Pydev with Eclipse

I find EMACS too much complex for me. I want to concentrate on the analysis of data with Pyhton, so I have looked for and alternative IDE for Python: Pydev.

The steps I have followed are:

  1. Download and install Eclipse (in reality I had it on my computer).
  2. Then, download, install and test Phyton.
  3. Once done, download and install Jython.
  4. Download and install IronPython.
  5. Install PyDev.
  6. Create a new PyDev project.
  7. I have created a file with print(“hello world”), then I have debugged it and it worked.
  8. Create a package to gather all the information.
  9. The set-up of the environment is very well explained. You first have to configure the interpreter, then you can develop all you need. In my case I’m starting importing some finance data from Yahoo Finance.
  10. Using Windows>Properties>Python Interpreter , install library “numpy” using “pip” as installer. This step is crucial to me as Python interpreter just work on PyDev in my computer.


I’m sure EMACS runs faster and it’s more efficient than Eclipse, but the point is that now I can concentrate on the development of the scripts I want to implement. 🙂

EMACS, how to install it for Python purposes

What IDE should I start with?

I have asked about some different IDEs for Python and this was the short list: EMACS, jupyter, syder and anaconda.

Then, I asked my friend: what is the more convenient for a basic learner as me? EMACS.

So this is the one I have installed. The basis of how to use EMACS can be read here. The guidelines recommends the installation of Elpy so this is the next step I have done.

Elpy – Python Development

Emacs is distributed with a python-mode (python.el), but if we want to have a more sophisticated IDE you can install Elpy (Emacs Lisp Python Environment) package.

Before to install Elpy, you have to install these 2 packages:

  • Flake8: flymake-python-pyflakes.
  • Jedi.

The document I used to install it is this one.

The issue I’m finding is that the these 2 packages are legacy and they seem to not work properly. I’m sure that in reality the issue is that I’m not able to enable them in the right way or to enable python properly. I’m stuck here by the moment.

Python extensions

Some basic notes about the python files extensions:

  • .py: This is normally the input source code that you’ve written (the basis).
  • .pyc : This is the compiled bytecode. If you import a module, python will build a *.pyc file that contains the bytecode to make importing it again later easier (and faster).
  • .pyo: This is a *.pyc file that was created while optimizations (-O) was on.
  • .pyd: This is basically a windows dll file.

EMACS basic commands I have learned today.

  • M-x list-packages: list the available packages you have in EMACS
  • M-x customize-group: enable you to customize a package (in my case: “package”. I have added Melpa packages to the list so these packages can be installed.
  • M-x package-install: to perform the installation of a package.

This video shows you how to install a package: .Emacs #3 – Installing Packages and Extensions. The series of videos are useful for new users as me.

III Maratón MTB ciudad de Tarifa

Family traveling, me preparing the vacations to the Pyrenees mountains, and suddenly the possibility to ride this race. For sure I’m still far away of the shape I need for the Pyrenees, but this was a good check point to know how I was.

The total distance, taking into account the launched start was 62.17 km that I covered in 4:22 hours. The majority of the route was a wide path with a lot of stones in the middle.

I was making well till the km 50 where a steep slope told me: “you should save some energy for the other 12 kms”.

The roads closed to the coast have really nice view of the coast. The fog did not let me see Africa.

The strong wind from east was present the whole ride and it made the day a little bit difficult.

Quantitative trading, Ernie Chang

This book contains basic concepts and approach (step by step) for does that want to initiate themselves on Quantitative trading. The focus is on statistical arbitrage trading, that deals with the simplest financial instruments: stocks, futures, and sometimes currencies.

The chapters cover the steps a trader should take:

  1. The Whats, Whos, and Whys of Quantitative Trading,
  2. Fishing for Ideas,
  3. Backtesting,
  4. Setting Up Your Business,
  5. Execution Systems,
  6. Money and Risk Management,
  7. Special Topics in Quantitative Trading, (reading it now)
  8. Conclusions.

You can follow the author blog that contains further valuable information and some nice examples.

A short list of common pitfalls related to how the back-test program is written:

  1. Survivor-ship bias: data does not contain companies that have fallen bankruptcy.
  2. Look-Ahead Bias: this phenomenon happens when you are using information that was available only at a time ahead of the instant the trade was made. For instance, “Buy when the stock is within 1 percent of the day’s low”.
  3. Data-Snooping Bias: the use too much parameters that make you build an over-optimized model. A rule of thumb: 5 parameters.
  4. Sample size: you need enough data to back test, how much? As a rule of thumb, let’s assume that the number of data points needed for optimizing your parameters is equal to 252 times the number of free parameters your model has. For instance, you define a daily trading model with three parameters, then you should have at least three years’ worth of back-test data with daily prices.
  5. Out-of-Sample Testing: divide your historical data into 2 parts. Use the first part for training, and keep the second part to test the resulting model.

Intro to Python for Data Science

DataCamp offers a set of courses oriented on programming and data scientist. I found some basic courses about python that have helped me to understand how the language works and practice with a good balance of theory and exercise.

Intro to Python for Data Science, is the free course that enables you to understand how basic things work.

I have also done a couple of chapters related to data import and data visualization that helped me to understand how to work on it.


Analysis of data with Python

The main goal is to learn, but not just through the completion of courses or certifications. After the achieving the PMP certification I though about a project to focus on programming and data analysis.

My goal is Learn Python is my next project. I have defined 3 months of project where I would like to achieve a goal.

Following the V2MOM model:

  • Vision: Be able to analyze data and predict behaviors.
  • Values: have fun, learn a lot, build a team with Dani, do practices and more practices.
  • Method: learn python, learn about patterns.
  • Obstacles: Time.
  • Measures: have an environment where I test a pattern with real time data from an external source. Have a list of learned lessons and experience.

Death line = December 2017