It's getting easier to look at a chart and figure out what to do. For example, recently I've been using bollinger bands on the D1 and trading infrequently when something presents itself.
It's more difficult to turn this intuition into an algorithm and quantify it. I suppose I need to come up with clever ways to localize a local bottoming or topping event and then characterize the shape of it.
What I am relatively good at is finding moments of extreme extension. Unless a major new trend is about to occur these can indicate reasonable entry points. For example, if the price is getting "far" away from a moving average and price action is respecting a boundary or throwing a good candlestick event then a robot can take action based on well defined situation.
There are also "warnings" available. If the price is moving very fast then what is considered a good entry point may be a lot riskier. This type of thing can be examined across multiple time frames to detect a larger scale event while trading on lower time periods.
The latest thought and robot experiment is to collect these little nuggets of interpretation. Accumulate the "value" of a list of reasons that it looks like a good time to trade and compare to the "value" of those reasons that a trade should not be entered.
To illustrate, let's assume our robot is "thinking" about entering a long trade on the USDJPY pair:
- Bid price is 8 pips below the m15 moving average (+ 0.08)
- Bid price is touching the lower h1 bollinger band (+ 0.10)
- Bid price is within one pip of middle h4 bollinger band (+ 0.05)
- The h1 bollinger band was recently pinched
- The previous m15 candle dropped 25 pips
Perhaps many robots would be better than people expected if they were able to look for more reasons not to enter a trade?
