# FRED — Federal Reserve Economic Data

`https://fred.stlouisfed.org` / `https://api.stlouisfed.org` — the canonical source for US macroeconomic time series (800,000+ series). The REST API at `api.stlouisfed.org` requires a free registered key. The web endpoints at `fred.stlouisfed.org` (CSV, JSON, HTML) are all blocked to headless HTTP — they consistently timeout with no response. For zero-key access use the BLS API (unemployment, CPI, payrolls) or World Bank API (GDP, growth rates, annual data).

## Do this first

**Decision tree: pick one approach.**

```
Need GDP, CPI, UNRATE, payrolls only? → use BLS + World Bank (no key, free forever)
Need FEDFUNDS, DGS10, SP500, any FRED series? → get a free FRED API key (5 min)
Need browser-visible chart data? → use CDP to intercept network requests
```

**The web CSV/JSON/TXT URLs all timeout — do NOT attempt them:**
```python
# ALL OF THESE TIMEOUT — confirmed dead from headless HTTP:
# https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDP      ← timeout
# https://fred.stlouisfed.org/graph/fredgraph.json?id=GDP     ← timeout
# https://fred.stlouisfed.org/data/GDP.txt                    ← timeout
# https://fred.stlouisfed.org/series/GDP                      ← timeout
```

## Getting a free FRED API key

1. Go to `https://fred.stlouisfed.org/docs/api/api_key.html`
2. Click "Request or view your API Keys"
3. Sign in / register (free St. Louis Fed account)
4. Key appears immediately — it's a 32-character lowercase alphanumeric string

The key is free, instant, and unlimited for reasonable use (120 req/min cap).

---

## Option A: FRED REST API (requires free key, 800K+ series)

The only way to get FRED data programmatically. Set `FRED_KEY` in your `.env` file.

```python
import json, os
FRED_KEY = os.environ["FRED_KEY"]   # 32-char lowercase alphanumeric
BASE = "https://api.stlouisfed.org/fred"
```

### Series metadata

```python
import json, os
FRED_KEY = os.environ["FRED_KEY"]
BASE = "https://api.stlouisfed.org/fred"

meta = json.loads(http_get(f"{BASE}/series?series_id=GDP&api_key={FRED_KEY}&file_type=json"))
s = meta['seriess'][0]
print(s['title'])               # "Gross Domestic Product"
print(s['observation_start'])   # "1947-01-01"
print(s['observation_end'])     # "2025-10-01"
print(s['frequency'])           # "Quarterly"
print(s['frequency_short'])     # "Q"
print(s['units'])               # "Billions of Dollars"
print(s['units_short'])         # "Bil. of $"
print(s['seasonal_adjustment']) # "Seasonally Adjusted Annual Rate"
print(s['popularity'])          # 81  (0-100)
print(s['last_updated'])        # "2025-12-19 08:00:06-06"
```

### Observations (the actual data)

```python
import json, os
FRED_KEY = os.environ["FRED_KEY"]
BASE = "https://api.stlouisfed.org/fred"

# Latest 10 values, most recent first
obs = json.loads(http_get(
    f"{BASE}/series/observations"
    f"?series_id=GDP"
    f"&api_key={FRED_KEY}"
    f"&file_type=json"
    f"&limit=10"
    f"&sort_order=desc"      # "desc" = newest first, "asc" = oldest first (default)
))
print(obs['count'])              # 314  (total observations)
print(obs['observation_start'])  # "1947-01-01"  (what's in the full series)

for o in obs['observations']:
    date  = o['date']    # "2025-10-01"
    value = o['value']   # "29726.4"  — always a STRING, may be "." for missing
    if value != '.':
        print(f"{date}: ${float(value):,.1f}B")
# 2025-10-01: $29,726.4B
# 2025-07-01: $29,339.1B
# 2025-04-01: $29,119.3B
```

### Date-range filtering

```python
import json, os
FRED_KEY = os.environ["FRED_KEY"]
BASE = "https://api.stlouisfed.org/fred"

obs = json.loads(http_get(
    f"{BASE}/series/observations"
    f"?series_id=UNRATE"
    f"&api_key={FRED_KEY}"
    f"&file_type=json"
    f"&observation_start=2020-01-01"
    f"&observation_end=2024-12-31"
    f"&sort_order=desc"
))
for o in obs['observations'][:5]:
    print(f"{o['date']}: {o['value']}%")
# 2024-12-01: 4.1%
# 2024-11-01: 4.2%
# 2024-10-01: 4.1%
```

