Predict binary options using brain.js
https://github.com/BrainJS/brain.js
This is a test using this course:
https://scrimba.com/playlist/pVZJQfg
For now it is underdevelopment.
The raw data(raw.json) was extracted from iqoption
it was handled to create a period like this:
Each expiration is a purchase time.
this image represents one expiration object bellow
{
"EURUSD": [
{
"expiration": 1546736640,
"candles": [
{
"active_id": 76,
"size": 5,
"at": 1546736642035200800,
"from": 1546736640,
"to": 1546736645,
"id": 7471732,
"open": 1.135453,
"close": 1.135449,
"min": 1.135449,
"max": 1.135453,
"ask": 1.135449,
"bid": 1.135449,
"volume": 0,
"phase": "T"
},
{
"active_id": 76,
"size": 5,
"at": 1546736642634810600,
"from": 1546736640,
"to": 1546736645,
"id": 7471732,
"open": 1.135453,
"close": 1.135452,
"min": 1.135449,
"max": 1.135453,
"ask": 1.135452,
"bid": 1.135452,
"volume": 0,
"phase": "T"
}
]
},
{
"expiration": 1546736640,
"candles": [
{
"active_id": 76,
"size": 5,
"at": 1546736642035200800,
"from": 1546736640,
"to": 1546736645,
"id": 7471732,
"open": 1.135453,
"close": 1.135449,
"min": 1.135449,
"max": 1.135453,
"ask": 1.135449,
"bid": 1.135449,
"volume": 0,
"phase": "T"
},
{
"active_id": 76,
"size": 5,
"at": 1546736642634810600,
"from": 1546736640,
"to": 1546736645,
"id": 7471732,
"open": 1.135453,
"close": 1.135452,
"min": 1.135449,
"max": 1.135453,
"ask": 1.135452,
"bid": 1.135452,
"volume": 0,
"phase": "T"
}
]
},
I simplified this data to first tests.
then my training data is like this:
where the inputs are all "close" from a purchase time
the output is what action is better for this period.
In a future version I will analyse all candles and see if this purchase time is profitable and when I should buy in this purshase time.
[
{
"input": [
1.000043156596794,
1.000043156596794,
1.000045798837414,
1.000045798837414,
1.0000493218249076
],
"output": [
"buy"
]
},
{
"input": [
1.0000493218249076,
1.0000493218249076,
1.0000493218249076,
1.000047560331161,
1.000047560331161
],
"output": [
"sell"
]
},
{
"input": [
1.0000502025717808,
1.0000502025717808,
1.000022899418707,
1.000022899418707,
1.0000237801655805
],
"output": [
"sell"
]
},
{
"input": [
1.0000237801655805,
1.0000299453936938,
1.0000299453936938,
1.0000264224062005,
1.0000264224062005
],
"output": [
"buy"
]
}
]
I'm using brain.recurrent.LSTMTimeStep
error on console: Uncaught Error: network error rate is unexpected NaN, check network configurations and try again
to calculate indicators https://www.npmjs.com/package/technicalindicators
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