Stocks Technical Analysis (TA) with Python & ChatGPT: A Comprehensive Guide

One effective way to gain insights and sentiments into potential stock price movements is through Technical Analysis (TA). By delving into historical market data, particularly price and volume, TA seeks to anticipate future price shifts. This methodology operates on the premise that historical price patterns tend to recur, thus shedding light on potential future trends.

Python provides robust tools for collecting, analyzing, and deriving Technical Analysis (TA) indicators from stock data. By harnessing the power of ChatGPT in conjunction with these tools, you can enable ChatGPT to analyze the TA indicators you’ve generated. This blog will guide you through the step-by-step process of performing stock TA analysis using Python’s yfinance and pandas_ta libraries, as well as demonstrate how you can leverage ChatGPT’s capabilities to interpret the analysis outcomes and forecast future trends.

Steps to Perform Stock TA Analysis Using Python and ChatGPT

1. Utilize yfinance to fetch historical stock price data and pandas_ta to compute technical indicators.
2. Gather indicators for the last trading day to input into the ChatGPT prompt.
3. Create a prompt for ChatGPT, incorporating the indicators for analysis.
4. Engage ChatGPT to interpret indicators and predict future trends.
5. Enhance insights by visualizing indicator trends using additional charts.

Python Script for Stock Price Data and TA Calculation

Below is the Python script to perform the function using the yfinance and pandas-ta libraries. Pandas_ta can compute various technical indicators, such as Moving Averages, RSI, and Bollinger Bands, providing insights into momentum, volatility, and potential reversals. For more details, consult the pandas-ta documentation.

!pip install yfinance
!pip install pandas_ta

import matplotlib.pyplot as plt
import pandas as pd
import yfinance as yf
import pandas_ta as ta
import matplotlib.dates as mdates
from datetime import datetime, timedelta

# Define the stock symbol and timeframe
symbol = 'DIS'
end_date = datetime.today()
start_date = end_date - timedelta(days=120)  # 4 months before today

# Fetch stock data using yfinance
stock_data = yf.download(symbol, start=start_date, end=end_date)

# Calculate technical indicators using pandas-ta
stock_data.ta.macd(append=True)
stock_data.ta.rsi(append=True)
stock_data.ta.bbands(append=True)
stock_data.ta.obv(append=True)

# Calculate additional technical indicators
stock_data.ta.sma(length=20, append=True)
stock_data.ta.ema(length=50, append=True)
stock_data.ta.stoch(append=True)
stock_data.ta.adx(append=True)

# Calculate other indicators
stock_data.ta.willr(append=True)
stock_data.ta.cmf(append=True)
stock_data.ta.psar(append=True)

#convert OBV to million
stock_data['OBV_in_million'] =  stock_data['OBV']/1e7
stock_data['MACD_histogram_12_26_9'] =  stock_data['MACDh_12_26_9'] # not to confuse chatGTP

# Summarize technical indicators for the last day
last_day_summary = stock_data.iloc[-1][['Adj Close',
    'MACD_12_26_9','MACD_histogram_12_26_9', 'RSI_14', 'BBL_5_2.0', 'BBM_5_2.0', 'BBU_5_2.0','SMA_20', 'EMA_50','OBV_in_million', 'STOCHk_14_3_3', 
    'STOCHd_14_3_3', 'ADX_14',  'WILLR_14', 'CMF_20', 
    'PSARl_0.02_0.2', 'PSARs_0.02_0.2'
]]

print("Summary of Technical Indicators for the Last Day:")
print(last_day_summary)

Setting Up the ChatGPT Prompt

Construct the prompt in a manner that invokes the expertise of legendary TA figures like Charles Dow and John Bollinger to conduct the analysis on your behalf.

Below is the Python script that establishes the prompt, delineating ChatGPT’s role and integrating the most recent trading day’s indicators. Following ChatGPT’s analysis, you can further engage by posing additional questions, delving into specific indicators, and requesting ChatGPT to elaborate further. Do note instead of pasting the prompt to ChatGPT manually, you can also use the openai API to automate the process.

## Work on the prompt
sys_prompt = """
Assume the role as a leading Technical Analysis (TA) expert in the stock market, \
a modern counterpart to Charles Dow, John Bollinger, and Alan Andrews. \
Your mastery encompasses both stock fundamentals and intricate technical indicators. \
You possess the ability to decode complex market dynamics, \
providing clear insights and recommendations backed by a thorough understanding of interrelated factors. \
Your expertise extends to practical tools like the pandas_ta module, \
allowing you to navigate data intricacies with ease. \
As a TA authority, your role is to decipher market trends, make informed predictions, and offer valuable perspectives.

given {} TA data as below on the last trading day, what will be the next few days possible stock price movement? 