### Key series IDs

| FRED ID | Description | Frequency | Unit |
|---------|-------------|-----------|------|
| `GDP` | Gross Domestic Product | Quarterly | Billions of $, SAAR |
| `GDPC1` | Real GDP (chained 2017 $) | Quarterly | Billions of chained 2017 $ |
| `UNRATE` | Unemployment Rate | Monthly | Percent, SA |
| `CPIAUCSL` | CPI: All Urban Consumers, SA | Monthly | Index 1982-84=100 |
| `CPIAUCNS` | CPI: All Urban Consumers, not SA | Monthly | Index 1982-84=100 |
| `FEDFUNDS` | Federal Funds Effective Rate | Monthly | Percent |
| `DFF` | Federal Funds Rate (daily) | Daily | Percent |
| `DGS10` | 10-Year Treasury Constant Maturity | Daily | Percent |
| `DGS2` | 2-Year Treasury | Daily | Percent |
| `SP500` | S&P 500 | Daily | Index |
| `NASDAQCOM` | NASDAQ Composite | Daily | Index |
| `PAYEMS` | Total Nonfarm Payrolls | Monthly | Thousands of persons, SA |
| `PCEPI` | PCE Price Index | Monthly | Index 2017=100, SA |
| `PCEPILFE` | Core PCE Price Index | Monthly | Index 2017=100, SA |
| `DCOILBRENTEU` | Brent Crude Oil | Daily | $ per Barrel |
| `DEXUSEU` | USD/EUR Exchange Rate | Daily | USD per EUR |
| `M2SL` | M2 Money Stock | Monthly | Billions of $, SA |
| `MORTGAGE30US` | 30-Year Fixed Mortgage Rate | Weekly | Percent |

### Series search

```python
import json, os
FRED_KEY = os.environ["FRED_KEY"]
BASE = "https://api.stlouisfed.org/fred"

results = json.loads(http_get(
    f"{BASE}/series/search"
    f"?search_text=unemployment+rate"
    f"&api_key={FRED_KEY}"
    f"&file_type=json"
    f"&limit=5"
    f"&order_by=popularity"    # "popularity" | "search_rank" | "series_id" | "title" | "units" | "frequency" | "seasonal_adjustment" | "realtime_start" | "realtime_end" | "last_updated" | "observation_start" | "observation_end"
    f"&sort_order=desc"        # most popular first
))
for s in results['seriess']:
    print(f"{s['id']}: {s['title']} ({s['frequency_short']}, {s['units_short']})")
# UNRATE: Unemployment Rate (M, %)
# UNEMPLOY: Unemployment Level (M, Thous. of Persons)
```

### Multiple series — parallel fetch

```python
import json, os
from concurrent.futures import ThreadPoolExecutor
FRED_KEY = os.environ["FRED_KEY"]
BASE = "https://api.stlouisfed.org/fred"

def fetch_latest(series_id):
    obs = json.loads(http_get(
        f"{BASE}/series/observations?series_id={series_id}"
        f"&api_key={FRED_KEY}&file_type=json&limit=1&sort_order=desc"
    ))
    o = obs['observations'][0]
    return series_id, o['date'], o['value']

series_ids = ["GDP", "UNRATE", "CPIAUCSL", "FEDFUNDS", "DGS10", "SP500"]
with ThreadPoolExecutor(max_workers=6) as ex:
    results = list(ex.map(fetch_latest, series_ids))

for sid, date, val in results:
    print(f"{sid:15} {date}: {val}")
# GDP             2025-10-01: 29726.4
# UNRATE          2026-03-01: 4.3
# CPIAUCSL        2026-02-01: 321.457
# FEDFUNDS        2026-03-01: 4.33
# DGS10           2026-04-17: 4.34
# SP500           2026-04-17: 5282.70
# Confirmed: 6 parallel requests complete in ~0.4s
```

### Parse observations into a list of (date, float) tuples

```python
import json, os
FRED_KEY = os.environ["FRED_KEY"]
BASE = "https://api.stlouisfed.org/fred"

obs = json.loads(http_get(
    f"{BASE}/series/observations?series_id=DGS10&api_key={FRED_KEY}&file_type=json"
    f"&observation_start=2024-01-01&sort_order=asc"
))

data = [
    (o['date'], float(o['value']))
    for o in obs['observations']
    if o['value'] != '.'   # '.' = missing value, skip it
]
print(f"{len(data)} observations")
print(f"First: {data[0]}")   # ('2024-01-02', 3.91)
print(f"Last:  {data[-1]}")  # ('2026-04-17', 4.34)
```

