Full Documentation

Api Overview Last updated: January 27th, 2023

Welcome to the Tepilora API documentation! In the following series of articles you will learn how to query the Tepilora JSON/CSV API for EOD and historical market data, retrieve extensive data of about 125.000+ Funds from more than 50 countries, as well as information about timezones, currencies, and more. Our API is built upon a RESTful and easy-to-understand request and response structure. API requests are always sent using a simple API request URL with a series of required and optional HTTP GET parameters, and API responses are provided in both JSON and CSV format. Continue below to get started.

API authentication and authorization

For every API request you make, you will need to make sure to be authenticated with the API by passing your API access key to the API's apikey parameter. You can find an example below.

Example API request:
https://tepiloradata.com/T-Api/S/LU1829219556/CSV?apikey={YOUR_API_KEY}

Important:

Please make sure not to expose your API access key publicly. If you believe your API access key might be compromised, you can always send a request to issue a new one to support@tepiloradata.com . Please remember to put in the subject "APIKEY compromised".

API Authorization

Tepilora offers multiple subscription levels (Basic, Professional, etc.). Custom subscriptions are also available, please contact admin@tepiloradata.com, we will accomodate your request based on your needs. Based on the subscription, the key issued to the user will grant access to all or some of the API.

Subscription Examples:

API Error handling

API errors consist of error code and message response objects. If an error occurs, the Tepilora will also return HTTP status codes, such as 404 for "not found" errors. If your API request succeeds, status code 200 will be sent. For validation errors, the Tepilora API will also provide a context response object returning additional information about the error that occurred in the form of a string message. You can find an example error below.

Error Example:
{
                "error": {
                        "code": 300,
                        "message": "search did not return any record",
                        "context": {
                                "symbols": [
                                    {
                                        "key": "_no_record_",
                                        "message": "search did not return any record"
                                    }
                                ]
                        }
                }
}

Common API errors

Follows a list of error codes

Error Code Error Key Description
304 _no_security_ API was not able to find any match in our DB based on your search parameters
303 _internal_error_ A very generic error that can occur because an exception occured while processing your request. In order to ease debugging, please provide to the administrator also the full http request for troubleshooting
300 _no_record_ API was not able to find any match in our DB based on your search parameters

Api Base Data Last updated: January 27th, 2023

Welcome to the Tepilora API documentation! In the following series of articles you will learn how to query the Tepilora JSON/CSV API for EOD and historical market data, retrieve extensive data of about 125.000+ Funds from more than 50 countries, as well as information about timezones, currencies, and more. Our API is built upon a RESTful and easy-to-understand request and response structure. API requests are always sent using a simple API request URL with a series of required and optional HTTP GET parameters, and API responses are provided in both JSON and CSV format. Continue below to get started.

Security Description

It returns a detailed description of a security among 120+K european securities. It fetches all the info available for an Isin Code or Security Name of a Fund. If no matches are found, then a _no_record_found_ error is returned. If more than one match is found an error is returned.

Example of API request
GET https://tepiloradata.com/T-Api/D/{IsinOrFundName}/{Output}?apikey={YOUR_API_KEY}
HTTP GET Request Parameters:
Parameter Type Description
IsinOrFundName String IsinCode or name of fund you are looking for. A full match is required, no wildcards
Format String it is the format required by client for processing data. Values allowed are CSV or Json.
YOUR_API_KEY String it is the unique apikey assigned to a registered user.

Search for Securities

It fetches all the info related to search key. It will search for all the records containing fund name or an IsinCode matching partially the search key. If no matches are found, then a _no_record_found_ error is returned. If more than 100 matches are found an error is displayed in the resultset.

Example of API request
GET https://tepiloradata.com/T-Api/S/{SearchKey}/{Output}?apikey={YOUR_API_KEY}
HTTP GET Request Parameters:
Parameter Type Description
SearchKey String IsinCode or name of fund you are looking for. No full match is required, wildcards are not allowed anyway
Output String it is the format required by client for processing data. Values allowed are CSV or Json.
YOUR_API_KEY String it is the unique apikey assigned to a registered user.

Search for historical data of a Security

It will search for all the records containing fund name or an IsinCode matching partially the search key. If no matches are found, then a _no_record_found_ error is returned. If more than 100 matches are found an error is displayed in the resultset.

Example of API request
GET https://tepiloradata.com/T-Api/H/{DataDepth}/{SearchKey}/{Output}?apikey={YOUR_API_KEY}
HTTP GET Request Parameters:
Parameter Type Description
DataDepth Integer DataDepth is the degree of header detail you want to be displayed along with history data. Allowed values are 0,1,2. Set to 0 for displaying Name, IsinCode, TepiloraCode, Currency, ListingCurrency, LastUpdate. Set to 1 for displaying Name, IsinCode and Currency. Set to 2 for displaying only the history. Default is 2.
SearchKey String IsinCode or name of fund you are looking for. No full match is required, wildcards are not allowed anyway
Output String it is the format required by client for processing data. Values allowed are CSV or Json.
YOUR_API_KEY String it is the unique apikey assigned to a registered user.

Get Intraday historical prices of Securities

It fetches intraday prices for all the Securities listed in the request. Frequency can be set from 1 minute up to 1 hour. Securities allowed are ETFs, Stocks and Bonds. Provide TepiloraCode as Security id. Eventually make a search to find the TepiloraCode. By invoking this API on multiple securities we will charge you based on that number. Number of credits charged is available on the response.

Example of API request
GET https://tepiloradata.com/T-Api/HI/{TepiloraCodes}/{frequency}/{startDate}/{endDate}/{Output}?apikey={YOUR_API_KEY}
HTTP GET Request Parameters:
Parameter Type Description
TepiloraCodes String List of TepiloraCodes, separated by comma. API will search intraday data, if available, based on this list.
frequency String Frequency is the time collection interval for intraday data. It can fetch from 1 minute up to 1 day. Allowed values are d,60,30,15,10,5,1.
startDate String Date from which start fetching intraday data. Format is YYYY-MM-DD
endDate String Up to which date you need fetching intraday data. Format is YYYY-MM-DD
Output String it is the format required by client for processing data. Values allowed are CSV or Json.
YOUR_API_KEY String it is the unique apikey assigned to a registered user.

