Newsletter

• seminar announcements
• stock picking strategies
• stock portfolio management for the lazy trader
• QQQ trading
• reviews of stock trading products and services
• tax issues, in particular with short-term trading
• short-term and intra-day trading
• statistical algorithms, parametrization
• strategy design and optimization
• impact of spread and trading errors
• designing robust strategies
• combining short and long term strategies on a single stock
• metrics used to assess the efficiency of a strategy
• statistical distributions of interest for stock traders
• data gathering and data quality issues
• statistical modeling, model fitting

Our First Article

• Too many parameters
• Selecting a metric to measure return
• How to efficiently use historical data

Over-parametrization can best be described as using a trading model with more than four parameters. The parameters are usually optimized on historical data. It is well known that this approach yields catastrophic results. However, it is possible to successfully work with 6 parameters if you follow a few rules. First, test the strategies on at least 200 trades. Then keep only the reliable strategies. To achieve this goal, test billions of parameter sets. For each set, introduce various forms of noise to see how it impacts performance. Then discard the vast majority of parameter sets that are too sensitive to noise. Do NOT look for the most efficient strategies, but instead for a strategy that is high-performing AND noise-resistant. To further improve the results, frequently update the parameters.

Metric Selection

When designing or using a strategy, one wants to measure its effectiveness. Always use two metrics: one that measures the average return over a long enough period of time (corresponding to at least 200 trades), and one that measures the volatility of the strategy. Strategy volatility is different from stock price volatility. While volatile stocks are interesting for short-term traders, volatile strategies are not. To reduce volatility, discard strategies with a large time span of negative growth at any given point.

Back-testing

Now, with the right metrics and the right number of parameters, let's try various kinds of testing. We will focus here on a strategy with daily buy and sell signals. One obvious way to simulate actual trading is to fit the strategy parameters with data that is between 31 and 180 days old, and then check the actual performance on the last 30 days. This is an improvement over classical backtesting. Also, when fine-tuning a strategy, put emphasis on performance during the last few weeks of the test-period. It helps to work with stocks that exhibit a rich variety of price patterns. Technical notes on all those topics are regularly published in Data Shaping's newsletter.