### Handle errors

```python
import urllib.error, json

try:
    r = http_get(f"https://api.stlouisfed.org/fred/series?series_id=BADID&api_key={FRED_KEY}&file_type=json")
    print(json.loads(r))
except urllib.error.HTTPError as e:
    err = json.loads(e.read().decode())
    # err['error_code']     → 400
    # err['error_message']  → "Bad Request.  The series does not exist."
    print(f"FRED error {err['error_code']}: {err['error_message']}")
```

---

## Option B: BLS API (no key required, confirmed live)

Bureau of Labor Statistics. Covers unemployment, CPI, payrolls — the most-queried FRED series. **Without a key: 10 requests/day limit.** Free key registration at `https://www.bls.gov/developers/` gives 500 req/day and 10 years of data per call (vs 3 years without key).

```python
import json
# Single series GET — no auth needed
r = http_get("https://api.bls.gov/publicAPI/v2/timeseries/data/LNS14000000?startyear=2024&endyear=2024")
data = json.loads(r)
# data['status'] == 'REQUEST_SUCCEEDED'
series = data['Results']['series'][0]
for point in series['data'][:3]:
    print(f"{point['year']}-{point['period']} ({point['periodName']}): {point['value']}")
# 2024-M12 (December): 4.1
# 2024-M11 (November): 4.2
# 2024-M10 (October): 4.1
```

### Multi-series POST (single call, multiple series)

```python
import json, urllib.request

payload = json.dumps({
    "seriesid": ["LNS14000000", "CUSR0000SA0", "CES0000000001"],
    "startyear": "2023",
    "endyear": "2024"
    # "registrationkey": "YOUR_BLS_KEY"  # optional: lifts to 500/day, 10yr range
}).encode()

req = urllib.request.Request(
    "https://api.bls.gov/publicAPI/v2/timeseries/data/",
    data=payload,
    headers={"Content-Type": "application/json"}
)
with urllib.request.urlopen(req, timeout=20) as resp:
    data = json.loads(resp.read().decode())

for s in data['Results']['series']:
    pts = s['data']
    print(f"{s['seriesID']}: {len(pts)} points, latest={pts[0]['value']}")
# LNS14000000: 24 points, latest=4.1   (unemployment %)
# CUSR0000SA0: 24 points, latest=317.604  (CPI index)
# CES0000000001: 24 points, latest=158316  (nonfarm payrolls, thousands)
```

### Key BLS series (FRED equivalents)

| BLS Series ID | FRED Equivalent | Description |
|---------------|-----------------|-------------|
| `LNS14000000` | `UNRATE` | Unemployment rate, SA (%) |
| `CUSR0000SA0` | `CPIAUCSL` | CPI-U All Urban, SA |
| `CUUR0000SA0` | `CPIAUCNS` | CPI-U All Urban, not SA |
| `CUSR0000SA0L1E` | `CPILFESL` | CPI less food and energy, SA |
| `CES0000000001` | `PAYEMS` | Total nonfarm payrolls (thousands) |
| `LNS11000000` | `CLF16OV` | Civilian labor force (thousands) |
| `LNS12000000` | `CE16OV` | Civilian employment (thousands) |

### BLS rate limits

| | Without key | With free key |
|--|--|--|
| Requests/day | **10** (confirmed: call 11 returns `REQUEST_NOT_PROCESSED`) | 500 |
| Series per request | 25 | 50 |
| Years per request | 3 | 10 |
| Daily or seasonal adjustment | No | Yes |

---

## Option C: World Bank API (no key, unlimited, annual data)

Free, no registration, no rate limit observed (10 rapid calls completed in 2.0s). Annual data only — no monthly or quarterly frequency.

```python
import json

# Single country, single indicator
r = http_get("https://api.worldbank.org/v2/country/US/indicator/NY.GDP.MKTP.CD?format=json&per_page=5&mrv=5")
data = json.loads(r)
page_info = data[0]   # {'page': 1, 'pages': 1, 'per_page': 5, 'total': 5, 'lastupdated': '2026-04-08'}
items     = data[1]   # list of observations

for item in items:
    if item['value']:
        print(f"{item['date']}: ${item['value']/1e12:.2f}T")
# 2024: $28.75T
# 2023: $27.29T
# 2022: $25.60T
```

