Browser Skills

https://www.macrotrends.net — long-term historical financial and economic charts. Three access patterns depending on page type; all work with plain http_get, no browser required.

All results validated against live site on 2026-04-18.

Do this first: pick your access pattern

Goal	Pattern	Latency	Variable
Stock OHLCV price history	Direct iframe PHP	~190ms	`dataDaily`
Stock market cap (daily)	Direct iframe PHP	~200ms	`chartData`
Stock fundamentals (PE, revenue, margins)	Direct iframe PHP	~140ms	`chartData`
S&P 500 / composite index charts	`chart_iframe_comp.php`	~90ms	`originalData`
Economic indicators (rates, yields, CPI)	`/economic-data/` JSON API	~150ms	`data[]` array
Gold, commodity prices	Either path (both work)	~150ms	`data[]` or `originalData`

Never use the browser for Macrotrends read-only tasks. All endpoints are accessible via http_get with the default Mozilla/5.0 UA. For pages that occasionally 403, switch to a Chrome UA (see gotchas).

Pattern 1: Stock price history (OHLCV)

Construct the iframe URL directly — no need to fetch the main page first.

python

import json, re
from helpers import http_get

def get_stock_ohlcv(ticker: str, years_back: int = None) -> list[dict]:
    """
    Returns daily OHLCV records for any US stock.

    ticker:     uppercase ticker symbol, e.g. 'AAPL', 'MSFT', 'TSLA', 'NVDA'
    years_back: number of years of history (1=~250 records, 15=~3772 records).
                Omit (None) to get ALL available history (AAPL goes back to 1980).
    """
    url = f"https://www.macrotrends.net/production/stocks/desktop/PRODUCTION/stock_price_history.php?t={ticker}"
    if years_back:
        url += f"&yb={years_back}"

    html = http_get(url)
    m = re.search(r'var\s+dataDaily\s*=\s*\[', html)
    if not m:
        raise ValueError(f"No dataDaily found for ticker {ticker!r}")

    si = html.index('[', m.start())
    bc = 0
    for j, ch in enumerate(html[si:], si):
        if ch == '[':   bc += 1
        elif ch == ']':
            bc -= 1
            if bc == 0: ei = j; break
    return json.loads(html[si:ei+1])

# Usage
records = get_stock_ohlcv('AAPL', years_back=15)
# [{'d': '2011-04-18', 'o': '9.771', 'h': '9.9547', 'l': '9.593', 'c': '9.9433', 'v': '18.275'}, ...]

latest = records[-1]
# {'d': '2026-04-17', 'o': '266.96', 'h': '272.3', 'l': '266.72', 'c': '270.23',
#  'v': '55.211', 'ma50': '260.554', 'ma200': '251.828'}

print(f"{latest['d']}: close=${latest['c']} vol={latest['v']}M shares")

dataDaily field reference

Field	Meaning	Type
`d`	Date (YYYY-MM-DD)	str
`o`	Open price (adjusted for splits)	str/float
`h`	High	str/float
`l`	Low	str/float
`c`	Close	str/float
`v`	Volume in millions of shares	str/float
`ma50`	50-day moving average	str/float (appears on recent records only)
`ma200`	200-day moving average	str/float (appears on recent records only)

Note: All price values are strings — cast with float(). Volume is millions: 55.211 = 55.2M shares traded.

Confirmed tickers (2026-04-18)

All tested with direct iframe URL, no page fetch needed:

python

# All work: AAPL, MSFT, TSLA, NVDA, GOOGL, AMZN, META, NFLX, etc.
# 3772 records for yb=15 (goes back to 2011-04-18)
# AAPL full history: 11428 records back to 1980-12-12

Pattern 2: Stock fundamentals (PE ratio, revenue, market cap, margins)

Different PHP files depending on metric. Construct directly.

