How does feature learning work?
Feature learning, also known as representation learning, refers to the automatic identification and extraction of relevant features from raw data. In the context of deep learning, this is often done through the hidden layers of neural networks. Let's consider an example in the context of Forex trading, where you might want to predict the direction of a currency pair like EUR/USD.
Old-School Tactics:
Back in the day, you’d play it by ear, using your smarts to pick out features like a mixtape:
Groove to the Averages: You’d roll with the classics, like 5-day or 10-day moving grooves.
Chart-Topping Indicators: RSI, MACD – the hits that every trader hums.
Pattern Spotting: You’d keep an eye out for those market rhythms like ‘Head and Shoulders’ or ‘Double Top.’
Then you’d let your machine learning DJ spin the tracks, maybe with logistic regression or a random forest, getting those features to dance.
Jamming with Deep Learning:
But hey, let’s crank it up with some deep learning:
- Raw Tunes: Just feed in the raw market beats – those OHLC (Open, High, Low, Close) numbers – straight to the system.
If you go for a CNN, think of it like layering beats:
Base Layer: It starts simple, picking up on basic vibes in the market, like price jumps and dips.
Middle Layer: Then, it gets a bit more complex, mixing those simple beats into ‘U’ turns that might signal a market spin.
Top Layer: Finally, it gets sophisticated, layering those mixes to spot a full-blown ‘Head and Shoulders’ track.
Or maybe you want to hit it with an LSTM, which is all about the flow:
Opening Act: It tunes into the latest market moves, getting a feel for the ‘momentum.’
Build-Up: Next up, it senses when the momentum’s hitting hard – kind of like a ‘rate of change.’
Headliner: Then, it learns the big moves – spotting the crowd-pleasers, those ‘reversal patterns.’
In both deep grooves – CNN or LSTM – the system learns the hits directly from the raw data while it trains. You can skip the manual mixtape and maybe even discover fresh beats that shake up the performance.
That’s the cool part of jamming with deep learning – it’s like having a smart system that can learn the best moves from the raw vibes, often spinning better tracks than the traditional, handcrafted playlists.