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yahooquery
Access Yahoo Finance data including real-time pricing, fundamentals, analyst estimates, options, news, and historical data via the yahooquery Python library.
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This page reorganizes the original catalog entry around fit, installability, and workflow context first. The original raw source lives below.
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Hot score
99
Updated
March 20, 2026
Overall rating
C5.4
Composite score
5.4
Best-practice grade
C62.8
Install command
npx @skill-hub/cli install openclaw-skills-yahooquery
Repository
openclaw/skills
Skill path: skills/512z/yahooquery
Access Yahoo Finance data including real-time pricing, fundamentals, analyst estimates, options, news, and historical data via the yahooquery Python library.
Open repositoryBest for
Primary workflow: Analyze Data & AI.
Technical facets: Full Stack, Data / AI.
Target audience: everyone.
License: Unknown.
Original source
Catalog source: SkillHub Club.
Repository owner: openclaw.
This is still a mirrored public skill entry. Review the repository before installing into production workflows.
What it helps with
- Install yahooquery into Claude Code, Codex CLI, Gemini CLI, or OpenCode workflows
- Review https://github.com/openclaw/skills before adding yahooquery to shared team environments
- Use yahooquery for development workflows
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Original source / Raw SKILL.md
---
name: yahooquery
description: Access Yahoo Finance data including real-time pricing, fundamentals, analyst estimates, options, news, and historical data via the yahooquery Python library.
---
# yahooquery Skill
Comprehensive access to Yahoo Finance data via the `yahooquery` Python library. This library provides programmatic access to nearly all Yahoo Finance endpoints, including real-time pricing, fundamentals, analyst estimates, options, news, and premium research.
## Core Classes
### 1. **Ticker** (Company-Specific Data)
The primary interface for retrieving data about one or more securities.
```python
from yahooquery import Ticker
# Single or multiple symbols
aapl = Ticker('AAPL')
tickers = Ticker('AAPL MSFT NVDA', asynchronous=True)
```
### 2. **Screener** (Predefined Stock Lists)
Access to pre-built screeners for discovering stocks by criteria.
```python
from yahooquery import Screener
s = Screener()
screeners = s.available_screeners # List all available screeners
data = s.get_screeners(['day_gainers', 'most_actives'], count=10)
```
### 3. **Research** (Premium Subscription Required)
Access proprietary research reports and trade ideas.
```python
from yahooquery import Research
r = Research(username='[email protected]', password='password')
reports = r.reports(report_type='Analyst Report', report_date='Last Week')
trades = r.trades(trend='Bullish', term='Short term')
```
---
## Ticker Class: Data Modules
The `Ticker` class exposes dozens of data endpoints via properties and methods.
### đ **Financial Statements**
- `.income_statement(frequency='a', trailing=True)` - Income statement (annual/quarterly)
- `.balance_sheet(frequency='a', trailing=True)` - Balance sheet
- `.cash_flow(frequency='a', trailing=True)` - Cash flow statement
- `.all_financial_data(frequency='a')` - Combined financials + valuation measures
- `.valuation_measures` - EV/EBITDA, P/E, P/B, P/S across periods
### đ **Pricing & Market Data**
- `.price` - Current pricing, market cap, 52-week range
- `.history(period='1y', interval='1d', start=None, end=None)` - Historical OHLC
- **period**: `1d`, `5d`, `1mo`, `3mo`, `6mo`, `1y`, `2y`, `5y`, `10y`, `ytd`, `max`
- **interval**: `1m`, `2m`, `5m`, `15m`, `30m`, `60m`, `90m`, `1h`, `1d`, `5d`, `1wk`, `1mo`, `3mo`
- `.option_chain` - Full options chain (all expirations)
### đ **Analysis & Estimates**
- `.calendar_events` - Next earnings date, EPS/revenue estimates
- `.earning_history` - Actual vs. estimated EPS (last 4 quarters)
- `.earnings` - Historical quarterly/annual earnings and revenue
- `.earnings_trend` - Analyst estimates for upcoming periods
- `.recommendation_trend` - Buy/Sell/Hold rating changes over time
- `.gradings` - Recent analyst upgrades/downgrades
### đą **Company Fundamentals**
- `.asset_profile` - Address, industry, sector, business summary, officers
- `.company_officers` - Executives with compensation details
- `.summary_profile` - Condensed company information
- `.key_stats` - Forward P/E, profit margin, beta, shares outstanding
- `.financial_data` - Financial KPIs (ROE, ROA, debt-to-equity, margins)
### đ„ **Ownership & Governance**
- `.insider_holders` - List of insider holders and positions
- `.insider_transactions` - Recent buy/sell transactions by insiders
- `.institution_ownership` - Top institutional holders
- `.fund_ownership` - Top mutual fund holders
- `.major_holders` - Ownership summary (institutional %, insider %, float)
### đ **ESG & Ratings**
- `.esg_scores` - Environmental, Social, Governance scores and controversies
- `.recommendation_rating` - Analyst consensus (Strong Buy â Strong Sell)
### đ° **News & Insights**
- `.news()` - Recent news articles
- `.technical_insights` - Bullish/bearish technical patterns
### đ° **Funds & ETFs Only**
- `.fund_holding_info` - Top holdings, bond/equity breakdown
- `.fund_performance` - Historical performance and returns
- `.fund_bond_holdings` / `.fund_bond_ratings` - Bond maturity and credit ratings
- `.fund_equity_holdings` - P/E, P/B, P/S for equity holdings
### đ **Other Modules**
- `.summary_detail` - Trading stats (day high/low, volume, avg volume)
- `.default_key_statistics` - Enterprise value, trailing P/E, forward P/E
- `.index_trend` - Performance relative to a benchmark index
- `.quote_type` - Security type, exchange, market
---
## Global Functions
```python
import yahooquery as yq
# Search
results = yq.search('NVIDIA')
# Market Data
market = yq.get_market_summary(country='US') # Major indices snapshot
trending = yq.get_trending(country='US') # Trending tickers
# Utilities
currencies = yq.get_currencies() # List of supported currencies
exchanges = yq.get_exchanges() # List of exchanges
rate = yq.currency_converter('USD', 'EUR') # Exchange rate
```
---
## Configuration & Keyword Arguments
The `Ticker`, `Screener`, and `Research` classes accept these optional parameters:
### Performance & Reliability
- `asynchronous=True` - Make requests asynchronously (for multiple symbols)
- `max_workers=8` - Number of concurrent workers (when async)
- `retry=5` - Number of retry attempts
- `backoff_factor=0.3` - Exponential backoff between retries
- `status_forcelist=[429, 500, 502, 503, 504]` - HTTP codes to retry
- `timeout=5` - Request timeout in seconds
### Data Format & Validation
- `formatted=False` - If `True`, returns data with `{raw, fmt, longFmt}` structure
- `validate=True` - Validate symbols on instantiation (invalid â `.invalid_symbols`)
- `country='United States'` - Regional data/news (france, germany, canada, etc.)
### Network & Auth
- `proxies={'http': 'http://proxy:port'}` - HTTP/HTTPS proxy
- `user_agent='...'` - Custom user agent string
- `verify=True` - SSL certificate verification
- `username='[email protected]'` / `password='...'` - Yahoo Finance Premium login
### Advanced (Shared Sessions)
- `session=...` / `crumb=...` - Share auth between `Research` and `Ticker` instances
---
## Best Practices
### 1. **Async for Multiple Symbols**
```python
tickers = Ticker('AAPL MSFT NVDA TSLA', asynchronous=True)
prices = tickers.price # Returns dict keyed by symbol
```
### 2. **Handling DataFrames**
Most financial methods return `pandas.DataFrame`. Convert for JSON output:
```python
df = aapl.income_statement()
print(df.to_json(orient='records', date_format='iso'))
```
### 3. **Historical Data - 1-Minute Intervals**
Yahoo limits 1-minute data to 7 days per request. For 30 days:
```python
tickers = Ticker('AAPL', asynchronous=True)
df = tickers.history(period='1mo', interval='1m') # Makes 4 requests automatically
```
### 4. **Premium Users: Combining Research + Ticker**
```python
r = Research(username='...', password='...')
reports = r.reports(sector='Technology', investment_rating='Bullish')
# Reuse session for Ticker
tickers = Ticker('AAPL', session=r.session, crumb=r.crumb)
data = tickers.asset_profile
```
---
## Common Use Cases
### Portfolio Analysis
```python
portfolio = Ticker('AAPL MSFT NVDA', asynchronous=True)
summary = portfolio.summary_detail
earnings = portfolio.earnings
history = portfolio.history(period='1y')
```
### Screening & Discovery
```python
s = Screener()
gainers = s.get_screeners(['day_gainers'], count=20)
# Returns DataFrame with price, volume, % change, etc.
```
### Options Analysis
```python
nvda = Ticker('NVDA')
options = nvda.option_chain
# Filter for calls/puts, strikes, expirations
```
### Earnings Calendar
```python
tickers = Ticker('AAPL MSFT NVDA')
calendar = tickers.calendar_events
# Shows next earnings date + analyst estimates
```
---
## Reference Documentation
Full API docs at: `/Users/henryzha/.openclaw/workspace-research/skills/yahooquery/references/`
- `index.md` - Overview of classes and functions
- `ticker/` - Detailed breakdown of all Ticker methods
- `screener.md` - Screener class guide
- `research.md` - Research class (Premium)
- `keyword_arguments.md` - Complete list of configuration options
- `misc.md` - Global utility functions
- `advanced.md` - Sharing sessions between Research and Ticker
---
## Environment
- **Installation**: `python3 -m pip install yahooquery`
- **Dependencies**: pandas, requests-futures, tqdm, beautifulsoup4, lxml
- **Python Version**: 3.7+
---
## Notes
- Yahoo Finance may rate-limit or block requests. Use `retry`, `backoff_factor`, and `status_forcelist` for robustness.
- Premium features (Research class) require a paid Yahoo Finance Premium subscription.
- Data accuracy and availability depend on Yahoo Finance's upstream data providers.
---
## Skill Companion Files
> Additional files collected from the skill directory layout.
### _meta.json
```json
{
"owner": "512z",
"slug": "yahooquery",
"displayName": "yahooquery",
"latest": {
"version": "1.0.0",
"publishedAt": 1770642451992,
"commit": "https://github.com/openclaw/skills/commit/28bae03c3fb1a50be95ade19efc8693cc2cce24a"
},
"history": []
}
```
### references/advanced.md
```markdown
!!! info
**For Yahoo Finance Premium Subscribers**
There might be a use case for combining the functionalities of both the Ticker and Research class. And, ideally, the user wouldn't have to utilize the login functionality in both instances. Here's how you would do that:
```python
from yahooquery import Research, Ticker
r = Research(username='[email protected]', password='password')
# I want to retrieve last week's Bullish Analyst Report's
# for the Financial Services sector
df = r.reports(
sector='Financial Services',
report_date='Last Week',
investment_rating='Bullish',
report_type='Analyst Report'
)
# But now I want to get the data I find relevant and run my own analysis
# Using aapl as a default symbol (we will change that later).
# But, the important part is passing the current session and crumb
# from our Research instance
tickers = Ticker('aapl', session=r.session, crumb=r.crumb)
# Now, I can loop through the dataframe and retrieve relevant data for
# each ticker within the dataframe utilizing the Ticker instance
for i, row in df.iterrows():
tickers.symbols = row['Tickers']
data = tickers.p_company_360
# Do something with data
# ...
# Or, pass all tickers to the Ticker instance
ticker_list = df['Tickers'].tolist()
ticker_list = list(set(flatten_list(ticker_list)))
tickers = Ticker(ticker_list, session=r.session, crumb=r.crumb)
data = tickers.p_company_360
# Do something with data
# ...
```
```
### references/index.md
```markdown
# Introduction
## Classes
The majority of the data available through the unofficial API can be obtained through the use of three classes:
- [Ticker](ticker/intro.md) - Retrieve company-specific data
- [Screener](screener.md) - Retrieve lists of stocks based on certain criteria
- [Research](research.md) - Retrieve proprietary research reports and trade ideas (**REQUIRES YAHOO FINANCE PREMIUM SUBSCRIPTION**).
Each class inherits functionality from a base class, `_YahooFinance`. As such, each class will accept the same [keyword arguments](keyword_arguments.md), which allows the user to make asynchronous requests, validate ticker symbols, retry failed requests, and much more.
## Functions
The functions below allow for additional data retrieval:
- [currency_converter](misc.md#currency_converter) - Retrieve current / historical conversion rate between two currencies
- [get_currencies](misc.md#get_currencies) - Retrieve list of currencies
- [get_exchanges](misc.md#get_exchanges) - Retrieve list of exchanges
- [get_market_summary](misc.md#get_market_summary) - Retrieve summary data relevant to a country
- [get_trending](misc.md#get_trending) - Retrieve trending securities relevant to a country
- [search](misc.md#search) - Query Yahoo Finance for anything
```
### references/keyword_arguments.md
```markdown
# Keyword Arguments
## Regular
### **asynchronous**
=== "Details"
- *Description*: When set to `True`, requests made to Yahoo Finance will be made asynchronously
- *Default*: `False`
- *Type*: `bool`
!!! tip
Only necessary when you have more than one symbol
=== "Example"
```python hl_lines="4"
symbols = 'fb aapl amzn nflx goog'
Ticker(
symbols,
asynchronous=True
)
```
### **backoff_factor**
=== "Details"
- *Description*: A factor, in seconds, to apply between attempts after the second try
- *Default*: `0.3`
- *Implementation*: `{backoff_factor} * (2 ** ({number of total retries} - 1))`
- *Example*: If the backoff factor is 0.1, then `sleep()` will sleep for [0.0s, 0.2s, 0.4s, ...] between retries
=== "Example"
```python hl_lines="3"
Ticker(
'aapl',
backoff_factor=1
)
```
### **country**
=== "Details"
- *Description*: Alter the language, region, and corsDomain that each request utilizes as a query parameter.
- *Default*: `United States`
!!! info
This functionality has not been thoroughly tested as far as comparing data returned for each country. You will see a difference, though, in the data returned from the [news](ticker/miscellaneous.md#news) method:
??? example "View Countries"
```python
{
'france': {
'lang': 'fr-FR',
'region': 'FR',
'corsDomain': 'fr.finance.yahoo.com'
},
'india': {
'lang': 'en-IN',
'region': 'IN',
'corsDomain': 'in.finance.yahoo.com'
},
'hong kong': {
'lang': 'zh-Hant-HK',
'region': 'HK',
'corsDomain': 'hk.finance.yahoo.com'
},
'germany': {
'lang': 'de-DE',
'region': 'DE',
'corsDomain': 'de.finance.yahoo.com'
},
'canada': {
'lang': 'en-CA',
'region': 'CA',
'corsDomain': 'ca.finance.yahoo.com'
},
'spain': {
'lang': 'es-ES',
'region': 'ES',
'corsDomain': 'es.finance.yahoo.com'
},
'italy': {
'lang': 'it-IT',
'region': 'IT',
'corsDomain': 'it.finance.yahoo.com'
},
'united states': {
'lang': 'en-US',
'region': 'US',
'corsDomain': 'finance.yahoo.com'
},
'australia': {
'lang': 'en-AU',
'region': 'AU',
'corsDomain': 'au.finance.yahoo.com'
},
'united kingdom': {
'lang': 'en-GB',
'region': 'GB',
'corsDomain': 'uk.finance.yahoo.com'
},
'brazil': {
'lang': 'pt-BR',
'region': 'BR',
'corsDomain': 'br.financas.yahoo.com'
},
'new zealand': {
'lang': 'en-NZ',
'region': 'NZ',
'corsDomain': 'nz.finance.yahoo.com'
},
'singapore': {
'lang': 'en-SG',
'region': 'SG',
'corsDomain': 'sg.finance.yahoo.com'
},
'taiwan': {
'lang': 'zh-tw',
'region': 'TW',
'corsDomain': 'tw.finance.yahoo.com'
},
}
```
=== "Example"
```python hl_lines="3"
Ticker(
'aapl',
country='France'
)
```
### **formatted**
=== "Details"
- *Description* - When `formatted=True`, most numerical data from the API will be returned as a dictionary:
```python
"totalCash": {
"raw": 94051000320
"fmt": "94.05B"
"longFmt": "94,051,000,320"
}
```
When formatted is set to False, an internal method will return the value in the "raw" key.
- *Default* - `False`
- *Type* - `bool`
!!! warning
When `formatted=True`, all data will be returned as a `dict`
=== "Example"
```python hl_lines="3"
Ticker(
'aapl',
formatted=True
)
```
### **max_workers**
=== "Details"
- *Description* - Defines the number of workers used to make asynchronous requests.
- *Default* - `8`
- *Type* - `int`
!!! tip
This is only relevant when `asynchronous=True`
=== "Example"
```python hl_lines="3 4"
Ticker(
'aapl',
asynchronous=True,
max_workers=4
)
```
### **progress**
=== "Details"
- *Description*: - Show a progress bar when downloading data
- *Default* - `False`
- *Type* - `bool`
=== "Example"
```python hl_lines="3"
Ticker(
'aapl',
progress=True
)
```
### **proxies**
=== "Details"
- *Description* - Make each request with a proxy. Simply pass a dictionary, mapping URL schemes to the URL to the proxy.
- *Default* - `None`
- *Type* - `dict`
!!! tip
You can also configure proxies by setting the environment variables `HTTP_PROXY` and `HTTPS_PROXY`.
=== "Example"
```python hl_lines="1 2 3 4 8"
proxies = {
'http': 'http://10.10.1.10:3128',
'https': 'http://10.10.1.10:1080',
}
Ticker(
'aapl',
proxies=proxies
)
```
### **retry**
=== "Details"
- *Description* - Number of times to retry a failed request
- *Default* - `5`
- *Type* - `int`
=== "Example"
```python hl_lines="3"
Ticker(
'aapl',
retry=10
)
```
### **status_forcelist**
=== "Details"
- *Description* - A set of integer HTTP status codes that we should force a retry on.
- *Default* - `[429, 500, 502, 503, 504]`
- *Type* - `list`
!!! tip
This is especially useful when retrieving historical pricing data for a large amount of symbols. Currently, Yahoo Finance has been displaying 404 errors for mass download requests.
=== "Example"
```python hl_lines="3"
Ticker(
'aapl',
status_forcelist=[404, 429, 500, 502, 503, 504]
)
```
### **timeout**
=== "Details"
- *Description* - Stop waiting for a response after a given number of seconds
- *Default* - `5`
- *Type* - `int`
!!! note
This is not a time limit on the entire response download; rather, an exception is raised if the server has not issued a response for timeout seconds (more precisely, if no bytes have been received on the underlying socket for timeout seconds). If no timeout is specified explicitly, requests do not time out.
=== "Example"
```python hl_lines="3"
Ticker(
'aapl',
timeout=3
)
```
### **user_agent**
=== "Details"
- *Description* - A browser's user-agent string that is sent with the headers with each request.
- *Default* - Random selection from the list below:
```python
USER_AGENT_LIST = [
'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/81.0.4044.138 Safari/537.36',
'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/81.0.4044.138 Safari/537.36',
'Mozilla/5.0 (Windows NT 10.0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/81.0.4044.138 Safari/537.36',
'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/81.0.4044.129 Safari/537.36',
'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/81.0.4044.138 Safari/537.36',
'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/81.0.4044.138 Safari/537.36',
]
```
- *Type* - `str`
=== "Example"
```python hl_lines="3"
Ticker(
'aapl',
user_agent="Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/81.0.4044.138 Safari/537.36"
)
```
### **validate**
=== "Details"
- *Description* - Validate existence of symbols during instantiation. Invalid symbols will be dropped but you can view them through the `invalid_symbols` property.
- *Default* - `False`
- *Type* - `bool`
=== "Example"
```python hl_lines="7 10"
from yahooquery import Ticker
symbols = 'fb facebook aapl apple amazon amzn netflix nflx goog alphabet'
t = Ticker(
symbols,
validate=True)
print(t.symbols)
['NFLX', 'GOOG', 'AAPL', 'FB', 'AMZN']
print(t.invalid_symbols)
['FACEBOOK', 'AMAZON', 'APPLE', 'NETFLIX', 'ALPHABET']
```
### **verify**
=== "Details"
- *Description* - Either a boolean, in which case it controls whether we verify the serverâs TLS certificate, or a string, in which case it must be a path to a CA bundle to use.
- *Default* - `True`
- *Type* - `bool` or `str`
=== "Example"
```python hl_lines="3"
Ticker(
'aapl',
verify=False
)
```
## Premium
### **username and password**
=== "Details"
- *Description*: If you're a subscriber to Yahoo Finance Premium, you'll be able to retrieve data available through that subscription. Simply pass your `username` and `password` to the `Ticker` class
!!! note
Selenium is utilized to login to Yahoo. It should take around 15-20 seconds to login.
!!! tip
Set environment variables for your username and password as `YF_USERNAME` and `YF_PASSWORD`, respectively.