Market cap (daily, in billions USD)

python

import json, re
from helpers import http_get

def get_market_cap(ticker: str, years_back: int = 15) -> list[dict]:
    url = f"https://www.macrotrends.net/production/stocks/desktop/PRODUCTION/market_cap.php?t={ticker}&yb={years_back}"
    html = http_get(url)
    m = re.search(r'var\s+chartData\s*=\s*\[', html)
    si = html.index('[', m.start())
    bc = 0
    for j, ch in enumerate(html[si:], si):
        if ch == '[':   bc += 1
        elif ch == ']':
            bc -= 1
            if bc == 0: ei = j; break
    return json.loads(html[si:ei+1])

data = get_market_cap('AAPL')
# [{'date': '2026-04-15', 'v1': 3929.35}, {'date': '2026-04-16', 'v1': 3884.67}, ...]
# v1 = market cap in billions USD

PE ratio, revenue, current ratio (quarterly/annual fundamentals)

python

import json, re
from helpers import http_get

def get_fundamental(ticker: str, metric_type: str, statement: str,
                    freq: str = 'Q', years_back: int = 15) -> list[dict]:
    """
    freq: 'Q' = quarterly, 'A' = annual
    """
    url = (
        f"https://www.macrotrends.net/production/stocks/desktop/PRODUCTION/"
        f"fundamental_iframe.php?t={ticker}&type={metric_type}&statement={statement}"
        f"&freq={freq}&sub=&yb={years_back}"
    )
    html = http_get(url)
    m = re.search(r'var\s+chartData\s*=\s*\[', html)
    si = html.index('[', m.start())
    bc = 0
    for j, ch in enumerate(html[si:], si):
        if ch == '[':   bc += 1
        elif ch == ']':
            bc -= 1
            if bc == 0: ei = j; break
    return json.loads(html[si:ei+1])

# PE ratio
pe = get_fundamental('AAPL', 'pe-ratio', 'price-ratios')
# [{'date': '2025-09-30', 'v1': 254.146, 'v2': 7.46, 'v3': 34.07}, ...]
# v1 = stock price, v2 = quarterly EPS, v3 = PE ratio

# Revenue
rev = get_fundamental('AAPL', 'revenue', 'income-statement')
# [{'date': '2025-12-31', 'v1': 435.617, 'v2': 143.756, 'v3': 15.65}, ...]
# v1 = TTM revenue ($B), v2 = quarterly revenue ($B), v3 = YoY growth %

# Total assets
assets = get_fundamental('AAPL', 'total-assets', 'balance-sheet')

# Current ratio
ratio = get_fundamental('AAPL', 'current-ratio', 'ratios')

Profit margins

python

def get_profit_margins(ticker: str, years_back: int = 15) -> list[dict]:
    url = (
        f"https://www.macrotrends.net/production/stocks/desktop/PRODUCTION/"
        f"fundamental_metric.php?t={ticker}&chart=profit-margin&sub=&yb={years_back}"
    )
    html = http_get(url)
    m = re.search(r'var\s+chartData\s*=\s*\[', html)
    si = html.index('[', m.start())
    bc = 0
    for j, ch in enumerate(html[si:], si):
        if ch == '[':   bc += 1
        elif ch == ']':
            bc -= 1
            if bc == 0: ei = j; break
    return json.loads(html[si:ei+1])

margins = get_profit_margins('AAPL')
# [{'date': '2025-12-31', 'v1': 47.33, 'v2': 32.38, 'v3': 27.04}, ...]
# v1 = gross margin %, v2 = operating margin %, v3 = net margin %

Dividend yield

python

def get_dividend_yield(ticker: str, years_back: int = 15) -> list[dict]:
    url = f"https://www.macrotrends.net/production/stocks/desktop/PRODUCTION/dividend_yield.php?t={ticker}&yb={years_back}"
    html = http_get(url)
    m = re.search(r'var\s+chartData\s*=\s*\[', html)
    si = html.index('[', m.start())
    bc = 0
    for j, ch in enumerate(html[si:], si):
        if ch == '[':   bc += 1
        elif ch == ']':
            bc -= 1
            if bc == 0: ei = j; break
    return json.loads(html[si:ei+1])

dy = get_dividend_yield('AAPL')
# [{'date': '2026-04-17', 'c': 270.23, 'ttm_d': 1.03848, 'ttm_dy': 0.3843}, ...]
# c = stock price, ttm_d = TTM dividend ($), ttm_dy = TTM yield (%)