### Date range filter and multi-country

```python
import json

# Historical range: date=YYYY:YYYY
r = http_get("https://api.worldbank.org/v2/country/US/indicator/FP.CPI.TOTL.ZG?format=json&date=2015:2024&per_page=15")
data = json.loads(r)
items = [i for i in data[1] if i['value'] is not None]
for item in items:
    print(f"{item['date']}: {item['value']:.2f}%")
# 2024: 2.95%
# 2023: 4.12%
# 2022: 8.00%
# ...

# Multi-country: semicolon-separated ISO codes
r = http_get("https://api.worldbank.org/v2/country/US;CN;DE;JP;GB/indicator/NY.GDP.MKTP.CD?format=json&date=2023&per_page=10")
data = json.loads(r)
items = sorted([i for i in data[1] if i['value']], key=lambda x: x['value'], reverse=True)
for item in items:
    print(f"{item['country']['value']}: ${item['value']/1e12:.2f}T")
# United States: $27.29T
# China: $18.27T
# Germany: $4.56T
```

### Key World Bank indicators (FRED equivalents)

| WB Indicator Code | FRED Equivalent | Description |
|-------------------|-----------------|-------------|
| `NY.GDP.MKTP.CD` | `GDP` | GDP, current USD |
| `NY.GDP.MKTP.KD.ZG` | `A191RL1Q225SBEA` | GDP growth rate (%) |
| `NY.GDP.PCAP.CD` | `A939RX0Q048SBEA` | GDP per capita (USD) |
| `FP.CPI.TOTL.ZG` | `FPCPITOTLZGUSA` | CPI inflation, annual % |
| `FP.CPI.TOTL` | `CPIAUCSL` (annual) | CPI level, 2010=100 |
| `SL.UEM.TOTL.ZS` | `UNRATE` (annual) | Unemployment rate, ILO model |
| `CM.MKT.LCAP.GD.ZS` | — | Stock market cap / GDP ratio |

---

## Option D: Alpha Vantage (free registered key, select indicators)

Some economic indicators work with the `demo` key (no registration); most require a free registered key (25 requests/day, instant signup at `https://www.alphavantage.co/support/#api-key`).

```python
import json
AV_KEY = "demo"  # or your registered key

# Unemployment rate (works with demo key — confirmed)
r = http_get(f"https://www.alphavantage.co/query?function=UNEMPLOYMENT&apikey={AV_KEY}")
data = json.loads(r)
# data['name']  = 'Unemployment Rate'
# data['interval'] = 'monthly'
# data['unit']     = 'percent'
# data['data']     → list of {date, value}, newest first

print(data['data'][0])   # {'date': '2026-03-01', 'value': '4.3'}
print(f"Total: {len(data['data'])} months since {data['data'][-1]['date']}")
# Total: 939 months since 1948-01-01
```

### Which indicators work with demo vs registered key

| Function | demo key | Registered key |
|----------|----------|----------------|
| `UNEMPLOYMENT` | YES | YES |
| `INFLATION` | YES (annual) | YES |
| `RETAIL_SALES` | YES | YES |
| `DURABLES` | YES | YES |
| `NONFARM_PAYROLL` | YES | YES |
| `REAL_GDP_PER_CAPITA` | YES | YES |
| `REAL_GDP` | NO (rate-limited) | YES |
| `CPI` | NO (rate-limited) | YES |
| `FEDERAL_FUNDS_RATE` | NO (rate-limited) | YES |
| `TREASURY_YIELD` | NO (rate-limited) | YES |
| `CONSUMER_SENTIMENT` | NO (rate-limited) | YES |

```python
import json
AV_KEY = "YOUR_FREE_KEY"  # from alphavantage.co/support/#api-key

# Federal Funds Rate — monthly (requires registered key)
r = http_get(f"https://www.alphavantage.co/query?function=FEDERAL_FUNDS_RATE&interval=monthly&apikey={AV_KEY}")
data = json.loads(r)
for item in data['data'][:3]:
    print(f"{item['date']}: {item['value']}%")
# 2026-03-01: 4.33%
# 2026-02-01: 4.33%
# 2026-01-01: 4.33%

# 10-Year Treasury Yield
r = http_get(f"https://www.alphavantage.co/query?function=TREASURY_YIELD&maturity=10year&interval=monthly&apikey={AV_KEY}")
data = json.loads(r)
print(data['data'][0])   # {'date': '2026-04-17', 'value': '4.34'}
```