=== "Example"
```python hl_lines="3 4"
Ticker(
'aapl',
username='fake_username',
password='fake_password'
)
```
## Advanced
### **crumb and session**
=== "Details"
- *Description*: Some requests to Yahoo Finance require a crumb to make the request. **This is only utilized for [advanced configuration](advanced.md)**
- *Default*: `None`
- *Type*: `str`
=== "Example"
See the [Advanced Section](advanced.md)
```
### references/misc.md
```markdown
# Miscellaneous Functions
Additional data can be obtained from Yahoo Finance outside of the three classes. The following functions can be utilized to retrieve additional data unrelated to a ticker symbol:
## Import Functions
```python
import yahooquery as yq
```
## Functions
### get_currencies
=== "Details"
- *Description*: List of currencies Yahoo Finance supports
- *Returns*: `list`
=== "Example"
```python hl_lines="2"
import yahooquery as yq
data = yq.get_currencies()
```
=== "Data"
```python
[{
'shortName': 'FJD',
'longName': 'Fijian Dollar',
'symbol': 'FJD',
'localLongName': 'Fijian Dollar'
}, {
'shortName': 'MXN',
'longName': 'Mexican Peso',
'symbol': 'MXN',
'localLongName': 'Mexican Peso'
}, {
'shortName': 'STD',
'longName': 'SĂŁo TomĂ© and PrĂncipe Dobra',
'symbol': 'STD',
'localLongName': 'SĂŁo TomĂ© and PrĂncipe Dobra'
}, {
'shortName': 'LVL',
'longName': 'Latvian Lats',
'symbol': 'LVL',
'localLongName': 'Latvian Lats'
}, {
'shortName': 'SCR',
'longName': 'Seychellois Rupee',
'symbol': 'SCR',
'localLongName': 'Seychellois Rupee'
}, {
'shortName': 'CDF',
'longName': 'Congolese Franc',
'symbol': 'CDF',
'localLongName': 'Congolese Franc'
}, {
'shortName': 'BBD',
'longName': 'Barbadian Dollar',
'symbol': 'BBD',
'localLongName': 'Barbadian Dollar'
}, {
'shortName': 'GTQ',
'longName': 'Guatemalan Quetzal',
'symbol': 'GTQ',
'localLongName': 'Guatemalan Quetzal'
}, {
'shortName': 'CLP',
'longName': 'Chilean Peso',
'symbol': 'CLP',
'localLongName': 'Chilean Peso'
}, {
'shortName': 'UGX',
'longName': 'Ugandan Shilling',
'symbol': 'UGX',
'localLongName': 'Ugandan Shilling'
}, {
'shortName': 'HNL',
'longName': 'Honduran Lempira',
'symbol': 'HNL',
'localLongName': 'Honduran Lempira'
}, {
'shortName': 'ZAR',
'longName': 'South African Rand',
'symbol': 'ZAR',
'localLongName': 'South African Rand'
}, {
'shortName': 'MXV',
'longName': 'Mexican Investment Unit',
'symbol': 'MXV',
'localLongName': 'Mexican Investment Unit'
}, {
'shortName': 'TND',
'longName': 'Tunisian Dinar',
'symbol': 'TND',
'localLongName': 'Tunisian Dinar'
}, {
'shortName': 'STN',
'longName': 'SĂŁo TomĂ© and PrĂncipe Dobra',
'symbol': 'STN',
'localLongName': 'SĂŁo TomĂ© and PrĂncipe Dobra'
}, {
'shortName': 'CUC',
'longName': 'Cuban Convertible Peso',
'symbol': 'CUC',
'localLongName': 'Cuban Convertible Peso'
}, {
'shortName': 'BSD',
'longName': 'Bahamian Dollar',
'symbol': 'BSD',
'localLongName': 'Bahamian Dollar'
}, {
'shortName': 'SLL',
'longName': 'Sierra Leonean Leone',
'symbol': 'SLL',
'localLongName': 'Sierra Leonean Leone'
}, {
'shortName': 'SDG',
'longName': 'Sudanese Pound',
'symbol': 'SDG',
'localLongName': 'Sudanese Pound'
}, {
'shortName': 'IQD',
'longName': 'Iraqi Dinar',
'symbol': 'IQD',
'localLongName': 'Iraqi Dinar'
}, {
'shortName': 'CUP',
'longName': 'Cuban Peso',
'symbol': 'CUP',
'localLongName': 'Cuban Peso'
}, {
'shortName': 'GMD',
'longName': 'Gambian Dalasi',
'symbol': 'GMD',
'localLongName': 'Gambian Dalasi'
}, {
'shortName': 'TWD',
'longName': 'New Taiwan Dollar',
'symbol': 'TWD',
'localLongName': 'New Taiwan Dollar'
}, {
'shortName': 'RSD',
'longName': 'Serbian Dinar',
'symbol': 'RSD',
'localLongName': 'Serbian Dinar'
}, {
'shortName': 'DOP',
'longName': 'Dominican Peso',
'symbol': 'DOP',
'localLongName': 'Dominican Peso'
}, {
'shortName': 'KMF',
'longName': 'Comorian Franc',
'symbol': 'KMF',
'localLongName': 'Comorian Franc'
}, {
'shortName': 'MYR',
'longName': 'Malaysian Ringgit',
'symbol': 'MYR',
'localLongName': 'Malaysian Ringgit'
}, {
'shortName': 'FKP',
'longName': 'Falkland Islands Pound',
'symbol': 'FKP',
'localLongName': 'Falkland Islands Pound'
}, {
'shortName': 'XOF',
'longName': 'CFA Franc BCEAO',
'symbol': 'XOF',
'localLongName': 'CFA Franc BCEAO'
}, {
'shortName': 'GEL',
'longName': 'Georgian Lari',
'symbol': 'GEL',
'localLongName': 'Georgian Lari'
}, {
'shortName': 'UYU',
'longName': 'Uruguayan Peso',
'symbol': 'UYU',
'localLongName': 'Uruguayan Peso'
}, {
'shortName': 'MAD',
'longName': 'Moroccan Dirham',
'symbol': 'MAD',
'localLongName': 'Moroccan Dirham'
}, {
'shortName': 'CVE',
'longName': 'Cape Verdean Escudo',
'symbol': 'CVE',
'localLongName': 'Cape Verdean Escudo'
}, {
'shortName': 'TOP',
'longName': 'Tongan Paʻanga',
'symbol': 'TOP',
'localLongName': 'Tongan Paʻanga'
}, {
'shortName': 'PGK',
'longName': 'Papua New Guinean Kina',
'symbol': 'PGK',
'localLongName': 'Papua New Guinean Kina'
}, {
'shortName': 'AZN',
'longName': 'Azerbaijan Manat',
'symbol': 'AZN',
'localLongName': 'Azerbaijan Manat'
}, {
'shortName': 'OMR',
'longName': 'Omani Rial',
'symbol': 'OMR',
'localLongName': 'Omani Rial'
}, {
'shortName': 'SEK',
'longName': 'Swedish Krona',
'symbol': 'SEK',
'localLongName': 'Swedish Krona'
}, {
'shortName': 'KES',
'longName': 'Kenyan Shilling',
'symbol': 'KES',
'localLongName': 'Kenyan Shilling'
}, {
'shortName': 'BTN',
'longName': 'Bhutanese Ngultrum',
'symbol': 'BTN',
'localLongName': 'Bhutanese Ngultrum'
}, {
'shortName': 'UAH',
'longName': 'Ukrainian Hryvnia',
'symbol': 'UAH',
'localLongName': 'Ukrainian Hryvnia'
}, {
'shortName': 'GNF',
'longName': 'Guinean Franc',
'symbol': 'GNF',
'localLongName': 'Guinean Franc'
}, {
'shortName': 'ERN',
'longName': 'Eritrean Nakfa',
'symbol': 'ERN',
'localLongName': 'Eritrean Nakfa'
}, {
'shortName': 'MZN',
'longName': 'Mozambican Metical',
'symbol': 'MZN',
'localLongName': 'Mozambican Metical'
}, {
'shortName': 'SVC',
'longName': 'Salvadoran ColĂłn',
'symbol': 'SVC',
'localLongName': 'Salvadoran ColĂłn'
}, {
'shortName': 'ARS',
'longName': 'Argentine Peso',
'symbol': 'ARS',
'localLongName': 'Argentine Peso'
}, {
'shortName': 'QAR',
'longName': 'Qatari Rial',
'symbol': 'QAR',
'localLongName': 'Qatari Rial'
}, {
'shortName': 'IRR',
'longName': 'Iranian Rial',
'symbol': 'IRR',
'localLongName': 'Iranian Rial'
}, {
'shortName': 'MRO',
'longName': 'Mauritanian Ouguiya',
'symbol': 'MRO',
'localLongName': 'Mauritanian Ouguiya'
}, {
'shortName': 'XPD',
'longName': 'Palladium',
'symbol': 'XPD',
'localLongName': 'Palladium'
}, {
'shortName': 'THB',
'longName': 'Thai Baht',
'symbol': 'THB',
'localLongName': 'Thai Baht'
}, {
'shortName': 'UZS',
'longName': 'Uzbekistan Som',
'symbol': 'UZS',
'localLongName': 'Uzbekistan Som'
}, {
'shortName': 'XPF',
'longName': 'CFP Franc',
'symbol': 'XPF',
'localLongName': 'CFP Franc'
}, {
'shortName': 'CNY',
'longName': 'Chinese Yuan',
'symbol': 'CNY',
'localLongName': 'Chinese Yuan'
}, {
'shortName': 'MRU',
'longName': 'Mauritanian Ouguiya',
'symbol': 'MRU',
'localLongName': 'Mauritanian Ouguiya'
}, {
'shortName': 'BDT',
'longName': 'Bangladeshi Taka',
'symbol': 'BDT',
'localLongName': 'Bangladeshi Taka'
}, {
'shortName': 'LYD',
'longName': 'Libyan Dinar',
'symbol': 'LYD',
'localLongName': 'Libyan Dinar'
}, {
'shortName': 'BMD',
'longName': 'Bermudan Dollar',
'symbol': 'BMD',
'localLongName': 'Bermudan Dollar'
}, {
'shortName': 'PHP',
'longName': 'Philippine Peso',
'symbol': 'PHP',
'localLongName': 'Philippine Peso'
}, {
'shortName': 'KWD',
'longName': 'Kuwaiti Dinar',
'symbol': 'KWD',
'localLongName': 'Kuwaiti Dinar'
}, {
'shortName': 'XPT',
'longName': 'Platinum',
'symbol': 'XPT',
'localLongName': 'Platinum'
}, {
'shortName': 'RUB',
'longName': 'Russian Ruble',
'symbol': 'RUB',
'localLongName': 'Russian Ruble'
}, {
'shortName': 'PYG',
'longName': 'Paraguayan Guarani',
'symbol': 'PYG',
'localLongName': 'Paraguayan Guarani'
}, {
'shortName': 'JMD',
'longName': 'Jamaican Dollar',
'symbol': 'JMD',
'localLongName': 'Jamaican Dollar'
}, {
'shortName': 'ISK',
'longName': 'Icelandic KrĂłna',
'symbol': 'ISK',
'localLongName': 'Icelandic KrĂłna'
}, {
'shortName': 'COP',
'longName': 'Colombian Peso',
'symbol': 'COP',
'localLongName': 'Colombian Peso'
}, {
'shortName': 'USD',
'longName': 'US Dollar',
'symbol': 'USD',
'localLongName': 'US Dollar'
}, {
'shortName': 'MKD',
'longName': 'Macedonian Denar',
'symbol': 'MKD',
'localLongName': 'Macedonian Denar'
}, {
'shortName': 'DZD',
'longName': 'Algerian Dinar',
'symbol': 'DZD',
'localLongName': 'Algerian Dinar'
}, {
'shortName': 'PAB',
'longName': 'Panamanian Balboa',
'symbol': 'PAB',
'localLongName': 'Panamanian Balboa'
}, {
'shortName': 'SGD',
'longName': 'Singapore Dollar',
'symbol': 'SGD',
'localLongName': 'Singapore Dollar'
}, {
'shortName': 'ETB',
'longName': 'Ethiopian Birr',
'symbol': 'ETB',
'localLongName': 'Ethiopian Birr'
}, {
'shortName': 'VEF',
'longName': 'Venezuelan BolĂvar',
'symbol': 'VEF',
'localLongName': 'Venezuelan BolĂvar'
}, {
'shortName': 'KGS',
'longName': 'Kyrgystani Som',
'symbol': 'KGS',
'localLongName': 'Kyrgystani Som'
}, {
'shortName': 'SOS',
'longName': 'Somali Shilling',
'symbol': 'SOS',
'localLongName': 'Somali Shilling'
}, {
'shortName': 'VUV',
'longName': 'Vanuatu Vatu',
'symbol': 'VUV',
'localLongName': 'Vanuatu Vatu'
}, {
'shortName': 'LAK',
'longName': 'Lao Kip',
'symbol': 'LAK',
'localLongName': 'Lao Kip'
}, {
'shortName': 'BND',
'longName': 'Brunei Dollar',
'symbol': 'BND',
'localLongName': 'Brunei Dollar'
}, {
'shortName': 'ZMK',
'longName': 'Zambian Kwacha',
'symbol': 'ZMK',
'localLongName': 'Zambian Kwacha'
}, {
'shortName': 'XAF',
'longName': 'CFA Franc BEAC',
'symbol': 'XAF',
'localLongName': 'CFA Franc BEAC'
}, {
'shortName': 'LRD',
'longName': 'Liberian Dollar',
'symbol': 'LRD',
'localLongName': 'Liberian Dollar'
}, {
'shortName': 'XAG',
'longName': 'Silver',
'symbol': 'XAG',
'localLongName': 'Silver'
}, {
'shortName': 'CHF',
'longName': 'Swiss Franc',
'symbol': 'CHF',
'localLongName': 'Swiss Franc'
}, {
'shortName': 'ITL',
'longName': 'Italian Lira',
'symbol': 'ITL',
'localLongName': 'Italian Lira'
}, {
'shortName': 'HRK',
'longName': 'Kuna',
'symbol': 'HRK',
'localLongName': 'Kuna'
}, {
'shortName': 'ALL',
'longName': 'Albanian Lek',
'symbol': 'ALL',
'localLongName': 'Albanian Lek'
}, {
'shortName': 'DJF',
'longName': 'Djiboutian Franc',
'symbol': 'DJF',
'localLongName': 'Djiboutian Franc'
}, {
'shortName': 'ZMW',
'longName': 'ZMW',
'symbol': 'ZMW',
'localLongName': 'ZMW'
}, {
'shortName': 'VES',
'longName': 'Venezuelan BolĂvar Soberano',
'symbol': 'VES',
'localLongName': 'Venezuelan BolĂvar Soberano'
}, {
'shortName': 'TZS',
'longName': 'Tanzanian Shilling',
'symbol': 'TZS',
'localLongName': 'Tanzanian Shilling'
}, {
'shortName': 'XAU',
'longName': 'Gold',
'symbol': 'XAU',
'localLongName': 'Gold'
}, {
'shortName': 'VND',
'longName': 'Vietnamese Dong',
'symbol': 'VND',
'localLongName': 'Vietnamese Dong'
}, {
'shortName': 'AUD',
'longName': 'Australian Dollar',
'symbol': 'AUD',
'localLongName': 'Australian Dollar'
}, {
'shortName': 'ILS',
'longName': 'Israeli New Sheqel',
'symbol': 'ILS',
'localLongName': 'Israeli New Sheqel'
}, {
'shortName': 'KPW',
'longName': 'North Korean Won',
'symbol': 'KPW',
'localLongName': 'North Korean Won'
}, {
'shortName': 'GYD',
'longName': 'Guyanaese Dollar',
'symbol': 'GYD',
'localLongName': 'Guyanaese Dollar'
}, {
'shortName': 'GHS',
'longName': 'Ghanaian Cedi',
'symbol': 'GHS',
'localLongName': 'Ghanaian Cedi'
}, {
'shortName': 'KHR',
'longName': 'Cambodian Riel',
'symbol': 'KHR',
'localLongName': 'Cambodian Riel'
}, {
'shortName': 'MDL',
'longName': 'Moldovan Leu',
'symbol': 'MDL',
'localLongName': 'Moldovan Leu'
}, {
'shortName': 'BOB',
'longName': 'Bolivian Boliviano',
'symbol': 'BOB',
'localLongName': 'Bolivian Boliviano'
}, {
'shortName': 'IDR',
'longName': 'Indonesian Rupiah',
'symbol': 'IDR',
'localLongName': 'Indonesian Rupiah'
}, {
'shortName': 'KYD',
'longName': 'Cayman Islands Dollar',
'symbol': 'KYD',
'localLongName': 'Cayman Islands Dollar'
}, {
'shortName': 'AMD',
'longName': 'Armenian Dram',
'symbol': 'AMD',
'localLongName': 'Armenian Dram'
}, {
'shortName': 'SHP',
'longName': 'Saint Helena Pound',
'symbol': 'SHP',
'localLongName': 'Saint Helena Pound'
}, {
'shortName': 'BWP',
'longName': 'Botswanan Pula',
'symbol': 'BWP',
'localLongName': 'Botswanan Pula'
}, {
'shortName': 'TRY',
'longName': 'Turkish Lira',
'symbol': 'TRY',
'localLongName': 'Turkish Lira'
}, {
'shortName': 'LBP',
'longName': 'Lebanese Pound',
'symbol': 'LBP',
'localLongName': 'Lebanese Pound'
}, {
'shortName': 'CYP',
'longName': 'Cypriot Pound',
'symbol': 'CYP',
'localLongName': 'Cypriot Pound'
}, {
'shortName': 'TJS',
'longName': 'Tajikistani Somoni',
'symbol': 'TJS',
'localLongName': 'Tajikistani Somoni'
}, {
'shortName': 'JOD',
'longName': 'Jordanian Dinar',
'symbol': 'JOD',
'localLongName': 'Jordanian Dinar'
}, {
'shortName': 'AED',
'longName': 'United Arab Emirates Dirham',
'symbol': 'AED',
'localLongName': 'United Arab Emirates Dirham'
}, {
'shortName': 'HKD',
'longName': 'Hong Kong Dollar',
'symbol': 'HKD',
'localLongName': 'Hong Kong Dollar'
}, {
'shortName': 'RWF',
'longName': 'Rwandan Franc',
'symbol': 'RWF',
'localLongName': 'Rwandan Franc'
}, {
'shortName': 'EUR',
'longName': 'Euro',
'symbol': 'EUR',
'localLongName': 'Euro'
}, {
'shortName': 'LSL',
'longName': 'Lesotho Loti',
'symbol': 'LSL',
'localLongName': 'Lesotho Loti'
}, {
'shortName': 'DKK',
'longName': 'Danish Krone',
'symbol': 'DKK',
'localLongName': 'Danish Krone'
}, {
'shortName': 'CAD',
'longName': 'Canadian Dollar',
'symbol': 'CAD',
'localLongName': 'Canadian Dollar'
}, {
'shortName': 'BGN',
'longName': 'Bulgarian Lev',
'symbol': 'BGN',
'localLongName': 'Bulgarian Lev'
}, {
'shortName': 'MMK',
'longName': 'Myanma Kyat',
'symbol': 'MMK',
'localLongName': 'Myanma Kyat'
}, {
'shortName': 'NOK',
'longName': 'Norwegian Krone',
'symbol': 'NOK',
'localLongName': 'Norwegian Krone'
}, {
'shortName': 'SYP',
'longName': 'Syrian Pound',
'symbol': 'SYP',
'localLongName': 'Syrian Pound'
}, {
'shortName': 'MUR',
'longName': 'Mauritian Rupee',
'symbol': 'MUR',
'localLongName': 'Mauritian Rupee'
}, {
'shortName': 'ZWL',
'longName': 'Zimbabwean Dollar (2009)',
'symbol': 'ZWL',
'localLongName': 'Zimbabwean Dollar (2009)'
}, {
'shortName': 'GIP',
'longName': 'Gibraltar Pound',
'symbol': 'GIP',
'localLongName': 'Gibraltar Pound'
}, {
'shortName': 'RON',
'longName': 'Romanian Leu',
'symbol': 'RON',
'localLongName': 'Romanian Leu'
}, {
'shortName': 'LKR',
'longName': 'Sri Lankan Rupee',
'symbol': 'LKR',
'localLongName': 'Sri Lankan Rupee'
}, {
'shortName': 'NGN',
'longName': 'Nigerian Naira',
'symbol': 'NGN',
'localLongName': 'Nigerian Naira'
}, {
'shortName': 'IEP',
'longName': 'Irish Pound',
'symbol': 'IEP',
'localLongName': 'Irish Pound'
}, {
'shortName': 'CZK',
'longName': 'Czech Republic Koruna',
'symbol': 'CZK',
'localLongName': 'Czech Republic Koruna'
}, {
'shortName': 'CRC',
'longName': 'Costa Rican ColĂłn',
'symbol': 'CRC',
'localLongName': 'Costa Rican ColĂłn'
}, {
'shortName': 'PKR',
'longName': 'Pakistani Rupee',
'symbol': 'PKR',
'localLongName': 'Pakistani Rupee'
}, {
'shortName': 'XCD',
'longName': 'East Caribbean Dollar',
'symbol': 'XCD',
'localLongName': 'East Caribbean Dollar'
}, {
'shortName': 'ANG',
'longName': 'Netherlands Antillean Guilder',
'symbol': 'ANG',
'localLongName': 'Netherlands Antillean Guilder'
}, {
'shortName': 'HTG',
'longName': 'Haitian Gourde',
'symbol': 'HTG',
'localLongName': 'Haitian Gourde'
}, {
'shortName': 'BHD',
'longName': 'Bahraini Dinar',
'symbol': 'BHD',
'localLongName': 'Bahraini Dinar'
}, {
'shortName': 'SIT',
'longName': 'Slovenian Tolar',
'symbol': 'SIT',
'localLongName': 'Slovenian Tolar'
}, {
'shortName': 'SZL',
'longName': 'Swazi Lilangeni',
'symbol': 'SZL',
'localLongName': 'Swazi Lilangeni'
}, {
'shortName': 'SRD',
'longName': 'Surinamese Dollar',
'symbol': 'SRD',
'localLongName': 'Surinamese Dollar'
}, {
'shortName': 'KZT',
'longName': 'Kazakhstani Tenge',
'symbol': 'KZT',
'localLongName': 'Kazakhstani Tenge'
}, {
'shortName': 'SAR',
'longName': 'Saudi Riyal',
'symbol': 'SAR',
'localLongName': 'Saudi Riyal'
}, {
'shortName': 'LTL',
'longName': 'Lithuanian Litas',
'symbol': 'LTL',
'localLongName': 'Lithuanian Litas'
}, {
'shortName': 'TTD',
'longName': 'Trinidad and Tobago Dollar',
'symbol': 'TTD',
'localLongName': 'Trinidad and Tobago Dollar'
}, {
'shortName': 'YER',
'longName': 'Yemeni Rial',
'symbol': 'YER',
'localLongName': 'Yemeni Rial'
}, {
'shortName': 'MVR',
'longName': 'Maldivian Rufiyaa',
'symbol': 'MVR',
'localLongName': 'Maldivian Rufiyaa'
}, {
'shortName': 'AFN',
'longName': 'Afghan Afghani',
'symbol': 'AFN',
'localLongName': 'Afghan Afghani'
}, {
'shortName': 'INR',
'longName': 'Indian Rupee',
'symbol': 'INR',
'localLongName': 'Indian Rupee'
}, {
'shortName': 'NPR',
'longName': 'Nepalese Rupee',
'symbol': 'NPR',
'localLongName': 'Nepalese Rupee'
}, {
'shortName': 'KRW',
'longName': 'South Korean Won',
'symbol': 'KRW',
'localLongName': 'South Korean Won'
}, {
'shortName': 'AWG',
'longName': 'Aruban Florin',
'symbol': 'AWG',
'localLongName': 'Aruban Florin'
}, {
'shortName': 'MNT',
'longName': 'Mongolian Tugrik',
'symbol': 'MNT',
'localLongName': 'Mongolian Tugrik'
}, {
'shortName': 'JPY',
'longName': 'Japanese Yen',
'symbol': 'JPY',
'localLongName': 'Japanese Yen'
}, {
'shortName': 'AOA',
'longName': 'Angolan Kwanza',
'symbol': 'AOA',
'localLongName': 'Angolan Kwanza'
}, {
'shortName': 'PLN',
'longName': 'Polish Zloty',
'symbol': 'PLN',
'localLongName': 'Polish Zloty'
}, {
'shortName': 'SBD',
'longName': 'Solomon Islands Dollar',
'symbol': 'SBD',
'localLongName': 'Solomon Islands Dollar'
}, {
'shortName': 'GBP',
'longName': 'British Pound Sterling',
'symbol': 'GBP',
'localLongName': 'British Pound Sterling'
}, {
'shortName': 'BYN',
'longName': 'Belarusian Ruble',
'symbol': 'BYN',
'localLongName': 'Belarusian Ruble'
}, {
'shortName': 'HUF',
'longName': 'Hungarian Forint',
'symbol': 'HUF',
'localLongName': 'Hungarian Forint'
}, {
'shortName': 'BYR',
'longName': 'Belarusian Ruble (2000-2016)',
'symbol': 'BYR',
'localLongName': 'Belarusian Ruble (2000-2016)'
}, {
'shortName': 'BIF',
'longName': 'Burundian Franc',
'symbol': 'BIF',
'localLongName': 'Burundian Franc'
}, {
'shortName': 'MWK',
'longName': 'Malawian Malawi Kwacha',
'symbol': 'MWK',
'localLongName': 'Malawian Malawi Kwacha'
}, {
'shortName': 'MGA',
'longName': 'Malagasy Ariary',
'symbol': 'MGA',
'localLongName': 'Malagasy Ariary'
}, {
'shortName': 'XDR',
'longName': 'Special Drawing Rights',
'symbol': 'XDR',
'localLongName': 'Special Drawing Rights'
}, {
'shortName': 'BZD',
'longName': 'Belize Dollar',
'symbol': 'BZD',
'localLongName': 'Belize Dollar'
}, {
'shortName': 'DEM',
'longName': 'German Mark',
'symbol': 'DEM',
'localLongName': 'German Mark'
}, {
'shortName': 'BAM',
'longName': 'Bosnia-Herzegovina Convertible Mark',
'symbol': 'BAM',
'localLongName': 'Bosnia-Herzegovina Convertible Mark'
}, {
'shortName': 'MOP',
'longName': 'Macanese Pataca',
'symbol': 'MOP',
'localLongName': 'Macanese Pataca'
}, {
'shortName': 'EGP',
'longName': 'Egyptian Pound',
'symbol': 'EGP',
'localLongName': 'Egyptian Pound'
}, {
'shortName': 'NAD',
'longName': 'Namibian Dollar',
'symbol': 'NAD',
'localLongName': 'Namibian Dollar'