Summary of Technical Indicators for the Last Day:
{}""".format(symbol,last_day_summary)

print(sys_prompt)

Visualizing Insights

Below is the Python code showcasing all the indicators by date, serving as a supplement to the analysis provided by ChatGPT.

# Plot the technical indicators
plt.figure(figsize=(14, 8))

# Price Trend Chart
plt.subplot(3, 3, 1)
plt.plot(stock_data.index, stock_data['Adj Close'], label='Adj Close', color='blue')
plt.plot(stock_data.index, stock_data['EMA_50'], label='EMA 50', color='green')
plt.plot(stock_data.index, stock_data['SMA_20'], label='SMA_20', color='orange')
plt.title("Price Trend")
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%b%d'))  # Format date as "Jun14"
plt.xticks(rotation=45, fontsize=8)  # Adjust font size
plt.legend()

# On-Balance Volume Chart
plt.subplot(3, 3, 2)
plt.plot(stock_data['OBV'], label='On-Balance Volume')
plt.title('On-Balance Volume (OBV) Indicator')
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%b%d'))  # Format date as "Jun14"
plt.xticks(rotation=45, fontsize=8)  # Adjust font size
plt.legend()

# MACD Plot
plt.subplot(3, 3, 3)
plt.plot(stock_data['MACD_12_26_9'], label='MACD')
plt.plot(stock_data['MACDh_12_26_9'], label='MACD Histogram')
plt.title('MACD Indicator')
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%b%d'))  # Format date as "Jun14"
plt.xticks(rotation=45, fontsize=8)  # Adjust font size
plt.title("MACD")
plt.legend()

# RSI Plot
plt.subplot(3, 3, 4)
plt.plot(stock_data['RSI_14'], label='RSI')
plt.axhline(y=70, color='r', linestyle='--', label='Overbought (70)')
plt.axhline(y=30, color='g', linestyle='--', label='Oversold (30)')
plt.legend()
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%b%d'))  # Format date as "Jun14"
plt.xticks(rotation=45, fontsize=8)  # Adjust font size
plt.title('RSI Indicator')

# Bollinger Bands Plot
plt.subplot(3, 3, 5)
plt.plot(stock_data.index, stock_data['BBU_5_2.0'], label='Upper BB')
plt.plot(stock_data.index, stock_data['BBM_5_2.0'], label='Middle BB')
plt.plot(stock_data.index, stock_data['BBL_5_2.0'], label='Lower BB')
plt.plot(stock_data.index, stock_data['Adj Close'], label='Adj Close', color='brown')
plt.title("Bollinger Bands")
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%b%d'))  # Format date as "Jun14"
plt.xticks(rotation=45, fontsize=8)  # Adjust font size
plt.legend()

# Stochastic Oscillator Plot
plt.subplot(3, 3, 6)
plt.plot(stock_data.index, stock_data['STOCHk_14_3_3'], label='Stoch %K')
plt.plot(stock_data.index, stock_data['STOCHd_14_3_3'], label='Stoch %D')
plt.title("Stochastic Oscillator")
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%b%d'))  # Format date as "Jun14"
plt.xticks(rotation=45, fontsize=8)  # Adjust font size
plt.legend()

# Williams %R Plot
plt.subplot(3, 3, 7)
plt.plot(stock_data.index, stock_data['WILLR_14'])
plt.title("Williams %R")
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%b%d'))  # Format date as "Jun14"
plt.xticks(rotation=45, fontsize=8)  # Adjust font size

# ADX Plot
plt.subplot(3, 3, 8)
plt.plot(stock_data.index, stock_data['ADX_14'])
plt.title("Average Directional Index (ADX)")
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%b%d'))  # Format date as "Jun14"
plt.xticks(rotation=45, fontsize=8)  # Adjust font size

# CMF Plot
plt.subplot(3, 3, 9)
plt.plot(stock_data.index, stock_data['CMF_20'])
plt.title("Chaikin Money Flow (CMF)")
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%b%d'))  # Format date as "Jun14"
plt.xticks(rotation=45, fontsize=8)  # Adjust font size

# Show the plots
plt.tight_layout()
plt.show()

Sample output

Conclusion

In conclusion, the combination of Python’s yfinance and pandas-ta modules, along with ChatGPT’s capabilities, provides a powerful method to analyze and interpret technical indicators, yielding valuable insights into stock sentiment.

The inclusion of additional charting enhances the depth of these insights. However, it’s important to recognize that a comprehensive analysis must also encompass macroeconomic and other relevant economic data to ensure well-informed decision-making.

If you are interesteed in stock market sentiment, analysis and price trends, do check out my other blog post on Decoding Market Sentiment: Exploring Options Data Analysis with Python and AI.