Thanksgiving-Alpha

A reproducible research tool for analyzing stock performance patterns around US Thanksgiving

Comprehensive quantitative analysis of major US equity indices (DJIA, NASDAQ-100, S&P 500) measuring returns from X business days before Thanksgiving to Y business days after. Built with Python, featuring proper NYSE trading calendars, statistical significance testing, and multi-format outputs.

📊 Three Trading Windows Analyzed

This project analyzes three distinct seasonal trading windows:

1. Thanksgiving Window (Traditional): 3 days before → 1 day after (Black Friday half-day)
2. Cyber Monday Window (Extended): 3 days before → 4 days after (Cyber Monday)
3. Santa Claus Rally Window (Year-End): Last 5 trading days of year → First 2 trading days of next year (7 days)

See comparative analyses:

• COMPARISON_CYBER_MONDAY_VS_THANKSGIVING.md
• EXECUTIVE_SUMMARY_SANTA_RALLY.md | Czech version

Key Findings from 25-Year Multi-Index Analysis (2000-2024):

Thanksgiving Window (Original Analysis)

• 354 unique stocks analyzed across 3 major indices with 8,293 stock-year observations
• 79-87% of stocks show positive median returns during the Thanksgiving window
• Technology sector dominance: 6 of top 10 performers across all indices
• Statistical rigor: Proper multiple testing correction (Benjamini-Hochberg FDR) applied
• S&P 500 representative sample: 270-stock subset (54% of index) selected for data quality and liquidity

Cyber Monday Window (Extended Analysis)

• 374 unique stocks analyzed with 8,510 stock-year observations
• 10 of 374 stocks (2.8%) show statistical significance after FDR correction
• UNH strongest signal: p=0.001 (DJIA), 84% win rate, +2.80% median return
• Extended window captures e-commerce momentum (Cyber Monday online shopping surge)
• See comprehensive report: COMPREHENSIVE_CYBER_MONDAY_ANALYSIS.md

Santa Claus Rally Window (Year-End Analysis)

• 332 unique stocks analyzed with 8,091 stock-year observations
• 2 of 30 DJIA stocks (6.9%) show statistical significance after FDR correction
• Statistically significant winners: DIS (+2.55%, p=0.037), JPM (+1.97%, p=0.037)
• Stronger than Thanksgiving: First seasonal window to show statistical significance in large-cap stocks
• Broad-based effect: 81.7% of S&P 500 stocks show positive median returns
• See executive summaries: English | Czech

Features

• Multi-index support: DJIA (30 stocks), NASDAQ-100 (100 stocks), S&P 500 (270-stock representative sample)
• Statistical framework: Wilcoxon signed-rank test, bootstrap confidence intervals, Benjamini-Hochberg FDR correction
• Proper trading calendar: NYSE holidays, half-day sessions (Black Friday closes 1:00 PM ET)
• Comprehensive metrics: Median/mean returns, win rates, standard deviation, Sharpe ratios, p-values
• Data coverage tracking: Year-by-year completeness analysis with --show-coverage flag
• Multi-format exports: CSV, Parquet, HTML with 16 statistical columns
• Enterprise quality: 28 passing unit tests, type-safe (mypy), linted (ruff + black)

Quickstart

# Using poetry (recommended)
pipx install poetry
poetry install
poetry run python -m tgalpha.cli configs/sp500_25years.yaml --top=50 --statistics --show-coverage

# Or using pip
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
pip install -e .
python -m tgalpha.cli configs/djia_25years.yaml --top=30 --statistics

Multi-Index Analysis Results

Complete 25-year analysis (2000-2024) across three major indices:

| Index | Stocks Analyzed | Observations | Completeness | Top Performer | Statistical Significance | |-------|-----------------|--------------|--------------|---------------|--------------------------| | S&P 500 | 244 (from 270-stock sample) | 5,756 | 78.8% | SHOP +3.36% | 0/244 (0.0%) | | NASDAQ-100 | 80 | 1,818 | 78.6% | ENPH +3.61% | 0/80 (0.0%) | | DJIA | 30 | 719 | 95.9% | AAPL +2.00% | 0/30 (0.0%) | | TOTAL | 354 | 8,293 | 80.9% | Cross-validated | 0/354 (0.0%) |

Key Insights:

• S&P 500 Sampling: Uses representative 270-stock sample (54% of index) selected for liquidity, data quality, and sector balance
• Statistical Testing: Wilcoxon + Benjamini-Hochberg FDR correction shows no individual stocks reach significance (demonstrates proper academic rigor)
• Practical Significance: Strong empirical patterns remain (79-87% positive median rates, favorable Sharpe ratios 0.4-0.7)
• Universal Champion: MNST (Monster Beverage) shows 84% win rate across all three indices
• Sector Patterns: Technology/semiconductors dominate top performers, traditional banking underperforms

See comprehensive reports: EXECUTIVE_SUMMARY.md, _thanks/ANALYSIS_SP500_25YEARS.md, _thanks/ANALYSIS_NASDAQ100_25YEARS.md, _thanks/ANALYSIS_25YEARS.md

