\n \n
import requests\n\nBASE = \"https:\/\/eodhd.com\/api\/mp\/unicornbay\/options\"\nTOKEN = \"demo\" # the demo token works for AAPL and AMZN, no registration\n\nr = requests.get(f\"{BASE}\/eod\", params={\n \"filter[contract]\": \"AAPL270115C00260000\",\n \"fields[options-eod]\": \"contract,strike,type,dte,moneyness,volatility\",\n \"page[limit]\": 5,\n \"api_token\": TOKEN,\n})\n\nfor row in r.json()[\"data\"]:\n a = row[\"attributes\"]\n print(row[\"id\"], a[\"dte\"], a[\"moneyness\"], a[\"volatility\"])<\/code><\/pre>\n\n <\/div>\n \n
\n \n <\/use>\n <\/svg>\n Note the record id: it’s the contract name plus the date, like AAPL270115C00260000-2026-08-31. One row is one contract on one trading day. That detail is about to matter a lot.<\/p>\n\n\n\n
<\/div>\n\n\n\n
Why the obvious approach cannot work<\/h2>\n\n\n\n The natural first instinct is to grab a whole expiry and sort through it locally. Let’s actually measure that instead of guessing. Apple’s January 2026 expiry, every strike, every date, walking the pagination to the end:<\/p>\n\n\n\n
\n
\n \n
params = {\n \"filter[underlying_symbol]\": \"AAPL\",\n \"filter[exp_date_eq]\": \"2026-01-16\",\n \"fields[options-eod]\": \"contract,strike,type,dte\",\n \"page[limit]\": 1000,\n \"api_token\": TOKEN,\n}\n\nurl, pages, rows = f\"{BASE}\/eod\", 0, []\nwhile url:\n payload = requests.get(url, params=params).json()\n if \"data\" not in payload: # we will hit this \u2014 see below\n print(\"stopped:\", payload)\n break\n pages += 1\n rows += payload[\"data\"]\n url = payload.get(\"links\", {}).get(\"next\")\n params = {\"api_token\": TOKEN} if url else None\n\nprint(f\"{len(rows)} rows over {pages} pages\")<\/code><\/pre>\n\n <\/div>\n \n
\n \n <\/use>\n <\/svg>\n This does not finish. It collects 11,000 rows across 11 pages and then stops with:<\/p>\n\n\n\n
\n
\n \n
stopped: {'errors': {'page.offset': ['The page.offset may not be greater than 10000.']}}<\/code><\/pre>\n\n <\/div>\n \n
\n \n <\/use>\n <\/svg>\n Pagination allows 1,000 records per request and caps the offset at 10,000, so a single query can reach about 11,000 rows and no further. And that one expiry is much bigger than that. Sweeping it in strike bands to count it properly, it comes to 62,978 rows \u2014 96 strikes across 585 trading dates, calls and puts<\/strong>. Sixty-three pages of data behind an eleven-page door.<\/p>\n\n\n\nFive hundred and eighty-five dates for a single expiry surprised me too, until it didn’t: those contracts were listed nearly two years before they expired, and the endpoint returns every day of their life. There is no filter for the row’s own date \u2014 the tradetime filters match the contract’s last market activity, not the date of the record \u2014 so asking for one expiry always means asking for its entire history.<\/p>\n\n\n\n
So brute force isn’t merely wasteful here, it’s impossible. What makes the job tractable is narrowing the strike range before you ask, using the one thing you already know: where the stock was trading.<\/p>\n\n\n\n
Here’s what band width actually buys you, measured on that same expiry:<\/p>\n\n\n\nStrike band around spot<\/th> Pages<\/th> Rows<\/th> Strikes<\/th> Usable dates in the 25\u201335 day window<\/th><\/tr><\/thead> \n\u00b11%<\/td> 2<\/td> 1,208<\/td> 2<\/td> 7<\/td><\/tr>\n \u00b12%<\/td> 3<\/td> 2,416<\/td> 4<\/td> 7<\/td><\/tr>\n \u00b13%<\/td> 4<\/td> 3,624<\/td> 6<\/td> 7<\/td><\/tr>\n \u00b16%<\/td> 9<\/td> 8,418<\/td> 13<\/td> 7<\/td><\/tr>\n<\/tbody><\/table><\/figure>\n\n\n\nEvery band yields the same seven usable dates \u2014 the wider ones just cost more. Tempting to take the cheapest row and move on. I tried that: a \u00b13% band over the full year still produced 253 daily observations from only 75 requests, which looks like a clear win \u2014 until you compare the output. On 28 of those 253 days the stock had drifted far enough from where it was when I picked the band that the genuinely at-the-money strike fell outside it, and the series quietly reported the second-best contract instead. The year’s volatility peak came out as 31.4% instead of 33.6%. Cheaper, and wrong exactly where it mattered.<\/p>\n\n\n\n
