Skip to main content

What software architects/developers frequently forget ...



When looking at code or architectures, sometimes I get the feel that developers forget that the art of programming, or any other problem solving task, is really the beauty of being able to transform a complex problem into a series of simple, well defined logical steps. Here is what some of the most intelligent people the world has ever seen remind us:

“If you can't explain it to a six year old, you don't understand it yourself.” 
― Albert Einstein


“Simplicity is the ultimate sophistication.” 
― Leonardo da Vinci

"Fools ignore complexity. Pragmatists suffer it. Some can avoid it. Geniuses remove it."
- Alan Perlis

So, the gist of the story, the role of an excellent developer or architect is not to complicate the simple, but to simplify the complicated. Sometimes people can easily get mislead about the beauty of a solution by admiring its complexity, but really and truly the greatness lies in the ability to simplify a complex problem.








Comments

Popular posts from this blog

Interfacing C# .Net and R - Integrating the best of both worlds

In specific software areas like in quantitative finance or else in other mathematical domains, data centric programming typically requires a good balance between three requirements - (1) a solid platform with rich mathematical/statistical functionality (2) having an easy to use, contemporary, programming environment which permits easy and flexible front end code development and (3) an easy to use interface between the two environments. In this artcile I am going to explain how such a balance can be attained by using two of the best products in their specific worlds - using the rich R library as the mathematical/statistical component but then interfacing with C# for the front end application design. As an interfacing option I banked on using R (D)COM which provides an easy to use interfacing method which keeps you away from spending hours identifying interfacing problems. The software required for this tutorial is the following: 1. R software ( download from here ) 2. R (D)COM Interf

Interfacing C# .Net and R - Integrating the best of both worlds (Part 2)

This post is a continuation from the previous post ( Part I ) focusing on interfacing C# with R using the R (D)COM. In this post I am going to enhance my previous exercise by creating a Facade .Net Class which facilitates access to specific functions in R. Creating the R Facade Class Creating a Facade Class (or a set of .Net classes) which acts as a .Net wrapper to R functions greatly facilitate the use of R functions and their integration within the .Net programming environment. Below I am showing an excerpt from the class RFacade that I have created in this example. using System; using System.Collections.Generic; using System.Linq; using System.Text; using StatConnectorCommonLib; using STATCONNECTORSRVLib; using System.Runtime.InteropServices; namespace R { class RFacade : IDisposable {      private StatConnector rconn;      private bool disposed = false;      public RFacade()      {           rconn = new STATCONNECTORSRVLib.StatConnector();           rcon

Simple moving average trading strategy using Python

Hi All, I am presenting simple boiler point code that can quickly be applied to test technical indicator strategies using Python. The code: 1. downloads daily stock data from google, 2. calculates the short and long moving averages 3. generates the trading signals 4. calculates the daily returns 5. runs the moving average strategy and calculates the cumulative return 6. plots cumulative return of our simple strategy Here is the code ... enjoy trying it out and extend it as required: import numpy as np import pandas_datareader as datar import datetime import matplotlib.pyplot as plt date_start = datetime.datetime( 2017 , 1 , 1 ) date_end = datetime.datetime( 2017 , 6 , 30 ) data = datar.get_data_google( 'AAPL' , date_start , date_end) short_ma = 5 long_ma = 20 data[ 'short_ma' ] = data[ 'Close' ].rolling(short_ma).mean() data[ 'long_ma' ] = data[ 'Close' ].rolling(long_ma).mean() data[ 'masig' ] = data[ 'short_ma'