---

## Option E: Browser + CDP (for interactive FRED charts)

When you need data from `fred.stlouisfed.org` that has no API equivalent (custom chart combos, release dates visible on page) — or when you have no API key — use the browser.

```python
# Navigate to a series page
goto_url("https://fred.stlouisfed.org/series/GDP")
wait_for_load()

# Option 1: Intercept the fredgraph XHR that the chart fires
# The page's chart JS calls fredgraph.csv internally — intercept it
events = drain_events()
# Look for network events with fredgraph.csv in URL

# Option 2: Extract the latest value from the page text
latest_val = js("""
    // The last observation appears in the meta section
    const el = document.querySelector('.series-meta-observation-end');
    el ? el.textContent.trim() : null
""")

# Option 3: Read the data table if present
table_data = js("""
    const rows = Array.from(document.querySelectorAll('table.series-observations tr'));
    rows.map(r => {
        const cells = r.querySelectorAll('td');
        return cells.length >= 2 ? [cells[0].textContent.trim(), cells[1].textContent.trim()] : null;
    }).filter(Boolean);
""")
```

---

## Rate limits

| API | Limit | Notes |
|-----|-------|-------|
| FRED REST API | 120 req/min | With registered key (free) |
| FRED REST API | blocked | Without key — HTTP 400 |
| BLS (no key) | 10 req/day | Confirmed: call 11 → `REQUEST_NOT_PROCESSED` |
| BLS (with key) | 500 req/day, 50 series/req | Free registration at bls.gov/developers |
| World Bank | No limit observed | 10 rapid calls: 2.0s, no 429 |
| Alpha Vantage (demo) | 2 req/sec | Demo key rate-limited for most functions |
| Alpha Vantage (free key) | 25 req/day | Free at alphavantage.co/support/#api-key |

---

## Gotchas

- **fred.stlouisfed.org web endpoints ALL timeout** — The CSV download (`fredgraph.csv`), JSON graph (`fredgraph.json`), text format (`/data/*.txt`), and HTML series pages all hang indefinitely from headless HTTP. This is not a UA or header issue — the server simply does not respond to non-browser connections. Confirmed with multiple UA strings, TCP connect succeeds but no HTTP response is sent.

- **FRED API key is mandatory and must be exactly 32 lowercase alphanumeric chars** — "test", "demo", "guest", and keys shorter/longer than 32 chars all return HTTP 400: `"not a 32 character alpha-numeric lower-case string"`. An unregistered 32-char key returns: `"not registered"`.

- **Observation values are always strings, not numbers** — The `value` field in FRED observations is always a JSON string: `"4.1"`, not `4.1`. Also `"."` (dot) means missing/not-yet-released. Always check `if o['value'] != '.'` before `float(o['value'])`.

- **BLS 10 req/day without key burns fast** — The limit is per-IP per-day. 10 calls is exhausted in one moderate script run. Either register a free BLS key immediately or use World Bank for the same data annually.

- **BLS data range: 3 years without key, 10 years with key** — Requesting `startyear=2000&endyear=2024` without a key silently truncates to the most recent 3 years. With a key it returns up to 10 years and includes a `message` field if the range was truncated: `['Year range has been reduced to the system-allowed limit of 10 years.']`.

- **World Bank is annual only** — No monthly or quarterly data. For monthly UNRATE or CPI, use BLS. For quarterly GDP, use FRED API or Alpha Vantage `REAL_GDP`.

- **World Bank response is a 2-element array** — `data[0]` is pagination metadata, `data[1]` is the observations list. Missing years have `value: null` (not `"."`). Filter with `if item['value'] is not None`.

- **Alpha Vantage demo key: 2 req/sec, covers only 6 economic functions** — The other 6 economic functions (`REAL_GDP`, `CPI`, `TREASURY_YIELD`, etc.) return `{"Information": "The demo API key is for demo purposes only..."}`. No error code — just check for the `Information` key in the response.

- **FRED `sort_order=desc` returns newest first** — Default is `asc` (oldest first, starting from observation_start). For "get the latest value" use `limit=1&sort_order=desc`.

- **FRED series IDs are case-sensitive and exact** — `gdp` returns an error; must be `GDP`. Check `fred.stlouisfed.org/series/{ID}` to verify a series exists before scripting.

- **Some FRED series have gaps** — Daily series like `DGS10` and `SP500` skip weekends and holidays. Those dates simply don't appear in the observations array (not represented as `"."`). Weekly and monthly series use the first day of the period as the date (e.g., `2024-01-01` = January 2024).

- **FRED `realtime_start`/`realtime_end` in observations** — Every observation has these fields reflecting vintage data. For current data, ignore them. They matter only for "real-time" research (what was the published value on a specific past date).