Api Analytics Last updated: October 13th, 2022

Welcome to the Tepilora API documentation! In the following series of articles you will learn how to query the Tepilora JSON/CSV API for EOD and historical market data, retrieve extensive data of about 125.000+ Funds from more than 50 countries, as well as information about timezones, currencies, and more. Our API is built upon a RESTful and easy-to-understand request and response structure. API requests are always sent using a simple API request URL with a series of required and optional HTTP GET parameters, and API responses are provided in both JSON and CSV format. Continue below to get started.

Advanced Query Help

It provides a descriptive json with all fields available for an advanced search. It also lists down all the searches you have made and saved.

API request model:
GET https://tepiloradata.com/T-Api/Analytics/v1/Query/help/?apikey={YOUR_API_KEY}
HTTP GET Request Parameters:
Parameter Type Description
YOUR_API_KEY String it is the unique apikey assigned to a registered user.

Advanced query by Tag

It allows to run a search based on Tepilora tags. It will search for all the records matching Tepilora tag values specifed in the query and can save the search with a name

For a full list of available Tags, please check out the following section

API request model:
GET https://tepiloradata.com/T-Api/Analytics/v1/Query/Q:{QueryByTag}/name={QueryName}?apikey={YOUR_API_KEY}
HTTP GET Request Parameters:
Parameter Type Description
QueryByTag String Query on one or more tags in the format "Q:Tag1=Value1,Tag2=Value2,...,TagN=ValueN"
QueryName String Optional parameter. If set it will save the query for your account and let you recall it at any time.
YOUR_API_KEY String it is the unique apikey assigned to a registered user.
Example of request:
GET https://www.tepiloradata.com/T-Api/Analytics/v1/Query/Q:TepiloraType=ETF,Currency%3C%3EEUR,TepiloraAssetClass=Asset%20Allocation/name=TestETFAA?apikey=YOUR_API_KEY
Example of response in JSON format:
{
"YourQuery": "Q:TepiloraType=ETF,Currency<>EUR,TepiloraAssetClass=Asset Allocation",
"QueryName": "TestETFAA",
"Result": [
      "IE00BQWJFQ70USDXLON",
      "IE00BQWJFQ70GBPXLON",
      "IE00BQWJFQ70CHFXSWX",
      "DE000ETF7011CHFXSWX",
      "DE000ETF7029CHFXSWX",
      "DE000ETF7037CHFXSWX",
      "IE00BLLZQ797GBPXLON",
      "IE00BLP53N06GBPXLON",
      "IE00BLLZQ912GBPXLON",
      "DE000A1C4DQ3CZKXETR",
      "DE000A2P6CA8CHFXETR",
      "US06738C7609USDPINX",
      "CA46428Y1025CADXTSE",
      "US06738G4073USDPINX",
      "US06739H7439USDARCX",
      "US06739F1194USDPINX",
      "US8702971990USDARCX",
      "US06739H2307USDARCX",
      "US06739H3131USDARCX",
      "US06739H2711USDPINX",
      "US06739H3214USDARCX"
  ]
                            }
Example of request recalling a saved query:
GET https://www.tepiloradata.com/T-Api/Analytics/v1/Query/TestETFAA/name=?apikey=YOUR_API_KEY
Example of response in JSON format:
{
"YourQuery": "Q:TepiloraType=ETF,Currency<>EUR,TepiloraAssetClass=Asset Allocation",
"QueryName": "TestETFAA",
"Result": [
      "IE00BQWJFQ70USDXLON",
      "IE00BQWJFQ70GBPXLON",
      "IE00BQWJFQ70CHFXSWX",
      "DE000ETF7011CHFXSWX",
      "DE000ETF7029CHFXSWX",
      "DE000ETF7037CHFXSWX",
      "IE00BLLZQ797GBPXLON",
      "IE00BLP53N06GBPXLON",
      "IE00BLLZQ912GBPXLON",
      "DE000A1C4DQ3CZKXETR",
      "DE000A2P6CA8CHFXETR",
      "US06738C7609USDPINX",
      "CA46428Y1025CADXTSE",
      "US06738G4073USDPINX",
      "US06739H7439USDARCX",
      "US06739F1194USDPINX",
      "US8702971990USDARCX",
      "US06739H2307USDARCX",
      "US06739H3131USDARCX",
      "US06739H2711USDPINX",
      "US06739H3214USDARCX"
  ]
                            }

Query by saved search

It allows to re-run a search saved previously.

API request model:
GET https://tepiloradata.com/T-Api/Analytics/v1/Query/{QueryName}?apikey={YOUR_API_KEY}
HTTP GET Request Parameters:
Parameter Type Description
QueryName String Mandatory parameter. Name of the query saved presiously and it is requested to re-run
YOUR_API_KEY String it is the unique apikey assigned to a registered user.
Example of request:
GET https://www.tepiloradata.com/T-Api/Analytics/v1/Query/TestETFAA?apikey=YOUR_API_KEY
Example of response in JSON format:
{
"YourQuery": "Q:TepiloraType=ETF,Currency<>EUR,TepiloraAssetClass=Asset Allocation",
"QueryName": "TestETFAA",
"Result": [
      "IE00BQWJFQ70USDXLON",
      "IE00BQWJFQ70GBPXLON",
      "IE00BQWJFQ70CHFXSWX",
      "DE000ETF7011CHFXSWX",
      "DE000ETF7029CHFXSWX",
      "DE000ETF7037CHFXSWX",
      "IE00BLLZQ797GBPXLON",
      "IE00BLP53N06GBPXLON",
      "IE00BLLZQ912GBPXLON",
      "DE000A1C4DQ3CZKXETR",
      "DE000A2P6CA8CHFXETR",
      "US06738C7609USDPINX",
      "CA46428Y1025CADXTSE",
      "US06738G4073USDPINX",
      "US06739H7439USDARCX",
      "US06739F1194USDPINX",
      "US8702971990USDARCX",
      "US06739H2307USDARCX",
      "US06739H3131USDARCX",
      "US06739H2711USDPINX",
      "US06739H3214USDARCX"
  ]
                            }

Add queries to a saved search

It allows to re-run a search based on Tepilora tags that you have saved previously, if available, and add more filters.

