Trading Order Out of Chaos
by Corvin Codirla Leave a Comment
In Part 1 we used simple Python to Improve our Backtesting times. Starting out with DataFrames we took a simple RSI strategy over a period of 7000 days and reduced from 7.3 seconds (which is a complete joke; sorry Pandas!) down to 0.003 seconds by converting everything to lists. But if you recall the comparison table at the end of Part 1
Implementation Time for RSI2 Backtests Python – Lists 0.003s Java 0.00005s C 0.…
Would you like to build consistently profitable trading systems from scratch? This article series does exactly that. Here are the steps which we’ll work through:That’s the theory. In this article-series we’ll go a step further and look at how to implemen… Read more
- Define the underlying principles of a successful trading approach
- Determine the assets which obey these principles
- Determine rule sets for trading these assets
- Work out a straightforward portfolio construction method
Intro
Backtesting is every systematic trader’s basic tool. And Python is becoming the lingua franca of programming. So putting Python into Backtesting to get fast results should be possible!
Yes and no!
In this article, we’ll cover how to really improve your Python backtesting and boost your speeds by several orders of magnitude!
“Greed is good”, but one shouldn’t always assume that the counterparty won’t notice. Last Wednesday, 27th January 2016, was FOMC day, news event day, and the markets went ape. Following on from the previous articles the idea was to continue to exploit the observed quote outages and extract value. Turns out that (presumably as expected), brokers aren’t that slow on their feet. I was moved from B-book status (i.e. rookie / loser status) to A-book status. Let’s just say that exces… Read moreFOMC Day: How the Golden Goose got cooked – Getting my A-book status
EURUSD tick data for the period 6th January 2016 to 8th January 2016. Zip file contains CSV files for each day. The format of the CSV files are: Local Time, Server Time, Server Time Milliseconds, Bid, Ask. The time resolution is in seconds. This means that there can be multiple quotes per second. Local Time is GMT+0 Server Time is GMT+1
EURUSD tick data for the period 3rd April 2016 to 8th April 2016. Zip file contains CSV files for each day. The format of the CSV files are: Local Time, Server Time, Server Time Milliseconds, Bid, Ask. The time resolution is in seconds. This means that there can be multiple quotes per second.
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