"Go West, young man" - Horace Greeley
- Project overview:
- train deep nueral network (DNN) model on historical cryptocurrency market data (BTC-USD, ETH-USD, etc.)
- features of the historical market data:
- date
- close (i.e., closing price of crypto product of choice)
- the model's hidden layers primarily utilize the LSTM architecture
- the DNN model's training process uses the following loss function(s):
- predict crypto prices given market data input (i.e., x_test)
- x_test is fed to the model manually for now, but will eventually pull from our internal DB of realtime prices
- make various trade decisions (buy/sell/hold) based on the confidence internal of our prediction
- make 95% successful trade predictions, barring global chaos
- This is an active side project that I maintain in my free time, don't use it in production environments.
If available in Hex, the package can be installed
by adding niner
to your list of dependencies in mix.exs
:
def deps do
[
{:niner, "~> 0.1.0"}
]
end
Documentation can be generated with ExDoc and published on HexDocs. Once published, the docs can be found at https://hexdocs.pm/niner.