Practical Checks for Backtesting a Value and Technical Stock Screen
Summary
The document considers a stock-selection score that awards points for improving return on equity, gross margin, sales, and leverage, as well as price above its 200-day average and a sequence of rising lows. It suggests testing whether the score relates to subsequent stock returns, then highlights practical issues that can make a backtest differ from live trading.
The recommendations include checking and correcting source data, and incorporating liquidity, slippage, commissions, financing costs, and realistic execution prices. A strategy’s portfolio role also needs consideration: capital allocation, interaction with other models, and fit with the investor’s risk and return objectives all matter. These points improve the realism of a test but do not establish that the proposed score predicts returns. The document gives no performance results or detailed statistical design; choices such as test period, survivorship bias, and out-of-sample validation remain unspecified.
Key ideas
- A point-based stock screen can be evaluated against subsequent returns, but a score-return relationship alone does not establish a robust strategy.
- Backtests should use carefully checked data and account for liquidity and execution constraints.
- Slippage, commissions, and financing costs can materially affect simulated performance.
- Portfolio sizing and the strategy’s interaction with other models should be considered before deployment.
- The recommendations do not specify a full statistical validation design or report test results.
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Full text
# Backtesting of value and technical analysis # Backtesting of value and technical analysis I am an economic last year student trying to figure out how to backtest my model. It consist on several requirements imposed to the stocks before buying them and a very simple exit strategy. I first impose the condition to the stocks of showing improvement in the following parameters: - Return on Equity - Gross margin - Sales - Leverage - Being above its 200 daily arithmetic mean - Achieve at least three increasing minimums Each company receives one point for each requirement accomplished, ranging from zero to six. My idea is to make a regression between the returns of the stocks and the punctuation obtained in the test to see if there is a positive correlation. I think that it may be very simple but since I just started in the quant finance it would be great to have some feedback and recommendations about how to improve my backtesting. ## Answer by amdopt (score 3) https://quant.stackexchange.com/a/32997 > I think that it may be very simple but since I just started in the quant finance it would be great to have some feedback and recommendations about how to improve my backtesting. From my experience, most who begin testing a model straight from academia overlook several things that are quite different in the real world. Factoring them in will help to make your model's test more accurate and will help you experience less variance when you take the step from simulation to live trading. - Clean your data. In all my years I have never found a data source that is always clean. Have some code that combs through your data before you put it into a database. You will have to correct errant pieces of data by hand at times and it can be tedious and annoying but it is necessary. - Factor in liquidity, slippage, commissions and financing costs to all your models. Portfolio's don't finance themselves, leverage is not free, commissions add up and you can't just buy or sell $1B of a security at a specific price just because your model wants to. Your execution price is the one at which someone else is willing to take the other side of your trade and it is often not your model's price. - If you become satisfied with a model, congratulations but you are only half way done. You now have to determine how you are going to deploy it. By this I mean that you will need to determine how much of your portfolio you will put to work with this model. Will this model run 100% of your portfolio? Will it be mixed with other models? Does the risk/return/volatility profile of this model fit in with what you are trying to accomplish? These are questions that only you can answer personally and the answer will come via an optimization process that you choose or create. Best of luck!
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