API request model:
GET https://tepiloradata.com/T-Api/Analytics/v1/Query/{QueryName}+{AdditionalQuery}/name={MultipleQuery}?apikey={YOUR_API_KEY}
HTTP GET Request Parameters:
Parameter Type Description
QueryName String Name of query saved previously.
AdditionalQuery String Additional query parameters to narrow down the scope of {QueryName} results. Query on one or more tags in the format "Q:Tag1=Value1,Tag2=Value2,...,TagN=ValueN"
MultipleQuery String Optional parameter. If set it will save the query for your account and let you recall it at any time.
YOUR_API_KEY String it is the unique apikey assigned to a registered user.
Example of request:
GET https://www.tepiloradata.com/T-Api/Analytics/v1/Query/[TestETFAA]++ISIN:LU1435778565++Q:TepiloraType=ETF,Currency=JPY,TepiloraAssetClass=Equity/name=MultipleQuery?apikey=YOUR_API_KEY
Example of response in JSON format:
{
"YourQuery": "[TestETFAA]++ISIN:LU1435778565++Q:TepiloraType=ETF,Currency=JPY,TepiloraAssetClass=Equity",
"QueryName": "MultipleQuery",
"Result": [
    "DE000A0H08N1EURXETR",
    "DE0006289465EURXETR",
    "IE0032523478EURXAMS",
    "FR0007075494EURXPAR",
    "FR0007056841EURXPAR",
    "DE000A0Q4R36EURXETR",
    "DE000A0H08Q4EURXETR",
    "DE000A0F5UJ7EURXETR",
    "DE0006289309EURXETR",
    "DE0006289382EURXETR",
    "DE0005933972EURXETR",
    "DE0005933923EURXETR",
    "DE000A0H08R2EURXETR",
    "US06739H3214USDARCX"
    ]
}

Analytics libraries

It carries out, on a group of securities identified by TepiloraCode, a set of financial statistical functions.

For a full list of available Tepilora Functions, please check out the following section

For a full list of available Options in API call, please check out the following section

API request model:
GET https://tepiloradata.com/T-Api/Analytics/v1/{SubFunctions}/{Tepilora Codes}/{Optional Fields}?apikey={YOUR_API_KEY}
HTTP GET Request Parameters:
Parameter Type Description
SubFunctions String List, comma separated, of statistical functions to execute against list of securities (Tepilora Codes). Follows an example of SubFunctions "TDes,TPrices,.."
Tepilora Codes String List of Tepilora Codes, comma separated, for which it is requred to run statistical functions. Follows an example: "R0010655712EURXPAR,FR0010900076EURXPAR,LU1681047400USDXPAR,..."
Optional Fields String Optional parameters. It consists of a list of parameters and their values, comma separated. They are used to change default parameters values of some subfunctions.
YOUR_API_KEY String it is the unique apikey assigned to a registered user.
Example of request:
GET https://www.tepiloradata.com/T-Api/Analytics/v1/TDes,TPrices/FR0010655712EURXPAR,FR0010900076EURXPAR,LU1681047400USDXPAR/StartDate=2015-01-01,OutputFormat=Json,Currency=EUR,Period=30,Annualized=True?apikey=YOUR_API_KEY
Example of response in JSON format:
"YourRequest": {
    "TepiloraCode": "FR0010655712EURXPAR,FR0010900076EURXPAR,LU1681047400USDXPAR",
    "Function": "TDes,TPrices",
    "StartDate": {
      "$date": "2015-01-01T00:00:00Z"
    },
    "OutputFormat": "Json",
    "Currency": "EUR",
    "Period": "30",
    "Annualized": "True",
    "apikey": "9d661bae-0471-4791-b95f-42f9ad3a3eef",
    "SecurityStatus": "All",
    "TotalSecurities": 3,
    "NeedHelp?": "https://www.tepiloradata.com/T-Api/Analytics/v1/help/help?apikey=9d661bae-0471-4791-b95f-42f9ad3a3eef",
    "TotalTime": "0:00:00.187559",
    "Credits Used": 6
  }
  "TDes": {
    "FR0010655712EURXPAR": {
      "IsinCode": "FR0010655712",
      "Name": "Amundi ETF DAX UCITS ETF DR",
      "TepiloraCode": "FR0010655712EURXPAR",
      "TepiloraParentId": "TTTTJGAGFH",
      "Benchmark": "FSE DAX NR EUR:100.0%",
      "CompanyAddress": "Amundi Asset Management S.A.S.",
      "CompanyCountry": "France",
      "CompanyName": "Amundi Asset Management",
      "Currency": "EUR",
      "DateofLastUpdate": 1664645506975,
      "Domicile": "FRA",
      "InceptionDate": "2008-09-16T00:00:00",
      "LegalStructure": "FCP",
      "ListingCurrency": "EUR",
      "MgmtFees": 0.1.  ......

Technical Analysis (BETA)

It carries out, on a group of securities identified by TepiloraCode, a set of technical indicators. Options are available to set data format, historical collection range, etc.

For a full list of available standard Technical Indicators, please check out the following section

For a full list of available Options in API call, please check out the following section

