Motivation behind this blog.


I am getting to the point in life, where accountability, radical truth and transparency is more important than being comfortable with our performance. The main reason to write about my trading, quant, python journey is to create a blog, that would be leading measurement tool to my accountability, because“ future results are lagging indicator of our present habits “ .


I am turning another page with my blog, where I am focusing on deep work, creating meaningful algorithms, learning basics of python for financial usage and data science, trying new things, and also sharing basics on the way of becoming a successful quantitative trader. This blog is more like a record for my past researches, problems that I solved as well as ultimate cheat-sheet for my future projects.



The goals for 2019:

-Obtaining level of mastery in programming financial models and building robust systems.

- Building top-down automatized approach framework for world view.

- Creating meaningful content that would inspire and help fellow investors/traders.


Investing Goals :

Long term – Long term investments made by quantitative and fundamental research ( mutual funds performance with the most favourable allocation )

Medium-term – Successful portfolio management held up to 3 months ( Long-short, market neutral portfolio, depending on priorities in given quarter)

Short term – Building profitable algorithmic machines for trading.( Mostly trades that takes 12 hours to 2 weeks with focus on probability and cycles)

Week 1.

Idea: research on performance of 4 mutual funds by historic data provided from UBS Luxembourg.

All 4 funds are based of similar equity structure, hence their weighting differs according to strategy they are holding, as well as fix income instrument and by that government bonds holding on book of specific fund.


My challenge is to figure out what was the best investment so far, stress test it, undergo rigorous analysis by specific instruments, and acquire findings of such funds.

The idea is to figure out whether isn’t better to purchase only stocks inside with specific weighting to outperform funds, as well as whether it is possible to get more for less risk.

The question we are asking is what is the optimum weighting for these funds, by Markowitz Efficient frontier, as well as how to create best possible returns out of these 4.


My guess is, that purchasing stocks inside would definitely outperform bond itself, as realistically we had bull market for some time, hence we can't underestimate downside and bad years, that might actually underperform in the long run because of negative returns in those periods.

Link :

Efficient Frontier on Git
or
Efficient Frontier on NBViewer

Blog Alpha.

What have I learned last week in python?

Got chance to go through 6 tutorials of Sentdex boy where I have tackled some real-life datasets from Kaggle.

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