=’100 days rolling’)
ax.set_ylabel(‘Closing price ($)’)

Lean, real life and theory of constrains


is the name of the team I have met today. A team composed by a 2 people who are analyzing a set of processes that they need to be improved in terms of efficiency and ability to absorb peaks.

The guys complained that all is in paper and that they were expecting data in a database or at least in an excel so they can analyze that.

I just broke out laughing: welcome to real life.

Real life

is the main fact these guys want to ignore. Yes, there are processes with legal papers that are scanned and managed, but legal communications are done by paper and we cannot change it.

This fact supposes a great wall that did not let them to see beyond. They just were paralyzed in front of this real fact.

“Come on guys, you have Six Sigma certifications, you share all these theory of Lean in LinkedIn, and now you come to me with this problem?”

Theory of constrains

is the basis of the history of the book The Goal (written by Eliyahu Goldratt), that simplifies the application of this theory in 5 steps.

I have commented them to go through this simple guidance and avoid a big bang approach to work on specific aspects. A big bang approach is not sustainable in a production line that cannot stop, this is a basic idea that they understand.

Finally I found that they perfectly understood the situation and my approach, the funny thing is that all they are asking is because their boss just want “data”.

Strava, other conditions: temperature

I use Strava as one of the environments that let me analyze the rides I do. I find very useful the kudos system, the way to show the data, how to compare with yourself, etc.

Yesterday I took my bike in Sevilla. It was October the 1st but there were around 35ºC at 17.30 that was the only slot I had to take the bike.

I did a route that I use to do, and the feeling of doing this route with such temperature is very different than when you perform it at 20ºC.

I understand that the effect of heat in every person is different, it’s also different for a single person depending of other factors. But in general, the effect of the heat reduces the performance of the workout.

For instance, the performance I had yesterday was very different in the first part of the ride than in the second part. Basically the difference was:

  • First half: speed = 2:31 min/km
  • Second half: speed = 2:45 min/km

The reason was not the slopes or the excessive speed during the first half, it was the temperature that provoked me to burn down slowly. The hot wind did not help either, in any case the effort done was useful for my goals, to suffer during the bad days is also a useful training for keeping the mind concentrated and minimize the lose of time.

I would suggest to Strava to add this variable in some type of kudos, or as a separate factor to measure.