  This study proposes a scheme for stationarity analysis of stock price
fluctuations based on KM$_2$O-Langevin theory. Using this scheme, we classify
the time-series data of stock price fluctuations into three periods:
stationary, non-stationary, and intermediate. We then suggest an example of a
low-risk stock trading strategy to demonstrate the usefulness of our scheme by
using actual stock index data. Our strategy uses a trend-based indicator,
moving averages, for stationary periods and an oscillator-based indicator,
psychological lines, for non-stationary periods to make trading decisions.
Finally, we confirm that our strategy is a safe trading strategy with small
maximum drawdown by back testing on the Nikkei Stock Average. Our study, the
first to apply the stationarity analysis of KM$_2$O-Langevin theory to actual
mechanical trading, opens up new avenues for stock price prediction.
