AAPL Stock Analysis 2021-2026— Python & Pandas

End-to-end time-series analysis and feature engineering on Apple Inc. (NASDAQ: AAPL) historical price data covering 5 years of market activity (2021–2026), built entirely in Python using pandas, numpy, and matplotlib.

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Project Overview

Ticker	AAPL (Apple Inc.)
Data Period	July 2021 – July 2026
Trading Days	1,255
Starting Price	$141.96
Latest Price	$316.15
Cumulative Return	+122.7%
Max Drawdown	-33.4% (2022 bear market)
Sharpe Ratio	0.72
Tools	Python · pandas · numpy · matplotlib

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Project Structure

AAPL-Stock-Analysis/
├── AAPL.csv                        # Raw price data (OHLCV)
├── AAPL_transformed.csv            # Cleaned dataset with all features
├── AAPL_monthly_summary.csv        # Monthly aggregation table
├── AAPL_quarterly_summary.csv      # Quarterly aggregation table
├── AAPL_yearly_summary.csv         # Yearly aggregation table
├── AAPL_pivot_close.csv            # Avg close pivot (Year x Quarter)
├── AAPL_pivot_volume.csv           # Total volume pivot (Year x Quarter)
├── AAPL_long_format.csv            # OHLC in long/melted format
├── AAPL_Report.pdf                 # Full analysis report (4 pages)
└── README.md                       # This file

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Analysis Steps

Step 1 — Load & Inspect

Profiled the raw CSV structure, identified 2 junk metadata header rows, confirmed all columns stored as object/str type.

Step 2 — Data Cleaning

df = df_raw.iloc[2:].copy()                        # skip junk rows
df.columns = ["Date","Close","High","Low","Open","Volume"]
df["Date"]   = pd.to_datetime(df["Date"])           # fix types
df["Close"]  = df["Close"].astype(float)
df["Volume"] = df["Volume"].astype(int)
df = df.sort_values("Date").reset_index(drop=True)  # sort oldest → newest

Step 3 — Missing Values

Dropped nulls on critical price columns — 0 rows removed after removing junk header rows.

Step 4 — Time Features

df["Year"]       = df["Date"].dt.year
df["Month_Name"] = df["Date"].dt.strftime("%b")
df["Quarter"]    = df["Date"].dt.quarter
df["Weekday"]    = df["Date"].dt.day_name()
df["Is_Month_End"]   = df["Date"].dt.is_month_end
df["Is_Quarter_End"] = df["Date"].dt.is_quarter_end

Step 5 — Feature Engineering

18+ new analytical columns across 6 categories:

Category	Features
Price	Daily_Range, Daily_Price_Change, Open_to_Close_Change, Overnight_Gap
Returns	Daily_Return_%, Open_to_Close_%, Rolling_Monthly_Return_%
Moving Averages	SMA_7/21/50/200, EMA_7/21/50/200, EMA_SMA_Diff, Dist_from_SMA200_%
Volatility	Volatility_7/21/30d, Ann_Volatility_30d, ATR_14/30, BB_Upper/Mid/Lower/Width
Momentum	RSI_14/7, MACD, MACD_Signal, MACD_Hist, ROC_5/10/20
Volume	Vol_SMA_20, Volume_Ratio, OBV, Volume_Spike

Step 6 — Filtering & Sorting

# Top 10 highest close days
df.sort_values("Close", ascending=False).head(10)

# High volume bullish sessions
df[(df["High_Volume"] == True) & (df["Direction"] == "UP")]

# Days above 30-day moving average
df[df["Close"] > df["MA_30"]]

Step 7 — Grouping & Aggregation

# Yearly summary
df.groupby("Year").agg(
    Avg_Close    = ("Close", "mean"),
    Max_Close    = ("Close", "max"),
    UP_Days      = ("Direction", lambda x: (x == "UP").sum()),
    Volatility   = ("Daily_Return_%", "std")
)

# Monthly summary
df.groupby(["Year","Month","Month_Name"]).agg(
    Open_Price   = ("Open",  "first"),
    Close_Price  = ("Close", "last"),
    Total_Volume = ("Volume","sum"),
    Trading_Days = ("Date",  "count")
)

Step 8 — Risk Metrics

sharpe  = (returns.mean() / returns.std()) * np.sqrt(252)      # 0.72
sortino = (returns.mean() / downside_std)  * np.sqrt(252)
var_95  = np.percentile(returns, 5)                             # -2.72%
max_dd  = df["Drawdown_%"].min()                                # -33.4%

Step 9 — Data Formatting & Restructuring

# Pivot table — avg close by year and quarter
df.pivot_table(values="Close", index="Year",
               columns="Quarter", aggfunc="mean")

# Melt OHLC to long format
df[["Date","Open","High","Low","Close"]].melt(
    id_vars="Date", value_vars=["Open","High","Low","Close"],
    var_name="Price_Type", value_name="Price"
)

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Key Findings

Trends

• AAPL delivered +122.7% cumulative return from Jul 2021 to Jul 2026
• 2022 bear market caused a -33.4% max drawdown with volatility spiking above 40% annualized
• Strong 2023–2024 recovery — best annual return year was 2023; MACD turned bullish in early 2023 and stayed positive
• Q4 seasonality is consistent — November is the strongest calendar month every year; September is the weakest
• Volume trended down year-over-year, suggesting institutional accumulation over retail speculation

Key Insights

• Sharpe Ratio of 0.72 — decent but not exceptional risk-adjusted return; the 2022 drawdown weighs on this
• VaR (95%) of -2.72% — on 1 in 20 trading days, losses can exceed this level
• RSI below 30 in 2022 closely coincided with major price bottoms and subsequent rallies
• OBV trended upward throughout — buying pressure outweighed selling even during dips
• Bollinger Band Width compressed significantly in 2025–2026, preceding the breakout to ATH of $316.22

Recommendations

• Use SMA 200 as a directional filter — hold when above, reduce exposure when below
• Buy September dips — historically the weakest month, consistently followed by Q4 strength
• Use RSI < 35 + MACD crossover as a combined entry signal
• Size positions using 1.5–2× ATR_14 as stop-loss distance (~$4.50–$7.50)
• Increase allocation heading into October–November each year; reduce after December peak

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Visualizations

Chart	Description
Price + SMA 50/200	Full 5-year price history with moving averages and bull/bear shading
Volume Trend	Daily volume with 20-day moving average (green=UP, red=DOWN days)
Monthly Returns	Bar chart of every month's return + seasonality average by calendar month
MACD + Volatility	MACD indicator with signal line and 30-day annualized volatility
Bollinger Bands	Dynamic support/resistance bands around 20-day SMA
RSI	14-day RSI with overbought (70) and oversold (30) zones
Cumulative vs Drawdown	Total return vs peak-to-trough drawdown on dual axis
Annual Returns	Year-by-year return bar chart

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Requirements

pip install pandas numpy matplotlib

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

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Disclaimer

This project is for educational purposes only and does not constitute financial advice. Past performance is not indicative of future results.

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Analysis by Jonas Nwachukwu · AAPL Stock Analysis 2021–2026