So: \u00b16%. Nine pages per expiry instead of two, and no silent substitutions.<\/p>\n\n\n\n
<\/div>\n\n\n\n
The second gotcha: next doesn’t carry your token<\/h2>\n\n\n\n You’ll notice the pagination loop above puts the token back on every follow-up request. That isn’t decoration. The next link keeps your filters, your sort and your offset \u2014 but not your api_token. Follow it verbatim and you get:<\/p>\n\n\n\n
\n
\n \n
401 Unauthenticated<\/code><\/pre>\n\n <\/div>\n \n
\n \n <\/use>\n <\/svg>\n Which is a genuinely nasty way to learn the lesson, because page one works perfectly and the failure only shows up on page two. If you’ve ever had a paging loop die exactly one page in, this is a good first thing to check.<\/p>\n\n\n\n
<\/div>\n\n\n\n
Building the series<\/h2>\n\n\n\n First the underlying’s price history \u2014 one request for the whole window, which is what lets us narrow the strike band later:<\/p>\n\n\n\n
\n
\n \n
from datetime import date, timedelta\nfrom collections import defaultdict\n\nSYMBOL = \"AAPL\"\nSTART, END = date(2025, 9, 1), date(2026, 9, 1)\n\npx = requests.get(f\"https:\/\/eodhd.com\/api\/eod\/{SYMBOL}.US\", params={\n \"from\": \"2025-08-01\", \"period\": \"d\", \"fmt\": \"json\", \"api_token\": TOKEN,\n}).json()\n\nclose = {row[\"date\"]: row[\"adjusted_close\"] for row in px}\n\ndef price_near(day):\n \"\"\"Closest available close on or before `day` \u2014 handles weekends and holidays.\"\"\"\n for back in range(8):\n key = (day - timedelta(days=back)).isoformat()\n if key in close:\n return close[key]\n return None<\/code><\/pre>\n\n <\/div>\n \n
\n \n <\/use>\n <\/svg>\n Then one function to pull an expiry inside a strike band, paginating properly and re-attaching the token:<\/p>\n\n\n\n
\n
\n \n
REQUESTS = 0\n\ndef fetch_expiry(expiry, lo, hi):\n \"\"\"Every row for one expiry inside a strike band, following pagination.\"\"\"\n global REQUESTS\n params = {\n \"filter[underlying_symbol]\": SYMBOL,\n \"filter[exp_date_eq]\": expiry.isoformat(),\n \"filter[strike_from]\": lo, \"filter[strike_to]\": hi,\n \"fields[options-eod]\": \"contract,strike,type,dte,moneyness,volatility\",\n \"page[limit]\": 1000, \"api_token\": TOKEN,\n }\n url, rows = f\"{BASE}\/eod\", []\n while url:\n payload = requests.get(url, params=params).json()\n REQUESTS += 1\n rows += payload.get(\"data\", [])\n url = payload.get(\"links\", {}).get(\"next\")\n params = {\"api_token\": TOKEN} if url else None\n return rows<\/code><\/pre>\n\n <\/div>\n \n
\n \n <\/use>\n <\/svg>\n Now the loop. US equity options overwhelmingly expire on Fridays, so rather than spending requests to enumerate expiries we generate every Friday in the window and let empty responses tell us which ones don’t exist. It isn’t a perfect rule \u2014 of the 89 past Apple expiries I pulled, two landed on a Thursday (27 March 2024 and 17 April 2025, both ahead of a holiday), and heavily traded ETFs like SPY also list Monday and Wednesday weeklies. For a single name it costs you the odd expiry; if you extend this to SPY, enumerate expiries properly instead.<\/p>\n\n\n\n
For each Friday, the dates where that expiry sits about 30 days out are roughly 25 to 35 days before it:<\/p>\n\n\n\n
\n
\n \n
fridays = []\nday = START\nwhile day <= END + timedelta(days=40):\n if day.weekday() == 4:\n fridays.append(day)\n day += timedelta(days=1)\n\nrows_by_date = defaultdict(list)\n\nfor expiry in fridays:\n spot = price_near(expiry - timedelta(days=30))\n if spot is None:\n continue\n for row in fetch_expiry(expiry, round(spot * 0.94), round(spot * 1.06)):\n a = row[\"attributes\"]\n row_date = row[\"id\"][-10:]\n if not a[\"volatility\"] or a[\"dte\"] is None:\n continue\n if 25 <= a[\"dte\"] <= 35 and START.isoformat() <= row_date <= END.isoformat():\n rows_by_date[row_date].append(a)<\/code><\/pre>\n\n <\/div>\n \n