}, {
'shortName': 'NIO',
'longName': 'Nicaraguan CĂłrdoba',
'symbol': 'NIO',
'localLongName': 'Nicaraguan CĂłrdoba'
}, {
'shortName': 'PEN',
'longName': 'Peruvian Sol',
'symbol': 'PEN',
'localLongName': 'Peruvian Sol'
}, {
'shortName': 'NZD',
'longName': 'New Zealand Dollar',
'symbol': 'NZD',
'localLongName': 'New Zealand Dollar'
}, {
'shortName': 'WST',
'longName': 'Samoan Tala',
'symbol': 'WST',
'localLongName': 'Samoan Tala'
}, {
'shortName': 'TMT',
'longName': 'Turkmenistani Manat',
'symbol': 'TMT',
'localLongName': 'Turkmenistani Manat'
}, {
'shortName': 'FRF',
'longName': 'French Franc',
'symbol': 'FRF',
'localLongName': 'French Franc'
}, {
'shortName': 'CLF',
'longName': 'Chilean Unit of Account (UF)',
'symbol': 'CLF',
'localLongName': 'Chilean Unit of Account (UF)'
}, {
'shortName': 'BRL',
'longName': 'Brazilian Real',
'symbol': 'BRL',
'localLongName': 'Brazilian Real'
}]
```
### get_exchanges
=== "Details"
- *Description*: List of exchanges and suffixes to use to to retrieve data on various exchanges
- *Returns*: `pandas.DataFrame`
=== "Example"
```python hl_lines="2"
import yahooquery as yq
df = yq.get_exchanges()
df.head()
```
=== "Data"
| | Country | Market, or Index | Suffix | Delay | Data Provider |
|---:|:-------------------------|:----------------------------------|:---------|:----------|:------------------|
| 0 | United States of America | Chicago Board of Trade (CBOT) | .CBT | 10 min | ICE Data Services |
| 1 | United States of America | Chicago Mercantile Exchange (CME) | .CME | 10 min | ICE Data Services |
| 2 | United States of America | Dow Jones Indexes | nan | Real-time | ICE Data Services |
| 3 | United States of America | Nasdaq Stock Exchange | nan | Real-time | ICE Data Services |
| 4 | United States of America | ICE Futures US | .NYB | 30 min | ICE Data Services |
### get_market_summary
=== "Details"
- *Description*: List of relevant exchanges for specific country
- *Returns*: `list`
- *Arguments*
| Argument | Description | Type | Default | Required | Options |
|:-----------|:-----------|:-------|:----------|:-----------|:------------------------------|
| country | Name of country | `str` | `United States` | optional | See below |
??? example "View Countries"
```python
{
'france': {
'lang': 'fr-FR',
'region': 'FR',
'corsDomain': 'fr.finance.yahoo.com'
},
'india': {
'lang': 'en-IN',
'region': 'IN',
'corsDomain': 'in.finance.yahoo.com'
},
'hong kong': {
'lang': 'zh-Hant-HK',
'region': 'HK',
'corsDomain': 'hk.finance.yahoo.com'
},
'germany': {
'lang': 'de-DE',
'region': 'DE',
'corsDomain': 'de.finance.yahoo.com'
},
'canada': {
'lang': 'en-CA',
'region': 'CA',
'corsDomain': 'ca.finance.yahoo.com'
},
'spain': {
'lang': 'es-ES',
'region': 'ES',
'corsDomain': 'es.finance.yahoo.com'
},
'italy': {
'lang': 'it-IT',
'region': 'IT',
'corsDomain': 'it.finance.yahoo.com'
},
'united states': {
'lang': 'en-US',
'region': 'US',
'corsDomain': 'finance.yahoo.com'
},
'australia': {
'lang': 'en-AU',
'region': 'AU',
'corsDomain': 'au.finance.yahoo.com'
},
'united kingdom': {
'lang': 'en-GB',
'region': 'GB',
'corsDomain': 'uk.finance.yahoo.com'
},
'brazil': {
'lang': 'pt-BR',
'region': 'BR',
'corsDomain': 'br.financas.yahoo.com'
},
'new zealand': {
'lang': 'en-NZ',
'region': 'NZ',
'corsDomain': 'nz.finance.yahoo.com'
},
'singapore': {
'lang': 'en-SG',
'region': 'SG',
'corsDomain': 'sg.finance.yahoo.com'
}
}
```
=== "Example"
```python hl_lines="2"
import yahooquery as yq
yq.get_market_summary(country='hong kong')
```
=== "Data"
```python
[{
'exchangeTimezoneName': 'America/New_York',
'fullExchangeName': 'SNP',
'symbol': '^GSPC',
'regularMarketChange': {
'raw': 24.900146,
'fmt': '24.90'
},
'gmtOffSetMilliseconds': -14400000,
'exchangeDataDelayedBy': 0,
'firstTradeDateMilliseconds': -1325583000000,
'language': 'en-US',
'regularMarketTime': {
'raw': 1596229659,
'fmt': '5:07PM EDT'
},
'exchangeTimezoneShortName': 'EDT',
'regularMarketChangePercent': {
'raw': 0.7670505,
'fmt': '0.77%'
},
'quoteType': 'INDEX',
'marketState': 'POSTPOST',
'regularMarketPrice': {
'raw': 3271.12,
'fmt': '3,271.12'
},
'market': 'us_market',
'quoteSourceName': 'Delayed Quote',
'priceHint': 2,
'tradeable': False,
'exchange': 'SNP',
'sourceInterval': 15,
'region': 'US',
'shortName': 'S&P 500',
'triggerable': False,
'regularMarketPreviousClose': {
'raw': 3246.22,
'fmt': '3,246.22'
}
}, {
'exchangeTimezoneName': 'America/New_York',
'fullExchangeName': 'DJI',
'symbol': '^DJI',
'regularMarketChange': {
'raw': 114.66992,
'fmt': '114.67'
},
'gmtOffSetMilliseconds': -14400000,
'exchangeDataDelayedBy': 0,
'firstTradeDateMilliseconds': 475857000000,
'language': 'en-US',
'regularMarketTime': {
'raw': 1596229659,
'fmt': '5:07PM EDT'
},
'exchangeTimezoneShortName': 'EDT',
'regularMarketChangePercent': {
'raw': 0.43578112,
'fmt': '0.44%'
},
'quoteType': 'INDEX',
'marketState': 'POSTPOST',
'regularMarketPrice': {
'raw': 26428.32,
'fmt': '26,428.32'
},
'market': 'us_market',
'quoteSourceName': 'Delayed Quote',
'priceHint': 2,
'tradeable': False,
'exchange': 'DJI',
'sourceInterval': 120,
'region': 'US',
'shortName': 'Dow 30',
'triggerable': False,
'regularMarketPreviousClose': {
'raw': 26313.65,
'fmt': '26,313.65'
}
}, {
'exchangeTimezoneName': 'America/New_York',
'fullExchangeName': 'Nasdaq GIDS',
'symbol': '^IXIC',
'regularMarketChange': {
'raw': 157.46094,
'fmt': '157.46'
},
'gmtOffSetMilliseconds': -14400000,
'exchangeDataDelayedBy': 0,
'firstTradeDateMilliseconds': 34612200000,
'language': 'en-US',
'regularMarketTime': {
'raw': 1596230159,
'fmt': '5:15PM EDT'
},
'exchangeTimezoneShortName': 'EDT',
'regularMarketChangePercent': {
'raw': 1.4871904,
'fmt': '1.49%'
},
'quoteType': 'INDEX',
'marketState': 'POSTPOST',
'regularMarketPrice': {
'raw': 10745.274,
'fmt': '10,745.27'
},
'market': 'us_market',
'quoteSourceName': 'Delayed Quote',
'priceHint': 2,
'tradeable': False,
'exchange': 'NIM',
'sourceInterval': 15,
'region': 'US',
'shortName': 'Nasdaq',
'triggerable': False,
'regularMarketPreviousClose': {
'raw': 10587.8,
'fmt': '10,587.80'
}
}, {
'exchangeTimezoneName': 'America/New_York',
'fullExchangeName': 'Chicago Options',
'symbol': '^RUT',
'regularMarketChange': {
'raw': -14.674194,
'fmt': '-14.67'
},
'gmtOffSetMilliseconds': -14400000,
'exchangeDataDelayedBy': 20,
'firstTradeDateMilliseconds': 558279000000,
'language': 'en-US',
'regularMarketTime': {
'raw': 1596227407,
'fmt': '4:30PM EDT'
},
'exchangeTimezoneShortName': 'EDT',
'regularMarketChangePercent': {
'raw': -0.981485,
'fmt': '-0.98%'
},
'quoteType': 'INDEX',
'marketState': 'POSTPOST',
'regularMarketPrice': {
'raw': 1480.427,
'fmt': '1,480.43'
},
'market': 'us_market',
'priceHint': 2,
'tradeable': False,
'exchange': 'WCB',
'sourceInterval': 15,
'region': 'US',
'shortName': 'Russell 2000',
'triggerable': False,
'regularMarketPreviousClose': {
'raw': 1495.1012,
'fmt': '1,495.10'
}
}, {
'fullExchangeName': 'NY Mercantile',
'symbol': 'CL=F',
'gmtOffSetMilliseconds': -14400000,
'headSymbolAsString': 'CL=F',
'language': 'en-US',
'regularMarketTime': {
'raw': 1596229198,
'fmt': '4:59PM EDT'
},
'regularMarketChangePercent': {
'raw': 1.2775606,
'fmt': '1.28%'
},
'quoteType': 'FUTURE',
'headSymbol': True,
'contractSymbol': False,
'tradeable': False,
'regularMarketPreviousClose': {
'raw': 39.92,
'fmt': '39.92'
},
'exchangeTimezoneName': 'America/New_York',
'regularMarketChange': {
'raw': 0.51000214,
'fmt': '0.51'
},
'firstTradeDateMilliseconds': 953701200000,
'exchangeDataDelayedBy': 30,
'exchangeTimezoneShortName': 'EDT',
'marketState': 'REGULAR',
'regularMarketPrice': {
'raw': 40.43,
'fmt': '40.43'
},
'market': 'us24_market',
'sourceInterval': 30,
'exchange': 'NYM',
'shortName': 'Crude Oil',
'region': 'US',
'triggerable': False
}, {
'fullExchangeName': 'COMEX',
'symbol': 'GC=F',
'gmtOffSetMilliseconds': -14400000,
'headSymbolAsString': 'GC=F',
'language': 'en-US',
'regularMarketTime': {
'raw': 1596229198,
'fmt': '4:59PM EDT'
},
'regularMarketChangePercent': {
'raw': 1.5960459,
'fmt': '1.60%'
},
'quoteType': 'FUTURE',
'headSymbol': True,
'contractSymbol': False,
'tradeable': False,
'regularMarketPreviousClose': {
'raw': 1942.3,
'fmt': '1,942.30'
},
'exchangeTimezoneName': 'America/New_York',
'regularMarketChange': {
'raw': 31.0,
'fmt': '31.00'
},
'firstTradeDateMilliseconds': 951714000000,
'exchangeDataDelayedBy': 30,
'exchangeTimezoneShortName': 'EDT',
'marketState': 'REGULAR',
'regularMarketPrice': {
'raw': 1973.3,
'fmt': '1,973.30'
},
'market': 'us24_market',
'sourceInterval': 15,
'exchange': 'CMX',
'shortName': 'Gold',
'region': 'US',
'triggerable': False
}, {
'fullExchangeName': 'COMEX',
'symbol': 'SI=F',
'gmtOffSetMilliseconds': -14400000,
'headSymbolAsString': 'SI=F',
'language': 'en-US',
'regularMarketTime': {
'raw': 1596229200,
'fmt': '5:00PM EDT'
},
'regularMarketChangePercent': {
'raw': 5.427616,
'fmt': '5.43%'
},
'quoteType': 'FUTURE',
'headSymbol': True,
'contractSymbol': False,
'tradeable': False,
'regularMarketPreviousClose': {
'raw': 23.362,
'fmt': '23.36'
},
'exchangeTimezoneName': 'America/New_York',
'regularMarketChange': {
'raw': 1.2679996,
'fmt': '1.27'
},
'firstTradeDateMilliseconds': 951714000000,
'exchangeDataDelayedBy': 30,
'exchangeTimezoneShortName': 'EDT',
'marketState': 'REGULAR',
'regularMarketPrice': {
'raw': 24.63,
'fmt': '24.63'
},
'market': 'us24_market',
'sourceInterval': 15,
'exchange': 'CMX',
'shortName': 'Silver',
'region': 'US',
'triggerable': False
}, {
'fullExchangeName': 'CCY',
'symbol': 'EURUSD=X',
'gmtOffSetMilliseconds': 3600000,
'language': 'en-US',
'regularMarketTime': {
'raw': 1596234582,
'fmt': '11:29PM BST'
},
'regularMarketChangePercent': {
'raw': -0.58878887,
'fmt': '-0.59%'
},
'quoteType': 'CURRENCY',
'tradeable': False,
'currency': 'USD',
'regularMarketPreviousClose': {
'raw': 1.1845534,
'fmt': '1.1846'
},
'exchangeTimezoneName': 'Europe/London',
'regularMarketChange': {
'raw': -0.0069744587,
'fmt': '-0.0070'
},
'exchangeDataDelayedBy': 0,
'firstTradeDateMilliseconds': 1070236800000,
'exchangeTimezoneShortName': 'BST',
'regularMarketPrice': {
'raw': 1.1775789,
'fmt': '1.1776'
},
'marketState': 'CLOSED',
'market': 'ccy_market',
'quoteSourceName': 'Delayed Quote',
'priceHint': 4,
'exchange': 'CCY',
'sourceInterval': 15,
'shortName': 'EUR/USD',
'region': 'US',
'triggerable': False
}, {
'exchangeTimezoneName': 'America/New_York',
'fullExchangeName': 'NYBOT',
'symbol': '^TNX',
'regularMarketChange': {
'raw': -0.004999995,
'fmt': '-0.0050'
},
'gmtOffSetMilliseconds': -14400000,
'exchangeDataDelayedBy': 30,
'firstTradeDateMilliseconds': -252356400000,
'language': 'en-US',
'regularMarketTime': {
'raw': 1596221996,
'fmt': '2:59PM EDT'
},
'exchangeTimezoneShortName': 'EDT',
'regularMarketChangePercent': {
'raw': -0.9242135,
'fmt': '-0.92%'
},
'quoteType': 'INDEX',
'marketState': 'REGULAR',
'regularMarketPrice': {
'raw': 0.536,
'fmt': '0.5360'
},
'market': 'us24_market',
'priceHint': 4,
'tradeable': False,
'exchange': 'NYB',
'sourceInterval': 30,
'region': 'US',
'shortName': '10-Yr Bond',
'triggerable': False,
'regularMarketPreviousClose': {
'raw': 0.541,
'fmt': '0.5410'
},
'longName': 'Treasury Yield 10 Years'
}, {
'exchangeTimezoneName': 'America/New_York',
'fullExchangeName': 'Chicago Options',
'symbol': '^VIX',
'regularMarketChange': {
'raw': -0.30000114,
'fmt': '-0.30'
},
'gmtOffSetMilliseconds': -14400000,
'exchangeDataDelayedBy': 20,
'firstTradeDateMilliseconds': 631290600000,
'language': 'en-US',
'regularMarketTime': {
'raw': 1596226490,
'fmt': '4:14PM EDT'
},
'exchangeTimezoneShortName': 'EDT',
'regularMarketChangePercent': {
'raw': -1.2116362,
'fmt': '-1.21%'
},
'quoteType': 'INDEX',
'marketState': 'POSTPOST',
'regularMarketPrice': {
'raw': 24.46,
'fmt': '24.46'
},
'market': 'us_market',
'priceHint': 2,
'tradeable': False,
'exchange': 'WCB',
'sourceInterval': 15,
'region': 'US',
'shortName': 'Vix',
'triggerable': False,
'regularMarketPreviousClose': {
'raw': 24.76,
'fmt': '24.76'
}
}, {
'fullExchangeName': 'CCY',
'symbol': 'GBPUSD=X',
'gmtOffSetMilliseconds': 3600000,
'language': 'en-US',
'regularMarketTime': {
'raw': 1596234582,
'fmt': '11:29PM BST'
},
'regularMarketChangePercent': {
'raw': -0.06541982,
'fmt': '-0.07%'
},
'quoteType': 'CURRENCY',
'tradeable': False,
'currency': 'USD',
'regularMarketPreviousClose': {
'raw': 1.3091918,
'fmt': '1.3092'
},
'exchangeTimezoneName': 'Europe/London',
'regularMarketChange': {
'raw': -0.00085651875,
'fmt': '-0.0009'
},
'exchangeDataDelayedBy': 0,
'firstTradeDateMilliseconds': 1070236800000,
'exchangeTimezoneShortName': 'BST',
'regularMarketPrice': {
'raw': 1.3083353,
'fmt': '1.3083'
},
'marketState': 'CLOSED',
'market': 'ccy_market',
'quoteSourceName': 'Delayed Quote',
'priceHint': 4,
'exchange': 'CCY',
'sourceInterval': 15,
'shortName': 'GBP/USD',
'region': 'US',
'triggerable': False
}, {
'fullExchangeName': 'CCY',
'symbol': 'JPY=X',
'gmtOffSetMilliseconds': 3600000,
'language': 'en-US',
'regularMarketTime': {
'raw': 1596232799,
'fmt': '10:59PM BST'
},
'regularMarketChangePercent': {
'raw': 1.078324,
'fmt': '1.08%'
},
'quoteType': 'CURRENCY',
'tradeable': False,
'currency': 'JPY',
'regularMarketPreviousClose': {
'raw': 104.792,
'fmt': '104.7920'
},
'exchangeTimezoneName': 'Europe/London',
'regularMarketChange': {
'raw': 1.1299973,
'fmt': '1.1300'
},
'exchangeDataDelayedBy': 0,
'firstTradeDateMilliseconds': 846633600000,
'exchangeTimezoneShortName': 'BST',
'regularMarketPrice': {
'raw': 105.922,
'fmt': '105.9220'
},
'marketState': 'CLOSED',
'market': 'ccy_market',
'quoteSourceName': 'Delayed Quote',
'priceHint': 4,
'exchange': 'CCY',
'sourceInterval': 15,
'shortName': 'USD/JPY',
'region': 'US',
'triggerable': False
}, {
'exchangeTimezoneName': 'Europe/London',
'fullExchangeName': 'CCC',
'symbol': 'BTC-USD',
'regularMarketChange': {
'raw': -2.7011719,
'fmt': '-2.70'
},
'gmtOffSetMilliseconds': 3600000,
'exchangeDataDelayedBy': 0,
'firstTradeDateMilliseconds': 1410908400000,
'language': 'en-US',
'regularMarketTime': {
'raw': 1596253293,
'fmt': '4:41AM BST'
},
'exchangeTimezoneShortName': 'BST',
'regularMarketChangePercent': {
'raw': -0.023798062,
'fmt': '-0.02%'
},
'quoteType': 'CRYPTOCURRENCY',
'marketState': 'REGULAR',
'regularMarketPrice': {
'raw': 11347.686,
'fmt': '11,347.69'
},
'market': 'ccc_market',
'quoteSourceName': 'CryptoCompare',
'tradeable': True,
'exchange': 'CCC',
'sourceInterval': 15,
'region': 'US',
'triggerable': False,
'regularMarketPreviousClose': {
'raw': 11350.387,
'fmt': '11,350.39'
}
}, {
'exchangeTimezoneName': 'America/New_York',
'fullExchangeName': 'Nasdaq GIDS',
'symbol': '^CMC200',
'regularMarketChange': {
'raw': 0.0,
'fmt': '0.00'
},
'gmtOffSetMilliseconds': -14400000,
'exchangeDataDelayedBy': 0,
'firstTradeDateMilliseconds': 1546266600000,
'language': 'en-US',
'regularMarketTime': {
'raw': 1594647950,
'fmt': '9:45AM EDT'
},
'exchangeTimezoneShortName': 'EDT',
'regularMarketChangePercent': {
'raw': 0.0,
'fmt': '0.00%'
},
'quoteType': 'INDEX',
'marketState': 'POSTPOST',
'regularMarketPrice': {
'raw': 156.7543,
'fmt': '156.75'
},
'market': 'us_market',
'quoteSourceName': 'Delayed Quote',
'priceHint': 2,
'tradeable': False,
'exchange': 'NIM',
'sourceInterval': 15,
'region': 'US',
'shortName': 'CMC Crypto 200',
'triggerable': False,
'regularMarketPreviousClose': {
'raw': 156.7543,
'fmt': '156.75'
}
}, {
'exchangeTimezoneName': 'Europe/London',
'fullExchangeName': 'FTSE Index',
'symbol': '^FTSE',
'regularMarketChange': {
'raw': -92.23047,
'fmt': '-92.23'
},
'gmtOffSetMilliseconds': 3600000,
'exchangeDataDelayedBy': 15,
'firstTradeDateMilliseconds': 441964800000,
'language': 'en-US',
'regularMarketTime': {
'raw': 1596210339,
'fmt': '4:45PM BST'
},
'exchangeTimezoneShortName': 'BST',
'regularMarketChangePercent': {
'raw': -1.5397432,
'fmt': '-1.54%'
},
'quoteType': 'INDEX',
'marketState': 'CLOSED',
'regularMarketPrice': {
'raw': 5897.76,
'fmt': '5,897.76'
},
'market': 'gb_market',
'priceHint': 2,
'tradeable': False,
'exchange': 'FGI',
'sourceInterval': 15,
'region': 'US',
'shortName': 'FTSE 100',
'triggerable': False,
'regularMarketPreviousClose': {
'raw': 5989.99,
'fmt': '5,989.99'
}
}, {
'exchangeTimezoneName': 'Asia/Tokyo',
'fullExchangeName': 'Osaka',
'symbol': '^N225',
'regularMarketChange': {
'raw': -629.23047,
'fmt': '-629.23'
},
'gmtOffSetMilliseconds': 32400000,
'exchangeDataDelayedBy': 0,
'firstTradeDateMilliseconds': -157420800000,
'language': 'en-US',
'regularMarketTime': {
'raw': 1596176103,
'fmt': '3:15PM JST'
},
'exchangeTimezoneShortName': 'JST',
'regularMarketChangePercent': {
'raw': -2.8167062,
'fmt': '-2.82%'
},
'quoteType': 'INDEX',
'marketState': 'CLOSED',
'regularMarketPrice': {
'raw': 21710.0,
'fmt': '21,710.00'
},
'market': 'jp_market',
'priceHint': 2,
'tradeable': False,
'exchange': 'OSA',
'sourceInterval': 20,
'region': 'US',
'shortName': 'Nikkei 225',
'triggerable': False,
'regularMarketPreviousClose': {
'raw': 22339.23,
'fmt': '22,339.23'
}
}]
```
### get_trending
=== "Details"
- *Description*: List of trending securities for specific country
- *Returns*: `list`
- *Arguments*
| Argument | Description | Type | Default | Required | Options |
|:-----------|:-----------|:-------|:----------|:-----------|:------------------------------|
| country | Name of country | `str` | `united states` | optional | See below |
??? example "View Countries"
```python
{
'france': {
'lang': 'fr-FR',
'region': 'FR',
'corsDomain': 'fr.finance.yahoo.com'
},
'india': {
'lang': 'en-IN',
'region': 'IN',
'corsDomain': 'in.finance.yahoo.com'
},
'hong kong': {
'lang': 'zh-Hant-HK',
'region': 'HK',
'corsDomain': 'hk.finance.yahoo.com'
},
'germany': {
'lang': 'de-DE',
'region': 'DE',
'corsDomain': 'de.finance.yahoo.com'
},
'canada': {
'lang': 'en-CA',
'region': 'CA',
'corsDomain': 'ca.finance.yahoo.com'
},
'spain': {
'lang': 'es-ES',
'region': 'ES',
'corsDomain': 'es.finance.yahoo.com'
},
'italy': {
'lang': 'it-IT',
'region': 'IT',
'corsDomain': 'it.finance.yahoo.com'
},
'united states': {
'lang': 'en-US',
'region': 'US',
'corsDomain': 'finance.yahoo.com'
},
'australia': {
'lang': 'en-AU',
'region': 'AU',
'corsDomain': 'au.finance.yahoo.com'
},
'united kingdom': {
'lang': 'en-GB',
'region': 'GB',
'corsDomain': 'uk.finance.yahoo.com'
},
'brazil': {
'lang': 'pt-BR',
'region': 'BR',
'corsDomain': 'br.financas.yahoo.com'
},
'new zealand': {
'lang': 'en-NZ',
'region': 'NZ',
'corsDomain': 'nz.finance.yahoo.com'
},
'singapore': {
'lang': 'en-SG',
'region': 'SG',
'corsDomain': 'sg.finance.yahoo.com'
}
}
```
=== "Example"
```python hl_lines="2"
import yahooquery as yq
data = yq.get_trending()
```
=== "Data"
```python
{
'count': 20,
'quotes': [{
'symbol': 'PINS'
}, {
'symbol': 'MARA'
}, {
'symbol': '^DJI'
}, {
'symbol': '^GSPC'
}, {
'symbol': '^IXIC'
}, {
'symbol': 'MSFT'
}, {
'symbol': 'SNY'
}, {
'symbol': 'JWN'
}, {
'symbol': 'AC.TO'
}, {
'symbol': 'CRUS'
}, {
'symbol': 'GSK'
}, {
'symbol': 'IZEA'
}, {
'symbol': 'SONN'
}, {
'symbol': 'TLSA'
}, {
'symbol': '^RUT'
}, {
'symbol': 'RVVTF'
}, {
'symbol': 'CLX'
}, {
'symbol': 'FLDM'
}, {
'symbol': 'KSU'
}, {
'symbol': 'BTC-USD'
}],
'jobTimestamp': 1596251351296,
'startInterval': 202008010200
}
```
### search
=== "Details"
- *Description*: Query Yahoo Finance for anything: companies, ticker symbols, cusips, news, etc.
