# 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](./main.ipynb)):

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](./main.ipynb)):
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:

```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](./main.ipynb)):
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):
```python
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):
```python
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:
```python
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:
```python
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):
```python
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.
...