You could apply superwised machine learning.
Features: the values of the last n data points.
Labels: -1 descreasing +1 increasing
Now you only need some labeled samples This will give a table like this (here n=5):
# t-4, t-3, t-2, t-1, t, label
#-----------------------------
54, 43, 98, 1, 45, +1
21, 12, 5, 98, 4, +1
6, 78, 45, 65, 37 -1
...
now take some ready-to-use machine-learning library (like WEKA) and train a classifer on this. Depending on the algorithm you'll also get a measurement for the certainty of the result. How many labeled training samples you'll need is difficult to predict. Try 100 for a start, but it could also be more than 1000. There is also a lot of potential in the features. Maybe it works better if you don't use absolute values but differences.
The labeled training data can be labeled by hand. But for market prediction its often possible to do this automatically based on historical data.
There should be lots of literature about stock prediction. People have been (and still are) researching in this area for ages. The above is of course very primitve.
Another approch would be to fit curve though your data and take the derivative of it. The more you know about the data the stronger this approch will be. For example if you have a good idea of the probability model that produces the noise, you can derive the 'optimal' fit (max likelyhood and such). If you know something about your underlying signal (the one you try to measure) this helps too (is it linear? quadratic?, libschiz cont.?. bounded?, ...).
This approch requires problem specific knowledge that might not be available and a good deal of math. But it can be very awarding since you don't end up with a black-box like you would with machine learning, but with a matematical model that you understand and can analyze.