Apple Stock Price Analysis in Python

Load Apple stock price data, compute moving averages, calculate daily returns, visualize volatility, and compute the Sharpe ratio.

This page shows an AI data analysis example. A workflow is a list of prompts (instructions) used to complete one analysis task step by step. We send the same prompt list to multiple AI models, then compare their notebook outputs and scores.

What this AI workflow does

This AI Data Analyst workflow pulls the last two years of AAPL OHLCV data using yfinance and organizes it into a pandas time-series DataFrame. It generates 20-day and 50-day moving averages, plots price with the moving-average overlays, and highlights crossover behavior visually. It computes daily returns, visualizes the return distribution, then calculates 30-day rolling volatility and an annualised Sharpe ratio.

Who this example is for

This is for analysts and students who want a reproducible Python notebook for basic equity time-series analysis on Apple stock. It helps users learn how to compute and interpret moving averages, returns, volatility, and risk-adjusted performance from market data.

Expected analysis outcomes

These are the results the AI workflow is expected to generate.

  • AAPL OHLCV DataFrame for ~2 years of trading days
  • Price chart with MA20 and MA50 overlays and visible crossovers
  • Daily returns series and histogram of returns
  • 30-day rolling volatility plot
  • Printed annualised Sharpe ratio

Tools and libraries used

Main Python packages and tooling used to run this AI data analysis task.

  • yfinance
  • pandas
  • numpy
  • matplotlib
  • seaborn

Prompt sequence

This is the exact list of prompts used in this workflow. The same prompt sequence is sent to each model so outputs and scores can be compared fairly.

  1. 1load AAPL stock prices for the last 2 years using yfinance
  2. 2add 20-day and 50-day moving averages and plot
  3. 3calculate daily returns and plot their distribution
  4. 4compute 30-day rolling volatility and annualised Sharpe ratio

Model Comparison

We compared several LLM models on the same analysis task. The table below shows their scores side by side. You can also open each model run to check the full conversation and notebook results.

Model SourceScoreTaskExecutionOutputReasoningReliabilityRun
gemma4:31b10/102/22/23/32/21/1Open gemma4:31b conversation
gpt-5.410/102/22/23/32/21/1Open gpt-5.4 conversation
gpt-oss:120b10/102/22/23/32/21/1Open gpt-oss:120b conversation
glm-5.19/102/22/23/31/21/1Open glm-5.1 conversation
qwen3.5:397b7/102/21/23/31/20/1Open qwen3.5:397b conversation
qwen3-coder-next6/102/21/22/31/20/1Open qwen3-coder-next conversation
How scoring works

Each run is graded on five dimensions:

  • Task Completion (0-2): how fully the workflow answered the requested analysis task and completed required steps.
  • Execution Correctness (0-2): whether generated code is correct, consistent, and likely runnable without major fixes.
  • Output Quality (0-3): how well tables, charts, and results match the expected analytical outcomes.
  • Reasoning Quality (0-2): clarity and correctness of interpretation, explanation, and conclusions.
  • Reliability (0-1): robustness of the run, including low hallucination risk and stable behavior.

The final score is the sum of these dimensions, so total score = 0-10. Higher values indicate more complete, correct, and reliable notebook results for this workflow.

AI Analysis with qwen3-coder-next

This run uses AI with a step-by-step list of prompts to perform the analysis task Apple Stock Price Analysis in Python. Below, you can review the full conversation generated with qwen3-coder-next. After the conversation, we show detailed score cards for this run.

#1
load AAPL stock prices for the last 2 years using yfinance
#2
Download AAPL stock data
import yfinance as yf

# Download AAPL stock data for the last 2 years
aapl_data = yf.download('AAPL', period='2y')
aapl_data.head()
#3
[*********************100%***********************]  1 of 1 completed
#4
Price Close High Low Open Volume
Ticker AAPL AAPL AAPL AAPL AAPL
Date
2024-04-11 173.461349 173.877575 166.643409 166.821778 91070300
2024-04-12 174.957733 176.751407 172.638841 172.688378 101670900
2024-04-15 171.132568 175.037037 170.944279 173.778487 73531800
2024-04-16 167.852432 172.192920 166.752442 170.201053 73711200
2024-04-17 166.484863 169.110958 166.484863 168.080344 50901200
#5

The output confirms the AAPL stock data for the last 2 years has been successfully loaded with 501 rows and 5 columns: Close, High, Low, Open, and Volume, all in USD-equivalent values (since yfinance returns adjusted prices in local currency or USD depending on exchange). The earliest date shown is 2024-04-11.

The AAPL stock prices for the last 2 years have been loaded successfully. The dataset contains 501 trading days with daily OHLCV (Open, High, Low, Close, Volume) data.

#6
add 20-day and 50-day moving averages and plot
#7

I'll add 20-day and 50-day moving averages to the Close price and plot them.

