Our skills in trading automation include knowledge and experience using Python, PineScript, R, Solidity; API for Binance, CryptoCompare, and TradingView.
It was developed software for processing and analyzing data from APIs, websites, web services, and databases with trading analytical data and exchanging information. This software uses such sources as MarketWatch, Kitco, Binance, CryptoCompare, BlockchainCenter, TradingView, DataRade.AI, YahooFinance, SimFin, and others sources.
Trading on the stock exchange differs in terms: short-term, medium-term and long-term. There are traders who manage to make money on minute timeframes trading - they are called scalpers. There are those who trade on hourly timeframes – intro-day traders. We create software products for trade analytics and conduct training on their usability.
'BTC', 'ETH', 'BNB', 'SOL', 'ADA', 'XRP', 'DOT', 'DOGE', 'AVAX', 'LTC', 'MATIC', 'ALGO', 'BCH', 'EGLD', 'VET', 'XLM', 'ICP', 'TRX', 'ATOM', 'THETA', 'FIL', 'ETC', 'FTM', 'HBAR', 'NEAR', 'HNT', 'XTZ', 'XMR', 'FLOW', 'EOS', 'RVN', 'KLAY', 'ONE', 'XEC','SRM', 'KSM', 'ONT', 'NEO', 'ZEC', 'STX', 'WAVES', 'AR', 'DASH', 'CELO', 'IOTX', 'BTG', 'XEM', 'DCR', 'QTUM', 'ROSE', 'MINA', 'ZIL', 'SC'.
Binance, TradingView, min-api.cryptocompare, blockchaincenter.net, alternative.me, marketwatch.com, kitco.com, and other APIs and websites.
indexes, metrics, indicators, strategies signals, and others. Analytical parameters after processing and calculations: 101.
Collected data were analyzed with R libraries. Technologies used: Pandas, Matplotlib, SeaBorn, Requests, HTTPAdapter, LXML, Binance API, TradingView API, NumPy, SymPy, Dplyr, Forecast, Tidyr.
Powerful artificial neural networks for predicting the course of cryptocurrencies. Technologies used: NumPy, Matplotlib, Keras, TensorFlow. It is developed 5 configurations for neural networks. One configuration as an example: neurons activation function - ELU, layers number - 53, input parameters- 101, output parameters - 4, layers type – Dense, optimizer - Adam, algorithm convergence step - 0.0000195, loss function – MAE, metrics – MAPE, Iterations of training: 600 epochs, batch size: 30. Percent of correct predictions: 72%.
Large ARM model for exchange course predicting. Based on statistical data that include indicators, indexes, support and resistance levels, and history of course changing. Technologies used: TensorFlow, NumPy, SymPy.
The dashboard is created to display the stock glass as a depth chart, recognize and display accumulations of buy and sell orders (bids/asks), and determine local and global support and resistance levels.
The indicator system is written in the PineScript language to display support and resistance channels.
The indicator system written in PineScript is developed for dynamic display of support and resistance blocks.
The indicator system written in the PineScript language is created to display local and global volume clusters.