\n \n <\/use>\n <\/svg>\n What we have now is a bucket of candidate contracts per trading date. Turning that into one number per day means picking the closest to the money \u2014 separately for the call and the put, then averaging the two.<\/p>\n\n\n\n
Averaging the pair is a convention rather than a law, and it’s worth knowing what it papers over. At a single strike near the money one leg is slightly in the money and the other slightly out, and the in-the-money leg is the less trustworthy of the two: wider spread, and for American-style puts an early-exercise premium the model has to guess at. Averaging keeps skew from leaking into what’s meant to be a level, which is what we want here. If you need a cleaner number, work from the out-of-the-money side or from a forward-implied ATM strike.<\/p>\n\n\n\n
\n
\n \n
series = []\n\nfor day in sorted(rows_by_date):\n picks = {}\n for kind in (\"call\", \"put\"):\n same = [c for c in rows_by_date[day] if c[\"type\"] == kind]\n if same:\n # closest to the money first, then closest to 30 days\n picks[kind] = min(same, key=lambda c: (abs(c[\"moneyness\"]), abs(c[\"dte\"] - 30)))\n\n if not picks:\n continue\n\n ivs = [p[\"volatility\"] for p in picks.values()]\n series.append({\n \"date\": day,\n \"iv\": round(sum(ivs) \/ len(ivs), 4),\n \"iv_call\": picks.get(\"call\", {}).get(\"volatility\"),\n \"iv_put\": picks.get(\"put\", {}).get(\"volatility\"),\n \"strike\": picks[next(iter(picks))][\"strike\"],\n })\n\nprint(f\"{len(series)} daily observations from {REQUESTS} requests\")<\/code><\/pre>\n\n <\/div>\n \n
\n \n <\/use>\n <\/svg>\n For Apple over the twelve months to 1 September 2026 that prints:<\/p>\n\n\n\n
\n
\n \n
253 daily observations from 97 requests<\/code><\/pre>\n\n <\/div>\n \n
\n \n <\/use>\n <\/svg>\n Essentially every trading day in the window, for under a hundred requests. Your exact count will differ by a page or two and will creep up over time \u2014 each new trading day adds rows to every live expiry, so a band that fits in four pages today needs five next month. The observation count and the volatility figures are unaffected; only the request tally drifts.<\/p>\n\n\n\n
Cheap enough to run across a watchlist without thinking about it \u2014 which is exactly what the naive version could not do at any price.<\/p>\n\n\n\n
<\/div>\n\n\n\n
What a year of Apple’s implied volatility shows<\/h2>\n\n\n\n Here’s the series, with Apple’s four earnings reports marked.<\/p>\n\n\n\nAAPL ATM 30-DTE implied volatility. Dashed lines are earnings reports.<\/figcaption><\/figure>\n\n\n\nOver the year, 30-day IV ranged from 17.2% to 33.6%<\/strong>, with a median of 25.1%. That range is the context that was missing at the start: 25% isn’t “high” or “low” for Apple, it’s dead average. The last observation, 1 September 2026, came in at 24.7% \u2014 the 47th percentile<\/strong> of the year. Neither cheap nor expensive. That’s a boring answer, and boring answers are worth a lot when the alternative is guessing.<\/p>\n\n\n\nNow look at what happens at those dashed lines. Every single one is followed by a cliff:<\/p>\n\n\n\nEarnings date<\/th> IV on the day<\/th> IV five sessions later<\/th> Change<\/th> Stock’s next-day move<\/th><\/tr><\/thead> \n30 Oct 2025<\/td> 25.8%<\/td> 23.6%<\/td> \u22122.1 pp<\/td> \u22120.38%<\/td><\/tr>\n 29 Jan 2026<\/td> 31.3%<\/td> 27.0%<\/td> \u22124.3 pp<\/td> +0.46%<\/td><\/tr>\n 30 Apr 2026<\/td> 29.2%<\/td> 22.8%<\/td> \u22126.4 pp<\/td> +3.24%<\/td><\/tr>\n 30 Jul 2026<\/td> 29.8%<\/td> 24.8%<\/td> \u22125.0 pp<\/td> \u22127.35%<\/td><\/tr>\n<\/tbody><\/table><\/figure>\n\n\n\nFour reports, four collapses, averaging 4.5 percentage points in five sessions<\/strong>. This is the well-known “IV crush”, and it’s satisfying to watch it fall out of data you assembled yourself rather than read about it in a textbook.<\/p>\n\n\n\nLook at that last row, though. Apple dropped 7.35% the day after its July report \u2014 a big move, exactly the kind of thing you’d want to own options for. And implied volatility still fell 5 points. If you had bought a straddle into that print, you were right about the stock moving and could still have lost money, because you paid for volatility that evaporated the moment the uncertainty did. Direction was never the whole trade.<\/p>\n\n\n\n