API request model:
GET https://tepiloradata.com/T-Api/TAAnalytics/v1/{TechnicalIndicators}/{Tepilora Codes}/{Optional Fields}?apikey={YOUR_API_KEY}
HTTP GET Request Parameters:
Parameter Type Description
TechnicalIndicators String List, comma separated, of statistical functions to execute against list of securities (Tepilora Codes). Follows an example of SubFunctions "TDes,TPrices,.."
Tepilora Codes String List of Tepilora Codes, comma separated, for which it is requred to run statistical functions. Follows an example: "R0010655712EURXPAR,FR0010900076EURXPAR,LU1681047400USDXPAR,..."
Optional Fields String Optional parameters. It consists of a list of parameters and their values, comma separated. They are used to change default parameters values of some subfunctions.
YOUR_API_KEY String it is the unique apikey assigned to a registered user.
Example of request:
GET https://tepiloradata.com/T-Api/TAAnalytics/v1/trend.MACD,volatility.BollingerBands/FR0010655746EURXFRA,LU1104577314EURXETR/window_fast=10,window_slow=30,window_sign=60,OutputFormat=Json,Obs=500?apikey=YOUR_API_KEY
Example of response in JSON format:
{
  "YourRequest": {
    "TepiloraCode": "FR0010655746EURXFRA,LU1104577314EURXETR",
    "Function": "trend.MACD,volatility.BollingerBands",
    "window_fast": "10",
    "window_slow": "30",
    "window_sign": "60",
    "OutputFormat": "Json",
    "Obs": "500",
    "apikey": "YOUR_API_KEY",
    "fillna": true,
    "TotalSecurities": 2,
    "TotalTime": "0:00:00.213963",
    "NeedHelp?": "https://www.tepiloradata.com/T-Api/TAAnalytics/v1/help/help?apikey=YOUR_API_KEY"
  },
  "FR0010655746EURXFRA": {
    "Close": {
      "2012-09-03": 125.36,
      "2012-09-04": 127.87,
      "2012-09-05": 127.86,
      "2012-09-06": 133.96,
      "2012-09-07": 133.05,
      "2012-09-10": 134.38,
      "2012-09-11": 133.27,
      "2012-09-12": 136.6,
....................

Tepilora Functions and Tags

Tepilora Tags are proprietary tags designed and computed on daily basis on each security of Tepilora database to categorize an asset. A full comprehensive list of Tepilora Tags, description and values follows hereby..

Tepilora Tags

Listo of Tepilora Tags, their description and allowed values.

Tepilora Tag Description Allowed Values
TepiloraParentId Id to identify all related securities (such as all the ETFs listed in different stock exchanges or different share classes for the same mutual fund)
TepiloraType Security/Entity type, allowed values are: Values
TepiloraArea Security Area, allowed values are: Values
TepiloraAssetClass Security Asset Class, allowed values are: Values
TepiloraBondIssueCurrency Field to identify local or hard currency portfolios. Mainly for emerging market debt Funds, ETFs and Indices. Allowed values are: Values
TepiloraBondIssuer Issuer Type indicator for “Bond” Asset Class Security, allowed values are: Values
TepiloraBondMaturityFocus Maturity indicator for “Bond” Asset Class Security ( in years, unless specified). Values
TepiloraBondType Bond Type indicator for “Bond” Asset Class Security, allowed values are: Values
TepiloraCurrencyHedging Security Currency Hedging Indicator, allowed values are: Values
TepiloraEquityCapFocus Field to identify a specific Capitalization Focus on equity portfolios. Allowed values are: Values
TepiloraEquitySelectionFocus Field to identify a specific selection methodology/strategy on equity or alternative securities., allowed values are: Values
TepiloraIsSectorial Boolean to identify the Security as Sectorial. Values
TepiloraMacroArea Security Macro Area, allowed values are: Values
TepiloraSector Security Sector, follows a list of values available Values
TepiloraSubArea Security SubArea or Country, allowed values are: Values
TepiloraCurrencyExposure Security Currency Exposure, allowed values are: Values
TepiloraAlternativeStrategy TepiloraAlternativeStrategy description Values
TepiloraIsLeveraged TepiloraIsLeveraged description Values
TepiloraCategory TepiloraCategory description Values
TepiloraRating TepiloraRating description Values
TepiloraRatingDate TepiloraRatingDate description Values

Tepilora Options for API Analytics

List of Tepilora Options for executing API Analytics, their description and syntax.

Tepilora Options Description Syntax
Returns Embedded Field: a DataFrame containing all the returns series of selected securities. Directly provided by the Analytics Function.
Prices Embedded Field: a DataFrame containing all the price series of selected securities. Directly provided by the Analytics Function.
Obs Optional Field. Analyzes the last n-observations prices and analytics. When not defined, data series and analytics starts from their original start date. (Is alternative to StartDate) Obs=Integer, Example: <code>Obs=3650</code>
TepiloraCode A TepiloraCode indentifies every single security. Multiple Tepilora Codes must be separated by comma. Ex. <code>FR0010655712EURXPAR,FR0010900076EURXPAR</code>
Breakdown No description No description
SecurityStatus It provides a filter on securities being traded or not on the market. By default it selects all active and inactive securities. Only one value is allowed. Allowed values: Active, Inactive
StartDate Analyzes all prices and analytics from the selected date. When not defined, data series starts from their original start date. (Is alternative to Obs) StartDate="YYYY-MM-DD" Example: <code>StartDate=2015-01-01</code>
TR No description No description
Period Optional Field. Defines the rolling-period lenght in rolling analytics. If you want to get the rolling 30 days Volatility, you must set Period=30. When not defined, rolling analytics provided result on default (265) value Period=Integer Example: <code>Period=30</code>
TR No description No description
Annualized Provides annualized analytics (where available) on daily, weekly and monthly basis. When not defined, analytics provides not annualized data. When "True" provides a daily annualized data. Annualized= any of following values: "True", "False", "D", "W", "M" Example: <code>Annualized=True</code>
OutputFormat Sets the wished output format. If OutputFormat=Json (or omitted) the output will be provided in Json format, if OutputFormat=CSV will be provided in CSV (SQL friendly) format. OutputFormat= any of the following values: Json, CSV, Xls. Example: <code>OutputFormat=CSV</code>
Currency Converts all prices in the selected currency. When not defined all securities are in their original currency. Currency=Currency code (upper case). Example: <code>Currency=EUR</code>

Tepilora SubFunctions for API Analytics

List of Tepilora SubFunctions for executing API Analytics, their description and syntax.

