A curated list of articles I’ve written about Stock Market and Cryptocurrency Analysis in Python.
This is a curated list of articles I’ve written about Stock Market and Cryptocurrency Analysis in Python. This list is regularly updated with new content about this topic.
It all started when I bought Stan Weinstein’s Secrets For Profiting in Bull and Bear Markets. In his book, Stan reveals his successful methods for timing investments to produce consistently profitable results.
Here are a few links that might interest you:
- Labeling and Data Engineering for Conversational AI and Analytics- Data Science for Business Leaders [Course]- Intro to Machine Learning with PyTorch [Course]- Become a Growth Product Manager [Course]- Deep Learning (Adaptive Computation and ML series) [Ebook]- Free skill tests for Data Scientists & Machine Learning Engineers
Some of the links above are affiliate links and if you go through them to make a purchase I’ll earn a commission. Keep in mind that I link courses because of their quality and not because of the commission I receive from your purchases.
Stock Market Analysis
3 Basic Steps of Stock Market Analysis in Python
Analyze Tesla stock in Python, calculate Trading Indicators and plot the OHLC chart. Includes a Jupyter Notebook with…
Buy and Hold Trading Strategy
Calculating the performance of the strategy with Backtester — a Python framework for backtes
Cryptocurrency Analysis with Python — MACD
Apply a simple trading strategy to cryptocurrency data
Cryptocurrency Analysis with Python — Buy and Hold
Which coin (Bitcoin, Ethereum or Litecoin) was the most profitable in the last quarter of 2017?
Cryptocurrency Analysis with Python — Log Returns
In the previous post, we analyzed raw price changes of cryptocurrencies. The problem with that approach is that prices…
LSTM for time series prediction
Training a Long Short Term Memory Neural Network with PyTorch and forecasting Bitcoin trading data
Backtesting a Bitcoin Trading Strategy
How to design and backtest a profitable Bitcoin Trading Strategy with a Python Backtesting framework.
Before you go
As usual, you can download this Jupyter Notebook to try examples on your machine.