This repository houses a collection of Jupyter Notebooks I've developed and shared publicly on Kaggle. Explore data analysis projects, machine learning models, and other insightful explorations across diverse datasets. Feel free to use, adapt, and contribute to these notebooks as you embark on your own data science journey!
In this notebook, we build and train a Multi-Step / Multi-Output Regression Model powered by TensorFlow & Keras in order to predict Bitcoin's future trend.
KeyZones are price levels that act as barriers to price movement. Support levels are prices where buyers are likely to step in and buy, preventing the price from falling further. Resistance levels …
Understanding the price state of Bitcoin, stocks or any other asset is essential to open/increase/reduce/close positions as it can reveal the trend, as well as overbought and oversold states.
When coding automated trading systems, it is essential to have a deep understanding of how trades are filled and how the price is affected by the existing liquidity (Bids & Asks).
When trading Bitcoin, stocks or any other asset, it is very common to execute several trades at different prices for different amounts. Therefore, the mean of the prices won't suffice to ...
When trading Futures Contracts, the allocated funds are used as collateral by the Exchange Platform in order to be able to open a position based on the desired leverage. If the asset's price moves against the position ...
The purpose of this Notebook/Utility Script is to provide a series of helper functions that will simplify the development of more complex projects.