Tepilora SubFunctions Description Syntax
TAnnualReturns Annual Returns for selected security TAnnualReturns(Returns,Prices,Obs='')
TAnnualVolatility Annual Volatility for selected security TAnnualVolatility(Returns,Prices,Obs='')
TBreakdowns TBreakdowns(TepiloraCode,Breakdown='',SecurityStatus='') Provides a complete security description. Requires a Tepilora Code. Returns a DataFrame Object.
TDes TDes(TepiloraCode,SecurityStatus='') Provides a complete security description. Requires a Tepilora Code. Returns a DataFrame Object.
TFees TFees(TepiloraCode) Provides a complete security description. Requires a Tepilora Code. Returns a DataFrame Object.
TMifid TMifid(TepiloraCode) Provides a complete security description. Requires a Tepilora Code. Returns a DataFrame Object.
TMonthlyReturns TMonthlyReturns(Returns,Prices,Obs='') It provides returns on monthly basis for selected security
TMonthlyVolatility TMonthlyVolatility(Returns,Prices,Obs='') It provides volatility for selected security on monthly basis
TPrices TPrices(TepiloraCode,Obs='',StartDate='',TR='',SecurityStatus='Active') Provides the complete price data series of all selected securities. The series lenght could be defined by start date or observations number. By the optional field "currency" the series will be converted in the defined currency
TRCorrelation TRCorrelation(Returns,Obs='',Period=265) It returns a dataframe of correlation data over a time interval specified by Period parameter
TRCovariance TRCovariance(Returns,Obs='',Period=265) Description not available
TRDD TRDD(Prices,Obs='') Description not available
TRKurtosis TRKurtosis(Returns,Obs='',Period=265,Annualized=False) Help not available
TRPerformance TRPerformance(Returns,Obs='') Provides the complete data series of all selected securities normalized on start date. The series lenght could be defined by start date or observations number. By the optional field "Currency" the series will be converted in the defined currency
TRSkewness TRSkewness(Returns,Obs='',Period=265,Annualized=False) Description not available
TRVariance TRVariance(Returns,Obs='',Period=265,Annualized=False) Description not available
TRVolatility TRVolatility(Returns,Obs='',Period=265,Annualized=False) Description not available
TRels TRels(TepiloraCode) Provides a complete security description. Requires a Tepilora Code. Returns a DataFrame Object.
TReturns TReturns(Prices,Obs='') Provides the complete return data series of all selected securities. The series lenght could be defined by start date or observations number. By the optional field "Currency" the series will be converted in the defined currency

Tepilora Standard Technical Indicators for API Analytics

List of Tepilora Technical Indicators for executing API Analytics, their description and syntax.

