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# ChatGPT Low Code Development V2: Finding New Factors
In addition to factor development, the quest for identifying effective factors is also a challenging journey. Our demo can expedite this process through the use of tailored prompts.
## Round 1
### init prompt:
Now, you are a senior professional stock factor researcher in top financial companies.You will help me to build some factors in Python today.
### ChatGPT's response (More in [this file](./main.ipynb)):
Of course! I'd be happy to help you build some factors in Python. Please let me know what specific factors you would like to build or if you have any specific requirements in mind.
## Round 2
### prompt 1:
Let's look at the data, it's stored in the CSV format and has the following columns: open, high, low, close, volume, and amount.
### ChatGPT's response (More in [this file](./main.ipynb)):
Great! To begin, we'll start by loading the data from the CSV file into a pandas DataFrame. Assuming the file is named "data.csv" and is located in the current working directory, you can use the following code:
```python
import pandas as pd
# Load data from CSV into a DataFrame
data = pd.read_csv('data.csv')
# Display a preview of the data
print(data.head())
```
...
## Round 3
### prompt 2:
OK. First, please show me a function to calculate the Fundamental Momentum Factor. Let's use FMF to indicate Fundamental Momentum Factor.
### ChatGPT's response (More in [this file](./main.ipynb)):
The Fundamental Momentum Factor (FMF) is a measure that quantifies the change in fundamental metrics over a specific period. To calculate the FMF, we need to select a fundamental metric (e.g., "amount") and a time period (e.g., 1 year). Here's how you can calculate the FMF in Python:
```python
import pandas as pd
def calculate_fmf(data, metric, period):
# Convert the date column to datetime format
data['date'] = pd.to_datetime(data['date'])
# Sort the data by date in ascending order
data = data.sort_values('date')
# Calculate the FMF
data['fmf'] = data[metric].pct_change(periods=period)
return data
# Load data from CSV into a DataFrame
data = pd.read_csv('data.csv')
# Calculate FMF using the 'amount' metric over a 1-year period
fmf_data = calculate_fmf(data, 'amount', 252)
# Display a preview of the FMF data
print(fmf_data.head())
```
...
## Round 4
### prompt 3:
Next, please show me a function to calculate the Price Momentum Factor. Let's use PMF to indicate Price Momentum Factor.
### ChatGPT's response (More in [this file](./main.ipynb)):
The Price Momentum Factor (PMF) is a measure that quantifies the price momentum of a stock. It is typically calculated as the percentage change in the closing price over a specific period. Here's how you can calculate the PMF in Python:
```python
import pandas as pd
def calculate_pmf(data, period):
# Convert the date column to datetime format
data['date'] = pd.to_datetime(data['date'])
# Sort the data by date in ascending order
data = data.sort_values('date')
# Calculate the PMF
data['pmf'] = data['close'].pct_change(periods=period)
return data
# Load data from CSV into a DataFrame
data = pd.read_csv('data.csv')
# Calculate PMF over a 1-year period
pmf_data = calculate_pmf(data, 252)
# Display a preview of the PMF data
print(pmf_data.head())
```
...
## Round 5
### prompt 4:
Next, please show me a function to calculate the Price Momentum Factor. Let's use PMF to indicate Price Momentum Factor.
### ChatGPT's response (More in [this file](./main.ipynb)):
Certainly! Let's combine the Fundamental Momentum Factor (FMF) and Price Momentum Factor (PMF) functions to create a more complex stock factor. Here's an example of how we can do it step by step:
1. Calculate FMF:
```python
fmf_data = calculate_fmf(data, 'amount', 252)
```
2. Calculate PMF:
```python
pmf_data = calculate_pmf(data, 252)
```
3. Merge FMF and PMF data on the 'date' column:
```python
merged_data = pd.merge(fmf_data, pmf_data[['date', 'pmf']], on='date', how='inner')
```
4. Create a new factor by multiplying FMF and PMF:
```python
merged_data['complex_factor'] = merged_data['fmf'] * merged_data['pmf']
```
5. Optionally, you may want to normalize the complex factor to a certain range or scale for easier interpretation. For instance, you can use the Min-Max scaling method:
```python
merged_data['complex_factor_scaled'] = (merged_data['complex_factor'] - merged_data['complex_factor'].min()) / (merged_data['complex_factor'].max() - merged_data['complex_factor'].min())
```
...
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