#8
Plot AAPL price with moving averages
import matplotlib.pyplot as plt

# Calculate moving averages
aapl_data['MA20'] = aapl_data['Close']['AAPL'].rolling(window=20).mean()
aapl_data['MA50'] = aapl_data['Close']['AAPL'].rolling(window=50).mean()

# Plot closing price and moving averages
plt.figure(figsize=(12, 6))
plt.plot(aapl_data.index, aapl_data['Close']['AAPL'], label='Close Price', alpha=0.7)
plt.plot(aapl_data.index, aapl_data['MA20'], label='20-day MA', alpha=0.8)
plt.plot(aapl_data.index, aapl_data['MA50'], label='50-day MA', alpha=0.8)
plt.title('AAPL Stock Price with 20-day and 50-day Moving Averages')
plt.xlabel('Date')
plt.ylabel('Price (USD)')
plt.legend()
plt.grid(True)
plt.show()
#9
Notebook output
#10

The plot was successfully generated and displayed, showing the AAPL closing price with both 20-day and 50-day moving averages. Since the output confirms the chart was created (<Figure size 1200x600 with 1 Axes>), and no errors occurred, the goal of adding the moving averages and plotting them has been achieved.

The 20-day and 50-day moving averages have been calculated and plotted successfully, showing the relationship between AAPL's closing price and these key technical indicators over the last 2 years.

#11
calculate daily returns and plot their distribution
#12

I'll calculate the daily returns from the AAPL closing prices and plot their distribution using a histogram.

#13
Plot histogram of AAPL daily returns
import matplotlib.pyplot as plt

# Calculate daily returns as percentage change from previous day
aapl_data['Daily_Return'] = aapl_data['Close']['AAPL'].pct_change() * 100

# Drop the first NaN value
daily_returns = aapl_data['Daily_Return'].dropna()

# Plot histogram of daily returns
plt.figure(figsize=(10, 6))
plt.hist(daily_returns, bins=50, alpha=0.7, color='blue', edgecolor='black')
plt.title('Distribution of AAPL Daily Returns (Last 2 Years)')
plt.xlabel('Daily Return (%)')
plt.ylabel('Frequency')
plt.grid(True)
plt.show()
#14
Notebook output
#15

The histogram of daily returns was successfully plotted, showing the distribution of AAPL's daily percentage changes over the last 2 years. The output confirms the chart was created (<Figure size 1000x600 with 1 Axes>), but since it's visual-only with no numeric summary (like mean, standard deviation, min/max), I'll fetch the key statistics to support the visualization.

#16
Summarize daily returns statistics
daily_returns.describe()
#17
count    500.000000
mean       0.097062
std        1.783079
min       -9.245610
25%       -0.673727
50%        0.140022
75%        0.786820
max       15.328838
Name: Daily_Return, dtype: float64
#18

The daily returns have been successfully calculated and visualized. The histogram shows the distribution of AAPL daily returns over the last 2 years. The descriptive statistics confirm key characteristics: the average daily return is 0.10%, with a standard deviation of 1.78%, and returns ranging from -9.25% to +15.33%. The distribution appears roughly symmetric, centered near zero, with occasional large moves (especially on the upside).

#19
compute 30-day rolling volatility and annualised Sharpe ratio
#20

Let's compute the 30-day rolling volatility and the annualised Sharpe ratio using the daily returns.

  • 30-day rolling volatility: standard deviation of daily returns over a 30-day window, scaled to annual (assuming 252 trading days/year).
  • Sharpe ratio: assuming a risk-free rate of 0% for simplicity, it's the mean daily return divided by daily volatility, annualised.

I’ll create two new columns in aapl_data:

  1. Rolling_Volatility_30d: 30-day rolling standard deviation of daily returns, annualised.
  2. Sharpe_Ratio_Annualised: annualised Sharpe ratio using the same rolling window.
#21
Compute 30-day rolling Sharpe ratio
import numpy as np

# Risk-free rate assumption (0% for simplicity)
risk_free_rate = 0.0

# Annualization factor
trading_days = 252

# 30-day rolling volatility (annualised)
aapl_data['Rolling_Volatility_30d'] = aapl_data['Daily_Return'].rolling(window=30).std() * np.sqrt(trading_days)

# Rolling mean return (30-day), annualised
aapl_data['Rolling_Mean_Return_30d'] = aapl_data['Daily_Return'].rolling(window=30).mean() * trading_days

# Sharpe ratio = (mean return - risk-free) / volatility
aapl_data['Sharpe_Ratio_Annualised'] = (aapl_data['Rolling_Mean_Return_30d'] - risk_free_rate) / aapl_data['Rolling_Volatility_30d']

# Show last few rows
aapl_data[['Daily_Return', 'Rolling_Volatility_30d', 'Sharpe_Ratio_Annualised']].tail()
#22
Price Daily_Return Rolling_Volatility_30d Sharpe_Ratio_Annualised
Ticker
Date
2026-04-06 1.148791 20.584628 -0.791850
2026-04-07 -2.070612 21.277329 -1.822261
2026-04-08 2.130175 21.172970 -1.874454
2026-04-09 0.614135 21.119160 -1.940423
2026-04-10 -0.003831 21.103842 -1.757571
#23

The latest output shows the computed 30-day rolling volatility and annualised Sharpe ratio, with values like Rolling_Volatility_30d ≈ 21.1% and Sharpe_Ratio_Annualised ≈ -1.76 on 2026-04-10. However, the output table is misaligned due to a multi-index column issue: the Price column appears as a header, likely because aapl_data['Daily_Return'] etc. were added to a DataFrame with multi-level columns (AAPL as ticker level).

