Crypto Trading BOTS

[El Bringy]

Hello!

Yesterday I got endorsed into cryptocurrencies and how I can benefit from it while asleep.

Developed a safe-to-use crypto-bot that should buy at a lower price than the market price and sell at a slightly higher price than the entry price (to ensure at least 0.2% rise after moving down) this bot is supposed to work as high-frequency trading bot it will connect with Binance, using limit_orders to make stopLoss and profit margins.

if anybody would like to share some info about Python crypto bots would be nice since I'm trying to finalize the bot as 100% safe to use adaptable to market as it uses machine learning to understand the market historical (OHCV) data & trends, which allows the code to predict the price of the following second and either to buy or not, the code re-trains itself on latest data to give the best input features for the LTSM model training.

I think I implemented enough techniques including market bands & RSI but it feels like something is missing, like for example if the code didn't make a purchase but it thinks it did but binance failed it will sell as it made a past purchase, anybody is doing the same with python or have idea how to make that code work seamlessly?

any instruction or tips into making this bot safe to use on spot account with 500+USD like safety measurements OR technique & logic required im sure im missing alot, i also don't mind sharing the whole code didn't feel like discussion is the good place for it.

 

[Tenzhiro]

Hello!

Yesterday I got endorsed into cryptocurrencies and how I can benefit from it while asleep.

Developed a safe-to-use crypto-bot that should buy at a lower price than the market price and sell at a slightly higher price than the entry price (to ensure at least 0.2% rise after moving down) this bot is supposed to work as high-frequency trading bot it will connect with Binance, using limit_orders to make stopLoss and profit margins.

if anybody would like to share some info about Python crypto bots would be nice since I'm trying to finalize the bot as 100% safe to use adaptable to market as it uses machine learning to understand the market historical (OHCV) data & trends, which allows the code to predict the price of the following second and either to buy or not, the code re-trains itself on latest data to give the best input features for the LTSM model training.

I think I implemented enough techniques including market bands & RSI but it feels like something is missing, like for example if the code didn't make a purchase but it thinks it did but binance failed it will sell as it made a past purchase, anybody is doing the same with python or have idea how to make that code work seamlessly?

(Sorry OCD)

 

[El Bringy]

Hello!

Yesterday I got endorsed into cryptocurrencies and how I can benefit from it while asleep.

Developed a safe-to-use crypto-bot that should buy at a lower price than the market price and sell at a slightly higher price than the entry price (to ensure at least 0.2% rise after moving down) this bot is supposed to work as high-frequency trading bot it will connect with Binance, using limit_orders to make stopLoss and profit margins.

if anybody would like to share some info about Python crypto bots would be nice since I'm trying to finalize the bot as 100% safe to use adaptable to market as it uses machine learning to understand the market historical (OHCV) data & trends, which allows the code to predict the price of the following second and either to buy or not, the code re-trains itself on latest data to give the best input features for the LTSM model training.

I think I implemented enough techniques including market bands & RSI but it feels like something is missing, like for example if the code didn't make a purchase but it thinks it did but binance failed it will sell as it made a past purchase, anybody is doing the same with python or have idea how to make that code work seamlessly?

(Sorry OCD)

Edited main post thanks :'D

 

[Monacomer]

I’ve been messing with a few self-made bots using simple strategies like RSI or MACD, but they need constant tweaking since market conditions shift fast and unexpected stuff can break everything.

 

[El Bringy]

I’ve been messing with a few self-made bots using simple strategies like RSI or MACD, but they need constant tweaking since market conditions shift fast and unexpected stuff can break everything.

Instead tweaking it myself i just let LTSM model (machine learning) study historical data and improve the signatures for each indicator to define the best entry position to catch the desired return of revenue %, since crypto is very volatile i also use high frequency trading for 0.5% more than the trading fee to make 100+ trade per hour succes rate used to be 90% last time i tried which is supposed to be huge, im still improving it to have a web application to monitor logs, execute & stop trading and better management.

Better use LTSM highest priority on the last 1 year and 1 month and general input on the whole historical data available, for binance you can use ccxt library (python)

It's easy to use class weights with TensorFlow for this purpose. See the class_weight parameter for model.fit(): (source : stackoverflow)

https://www.tensorflow.org/api_docs/python/tf/keras/Model#fit

 

[Monacomer]

I’ve been messing with a few self-made bots using simple strategies like RSI or MACD, but they need constant tweaking since market conditions shift fast and unexpected stuff can break everything.

I messed around with a few bots last winter, and most of them were either too basic or too risky without much control. Ended up checking out https://quantumaiofficial.com/ after a buddy mentioned it, and it's been decent so far—super simple to set up, and the settings give you enough control without needing to be a pro. Still testing it, but no major complaints yet.