The key to profitable trading under real out-of-sample (OS) conditions – meaning under data that didn’t exist when you created the strategy – is to be able to draw some type of predictions about which strategies will be better performing in the future. This means that you must have some idea about how the past […]
Posts Tagged ‘system development’
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November 2018
December 2017
November 2017
October 2017
March 2017
December 2016
- Our Reinforcement Learning mining and repository: Now live trading!
- Using a raspberry pi in trading: Four interesting DIY project ideas
November 2016
- Introducing pKantuRL: GPU enabled mining for reinforcement learning based systems
- Reinforcement learning and trading: Approaching the markets like a game
September 2016
- Simple long term trend following in Forex minor pairs
- Hidden consequences of classification model overfitting in Random Forests
August 2016
- A current picture of our machine learning based mining and trading system portfolio
- Irreducible error in trading: Beyond the bias-variance trade off
- How long does it take to see in-sample/out-of-sample variable correlations?
- Making Random Forests cheap enough for machine learning mining
July 2016
- Is there anything in live trading we can predict from in-sample data?
- Expanding price action based mining: Magnitudes and asymmetries
- Why do historically top performing trading systems almost always under perform?
- Is the key to more effective diversification the Risk to Reward ratio?
June 2016
May 2016
- Quantitative Finance Research is in Trouble: The p-hacking issue
- Do Monte Carlo simulations say anything about system robustness?
- Are multiple pair strategies better than single pair strategies?
- Using qqpat: When should you expect to see long term statistics?
March 2016
- Using R in trading: Backtesting a Chaos based prediction system
- Can fractal dimensions predict system abundance in Forex pairs?
- Bugs in Trading: My story with a difficult problem
- Dissecting the performance of our Forex machine learning strategies
February 2016
- Trading and the GPU: The super power that almost no one uses
- The Future is here: Expanding Forex GPU based mining using shifted timeframes
- New Algorithmic Trading Timeframes: Just shift it!
- Bugs in algorithmic trading: You'll have them so have a plan for them
- Building an algorithmic trading plan: Answer these questions
- Thousands of Forex Trading Systems: Lessons from massive algo trading
- Machine Learning in FX Trading: Measuring the quality of Input/Output structures
- Using luck to your advantage: Improving on trailing stop mechanisms in FX trading
December 2015
- Creating Successful Machine Learning Systems: My journey in the world of self-adapting strategies, part 1
- Using OpenKantu in Practice: Is old Forex data irrelevant in today's market?
November 2015
June 2015
May 2015
- Building Algorithmic Trading Systems for the Forex market. Part 2: Where to look
- Building Algorithmic Trading Systems for the Forex market. Part 1: Where to start
January 2015
August 2014
- Building a Machine Learning Library for Forex Trading: Creating a toolbox of input/output generators
May 2014
March 2014
- Accurate and systematic evaluation of Data Mining Bias (DMB) in trading strategy creation
- Taking algorithmic system generation to the next level: Using the power of GPUs
- Taking a big step in machine learning: Profitable historical results across multiple Forex pairs
- Reducing your data-mining bias: Creating trading systems with limited data
- Going below the one minute timeframe: Implementing simulations with Tick-by-tick resolution
February 2014
- The dynamic stop-loss: Changing exits as a function of time
- Oops, you did it again: Dealing with the latest Metatrader 4 build 600+
- The F4 Programming Framework and Asirikuy Tester: A simple FAQ
January 2014
- Reality Check: Why there is no such thing as a true "out-of-sample test" when using historical data
- Data-mining in Algorithmic Trading: Determining your data-mining bias through the use of random data. Part two.
- Data-mining in Algorithmic Trading: Determining your data-mining bias through the use of random data. Part one.
February 2013
January 2013
December 2012
November 2012
October 2012
- Avoiding Binary Choices: Using Fuzzy Logic Without Lot Size Granularity Problems
- A Switch, a Switch, My Kingdom for a Switch: Changing Trading Logic in Algorithmic Trading Strategies
- Walk Forward Analysis: Degrees of Freedom, Adaptability and Survivability
- Implementing Next-Generation Testing: The Power of the New F4 Strategy Tester Module
September 2012
May 2012
February 2011
- The Indicator Series: Following the Markets with The Zig-Zag Indicator
- Looking at myforexdot.org.uk, an Excellent Website with a Lot to Say
