name: weekly-analysis description: "Complete weekly stock analysis workflow: fetch end-of-week prices, calculate returns/momentum, and merge with fundamentals from Rice Data Portal. Self-contained for all weekly analysis tasks."

Weekly Stock Analysis

Provide complete, self-contained workflows for weekly stock analysis including price data, returns, momentum, and fundamental data from SEC filings.

When to Use This Skill

Use this skill when the user requests:

Weekly stock returns or prices
End-of-week data analysis
Financial data at weekly frequency
Combining price and fundamental data weekly
Weekly portfolio analysis or backtesting
Higher frequency analysis than monthly

Core Workflow

Step 1: Check for Precomputed Ratios

CRITICAL: Before calculating any financial ratio, check if it already exists in the database.

Use the precomputed check script (shared across both skills):

python .claude/skills/references/scripts/check_precomputed.py

This verifies which variables are available in:

DAILY table: marketcap, pb, pe, ps, ev, evebit, evebitda
SF1 table: All income statement, balance sheet, cash flow items, and financial ratios

Common precomputed ratios:

pb (price-to-book), pe (price-to-earnings), ps (price-to-sales)
roe (return on equity), roa, roic
grossmargin, netmargin, ebitdamargin
de (debt-to-equity ratio - this is "leverage")
assetturnover, currentratio, quickratio

Rule: Fetch precomputed ratios from the database. Do NOT calculate manually if they exist.

Step 2: Fetch Weekly Returns

Use the fetch script to get end-of-week prices and calculate returns:

python .claude/skills/weekly-analysis/scripts/fetch_weekly_data.py START_DATE OUTPUT_FILE
# Example: python .claude/skills/weekly-analysis/scripts/fetch_weekly_data.py 2020-01-01 weekly.parquet

Automatically includes:

ticker, week (ISO week format: YYYY-WW), date
close (split-adjusted price)
return (weekly return, decimal format)
momentum (48-week momentum: week t-53 to t-5)
lag_month (4-week return: week t-5 to t-1)
lag_week (prior week's return)
marketcap (from DAILY table, already shifted by 1 week)
sector, industry, size (market cap category)

ISO Week Format:

Weeks labeled as '2025-W01', '2025-W02', etc.
Week 1 = first week with at least 4 days in new year
Some years have 53 weeks (e.g., 2020, 2026, 2032)

Step 3: Fetch Additional Variables

From DAILY Table (for precomputed ratios)

Fetch end-of-week values for ratios like pb, pe, ps:

# Pattern: Get end-of-week values, then shift by 1 week
# Query year-by-year to avoid timeouts
# See .claude/skills/references/rice_database_schema.md for query patterns

Then shift: df['pb'] = df.groupby('ticker')['pb'].shift(1)

From SF1 Table (for fundamental data)

Fetch annual (ARY) or quarterly (ARQ) fundamental data:

# Query SF1 table with dimension='ARY' for annual data
# Order by ticker, datekey
# See .claude/skills/references/rice_database_schema.md for schema details

CRITICAL: Calculate growth rates BEFORE merging:

# Calculate year-over-year growth from fundamental data
df_fund['asset_growth'] = df_fund.groupby('ticker')['assets'].pct_change().round(4)

Step 4: Merge with Fundamentals

python .claude/skills/weekly-analysis/scripts/merge_weekly_fundamentals.py WEEKLY_FILE FUNDAMENTALS_FILE OUTPUT_FILE

The merge script automatically:

Converts filing date to ISO week format
Shifts data one week forward (filed in week N → available week N+1)
Forward fills fundamental data until next filing
Shifts close prices to represent prior week's closing price

Step 5: Shift SF1 Variables

After merging, shift all SF1 variables by 1 week to avoid look-ahead bias:

# Shift each SF1 variable by 1 week (grouped by ticker)
df['roe'] = df.groupby('ticker')['roe'].shift(1)
df['grossmargin'] = df.groupby('ticker')['grossmargin'].shift(1)
df['de'] = df.groupby('ticker')['de'].shift(1)
# Repeat for all SF1 variables

Important Rules

Avoiding Look-Ahead Bias

ALL variables from DAILY and SF1 tables must be shifted:

DAILY variables: Shift by 1 week after fetching
SF1 variables: Shift by 1 week after merging
This ensures variables represent information known at the start of the week

Return Calculations

ALWAYS group by ticker when calculating returns:

df['return'] = df.groupby('ticker')['closeadj'].pct_change().round(4)
df['momentum'] = (
    df.groupby('ticker')['closeadj'].shift(5) /
    df.groupby('ticker')['closeadj'].shift(53) - 1
).round(4)

Returns are expressed as decimals (0.02 = 2% return), rounded to 4 decimal places.

Growth Rate Calculations

Calculate growth rates from fundamental data BEFORE merging:

Use year-over-year for annual data (ARY)
Use quarter-over-quarter for quarterly data (ARQ)
Do NOT calculate after merging (forward-fill creates zero growth)

Query Performance

Query year-by-year to avoid API timeouts:

for year in range(start_year, end_year + 1):
    sql = f"SELECT ... WHERE date::DATE >= '{year}-01-01' AND date::DATE < '{year+1}-01-01'"
    # Execute and append results

ISO Week Edge Cases

Early January dates may belong to prior year's Week 52/53
Late December dates may belong to next year's Week 01
This is normal ISO 8601 week numbering behavior

Output Format

Final dataset columns:

ticker: Stock symbol
week: ISO week label (YYYY-WW format, e.g., '2025-W01')
return: Weekly return (decimal)
momentum: 48-week momentum (decimal)
lag_month: 4-week return (decimal)
lag_week: Prior week return (decimal)
close: Prior week's closing price (shifted)
marketcap: Market cap in thousands (from DAILY, shifted)
sector, industry: Classifications
size: Market cap category (Nano, Micro, Small, Mid, Large, Mega)
Additional variables from DAILY/SF1 as requested (all shifted)

Database Schema Reference

For complete database schema, table structures, and query patterns, see: .claude/skills/references/rice_database_schema.md

Search for:

"DAILY Table" - precomputed valuation ratios
"SF1 Table" - fundamental data from SEC filings
"SEP Table" - price data
"Common Query Patterns" - SQL templates

Scripts Available

Scripts in .claude/skills/weekly-analysis/scripts/:

fetch_weekly_data.py - Get end-of-week prices and returns
merge_weekly_fundamentals.py - Merge returns with fundamental data

Shared scripts in .claude/skills/references/scripts/:

check_precomputed.py - Verify which ratios exist in database
precomputed_variables.py - List of all precomputed variables

Key Reminders

✅ Check for precomputed ratios BEFORE calculating
✅ Fetch from DAILY/SF1 tables when variables exist
✅ Shift ALL DAILY variables by 1 week after fetching
✅ Calculate growth rates BEFORE merging
✅ Shift ALL SF1 variables by 1 week after merging
✅ Always group by ticker for returns and growth rates
✅ Query year-by-year to avoid timeouts
✅ Use decimal format for returns (not percentages)
✅ Understand ISO week format (weeks may span year boundaries)

weekly-analysis

SKILL.md