machine learning classification with financial instrument/time series data
Consider that:


a "sliding window" approach can be used with any standard regression / classification algorithm. E.g. given the following time series

Time High Low Open Close Volume
  1   H1   L1  O1   C1     V1
  2   H2   L2  O2   C2     V2
  ...
  i   Hi   Li  Oi   Ci     Vi


you can feed the ML algorithm with these examples:

  O0, H1, L1, O1, C1, V1, H2, L2, O2, C2, V2, H3, L3, O3, C3, V3
  O1, H2, L2, O2, C2, V2, H3, L3, O3, C3, V3, H4, V4, O4, C4, V4
  O2, H3, L3, O3, C3, V3, H4, V4, O4, C4, V4, H5, V5, O5, C5, V5
  ...


(the first value is the target)

The size of the window (s) is an important limitation: the algorithm cannot correlate "candles" that lie more than s timesteps apart.

On the other end, with a large s, all examples appear to be sparse and dissimilar in many ways (see Curse of dimensionality).

You may use other features (e.g. averages, multiple timeframes, Japanese candlestick patterns...) but not without a good knowledge of the specific domain.
ML has tools that, in theory, can deal with timeseries. E.g. Recurrent neural networks can take advantage of their internal memory to process arbitrary sequences of inputs and exhibit dynamic temporal behavior. See Which machine learning algorithms can be used for time series forecasts? for references.