Here’s something else the data says, and it contradicts what everybody repeats. The standard line is that IV ramps up into earnings. Across these four reports it didn’t: IV ten sessions before the announcement averaged 0.4 points higher<\/strong> than on the day itself. Four reports is a small sample and I wouldn’t generalise from it \u2014 but in this window the run-up simply wasn’t there, while the collapse was, every time. That asymmetry is the kind of thing you can only check with history.<\/p>\n\n\n\nAnd the biggest volatility spike of the entire year had nothing to do with earnings at all. On 30 March 2026 Apple’s 30-day IV hit 33.6%, the high of the sample, with no report anywhere near. The stock sat at $246 and barely moved that week. Then it rallied to $272 by 20 April \u2014 up nearly 11%. The options market had priced a storm; what arrived was a rally. Implied volatility is the price of expected movement, not a prediction of which way, and it is frequently just wrong.<\/p>\n\n\n\n
One more series worth plotting, almost free now that the pieces are in place. We kept the call and put IV separately, so subtracting one from the other gives a running measure of what downside protection costs relative to upside:<\/p>\n\n\n\n
\n
\n \n
gap = [\n (s[\"date\"], (s[\"iv_put\"] - s[\"iv_call\"]) * 100)\n for s in series\n if s[\"iv_put\"] and s[\"iv_call\"]\n]<\/code><\/pre>\n\n <\/div>\n \n
\n \n <\/use>\n <\/svg>\n At-the-money put IV minus call IV. When it climbs, puts are getting relatively pricier.<\/figcaption><\/figure>\n\n\n\nAnd the practical version of the original question \u2014 a percentile, not a level:<\/p>\n\n\n\n
\n
\n \n
def iv_percentile(series):\n values = sorted(s[\"iv\"] for s in series)\n latest = series[-1][\"iv\"]\n rank = sum(1 for v in values if v <= latest) \/ len(values)\n return latest, rank\n\niv, rank = iv_percentile(series)\nprint(f\"latest IV {iv:.1%} \u2014 {rank:.0%} percentile of the window\")<\/code><\/pre>\n\n <\/div>\n \n
\n \n <\/use>\n <\/svg>\n A year of readings in one picture: the latest value against the full distribution.<\/figcaption><\/figure>\n\n\n\n“Apple’s 30-day IV is at the 47th percentile of the past year” is a sentence you can act on. “Apple’s IV is 24.7%” is not.<\/p>\n\n\n\n
Change the symbol to AMZN and the same code gives you Amazon’s volatility index. Put the two side by side and you get the clearest possible argument for why levels are useless on their own.<\/p>\n\n\n\nSame code, two tickers. Amazon’s 30-day IV sat above Apple’s on every day of the sample.<\/figcaption><\/figure>\n\n\n\nAmazon’s 30-day IV sat above Apple’s on 252 of the 253 days<\/strong> \u2014 every day but one \u2014 averaging 9.3 points higher, median 32.8% against 25.1%. Now look what that does to the original question. On 1 September 2026 Amazon printed 28.8% and Apple 24.7%. The bigger number is the cheaper<\/em> one: 28.8% is Amazon’s 22nd percentile for the year, while Apple’s 24.7% is its 47th. Sort a watchlist by raw implied volatility and you will get Amazon above Apple every single day, which tells you nothing you didn’t know. Rank each name against its own history and you find out which options are actually on sale.<\/p>\n\n\n\n<\/div>\n\n\n\n
Where this breaks<\/h2>\n\n\n\n Four things to keep in mind before you lean on this.<\/p>\n\n\n\n