Stock metric URL reference

Metric	PHP file	Extra params
Stock price OHLCV	`stock_price_history.php`	—
Market cap (daily)	`market_cap.php`	—
Dividend yield	`dividend_yield.php`	—
Stock splits (price history)	`stock_splits.php`	—
PE ratio	`fundamental_iframe.php`	`type=pe-ratio&statement=price-ratios`
Revenue	`fundamental_iframe.php`	`type=revenue&statement=income-statement`
Total assets	`fundamental_iframe.php`	`type=total-assets&statement=balance-sheet`
Current ratio	`fundamental_iframe.php`	`type=current-ratio&statement=ratios`
Profit margins	`fundamental_metric.php`	`chart=profit-margin`

Base URL prefix: https://www.macrotrends.net/production/stocks/desktop/PRODUCTION/

All take ?t={TICKER}&yb={N} (or &sub=&yb={N} for the fundamental ones).

Pattern 3: Index and composite charts (S&P 500, Shiller PE, etc.)

These pages embed chart data via chart_iframe_comp.php. The variable is originalData.

python

import json, re
from helpers import http_get

def extract_index_chart(page_id: int, url_slug: str) -> list[dict]:
    """
    page_id:  the numeric ID from the page URL, e.g. 2577
    url_slug: last segment of the page URL, e.g. 'sp500-pe-ratio-price-to-earnings-chart'
    """
    url = f"https://www.macrotrends.net/assets/php/chart_iframe_comp.php?id={page_id}&url={url_slug}"
    html = http_get(url)
    m = re.search(r'var\s+originalData\s*=\s*\[', html)
    if not m:
        raise ValueError("originalData not found — this page may use a different pattern")
    si = html.index('[', m.start())
    bc = 0
    for j, ch in enumerate(html[si:], si):
        if ch == '[':   bc += 1
        elif ch == ']':
            bc -= 1
            if bc == 0: ei = j; break
    return json.loads(html[si:ei+1])

# S&P 500 PE ratio (1180 monthly records, 1927-2026)
pe_data = extract_index_chart(2577, 'sp500-pe-ratio-price-to-earnings-chart')
# [{'date': '1927-12-01', 'close': '15.9099'}, ..., {'date': '2026-03-01', 'close': '27.8925'}]
# 'close' is the PE ratio value

# Gold prices (1336 monthly records, 1915-2026)
gold_data = extract_index_chart(1333, 'historical-gold-prices-100-year-chart')
# [{'id': 'GOLDAMGBD228NLBM', 'date': '1915-01-01', 'close': '629.36', 'close1': '19.250'}, ...]
# 'close' = inflation-adjusted price, 'close1' = nominal USD price

print(f"Latest S&P PE: {pe_data[-1]}")   # {'date': '2026-03-01', 'close': '27.8925'}
print(f"Latest gold:   {gold_data[-1]}") # {'id': ..., 'date': '2026-04-01', 'close': '5177.19', 'close1': '5177.190'}

Detecting which pattern a page uses

python

def get_page_pattern(page_url: str) -> str:
    html = http_get(page_url)
    if 'chart_iframe_comp.php' in html:
        return 'index_chart'           # use extract_index_chart()
    elif 'generateChart' in html and 'highchartsURL' in html:
        return 'economic_api'          # use get_economic_data()
    elif '/production/stocks/desktop/PRODUCTION/' in html:
        return 'stock_iframe'          # use get_stock_ohlcv() etc.
    return 'unknown'