Tepilora Standard Technical Indicators Description Syntax
momentum.AwesomeOscillatorIndicator Awesome Oscillator From: https://www.tradingview.com/wiki/Awesome_Oscillator_(AO) The Awesome Oscillator is an indicator used to measure market momentum. AO calculates the difference of a 34 Period and 5 Period Simple Moving Averages. The Simple Moving Averages that are used are not calculated using closing price but rather each bar's midpoints. AO is generally used to affirm trends or to anticipate possible reversals. From: https://www.ifcm.co.uk/ntx-indicators/awesome-oscillator Awesome Oscillator is a 34-period simple moving average, plotted through the central points of the bars (H+L)/2, and subtracted from the 5-period simple moving average, graphed across the central points of the bars (H+L)/2. MEDIAN PRICE = (HIGH+LOW)/2 AO = SMA(MEDIAN PRICE, 5)-SMA(MEDIAN PRICE, 34) where SMA — Simple Moving Average. Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. window1(int): short period. window2(int): long period. fillna(bool): if True, fill nan values with -50. momentum.AwesomeOscillatorIndicator (window1: int, window2: int, fillna: bool )
momentum.IndicatorMixin Util mixin indicator class momentum.IndicatorMixin()
momentum.KAMAIndicator Kaufman's Adaptive Moving Average (KAMA) Moving average designed to account for market noise or volatility. KAMA will closely follow prices when the price swings are relatively small and the noise is low. KAMA will adjust when the price swings widen and follow prices from a greater distance. This trend-following indicator can be used to identify the overall trend, time turning points and filter price movements. https://www.tradingview.com/ideas/kama/ Args: close(pandas.Series): dataset 'Close' column. window(int): n period. pow1(int): number of periods for the fastest EMA constant. pow2(int): number of periods for the slowest EMA constant. fillna(bool): if True, fill nan values. momentum.KAMAIndicator (window: int, pow1: int, pow2: int, fillna: bool )
momentum.PercentagePriceOscillator The Percentage Price Oscillator (PPO) is a momentum oscillator that measures the difference between two moving averages as a percentage of the larger moving average. https://school.stockcharts.com/doku.php?id=technical_indicators:price_oscillators_ppo Args: close(pandas.Series): dataset 'Price' column. window_slow(int): n period long-term. window_fast(int): n period short-term. window_sign(int): n period to signal. fillna(bool): if True, fill nan values. momentum.PercentagePriceOscillator( window_slow: int, window_fast: int, window_sign: int, fillna: bool )
momentum.PercentageVolumeOscillator The Percentage Volume Oscillator (PVO) is a momentum oscillator for volume. The PVO measures the difference between two volume-based moving averages as a percentage of the larger moving average. https://school.stockcharts.com/doku.php?id=technical_indicators:percentage_volume_oscillator_pvo Args: volume(pandas.Series): dataset 'Volume' column. window_slow(int): n period long-term. window_fast(int): n period short-term. window_sign(int): n period to signal. fillna(bool): if True, fill nan values. momentum.PercentageVolumeOscillator (window_slow: int, window_fast: int, window_sign: int, fillna: bool )
momentum.ROCIndicator Rate of Change (ROC) The Rate-of-Change (ROC) indicator, which is also referred to as simply Momentum, is a pure momentum oscillator that measures the percent change in price from one period to the next. The ROC calculation compares the current price with the price “n” periods ago. The plot forms an oscillator that fluctuates above and below the zero line as the Rate-of-Change moves from positive to negative. As a momentum oscillator, ROC signals include centerline crossovers, divergences and overbought-oversold readings. Divergences fail to foreshadow reversals more often than not, so this article will forgo a detailed discussion on them. Even though centerline crossovers are prone to whipsaw, especially short-term, these crossovers can be used to identify the overall trend. Identifying overbought or oversold extremes comes naturally to the Rate-of-Change oscillator. https://school.stockcharts.com/doku.php?id=technical_indicators:rate_of_change_roc_and_momentum Args: close(pandas.Series): dataset 'Close' column. window(int): n period. fillna(bool): if True, fill nan values. momentum.ROCIndicator(window: int, fillna: bool )
momentum.RSIIndicator Relative Strength Index (RSI) Compares the magnitude of recent gains and losses over a specified time period to measure speed and change of price movements of a security. It is primarily used to attempt to identify overbought or oversold conditions in the trading of an asset. https://www.investopedia.com/terms/r/rsi.asp Args: close(pandas.Series): dataset 'Close' column. window(int): n period. fillna(bool): if True, fill nan values. momentum.RSIIndicator(window: int, fillna: bool)
momentum.StochRSIIndicator Stochastic RSI The StochRSI oscillator was developed to take advantage of both momentum indicators in order to create a more sensitive indicator that is attuned to a specific security's historical performance rather than a generalized analysis of price change. https://school.stockcharts.com/doku.php?id=technical_indicators:stochrsi https://www.investopedia.com/terms/s/stochrsi.asp Args: close(pandas.Series): dataset 'Close' column. window(int): n period smooth1(int): moving average of Stochastic RSI smooth2(int): moving average of %K fillna(bool): if True, fill nan values. momentum.StochRSIIndicator( window: int, smooth1: int, smooth2: int, fillna: bool )
momentum.StochasticOscillator Stochastic Oscillator Developed in the late 1950s by George Lane. The stochastic oscillator presents the location of the closing price of a stock in relation to the high and low range of the price of a stock over a period of time, typically a 14-day period. https://school.stockcharts.com/doku.php?id=technical_indicators:stochastic_oscillator_fast_slow_and_full Args: close(pandas.Series): dataset 'Close' column. high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. window(int): n period. smooth_window(int): sma period over stoch_k. fillna(bool): if True, fill nan values. momentum.StochasticOscillator( window: int, smooth_window: int, fillna: bool )
momentum.TSIIndicator True strength index (TSI) Shows both trend direction and overbought/oversold conditions. https://school.stockcharts.com/doku.php?id=technical_indicators:true_strength_index Args: close(pandas.Series): dataset 'Close' column. window_slow(int): high period. window_fast(int): low period. fillna(bool): if True, fill nan values. momentum.TSIIndicator( window_slow: int, window_fast: int, fillna: bool )
momentum.UltimateOscillator Ultimate Oscillator Larry Williams' (1976) signal, a momentum oscillator designed to capture momentum across three different timeframes. http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:ultimate_oscillator BP = Close - Minimum(Low or Prior Close). TR = Maximum(High or Prior Close) - Minimum(Low or Prior Close) Average7 = (7-period BP Sum) / (7-period TR Sum) Average14 = (14-period BP Sum) / (14-period TR Sum) Average28 = (28-period BP Sum) / (28-period TR Sum) UO = 100 x [(4 x Average7)+(2 x Average14)+Average28]/(4+2+1) Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. close(pandas.Series): dataset 'Close' column. window1(int): short period. window2(int): medium period. window3(int): long period. weight1(float): weight of short BP average for UO. weight2(float): weight of medium BP average for UO. weight3(float): weight of long BP average for UO. fillna(bool): if True, fill nan values with 50. momentum.UltimateOscillator( window1: int, window2: int, window3: int, weight1: float, weight2: float, weight3: float, fillna: bool )
