Developing a trading strategy for Bitcoin Market using Long Short-term Memory (LSTM) architecture

Prediction of financial markets time series market direction is a challenging task mainly due to the unprecedented changes in economic trends and conditions in one hand and incomplete information on the other hand. Therefore, developing different forecasting, like LSTMs, have been employed by quantitative traders recently.Long Short Term Memory networks (LSTMs”) – are a special kind of recurrent neural network (RNN), capable of learning long-term dependencies.

Modeling Commodity Marketplace for Proof of Work Networks

Recent market instability, volatility, and Bitcion (BTC) halving event combined to create significant challenges for this sector, resulting in many hashing power producers being forced into bankruptcy. The sector has grown very rapidly and has been plagued with boom-bust cycles that have been difficult for producers to weather due to the lack of hedging tools/financial instruments at their disposal.
Pow.re Corporation offers clearinghouse-type services providing hashing power producers the ability to sell their risk to speculators.

Modeling Commodity Marketplace for Proof of Work Networks

Recent market instability, volatility, and Bitcion (BTC) halving event combined to create significant challenges for this sector, resulting in many hashing power producers being forced into bankruptcy. The sector has grown very rapidly and has been plagued with boom-bust cycles that have been difficult for producers to weather due to the lack of hedging tools/financial instruments at their disposal. Pow.re Corporation offers clearinghouse-type services providing hashing power producers the ability to sell their risk to speculators.