Trading systems come in two flavors: model-based and data-mining. This article deals with model based strategies. Even when the basic algorithms are not complex, properly developing them has its difficulties and pitfalls (otherwise anyone would be doing it). A significant market inefficiency gives a system only a relatively small edge. Any little mistake can turn a winning strategy into a losing one. And you will not necessarily notice this in the backtest. Continue reading “Build Better Strategies! Part 2: Model-Based Systems”
The more data you use for testing or training your strategy, the less bias will affect the test result and the more accurate will be the training. The problem: price data is always in short supply. Even shorter when you must put aside some part for out-of-sample tests. Extending the test or training period far into the past is not always a solution. The markets of the 1990s or 1980s were very different from today, so their price data can cause misleading results.
In this article I’ll describe a simple method to produce more trades for testing, training, and optimizing from the same amount of price data. The method is tested with a price action system based on data mining price patterns. Continue reading “Better Tests with Oversampling”
Enough blog posts, papers, and books deal with how to properly optimize and test trading systems. But there is little information about how to get to such a system in the first place. The described strategies often seem to have appeared out of thin air. Does a trading system require some sort of epiphany? Or is there a systematic approach to developing it?
This post is the first of a small series in which I’ll attempt a methodical way to build trading strategies. The first part deals with the two main methods of strategy development, with market hypotheses and with a Swiss Franc case study. Continue reading “Build Better Strategies!”
We will now repeat our experiment with the 900 trend trading strategies, but this time with trades filtered by the Market Meanness Index. In our first experiment we found many profitable strategies, some even with high profit factors, but none of them passed White’s Reality Check. So they all would probably fail in real trading in spite of their great results in the backtest. This time we hope that the MMI improves most systems by filtering out trades in non-trending market situations. Continue reading “Boosting Strategies with MMI”
This is the second part of the trend experiment article series, involving 900 systems and 10 different “smoothing” or “low-lag” indicators for finding out if trend really exists and can be exploited by a simple algorithmic system. When you do such an experiment, you have normally some expectations about the outcome, such as: Continue reading “The Trend Experiment”