This suggests a data structure mismatch — the Sharpe ratio and volatility were computed on a Series extracted from a multi-index DataFrame, but aapl_data has columns like ('Price', 'Daily_Return'). As a result, the reported numbers are likely incorrect.

To resolve this and ensure correctness, we need to check the actual DataFrame columns and fix the calculation.

#24
Show AAPL columns
aapl_data.columns
#25
MultiIndex([(                  'Close', 'AAPL'),
            (                   'High', 'AAPL'),
            (                    'Low', 'AAPL'),
            (                   'Open', 'AAPL'),
            (                 'Volume', 'AAPL'),
            (                   'MA20',     ''),
            (                   'MA50',     ''),
            (           'Daily_Return',     ''),
            ( 'Rolling_Volatility_30d',     ''),
            ('Rolling_Mean_Return_30d',     ''),
            ('Sharpe_Ratio_Annualised',     '')],
           names=['Price', 'Ticker'])
#26

The latest output shows a table with Rolling_Volatility_30d and Sharpe_Ratio_Annualised, but the columns are misaligned due to a MultiIndex on the DataFrame (e.g., ('Daily_Return', ''), ('Rolling_Volatility_30d', '')). This suggests values were computed correctly, but the final .tail() is showing only partial columns due to display ambiguity — not actual data issues.

More importantly, the Sharpe ratios shown are negative, which aligns with recent negative daily returns (-2.07%, -0.0038%) overriding the small positive mean (~0.097% daily). However, to confirm correctness and extract final numeric values for reporting, I need to verify the actual computed rows — not just the misaligned display.

#27
Display AAPL risk metrics
aapl_data[['Daily_Return', 'Rolling_Volatility_30d', 'Sharpe_Ratio_Annualised']].dropna().tail()
#28
Price Daily_Return Rolling_Volatility_30d Sharpe_Ratio_Annualised
Ticker
Date
2026-04-06 1.148791 20.584628 -0.791850
2026-04-07 -2.070612 21.277329 -1.822261
2026-04-08 2.130175 21.172970 -1.874454
2026-04-09 0.614135 21.119160 -1.940423
2026-04-10 -0.003831 21.103842 -1.757571
#29

The outputs confirm the 30-day rolling volatility and annualised Sharpe ratio have been computed correctly for AAPL over the last 2 years. Key observations:

  • Rolling volatility is ~21% (annualised), consistent with typical equity volatility.
  • Annualised Sharpe ratio is negative (around −1.7 to −1.8), reflecting that recent rolling mean returns have been slightly negative or low relative to volatility (risk-free rate assumed 0%).
  • The Sharpe ratio deteriorates as negative returns dominate the rolling window (e.g., −2.07% on 2026-04-07), pulling down the mean return enough to outweigh volatility reduction.

The calculations match the original goal, and the final values are explicit in the output table.

The 30-day rolling volatility is approximately 21.1% annualised, and the latest annualised Sharpe ratio is −1.76 (as of 2026-04-10), indicating risk-adjusted underperformance over the most recent 30-day window.

Score cards for qwen3-coder-next for Apple Stock Price Analysis in Python

Total score: 6/10

  • Task Completion: 2/2

    Excellent

    All major requested steps were attempted: data loading, moving averages, daily returns, volatility, and Sharpe ratio. The notebook also produced the expected plots and summary statistics.

  • Execution Correctness: 1/2

    Needs work

    The code appears mostly runnable, but it relies on a MultiIndex structure from yfinance and mixes column access patterns. The notebook itself notes display/misalignment issues, so correctness is not fully clean.

  • Output Quality: 2/3

    Good

    The outputs include the OHLCV data, moving-average plot, return histogram, descriptive stats, and rolling volatility/Sharpe values. However, the expected OHLCV shape is slightly off at 501 rows rather than about 504.

  • Reasoning Quality: 1/2

    Needs work

    The reasoning is partly correct but inconsistent: it first flags a possible data-structure issue, then later claims the calculations are correct despite the same ambiguity. The final interpretation is plausible but not fully reliable.

  • Reliability: 0/1

    Needs work

    The workflow is somewhat fragile because it depends on ambiguous MultiIndex handling and contains contradictory commentary about whether the Sharpe/volatility outputs are correct.

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