- *Returns*: `dict`
- *Arguments*
| Argument | Description | Type | Default | Required | Options |
|:-----------|:-----------|:-------|:----------|:-----------|:------------------------------|
| query | What you'd like to query from Yahoo Finance | `str` | None | **required** | N/A |
| quotes_count | Maximum number of quotes to return | `int` | 10 | optional | N/A |
| news_count | Maximum number of news items to return | `int` | 10 | optional | N/A |
| first_quote | Return only the first quote result | `bool` | `False` | optional | `True`, `False` |
| country | Name of country | `str` | `United States` | optional | See below |
??? example "View Countries"
```python
{
'france': {
'lang': 'fr-FR',
'region': 'FR',
'corsDomain': 'fr.finance.yahoo.com'
},
'india': {
'lang': 'en-IN',
'region': 'IN',
'corsDomain': 'in.finance.yahoo.com'
},
'hong kong': {
'lang': 'zh-Hant-HK',
'region': 'HK',
'corsDomain': 'hk.finance.yahoo.com'
},
'germany': {
'lang': 'de-DE',
'region': 'DE',
'corsDomain': 'de.finance.yahoo.com'
},
'canada': {
'lang': 'en-CA',
'region': 'CA',
'corsDomain': 'ca.finance.yahoo.com'
},
'spain': {
'lang': 'es-ES',
'region': 'ES',
'corsDomain': 'es.finance.yahoo.com'
},
'italy': {
'lang': 'it-IT',
'region': 'IT',
'corsDomain': 'it.finance.yahoo.com'
},
'united states': {
'lang': 'en-US',
'region': 'US',
'corsDomain': 'finance.yahoo.com'
},
'australia': {
'lang': 'en-AU',
'region': 'AU',
'corsDomain': 'au.finance.yahoo.com'
},
'united kingdom': {
'lang': 'en-GB',
'region': 'GB',
'corsDomain': 'uk.finance.yahoo.com'
},
'brazil': {
'lang': 'pt-BR',
'region': 'BR',
'corsDomain': 'br.financas.yahoo.com'
},
'new zealand': {
'lang': 'en-NZ',
'region': 'NZ',
'corsDomain': 'nz.finance.yahoo.com'
},
'singapore': {
'lang': 'en-SG',
'region': 'SG',
'corsDomain': 'sg.finance.yahoo.com'
}
}
```
=== "Example"
```python hl_lines="2"
import yahooquery as yq
data = yq.search("38141G104", first_quote=True)
```
=== "Data"
```python
{
'exchange': 'NYQ',
'quoteType': 'EQUITY',
'symbol': 'GS',
'index': 'quotes',
'score': 33156.0,
'typeDisp': 'Equity',
'longname': 'The Goldman Sachs Group, Inc.',
'isYahooFinance': True
}
```
```
### references/research.md
```markdown
The Research class is the access point to retrieve either research reports or trade ideas from Yahoo Finance. **You must be a subscriber to Yahoo Finance Premium to utilize this class.**
## Import
```python
from yahooquery import Research
```
## Create Instance
```python
r = Research(username='[email protected]', password='password')
```
## Research
### **reports**
=== "Details"
- *Description*: Retrieve research reports from Yahoo Finance
- *Return*: `pandas.DataFrame`
- *Arguments*
| Argument | Description | Type | Default | Required | Options |
|:-----------|:-----------|:-------|:----------|:-----------|:------------------------------|
| size | Number of trades to return | `int` | `100` | optional | |
| investment_rating | Type of investment rating |`str` or `list` | None | optional | See below |
| sector | Sector | `str` or `list` | None | optional | See below |
| report_type | Report types | `str` or `list` | None | optional | See below |
| report_date | Date range | `str` | None | optional | See below |
=== "investment_rating"
```python
{
'options': ['Bearish', 'Bullish'],
'multiple': True
}
```
=== "sector"
```python
{
'options': [
'Basic Materials', 'Communication Services', 'Consumer Cyclical',
'Consumer Defensive', 'Energy', 'Financial Services', 'Healthcare',
'Industrial', 'Real Estate', 'Technology', 'Utilities'],
'multiple': True
}
```
=== "report_type"
```python
{
'options': [
'Analyst Report', 'Insider Activity', 'Market Outlook', 'Market Summary',
'Market Update', 'Portfolio Ideas', 'Quantitative Report', 'Sector Watch',
'Stock Picks', 'Technical Analysis', 'Thematic Portfolio', 'Top/Bottom Insider Activity'
],
'multiple': True
}
```
=== "report_date"
```python
{
'options': {
'Last Week': 7,
'Last Month': 30,
'Last Year': 365
},
'multiple': False
}
```
!!! warning
If using a `str` for the arguments that can either be a `str` or `list`, they have to be comma separated, i.e. `sector='Financial Services, Technology'`.
=== "Example"
```python hl_lines="2 3 4 5"
r = Research(username='[email protected]', password=password)
r.reports(
report_type='Analyst Report, Insider Activity',
report_date='Last Week'
)
```
=== "Data"
| | Report Date | Report type | Report title | Tickers | Sector | Rating | Investment Rating | Target Price | Earnings Estimates |
|---:|:-------------------------|:---------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------|:-----------------------|:-----------|:--------------------|:---------------|:---------------------|
| 0 | 2020-07-31T00:00:00.000Z | Analyst Report | CME Group is a futures and derivatives exchange and clearing company. It operates exchanges such as the Chicago Mercantile Exchange (CME), Chicago Board of Trade (CBOT), New York Mercantile Exchange (NYMEX), Commodity Exchange (COMEX) and the Kansas City Board of Trade (KCBT). In addition, CME offers a range of market data and information services. CME shares are a component of the S&P 500. | ['CME'] | ['Financial Services'] | Maintained | Bullish | Decreased | Decreased |
| 1 | 2020-07-31T00:00:00.000Z | Analyst Report | Mastercard operates the world's second-largest electronic payments network, providing processing services and payment product platforms, including credit, debit, ATM, prepaid and commercial payments under the Mastercard, Maestro, and Cirrus brands. Mastercard went public in 2006 and is a member of the S&P 500. | ['MA'] | ['Financial Services'] | Maintained | Bullish | Increased | Decreased |
| 2 | 2020-07-31T00:00:00.000Z | Analyst Report | Blackstone Group is one of the world's leading managers of alternative assets, including private equity, real estate, hedge funds, credit-oriented funds, and closed-end mutual funds. In recent years, Blackstone has rapidly grown its fee-earning assets under management, and its assets are relatively well balanced among private equity, real estate, hedge funds, and credit. The company converted from a publicly traded partnership to a corporation on July 1, 2019. | ['BX'] | ['Financial Services'] | Maintained | Bullish | Maintained | Decreased |
| 3 | 2020-07-31T00:00:00.000Z | Analyst Report | Northrop Grumman is a leading global defense contractor, providing systems integration, defense electronics, information technology, and advanced aircraft and space technology. The shares are a component of the S&P 500. The company has 90,000 employees. | ['NOC'] | ['Industrials'] | Maintained | Bullish | Maintained | Increased |
| 4 | 2020-07-31T00:00:00.000Z | Analyst Report | Starbucks is a leading retailer of fresh-brewed coffee and branded merchandise. Its brands include Starbucks, Tazo Tea, and Frappuccino. With a market cap of more than $90 billion, SBUX shares are generally considered large-cap growth. | ['SBUX'] | ['Consumer Cyclical'] | Maintained | Bullish | Maintained | Decreased |
### **trades**
=== "Details"
- *Description*: Retrieve trade ideas from Yahoo Finance
- *Return*: `pandas.DataFrame`
- *Arguments*
| Argument | Description | Type | Default | Required | Options |
|:-----------|:-----------|:-------|:----------|:-----------|:------------------------------|
| size | Number of trades to return | `int` | `100` | optional | |
| trend | Type of investment rating |`str` or `list` | None | optional | See below |
| sector | Sector | `str` or `list` | None | optional | See below |
| term | Term length (short, mid, long) | `str` or `list` | None | optional | See below |
| startdatetime | Date range | `str` | None | optional | See below |
=== "trend"
```python
{
'options': ['Bearish', 'Bullish'],
'multiple': True
}
```
=== "sector"
```python
{
'options': [
'Basic Materials', 'Communication Services', 'Consumer Cyclical',
'Consumer Defensive', 'Energy', 'Financial Services', 'Healthcare',
'Industrial', 'Real Estate', 'Technology', 'Utilities'],
'multiple': True
}
```
=== "term"
```python
{
'options': ['Short term', 'Mid term', 'Long term'],
'multiple': True
}
```
=== "startdatetime"
```python
{
'options': {
'Last Week': 7,
'Last Month': 30,
'Last Year': 365
},
'multiple': False
}
```
!!! warning
If using a `str` for the arguments that can either be a `str` or `list`, they have to be comma separated, i.e. `sector='Financial Services, Technology'`.
=== "Example"
```python hl_lines="2 3 4 5 6"
r = Research(username='[email protected]', password=password)
r.trades(
sector=['Financial Services', 'Technology'],
term='Short term',
startdatetime='Last Week'
)
```
=== "Data"
| | Idea Date | Term | Ticker | Rating | Price Target | Rate of Return | ID | Image URL | Company Name | Price Timestamp | Current Price | Title | Highlights | Description |
|---:|:-------------------------|:-----------|:---------|:---------|---------------:|-----------------:|:--------------------------------|:-------------------------------------------------------------|:------------------------------|------------------:|----------------:|:----------------------------------------------------------------------------------|:-------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 2020-07-30T00:00:00.000Z | Short term | BHLB | Sell | 8.3 | -0.177134 | tc_USvyP6AAPGJwApgABAACAAAD6CIg | https://s.yimg.com/uc/fin/img/bearish-continuation-wedge.svg | Berkshire Hills Bancorp, Inc. | 1596225965000 | 9.96 | Berkshire Hills Bancorp, Inc. - BHLB forms a Continuation Wedge (Bearish) pattern | | ['This stock has formed a pattern called Continuation Wedge (Bearish), providing a target price for the short-term in the range of 8.10 to 8.50.', 'The price recently crossed below its moving average signaling a new downtrend has been established.'] |
| 1 | 2020-07-30T00:00:00.000Z | Short term | FISV | Buy | 107.82 | 0.070492 | tc_USvyO_AAPKkQAGgABAACAAAD6CJg | https://s.yimg.com/uc/fin/img/bullish-double-bottom.svg | Fiserv, Inc. | 1596225601000 | 99.79 | Fiserv, Inc. - FISV forms a Double Bottom pattern | | ['This stock has formed a pattern called Double Bottom, providing a target price for the short-term in the range of 107.00 to 108.50.', 'The Intermediate-Term KST indicator has triggered a bullish signal by rising above its moving average.'] |
```
### references/screener.md
```markdown
Currently, the Screener class allows the user to retrieve predefined screeners. Future versions will eventually allow for users to create screeners on the fly as well as utilize their own screeners they've created on Yahoo Finance.
## Import
```python
from yahooquery import Screener
```
## Create Instance
```python
s = Screener()
```
## Screener
### available_screeners
=== "Details"
- *Description*: Return list of predefined screeners available to pass to [get_screeners](#get_screeners)
- *Return*: `dict`
=== "Example"
```python hl_lines="2"
s = Screener()
s.available_screeners
```
=== "Data"
```python
[
'accident_health_insurance',
'advertising_agencies',
'aerospace_defense_major_diversified',
'aerospace_defense_products_services',
'aggressive_small_caps',
'agricultural_chemicals',
'air_delivery_freight_services',
'air_services_other',
'all_cryptocurrencies_au',
'all_cryptocurrencies_ca',
'all_cryptocurrencies_eu',
'all_cryptocurrencies_gb',
'all_cryptocurrencies_in',
'all_cryptocurrencies_us',
'aluminum',
'apparel_stores',
'appliances',
'application_software',
'asset_management',
'auto_dealerships',
'auto_manufacturers_major',
'auto_parts',
'auto_parts_stores',
'auto_parts_wholesale',
'basic_materials',
'basic_materials_wholesale',
'beverages_brewers',
'beverages_soft_drinks',
'beverages_wineries_distillers',
'biotechnology',
'broadcasting_radio',
'broadcasting_tv',
'building_materials_wholesale',
'business_equipment',
'business_services',
'business_software_services',
'catalog_mail_order_houses',
'catv_systems',
'cement',
'chemicals_major_diversified',
'cigarettes',
'cleaning_products',
'closedend_fund_debt',
'closedend_fund_equity',
'closedend_fund_foreign',
'communication_equipment',
'computer_based_systems',
'computer_peripherals',
'computers_wholesale',
'confectioners',
'conglomerates',
'conservative_foreign_funds',
'consumer_defensive',
'consumer_goods',
'consumer_services',
'copper',
'credit_services',
'dairy_products',
'data_storage_devices',
'day_gainers',
'day_gainers_americas',
'day_gainers_asia',
'day_gainers_au',
'day_gainers_br',
'day_gainers_ca',
'day_gainers_de',
'day_gainers_dji',
'day_gainers_es',
'day_gainers_europe',
'day_gainers_fr',
'day_gainers_gb',
'day_gainers_hk',
'day_gainers_in',
'day_gainers_it',
'day_gainers_ndx',
'day_gainers_nz',
'day_gainers_sg',
'day_losers',
'day_losers_americas',
'day_losers_asia',
'day_losers_au',
'day_losers_br',
'day_losers_ca',
'day_losers_de',
'day_losers_dji',
'day_losers_es',
'day_losers_europe',
'day_losers_fr',
'day_losers_gb',
'day_losers_hk',
'day_losers_in',
'day_losers_it',
'day_losers_ndx',
'day_losers_nz',
'day_losers_sg',
'department_stores',
'diagnostic_substances',
'discount_variety_stores',
'diversified_communication_services',
'diversified_computer_systems',
'diversified_electronics',
'diversified_investments',
'diversified_machinery',
'diversified_utilities',
'drug_delivery',
'drug_manufacturers_major',
'drug_manufacturers_other',
'drug_related_products',
'drug_stores',
'drugs_generic',
'drugs_wholesale',
'education_training_services',
'electric_utilities',
'electronic_equipment',
'electronics_stores',
'electronics_wholesale',
'entertainment_diversified',
'fair_value_screener',
'farm_construction_machinery',
'farm_products',
'financial',
'food_major_diversified',
'food_wholesale',
'foreign_money_center_banks',
'foreign_regional_banks',
'foreign_utilities',
'gaming_activities',
'gas_utilities',
'general_building_materials',
'general_contractors',
'general_entertainment',
'gold',
'grocery_stores',
'growth_technology_stocks',
'health_care_plans',
'healthcare',
'healthcare_information_services',
'heavy_construction',
'high_yield_bond',
'home_furnishing_stores',
'home_furnishings_fixtures',
'home_health_care',
'home_improvement_stores',
'hospitals',
'housewares_accessories',
'independent_oil_gas',
'industrial_electrical_equipment',
'industrial_equipment_components',
'industrial_equipment_wholesale',
'industrial_goods',
'industrial_metals_minerals',
'information_delivery_services',
'information_technology_services',
'insurance_brokers',
'internet_information_providers',
'internet_service_providers',
'internet_software_services',
'investment_brokerage_national',
'investment_brokerage_regional',
'jewelry_stores',
'life_insurance',
'lodging',
'long_distance_carriers',
'longterm_care_facilities',
'lumber_wood_production',
'machine_tools_accessories',
'major_airlines',
'major_integrated_oil_gas',
'management_services',
'marketing_services',
'meat_products',
'medical_appliances_equipment',
'medical_equipment_wholesale',
'medical_instruments_supplies',
'medical_laboratories_research',
'mega_cap_hc',
'metal_fabrication',
'money_center_banks',
'mortgage_investment',
'most_actives',
'most_actives_americas',
'most_actives_asia',
'most_actives_au',
'most_actives_br',
'most_actives_ca',
'most_actives_de',
'most_actives_dji',
'most_actives_es',
'most_actives_europe',
'most_actives_fr',
'most_actives_gb',
'most_actives_hk',
'most_actives_in',
'most_actives_it',
'most_actives_ndx',
'most_actives_nz',
'most_actives_sg',
'most_watched_tickers',
'movie_production_theaters',
'ms_basic_materials',
'ms_communication_services',
'ms_consumer_cyclical',
'ms_consumer_defensive',
'ms_energy',
'ms_financial_services',
'ms_healthcare',
'ms_industrials',
'ms_real_estate',
'ms_technology',
'ms_utilities',
'multimedia_graphics_software',
'networking_communication_devices',
'nonmetallic_mineral_mining',
'office_supplies',
'oil_gas_drilling_exploration',
'oil_gas_equipment_services',
'oil_gas_pipelines',
'oil_gas_refining_marketing',
'packaging_containers',
'paper_paper_products',
'personal_products',
'personal_services',
'photographic_equipment_supplies',
'pollution_treatment_controls',
'portfolio_anchors',
'printed_circuit_boards',
'processed_packaged_goods',
'processing_systems_products',
'property_casualty_insurance',
'property_management',
'publishing_books',
'publishing_newspapers',
'publishing_periodicals',
'railroads',
'real_estate_development',
'recreational_goods_other',
'recreational_vehicles',
'regional_airlines',
'regional_midatlantic_banks',
'regional_midwest_banks',
'regional_northeast_banks',
'regional_pacific_banks',
'regional_southeast_banks',
'regional_southwest_banks',
'reit_diversified',
'reit_healthcare_facilities',
'reit_hotel_motel',
'reit_industrial',
'reit_office',
'reit_residential',
'reit_retail',
'rental_leasing_services',
'research_services',
'residential_construction',
'resorts_casinos',
'restaurants',
'rubber_plastics',
'savings_loans',
'scientific_technical_instruments',
'security_protection_services',
'security_software_services',
'semiconductor_broad_line',
'semiconductor_equipment_materials',
'semiconductor_integrated_circuits',
'semiconductor_memory_chips',
'semiconductor_specialized',
'services',
'shipping',
'silver',
'small_cap_gainers',
'small_tools_accessories',
'solid_large_growth_funds',
'solid_midcap_growth_funds',
'specialized_health_services',
'specialty_chemicals',
'specialty_eateries',
'specialty_retail_other',
'sporting_activities',
'sporting_goods',
'sporting_goods_stores',
'staffing_outsourcing_services',
'steel_iron',
'surety_title_insurance',
'synthetics',
'technical_services',
'technical_system_software',
'technology',
'telecom_services_domestic',
'telecom_services_foreign',
'textile_apparel_clothing',
'textile_apparel_footwear_accessories',
'textile_industrial',
'tobacco_products_other',
'top_energy_us',
'top_etfs',
'top_etfs_hk',
'top_etfs_in',
'top_etfs_us',
'top_iv_options_us',
'top_mutual_funds',
'top_mutual_funds_au',
'top_mutual_funds_br',
'top_mutual_funds_ca',
'top_mutual_funds_de',
'top_mutual_funds_es',
'top_mutual_funds_fr',
'top_mutual_funds_gb',
'top_mutual_funds_hk',
'top_mutual_funds_in',
'top_mutual_funds_it',
'top_mutual_funds_nz',
'top_mutual_funds_sg',
'top_mutual_funds_us',
'top_options_implied_volatality',
'top_options_open_interest',
'toy_hobby_stores',
'toys_games',
'trucking',
'trucks_other_vehicles',
'undervalued_growth_stocks',
'undervalued_large_caps',
'utilities',
'waste_management',
'water_utilities',
'wireless_communications'
]
```
### get_screeners
=== "Details"
- *Description*: List of securities from a predefined screener(s)
- *Return*: `pandas.DataFrame`
- *Arguments*
| Argument | Type | Default | Required | Options |
|:-----------|:-------|:----------|:-----------|:------------------------------|
| screen_ids | `str` or `list` | None | **Required** | See [available screeners](#available_screeners) |
| count | `int` | `25` | optional | |
=== "Example"
```python hl_lines="2"
s = Screener()
s.get_screeners(['most_actives', 'day_gainers'], 5)
```
=== "Data"
```python hl_lines="2 394"
{
'most_actives': {
'id': '437465ef-980e-4d8c-a860-de7cbfbab373',
'title': 'Most Actives - US',
'description': 'Stocks ordered in descending order by intraday trade volume',
'canonicalName': 'MOST_ACTIVES',
'criteriaMeta': {
'size': 5,
'offset': 0,
'sortField': 'dayvolume',
'sortType': 'DESC',
'quoteType': 'EQUITY',
'topOperator': 'AND',
'criteria': [{
'field': 'region',
'operators': ['EQ'],
'values': [],
'labelsSelected': [53]
}, {
'field': 'intradaymarketcap',
'operators': ['EQ'],
'values': [],
'labelsSelected': [1, 2, 3]
}, {
'field': 'dayvolume',
'operators': ['GT'],
'values': [5000000],
'labelsSelected': []
}]
},
'rawCriteria': '{"offset":0,"size":5,"sortField":"dayvolume","sortType":"DESC","quoteType":"EQUITY","query":{"operator":"AND","operands":[{"operator":"eq","operands":["region","us"]},{"operator":"or","operands":[{"operator":"BTWN","operands":["intradaymarketcap",2000000000,10000000000]},{"operator":"BTWN","operands":["intradaymarketcap",10000000000,100000000000]},{"operator":"GT","operands":["intradaymarketcap",100000000000]}]},{"operator":"gt","operands":["dayvolume",5000000]}]}}',
'start': 0,
'count': 5,
'total': 203,
'quotes': [{
'language': 'en-US',
'region': 'US',
'quoteType': 'EQUITY',
'quoteSourceName': 'Nasdaq Real Time Price',
'triggerable': False,
'currency': 'USD',
'firstTradeDateMilliseconds': -252322200000,
'priceHint': 2,
'postMarketChangePercent': 0.0,
'postMarketTime': 1596234164,
'postMarketPrice': 6.08,
'postMarketChange': 0.0,
'regularMarketChange': -0.19000006,
'regularMarketTime': 1596225602,
'regularMarketPrice': 6.07,
'regularMarketDayHigh': 6.29,
'regularMarketDayRange': '6.0 - 6.29',
'regularMarketDayLow': 6.0,
'regularMarketVolume': 142268197,
'regularMarketPreviousClose': 6.26,
'bid': 6.07,
'ask': 6.08,
'bidSize': 473,
'askSize': 280,
'exchange': 'NYQ',
'market': 'us_market',
'messageBoardId': 'finmb_177031',
'fullExchangeName': 'NYSE',
'shortName': 'General Electric Company',
'longName': 'General Electric Company',
'financialCurrency': 'USD',
'regularMarketOpen': 6.25,
'averageDailyVolume3Month': 103055092,
'averageDailyVolume10Day': 86456425,
'fiftyTwoWeekLowChange': 0.59000015,
'fiftyTwoWeekLowChangePercent': 0.107664265,
'fiftyTwoWeekRange': '5.48 - 13.26',
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'firstTradeDateMilliseconds': 791735400000,
'priceHint': 2,
'postMarketChangePercent': 2.8466468,
'postMarketTime': 1596230968,
'postMarketPrice': 56.0,
'postMarketChange': 1.5499992,
'regularMarketChange': 7.1399994,
'regularMarketTime': 1596225602,
'regularMarketPrice': 54.45,
'regularMarketDayHigh': 54.53,
'regularMarketDayRange': '49.02 - 54.53',
'regularMarketDayLow': 49.02,
'regularMarketVolume': 1196752,
'regularMarketPreviousClose': 47.31,
'bid': 54.41,
'ask': 54.42,
'bidSize': 8,
'askSize': 8,
'exchange': 'NMS',
'market': 'us_market',
'messageBoardId': 'finmb_336648',
'fullExchangeName': 'NasdaqGS',
'shortName': 'Brooks Automation, Inc.',
'longName': 'Brooks Automation, Inc.',
'financialCurrency': 'USD',
'regularMarketOpen': 50.0,
'averageDailyVolume3Month': 505335,
'averageDailyVolume10Day': 407887,
'fiftyTwoWeekLowChange': 33.260002,
'fiftyTwoWeekLowChangePercent': 1.5696083,
'fiftyTwoWeekRange': '21.19 - 54.53',
'fiftyTwoWeekHighChange': -0.07999802,
'fiftyTwoWeekHighChangePercent': -0.001467046,
'fiftyTwoWeekLow': 21.19,
'fiftyTwoWeekHigh': 54.53,
'dividendDate': 1593129600,
'earningsTimestamp': 1596124800,
'earningsTimestampStart': 1596124800,
'earningsTimestampEnd': 1596124800,
'trailingAnnualDividendRate': 0.4,
'trailingPE': 8.9262295,
'trailingAnnualDividendYield': 0.008454872,
'marketState': 'POST',
'epsTrailingTwelveMonths': 6.1,
'epsForward': 1.21,
'sharesOutstanding': 73759000,
'bookValue': 15.928,
'fiftyDayAverage': 44.00857,
'fiftyDayAverageChange': 10.441429,
'fiftyDayAverageChangePercent': 0.23725899,
'twoHundredDayAverage': 38.295036,
'twoHundredDayAverageChange': 16.154964,
'twoHundredDayAverageChangePercent': 0.4218553,
'marketCap': 4016177664,
'forwardPE': 45.0,
'priceToBook': 3.4185083,
'sourceInterval': 15,
'exchangeDataDelayedBy': 0,
'exchangeTimezoneName': 'America/New_York',
'exchangeTimezoneShortName': 'EDT',
'gmtOffSetMilliseconds': -14400000,
'esgPopulated': False,
'tradeable': True,
'regularMarketChangePercent': 15.091946,
'displayName': 'Brooks Automation',
'symbol': 'BRKS'
}],
'predefinedScr': True,
'versionId': 6
}
}
```
```
### references/ticker/financials.md
```markdown
## Individual
### **balance_sheet**
=== "Details"
- *Description*: Retrieves balance sheet data for most recent four quarters or most recent four years as well as trailing 12 months.
