All URIs are relative to https://api-v2.intrinio.com
| Method | HTTP request | Description |
|---|---|---|
| get_security_price_technicals_adi | GET /securities/{identifier}/prices/technicals/adi | Accumulation/Distribution Index |
| get_security_price_technicals_adtv | GET /securities/{identifier}/prices/technicals/adtv | Average Daily Trading Volume |
| get_security_price_technicals_adx | GET /securities/{identifier}/prices/technicals/adx | Average Directional Index |
| get_security_price_technicals_ao | GET /securities/{identifier}/prices/technicals/ao | Awesome Oscillator |
| get_security_price_technicals_atr | GET /securities/{identifier}/prices/technicals/atr | Average True Range |
| get_security_price_technicals_bb | GET /securities/{identifier}/prices/technicals/bb | Bollinger Bands |
| get_security_price_technicals_cci | GET /securities/{identifier}/prices/technicals/cci | Commodity Channel Index |
| get_security_price_technicals_cmf | GET /securities/{identifier}/prices/technicals/cmf | Chaikin Money Flow |
| get_security_price_technicals_dc | GET /securities/{identifier}/prices/technicals/dc | Donchian Channel |
| get_security_price_technicals_dpo | GET /securities/{identifier}/prices/technicals/dpo | Detrended Price Oscillator |
| get_security_price_technicals_eom | GET /securities/{identifier}/prices/technicals/eom | Ease of Movement |
| get_security_price_technicals_fi | GET /securities/{identifier}/prices/technicals/fi | Force Index |
| get_security_price_technicals_ichimoku | GET /securities/{identifier}/prices/technicals/ichimoku | Ichimoku Kinko Hyo |
| get_security_price_technicals_kc | GET /securities/{identifier}/prices/technicals/kc | Keltner Channel |
| get_security_price_technicals_kst | GET /securities/{identifier}/prices/technicals/kst | Know Sure Thing |
| get_security_price_technicals_macd | GET /securities/{identifier}/prices/technicals/macd | Moving Average Convergence Divergence |
| get_security_price_technicals_mfi | GET /securities/{identifier}/prices/technicals/mfi | Money Flow Index |
| get_security_price_technicals_mi | GET /securities/{identifier}/prices/technicals/mi | Mass Index |
| get_security_price_technicals_nvi | GET /securities/{identifier}/prices/technicals/nvi | Negative Volume Index |
| get_security_price_technicals_obv | GET /securities/{identifier}/prices/technicals/obv | On-balance Volume |
| get_security_price_technicals_obv_mean | GET /securities/{identifier}/prices/technicals/obv_mean | On-balance Volume Mean |
| get_security_price_technicals_rsi | GET /securities/{identifier}/prices/technicals/rsi | Relative Strength Index |
| get_security_price_technicals_sma | GET /securities/{identifier}/prices/technicals/sma | Simple Moving Average |
| get_security_price_technicals_sr | GET /securities/{identifier}/prices/technicals/sr | Stochastic Oscillator |
| get_security_price_technicals_trix | GET /securities/{identifier}/prices/technicals/trix | Triple Exponential Average |
| get_security_price_technicals_tsi | GET /securities/{identifier}/prices/technicals/tsi | True Strength Index |
| get_security_price_technicals_uo | GET /securities/{identifier}/prices/technicals/uo | Ultimate Oscillator |
| get_security_price_technicals_vi | GET /securities/{identifier}/prices/technicals/vi | Vortex Indicator |
| get_security_price_technicals_vpt | GET /securities/{identifier}/prices/technicals/vpt | Volume-price Trend |
| get_security_price_technicals_vwap | GET /securities/{identifier}/prices/technicals/vwap | Volume Weighted Average Price |
| get_security_price_technicals_wr | GET /securities/{identifier}/prices/technicals/wr | Williams %R |
View Intrinio API Documentation
ApiResponseSecurityAccumulationDistributionIndex get_security_price_technicals_adi(identifier, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
The Accumulation / Distribution Indicator is a volume-based technical indicator which uses the relationship between the stock`s price and volume flow to determine the underlying trend of a stock, up, down, or sideways trend of a stock.
