Seasonality Analysis
Historical Prices Year-by-Year
Historical Returns (%) By Years/Months
| Year | Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec | Min | Max | avg |
| 2026 | 4.39 | -1.88 | -4.56 | 2.96 | -1.58 | -0.33 | -1.32 | -2.73 | -0.15 | -4.56 | 4.39 | -0.58 | |||
| 2025 | 0.33 | -4.33 | -1.85 | 0.69 | -1.03 | 3.78 | 2.06 | 2.63 | -1.56 | -2.23 | 1.76 | -4.33 | 3.78 | 0.02 | |
| Summary | |||||||||||||||
| Avg Returns (%) | 4.39 | -0.78 | -4.45 | 0.56 | -0.45 | -0.68 | 1.23 | -0.34 | 1.24 | -1.56 | -2.23 | 1.76 | -4.45 | 4.39 | -0.11 |
| Max Pos Return (%) | 4.39 | 0.33 | -4.33 | 2.96 | 0.69 | -0.33 | 3.78 | 2.06 | 2.63 | -1.56 | -2.23 | 1.76 | -4.33 | 4.39 | 0.85 |
| Max Neg Return (%) | 4.39 | -1.88 | -4.56 | -1.85 | -1.58 | -1.03 | -1.32 | -2.73 | -0.15 | -1.56 | -2.23 | 1.76 | -4.56 | 4.39 | -1.06 |
| Pos Occurances (%) | 100 | 50 | 0 | 50 | 50 | 0 | 50 | 50 | 50 | 0 | 0 | 100 | 0 | 100 | 42 |
| Neg Occurance (%) | 0 | 50 | 100 | 50 | 50 | 100 | 50 | 50 | 50 | 100 | 100 | 0 | 0 | 100 | 58 |
- Seasonality here is suggestive, not decisive: split-half rank correlation of 0.04 across the twelve months, with 6 of 12 months keeping the same sign in both halves.
- Jul is the strongest month (edge 58/100, Moderate): median +1.38%, mean +2.89%, higher in 6 of 7 years (86%), t = 2.18, p = 0.072.
- Feb is the weakest month (edge 56/100, Moderate): median -1.88%, mean -1.66%, lower in 5 of 7 years, t = -2.48, p = 0.048.
- Best stretch of the year is Jul–Nov: median +4.79% compounded, positive in 5 of 6 seasons (83%), p = 0.174.
- Worst stretch is Feb–Jun: median -5.86% compounded, positive in only 2 of 7 seasons (29%).
- We are in Sep, historically a +0.43% median month with a 50% hit rate. Month-to-date STT-PG is -0.15% — behind the seasonal norm.
- Seasonal path from here: Oct -0.64% (50% hit), Oct–Dec -1.48% (33%), Oct–Mar +0.53% (50%) — medians of the compounded window return.
- The median seasonal year peaks around Sep at +5.78%, troughs around Jun at -1.06% and finishes the year at +3.32% (5 complete years).
- Best weekday is Tuesday at +0.054% a session (not significant); weakest is Monday at -0.062%.
- Turn-of-month (last 3 sessions plus first 3) averages -0.007% a session against +0.012% for the rest of the month — a -0.019% daily spread across 483 sessions.
- By quarter, Q3 is the best (median +3.31%, 67% positive) and Q4 the worst (median -1.48%).
- Risk is seasonal too: Mar is the most volatile month (22.7% annualised) and Jul the calmest (8.1%); participation peaks in Mar at +44% of the average month's volume.
- Twelve months were tested at once, so the 5% bar is really 0.0042 after a Bonferroni correction — no month clears it. With 7 seasons of data, seasonality is context, not a trade on its own.
Ask the market a question. Get a calculated answer.
The AI is not a chatbot bolted onto a document store. It calls the same analytics engine that powers every screen on this platform — so what comes back is a number it computed from raw history, with the command that produced it.
86,000+ instruments
Global equities, ETFs, funds, options, FX, commodities, crypto, economics, filings, transcripts and news — one normalised symbol universe with adjusted history.
A real analytics engine
Screening, backtesting, technicals, options analytics, correlations, seasonality and factor models — computed on demand from raw prices, never a stale cache.
It shows its working
Answers arrive with the charts, tables and tool calls behind them, so you can check the number instead of trusting a paraphrase.
Your own documents
Upload filings, decks and research. Ask across them and the answer cites the page it came from.
Agents and workflows
Multi-step research that runs the platform's tools for you — screen, pull the history, compute, compare, then write it up.
MCP, CLI and API
The same command catalogue from Claude, your own agent, a shell or your pipeline. The answer on screen is the answer your job gets at 4am.
You ask
“How does NVDA usually trade through earnings?”
It calls
→ ka.options_expected_move(NVDA)
It answers
NVDA has averaged a 9.2% absolute move on the day after earnings and closed higher 67% of the time. Two in three reactions land between −4.2% and +16.3% — the distribution is skewed right, not symmetric.
Every figure computed live from our own history — not scraped, not summarised.
Or start with