- *Return*: `pandas.DataFrame`
- *Arguments*
| Argument | Description |Type | Default | Required | Options |
|:-----------|:-------------|:-------|:----------|:-----------|:------------------------------|
| frequency | Display either quarterly or annual data |`str` | `a` | optional | Annual - `a`<br>Quarter - `q` |
| trailing | Include or exclude trailing twelve month (TTM) data |`bool` | `True` | optional | `True`<br>`False` |
=== "Example"
```python hl_lines="2"
aapl = Ticker('aapl')
aapl.balance_sheet()
```
=== "Data"
| symbol | asOfDate | periodType | AccountsPayable | AccountsReceivable | AccumulatedDepreciation | AllowanceForDoubtfulAccountsReceivable | AvailableForSaleSecurities | CapitalStock | CashAndCashEquivalents | CashCashEquivalentsAndShortTermInvestments | CashEquivalents | CashFinancial | CommercialPaper | CommonStock | CommonStockEquity | CurrentAccruedExpenses | CurrentAssets | CurrentDebt | CurrentDebtAndCapitalLeaseObligation | CurrentDeferredLiabilities | CurrentDeferredRevenue | CurrentLiabilities | GainsLossesNotAffectingRetainedEarnings | Goodwill | GoodwillAndOtherIntangibleAssets | GrossAccountsReceivable | GrossPPE | Inventory | InvestedCapital | InvestmentinFinancialAssets | InvestmentsAndAdvances | LandAndImprovements | Leases | LongTermDebt | LongTermDebtAndCapitalLeaseObligation | MachineryFurnitureEquipment | NetDebt | NetPPE | NetTangibleAssets | NonCurrentDeferredLiabilities | NonCurrentDeferredRevenue | NonCurrentDeferredTaxesLiabilities | OrdinarySharesNumber | OtherCurrentAssets | OtherCurrentBorrowings | OtherCurrentLiabilities | OtherIntangibleAssets | OtherNonCurrentAssets | OtherNonCurrentLiabilities | OtherReceivables | OtherShortTermInvestments | Payables | PayablesAndAccruedExpenses | Properties | Receivables | RetainedEarnings | ShareIssued | StockholdersEquity | TangibleBookValue | TotalAssets | TotalCapitalization | TotalDebt | TotalEquityGrossMinorityInterest | TotalLiabilitiesNetMinorityInterest | TotalNonCurrentAssets | TotalNonCurrentLiabilitiesNetMinorityInterest | TradeandOtherPayablesNonCurrent | WorkingCapital |
|:---------|:--------------------|:-------------|------------------:|---------------------:|--------------------------:|-----------------------------------------:|-----------------------------:|---------------:|-------------------------:|---------------------------------------------:|------------------:|----------------:|------------------:|--------------:|--------------------:|-------------------------:|----------------:|--------------:|---------------------------------------:|-----------------------------:|-------------------------:|---------------------:|------------------------------------------:|------------:|-----------------------------------:|--------------------------:|-----------:|------------:|------------------:|------------------------------:|-------------------------:|----------------------:|----------:|---------------:|----------------------------------------:|------------------------------:|-----------:|-----------:|--------------------:|--------------------------------:|----------------------------:|-------------------------------------:|-----------------------:|---------------------:|-------------------------:|--------------------------:|------------------------:|------------------------:|-----------------------------:|-------------------:|----------------------------:|-----------:|-----------------------------:|-------------:|--------------:|-------------------:|--------------:|---------------------:|--------------------:|--------------:|----------------------:|------------:|-----------------------------------:|--------------------------------------:|------------------------:|------------------------------------------------:|----------------------------------:|-----------------:|
| aapl | 2016-09-30 00:00:00 | 12M | 3.7294e+10 | 1.5754e+10 | -3.4235e+10 | -5.3e+07 | 1.7043e+11 | 3.1251e+10 | 2.0484e+10 | 6.7155e+10 | 1.1883e+10 | 8.601e+09 | 8.105e+09 | 3.1251e+10 | 1.28249e+11 | 2.2027e+10 | 1.06869e+11 | 1.1605e+10 | 1.1605e+10 | 8.08e+09 | 8.08e+09 | 7.9006e+10 | 6.34e+08 | 5.414e+09 | 8.62e+09 | 1.5807e+10 | 6.1245e+10 | 2.132e+09 | 2.15281e+11 | 1.7043e+11 | 1.7043e+11 | 1.0185e+10 | 6.517e+09 | 7.5427e+10 | 7.5427e+10 | 4.4543e+10 | 6.6548e+10 | 2.701e+10 | 1.19629e+11 | 2.8949e+10 | 2.93e+09 | 2.6019e+10 | 5.33617e+09 | 8.283e+09 | 3.5e+09 | nan | 3.206e+09 | 8.757e+09 | 1.0055e+10 | 1.3545e+10 | 4.6671e+10 | 3.7294e+10 | 5.9321e+10 | 0 | 2.9299e+10 | 9.6364e+10 | 5.33617e+09 | 1.28249e+11 | 1.19629e+11 | 3.21686e+11 | 2.03676e+11 | 8.7032e+10 | 1.28249e+11 | 1.93437e+11 | 2.14817e+11 | 1.14431e+11 | nan | 2.7863e+10 |
| aapl | 2017-09-30 00:00:00 | 12M | 4.9049e+10 | 1.7874e+10 | -4.1293e+10 | -5.8e+07 | 1.94714e+11 | 3.5867e+10 | 2.0289e+10 | 7.4181e+10 | 1.2307e+10 | 7.982e+09 | 1.1977e+10 | 3.5867e+10 | 1.34047e+11 | 2.5744e+10 | 1.28645e+11 | 1.8473e+10 | 1.8473e+10 | 7.548e+09 | 7.548e+09 | 1.00814e+11 | -1.5e+08 | 5.717e+09 | 8.015e+09 | 1.7932e+10 | 7.5076e+10 | 4.855e+09 | 2.49727e+11 | 1.94714e+11 | 1.94714e+11 | 1.3587e+10 | 7.279e+09 | 9.7207e+10 | 9.7207e+10 | 5.421e+10 | 9.5391e+10 | 3.3783e+10 | 1.26032e+11 | 3.434e+10 | 2.836e+09 | 3.1504e+10 | 5.1262e+09 | 1.3936e+10 | 6.496e+09 | nan | 2.298e+09 | 1.0162e+10 | 8.911e+09 | 1.7799e+10 | 5.3892e+10 | 4.9049e+10 | 7.4793e+10 | 0 | 3.5673e+10 | 9.833e+10 | 5.1262e+09 | 1.34047e+11 | 1.26032e+11 | 3.75319e+11 | 2.31254e+11 | 1.1568e+11 | 1.34047e+11 | 2.41272e+11 | 2.46674e+11 | 1.40458e+11 | nan | 2.7831e+10 |
| aapl | 2018-09-30 00:00:00 | 12M | 5.5888e+10 | 2.3186e+10 | -4.9099e+10 | nan | 1.70799e+11 | 4.0201e+10 | 2.5913e+10 | 6.6301e+10 | 1.4338e+10 | 1.1575e+10 | 1.1964e+10 | 4.0201e+10 | 1.07147e+11 | nan | 1.31339e+11 | 2.0748e+10 | 2.0748e+10 | 7.543e+09 | 7.543e+09 | 1.16866e+11 | -3.454e+09 | nan | nan | nan | 9.0403e+10 | 3.956e+09 | 2.2163e+11 | 1.70799e+11 | 1.70799e+11 | 1.6216e+10 | 8.205e+09 | 9.3735e+10 | 9.3735e+10 | 6.5982e+10 | 8.857e+10 | 4.1304e+10 | 1.07147e+11 | 3.223e+09 | 2.797e+09 | 4.26e+08 | 4.75499e+09 | 1.2087e+10 | 8.784e+09 | 3.2687e+10 | nan | 2.2283e+10 | 1.1165e+10 | 2.5809e+10 | 4.0388e+10 | 5.5888e+10 | 5.5888e+10 | 0 | 4.8995e+10 | 7.04e+10 | 4.75499e+09 | 1.07147e+11 | 1.07147e+11 | 3.65725e+11 | 2.00882e+11 | 1.14483e+11 | 1.07147e+11 | 2.58578e+11 | 2.34386e+11 | 1.41712e+11 | 3.3589e+10 | 1.4473e+10 |
| aapl | 2019-09-30 00:00:00 | 12M | 4.6236e+10 | 2.2926e+10 | -5.8579e+10 | nan | 1.05341e+11 | 4.5174e+10 | 4.8844e+10 | 1.00557e+11 | 3.664e+10 | 1.2204e+10 | 5.98e+09 | 4.5174e+10 | 9.0488e+10 | nan | 1.62819e+11 | 1.624e+10 | 1.624e+10 | 5.522e+09 | 5.522e+09 | 1.05718e+11 | -5.84e+08 | nan | nan | nan | 9.5957e+10 | 4.106e+09 | 1.98535e+11 | 1.05341e+11 | 1.05341e+11 | 1.7085e+10 | 9.075e+09 | 9.1807e+10 | 9.1807e+10 | 6.9797e+10 | 5.9203e+10 | 3.7378e+10 | 9.0488e+10 | nan | nan | nan | 4.44324e+09 | 1.2352e+10 | 1.026e+10 | 3.772e+10 | nan | 3.2978e+10 | 2.0958e+10 | 2.2878e+10 | 5.1713e+10 | 4.6236e+10 | 4.6236e+10 | 0 | 4.5804e+10 | 4.5898e+10 | 4.44324e+09 | 9.0488e+10 | 9.0488e+10 | 3.38516e+11 | 1.82295e+11 | 1.08047e+11 | 9.0488e+10 | 2.48028e+11 | 1.75697e+11 | 1.4231e+11 | 2.9545e+10 | 5.7101e+10 |
### **cash_flow**
=== "Details"
- *Description*: Retrieves cash flow data for most recent four quarters or most recent four years as well as the trailing 12 months
- *Return*: `pandas.DataFrame`
- *Arguments*
| Argument | Description |Type | Default | Required | Options |
|:-----------|:-------------|:-------|:----------|:-----------|:------------------------------|
| frequency | Display either quarterly or annual data |`str` | `a` | optional | Annual - `a`<br>Quarter - `q` |
| trailing | Include or exclude trailing twelve month (TTM) data |`bool` | `True` | optional | `True`<br>`False` |
=== "Example"
```python hl_lines="2"
aapl = Ticker('aapl')
aapl.cash_flow(trailing=False)
```
=== "Data"
| symbol | asOfDate | periodType | BeginningCashPosition | CapitalExpenditure | CashDividendsPaid | CashFlowFromContinuingFinancingActivities | ChangeInAccountPayable | ChangeInCashSupplementalAsReported | ChangeInInventory | ChangeInWorkingCapital | ChangesInAccountReceivables | CommonStockIssuance | DeferredIncomeTax | DepreciationAndAmortization | EndCashPosition | FreeCashFlow | InvestingCashFlow | NetIncome | NetOtherFinancingCharges | NetOtherInvestingChanges | OperatingCashFlow | OtherNonCashItems | PurchaseOfBusiness | PurchaseOfInvestment | RepaymentOfDebt | RepurchaseOfCapitalStock | SaleOfInvestment | StockBasedCompensation |
|:---------|:--------------------|:-------------|------------------------:|---------------------:|--------------------:|--------------------------------------------:|-------------------------:|-------------------------------------:|--------------------:|-------------------------:|------------------------------:|----------------------:|--------------------:|------------------------------:|------------------:|---------------:|--------------------:|------------:|---------------------------:|---------------------------:|--------------------:|--------------------:|---------------------:|-----------------------:|------------------:|---------------------------:|-------------------:|-------------------------:|
| aapl | 2016-09-30 00:00:00 | 12M | 2.112e+10 | -1.3548e+10 | -1.215e+10 | -2.0483e+10 | 1.791e+09 | -6.36e+08 | 2.17e+08 | 4.84e+08 | 1.095e+09 | 4.95e+08 | 4.938e+09 | 1.0505e+10 | 2.0484e+10 | 5.2276e+10 | -4.5977e+10 | 4.5687e+10 | -1.163e+09 | -1.1e+08 | 6.5824e+10 | nan | -2.97e+08 | -1.43816e+11 | -2.5e+09 | -2.9722e+10 | 1.11794e+11 | 4.21e+09 |
| aapl | 2017-09-30 00:00:00 | 12M | 2.0484e+10 | -1.2795e+10 | -1.2769e+10 | -1.7347e+10 | 9.618e+09 | -1.95e+08 | -2.723e+09 | -5.55e+09 | -2.093e+09 | 5.55e+08 | 5.966e+09 | 1.0157e+10 | 2.0289e+10 | 5.0803e+10 | -4.6446e+10 | 4.8351e+10 | -1.247e+09 | 2.2e+08 | 6.3598e+10 | -1.66e+08 | -3.29e+08 | -1.59881e+11 | -3.5e+09 | -3.29e+10 | 1.26339e+11 | 4.84e+09 |
| aapl | 2018-09-30 00:00:00 | 12M | 2.0289e+10 | -1.3313e+10 | -1.3712e+10 | -8.7876e+10 | 9.175e+09 | 5.624e+09 | 8.28e+08 | 3.4694e+10 | -5.322e+09 | 6.69e+08 | -3.259e+10 | 1.0903e+10 | 2.5913e+10 | 6.4121e+10 | 1.6066e+10 | 5.9531e+10 | -2.527e+09 | -7.45e+08 | 7.7434e+10 | -4.44e+08 | -7.21e+08 | -7.3227e+10 | -6.5e+09 | -7.2738e+10 | 1.04072e+11 | 5.34e+09 |
| aapl | 2019-09-30 00:00:00 | 12M | 2.5913e+10 | -1.0495e+10 | -1.4119e+10 | -9.0976e+10 | -1.923e+09 | 2.4311e+10 | -2.89e+08 | -3.488e+09 | 2.45e+08 | 7.81e+08 | -3.4e+08 | 1.2547e+10 | 5.0224e+10 | 5.8896e+10 | 4.5896e+10 | 5.5256e+10 | -2.922e+09 | -1.078e+09 | 6.9391e+10 | -6.52e+08 | -6.24e+08 | -4.0631e+10 | -8.805e+09 | -6.6897e+10 | 9.8724e+10 | 6.068e+09 |
### **income_statement**
=== "Details"
- *Description*: Retrieves income statement data for most recent four quarters or most recent four years as well as trailing 12 months.
- *Return*: `pandas.DataFrame`
- *Arguments*
| Argument | Description |Type | Default | Required | Options |
|:-----------|:-------------|:-------|:----------|:-----------|:------------------------------|
| frequency | Display either quarterly or annual data |`str` | `a` | optional | Annual - `a`<br>Quarter - `q` |
| trailing | Include or exclude trailing twelve month (TTM) data |`bool` | `True` | optional | `True`<br>`False` |
=== "Example"
```python hl_lines="2"
aapl = Ticker('aapl')
aapl.income_statement()
```
=== "Data"
| symbol | asOfDate | periodType | BasicAverageShares | BasicEPS | CostOfRevenue | DilutedAverageShares | DilutedEPS | DilutedNIAvailtoComStockholders | EBIT | EBITDA | GrossProfit | InterestExpense | InterestExpenseNonOperating | InterestIncome | InterestIncomeNonOperating | NetIncome | NetIncomeCommonStockholders | NetIncomeContinuousOperations | NetIncomeFromContinuingAndDiscontinuedOperation | NetIncomeFromContinuingOperationNetMinorityInterest | NetIncomeIncludingNoncontrollingInterests | NetInterestIncome | NetNonOperatingInterestIncomeExpense | NormalizedEBITDA | NormalizedIncome | OperatingExpense | OperatingIncome | OperatingRevenue | OtherIncomeExpense | OtherNonOperatingIncomeExpenses | PretaxIncome | ReconciledCostOfRevenue | ReconciledDepreciation | ResearchAndDevelopment | SellingGeneralAndAdministration | TaxEffectOfUnusualItems | TaxProvision | TaxRateForCalcs | TotalExpenses | TotalOperatingIncomeAsReported | TotalRevenue |
|:---------|:--------------------|:-------------|---------------------:|-----------:|----------------:|-----------------------:|-------------:|----------------------------------:|-----------:|-------------:|--------------:|------------------:|------------------------------:|-----------------:|-----------------------------:|------------:|------------------------------:|--------------------------------:|--------------------------------------------------:|------------------------------------------------------:|--------------------------------------------:|--------------------:|---------------------------------------:|-------------------:|-------------------:|-------------------:|------------------:|-------------------:|---------------------:|----------------------------------:|---------------:|--------------------------:|-------------------------:|-------------------------:|----------------------------------:|--------------------------:|---------------:|------------------:|----------------:|---------------------------------:|---------------:|
| aapl | 2016-09-30 00:00:00 | 12M | 5.47082e+09 | 8.35 | 1.31376e+11 | 5.50028e+09 | 8.31 | 4.5687e+10 | 6.2828e+10 | nan | 8.4263e+10 | 1.456e+09 | 1.456e+09 | 3.999e+09 | 3.999e+09 | 4.5687e+10 | 4.5687e+10 | 4.5687e+10 | 4.5687e+10 | 4.5687e+10 | 4.5687e+10 | 2.543e+09 | 2.543e+09 | 7.3333e+10 | 4.5687e+10 | 2.4239e+10 | 6.0024e+10 | 2.15639e+11 | -1.195e+09 | -1.195e+09 | 6.1372e+10 | 1.31376e+11 | 1.0505e+10 | 1.0045e+10 | 1.4194e+10 | 0 | 1.5685e+10 | 0.256 | 1.55615e+11 | 6.0024e+10 | 2.15639e+11 |
| aapl | 2017-09-30 00:00:00 | 12M | 5.21724e+09 | 9.27 | 1.41048e+11 | 5.25169e+09 | 9.21 | 4.8351e+10 | 6.6412e+10 | nan | 8.8186e+10 | 2.323e+09 | 2.323e+09 | 5.201e+09 | 5.201e+09 | 4.8351e+10 | 4.8351e+10 | 4.8351e+10 | 4.8351e+10 | 4.8351e+10 | 4.8351e+10 | 2.878e+09 | 2.878e+09 | 7.6569e+10 | 4.8351e+10 | 2.6842e+10 | 6.1344e+10 | 2.29234e+11 | -1.33e+08 | -1.33e+08 | 6.4089e+10 | 1.41048e+11 | 1.0157e+10 | 1.1581e+10 | 1.5261e+10 | 0 | 1.5738e+10 | 0.246 | 1.6789e+11 | 6.1344e+10 | 2.29234e+11 |
| aapl | 2018-09-30 00:00:00 | 12M | 4.95538e+09 | 12.01 | 1.63756e+11 | 5.00011e+09 | 11.91 | 5.9531e+10 | 7.6143e+10 | nan | 1.01839e+11 | 3.24e+09 | 3.24e+09 | 5.686e+09 | 5.686e+09 | 5.9531e+10 | 5.9531e+10 | 5.9531e+10 | 5.9531e+10 | 5.9531e+10 | 5.9531e+10 | 2.446e+09 | 2.446e+09 | 8.7046e+10 | 5.9531e+10 | 3.0941e+10 | 7.0898e+10 | 2.65595e+11 | -4.41e+08 | -4.41e+08 | 7.2903e+10 | 1.63756e+11 | 1.0903e+10 | 1.4236e+10 | 1.6705e+10 | 0 | 1.3372e+10 | 0.183 | 1.94697e+11 | 7.0898e+10 | 2.65595e+11 |
| aapl | 2019-09-30 00:00:00 | 12M | 4.61783e+09 | 11.97 | 1.61782e+11 | 4.64891e+09 | 11.89 | 5.5256e+10 | 6.9313e+10 | nan | 9.8392e+10 | 3.576e+09 | 3.576e+09 | 4.961e+09 | 4.961e+09 | 5.5256e+10 | 5.5256e+10 | 5.5256e+10 | 5.5256e+10 | 5.5256e+10 | 5.5256e+10 | 1.385e+09 | 1.385e+09 | 8.186e+10 | 5.5256e+10 | 3.4462e+10 | 6.393e+10 | 2.60174e+11 | 4.22e+08 | 4.22e+08 | 6.5737e+10 | 1.61782e+11 | 1.2547e+10 | 1.6217e+10 | 1.8245e+10 | 0 | 1.0481e+10 | 0.159 | 1.96244e+11 | 6.393e+10 | 2.60174e+11 |
| aapl | 2020-03-31 00:00:00 | TTM | nan | nan | 1.65854e+11 | nan | nan | 5.7215e+10 | 7.0309e+10 | 8.2023e+10 | 1.02127e+11 | 3.218e+09 | 3.218e+09 | 4.39e+09 | 4.39e+09 | 5.7215e+10 | 5.7215e+10 | 5.7215e+10 | 5.7215e+10 | 5.7215e+10 | 5.7215e+10 | 1.172e+09 | 1.172e+09 | 8.2023e+10 | 5.7215e+10 | 3.6536e+10 | 6.5591e+10 | 2.67981e+11 | 3.28e+08 | 3.28e+08 | 6.7091e+10 | 1.65854e+11 | 1.1714e+10 | 1.7383e+10 | 1.9153e+10 | 0 | 9.876e+09 | 0.147203 | 2.0239e+11 | 6.5591e+10 | 2.67981e+11 |
### **valuation_measures**
=== "Details"
- *Description*: Retrieves valuation measures for most recent four quarters as well as the most recent date
- *Return*: `pandas.DataFrame`
=== "Example"
```python hl_lines="2"
aapl = Ticker('aapl')
aapl.valuation_measures
```
=== "Data"
| symbol | asOfDate | periodType | EnterpriseValue | EnterprisesValueEBITDARatio | EnterprisesValueRevenueRatio | ForwardPeRatio | MarketCap | PbRatio | PeRatio | PegRatio | PsRatio |
|:---------|:--------------------|:-------------|------------------:|------------------------------:|-------------------------------:|-----------------:|--------------:|----------:|----------:|-----------:|----------:|
| aapl | 2019-06-30 00:00:00 | 3M | 9.43183e+11 | 60.0371 | 17.5283 | 15.9744 | 9.10645e+11 | 8.47207 | 16.5762 | 1.4501 | 3.68445 |
| aapl | 2019-09-30 00:00:00 | 3M | 1.00896e+12 | 50.1569 | 15.7551 | 17.2712 | 9.95152e+11 | 10.3172 | 19.0127 | 2.0355 | 4.09034 |
| aapl | 2019-12-31 00:00:00 | 3M | 1.29513e+12 | 43.8746 | 14.1053 | 22.1729 | 1.28764e+12 | 14.23 | 24.6972 | 2.0292 | 5.24708 |
| aapl | 2020-03-05 00:00:00 | TTM | 1.2828e+12 | nan | nan | 22.1239 | 1.28167e+12 | 14.3153 | 23.0646 | 1.7784 | 5.00004 |
| aapl | 2020-03-08 00:00:00 | TTM | nan | 15.4204 | 4.79222 | nan | nan | nan | nan | nan | nan |
| aapl | 2020-03-31 00:00:00 | 3M | 1.10068e+12 | 65.9957 | 18.8753 | 19.6464 | 1.09955e+12 | 12.2812 | 20.0228 | 1.5803 | 4.34064 |
| aapl | 2020-05-08 00:00:00 | TTM | 1.35966e+12 | nan | nan | 26.178 | 1.34421e+12 | 17.14 | 24.2478 | 2.171 | 5.20229 |
| aapl | 2020-05-11 00:00:00 | TTM | nan | 16.5766 | 5.07373 | nan | nan | nan | nan | nan | nan |
| aapl | 2020-05-28 00:00:00 | TTM | 1.39486e+12 | nan | nan | 26.8097 | 1.3794e+12 | 17.5888 | 24.8827 | 2.2278 | 5.3385 |
| aapl | 2020-05-29 00:00:00 | TTM | nan | 17.0057 | 5.20506 | nan | nan | nan | nan | nan | nan |
| aapl | 2020-06-12 00:00:00 | TTM | 1.48393e+12 | nan | nan | 22.5734 | 1.46847e+12 | 18.7245 | 26.4894 | 1.8786 | 5.68322 |
| aapl | 2020-06-15 00:00:00 | TTM | nan | 18.0916 | 5.53744 | nan | nan | nan | nan | nan | nan |
| aapl | 2020-07-14 00:00:00 | TTM | 1.69817e+12 | nan | nan | 25.8398 | 1.68272e+12 | 21.4564 | 30.3542 | 2.1527 | 6.51239 |
| aapl | 2020-07-15 00:00:00 | TTM | nan | 20.7036 | 6.33692 | nan | nan | nan | nan | nan | nan |
| aapl | 2020-07-16 00:00:00 | TTM | 1.6889e+12 | nan | nan | 26.3158 | 1.67344e+12 | 21.3381 | 30.1869 | 2.1897 | 6.47649 |
| aapl | 2020-07-18 00:00:00 | TTM | nan | 20.5906 | 6.30231 | nan | nan | nan | nan | nan | nan |
| aapl | 2020-07-29 00:00:00 | TTM | 1.6632e+12 | nan | nan | 25.1256 | 1.64774e+12 | 21.0104 | 29.7232 | 2.0905 | 6.37702 |
| aapl | 2020-07-30 00:00:00 | TTM | nan | 20.2772 | 6.2064 | nan | nan | nan | nan | nan | nan |
## Multiple
### all_financial_data
=== "Details"
- *Description*: Retrieve all financial data, including income statement, balance sheet, cash flow, and valuation measures.
