Hi friends,
Is there any guide on how to optimize the parameters for neural strategies? I found several posts about using the genetic algorithm to optimize the parameters. I did not see any information if it can be used for neural strategies as well. However, I could not run the genetic algorithm and appreciate if anybody could help. The steps mentioned by Gekko warez did not work for me. I found some others had the same problem with running the genetic algorithm.
Thanks,
I help you with the config.
All other things solve by yourself.
Apply that pull request from git.
Code: const randomExt = require('random-ext');
const config = {
stratName: 'neuralnet',
gekkoConfig: {
watch: {
exchange: 'binance',
currency: 'BTC',
asset: 'NCASH'
},
daterange: {
from: '2018-04-10 19:15',
to: '2018-04-14 19:13'
},
simulationBalance: {
'asset': 0,
'currency': 100
},
slippage: 0.10,
feeTaker: 0.10,
feeMaker: 0.10,
feeUsing: 'maker', // maker || taker
},
apiUrl: 'http://localhost:3000',
// Population size, better reduce this for larger data
populationAmt: 20,
// How many completely new units will be added to the population (populationAmt * variation must be a whole number!!)
variation: 0.5,
// How many components maximum to mutate at once
mutateElements: 3,
// How many parallel queries to run at once
parallelqueries: 8,
// profit || score
// score = profit * sharpe -- feedback?
// profit = recommended!
mainObjective: 'profit',
// optionally recieve and archive new all time high every new all time high
notifications: {
email: {
enabled: false,
receiver: '',
senderservice: 'gmail',
sender: '',
senderpass: '',
},
},
candleValues: [ 2, 3, 5 ],
getProperties: () => ({
// Strat settings must be flattened and cannot be nested for mutation to work properly!
historySize: 20,
threshold_buy: randomExt.float(1.20,0.30).toFixed(2),
threshold_sell: randomExt.float(-0.30,-0.80).toFixed(2),
price_buffer_len: 100,
learning_rate: randomExt.float(1.10,0.01).toFixed(2),
momentum: 0.10,
decay: 0.10,
min_predictions: 20,
candleSize: randomExt.pick(config.candleValues)
})
};
module.exports = config;
And ofc tailor the parameters as you wish
Good luck
Hi Remo,
I appreciate your help. It's working now, let's see what will be the best output. On a different note, if you are using the neural network, I'm wondering what is your impression about it. There are many classical strategies which use a combination of indicators like RSI and MACD. Do you think sophisticated strategies like neural network works better than those classical ones? Any advantage or disadvantage? Also, since we are in a bearish market now, what is your suggestion for the time period of backtesting? One month, two or more?
Thanks
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