Python Code Streamliner
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Python coding assistant. Ask Python-related questions and PCS will answer. Request code and PCS will generate it. Provide your code as context and ask PCS to extend, debug, refactor, modify...
Python Code Streamliner Features
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ChatGPT Bot
Custom bot powered by ChatGPT technology. May behave differently from regular ChatGPT.
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Try These Prompts
Click on an example to start a conversation:
- 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?
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Tips for Best Results
- 🔍 Be Specific: The more detail you provide in your prompts, the better Python Code Streamliner can understand and fulfill your request.
- 🌍 Provide Context: If your query relates to something specific, include that context. For example, instead of "Write a story," try "Write a story about a brave knight in a medieval setting."
- 🔄 Iterate and Refine: Don't expect the perfect answer on the first try. If the initial response isn't quite right, ask follow-up questions or rephrase your prompt to guide the AI.
- 🧪 Experiment: Try different types of questions and tasks to explore the capabilities of Python Code Streamliner.
- 💬 Use Clear Language: Avoid ambiguity and overly complex sentences for clearer communication.