- Run
./src/data-pre-processing.ipynb
Change parameters as below:
# Currency use in comparison to Bitcoin.
currency = 'ETH'
# Time Interval (oneMin, fiveMin, halfHour, hour, day...).
tickInterval = 'fiveMin'
# Row limit to process.
limit = 2000- Run
./src/prediction.ipynb
The main component in any Market prediction problem is the price. But this time we also need another 3 features. It's suggested to use some textual features but following Siraj's advice "Be creative, good luck!", we are going to use three popular technical indicators that (hopefully) will help the model predict the price (as they help human traders to do the same task).
There are A LOT of different technical indicators for market analysis. The universe of "technical analysis" for trading is vast. Most of it is out of our scope, so we're just going to present three simple technical indicators, each specialized in a different task.
The Moving Average Convergence/Divergence oscillator (MACD) is one of the simplest and most effective momentum indicators available. The MACD turns two trend-following indicators, moving averages, into a momentum oscillator by subtracting the longer moving average from the shorter moving average.
The Stochastic Oscillator is a momentum indicator that shows the location of the close relative to the high-low range over a set number of periods.
Is an indicator to measure the volalitility (NOT price direction). The largest of:
- Method A: Current High less the current Low
- Method B: Current High less the previous Close (absolute value)
- Method C: Current Low less the previous Close (absolute value)
A (more appropiate) recurrent neural network model. Details in the corresponding Notebook "./src/prediction.ipynb"
- jupyter
- json
- urllib (for fetching data)
- matplotlib (for plotting)
- pandas (for dataset manipulation)
- numpy
- tensorflow