fingpt/FinGPT_Others/FinGPT_Low_Code_Development/chatgpt-low-code-development-v1/readme.md

ChatGPT Low Code Development V1: Factors Development

In financial companies, software development is an indispensable process, particularly the development of factors. Building a factor library has historically been a time-consuming and complex endeavor. We demonstrate that the strong code generation capability of ChatGPT significantly reduces the time and effort required by using prompt engineering

Round 1

init prompt:

You are now a senior software development engineer in top financial company. You will help me to build a factor library in Python today.

ChatGPT's response (More in this file):

Sure, I'd be happy to help you build a factor library in Python for your financial company. Let's get started!

A factor library is a useful tool for quantitative analysis and modeling, which helps in evaluating and creating investment strategies. It provides a set of reusable functions and classes that can be used to calculate various factors or indicators based on financial data.

To build a factor library in Python, we'll start by creating a new Python module to house our library. Let's call it "factor_library.py". Open a text editor or an integrated development environment (IDE) and create a new file with that name.

Here's a basic outline of the steps we'll take to build the factor library: ...

Round 2

prompt 1:

First, please help me design the base class of all factors

ChatGPT's response (More in this file):

Certainly! When designing the base class for all factors, it's important to consider the common functionality and attributes that factors will share. Here's an example of how you can design the base class for factors in Python:

import pandas as pd

class Factor:
    def __init__(self, df):
        self.df = df

    def calculate(self):
        """
        This method should be implemented in each derived factor class.
        It should contain the specific logic to calculate the factor value.
        """
        raise NotImplementedError("Subclasses must implement calculate method.")

...

Round 3

prompt 2:

Second, please help me design 5 fundamental factors based on the base class

ChatGPT's response (More in this file):

Certainly! Here are five examples of fundamental factors that you can design based on the base Factor class:

1. Price-to-Earnings Ratio (P/E Ratio):

class PERatioFactor(Factor):
    def calculate(self):
        return self.df['close'] / self.df['earnings_per_share']

In this example, the calculate method divides the closing price of each data point by the corresponding earnings per share to calculate the P/E ratio factor.

2. Price-to-Book Ratio (P/B Ratio):

class PBRatioFactor(Factor):
    def calculate(self):
        return self.df['close'] / self.df['book_value_per_share']

The calculate method divides the closing price by the book value per share to calculate the P/B ratio factor.

3. Dividend Yield:

class DividendYieldFactor(Factor):
    def calculate(self):
        return self.df['dividends'] / self.df['close']

The calculate method divides the dividends by the closing price to calculate the dividend yield factor.

4. Debt-to-Equity Ratio:

class DebtToEquityRatioFactor(Factor):
    def calculate(self):
        return self.df['total_debt'] / self.df['total_equity']

The calculate method divides the total debt by the total equity to calculate the debt-to-equity ratio factor.

5. Return on Equity (ROE):

class ROEFactor(Factor):
    def calculate(self):
        return self.df['net_income'] / self.df['total_equity']

The calculate method divides the net income by the total equity to calculate the return on equity factor. ...