AI

We explore what is carry trading and how to apply it in Python. Why today's economic landscape it is a profitable strategy and what risks are associated with it.

Uncover hidden insights in your data with these 4 methods for smoothing a line. From rolling windows to spline interpolation, we'll reveal the secrets you need to know to unlock the full potential of your data.

Learn tips for successful web scraping in Python: check website's rules, request rate, dynamic content, and data format. Scrape responsibly!

"Use the schedule library in Python to schedule tasks to run periodically by installing the library, defining a function to be run, scheduling the function using schedule.every(interval).unit.do(function), running pending jobs using a while loop, and giving the CPU a break using time.sleep(1)."

Learn how to compute the Kelly Criterion in Python with this script example, which shows how to use the formula to determine the optimal fraction of bankroll to bet, using the probability of winning, payout ratio, and probability of losing. #KellyCriterion #Python #Trading

Cython allows you to write high-performance Python code by using C-level parallelism and low-level operations. Learn how to install, create and import Cython files, and optimize performance with Cython. #Cython #Python #Performance

Discover how to implement a mean-reverting strategy in Python using the Pandas library, and how to use it to make buy/sell decisions in financial markets. #MeanReverting #Python #Finance

Learn how to implement a simple trend-following algorithm in Python using the Pandas library, and how to use it to make buy/sell decisions in financial markets. #TrendFollowing #Python #Finance

Discover various methods to analyze the correlation between two variables in Python using functions such as numpy.corrcoef(), pandas.DataFrame.corr(), scipy.stats.pearsonr(), scipy.stats.spearmanr() and seaborn.pairplot() to understand the relationship between the variables.