from __future__ import print_function
import time
import intrinio_sdk as intrinio
from intrinio_sdk.rest import ApiException
intrinio.ApiClient().set_api_key('YOUR_API_KEY')
intrinio.ApiClient().allow_retries(True)
identifier = 'AAPL'
start_date = '2018-01-01'
end_date = '2019-01-01'
page_size = 100
next_page = ''
response = intrinio.TechnicalApi().get_security_price_technicals_adi(identifier, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
print(response)
# Note: For a Pandas DataFrame, import Pandas and use pd.DataFrame(response.property_name_dict) | Name | Type | Description | Notes |
|---|---|---|---|
| identifier | str | A Security identifier (Ticker, FIGI, ISIN, CUSIP, Intrinio ID) | |
| start_date | str | Return technical indicator values on or after the date | [optional] |
| end_date | str | Return technical indicator values on or before the date | [optional] |
| page_size | int | The number of results to return | [optional] [default to 100] |
| next_page | str | Gets the next page of data from a previous API call | [optional] |
ApiResponseSecurityAccumulationDistributionIndex
View Intrinio API Documentation
ApiResponseSecurityAverageDailyTradingVolume get_security_price_technicals_adtv(identifier, period=period, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
Average Daily Trading Volume is the average number of shares traded over a given period, usually between 20 to 30 trading days.
from __future__ import print_function
import time
import intrinio_sdk as intrinio
from intrinio_sdk.rest import ApiException
intrinio.ApiClient().set_api_key('YOUR_API_KEY')
intrinio.ApiClient().allow_retries(True)
identifier = 'AAPL'
period = 22
start_date = '2018-01-01'
end_date = '2019-01-01'
page_size = 100
next_page = ''
response = intrinio.TechnicalApi().get_security_price_technicals_adtv(identifier, period=period, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
print(response)
# Note: For a Pandas DataFrame, import Pandas and use pd.DataFrame(response.property_name_dict) | Name | Type | Description | Notes |
|---|---|---|---|
| identifier | str | A Security identifier (Ticker, FIGI, ISIN, CUSIP, Intrinio ID) | |
| period | int | The number of observations, per period, to calculate Average Daily Trading Volume | [optional] [default to 22] |
| start_date | str | Return technical indicator values on or after the date | [optional] |
| end_date | str | Return technical indicator values on or before the date | [optional] |
| page_size | int | The number of results to return | [optional] [default to 100] |
| next_page | str | Gets the next page of data from a previous API call | [optional] |
ApiResponseSecurityAverageDailyTradingVolume
View Intrinio API Documentation
ApiResponseSecurityAverageDirectionalIndex get_security_price_technicals_adx(identifier, period=period, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
The Average Directional Index indicator is often used to identify decreasing or increasing price momentum for an underlying security, it is composed of a total of three indicators, the current trendline (adx), a positive directional indicator (di_pos), and a negative directional indicator (di_neg).
from __future__ import print_function
import time
import intrinio_sdk as intrinio
from intrinio_sdk.rest import ApiException
intrinio.ApiClient().set_api_key('YOUR_API_KEY')
intrinio.ApiClient().allow_retries(True)
identifier = 'AAPL'
period = 14
start_date = '2018-01-01'
end_date = '2019-01-01'
page_size = 100
next_page = ''
response = intrinio.TechnicalApi().get_security_price_technicals_adx(identifier, period=period, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
print(response)
# Note: For a Pandas DataFrame, import Pandas and use pd.DataFrame(response.property_name_dict) | Name | Type | Description | Notes |
|---|---|---|---|
| identifier | str | A Security identifier (Ticker, FIGI, ISIN, CUSIP, Intrinio ID) | |
| period | int | The number of observations, per period, to calculate Average Directional Index | [optional] [default to 14] |
| start_date | str | Return technical indicator values on or after the date | [optional] |
| end_date | str | Return technical indicator values on or before the date | [optional] |
| page_size | int | The number of results to return | [optional] [default to 100] |
| next_page | str | Gets the next page of data from a previous API call | [optional] |
ApiResponseSecurityAverageDirectionalIndex
View Intrinio API Documentation
ApiResponseSecurityAwesomeOscillator get_security_price_technicals_ao(identifier, short_period=short_period, long_period=long_period, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
The Awesome Oscillator (ao) is a momentum indicator and is calculated by taking the difference between the latest 5 period simple moving average and the 34 period simple moving average. Rather than using the closing price like other indicators, the Awesome Oscillator uses the latest period`s midpoint value (period_high - period_low / 2). The Awesome Oscillator is useful in identifying and trading, zero-line crossovers, twin-peaks trading, and bullish/bearish saucers - Awesome Oscillator is often aggregated with additional technical indicators.
