CryptoPredictions

CryptoPredictions is an open-source toolbox for price prediction/forecasting a sequence of prices of cryptocurrencies given an observed sequence.

Why CryptoPredictions?

This library offers you a wide range of services that you may not find anywhere else. The exclusive benefits of CryptoPredictions are:

• At the outset of our work, we faced a serious challenge of dataset scarcity. Many papers and repos fetched the data through different websites, such as Yahoo Finance. However, we have overcome this obstacle by using platforms such as Bitmex, which offer a common structure for different currencies.

• Before the advent of our library, users had to run different codes for different models, making it difficult to compare them fairly. Fortunately, CryptoPredictions has made it possible to conduct a unified and equitable evaluation of different models. With Hydra, users can easily structure and understand arguments, making it easier to run codes on different settings and check results.

• By using Hydra, users have a better understanding of the arguments. Furthermore, it is far easier to run a code on different settings and check the result.

• While some models may perform exceptionally well in terms of accuracy, they often require a well-defined strategy for successful trading. Our backtester can help you determine the effectiveness of your model in real-world scenarios.

• We understand that evaluating models can be challenging, which is why we offer a variety of metrics to help you measure progress towards accomplishing your tasks. By analyzing multiple metrics, you can identify areas for improvement and correct what is not working. To learn more about the pros and cons of each metric, please refer to the metrics section here.

• At CryptoPredictions, we do not fetch indicators from different websites, because it leads to problems such as null rows and the lack of information on indicators for all cryptocurrencies. Instead, CryptoPredictions calculates them in a way that doesn't carry the mentioned problems and could be generalized to other datasets.

• We hope that it will inspire you to develop even better projects, and we look forward to your feedback. If you find CryptoPredictions useful and valuable, we would greatly appreciate it if you could take a moment to give it a star on Github. Your support would mean a lot to us and help us to continue improving the library for the community :)

Overview

The main parts of the library are as follows:

CryptoPredictions
├── train.py                            -- script to train the models, runs factory.trainer.py
├── backtester.py                       -- script to calculate the profit by selecting a strategy to buy and sell based on the prediction
├── models                    
│   ├── orbit.py
|   ├── prophet.py
|   ├── LSTM.py
│   ├── sarimax.py
|   ├── random_forest.py
|   ├── xgboost.py
|   ├── ...
├── data_loader
|   ├── CoinMarketDataset.py        
|   ├── Bitmex.py        
|   ├── ...

Getting Started

To get started as quickly as possible, follow the instructions in this section. This should allow you train a model from scratch, evaluate your pretrained models, and produce basic visualizations.

Dependencies

Make sure you have the following dependencies installed before proceeding:

• Python 3.7+ distribution
• pip >= 21.3.1

Virtualenv

You can create and activate virtual environment like below:

pip install --upgrade virtualenv

virtualenv -p python3.7 <venvname>  

source <venvname>/bin/activate  

pip install --upgrade pip

Requirements

Furthermore, you just have to install all the packages you need:

pip install -r requirements.txt  

Before moving forward, you need to install Hydra and know its basic functions to run different modules and APIs.
hydra is A framework for elegantly configuring complex applications with hierarchical structure. For more information about Hydra, read their official page documentation.

Hydra

In order to have a better structure and understanding of our arguments, we use Hydra to dynamically create a hierarchical configuration by composition and override it through config files and the command line. If you have any issues and errors install hydra like below:

pip install hydra-core --upgrade

for more information about Hydra and config files please visit here.

Dataset

You can use more than 15 cryptocurrencies data by giving the symbol of the selected cryptocurrency to the config files. Moreover, the csv files of these cryptocurrencies could be found in ./data .

Name	Symbol	Name	Symbol	Name	Symbol
Bitcoin	XBTUSD	Ethereum	ETHUSD	BNB	BNBUSD
Cardano	ADAUSD	Dogecoin	DOGEUSD	Solana	SOLUSD
Polkadot	DOTUSD	Litecoin	LTCUSD	TRON	TRXUSD
Avalanche	AVAXUSD	Chainlink	LINKUSD	Aptos	APTUSD
Bitcoin Cash	BCHUSD	NEAR Protocol	NEARUSD	ApeCoin	APEUSD
Cronos	CROUSD	Axie Infinity	AXSUSD	EOS	EOSUSD

Indicators

In order to have a richer dataset, library provides you with more than 30 indicators. You could select the indicators you want to have in your dataset and the library will calculate them and add them to the dataset.

The list of of available indicators supported by the library is as follow:

Name	Symbol	Name	Symbol	Name	Symbol
Simple Moving Average	sma	Weighted Moving Average	wma	Cumulative Moving Average	cma
Exponential Moving Average	ema	Double Exponential Moving Average	dema	Triple Exponential Moving Average	trix
Moving Average Convergence Divergence	macd	Stochastic	stoch	KDJ	kdj
William %R	wpr	Relative Strengh Index	rsi	Stochastic Relative Strengh Index	srsi
Chande Momentum Oscillator	cmo	Bollinger Bands	bollinger	Keltner Channel	kc
Donchian Channel	dc	Heiken Ashi	heiken	Ichimoku	ichi
Volume Profile	vp	True Range	tr	Average True Range	atr
Average Directionnal Index	adx	On Balance Volume	obv	Momentum	mmt
Rate Of Change	roc	Aroon	aroon	Chaikin Money Flow	cmf
Volatility Index	vix	Chopiness Index	chop	Center Of Gravity	cog

Metrics

The essential step in any machine learning model is to evaluate the accuracy of the model. The list of of available metrics supported by the library is as follow:

• accuracy_score: Number of correct predictions/Total number of predictions
• precision_score: the proportion of positively predicted labels that are actually correct
• recall_score: the model's ability to correctly predict the positives out of actual positives
• f1_score: 2.Precision.Recall/(Precision+Recall)
• MAE: Mean Absolute Error
• MAPE: Mean Absolute Percentage Error
• MASE: Mean Absolute Scaled Error
• RMSE: Root Mean Square Error
• SMAPE: Symmetric Mean Absolute Percentage Error
• Stochastic: the possibility that the outcome is not that expected, given that both the model and parameters are correct

Results

As a conclusion, we tested models using different cryptocurrencies on the various available metrics and the result is as follows:

You can see other graphs here.

Report

To gain a better understanding of the models, metrics, and validation method used in this library, we prepared a report and you can read it from here.

Our Team

• Amirhossein Alimohammadi
• Ahmad Nosratbakhsh