- *Return*: `pandas.DataFrame`
- *Arguments*
| Argument | Description |Type | Default | Required | Options |
|:-----------|:-------------|:-------|:----------|:-----------|:------------------------------|
| frequency | Display either quarterly or annual data |`str` | `a` | optional | Annual - `a`<br>Quarter - `q` |
!!! warning
No trailing twelve month (TTM) data will be returned with this method
=== "Example"
```python
aapl = Ticker('aapl')
aapl.all_financial_data()
```
=== "Data"
| symbol | asOfDate | periodType | AccountsPayable | AccountsReceivable | AccumulatedDepreciation | AllowanceForDoubtfulAccountsReceivable | AvailableForSaleSecurities | BasicAverageShares | BasicEPS | BeginningCashPosition | CapitalExpenditure | CapitalStock | CashAndCashEquivalents | CashCashEquivalentsAndShortTermInvestments | CashDividendsPaid | CashEquivalents | CashFinancial | CashFlowFromContinuingFinancingActivities | ChangeInAccountPayable | ChangeInCashSupplementalAsReported | ChangeInInventory | ChangeInWorkingCapital | ChangesInAccountReceivables | CommercialPaper | CommonStock | CommonStockEquity | CommonStockIssuance | CostOfRevenue | CurrentAccruedExpenses | CurrentAssets | CurrentDebt | CurrentDebtAndCapitalLeaseObligation | CurrentDeferredLiabilities | CurrentDeferredRevenue | CurrentLiabilities | DeferredIncomeTax | DepreciationAndAmortization | DilutedAverageShares | DilutedEPS | DilutedNIAvailtoComStockholders | EBIT | EndCashPosition | EnterpriseValue | EnterprisesValueEBITDARatio | EnterprisesValueRevenueRatio | ForwardPeRatio | FreeCashFlow | GainsLossesNotAffectingRetainedEarnings | Goodwill | GoodwillAndOtherIntangibleAssets | GrossAccountsReceivable | GrossPPE | GrossProfit | InterestExpense | InterestExpenseNonOperating | InterestIncome | InterestIncomeNonOperating | Inventory | InvestedCapital | InvestingCashFlow | InvestmentinFinancialAssets | InvestmentsAndAdvances | LandAndImprovements | Leases | LongTermDebt | LongTermDebtAndCapitalLeaseObligation | MachineryFurnitureEquipment | MarketCap | NetDebt | NetIncome | NetIncomeCommonStockholders | NetIncomeContinuousOperations | NetIncomeFromContinuingAndDiscontinuedOperation | NetIncomeFromContinuingOperationNetMinorityInterest | NetIncomeIncludingNoncontrollingInterests | NetInterestIncome | NetNonOperatingInterestIncomeExpense | NetOtherFinancingCharges | NetOtherInvestingChanges | NetPPE | NetTangibleAssets | NonCurrentDeferredLiabilities | NonCurrentDeferredRevenue | NonCurrentDeferredTaxesLiabilities | NormalizedEBITDA | NormalizedIncome | OperatingCashFlow | OperatingExpense | OperatingIncome | OperatingRevenue | OrdinarySharesNumber | OtherCurrentAssets | OtherCurrentBorrowings | OtherCurrentLiabilities | OtherIncomeExpense | OtherIntangibleAssets | OtherNonCashItems | OtherNonCurrentAssets | OtherNonCurrentLiabilities | OtherNonOperatingIncomeExpenses | OtherReceivables | OtherShortTermInvestments | Payables | PayablesAndAccruedExpenses | PbRatio | PeRatio | PegRatio | PretaxIncome | Properties | PsRatio | PurchaseOfBusiness | PurchaseOfInvestment | Receivables | ReconciledCostOfRevenue | ReconciledDepreciation | RepaymentOfDebt | RepurchaseOfCapitalStock | ResearchAndDevelopment | RetainedEarnings | SaleOfInvestment | SellingGeneralAndAdministration | ShareIssued | StockBasedCompensation | StockholdersEquity | TangibleBookValue | TaxEffectOfUnusualItems | TaxProvision | TaxRateForCalcs | TotalAssets | TotalCapitalization | TotalDebt | TotalEquityGrossMinorityInterest | TotalExpenses | TotalLiabilitiesNetMinorityInterest | TotalNonCurrentAssets | TotalNonCurrentLiabilitiesNetMinorityInterest | TotalOperatingIncomeAsReported | TotalRevenue | TradeandOtherPayablesNonCurrent | WorkingCapital |
|:---------|:--------------------|:-------------|------------------:|---------------------:|--------------------------:|-----------------------------------------:|-----------------------------:|---------------------:|-----------:|------------------------:|---------------------:|---------------:|-------------------------:|---------------------------------------------:|--------------------:|------------------:|----------------:|--------------------------------------------:|-------------------------:|-------------------------------------:|--------------------:|-------------------------:|------------------------------:|------------------:|--------------:|--------------------:|----------------------:|----------------:|-------------------------:|----------------:|--------------:|---------------------------------------:|-----------------------------:|-------------------------:|---------------------:|--------------------:|------------------------------:|-----------------------:|-------------:|----------------------------------:|-----------:|------------------:|------------------:|------------------------------:|-------------------------------:|-----------------:|---------------:|------------------------------------------:|------------:|-----------------------------------:|--------------------------:|-----------:|--------------:|------------------:|------------------------------:|-----------------:|-----------------------------:|------------:|------------------:|--------------------:|------------------------------:|-------------------------:|----------------------:|----------:|---------------:|----------------------------------------:|------------------------------:|------------:|-----------:|------------:|------------------------------:|--------------------------------:|--------------------------------------------------:|------------------------------------------------------:|--------------------------------------------:|--------------------:|---------------------------------------:|---------------------------:|---------------------------:|-----------:|--------------------:|--------------------------------:|----------------------------:|-------------------------------------:|-------------------:|-------------------:|--------------------:|-------------------:|------------------:|-------------------:|-----------------------:|---------------------:|-------------------------:|--------------------------:|---------------------:|------------------------:|--------------------:|------------------------:|-----------------------------:|----------------------------------:|-------------------:|----------------------------:|-----------:|-----------------------------:|----------:|----------:|-----------:|---------------:|-------------:|----------:|---------------------:|-----------------------:|--------------:|--------------------------:|-------------------------:|------------------:|---------------------------:|-------------------------:|-------------------:|-------------------:|----------------------------------:|--------------:|-------------------------:|---------------------:|--------------------:|--------------------------:|---------------:|------------------:|--------------:|----------------------:|------------:|-----------------------------------:|----------------:|--------------------------------------:|------------------------:|------------------------------------------------:|---------------------------------:|---------------:|----------------------------------:|-----------------:|
| aapl | 2016-09-30 00:00:00 | 12M | 3.7294e+10 | 1.5754e+10 | -3.4235e+10 | -5.3e+07 | 1.7043e+11 | 5.47082e+09 | 8.35 | 2.112e+10 | -1.3548e+10 | 3.1251e+10 | 2.0484e+10 | 6.7155e+10 | -1.215e+10 | 1.1883e+10 | 8.601e+09 | -2.0483e+10 | 1.791e+09 | -6.36e+08 | 2.17e+08 | 4.84e+08 | 1.095e+09 | 8.105e+09 | 3.1251e+10 | 1.28249e+11 | 4.95e+08 | 1.31376e+11 | 2.2027e+10 | 1.06869e+11 | 1.1605e+10 | 1.1605e+10 | 8.08e+09 | 8.08e+09 | 7.9006e+10 | 4.938e+09 | 1.0505e+10 | 5.50028e+09 | 8.31 | 4.5687e+10 | 6.2828e+10 | 2.0484e+10 | 6.26433e+11 | 8.5423 | 2.90501 | 12.6582 | 5.2276e+10 | 6.34e+08 | 5.414e+09 | 8.62e+09 | 1.5807e+10 | 6.1245e+10 | 8.4263e+10 | 1.456e+09 | 1.456e+09 | 3.999e+09 | 3.999e+09 | 2.132e+09 | 2.15281e+11 | -4.5977e+10 | 1.7043e+11 | 1.7043e+11 | 1.0185e+10 | 6.517e+09 | 7.5427e+10 | 7.5427e+10 | 4.4543e+10 | 6.03254e+11 | 6.6548e+10 | 4.5687e+10 | 4.5687e+10 | 4.5687e+10 | 4.5687e+10 | 4.5687e+10 | 4.5687e+10 | 2.543e+09 | 2.543e+09 | -1.163e+09 | -1.1e+08 | 2.701e+10 | 1.19629e+11 | 2.8949e+10 | 2.93e+09 | 2.6019e+10 | 7.3333e+10 | 4.5687e+10 | 6.5824e+10 | 2.4239e+10 | 6.0024e+10 | 2.15639e+11 | 5.33617e+09 | 8.283e+09 | 3.5e+09 | nan | -1.195e+09 | 3.206e+09 | nan | 8.757e+09 | 1.0055e+10 | -1.195e+09 | 1.3545e+10 | 4.6671e+10 | 3.7294e+10 | 5.9321e+10 | 4.76726 | 13.1607 | 2.4275 | 6.1372e+10 | 0 | 2.8598 | -2.97e+08 | -1.43816e+11 | 2.9299e+10 | 1.31376e+11 | 1.0505e+10 | -2.5e+09 | -2.9722e+10 | 1.0045e+10 | 9.6364e+10 | 1.11794e+11 | 1.4194e+10 | 5.33617e+09 | 4.21e+09 | 1.28249e+11 | 1.19629e+11 | 0 | 1.5685e+10 | 0.256 | 3.21686e+11 | 2.03676e+11 | 8.7032e+10 | 1.28249e+11 | 1.55615e+11 | 1.93437e+11 | 2.14817e+11 | 1.14431e+11 | 6.0024e+10 | 2.15639e+11 | nan | 2.7863e+10 |
| aapl | 2017-09-30 00:00:00 | 12M | 4.9049e+10 | 1.7874e+10 | -4.1293e+10 | -5.8e+07 | 1.94714e+11 | 5.21724e+09 | 9.27 | 2.0484e+10 | -1.2795e+10 | 3.5867e+10 | 2.0289e+10 | 7.4181e+10 | -1.2769e+10 | 1.2307e+10 | 7.982e+09 | -1.7347e+10 | 9.618e+09 | -1.95e+08 | -2.723e+09 | -5.55e+09 | -2.093e+09 | 1.1977e+10 | 3.5867e+10 | 1.34047e+11 | 5.55e+08 | 1.41048e+11 | 2.5744e+10 | 1.28645e+11 | 1.8473e+10 | 1.8473e+10 | 7.548e+09 | 7.548e+09 | 1.00814e+11 | 5.966e+09 | 1.0157e+10 | 5.25169e+09 | 9.21 | 4.8351e+10 | 6.6412e+10 | 2.0289e+10 | 8.27645e+11 | 10.8091 | 3.61048 | 14.245 | 5.0803e+10 | -1.5e+08 | 5.717e+09 | 8.015e+09 | 1.7932e+10 | 7.5076e+10 | 8.8186e+10 | 2.323e+09 | 2.323e+09 | 5.201e+09 | 5.201e+09 | 4.855e+09 | 2.49727e+11 | -4.6446e+10 | 1.94714e+11 | 1.94714e+11 | 1.3587e+10 | 7.279e+09 | 9.7207e+10 | 9.7207e+10 | 5.421e+10 | 7.96065e+11 | 9.5391e+10 | 4.8351e+10 | 4.8351e+10 | 4.8351e+10 | 4.8351e+10 | 4.8351e+10 | 4.8351e+10 | 2.878e+09 | 2.878e+09 | -1.247e+09 | 2.2e+08 | 3.3783e+10 | 1.26032e+11 | 3.434e+10 | 2.836e+09 | 3.1504e+10 | 7.6569e+10 | 4.8351e+10 | 6.3598e+10 | 2.6842e+10 | 6.1344e+10 | 2.29234e+11 | 5.1262e+09 | 1.3936e+10 | 6.496e+09 | nan | -1.33e+08 | 2.298e+09 | -1.66e+08 | 1.0162e+10 | 8.911e+09 | -1.33e+08 | 1.7799e+10 | 5.3892e+10 | 4.9049e+10 | 7.4793e+10 | 5.96602 | 17.4541 | 2.087 | 6.4089e+10 | 0 | 3.65748 | -3.29e+08 | -1.59881e+11 | 3.5673e+10 | 1.41048e+11 | 1.0157e+10 | -3.5e+09 | -3.29e+10 | 1.1581e+10 | 9.833e+10 | 1.26339e+11 | 1.5261e+10 | 5.1262e+09 | 4.84e+09 | 1.34047e+11 | 1.26032e+11 | 0 | 1.5738e+10 | 0.246 | 3.75319e+11 | 2.31254e+11 | 1.1568e+11 | 1.34047e+11 | 1.6789e+11 | 2.41272e+11 | 2.46674e+11 | 1.40458e+11 | 6.1344e+10 | 2.29234e+11 | nan | 2.7831e+10 |
| aapl | 2018-09-30 00:00:00 | 12M | 5.5888e+10 | 2.3186e+10 | -4.9099e+10 | nan | 1.70799e+11 | 4.95538e+09 | 12.01 | 2.0289e+10 | -1.3313e+10 | 4.0201e+10 | 2.5913e+10 | 6.6301e+10 | -1.3712e+10 | 1.4338e+10 | 1.1575e+10 | -8.7876e+10 | 9.175e+09 | 5.624e+09 | 8.28e+08 | 3.4694e+10 | -5.322e+09 | 1.1964e+10 | 4.0201e+10 | 1.07147e+11 | 6.69e+08 | 1.63756e+11 | nan | 1.31339e+11 | 2.0748e+10 | 2.0748e+10 | 7.543e+09 | 7.543e+09 | 1.16866e+11 | -3.259e+10 | 1.0903e+10 | 5.00011e+09 | 11.91 | 5.9531e+10 | 7.6143e+10 | 2.5913e+10 | 1.13394e+12 | 13.0269 | 4.26942 | 16.6945 | 6.4121e+10 | -3.454e+09 | nan | nan | nan | 9.0403e+10 | 1.01839e+11 | 3.24e+09 | 3.24e+09 | 5.686e+09 | 5.686e+09 | 3.956e+09 | 2.2163e+11 | 1.6066e+10 | 1.70799e+11 | 1.70799e+11 | 1.6216e+10 | 8.205e+09 | 9.3735e+10 | 9.3735e+10 | 6.5982e+10 | 1.09031e+12 | 8.857e+10 | 5.9531e+10 | 5.9531e+10 | 5.9531e+10 | 5.9531e+10 | 5.9531e+10 | 5.9531e+10 | 2.446e+09 | 2.446e+09 | -2.527e+09 | -7.45e+08 | 4.1304e+10 | 1.07147e+11 | 3.223e+09 | 2.797e+09 | 4.26e+08 | 8.7046e+10 | 5.9531e+10 | 7.7434e+10 | 3.0941e+10 | 7.0898e+10 | 2.65595e+11 | 4.75499e+09 | 1.2087e+10 | 8.784e+09 | 3.2687e+10 | -4.41e+08 | nan | -4.44e+08 | 2.2283e+10 | 1.1165e+10 | -4.41e+08 | 2.5809e+10 | 4.0388e+10 | 5.5888e+10 | 5.5888e+10 | 9.33797 | 20.4105 | 1.2848 | 7.2903e+10 | 0 | 4.49591 | -7.21e+08 | -7.3227e+10 | 4.8995e+10 | 1.63756e+11 | 1.0903e+10 | -6.5e+09 | -7.2738e+10 | 1.4236e+10 | 7.04e+10 | 1.04072e+11 | 1.6705e+10 | 4.75499e+09 | 5.34e+09 | 1.07147e+11 | 1.07147e+11 | 0 | 1.3372e+10 | 0.183 | 3.65725e+11 | 2.00882e+11 | 1.14483e+11 | 1.07147e+11 | 1.94697e+11 | 2.58578e+11 | 2.34386e+11 | 1.41712e+11 | 7.0898e+10 | 2.65595e+11 | 3.3589e+10 | 1.4473e+10 |
| aapl | 2019-09-30 00:00:00 | 12M | 4.6236e+10 | 2.2926e+10 | -5.8579e+10 | nan | 1.05341e+11 | 4.61783e+09 | 11.97 | 2.5913e+10 | -1.0495e+10 | 4.5174e+10 | 4.8844e+10 | 1.00557e+11 | -1.4119e+10 | 3.664e+10 | 1.2204e+10 | -9.0976e+10 | -1.923e+09 | 2.4311e+10 | -2.89e+08 | -3.488e+09 | 2.45e+08 | 5.98e+09 | 4.5174e+10 | 9.0488e+10 | 7.81e+08 | 1.61782e+11 | nan | 1.62819e+11 | 1.624e+10 | 1.624e+10 | 5.522e+09 | 5.522e+09 | 1.05718e+11 | -3.4e+08 | 1.2547e+10 | 4.64891e+09 | 11.89 | 5.5256e+10 | 6.9313e+10 | 5.0224e+10 | 1.00896e+12 | 12.3254 | 3.878 | 17.2712 | 5.8896e+10 | -5.84e+08 | nan | nan | nan | 9.5957e+10 | 9.8392e+10 | 3.576e+09 | 3.576e+09 | 4.961e+09 | 4.961e+09 | 4.106e+09 | 1.98535e+11 | 4.5896e+10 | 1.05341e+11 | 1.05341e+11 | 1.7085e+10 | 9.075e+09 | 9.1807e+10 | 9.1807e+10 | 6.9797e+10 | 9.95152e+11 | 5.9203e+10 | 5.5256e+10 | 5.5256e+10 | 5.5256e+10 | 5.5256e+10 | 5.5256e+10 | 5.5256e+10 | 1.385e+09 | 1.385e+09 | -2.922e+09 | -1.078e+09 | 3.7378e+10 | 9.0488e+10 | nan | nan | nan | 8.186e+10 | 5.5256e+10 | 6.9391e+10 | 3.4462e+10 | 6.393e+10 | 2.60174e+11 | 4.44324e+09 | 1.2352e+10 | 1.026e+10 | 3.772e+10 | 4.22e+08 | nan | -6.52e+08 | 3.2978e+10 | 2.0958e+10 | 4.22e+08 | 2.2878e+10 | 5.1713e+10 | 4.6236e+10 | 4.6236e+10 | 10.3172 | 19.0127 | 2.0355 | 6.5737e+10 | 0 | 4.09034 | -6.24e+08 | -4.0631e+10 | 4.5804e+10 | 1.61782e+11 | 1.2547e+10 | -8.805e+09 | -6.6897e+10 | 1.6217e+10 | 4.5898e+10 | 9.8724e+10 | 1.8245e+10 | 4.44324e+09 | 6.068e+09 | 9.0488e+10 | 9.0488e+10 | 0 | 1.0481e+10 | 0.159 | 3.38516e+11 | 1.82295e+11 | 1.08047e+11 | 9.0488e+10 | 1.96244e+11 | 2.48028e+11 | 1.75697e+11 | 1.4231e+11 | 6.393e+10 | 2.60174e+11 | 2.9545e+10 | 5.7101e+10 |
### get_financial_data
=== "Details"
- *Description*: Obtain specific data from either cash flow, income statement, balance sheet, or valuation measures.
