The approach to most market timing in the early 1980s went something like this: Calculate a particular indicator over N days, buy the market when the indicator went to X and sell when it got to Y and, possibly, sell and go short when it got to Z. There usually would be a chart associated to demonstrate the virtues of the strategy based on the last half a dozen or so signals. This was around the same time when desktop computers were becoming common, if not at home, at least on the majority of traders' office desks.
With a background in probability and statistics, it was a simple leap to consider taking any simple indicator, such as a 10-day put/call ratio on an index of interest, have the computer calculate the measure for you and go back through history and quantify how the corresponding market had behaved in the past based on similar observations. Then, going forward, you would have not only a buy/sell signal, but a confidence of probability associated with each day. Somewhat basic at the time, the ease of obtaining and testing data in this way has come a long way in 25 years.
For traders, it seems turnkey. If the indicator is at, say, 1.20 (with 1.0 being the mean), go find all the 1.19-1.21s and see how much damage was done in this spot in the past. Trade accordingly going forward. Get rich. Retire.
Room for improvement
Most analysts hit on nuances of this approach almost instantly. For example, a 1.2 coming from a 1.3 is probably a different animal than a 1.2 that is coming from a 1.1. Certainly, there are profits to be had by making this distinction.
So, rather than searching for a simple 10-day moving average, let's add a second level of detail and calculate both a 10-day and a 20-day moving average and search on both. That way, we would know whether the 10-day moving average is coming from a higher or lower level depending on the measurement taken on the longer 20-day moving average.
It's also not much of a stretch to consider whether, say, high put/call ratings (around 1.20) could possibly mean one thing when the market itself is in an uptrend and something different when the market is in a downtrend. So, similarly, you can search for two additional variables based on the trailing price trend.
The next logical round of analysis is teaching the computer how to simulate the trading of the strategy and then mixing the indicator with a series of other relevant indicators for a slightly more complex multi-indicator model. Once you do this, you can experiment with different variations of the time intervals (10 and 20) and choose those that fit the indicator more precisely. With a little work, you can set the computer up to study millions of combinations of all the variables and optimize to get the best performance over history.