Python Code Streamliner on the GPT Store
Introduction to Python Code Streamliner
With its extensive knowledge base and intuitive understanding of programming concepts,
Whether you're a beginner looking to learn Python, a data scientist working with Pandas dataframes, or a developer in need of assistance with algorithmic trading or data visualization,
GPT Description
GPT Prompt Starters
- Add ReduceLROnPlateau and Early Stopping callbacks to this training: hist = model.fit(X_train, y_train, validation_split=0.1, epochs=50, batch_size = 35)
- Write a function that simulates a stochastic oscillator for algorithmic trading
- Define a daily date range spanning 20 years. Then write the Matplotlib code that, given an interest rate r and an initial capital Ci, plots the following two time series Cf_sIR = Ci * (1+ r* (t/365) ) \ Cf_cIR = Ci * (1+r)**t
- date.today() returns 2024-02-20. Write the code that returns a string with '20/2/2024' in this case
- Add ReduceLROnPlateau and Early Stopping callbacks to this training: hist = model.fit(X_train, y_train, validation_split=0.1, epochs=50, batch_size = 35)
- Create a function that calculates a one-hot encoded matrix given an input data frame and a list with the columns we want to one-hot encode. Then create a function that calculates the Euclidean distance between any two items in the original data frame (identified by their index label), based on the one-hot encoded values obtained from the first function
- Given a Pandas dataframe markowitz_rets, with expected daily returns for a given year (index: date, columns: assets), the covariance matrix (mat_cov), and a risk free rate (rfr), write a function (using a library such as scipy) that optimizes the Sharpe ratio of a portfolio, returning weights, expected returns, expected volatility (standard deviation) and Sharpe ratio
- Who is your author?
Python Code Streamliner GPT FAQs
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