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18 changes: 9 additions & 9 deletions README.md
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Expand Up @@ -9,10 +9,10 @@

##

### Introduction
### 1) Introduction
This repo makes use of the state-of-art Deep Learning algorithm to predict the price of Bitcoin, which has the potential to generalize to other cryptocurrency. It leverages models such as CNN and RNN implemented by [Keras](https://github.com/keras-team/keras) running on top of [Tensorflow](https://github.com/tensorflow/tensorflow). You can find more detailed illustration in this [blog post](https://blog.goodaudience.com/predicting-cryptocurrency-price-with-tensorflow-and-keras-e1674b0dc58a).

### Getting Started
### 2) Getting Started
To run this repo, be sure to install the following environment and library:

1. Python 2.7
Expand All @@ -23,22 +23,22 @@ To run this repo, be sure to install the following environment and library:
6. h5py=2.7.0
7. sklearn=0.19.1

### File Illustration
#### There are currently three different models:
### 3) File Illustration
#### i) There are currently three different models:
1. LSTM.py
2. GRU.py
3. CNN.py (1 dimensional CNN)

#### The validation result is plotted in:
#### ii) The validation result is plotted in:
1. Plot_LSTM.ipynb
2. Plot_GRU.ipynb
3. Plot_CNN.ipynb

#### Data is collected from [poloniex](https://poloniex.com) and parse to h5py file:
#### iii) Data is collected from [poloniex](https://poloniex.com) and parse to h5py file:
1. DataCollection.ipynb
2. PastSampler.ipynb

### Run
### 4) Run
To run the prediction model, select one of the model. For instance,
```
python CNN.py
Expand All @@ -48,11 +48,11 @@ To run iPython file, you need to run jupyter notebook
jupyter notebook
```
__Be sure to run DataCollection.ipynb and PastSampler.ipynb first to create database for training models.__
### Input & Output & Loss
### 5) Input & Output & Loss
The input consists of a list of past Bitcoin data with step size of 256.
The output is the predicted value of the future data with step size of 16. Note that since the data is ticked every five minutes, the input data spans over the past 1280 minutes, while the output cover the future 80 minutes. The datas are scaled with MinMaxScaler provided by sklearn over the entire dataset. The loss is defined as Mean Square Error (MSE).

### Result
### 7) Result
|Model | #Layers | Activation | Validation Loss |Test Loss (Scale Inverted) |
|----------| ------------- |------|-------| -----|
| CNN | 2 | ReLU | 0.00029 | 114308 |
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