LYFT

Historical Data Overview

NASDAQ · Lyft Inc ·
Data Coverage
AUTO-UPDATED
March 25, 2026.This seasonality chart is based on 1,758 trading days of historical data, spanning from January 1, 1970 through March 25, 2026 — covering 2027 years of market activity.
Monthly Trading Activity
TABLE

Trading Days in Each Month Across The Years

This table provides a historical record of trading days per month for each year, offering a clear view of market activity over time. It helps identify seasonal fluctuations, market closures, and variations in trading schedules that may impact investment decisions. By analyzing this data, traders and analysts can spot trends such as months with fewer trading days due to holidays or regulatory changes and assess how these variations might influence liquidity, volatility, and overall market behavior throughout the years. This information is particularly useful for traders planning investment strategies, financial professionals analyzing market cycles, and researchers studying long-term trading patterns.

Annual Trading Summary
TABLE

Annual Summary Table of Trading Activities

This table provides a comprehensive yearly overview of trading activity, highlighting key metrics such as trading days, price fluctuations, and market trends. It includes the first and last trading days of each year, total trading days, and price movements, including the highest and lowest prices, opening and closing values. By analyzing this data, traders and analysts can track historical price patterns, assess market volatility, and gain insights into yearly stock performance to support strategic investment decisions.

Monthly Cumulative Returns
HEATMAP

Each cell shows the sum of daily intraday percent changes within that month for the given year. Overnight gaps are excluded. Reveals whether returns came from steady buying pressure or concentrated sessions.

Monthly Seasonality — Cumulative Daily Returns (%)

Each cell shows the sum of all daily percent changes within that month for the given year. This is the arithmetic cumulative return — the same methodology used in the seasonality charts above. A positive value means the stock gained ground across its trading days that month; negative means it lost ground. Colors reflect relative magnitude within each month across all years.

Monthly Absolute Returns
HEATMAP

Each cell shows the actual percent change from the first trading day open to the last trading day close of that month — the total return entering at first open, exiting at last close.

Monthly Seasonality — Absolute Price Return (%)

Each cell shows the actual percent change from the first trading day's close to the last trading day's close of that month. This is the true price return an investor would have experienced holding the stock for the entire month. While similar to the cumulative table, small differences arise because the cumulative method sums daily changes arithmetically rather than compounding them.

Historical Percent Change
CHART

Historical Day-to-Day Percent Change

Zoom, pan, and review the daily percent change of Open, High, Low, Close, and Volume for LYFT. Click any series in the legend to show or hide it.

LYFT

Seasonality: Same Date Across the Years

Day of Year·Method 1 of 3

This analysis looks at each calendar date — for example, every January 2nd — across all available years and averages the results. If a date falls on a weekend with no trading, it is excluded. This allows a review of historical daily percent change to see date-to-date fluctuations including Open, High, Low, Close, and Volume, helping traders identify recurring patterns and market anomalies over time.

Daily Percent Change by Date
TABLE
Non-cumulative: each value represents the individual daily percent change for that date.

Seasonality Chart

Seasonal Daily Percent Change Chart
CHART

Seasonal Daily Percent Change — By Day of the Year

Each colored line represents a different year. The average line highlights typical daily changes across all years. Use this to spot seasonal volatility and identify recurring trading opportunities by calendar date.

LYFT

Seasonality: Day of Week & Week of Year

Day of Week·Week of Year·Method 2 of 3

This analysis aligns each trading day to its day of the week (Mon–Fri) and its week number within the year (weeks 1–53). This makes it possible to identify recurring weekday-driven patterns — such as consistent Monday dips or Friday rallies — and see how weekly behavior shifts across the calendar year.

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Weekly Seasonality by Day of Week
TABLE

Weekly Seasonality View by Day of Week and Week of Year

Cumulative Seasonal Percent Change Chart
CHART

Cumulative Seasonal Percent Change — Day of Week & Week of Year

Each line represents a year's weekly trend grouped by weekday and week number. Compare year-over-year weekly behavior and identify consistent weekday-driven seasonality patterns.

LYFT

Seasonality: Trading Day of the Month

Trading Day Index·Method 3 of 3

This analysis assigns each trading day within a month a sequential index — trading day 1 is the first market-open day of the month, day 2 the second, and so on regardless of calendar date. Most months have 19–23 trading days. By aligning all months across all years to this index, recurring patterns at the start or end of the trading month become visible — such as consistent strength in the first few days or weakness in the final stretch.

Cumulative Seasonal Percent Change by Trading Day
CHART

Cumulative Seasonal Percent Change — By Trading Day of the Month

Each line represents a year, showing how cumulative percent change in closing price builds across trading days 1 through 23 within a month. The average line highlights the typical intra-month pattern across all years.

Seasonality Chart

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