- *Return*: `pandas.DataFrame`
- *Arguments*
| Argument | Description | Type | Default | Required | Options |
|:-----------|:-------|:-------|:----------|:-----------|:------------------------------|
| types | Specific item(s) to retrieve from financials | `list` or `str` | `None` | **Required** | See options below |
| frequency | Display either quarterly or annual data |`str` | `a` | optional | Annual - `a`<br>Quarter - `q` |
| trailing | Include or exclude trailing twelve month (TTM) data |`bool` | `True` | optional | `True`<br>`False` |
!!! warning
If you try and use all of the options below **AND** `trailing=True`, you will
receive an error because the requested URL will be too long
=== "Balance Sheet"
```python
[
'AccountsPayable', 'AccountsReceivable', 'AccruedInterestReceivable',
'AccumulatedDepreciation', 'AdditionalPaidInCapital',
'AllowanceForDoubtfulAccountsReceivable', 'AssetsHeldForSaleCurrent',
'AvailableForSaleSecurities', 'BuildingsAndImprovements', 'CapitalLeaseObligations',
'CapitalStock', 'CashAndCashEquivalents', 'CashCashEquivalentsAndShortTermInvestments',
'CashEquivalents', 'CashFinancial', 'CommercialPaper', 'CommonStock',
'CommonStockEquity', 'ConstructionInProgress', 'CurrentAccruedExpenses',
'CurrentAssets', 'CurrentCapitalLeaseObligation', 'CurrentDebt',
'CurrentDebtAndCapitalLeaseObligation', 'CurrentDeferredAssets',
'CurrentDeferredLiabilities', 'CurrentDeferredRevenue', 'CurrentDeferredTaxesAssets',
'CurrentDeferredTaxesLiabilities', 'CurrentLiabilities', 'CurrentNotesPayable',
'CurrentProvisions', 'DefinedPensionBenefit', 'DerivativeProductLiabilities',
'DividendsPayable', 'DuefromRelatedPartiesCurrent', 'DuefromRelatedPartiesNonCurrent',
'DuetoRelatedPartiesCurrent', 'DuetoRelatedPartiesNonCurrent', 'EmployeeBenefits',
'FinancialAssets', 'FinancialAssetsDesignatedasFairValueThroughProfitorLossTotal',
'FinishedGoods', 'FixedAssetsRevaluationReserve', 'ForeignCurrencyTranslationAdjustments',
'GainsLossesNotAffectingRetainedEarnings', 'GeneralPartnershipCapital', 'Goodwill',
'GoodwillAndOtherIntangibleAssets', 'GrossAccountsReceivable', 'GrossPPE',
'HedgingAssetsCurrent', 'HeldToMaturitySecurities', 'IncomeTaxPayable',
'InterestPayable', 'InventoriesAdjustmentsAllowances', 'Inventory',
'InvestedCapital', 'InvestmentProperties', 'InvestmentinFinancialAssets',
'InvestmentsAndAdvances', 'InvestmentsInOtherVenturesUnderEquityMethod',
'InvestmentsinAssociatesatCost', 'InvestmentsinJointVenturesatCost',
'InvestmentsinSubsidiariesatCost', 'LandAndImprovements', 'Leases',
'LiabilitiesHeldforSaleNonCurrent', 'LimitedPartnershipCapital',
'LineOfCredit', 'LoansReceivable', 'LongTermCapitalLeaseObligation',
'LongTermDebt', 'LongTermDebtAndCapitalLeaseObligation', 'LongTermEquityInvestment',
'LongTermProvisions', 'MachineryFurnitureEquipment', 'MinimumPensionLiabilities',
'MinorityInterest', 'NetDebt', 'NetPPE', 'NetTangibleAssets', 'NonCurrentAccountsReceivable',
'NonCurrentAccruedExpenses', 'NonCurrentDeferredAssets', 'NonCurrentDeferredLiabilities',
'NonCurrentDeferredRevenue', 'NonCurrentDeferredTaxesAssets', 'NonCurrentDeferredTaxesLiabilities',
'NonCurrentNoteReceivables', 'NonCurrentPensionAndOtherPostretirementBenefitPlans',
'NonCurrentPrepaidAssets', 'NotesReceivable', 'OrdinarySharesNumber',
'OtherCapitalStock', 'OtherCurrentAssets', 'OtherCurrentBorrowings',
'OtherCurrentLiabilities', 'OtherEquityAdjustments', 'OtherEquityInterest',
'OtherIntangibleAssets', 'OtherInventories', 'OtherInvestments', 'OtherNonCurrentAssets',
'OtherNonCurrentLiabilities', 'OtherPayable', 'OtherProperties', 'OtherReceivables',
'OtherShortTermInvestments', 'Payables', 'PayablesAndAccruedExpenses',
'PensionandOtherPostRetirementBenefitPlansCurrent', 'PreferredSecuritiesOutsideStockEquity',
'PreferredSharesNumber', 'PreferredStock', 'PreferredStockEquity',
'PrepaidAssets', 'Properties', 'RawMaterials', 'Receivables',
'ReceivablesAdjustmentsAllowances', 'RestrictedCash', 'RestrictedCommonStock',
'RetainedEarnings', 'ShareIssued', 'StockholdersEquity', 'TangibleBookValue',
'TaxesReceivable', 'TotalAssets', 'TotalCapitalization', 'TotalDebt',
'TotalEquityGrossMinorityInterest', 'TotalLiabilitiesNetMinorityInterest',
'TotalNonCurrentAssets', 'TotalNonCurrentLiabilitiesNetMinorityInterest',
'TotalPartnershipCapital', 'TotalTaxPayable', 'TradeandOtherPayablesNonCurrent',
'TradingSecurities', 'TreasurySharesNumber', 'TreasuryStock', 'UnrealizedGainLoss',
'WorkInProcess', 'WorkingCapital'
]
```
=== "Cash Flow"
```python
[
'RepaymentOfDebt', 'RepurchaseOfCapitalStock', 'CashDividendsPaid',
'CommonStockIssuance', 'ChangeInWorkingCapital',
'CapitalExpenditure',
'CashFlowFromContinuingFinancingActivities', 'NetIncome',
'FreeCashFlow', 'ChangeInCashSupplementalAsReported',
'SaleOfInvestment', 'EndCashPosition', 'OperatingCashFlow',
'DeferredIncomeTax', 'NetOtherInvestingChanges',
'ChangeInAccountPayable', 'NetOtherFinancingCharges',
'PurchaseOfInvestment', 'ChangeInInventory',
'DepreciationAndAmortization', 'PurchaseOfBusiness',
'InvestingCashFlow', 'ChangesInAccountReceivables',
'StockBasedCompensation', 'OtherNonCashItems',
'BeginningCashPosition'
]
```
=== "Income Statement"
```python
[
'Amortization', 'AmortizationOfIntangiblesIncomeStatement',
'AverageDilutionEarnings', 'BasicAccountingChange', 'BasicAverageShares',
'BasicContinuousOperations', 'BasicDiscontinuousOperations', 'BasicEPS',
'BasicEPSOtherGainsLosses', 'BasicExtraordinary', 'ContinuingAndDiscontinuedBasicEPS',
'ContinuingAndDiscontinuedDilutedEPS', 'CostOfRevenue', 'DepletionIncomeStatement',
'DepreciationAmortizationDepletionIncomeStatement', 'DepreciationAndAmortizationInIncomeStatement',
'DepreciationIncomeStatement', 'DilutedAccountingChange', 'DilutedAverageShares',
'DilutedContinuousOperations', 'DilutedDiscontinuousOperations', 'DilutedEPS',
'DilutedEPSOtherGainsLosses', 'DilutedExtraordinary', 'DilutedNIAvailtoComStockholders',
'DividendPerShare', 'EBIT', 'EBITDA', 'EarningsFromEquityInterest',
'EarningsFromEquityInterestNetOfTax', 'ExciseTaxes', 'GainOnSaleOfBusiness',
'GainOnSaleOfPPE', 'GainOnSaleOfSecurity', 'GeneralAndAdministrativeExpense',
'GrossProfit', 'ImpairmentOfCapitalAssets', 'InsuranceAndClaims',
'InterestExpense', 'InterestExpenseNonOperating', 'InterestIncome',
'InterestIncomeNonOperating', 'MinorityInterests', 'NetIncome', 'NetIncomeCommonStockholders',
'NetIncomeContinuousOperations', 'NetIncomeDiscontinuousOperations',
'NetIncomeExtraordinary', 'NetIncomeFromContinuingAndDiscontinuedOperation',
'NetIncomeFromContinuingOperationNetMinorityInterest', 'NetIncomeFromTaxLossCarryforward',
'NetIncomeIncludingNoncontrollingInterests', 'NetInterestIncome',
'NetNonOperatingInterestIncomeExpense', 'NormalizedBasicEPS', 'NormalizedDilutedEPS',
'NormalizedEBITDA', 'NormalizedIncome', 'OperatingExpense', 'OperatingIncome',
'OperatingRevenue', 'OtherGandA', 'OtherIncomeExpense', 'OtherNonOperatingIncomeExpenses',
'OtherOperatingExpenses', 'OtherSpecialCharges', 'OtherTaxes',
'OtherunderPreferredStockDividend', 'PreferredStockDividends',
'PretaxIncome', 'ProvisionForDoubtfulAccounts', 'ReconciledCostOfRevenue',
'ReconciledDepreciation', 'RentAndLandingFees', 'RentExpenseSupplemental',
'ReportedNormalizedBasicEPS', 'ReportedNormalizedDilutedEPS', 'ResearchAndDevelopment',
'RestructuringAndMergernAcquisition', 'SalariesAndWages', 'SecuritiesAmortization',
'SellingAndMarketingExpense', 'SellingGeneralAndAdministration', 'SpecialIncomeCharges',
'TaxEffectOfUnusualItems', 'TaxLossCarryforwardBasicEPS', 'TaxLossCarryforwardDilutedEPS',
'TaxProvision', 'TaxRateForCalcs', 'TotalExpenses', 'TotalOperatingIncomeAsReported',
'TotalOtherFinanceCost', 'TotalRevenue', 'TotalUnusualItems',
'TotalUnusualItemsExcludingGoodwill', 'WriteOff'
]
```
=== "Valuation Measures"
```python
[
'ForwardPeRatio', 'PsRatio', 'PbRatio',
'EnterprisesValueEBITDARatio', 'EnterprisesValueRevenueRatio',
'PeRatio', 'MarketCap', 'EnterpriseValue', 'PegRatio'
]
```
=== "Example"
```python hl_lines="2"
aapl = Ticker('aapl')
types = ['TotalDebt', 'TotalAssets', 'EBIT', 'EBITDA', 'PeRatio']
aapl.get_financial_data(types, trailing=False)
```
=== "Data"
| symbol | asOfDate | periodType | EBIT | PeRatio | TotalAssets | TotalDebt |
|:---------|:--------------------|:-------------|-----------:|----------:|--------------:|------------:|
| aapl | 2016-09-30 00:00:00 | 12M | 6.2828e+10 | 13.1607 | 3.21686e+11 | 8.7032e+10 |
| aapl | 2017-09-30 00:00:00 | 12M | 6.6412e+10 | 17.4541 | 3.75319e+11 | 1.1568e+11 |
| aapl | 2018-09-30 00:00:00 | 12M | 7.6143e+10 | 20.4105 | 3.65725e+11 | 1.14483e+11 |
| aapl | 2019-09-30 00:00:00 | 12M | 6.9313e+10 | 19.0127 | 3.38516e+11 | 1.08047e+11 |
```
### references/ticker/historical.md
```markdown
# Historical Prices
### **history**
=== "Details"
- *Description*: Retreives historical pricing data (OHLC) for given symbol(s)
- *Return*: `pandas.DataFrame`
- *Arguments*
| Argument | Description | Type | Default | Required | Options |
|:-----------|:-----------------------------------------|:-----------------------------|:----------|:-----------|:--------------------------------------------------------------------------------------|
| period | Length of time | `str` | `ytd` | optional | `['1d', '5d', '7d', '60d', '1mo', '3mo', '6mo', '1y', '2y', '5y', '10y', 'ytd', 'max']` |
| interval | Time between data points | `str` | `1d` | optional | `['1m', '2m', '5m', '15m', '30m', '60m', '90m', '1h', '1d', '5d', '1wk', '1mo', '3mo']` |
| start | Specific starting date to pull data from | `str` or `datetime.datetime` | | optional | If a string is passed, use the format `YYYY-MM-DD` |
| end | Specific ending date | `str` or `datetime.datetime` | | optional | If a string is passed, use the format `YYYY-MM-DD`
| adj_timezone | Adjust datetime to the specific symbol's timezone | `bool` | `True` | optional | `True`<br>`False`
| adj_ohlc | Calculates an adjusted open, high, low and close prices according to split and dividend information | `bool` | `False` | optional | `True`<br>`False`
!!! tip "One Minute Interval Data"
The Yahoo Finance API restricts the amount of one minute interval data to seven days per request. However, the data availability extends to 30 days. The following will allow the user to retrieve the last 30 days of one minute interval data, with the one caveat that **4 requests are made in 7 day ranges to retrieve the desired data**:
```python
tickers = Ticker('fb aapl nflx', asynchronous=True)
df = tickers.history(period='1mo', interval='1m')
```
Thanks to [@rodrigobercini](https://github.com/rodrigobercini) for finding [this](https://github.com/dpguthrie/yahooquery/issues/32).
=== "Example"
```python
tickers = Ticker('fb aapl nflx', asynchronous=True)
# Default period = ytd, interval = 1d
df = tickers.history()
df.head()
```
=== "Data"
| | high | volume | close | low | open | adjclose | dividends |
|:----------------------------------|-------:|------------:|--------:|-------:|-------:|-----------:|------------:|
| ('fb', datetime.date(2020, 1, 2)) | 209.79 | 1.20771e+07 | 209.78 | 206.27 | 206.75 | 209.78 | 0 |
| ('fb', datetime.date(2020, 1, 3)) | 210.4 | 1.11884e+07 | 208.67 | 206.95 | 207.21 | 208.67 | 0 |
| ('fb', datetime.date(2020, 1, 6)) | 212.78 | 1.70589e+07 | 212.6 | 206.52 | 206.7 | 212.6 | 0 |
| ('fb', datetime.date(2020, 1, 7)) | 214.58 | 1.49124e+07 | 213.06 | 211.75 | 212.82 | 213.06 | 0 |
| ('fb', datetime.date(2020, 1, 8)) | 216.24 | 1.3475e+07 | 215.22 | 212.61 | 213 | 215.22 | 0 |
```
### references/ticker/intro.md
```markdown
# Ticker - Intro
## Import Ticker
```python
from yahooquery import Ticker
```
## Create Instance
To retrieve data from Yahoo Finance for a single stock, create an instance of the `Ticker` class by passing the company's ticker symbol as an argument:
```python
aapl = Ticker('aapl')
```
Or, pass in multiple symbols to retrieve data for multiple companies. Symbols can be passed in as a list:
```python
symbols = ['fb', 'aapl', 'amzn', 'nflx', 'goog']
tickers = Ticker(symbols)
```
They can also be passed in as a string:
```python
symbols = 'fb aapl amzn nflx goog'
tickers = Ticker(symbols)
```
!!! note
Outside of a few properties / methods, each symbol will represent one request.
For example:
```python
from yahooquery import Ticker
symbols = 'fb aapl amzn nflx goog'
tickers = Ticker(symbols)
# Retrieve each company's profile information
data = tickers.asset_profile
```
By calling the `asset_profile` property on the `tickers` instance, we're making 5 separate calls to Yahoo Finance (determined by the number of symbols).
```
### references/ticker/miscellaneous.md
```markdown
### **corporate_events**
=== "Details"
- *Description*: Significant events related to a given symbol(s)
- *Return*: `pandas.DataFrame`
=== "Example"
```python hl_lines="2"
aapl = Ticker('aapl')
df = aapl.corporate_events
df.head()
```
=== "Data"
| | date | significance | headline | parentTopics |
|---:|:--------------------|---------------:|:--------------------------------------------------------------------------------------------------------------|:-------------------|
| 0 | 2012-03-19 00:00:00 | 1 | Apple Inc. Announces Plans To Initiate Dividend And Share Repurchase Program | Performance |
| 1 | 2012-10-25 00:00:00 | 3 | Apple Inc Declares Cash Dividend | Performance |
| 2 | 2013-01-23 00:00:00 | 1 | Apple Inc Issues Q2 2013 Revenue Guidance Below Analysts' Estimates; Declares Cash Dividend | Corporate Guidance |
| 3 | 2013-04-23 00:00:00 | 1 | Apple Inc Increases Repurchase Authorization To $60 Billion From The $10 Billion; Approves Quarterly Dividend | Ownership/Control |
| 4 | 2013-07-23 00:00:00 | 3 | Apple Inc Declares Cash Dividend | Performance |
### **news**
=== "Details"
- *Description*: Get news headline and summary information for given symbol(s)
- *Return*: `list`
- *Arguments*:
| Argument | Type | Default | Required | Options |
|:-----------|:-------|:----------|:-----------|:------------------------------|
| count | `int` | `25` | optional | |
| start | `str` or `datetime.datetime` | `None` | optional | If a `str` is used, the format should be YYYY-MM-DD |
!!! warning
It's recommended to use one symbol when utilizing this method as there's no discernible way to group resulting news items to the symbols they belong to.
!!! tip
If multiple symbols are used, only one request will be made regardless of the number of symbols
=== "Example"
```python hl_lines="2"
aapl = Ticker('aapl')
aapl.news(5)
```
=== "Data"
```python
[{
'rank': 0,
'id': '3a3b4532-38fb-3fcb-85b3-3cdc4d5bbabe',
'tag': 'news',
'title': 'Big Techâs CEOs Got the Last Word. Hereâs Why Tech Earnings Were So Important.',
'summary': 'Amazonâs sales were up 40% in the June quarter, which one analyst referred to as shocking levels of growth. Meanwhile, Apple couldnât keep up with demand for Macs and iPads.',
'url': 'https://www.barrons.com/articles/heres-why-amazon-apple-and-facebook-earnings-were-so-important-51596232561?siteid=yhoof2&yptr=yahoo',
'author_name': 'Eric J. Savitz',
'provider_publish_time': 1596232560,
'provider_name': 'Barrons.com',
'hosted': False,
'tickers': [],
'featured': False,
'timeZoneShortName': 'EDT',
'timeZoneFullName': 'America/New_York',
'gmtOffSetMilliseconds': -14400000,
'imageSet': {}
}, {
'rank': 1,
'id': 'e0ea15cf-9cd4-33fa-b8ef-87ea7ee40c32',
'tag': 'news',
'title': 'Microsoft Is in Talks to Buy TikTok in U.S.',
'summary': '(Bloomberg) -- Microsoft Corp. is exploring an acquisition of TikTokâs operations in the U.S., according to a people familiar with the matter. A deal would give the software company a popular social-media service and relieve U.S. government pressure on the Chinese owner of the video-sharing app.The Trump administration has been weighing whether to direct China-based ByteDance Ltd. to divest its stake in TikTokâs U.S. operations, according to several people familiar with the issue. The U.S. has been investigating potential national security risks due to the Chinese companyâs control of the app.While the administration was prepared to announce an order as soon as Friday, according to three people familiar with the matter, another person said later that the decision was on hold, pending further review by President Donald Trump. All of the people asked not to be identified because the deliberations are private.Spokespeople for Microsoft and TikTok declined to comment on any potential talks. The software companyâs interest in the app was reported earlier by Fox Business Network.âWe are looking at TikTok. We may be banning TikTok,â Trump told reporters Friday at the White House. âWe are looking at a lot of alternatives with respect to TikTok.âAny transaction could face regulatory hurdles. ByteDance bought Musical.ly Inc. in 2017 and merged it with TikTok, creating a social-media hit in the U.S -- the first Chinese app to make such inroads. As TikTok became more popular, U.S. officials grew concerned about the potential for the Chinese government to use the app to gain data on U.S. citizens.The Committee on Foreign Investment in the U.S. began a review in 2019 of the Musical.ly purchase. In recent years, CFIUS, which investigates overseas acquisitions of U.S. businesses, has taken a much more aggressive role in reviewing and approving deals that may threaten national security. It can recommend that the president block or unwind transactions.Itâs also possible that other potential buyers could come forward, said another person familiar with the discussions. Microsoftâs industry peers -- Facebook Inc., Apple Inc., Amazon.com Inc. and Alphabet Inc. -- fit the profile of potential suitors, though all are under antitrust scrutiny from U.S. regulators, which would likely complicate a deal.A purchase of TikTok would represent a huge coup for Microsoft, which would gain a popular consumer app that has won over young people with a steady diet of dance videos, lip-syncing clips and viral memes. The company has dabbled in social-media investments in the past, but hasnât developed a popular service of its own in the lucrative sector. Microsoft acquired the LinkedIn job-hunting and corporate networking company for $26.2 billion in 2016.Microsoft can point to one acquisition that came with a massive existing community of users that has increased under its ownership -- the 2014 deal for Minecraft, the best-selling video game ever.Other purchases of popular services have gone less well. The 2011 pickup of Skype led to several years of stagnation for the voice-calling service and Microsoft fell behind newer products in the category. Outside of Xbox, the company hasnât focused on younger consumers. A TikTok deal could change that, though, and give Microsoft âa crown jewel on the consumer social media front,â Dan Ives, an analyst at Wedbush Securities, wrote in a note to investors Friday.TikTok has repeatedly rejected accusations that it feeds user data to China or is beholden to Beijing, even though ByteDance is based there. TikTok now has a U.S.-based chief executive officer and ByteDance has considered making other organizational changes to satisfy U.S. authorities.âHundreds of millions of people come to TikTok for entertainment and connection, including our community of creators and artists who are building livelihoods from the platform,â a TikTok spokeswoman said Friday. âWeâre motivated by their passion and creativity, and committed to protecting their privacy and safety as we continue working to bring joy to families and meaningful careers to those who create on our platform.âThe mechanics of separating the TikTok app in the U.S. from the rest of its operations wonât come without complications. Unlike many tech companies in the U.S. where engineers for, say, Google, work on particular products like YouTube or Google Maps, many of ByteDanceâs engineers work across its different platforms and services and continue to work on TikTok globally.On Thursday, U.S. Senators Josh Hawley, a Missouri Republican, and Richard Blumenthal, a Connecticut Democrat, wrote the Justice Department asking for an investigation of whether TikTok has violated the constitutional rights of Americans by sharing private information with the Chinese government.A deal with Microsoft could potentially help extract ByteDance from the political war between the U.S. and China.U.S. Senator Marco Rubio, a Florida Republican and member of the Senateâs Select Committee on Intelligence, applauded the idea of a TikTok sale. âIn its current form, TikTok represents a potential threat to personal privacy and our national security,â Rubio said in a statement. âWe must do more than simply remove ByteDance from the equation. Moving forward, we must establish a framework of standards that must be met before a high-risk, foreign-based app is allowed to operate on American telecommunications networks and devices.â(Updates with details of TikTokâs operations in the 14th paragraph.)For more articles like this, please visit us at bloomberg.comSubscribe now to stay ahead with the most trusted business news source.©2020 Bloomberg L.P.',
'url': 'https://finance.yahoo.com/news/microsoft-said-talks-buy-tiktok-185221680.html',
'author_name': 'Kurt Wagner, Jennifer Jacobs, Saleha Mohsin and Jenny Leonard',
'provider_publish_time': 1596232267,
'provider_name': 'Bloomberg',
'hosted': True,
'tickers': [],
'thumbnail': 'https://media.zenfs.com/en/bloomberg_technology_68/d998c0ffe5e9a652dce2e465c5d6d8a3',
