>be you
>think about investments constantly
>no life
>be wrong and get rekt
>live psychological roller-coaster
>be me
>let ml algorithm think for me
>live my live without think about investments
>algo's right 80% of time
>never miss more than 60% of good trades to make
You just never learn...
Other urls found in this thread:
en.wikipedia.org
github.com
github.com
github.com
github.com
twitter.com
Make discord fren
What does it trade?
How long have you been studying ML?
nope, fren
I am not instagram influencer
>How long have you been studying ML?
2 month at good pace with lots of book
>What does it trade ?
BTCUSD scalp at even tp/sl distance from entry price
Also, I have not tested extensively the algorithm, it's just cross validation scores from the training test, not the actual test. But its classifies quite well I'm surprised.
When you have a lot of features (indicators), Ridge Regressors seems to outperform anything.
I'm testing decision tree RN.
Pic related is my homemade trading bot, I have not run it in a while.
Actually someone paid me to do it, but back in the days I had not extensive time to work on algorithms, I was just sprinting on the framework and ui dev.
What does the architecture look like? RNN? LSTM?
What does the test data look like? Has to be more than just labels and data right? Doesn't the bot have to choose when to sell/buy in real time? What does this "precision" metric actually mean?
where do I start to learn this?
t. electrician
If you don't know maths it will sometimes be difficult, but I read:
Hands On Machine Learning by someone at Google.
Use it with either google collaboratory or the new Jupyter Lab, and read scikit-learn documentation and user manual as much as you can.
>What does the architecture look like? RNN? LSTM?
God, it's machine learning, not deep learning.
Deep Learning is very costly in processing power, and it would often overfit. None of this memetic shit is required for such serious problems. Here I'm just using Ridge Polynomial Regressor (Linear Regressor witch minimises the sum of features total weight sum, and that extends features with polynomial features module). Also data is scaled.
I have put 77 features in. But with poly augmentation (2nd degree), it makes many thousands.
ML is based on retrofitting so if you use retro data it'll always perform above average. Come back when can sustain 1 year of real gains.
also come back when we're not in a ridiculous bull market where any buy & holder is making much more than a trader or a trading algo (who do you think made more money, those who held TSLA or those who've been trading it up and down?)