Posted 2 CommentsPosted in Datathons Solutions, Learn, Team solutions

TOOL USED R MICROSOFT AZURE MICROSOFT EXCEL SUMMARY The Dataset is time series data of crypto currency consisting of of 1869 observation and 21 features(each feature showing different crypto currency). Frequency of the observations is 5 min showing from date 18/01/18 to 24/01/18. BUSINESS UNDERSTANDING As the data belongs to the crypto exchange. Intraday short-term […]

Prediction Model for Crypto Currency in R

Posted 2 CommentsPosted in Datathons Solutions, Learn, Team solutions

Bitcoins are cryptocurrency systems, that enable its users to exchange payments without passing through a central authority (Eg. Reserve Bank of India, Federal Bank etc). They were developed in 2008, using the Blockchain Technology. In the present article, methods to create prediction models have been implemented. The model considers a sample data of 3 months spaced over 5 minutes for each day. The Training data and Testing data are developed on that dataset for twenty bitcoins; viz: Bitcoin, Bitcoin Cash, Bitcoin Gold, Cardeno, Dash, Dogecoin, Eos, Ethereum, Ethereum Class, Iota, Lisk, Litrcoin, Monero, NEMcoin, Neo, Ripple, Stellar, Tether, Tron, Zcash.
The prediction models used are ARIMA, Exponential Smoothing and Neural Networks on R. The models calculate the values for the next time instant, i.e. next five minutes and the code developed goes on continuing it (predicting next 5-minute price) for all 288 time-points in a day.

THE A.I. CRYPTO TRADER: cryptomonkeys

Posted 3 CommentsPosted in Datathons Solutions, Learn, Team solutions

  The Folder where you can locate the data & the code (using Jupyter). Академичен дататон¶ In [1]: import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns In [2]: WRITE_TO_FILE = False In [3]:‘bmh’) pd.options.display.precision = 3 Зараждане на данните от price_data.csv¶ In [4]: %%time raw_df = pd.read_csv(‘./raw_data/price_data.csv’) # CPU […]

Cryptocurrency Prediction by Kautilya

Posted 6 CommentsPosted in Datathons Solutions, Learn, Team solutions

Given the cryptocurrencies’ data, we aim to forecast the future cryptocurrencies’ prices so as to execute profitable trades. We show that the cryptocurrencies’ prices also exhibit desirable properties such as stationarity and mixing. Some classical time series prediction models that exploit this behavior, such as “Arima” models produce poor predictions and also lack good probabilistic interpretations. We have introduced a theoretical framework in the 1st place and for predicting and trading prices of the cryptocurrencies for future and based on that we have designed our model which is based on “Neural Network” model which can give better prediction values as compared to the other models.

DAB PANDA: The A.I. Crypto Trader

Posted 7 CommentsPosted in Datathons Solutions, Team solutions

Team members: Ana Popova, @anie Izabella Taskova, @ izabellataskova Kamelia Kosekova, @kameliak Kameliya Lokmadzhieva, @kameliyalokmadzhieva Nikolay Bojurin, @nikolay Mentors: @boryana @alex-efremov @pepe   Team name: DAB PANDA Team logo:   NB!!!! OUR NOTEBOOKS ARE AVAILABLE HERE:  DAB PANDA Rmds   Data Understanding and Preparation You may see our code with results and brief comments if you […]

A venture in crypto-currency trading

Posted 5 CommentsPosted in Datathons Solutions, Learn, Team solutions

In [315]: import numpy as np import pandas as pd import matplotlib.pyplot as plt from statsmodels.tsa.seasonal import seasonal_decompose from statsmodels.tsa.arima_model import ARIMA from import plot_acf, plot_pacf import time Enthusiast Team: Datathon Case¶Predicting Cryptocurrency prices¶Reading the Currencies for the First Problem¶ In [88]: currency_info = pd.read_csv(“currencies.txt”, sep = “\t”) currency_info Out[88]: Currency ticker CoinID 0 Bitcoin BTC […]