It’s end-of-day, and that’s a feature here.<\/strong> One clean reading per session is exactly what you want for a historical series. But it means you cannot use this to react intraday, and you shouldn’t pretend otherwise \u2014 if your idea depends on where IV was at 11:30, this dataset can’t tell you.<\/p>\n\n\n\nThe 25-to-35-day window is a compromise.<\/strong> Some days you’ll land on a 26-day contract and some on a 34-day one, and since the volatility term structure slopes, that introduces a little jitter that isn’t real vol movement. If it bothers you, interpolate between the two nearest expiries to hit exactly 30 days. You have everything you need to do that; I left it out to keep the code readable.<\/p>\n\n\n\nNearest-to-the-money is not the same as at-the-money.<\/strong> When strikes are spaced $5 apart and the stock sits between two of them, your “ATM” contract is up to $2.50 off. On a $250 stock that’s noise; on a $12 stock it isn’t. This is also why the band width mattered: get it wrong and you don’t get an error, you get a slightly worse contract and a slightly wrong number.<\/p>\n\n\n\nLiquidity decides whether any of this means anything.<\/strong> Apple and Amazon have deep, tightly quoted chains, so their implied volatility is a real market price. Go far enough down the market-cap ladder and you’ll find contracts whose IV is derived from a quote nobody would trade against. The data will happily hand you a number; the bid-ask spread, volume and open interest fields are how you decide whether to believe it. Filter on them before you trust a thin name.<\/p>\n\n\n\n<\/div>\n\n\n\n
Run it on your own ticker<\/h2>\n\n\n\n Everything above runs on the demo token, which covers AAPL and AMZN without registration \u2014 including the price history call. Change one variable and you have Amazon’s volatility index instead. Copy the code, run it, and you’ll have a year of ticker-level IV history in a couple of minutes.<\/p>\n\n\n\n
Beyond those two tickers you’ll need a key. The US Stock Options Data API<\/a> covers roughly 6,900 US underlyings with history back to October 2023, and every contract row carries implied volatility, all five Greeks, open interest and bid\/ask alongside the days-to-expiry and moneyness fields we leaned on here. The API documentation<\/a> has the full parameter list.<\/p>\n\n\n\nTwo directions worth taking this next. Run the loop across a basket and you can rank names by how expensive their options are relative to their own history \u2014 a far more useful screen than sorting by raw IV, which just surfaces the most volatile stocks every time. Or hold the date fixed and vary strike and expiry instead of pinning them, and the same rows give you the volatility surface: skew across strikes, term structure across expiries.<\/p>\n\n\n\n
Next time I want to go after a noisier question \u2014 spotting unusual options activity from end-of-day data, and being honest about what “unusual” can and can’t mean when you only see the close.<\/p>\n\n","protected":false},"excerpt":{"rendered":"
Here is a question that sounds simple and turns out to be surprisingly hard to answer: are options on Apple expensive right now? Your broker will happily show you today’s implied volatility. Fine \u2014 but 25% implied volatility means nothing on its own. Is that high for Apple? Low? Perfectly ordinary for a Tuesday in […]<\/p>\n","protected":false},"author":25,"featured_media":6861,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[93],"tags":[],"coding-language":[],"ready-to-go-solution":[],"qualification":[],"financial-apis-category":[],"financial-apis-manuals":[],"class_list":["post-6860","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-stock-options","has_thumb"],"yoast_head":"\n
How to Build a Historical Implied Volatility Series for Any US Stock (Python) | EODHD APIs Academy<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/eodhd.com\/financial-academy\/stock-options\/build-your-own-volatility-index-python\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How to Build a Historical Implied Volatility Series for Any US Stock (Python)\" \/>\n<meta property=\"og:description\" content=\"Here is a question that sounds simple and turns out to be surprisingly hard to answer: are options on Apple expensive right now? Your broker will happily show you today’s implied volatility. Fine \u2014 but 25% implied volatility means nothing on its own. Is that high for Apple? Low? 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