Many traders use stop-losses in their techniques to minimize losses. But what is the right stop-loss to choose? 3%? 9%? 15%? In this research project, I tried to answer that question and find optimal stop-losses.
I used a simple conditional probability model to find optimal stop-losses for tech stocks satisfying the following criteria:
- $1 - $10bn market cap
- Middle 75% in volatility
- Middle 75% in volume
I found that stop-losses are not useful for protecting against future losses. Main reasons:
1. Percent loss so far is not a good predictor of further losses.
2. It appears that when given a percent loss, the investor can, on average, expect to gain back substantially by staying invested, waiting for the rebound, and then selling.
There is still much work to be done -- this is a very big topic, and I've only put a small dent in it. In the notebook, I indicated opportunities for further investigation. Engage the analysis by:
1. Cloning the notebook
2. Playing with the parameters (it's easy! The final section explains how).
3. Sharing what you find! I'd be interested in further collaboration.
Error Correction July-28-2015: In the "get_volatility" function, the line
volatility = sigma_period * math.sqrt(len(opens)) should actually be
volatility = sigma_period / math.sqrt(len(opens)). Thankfully, this doesn't impact the results at all.