Post di Andrea Carratta

C# Tip 💡 - 𝗚𝗲𝘁 𝗺𝗲𝘁𝗵𝗼𝗱 𝗰𝗮𝗹𝗹𝗲𝗿 𝗶𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 Did you know we can use info attributes in C# to get 𝗶𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗮𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝗰𝗮𝗹𝗹𝗲𝗿 of a method? The example below shows the caller member name, file path and line number being outputted. These are really useful and can help with logging and debugging as we don't have to try and visually parse a long stack trace to pinpoint where something might have went wrong. There is 𝗻𝗼 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗼𝘃𝗲𝗿𝗵𝗲𝗮𝗱 as the compiler simply replaces the parameters with 𝗰𝗼𝗻𝘀𝘁𝗮𝗻𝘁𝘀, so it is no different from passing your own constant string at runtime. An example of the IL is in the comments below. What do you think? Have you used these before? ---------------------------------------------------- Please hit the 🔔 icon on my profile to get notified when I post, otherwise LI will just show 'top' ones as per their algorithm

Just finished Backtesting your strategy on 1 stock and already feel confident in deploying it? Imagine, the confidence (or rather arrogance) in Backtesting your strategy on the same stock OVER A 100 TIMES! But how is that even possible?! Let me show you…   Monte Carlo Simulation is a mathematical technique used to estimate the possible outcome of an uncertain event. The name comes from a city called Monte Carlo, which is known for gambling. It is a Randomly Evolving Simulation Monte Carlo Simulations can help you with bettering your Backtesting results. Since, you can only Backtest over one historical run manually, you cannot hope to predict the future prices with it but, with Monte Carlo, you can create as many price simulations as you want.   It models the probability of different outcomes of a situation which are difficult to predict due to intervention of random variables. It works on the law of large number.   It uses random sampling which is a method used to produce multiple possible outcomes from random samples of data, and calculate the average result.   Monte Carlo is used in fields like Portfolio Management, which is run under multiple scenarios, to give the investors an idea of how their portfolio will perform under several market conditions. It is also used in risk analysis, option pricing and planning for spare capacity. It is not only used in finance but also in medicine and astrophysics   A simple procedure to run a Monte Carlo Simulation is as follows: ❇️ Set up a predictive model ❇️ Specify probability distribution ❇️ Run repeated simulations. ❇️ Calculate variance and standard deviation to analyse your data better   If the data is too big to run a simulation on. You can also use confidence levels which is the probability that the sampled results contain the true value of your parameter.   These simulations also help in exploring the Max Historical Drawdown. To put it simply, we get only a single drawdown value after Manual Backtesting. With Monte Carlo Simulations, you can create a Drawdown distribution which would give you a way better idea of what your strategy actually looks like in front of the real market forces.   The Python program attached below, demonstrates a very simple Monte Carlo Simulation over 3 stocks on a daily time frame over 252 days. The program uses Cholesky Decomposition to introduce a correlation structure to the assets in the portfolio. Test it out and have fun! #quantitativefinance #quant #trading #algorithmictrading

🎬 Discover the first spoiler of SPOT – the Test Sequencer that makes creating test sequences easier and faster! 🎬 With SPOT, you can finally say goodbye to long hours of programming. Thanks to no-code actions and its intuitive interface, creating test sequences becomes a simple and quick process. Less code, more simplicity and standardization. SPOT not only allows you to focus on what really matters but also facilitates the standardization of your testing processes, ensuring more consistent and reliable results. 🌟 Are you ready to optimize the creation of your test sequences? This is just the beginning. Contact us to discover how SPOT can make your work more effective, satisfying, and standardized. #testautomation #simplification #efficiency #standardization #spot #lesscoding #fastertesting #labview

A brief history of configuration files. 1981: config.sys 1992: config.ini 1999: config.xml 2007: config.json 2018: config.yaml 2025: config.toml ---- 2035: config.??? #programming #softwareengineering #coding

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MachTT API threading

After so many years of constant joy, riddles and puzzles, it's probably time to retire. Machines convince machines that they are not machines. Bye, bye CAPTCHA. You served us well