from __future__ import print_function
import time
import intrinio_sdk as intrinio
from intrinio_sdk.rest import ApiException
intrinio.ApiClient().set_api_key('YOUR_API_KEY')
intrinio.ApiClient().allow_retries(True)
identifier = 'AAPL'
short_period = 5
long_period = 34
start_date = '2018-01-01'
end_date = '2019-01-01'
page_size = 100
next_page = ''
response = intrinio.TechnicalApi().get_security_price_technicals_ao(identifier, short_period=short_period, long_period=long_period, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
print(response)
# Note: For a Pandas DataFrame, import Pandas and use pd.DataFrame(response.property_name_dict) | Name | Type | Description | Notes |
|---|---|---|---|
| identifier | str | A Security identifier (Ticker, FIGI, ISIN, CUSIP, Intrinio ID) | |
| short_period | int | The number of observations, per period, to calculate short period Simple Moving Average of the Awesome Oscillator | [optional] [default to 5] |
| long_period | int | The number of observations, per period, to calculate long period Simple Moving Average of the Awesome Oscillator | [optional] [default to 34] |
| start_date | str | Return technical indicator values on or after the date | [optional] |
| end_date | str | Return technical indicator values on or before the date | [optional] |
| page_size | int | The number of results to return | [optional] [default to 100] |
| next_page | str | Gets the next page of data from a previous API call | [optional] |
ApiResponseSecurityAwesomeOscillator
View Intrinio API Documentation
ApiResponseSecurityAverageTrueRange get_security_price_technicals_atr(identifier, period=period, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
The Average True Range (ATR) is a non-directional market volatility indicator often used to generate stop-out or entry indications. An increasing or expanding ATR typically indicates higher volatility, and a decreasing ATR indicates sideways price action and lower volatility.
from __future__ import print_function
import time
import intrinio_sdk as intrinio
from intrinio_sdk.rest import ApiException
intrinio.ApiClient().set_api_key('YOUR_API_KEY')
intrinio.ApiClient().allow_retries(True)
identifier = 'AAPL'
period = 14
start_date = '2018-01-01'
end_date = '2019-01-01'
page_size = 100
next_page = ''
response = intrinio.TechnicalApi().get_security_price_technicals_atr(identifier, period=period, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
print(response)
# Note: For a Pandas DataFrame, import Pandas and use pd.DataFrame(response.property_name_dict) | Name | Type | Description | Notes |
|---|---|---|---|
| identifier | str | A Security identifier (Ticker, FIGI, ISIN, CUSIP, Intrinio ID) | |
| period | int | The number of observations, per period, to calculate Average True Range | [optional] [default to 14] |
| start_date | str | Return technical indicator values on or after the date | [optional] |
| end_date | str | Return technical indicator values on or before the date | [optional] |
| page_size | int | The number of results to return | [optional] [default to 100] |
| next_page | str | Gets the next page of data from a previous API call | [optional] |
ApiResponseSecurityAverageTrueRange
View Intrinio API Documentation
ApiResponseSecurityBollingerBands get_security_price_technicals_bb(identifier, period=period, standard_deviations=standard_deviations, price_key=price_key, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
Bollinger Bands can be a useful technical analysis tool for generating oversold or overbought indicators. Bollinger Bands are composed of three lines, a simple moving average (middle band) and an upper and lower band – the upper and lower bands are typically 2 standard deviations +/- from a 20-day simple moving average, but can be modified. Traders typically consider an underlying security to be overbought as the underlying`s price moves towards the upper band and oversold as the underlying price moves towards the lower band.
from __future__ import print_function
import time
import intrinio_sdk as intrinio
from intrinio_sdk.rest import ApiException
intrinio.ApiClient().set_api_key('YOUR_API_KEY')
intrinio.ApiClient().allow_retries(True)
identifier = 'AAPL'
period = 20
standard_deviations = 2.0
price_key = 'close'
start_date = '2018-01-01'
end_date = '2019-01-01'
page_size = 100
next_page = ''
response = intrinio.TechnicalApi().get_security_price_technicals_bb(identifier, period=period, standard_deviations=standard_deviations, price_key=price_key, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
print(response)
# Note: For a Pandas DataFrame, import Pandas and use pd.DataFrame(response.property_name_dict) | Name | Type | Description | Notes |
|---|---|---|---|
| identifier | str | A Security identifier (Ticker, FIGI, ISIN, CUSIP, Intrinio ID) | |
| period | int | The number of observations, per period, to calculate Bollinger Bands | [optional] [default to 20] |
| standard_deviations | float | The number of standard deviations to calculate the upper and lower bands of the Bollinger Bands | [optional] [default to 2.0] |
| price_key | str | The Stock Price field to use when calculating Bollinger Bands | [optional] [default to close] |
| start_date | str | Return technical indicator values on or after the date | [optional] |
| end_date | str | Return technical indicator values on or before the date | [optional] |
| page_size | int | The number of results to return | [optional] [default to 100] |
| next_page | str | Gets the next page of data from a previous API call | [optional] |
ApiResponseSecurityBollingerBands
View Intrinio API Documentation
ApiResponseSecurityCommodityChannelIndex get_security_price_technicals_cci(identifier, period=period, constant=constant, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
The Commodity Channel Index (CCI) is a technical indicator used to generate buy and sell signals by indicating periods of strength and weakness in the market. CCI signals that fall below -100 are often perceived as weakness in the underlying price movement and CCI signals that rise above 100 indicate strength behind the underlying price movement.