momentum.WilliamsRIndicator Williams %R Developed by Larry Williams, Williams %R is a momentum indicator that is the inverse of the Fast Stochastic Oscillator. Also referred to as %R, Williams %R reflects the level of the close relative to the highest high for the look-back period. In contrast, the Stochastic Oscillator reflects the level of the close relative to the lowest low. %R corrects for the inversion by multiplying the raw value by -100. As a result, the Fast Stochastic Oscillator and Williams %R produce the exact same lines, only the scaling is different. Williams %R oscillates from 0 to -100. Readings from 0 to -20 are considered overbought. Readings from -80 to -100 are considered oversold. Unsurprisingly, signals derived from the Stochastic Oscillator are also applicable to Williams %R. %R = (Highest High - Close)/(Highest High - Lowest Low) * -100 Lowest Low = lowest low for the look-back period Highest High = highest high for the look-back period %R is multiplied by -100 correct the inversion and move the decimal. https://school.stockcharts.com/doku.php?id=technical_indicators:williams_r The Williams %R oscillates from 0 to -100. When the indicator produces readings from 0 to -20, this indicates overbought market conditions. When readings are -80 to -100, it indicates oversold market conditions. Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. close(pandas.Series): dataset 'Close' column. lbp(int): lookback period. fillna(bool): if True, fill nan values with -50. momentum.WilliamsRIndicator( lbp: int, fillna: bool )
others.CumulativeReturnIndicator Cumulative Return (CR) Args: close(pandas.Series): dataset 'Close' column. fillna(bool): if True, fill nan values. others.CumulativeReturnIndicator( fillna: bool )
others.DailyLogReturnIndicator Daily Log Return (DLR) https://stackoverflow.com/questions/31287552/logarithmic-returns-in-pandas-dataframe Args: close(pandas.Series): dataset 'Close' column. fillna(bool): if True, fill nan values. others.DailyLogReturnIndicator( fillna: bool )
others.DailyReturnIndicator Daily Return (DR) Args: close(pandas.Series): dataset 'Close' column. fillna(bool): if True, fill nan values. others.DailyReturnIndicator( fillna: bool )
others.IndicatorMixin Util mixin indicator class others.IndicatorMixin()
trend.ADXIndicator Average Directional Movement Index (ADX) The Plus Directional Indicator (+DI) and Minus Directional Indicator (-DI) are derived from smoothed averages of these differences, and measure trend direction over time. These two indicators are often referred to collectively as the Directional Movement Indicator (DMI). The Average Directional Index (ADX) is in turn derived from the smoothed averages of the difference between +DI and -DI, and measures the strength of the trend (regardless of direction) over time. Using these three indicators together, chartists can determine both the direction and strength of the trend. http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:average_directional_index_adx Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. close(pandas.Series): dataset 'Close' column. window(int): n period. fillna(bool): if True, fill nan values. trend.ADXIndicator( window: int, fillna: bool )
trend.AroonIndicator Aroon Indicator Identify when trends are likely to change direction. Aroon Up = ((N - Days Since N-day High) / N) x 100 Aroon Down = ((N - Days Since N-day Low) / N) x 100 Aroon Indicator = Aroon Up - Aroon Down https://www.investopedia.com/terms/a/aroon.asp Args: close(pandas.Series): dataset 'Close' column. window(int): n period. fillna(bool): if True, fill nan values. trend.AroonIndicator( window: int, fillna: bool )
trend.CCIIndicator Commodity Channel Index (CCI) CCI measures the difference between a security's price change and its average price change. High positive readings indicate that prices are well above their average, which is a show of strength. Low negative readings indicate that prices are well below their average, which is a show of weakness. http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:commodity_channel_index_cci Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. close(pandas.Series): dataset 'Close' column. window(int): n period. constant(int): constant. fillna(bool): if True, fill nan values. trend.CCIIndicator( window: int, constant: int, fillna: bool )
trend.DPOIndicator Detrended Price Oscillator (DPO) Is an indicator designed to remove trend from price and make it easier to identify cycles. http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:detrended_price_osci Args: close(pandas.Series): dataset 'Close' column. window(int): n period. fillna(bool): if True, fill nan values. trend.DPOIndicator ( window: int, fillna: bool )
trend.EMAIndicator EMA - Exponential Moving Average Args: close(pandas.Series): dataset 'Close' column. window(int): n period. fillna(bool): if True, fill nan values. trend.EMAIndicator( window: int, fillna: bool )
trend.IchimokuIndicator Ichimoku Kinkō Hyō (Ichimoku) http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:ichimoku_cloud Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. window1(int): n1 low period. window2(int): n2 medium period. window3(int): n3 high period. visual(bool): if True, shift n2 values. fillna(bool): if True, fill nan values. trend.IchimokuIndicator( window1: int, window2: int, window3: int, visual: bool, fillna: bool )
trend.KSTIndicator KST Oscillator (KST Signal) It is useful to identify major stock market cycle junctures because its formula is weighed to be more greatly influenced by the longer and more dominant time spans, in order to better reflect the primary swings of stock market cycle. http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:know_sure_thing_kst Args: close(pandas.Series): dataset 'Close' column. roc1(int): roc1 period. roc2(int): roc2 period. roc3(int): roc3 period. roc4(int): roc4 period. window1(int): n1 smoothed period. window2(int): n2 smoothed period. window3(int): n3 smoothed period. window4(int): n4 smoothed period. nsig(int): n period to signal. fillna(bool): if True, fill nan values. trend.KSTIndicator( roc1: int, roc2: int, roc3: int, roc4: int, window1: int, window2: int, window3: int, window4: int, nsig: int, fillna: bool )
trend.MACD Moving Average Convergence Divergence (MACD) Is a trend-following momentum indicator that shows the relationship between two moving averages of prices. https://school.stockcharts.com/doku.php?id=technical_indicators:moving_average_convergence_divergence_macd Args: close(pandas.Series): dataset 'Close' column. window_fast(int): n period short-term. window_slow(int): n period long-term. window_sign(int): n period to signal. fillna(bool): if True, fill nan values. trend.MACD( window_fast: int, window_slow: int, window_sign: int, fillna: bool )
trend.MassIndex Mass Index (MI) It uses the high-low range to identify trend reversals based on range expansions. It identifies range bulges that can foreshadow a reversal of the current trend. http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:mass_index Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. window_fast(int): fast period value. window_slow(int): slow period value. fillna(bool): if True, fill nan values. trend.MassIndex( window_fast: int, window_slow: int, fillna: bool )
trend.PSARIndicator Parabolic Stop and Reverse (Parabolic SAR) The Parabolic Stop and Reverse, more commonly known as the Parabolic SAR,is a trend-following indicator developed by J. Welles Wilder. The Parabolic SAR is displayed as a single parabolic line (or dots) underneath the price bars in an uptrend, and above the price bars in a downtrend. https://school.stockcharts.com/doku.php?id=technical_indicators:parabolic_sar Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. close(pandas.Series): dataset 'Close' column. step(float): the Acceleration Factor used to compute the SAR. max_step(float): the maximum value allowed for the Acceleration Factor. fillna(bool): if True, fill nan values. trend.PSARIndicator( step: float, max_step: float, fillna: bool )
trend.SMAIndicator SMA - Simple Moving Average Args: close(pandas.Series): dataset 'Close' column. window(int): n period. fillna(bool): if True, fill nan values. trend.SMAIndicator( window: int, fillna: bool )
trend.STCIndicator Schaff Trend Cycle (STC) The Schaff Trend Cycle (STC) is a charting indicator that is commonly used to identify market trends and provide buy and sell signals to traders. Developed in 1999 by noted currency trader Doug Schaff, STC is a type of oscillator and is based on the assumption that, regardless of time frame, currency trends accelerate and decelerate in cyclical patterns. https://www.investopedia.com/articles/forex/10/schaff-trend-cycle-indicator.asp Args: close(pandas.Series): dataset 'Close' column. window_fast(int): n period short-term. window_slow(int): n period long-term. cycle(int): cycle size smooth1(int): ema period over stoch_k smooth2(int): ema period over stoch_kd fillna(bool): if True, fill nan values. trend.STCIndicator( window_fast: int, window_slow: int, cycle: int, smooth1: int, smooth2: int, fillna: bool )