To get the ID and slug from a page

python

import re
from helpers import http_get

page_url = "https://www.macrotrends.net/2577/sp500-pe-ratio-price-to-earnings-chart"
html = http_get(page_url)

# Option A: parse from the iframe src in the HTML
m = re.search(r'chart_iframe_comp\.php\?id=(\d+)&url=([^"&]+)', html)
if m:
    page_id, url_slug = int(m.group(1)), m.group(2)

# Option B: derive from the page URL (works when slug matches)
import urllib.parse
parts = page_url.rstrip('/').split('/')
page_id  = int(parts[-2])   # 2577
url_slug = parts[-1]         # 'sp500-pe-ratio-price-to-earnings-chart'

Pattern 4: Economic indicator API

Pages that use generateChart() in their JS load data from /economic-data/{pageID}/{freq}. This endpoint requires a Referer header matching the page URL.

python

import json, datetime, gzip, urllib.request
from helpers import http_get

def get_economic_data(page_id: int, referer_url: str, freq: str = 'D') -> dict:
    """
    page_id:     numeric ID from the page URL (e.g. 2015 for Fed Funds Rate)
    referer_url: the full page URL — required as Referer header
    freq:        'D' = daily, 'M' = monthly (not all support both)
    
    Returns {'data': [[ts_ms, value], ...], 'metadata': {...}}
    """
    url = f"https://www.macrotrends.net/economic-data/{page_id}/{freq}"
    headers = {
        "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36",
        "Accept": "application/json, */*",
        "Accept-Encoding": "gzip",
        "Referer": referer_url,
    }
    with urllib.request.urlopen(urllib.request.Request(url, headers=headers), timeout=20) as r:
        raw = r.read()
        if r.headers.get("Content-Encoding") == "gzip":
            raw = gzip.decompress(raw)
        result = json.loads(raw)
    if result is None:
        raise ValueError(f"pageID={page_id} does not support freq={freq!r}")
    return result

# Fed Funds Rate (daily, 25319 records)
ffr = get_economic_data(2015, "https://www.macrotrends.net/2015/fed-funds-rate-historical-chart", freq='D')
print(ffr['metadata']['name'])  # 'Fed Funds Interest Rate'
print(ffr['metadata']['label']) # '%'

# Convert timestamps to dates
for ts_ms, value in ffr['data'][-3:]:
    dt = datetime.datetime.fromtimestamp(ts_ms / 1000, datetime.UTC)
    print(f"{dt.strftime('%Y-%m-%d')}: {value}%")
# 2026-04-13: 3.64%
# 2026-04-14: 3.64%
# 2026-04-15: 3.64%

# 10-Year Treasury yield (daily, 16074 records)
t10 = get_economic_data(2016, "https://www.macrotrends.net/2016/10-year-treasury-bond-rate-yield-chart", freq='D')
# Last: 2026-04-15: 4.29%

# Gold prices (monthly, 1336 records, 1915-present) — template=5
gold = get_economic_data(1333, "https://www.macrotrends.net/1333/historical-gold-prices-100-year-chart", freq='M')
# metadata: {'name': 'Gold Prices', 'currency': '$', 'label': ''}

# US Unemployment Rate (monthly, 938 records)
unemp = get_economic_data(1316, "https://www.macrotrends.net/1316/us-national-unemployment-rate", freq='M')
# metadata: {'name': 'U.S. Unemployment Rate', 'label': '%'}

# Debt-to-GDP ratio (monthly, 712 records)
debt_gdp = get_economic_data(1381, "https://www.macrotrends.net/1381/debt-to-gdp-ratio-historical-chart", freq='M')

metadata fields

python

{
    'name':            'Fed Funds Interest Rate',  # chart title
    'tableHeaderName': 'Fed Funds Interest Rate',
    'currency':        '',            # '$' for dollar-denominated series
    'label':           '%',          # units label
    'chartType':       'line',
    'mobileChartType': 'line',
    'lineWidth':       2,
    'positiveColor':   '#2caffe',
    'negativeColor':   '',
    'decimals':        '',
    'chartScale':      'linear',
    'seriesUnits':     ''
}