from __future__ import print_function
import time
import intrinio_sdk as intrinio
from intrinio_sdk.rest import ApiException
intrinio.ApiClient().set_api_key('YOUR_API_KEY')
intrinio.ApiClient().allow_retries(True)
identifier = 'AAPL'
period = 20
constant = 0.015
start_date = '2018-01-01'
end_date = '2019-01-01'
page_size = 100
next_page = ''
response = intrinio.TechnicalApi().get_security_price_technicals_cci(identifier, period=period, constant=constant, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
print(response)
# Note: For a Pandas DataFrame, import Pandas and use pd.DataFrame(response.property_name_dict) | Name | Type | Description | Notes |
|---|---|---|---|
| identifier | str | A Security identifier (Ticker, FIGI, ISIN, CUSIP, Intrinio ID) | |
| period | int | The number of observations, per period, to calculate Commodity Channel Index | [optional] [default to 20] |
| constant | float | The number of observations, per period, to calculate Commodity Channel Index | [optional] [default to 0.015] |
| start_date | str | Return technical indicator values on or after the date | [optional] |
| end_date | str | Return technical indicator values on or before the date | [optional] |
| page_size | int | The number of results to return | [optional] [default to 100] |
| next_page | str | Gets the next page of data from a previous API call | [optional] |
ApiResponseSecurityCommodityChannelIndex
View Intrinio API Documentation
ApiResponseSecurityChaikinMoneyFlow get_security_price_technicals_cmf(identifier, period=period, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
The Chaikin Money Flow (CMF) utilizes exponential moving averages as an indicator to monitor the flow of money and momentum. The CMF indicator oscillates around a midrange 0-line and ranges between 100 and -100.
from __future__ import print_function
import time
import intrinio_sdk as intrinio
from intrinio_sdk.rest import ApiException
intrinio.ApiClient().set_api_key('YOUR_API_KEY')
intrinio.ApiClient().allow_retries(True)
identifier = 'AAPL'
period = 20
start_date = '2018-01-01'
end_date = '2019-01-01'
page_size = 100
next_page = ''
response = intrinio.TechnicalApi().get_security_price_technicals_cmf(identifier, period=period, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
print(response)
# Note: For a Pandas DataFrame, import Pandas and use pd.DataFrame(response.property_name_dict) | Name | Type | Description | Notes |
|---|---|---|---|
| identifier | str | A Security identifier (Ticker, FIGI, ISIN, CUSIP, Intrinio ID) | |
| period | int | The number of observations, per period, to calculate Chaikin Money Flow | [optional] [default to 20] |
| start_date | str | Return technical indicator values on or after the date | [optional] |
| end_date | str | Return technical indicator values on or before the date | [optional] |
| page_size | int | The number of results to return | [optional] [default to 100] |
| next_page | str | Gets the next page of data from a previous API call | [optional] |
ApiResponseSecurityChaikinMoneyFlow
View Intrinio API Documentation
ApiResponseSecurityDonchianChannel get_security_price_technicals_dc(identifier, period=period, price_key=price_key, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
The Donchian Channel consists of an Upper Bound (upper_bound) and Lower Bound (lower_bound) that track the recent highs and lows and is often used to signal entry and exit points for a position. As the price of the underlying symbol increases the Upper Bound raises, if the price becomes range bound the Upper Bound will remain flat and if the price begins to decrease, the Upper Bound will fall (and vice-versa for the Lower Bound).
