Neural network strategy for Gekko
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README.md

gekko-neuralnet

Neural network strategy for Gekko

Install

copy the file(s) from /strategies/ into the strategies folder of your gekko install copy the file(s) from /toml/ into the /config/strategies/ folder of your gekko install

Install the modules in your gekko folder: npm install convnetjs mathjs

Example Configurations

config.NNv2 = {
  threshold_buy_bear: 2.0,
  threshold_buy_bull: 0.5,
  threshold_sell_bear: -0.5,
  threshold_sell_bull: -0.5,
  NN_SMMA_Length: 4,
  maFast: 20,
  maSlow: 720,
  decay: 0.5,
  price_buffer_len: 120,
  stoploss_threshold: 5
}

Usage / Configuration

// the neural network training method to use. Options are 'sgd', 'adagrad', 'windowgrad', 'nesterov'
method = 'adadelta'

// the treshold for buying into a currency. e.g.: The predicted price is 1% above the current candle.close

threshold_buy = 1.00

// the treshold for selling a currency. e.g.: The predicted price is 1% under the current candle.close
threshold_sell = -1.00

// The length of the candle.close price buffer. It's used to train the network on every update cycle.
price_buffer_len = 100

// The learning rate of net - not used for adadelta or adagrad
learning_rate = 0.01


// learning speed - not used for adadelta or adagrad
momentum = 0.9
//l2 decay
decay = 0.1

//minimum number of prictions until the network is considered 'trained'. History size should be equal
min_predictions = 720

//enables stoploss function
stoploss_enabled = false

//trigger stoploss 5% under last buyprice
stoploss_threshold = 0.95

If this strategy is useful for you and generates profits. Buy me a coffee, or two:

ETH 0xeb969152217062760104b2e17545647e05f1673b

BTC 16k9vwf4vDfF9ufnuu91WDjheVokPjh2X4

NANO xrb_3boer7rzaewcn583jrfid687znn5ncagp7tqi97pjnupxpr8ep6aor1dqrzy