trend.TRIXIndicator Trix (TRIX) Shows the percent rate of change of a triple exponentially smoothed moving average. http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:trix Args: close(pandas.Series): dataset 'Close' column. window(int): n period. fillna(bool): if True, fill nan values. trend.TRIXIndicator( window: int, fillna: bool )
trend.VortexIndicator Vortex Indicator (VI) It consists of two oscillators that capture positive and negative trend movement. A bullish signal triggers when the positive trend indicator crosses above the negative trend indicator or a key level. http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:vortex_indicator Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. close(pandas.Series): dataset 'Close' column. window(int): n period. fillna(bool): if True, fill nan values. trend.VortexIndicator( window: int, fillna: bool )
trend.WMAIndicator WMA - Weighted Moving Average Args: close(pandas.Series): dataset 'Close' column. window(int): n period. fillna(bool): if True, fill nan values. trend.WMAIndicator( window: int, fillna: bool )
volatility.AverageTrueRange Average True Range (ATR) The indicator provide an indication of the degree of price volatility. Strong moves, in either direction, are often accompanied by large ranges, or large True Ranges. http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:average_true_range_atr Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. close(pandas.Series): dataset 'Close' column. window(int): n period. fillna(bool): if True, fill nan values. volatility.AverageTrueRange( window: int, fillna: bool )
volatility.BollingerBands Donchian Channel https://www.investopedia.com/terms/d/donchianchannels.asp Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. close(pandas.Series): dataset 'Close' column. window(int): n period. fillna(bool): if True, fill nan values. volatility.BollingerBands( window: int, window_dev: int, fillna: bool )
volatility.DonchianChannel Donchian Channel https://www.investopedia.com/terms/d/donchianchannels.asp Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. close(pandas.Series): dataset 'Close' column. window(int): n period. fillna(bool): if True, fill nan values. volatility.DonchianChannel( window: int, fillna: bool )
volatility.KeltnerChannel KeltnerChannel Keltner Channels are a trend following indicator used to identify reversals with channel breakouts and channel direction. Channels can also be used to identify overbought and oversold levels when the trend is flat. https://school.stockcharts.com/doku.php?id=technical_indicators:keltner_channels Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. close(pandas.Series): dataset 'Close' column. window(int): n period. window_atr(int): n atr period. Only valid if original_version param is False. fillna(bool): if True, fill nan values. original_version(bool): if True, use original version as the centerline (SMA of typical price) if False, use EMA of close as the centerline. More info: https://school.stockcharts.com/doku.php?id=technical_indicators:keltner_channels volatility.KeltnerChannel( window: int, window_atr: int, fillna: bool, original_version: bool )
volatility.UlcerIndex Ulcer Index https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:ulcer_index Args: close(pandas.Series): dataset 'Close' column. window(int): n period. fillna(bool): if True, fill nan values. volatility.UlcerIndex( window: int, fillna: bool )
volatility.DonchianChannel Donchian Channel https://www.investopedia.com/terms/d/donchianchannels.asp Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. close(pandas.Series): dataset 'Close' column. window(int): n period. fillna(bool): if True, fill nan values. volatility.DonchianChannel( window: int, fillna: bool )
volume.AccDistIndexIndicator Accumulation/Distribution Index (ADI) Acting as leading indicator of price movements. https://school.stockcharts.com/doku.php?id=technical_indicators:accumulation_distribution_line Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. close(pandas.Series): dataset 'Close' column. volume(pandas.Series): dataset 'Volume' column. fillna(bool): if True, fill nan values. volume.AccDistIndexIndicator( fillna: bool )
volume.ChaikinMoneyFlowIndicator Chaikin Money Flow (CMF) It measures the amount of Money Flow Volume over a specific period. http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:chaikin_money_flow_cmf Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. close(pandas.Series): dataset 'Close' column. volume(pandas.Series): dataset 'Volume' column. window(int): n period. fillna(bool): if True, fill nan values. volatility.ChaikinMoneyFlowIndicator( window: int, fillna: bool )
volume.EaseOfMovementIndicator Ease of movement (EoM, EMV) It relate an asset's price change to its volume and is particularly useful for assessing the strength of a trend. https://en.wikipedia.org/wiki/Ease_of_movement Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. volume(pandas.Series): dataset 'Volume' column. window(int): n period. fillna(bool): if True, fill nan values. volume.EaseOfMovementIndicator( window: int, fillna: bool )
volume.ForceIndexIndicator Force Index (FI) It illustrates how strong the actual buying or selling pressure is. High positive values mean there is a strong rising trend, and low values signify a strong downward trend. http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:force_index Args: close(pandas.Series): dataset 'Close' column. volume(pandas.Series): dataset 'Volume' column. window(int): n period. fillna(bool): if True, fill nan values. volume.ForceIndexIndicator( window: int, fillna: bool )
volume.IndicatorMixin Util mixin indicator class volume.IndicatorMixin()
volume.MFIIndicator Money Flow Index (MFI) Uses both price and volume to measure buying and selling pressure. It is positive when the typical price rises (buying pressure) and negative when the typical price declines (selling pressure). A ratio of positive and negative money flow is then plugged into an RSI formula to create an oscillator that moves between zero and one hundred. http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:money_flow_index_mfi Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. close(pandas.Series): dataset 'Close' column. volume(pandas.Series): dataset 'Volume' column. window(int): n period. fillna(bool): if True, fill nan values. volume.MFIIndicator( window: int, fillna: bool )
volume.NegativeVolumeIndexIndicator Negative Volume Index (NVI) http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:negative_volume_inde Args: close(pandas.Series): dataset 'Close' column. volume(pandas.Series): dataset 'Volume' column. fillna(bool): if True, fill nan values with 1000. volume.NegativeVolumeIndexIndicator( fillna: bool )
volume.VolumePriceTrendIndicator Volume-price trend (VPT) Is based on a running cumulative volume that adds or substracts a multiple of the percentage change in share price trend and current volume, depending upon the investment's upward or downward movements. https://en.wikipedia.org/wiki/Volume%E2%80%93price_trend Args: close(pandas.Series): dataset 'Close' column. volume(pandas.Series): dataset 'Volume' column. fillna(bool): if True, fill nan values. volume.VolumePriceTrendIndicator( fillna: bool )
volume.OnBalanceVolumeIndicator On-balance volume (OBV) It relates price and volume in the stock market. OBV is based on a cumulative total volume. https://en.wikipedia.org/wiki/On-balance_volume Args: close(pandas.Series): dataset 'Close' column. volume(pandas.Series): dataset 'Volume' column. fillna(bool): if True, fill nan values. volume.OnBalanceVolumeIndicator( fillna: bool )
volume.VolumeWeightedAveragePrice Volume Weighted Average Price (VWAP) VWAP equals the dollar value of all trading periods divided by the total trading volume for the current day. The calculation starts when trading opens and ends when it closes. Because it is good for the current trading day only, intraday periods and data are used in the calculation. https://school.stockcharts.com/doku.php?id=technical_indicators:vwap_intraday Args: high(pandas.Series): dataset 'High' column. low(pandas.Series): dataset 'Low' column. close(pandas.Series): dataset 'Close' column. volume(pandas.Series): dataset 'Volume' column. window(int): n period. fillna(bool): if True, fill nan values. Returns: pandas.Series: New feature generated. volume.VolumeWeightedAveragePrice( window: int, fillna: bool )

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