Available frequency codes

Code	Meaning	Notes
`D`	Daily	Most series support this
`M`	Monthly	Returns `null` if not available
`Q`	Quarterly	Usually `null` — use `M` instead
`A`	Annual	Usually `null` — use `M` instead
`DEFAULT`	Default (usually monthly)	Same data as `M` for most series
`INDEXMONTHLY`	Monthly index close	Some commodity/index series
`INDEXDAILY`	Daily index	Some series
`DAILYEXCHANGERATE`	Daily FX rate	Currency pairs
`10YD`	10-year daily	Specialized series

Try D first, fall back to M if you get null.

Known economic page IDs

ID	URL slug	Description
1316	us-national-unemployment-rate	U.S. Unemployment Rate (monthly, back to 1948)
1333	historical-gold-prices-100-year-chart	Gold Prices (monthly, back to 1915)
1381	debt-to-gdp-ratio-historical-chart	U.S. Debt to GDP Ratio
2015	fed-funds-rate-historical-chart	Fed Funds Interest Rate (daily, back to 1954)
2016	10-year-treasury-bond-rate-yield-chart	10-Year Treasury Yield (daily, back to 1962)
2577	sp500-pe-ratio-price-to-earnings-chart	S&P 500 PE Ratio (uses `chart_iframe_comp.php`)

Generic extraction helper

One function that handles all three embedded-JS patterns:

python

import json, re
from helpers import http_get

def extract_chart_var(html: str, var_name: str) -> list:
    """Extract a JS array variable from Macrotrends iframe HTML."""
    m = re.search(rf'var\s+{re.escape(var_name)}\s*=\s*\[', html)
    if not m:
        return []
    si = html.index('[', m.start())
    bc = 0
    for j, ch in enumerate(html[si:], si):
        if ch == '[':   bc += 1
        elif ch == ']':
            bc -= 1
            if bc == 0:
                return json.loads(html[si:j+1])
    return []

# Works for dataDaily, chartData, or originalData:
html = http_get("https://www.macrotrends.net/production/stocks/desktop/PRODUCTION/stock_price_history.php?t=AAPL&yb=15")
daily = extract_chart_var(html, 'dataDaily')

html2 = http_get("https://www.macrotrends.net/assets/php/chart_iframe_comp.php?id=2577&url=sp500-pe-ratio-price-to-earnings-chart")
pe_data = extract_chart_var(html2, 'originalData')

URL construction guide

Stock pages

python

STOCK_BASE = "https://www.macrotrends.net/production/stocks/desktop/PRODUCTION/"

# Price history OHLCV
f"{STOCK_BASE}stock_price_history.php?t={ticker}"                        # all history
f"{STOCK_BASE}stock_price_history.php?t={ticker}&yb={years}"             # last N years

# Market cap
f"{STOCK_BASE}market_cap.php?t={ticker}&yb={years}"

# Fundamentals
f"{STOCK_BASE}fundamental_iframe.php?t={ticker}&type={type}&statement={stmt}&freq={freq}&sub=&yb={years}"
# type/statement combos: pe-ratio/price-ratios, revenue/income-statement,
#                        total-assets/balance-sheet, current-ratio/ratios

# Metrics
f"{STOCK_BASE}fundamental_metric.php?t={ticker}&chart={metric}&sub=&yb={years}"
# metrics: profit-margin

# Dividend yield
f"{STOCK_BASE}dividend_yield.php?t={ticker}&yb={years}"

Economic / index pages

python

# From numeric ID + URL slug (read from page source or page URL)
f"https://www.macrotrends.net/assets/php/chart_iframe_comp.php?id={id}&url={slug}"

