I have a great trading idea!! I do my research, and whoala!! It looks great!! Time to test it… I open a demo account with some renamed broker… I start to paper trade, and then I see how good is performing… ohh wow… Should I sell this strategy to some hedge fund? No!! I will … Continue reading Why trading on DEMO account is SO different from REAL accounts
Data structures that are contained within a single cache-line are more efficient. Use appropriate containers (e.g. prefer reserved std::vector than std::list) Organize your data to avoid alignment holes (sorting your struct members by decreasing size is one way) Don’t neglect the cache in data structure and algorithm design Use smaller data types Beware of the … Continue reading Unlock the secrets of High-Frequency Trading systems
Iron Condor Backtest - SPX - 38 DTE We will look at the automated backtesting results for four variations of a 38 days-to-expiration (DTE) SPX "no touch" Iron Condor (IC). As with the prior backtests, the short strikes for both the call credit spreads and put credit spreads will be at approximately the same delta. … Continue reading Options Backtest: Iron Condor – SPX – 38 DTE
Choose the right language: FORGET about scripting languages, they won’t work. When you are looking to shave those last few microseconds off your processing time you cannot have the overhead of an interpreted language. Additionally, you will want a strong memory model to enable lock free programming so you should be looking at Java, Scala … Continue reading What I’ve learned after coding for HFT and Low Latency Systems
The simplest ideas are often some of the best. This is a mantra that should often be repeated by traders and investors. The simplest ideas persistently produce profits for long periods of time. I don’t know if this is because they are so simple that they are ignored or because they identify and exploit the … Continue reading Backtesting the $SPY (S&P 500): SMA 10/100 System
Often, trading model developers “spoil” the eventual results of their model by making errors early in the process. These errors could be using poorly-collected data, not accounting for survivorship bias, or testing too many specifications of a similar model. Data snooping such as that can be particularly costly in that it is an error that … Continue reading How do I develop Trading Systems?
One of the biggest lessons I’ve learned is the important of separating the trading models–the mathematical or algorithmic logic that analyzes data and decides how to trade–from the trading system–the general framework which provides interfaces for receiving data and interacting with the market to the trading models. The nature of high frequency trading is such … Continue reading Building a Trading System – General Considerations
Low latency networks: location, location, location…. Or Colocation! This is key to creating an HFT business… without it, you will be another retail investor. Low Latency systems: usually written in Java or C++. Personally, because of my background, I prefer C++. There have been non-sense discussions on what is better C++ or Java? For me, … Continue reading Why High Frequency Trading is so profitable?
Black box trading, algorithmic trading, automated trading, or whatever you’d like to call it, is on the rise. Technology is better, cheaper, and faster than ever before allowing retail traders to take part in an area of trading that was historically reserved only for the big boys. Speaking with Forex traders on a daily basis … Continue reading 3 Ways to Use Black Box Automated Trading Systems in Forex
Automated trading requires a server, and it often makes sense to collocate it. Even discretionary traders these days can rely on certain automation that may be better off on a server. Tools like NinjaTrader allow “customer assisted trading” where a position automatically gets a profit target and stop loss, even with automatic adjustments. While not … Continue reading Exchange Colocation – worth it or not?