WEEKLY DIGEST 2014, October 05 - 12 for Quantitative Analysis, Trading and Developing - mql5 digest Quantitative Finance: Analysis of Gold Mining ETF - 5 Reasons Why the Market Vectors Gold Mining ETF Could Be The Next Monster Trade...

Many of you who wished to use the Matlab’s Financial Toolbox for portfolio visualization most probably realised that something was wrong. Not every time the annualised risk and return for different time-series matched with what was expected...

With every year the competition in the field of quantitative finance gets tougher...

All the time in the displaying of uncommon occasions in money and protection industry the investigators are keen on both the likelihood of occasions and their monetary results. That rotates around the part of a likelihood of ruin, i.e...

FX trading styles: Different strokes for different folks The short term trader is also not immune from the competition that bank and hedge fund professionals offer, but has a greater margin of error...

Limit orders provide investors with an opportunity to buy or sell a specific number of shares at a given/demanded price. In many instances this solution is favored to market orders because of a slightly higher degree of control over risk or potential losses. We need to include it in estimations...

In my previous post I provided an introduction to the trading model invention and design. We made use of FX data of AUDUSD pair sampled hourly and splitting data into weekly time-series. Today, we will work a bit harder over formulation of the very first rules for the model...

Feeding our models with data for backtesting purposes requires pulling data from the server...

When you think about an invention of a new model for algorithmic trading, there are only three key elements you need to start your work with: creativity, data, and programming tool. Assuming that the last two are already in your possession, all what remains is seeking and finding a great new idea...

Dealing with financial data sets in a raw format sometimes we find ourselves in a need of converting a specific date and time into continuous measure of time expressed by a single number...

The tick-data provided in the .csv (comma separated values) file format sometimes may be a real problem to handle quickly, especially when the total size starts to count in hundreds of GB. If your goal is to extract a time-series with, say, hourly time resolution only, Read more -> Sources...

function [str]=bankformat(num) num2=floor(num*1000)/1000; r=int32(100*(num-floor(num))); str = num2str(num2); k=find(str == '.'); if(isempty(k)) str=[str,'.00']; end FIN = min(length(str),find(str == '...

A precious lesson I learned during my venture over programming an independent backtesting engine for new trading model was slippage. Simply speaking, slippage is a fraction of stock price which you need to assume as a deviation from the price you are willing to pay...

It is one of the most fundamental approaches in measuring the risk, but truly worth revising its calculation. Value-at-Risk (VaR...

When I read the book of Nassim Nicholas Taleb Black Swan my mind was captured by the beauty of extremely rare events and, concurrently, devastated by the message the book sent: the non-computability of the probability of the consequential rare events using scientific methods (as owed to the very...

Having in mind the upcoming series of articles on building a backtesting engine for algo traded portfolios, today I decided to drop a short post on a simulation of the portfolio realised profit and loss (P&L...

Forecasting future has always been a part of human untamed skill to posses. In an enjoyable Hollywood production of Next Nicolas Cage playing a character of Frank Cadillac has an ability to see the future just up to a few minutes ahead...

Working with financial time-series, especially in trading and its following analysis of data trends or so on, we wish to rebin (resample) our data into a new time-series which would provide us with some sort of a new information on Read More -> Sources...

I decided to take the data analysis on Black Swan and Extreme Loss Modeling to the next level and examine the time distribution of extreme losses across the entire S&P 500 universe of traded stocks...

Everybody would agree on one thing: the nervousness among traders may lead to massive sells of stocks, the avalanche of prices, and huge losses. We have witnessed this sort of behaviour many times...