# Economic indicator JSON API (requires Referer header)
f"https://www.macrotrends.net/economic-data/{page_id}/{freq}"

Rate limits and anti-bot

No rate limiting observed at any tested volume. 10 rapid requests to the same stock iframe completed in 1.8s with no throttling, CAPTCHA, or 429 errors.
Default UA works (Mozilla/5.0) for most endpoints. The iframe PHP files never 403'd.
Chrome UA needed for some main HTML pages (not data endpoints): use when fetching /stocks/charts/... or /2015/... wrapper pages if you get 403. Switch to:
python
```
headers = {"User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36"}
```
Referer required for /economic-data/{id}/{freq} — send the page URL as Referer. Without it, the request is allowed but you get a 403 on some pages.
No cookies, sessions, or auth tokens needed for any endpoint.

Gotchas

Main page URL ≠ data page: Some URLs redirect to different content. /1316/us-national-debt-by-year redirects to /1316/us-national-unemployment-rate. Always check the final URL with r.url if the returned data looks wrong. Use the final URL as the Referer.

yb parameter controls history depth:

yb=1 → ~250 records (last year)
yb=15 → ~3772 records (last 15 years)
omit → full history (AAPL: 11428 records to 1980; default for most queries)

Two iframe patterns for economic pages: Pages at macrotrends.net/NNNN/slug use either chart_iframe_comp.php (→ originalData) or generateChart + /economic-data/ API. Check the main page HTML to detect which:

python

if 'chart_iframe_comp.php' in html:   # use extract_index_chart()
elif 'highchartsURL' in html:          # use get_economic_data()

Gold data has two price columns:

python

{'id': 'GOLDAMGBD228NLBM', 'date': '2026-04-01', 'close': '5177.19', 'close1': '5177.190'}
# 'close'  = inflation-adjusted price (base year adjusts over time)
# 'close1' = nominal USD price (the raw market price)

Economic API frequency codes: Only D and M consistently return data across most series. A and Q return null for most economic indicators. Always try D first.

chartData fields vary by metric:

market_cap.php → {'date', 'v1'} (v1 = market cap in $B)
fundamental_iframe.php type=pe-ratio → {'date', 'v1', 'v2', 'v3'} (stock price, EPS, PE)
fundamental_iframe.php type=revenue → {'date', 'v1', 'v2', 'v3'} (TTM revenue, quarterly revenue, YoY%)
fundamental_metric.php chart=profit-margin → {'date', 'v1', 'v2', 'v3'} (gross%, operating%, net%)
dividend_yield.php → {'date', 'c', 'ttm_d', 'ttm_dy'} (price, dividend, yield%)

Bracket matching required for large arrays: The var dataDaily = [...] in stock iframes is ~450KB with 3772 OHLCV records. The re.DOTALL greedy approach works but is slow; bracket-counting (bc pattern above) is O(n) and fast.

No public API for ticker lookup: To find the company slug for a URL, check the search endpoint: https://www.macrotrends.net/production/stocks/desktop/PRODUCTION/ticker_search_list.php?v=YYYYMMDD — but the stock price iframe only needs the ticker symbol (?t=AAPL), not the slug.

macrotrends

Use this skill

What the agent will read

Data Extraction

Do this first: pick your access pattern

Pattern 1: Stock price history (OHLCV)

dataDaily field reference

Confirmed tickers (2026-04-18)

Pattern 2: Stock fundamentals (PE ratio, revenue, market cap, margins)

Market cap (daily, in billions USD)

PE ratio, revenue, current ratio (quarterly/annual fundamentals)

Profit margins

Dividend yield

Stock metric URL reference

Pattern 3: Index and composite charts (S&P 500, Shiller PE, etc.)

Detecting which pattern a page uses

To get the ID and slug from a page

Pattern 4: Economic indicator API

metadata fields

Available frequency codes

Known economic page IDs

Generic extraction helper

URL construction guide

Stock pages

Economic / index pages

Rate limits and anti-bot

Gotchas