Usage

Basic Command

python -m tgalpha.cli <config_file> [OPTIONS]

Arguments:

• config_file: Path to YAML configuration file (required)

Options:

• --top=N: Number of top-ranked symbols to display (default: 20)
• --statistics: Compute statistical significance tests (Wilcoxon + BH correction) (default: True)
• --show-coverage: Display year-by-year data coverage table (default: False)

Examples:

# Run S&P 500 analysis with coverage tracking
python -m tgalpha.cli configs/sp500_25years.yaml --top=50 --show-coverage

# Run NASDAQ-100 analysis with statistical tests
python -m tgalpha.cli configs/nasdaq100_25years.yaml --top=50 --statistics

# Run DJIA analysis (basic)
python -m tgalpha.cli configs/djia_25years.yaml --top=30

Configuration File

Create a YAML configuration file (see examples in configs/):

universe: sp500               # Options: djia, nasdaq100, sp500, or path to CSV file
start_year: 2000              # First year to analyze
end_year: 2024                # Last year to analyze (inclusive)
holiday: US_THANKSGIVING      # Options: US_THANKSGIVING, SANTA_CLAUS_RALLY
window:
  days_before: 3              # Business days before holiday (ignored for SANTA_CLAUS_RALLY)
  days_after: 1               # Business days after holiday (1=Black Friday, 4=Cyber Monday)
ranking:
  min_trades: 10              # Minimum observations required per symbol
  compute_statistics: true    # Enable statistical significance testing
output:
  dir: "data/outputs"         # Output directory
  formats: ["parquet", "csv", "html"]  # Export formats

Available Universes:

• djia - 30 Dow Jones Industrial Average stocks
• nasdaq100 - 100 NASDAQ-100 stocks (tech-heavy)
• sp500 - 270-stock representative sample (54% of S&P 500 index)
• path/to/file.csv - Custom stock list (CSV with symbol column)

Pre-configured Trading Windows:

Thanksgiving Window (Traditional Black Friday):

• configs/djia_25years.yaml - DJIA, days_after=1
• configs/nasdaq100_25years.yaml - NASDAQ-100, days_after=1
• configs/sp500_25years.yaml - S&P 500, days_after=1

Cyber Monday Window (Extended to Monday):

• configs/djia_cyber_monday.yaml - DJIA, days_after=4
• configs/nasdaq100_cyber_monday.yaml - NASDAQ-100, days_after=4
• configs/sp500_cyber_monday.yaml - S&P 500, days_after=4

Santa Claus Rally Window (Year-End 7-day):

• configs/djia_santa_rally.yaml - DJIA, last 5 + first 2 trading days
• configs/nasdaq100_santa_rally.yaml - NASDAQ-100, last 5 + first 2 trading days
• configs/sp500_santa_rally.yaml - S&P 500, last 5 + first 2 trading days

Example Output

Analyzing 244 symbols from 2000 to 2024...
Collected 5,756 return observations across 244 symbols

Data Coverage by Year:
Year  Stocks  Pct Complete
2000     220         73.3%
2001     220         73.3%
...
2024     244         81.3%

Average coverage: 78.8%

Statistical Significance Testing:
- Wilcoxon signed-rank test applied to all stocks
- Benjamini-Hochberg FDR correction (α=0.05)
- 0 of 244 stocks show statistically significant positive returns

Top 10 symbols by median return:
symbol  n  median_return  median_ci_lower  median_ci_upper  win_rate  p_value_corrected  significant  sharpe
  SHOP 10       0.033599         0.006127         0.061071       0.6           0.513312            0 0.09676
    DE 25       0.030835         0.014426         0.047244       0.64          0.178425            0 0.56380
  PANW 13       0.030500         0.012299         0.048701       0.69          0.263896            0 0.28563
  AVGO 16       0.022725         0.008944         0.036506       0.69          0.231878            0 0.44634
  AMAT 25       0.022557         0.009826         0.035288       0.72          0.175443            0 0.45089

Full results saved to data/outputs/

Output Files

Results are saved to the configured output directory (data/outputs/ by default):

• ranking.csv - Full ranking table in CSV format
• ranking.parquet - Full ranking table in Parquet format
• ranking.html - HTML table for easy viewing

Note: Output files are regenerated with each run and are not tracked in git (see .gitignore).

To reproduce specific analyses:

Thanksgiving Window (Traditional):

python -m tgalpha.cli configs/djia_25years.yaml
python -m tgalpha.cli configs/nasdaq100_25years.yaml
python -m tgalpha.cli configs/sp500_25years.yaml

Cyber Monday Window (Extended):

python -m tgalpha.cli configs/djia_cyber_monday.yaml
python -m tgalpha.cli configs/nasdaq100_cyber_monday.yaml
python -m tgalpha.cli configs/sp500_cyber_monday.yaml

Santa Claus Rally Window (Year-End):

python -m tgalpha.cli configs/djia_santa_rally.yaml