'featured': False,
'timeZoneShortName': 'EDT',
'timeZoneFullName': 'America/New_York',
'gmtOffSetMilliseconds': -14400000,
'imageSet': {}
}, {
'rank': 2,
'id': '8cc7e7d9-87ec-3aaf-8a51-24f8ae1f6eca',
'tag': 'news',
'title': 'Bitcoin Is Rising Again. Asking Why Takes You Down the Financial Rabbit Hole.',
'summary': 'How to explain Bitcoinâs reversal of fortune? In search of an answer our columnist explores the dollar, the euro, gold, stocks, inflation, and more.',
'url': 'https://www.barrons.com/articles/bitcoin-is-rising-again-asking-why-takes-you-down-a-financial-rabbit-hole-51596231804?siteid=yhoof2&yptr=yahoo',
'author_name': 'Jack Hough',
'provider_publish_time': 1596231780,
'provider_name': 'Barrons.com',
'hosted': False,
'tickers': [],
'featured': False,
'timeZoneShortName': 'EDT',
'timeZoneFullName': 'America/New_York',
'gmtOffSetMilliseconds': -14400000,
'imageSet': {}
}, {
'rank': 3,
'id': '14ba64e7-a900-3e30-b513-94a5efce0dd7',
'tag': 'news',
'title': 'Apple Stock Rallied 10% Friday. Hereâs Why.',
'summary': 'One day after reporting June quarter results, Apple (ticker: AAPL) stock surged 10.5% to $425.04, the stockâs first-ever close above $400. The stock wonât stay there for long, though: The company Thursday declared a 4-for-1 stock split, effective August 31. A big revenue beat: Apple had $59.7 billion in sales in the quarter, up 11% from a year ago, and beating consensus by $7.6 billion.',
'url': 'https://www.barrons.com/articles/apple-stock-hits-new-highs-earnings-51596215820?siteid=yhoof2&yptr=yahoo',
'author_name': 'Eric J. Savitz',
'provider_publish_time': 1596231300,
'provider_name': 'Barrons.com',
'hosted': False,
'tickers': [],
'featured': False,
'timeZoneShortName': 'EDT',
'timeZoneFullName': 'America/New_York',
'gmtOffSetMilliseconds': -14400000,
'imageSet': {}
}, {
'rank': 4,
'id': '5894f228-5bad-3eda-8a48-d8fc0bdced0c',
'tag': 'news',
'title': 'Apple Tops Saudi Aramco as Worldâs Most Valuable Company',
'summary': '(Bloomberg) -- Apple Inc. became the worldâs most valuable company with its market value overtaking Saudi Aramco in the wake of better-than-expected earnings.Apple jumped 10% on Friday, ending the day with a record market capitalization of $1.817 trillion. Itâs the first time the companyâs valuation has surpassed that of Saudi Arabiaâs national oil company, which made its market debut in Riyadh in December, and is valued at $1.76 trillion. Before that, Apple had vied with Microsoft Corp. for the title of the U.S.âs largest public company.The dethroning of Aramco comes after a tumultuous period for the Saudi company. Its initial public offering fell short of Crown Price Mohammed bin Salmanâs expectations. The kingdomâs de facto ruler initially wanted a valuation of $2 trillion and to raise $100 billion. But after foreign investors balked at the pricing, the government settled on a smaller domestic offering and raised about $30 billion, still the largest IPO ever.Then came this yearâs plunge in crude prices as energy demand crashed with the spread of the virus. Aramcoâs second-quarter revenue probably dropped to about $37 billion from $76 billion a year earlier, according to analyst estimates compiled by Bloomberg. Thatâs less than the $59.7 billion in sales that Apple reported for its most recent period.Aramcoâs stock is down 6.4% since the end of December, though thatâs far less than the fall of other oil majors. Exxon Mobil Corp. has declined 40% and Royal Dutch Shell Plc has dropped 50%.Apple, meanwhile, has benefited as the pandemic has strengthened the market positions of the worldâs biggest technology companies, which boast strong balance sheets and fast-growing businesses thanks to an acceleration in the shift to digital services. The iPhone makerâs shares have gained 45% so far this year.(Adds closing share values throughout. A previous version of this story corrected the market cap gain.)For more articles like this, please visit us at bloomberg.comSubscribe now to stay ahead with the most trusted business news source.©2020 Bloomberg L.P.',
'url': 'https://finance.yahoo.com/news/apple-briefly-tops-saudi-aramco-153503708.html',
'author_name': 'Jeran Wittenstein and Matthew Martin',
'provider_publish_time': 1596229879,
'provider_name': 'Bloomberg',
'hosted': True,
'tickers': [],
'thumbnail': 'https://media.zenfs.com/en/bloomberg_markets_842/8a17157f40b14e617bb6ed1b8e2774f5',
'featured': False,
'timeZoneShortName': 'EDT',
'timeZoneFullName': 'America/New_York',
'gmtOffSetMilliseconds': -14400000,
'imageSet': {}
}]
```
### **quotes**
=== "Details"
- *Description*: Get real-time quote information for given symbol(s)
- *Return*: `list`
!!! tip
If multiple symbols are used, only one request will be made regardless of the number of symbols
=== "Example"
```python hl_lines="2"
tickers = Ticker('fb aapl amzn nflx goog')
tickers.quotes
```
=== "Data"
```python
[{
'language': 'en-US',
'region': 'US',
'quoteType': 'EQUITY',
'quoteSourceName': 'Nasdaq Real Time Price',
'triggerable': True,
'currency': 'USD',
'tradeable': False,
'firstTradeDateMilliseconds': 1337347800000,
'priceHint': 2,
'postMarketChangePercent': -0.067015484,
'postMarketTime': 1596233013,
'postMarketPrice': 253.5,
'postMarketChange': -0.16999817,
'regularMarketChange': 19.169998,
'regularMarketChangePercent': 8.17484,
'regularMarketTime': 1596225602,
'regularMarketPrice': 253.67,
'regularMarketDayHigh': 255.85,
'regularMarketDayRange': '249.0 - 255.85',
'regularMarketDayLow': 249.0,
'regularMarketVolume': 52105898,
'regularMarketPreviousClose': 234.5,
'bid': 253.31,
'ask': 253.7,
'bidSize': 11,
'askSize': 12,
'fullExchangeName': 'NasdaqGS',
'financialCurrency': 'USD',
'regularMarketOpen': 255.82,
'averageDailyVolume3Month': 24501318,
'averageDailyVolume10Day': 17021500,
'fiftyTwoWeekLowChange': 116.56999,
'fiftyTwoWeekLowChangePercent': 0.8502552,
'fiftyTwoWeekRange': '137.1 - 255.85',
'fiftyTwoWeekHighChange': -2.180008,
'fiftyTwoWeekHighChangePercent': -0.008520649,
'fiftyTwoWeekLow': 137.1,
'fiftyTwoWeekHigh': 255.85,
'earningsTimestamp': 1596124800,
'earningsTimestampStart': 1603868340,
'earningsTimestampEnd': 1604304000,
'trailingPE': 34.80653,
'epsTrailingTwelveMonths': 7.288,
'epsForward': 9.74,
'sharesOutstanding': 2408470016,
'bookValue': 36.936,
'fiftyDayAverage': 235.86743,
'fiftyDayAverageChange': 17.802567,
'fiftyDayAverageChangePercent': 0.075477004,
'twoHundredDayAverage': 207.2297,
'twoHundredDayAverageChange': 46.440292,
'twoHundredDayAverageChangePercent': 0.22410056,
'marketCap': 723725582336,
'forwardPE': 26.044147,
'priceToBook': 6.867825,
'sourceInterval': 15,
'exchangeDataDelayedBy': 0,
'marketState': 'POST',
'exchange': 'NMS',
'shortName': 'Facebook, Inc.',
'longName': 'Facebook, Inc.',
'messageBoardId': 'finmb_20765463',
'exchangeTimezoneName': 'America/New_York',
'exchangeTimezoneShortName': 'EDT',
'gmtOffSetMilliseconds': -14400000,
'market': 'us_market',
'esgPopulated': False,
'displayName': 'Facebook',
'symbol': 'FB'
}, {
'language': 'en-US',
'region': 'US',
'quoteType': 'EQUITY',
'quoteSourceName': 'Delayed Quote',
'triggerable': True,
'currency': 'USD',
'tradeable': False,
'firstTradeDateMilliseconds': 345479400000,
'priceHint': 2,
'postMarketChangePercent': 0.519949,
'postMarketTime': 1596233031,
'postMarketPrice': 427.25,
'postMarketChange': 2.2099915,
'regularMarketChange': 40.28,
'regularMarketChangePercent': 10.468863,
'regularMarketTime': 1596225602,
'regularMarketPrice': 425.04,
'regularMarketDayHigh': 425.66,
'regularMarketDayRange': '403.36 - 425.66',
'regularMarketDayLow': 403.36,
'regularMarketVolume': 91201476,
'regularMarketPreviousClose': 384.76,
'bid': 427.26,
'ask': 426.2,
'bidSize': 30,
'askSize': 31,
'fullExchangeName': 'NasdaqGS',
'financialCurrency': 'USD',
'regularMarketOpen': 411.535,
'averageDailyVolume3Month': 34664412,
'averageDailyVolume10Day': 32670312,
'fiftyTwoWeekLowChange': 232.46,
'fiftyTwoWeekLowChangePercent': 1.2070827,
'fiftyTwoWeekRange': '192.58 - 425.66',
'fiftyTwoWeekHighChange': -0.6199951,
'fiftyTwoWeekHighChangePercent': -0.0014565501,
'fiftyTwoWeekLow': 192.58,
'fiftyTwoWeekHigh': 425.66,
'dividendDate': 1589414400,
'earningsTimestamp': 1596124800,
'earningsTimestampStart': 1603868340,
'earningsTimestampEnd': 1604304000,
'trailingAnnualDividendRate': 3.08,
'trailingPE': 32.236633,
'trailingAnnualDividendYield': 0.0080049895,
'epsTrailingTwelveMonths': 13.185,
'epsForward': 14.97,
'sharesOutstanding': 4334329856,
'bookValue': 16.761,
'fiftyDayAverage': 369.66028,
'fiftyDayAverageChange': 55.37973,
'fiftyDayAverageChangePercent': 0.1498125,
'twoHundredDayAverage': 313.8948,
'twoHundredDayAverageChange': 111.1452,
'twoHundredDayAverageChangePercent': 0.35408422,
'marketCap': 1842263621632,
'forwardPE': 28.392786,
'priceToBook': 25.35887,
'sourceInterval': 15,
'exchangeDataDelayedBy': 0,
'marketState': 'POST',
'exchange': 'NMS',
'shortName': 'Apple Inc.',
'longName': 'Apple Inc.',
'messageBoardId': 'finmb_24937',
'exchangeTimezoneName': 'America/New_York',
'exchangeTimezoneShortName': 'EDT',
'gmtOffSetMilliseconds': -14400000,
'market': 'us_market',
'esgPopulated': False,
'displayName': 'Apple',
'symbol': 'AAPL'
}, {
'language': 'en-US',
'region': 'US',
'quoteType': 'EQUITY',
'quoteSourceName': 'Nasdaq Real Time Price',
'triggerable': True,
'currency': 'USD',
'tradeable': False,
'firstTradeDateMilliseconds': 863703000000,
'priceHint': 2,
'postMarketChangePercent': -0.2673244,
'postMarketTime': 1596232724,
'postMarketPrice': 3156.22,
'postMarketChange': -8.459961,
'regularMarketChange': 112.80005,
'regularMarketChangePercent': 3.696084,
'regularMarketTime': 1596225602,
'regularMarketPrice': 3164.68,
'regularMarketDayHigh': 3244.5,
'regularMarketDayRange': '3151.02 - 3244.5',
'regularMarketDayLow': 3151.02,
'regularMarketVolume': 7862360,
'regularMarketPreviousClose': 3051.88,
'bid': 3160.52,
'ask': 3160.0,
'bidSize': 8,
'askSize': 14,
'fullExchangeName': 'NasdaqGS',
'financialCurrency': 'USD',
'regularMarketOpen': 3244.0,
'averageDailyVolume3Month': 4675034,
'averageDailyVolume10Day': 4733650,
'fiftyTwoWeekLowChange': 1538.6499,
'fiftyTwoWeekLowChangePercent': 0.9462617,
'fiftyTwoWeekRange': '1626.03 - 3344.29',
'fiftyTwoWeekHighChange': -179.6101,
'fiftyTwoWeekHighChangePercent': -0.053706497,
'fiftyTwoWeekLow': 1626.03,
'fiftyTwoWeekHigh': 3344.29,
'earningsTimestamp': 1596124800,
'earningsTimestampStart': 1603382400,
'earningsTimestampEnd': 1603728000,
'trailingPE': 151.15971,
'epsTrailingTwelveMonths': 20.936,
'epsForward': 37.79,
'sharesOutstanding': 498776000,
'bookValue': 130.806,
'fiftyDayAverage': 2896.579,
'fiftyDayAverageChange': 268.10083,
'fiftyDayAverageChangePercent': 0.09255774,
'twoHundredDayAverage': 2323.4736,
'twoHundredDayAverageChange': 841.2063,
'twoHundredDayAverageChangePercent': 0.36204684,
'marketCap': 1578466410496,
'forwardPE': 83.74384,
'priceToBook': 24.193691,
'sourceInterval': 15,
'exchangeDataDelayedBy': 0,
'marketState': 'POST',
'exchange': 'NMS',
'shortName': 'Amazon.com, Inc.',
'longName': 'Amazon.com, Inc.',
'messageBoardId': 'finmb_18749',
'exchangeTimezoneName': 'America/New_York',
'exchangeTimezoneShortName': 'EDT',
'gmtOffSetMilliseconds': -14400000,
'market': 'us_market',
'esgPopulated': False,
'displayName': 'Amazon.com',
'symbol': 'AMZN'
}, {
'language': 'en-US',
'region': 'US',
'quoteType': 'EQUITY',
'quoteSourceName': 'Nasdaq Real Time Price',
'triggerable': True,
'currency': 'USD',
'tradeable': False,
'firstTradeDateMilliseconds': 1022160600000,
'priceHint': 2,
'postMarketChangePercent': -0.5584215,
'postMarketTime': 1596232099,
'postMarketPrice': 486.15,
'postMarketChange': -2.730011,
'regularMarketChange': 3.080017,
'regularMarketChangePercent': 0.6340093,
'regularMarketTime': 1596225602,
'regularMarketPrice': 488.88,
'regularMarketDayHigh': 494.795,
'regularMarketDayRange': '484.5 - 494.795',
'regularMarketDayLow': 484.5,
'regularMarketVolume': 5797772,
'regularMarketPreviousClose': 485.8,
'bid': 487.3,
'ask': 488.56,
'bidSize': 9,
'askSize': 40,
'fullExchangeName': 'NasdaqGS',
'financialCurrency': 'USD',
'regularMarketOpen': 488.29,
'averageDailyVolume3Month': 7602690,
'averageDailyVolume10Day': 7325312,
'fiftyTwoWeekLowChange': 236.6,
'fiftyTwoWeekLowChangePercent': 0.93784684,
'fiftyTwoWeekRange': '252.28 - 575.37',
'fiftyTwoWeekHighChange': -86.48999,
'fiftyTwoWeekHighChangePercent': -0.15032065,
'fiftyTwoWeekLow': 252.28,
'fiftyTwoWeekHigh': 575.37,
'earningsTimestamp': 1594900801,
'earningsTimestampStart': 1602676800,
'earningsTimestampEnd': 1603123200,
'trailingPE': 82.55319,
'epsTrailingTwelveMonths': 5.922,
'epsForward': 8.78,
'sharesOutstanding': 441015008,
'bookValue': 21.166,
'fiftyDayAverage': 478.79858,
'fiftyDayAverageChange': 10.081421,
'fiftyDayAverageChangePercent': 0.021055661,
'twoHundredDayAverage': 409.49237,
'twoHundredDayAverageChange': 79.387634,
'twoHundredDayAverageChangePercent': 0.19386841,
'marketCap': 215603412992,
'forwardPE': 55.681095,
'priceToBook': 23.09742,
'sourceInterval': 15,
'exchangeDataDelayedBy': 0,
'marketState': 'POST',
'exchange': 'NMS',
'shortName': 'Netflix, Inc.',
'longName': 'Netflix, Inc.',
'messageBoardId': 'finmb_32012',
'exchangeTimezoneName': 'America/New_York',
'exchangeTimezoneShortName': 'EDT',
'gmtOffSetMilliseconds': -14400000,
'market': 'us_market',
'esgPopulated': False,
'displayName': 'Netflix',
'symbol': 'NFLX'
}, {
'language': 'en-US',
'region': 'US',
'quoteType': 'EQUITY',
'quoteSourceName': 'Delayed Quote',
'triggerable': True,
'currency': 'USD',
'tradeable': False,
'firstTradeDateMilliseconds': 1092922200000,
'priceHint': 2,
'postMarketChangePercent': 0.0026999423,
'postMarketTime': 1596231815,
'postMarketPrice': 1483.0,
'postMarketChange': 0.040039062,
'regularMarketChange': -48.48999,
'regularMarketChangePercent': -3.16628,
'regularMarketTime': 1596225602,
'regularMarketPrice': 1482.96,
'regularMarketDayHigh': 1508.95,
'regularMarketDayRange': '1454.04 - 1508.95',
'regularMarketDayLow': 1454.04,
'regularMarketVolume': 3368287,
'regularMarketPreviousClose': 1531.45,
'bid': 1483.0,
'ask': 1484.0,
'bidSize': 12,
'askSize': 8,
'fullExchangeName': 'NasdaqGS',
'financialCurrency': 'USD',
'regularMarketOpen': 1505.01,
'averageDailyVolume3Month': 1643406,
'averageDailyVolume10Day': 1380212,
'fiftyTwoWeekLowChange': 469.42395,
'fiftyTwoWeekLowChangePercent': 0.46315467,
'fiftyTwoWeekRange': '1013.536 - 1586.99',
'fiftyTwoWeekHighChange': -104.03003,
'fiftyTwoWeekHighChangePercent': -0.06555179,
'fiftyTwoWeekLow': 1013.536,
'fiftyTwoWeekHigh': 1586.99,
'trailingPE': 29.915276,
'epsTrailingTwelveMonths': 49.572,
'epsForward': 55.06,
'sharesOutstanding': 336161984,
'bookValue': 297.759,
'fiftyDayAverage': 1479.2025,
'fiftyDayAverageChange': 3.7574463,
'fiftyDayAverageChangePercent': 0.0025401837,
'twoHundredDayAverage': 1375.5465,
'twoHundredDayAverageChange': 107.41345,
'twoHundredDayAverageChangePercent': 0.07808784,
'marketCap': 1014620422144,
'forwardPE': 26.933525,
'priceToBook': 4.9804034,
'sourceInterval': 15,
'exchangeDataDelayedBy': 0,
'marketState': 'POST',
'exchange': 'NMS',
'shortName': 'Alphabet Inc.',
'longName': 'Alphabet Inc.',
'messageBoardId': 'finmb_29096',
'exchangeTimezoneName': 'America/New_York',
'exchangeTimezoneShortName': 'EDT',
'gmtOffSetMilliseconds': -14400000,
'market': 'us_market',
'esgPopulated': False,
'displayName': 'Alphabet',
'symbol': 'GOOG'
}]
```
### **recommendations**
=== "Details"
- *Description*: Get real-time quote information for given symbol(s)
- *Return*: `dict`
=== "Example"
```python hl_lines="2"
tickers = Ticker('aapl gs hasgx ^GSPC ezu')
tickers.recommendations
```
=== "Data"
```python
{
'aapl': {
'symbol': 'AAPL',
'recommendedSymbols': [{
'symbol': 'GOOG',
'score': 0.279041
}, {
'symbol': 'AMZN',
'score': 0.278376
}, {
'symbol': 'FB',
'score': 0.274481
}, {
'symbol': 'TSLA',
'score': 0.225957
}, {
'symbol': 'NFLX',
'score': 0.207756
}]
},
'gs': {
'symbol': 'GS',
'recommendedSymbols': [{
'symbol': 'MS',
'score': 0.195796
}, {
'symbol': 'JPM',
'score': 0.160104
}, {
'symbol': 'WFC',
'score': 0.139129
}, {
'symbol': 'C',
'score': 0.137378
}, {
'symbol': 'BAC',
'score': 0.125276
}]
},
'hasgx': {
'symbol': 'HASGX',
'recommendedSymbols': [{
'symbol': 'HAVLX',
'score': 0.020499
}, {
'symbol': 'HAMGX',
'score': 0.016157
}, {
'symbol': 'HASCX',
'score': 0.014594
}, {
'symbol': 'HAIGX',
'score': 0.012841
}, {
'symbol': 'HAMVX',
'score': 0.012294
}]
},
'^GSPC': {
'symbol': '^GSPC',
'recommendedSymbols': [{
'symbol': '^TYX',
'score': 0.187618
}, {
'symbol': '^IXIC',
'score': 0.157791
}, {
'symbol': '^DJI',
'score': 0.134881
}, {
'symbol': 'GE',
'score': 0.10353
}, {
'symbol': 'MCD',
'score': 0.102003
}]
},
'ezu': {
'symbol': 'EZU',
'recommendedSymbols': [{
'symbol': 'EWQ',
'score': 0.152994
}, {
'symbol': 'EWU',
'score': 0.146443
}, {
'symbol': 'EWN',
'score': 0.145267
}, {
'symbol': 'IEV',
'score': 0.143627
}, {
'symbol': 'EWD',
'score': 0.141428
}]
}
}
```
### **technical_insights**
=== "Details"
- *Description*: Technical indicators for given symbol(s)
- *Return*: `dict`
=== "Example"
```python hl_lines="2"
aapl = Ticker('aapl')
aapl.technical_insights
```
=== "Data"
```python
{
'aapl': {
'symbol': 'aapl',
'instrumentInfo': {
'technicalEvents': {
'provider': 'Trading Central',
'sector': 'Technology',
'shortTermOutlook': {
'stateDescription': 'Recent bearish events outweigh bullish events.',
'direction': 'Bearish',
'score': 3,
'scoreDescription': 'Strong Bearish Evidence',
'sectorDirection': 'Bullish',
'sectorScore': 2,
'sectorScoreDescription': 'Bullish Evidence',
'indexDirection': 'Bearish',
'indexScore': 2,
'indexScoreDescription': 'Bearish Evidence'
},
'intermediateTermOutlook': {
'stateDescription': 'Bullish events outweigh bearish events.',
'direction': 'Bullish',
'score': 1,
'scoreDescription': 'Weak Bullish Evidence',
'sectorDirection': 'Bullish',
'sectorScore': 2,
'sectorScoreDescription': 'Bullish Evidence',
'indexDirection': 'Bullish',
'indexScore': 2,
'indexScoreDescription': 'Bullish Evidence'
},
'longTermOutlook': {
'stateDescription': 'All events are bullish.',
'direction': 'Bullish',
'score': 2,
'scoreDescription': 'Bullish Evidence',
'sectorDirection': 'Bullish',
'sectorScore': 2,
'sectorScoreDescription': 'Bullish Evidence',
'indexDirection': 'Bullish',
'indexScore': 3,
'indexScoreDescription': 'Strong Bullish Evidence'
}
},
'keyTechnicals': {
'provider': 'Trading Central',
'support': 203.77,
'resistance': 388.23,
'stopLoss': 355.460616
},
'valuation': {
'color': 0.0,
'description': 'Overvalued',
'discount': '-8%',
'relativeValue': 'Premium',
'provider': 'Trading Central'
}
},
'companySnapshot': {
'sectorInfo': 'Technology',
'company': {
'innovativeness': 0.9983,
'hiring': 0.9795,
'sustainability': 0.8240000000000001,
'insiderSentiments': 0.2217,
'earningsReports': 0.8340000000000001,
'dividends': 0.25
},
'sector': {
'innovativeness': 0.5,
'hiring': 0.5,
'sustainability': 0.5,
'insiderSentiments': 0.5,
'earningsReports': 0.5,
'dividends': 0.5
}
},
'recommendation': {
'targetPrice': 450.0,
'provider': 'Argus Research',
'rating': 'BUY'
},
'sigDevs': [{
'headline': 'Apple Reports Q3 Earnings Of $2.58 Per Share',
'date': '2020-07-30'
}]
}
}
```
```
### references/ticker/options.md
```markdown
# Options
### **option_chain**
=== "Details"
- *Description*: View option chain data for all expiration dates for a given symbol(s)
- *Return*: `pandas.DataFrame`
=== "Example"
```python
faang = Ticker('fb aapl amzn nflx goog')
df = faang.option_chain
df.head()
```
=== "Data"
| | contractSymbol | strike | currency | lastPrice | change | percentChange | volume | openInterest | bid | ask | contractSize | lastTradeDate | impliedVolatility | inTheMoney |
|:----------------------------------------------------|:--------------------|---------:|:-----------|------------:|---------:|----------------:|---------:|---------------:|-------:|------:|:---------------|:--------------------|--------------------:|:-------------|
| ('aapl', Timestamp('2020-07-31 00:00:00'), 'calls') | AAPL200731C00170000 | 170 | USD | 237.49 | 28.78 | 13.7895 | 1 | 4 | 239.8 | 244.1 | REGULAR | 2020-07-31 13:32:22 | 9.42383 | True |
| ('aapl', Timestamp('2020-07-31 00:00:00'), 'calls') | AAPL200731C00175000 | 175 | USD | 206.7 | 0 | 0 | 1 | 1 | 235.15 | 239.1 | REGULAR | 2020-07-30 16:14:43 | 9.14454 | True |
| ('aapl', Timestamp('2020-07-31 00:00:00'), 'calls') | AAPL200731C00180000 | 180 | USD | 191.6 | 0 | 0 | 0 | 1 | 229.8 | 234 | REGULAR | 2020-07-23 18:23:16 | 8.78321 | True |
| ('aapl', Timestamp('2020-07-31 00:00:00'), 'calls') | AAPL200731C00185000 | 185 | USD | 188.38 | 0 | 0 | 1 | 0 | 224.9 | 229.1 | REGULAR | 2020-07-06 19:30:06 | 8.60938 | True |
| ('aapl', Timestamp('2020-07-31 00:00:00'), 'calls') | AAPL200731C00190000 | 190 | USD | 173.7 | 0 | 0 | 0 | 1 | 178.4 | 182.9 | REGULAR | 2020-06-24 13:58:56 | 1e-05 | True |
=== "Filtering Examples"
```python
faang = Ticker('fb aapl amzn nflx goog')
df = faang.option_chain
# The dataframe contains a MultiIndex
df.index.names
FrozenList(['symbol', 'expiration', 'optionType'])
# Get specific expiration date for specified symbol
df.loc['aapl', '2022-07-31']
# Get specific option type for expiration date for specified symbol
df.loc['aapl', '2022-07-31', 'calls']
# Retrieve only calls for all symbols
df.xs('calls', level=2)
# Only include Apple in the money options
df.loc[df['inTheMoney'] == True].xs('aapl')
```
```