from __future__ import print_function
import time
import intrinio_sdk as intrinio
from intrinio_sdk.rest import ApiException
intrinio.ApiClient().set_api_key('YOUR_API_KEY')
intrinio.ApiClient().allow_retries(True)
identifier = 'AAPL'
period = 20
price_key = 'close'
start_date = '2018-01-01'
end_date = '2019-01-01'
page_size = 100
next_page = ''
response = intrinio.TechnicalApi().get_security_price_technicals_dc(identifier, period=period, price_key=price_key, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
print(response)
# Note: For a Pandas DataFrame, import Pandas and use pd.DataFrame(response.property_name_dict) | Name | Type | Description | Notes |
|---|---|---|---|
| identifier | str | A Security identifier (Ticker, FIGI, ISIN, CUSIP, Intrinio ID) | |
| period | int | The number of observations, per period, to calculate Donchian Channel | [optional] [default to 20] |
| price_key | str | The Stock Price field to use when calculating Donchian Channel | [optional] [default to close] |
| start_date | str | Return technical indicator values on or after the date | [optional] |
| end_date | str | Return technical indicator values on or before the date | [optional] |
| page_size | int | The number of results to return | [optional] [default to 100] |
| next_page | str | Gets the next page of data from a previous API call | [optional] |
ApiResponseSecurityDonchianChannel
View Intrinio API Documentation
ApiResponseSecurityDetrendedPriceOscillator get_security_price_technicals_dpo(identifier, period=period, price_key=price_key, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
The Detrended Price Oscillator (DPO) signals the peaks and troughs of the underlying symbol’s price for a set period of time and is often used by traders to estimate future peaks and troughs using this as guidance to enter or exit a position.
from __future__ import print_function
import time
import intrinio_sdk as intrinio
from intrinio_sdk.rest import ApiException
intrinio.ApiClient().set_api_key('YOUR_API_KEY')
intrinio.ApiClient().allow_retries(True)
identifier = 'AAPL'
period = 20
price_key = 'close'
start_date = '2018-01-01'
end_date = '2019-01-01'
page_size = 100
next_page = ''
response = intrinio.TechnicalApi().get_security_price_technicals_dpo(identifier, period=period, price_key=price_key, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
print(response)
# Note: For a Pandas DataFrame, import Pandas and use pd.DataFrame(response.property_name_dict) | Name | Type | Description | Notes |
|---|---|---|---|
| identifier | str | A Security identifier (Ticker, FIGI, ISIN, CUSIP, Intrinio ID) | |
| period | int | The number of observations, per period, to calculate Detrended Price Oscillator | [optional] [default to 20] |
| price_key | str | The Stock Price field to use when calculating Detrended Price Oscillator | [optional] [default to close] |
| start_date | str | Return technical indicator values on or after the date | [optional] |
| end_date | str | Return technical indicator values on or before the date | [optional] |
| page_size | int | The number of results to return | [optional] [default to 100] |
| next_page | str | Gets the next page of data from a previous API call | [optional] |
ApiResponseSecurityDetrendedPriceOscillator
View Intrinio API Documentation
ApiResponseSecurityEaseOfMovement get_security_price_technicals_eom(identifier, period=period, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
The Ease of Movement (EOM) is a volume based oscillator that fluctuates around a midrange 0-line into positive and negative values. Positive values indicate that the underlying symbols price is rising with relative ease and negative value indicates the underlying symbols price is failing with relative ease.
from __future__ import print_function
import time
import intrinio_sdk as intrinio
from intrinio_sdk.rest import ApiException
intrinio.ApiClient().set_api_key('YOUR_API_KEY')
intrinio.ApiClient().allow_retries(True)
identifier = 'AAPL'
period = 20
start_date = '2018-01-01'
end_date = '2019-01-01'
page_size = 100
next_page = ''
response = intrinio.TechnicalApi().get_security_price_technicals_eom(identifier, period=period, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
print(response)
# Note: For a Pandas DataFrame, import Pandas and use pd.DataFrame(response.property_name_dict) | Name | Type | Description | Notes |
|---|---|---|---|
| identifier | str | A Security identifier (Ticker, FIGI, ISIN, CUSIP, Intrinio ID) | |
| period | int | The number of observations, per period, to calculate Ease of Movement | [optional] [default to 20] |
| start_date | str | Return technical indicator values on or after the date | [optional] |
| end_date | str | Return technical indicator values on or before the date | [optional] |
| page_size | int | The number of results to return | [optional] [default to 100] |
| next_page | str | Gets the next page of data from a previous API call | [optional] |
ApiResponseSecurityEaseOfMovement
View Intrinio API Documentation
ApiResponseSecurityForceIndex get_security_price_technicals_fi(identifier, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
The Force Index (FI) is an oscillator that takes into account the intensity of an underlying symbol`s price movement and its corresponding volume. It is used to confirm price breakouts and signal underlying trends.
from __future__ import print_function
import time
import intrinio_sdk as intrinio
from intrinio_sdk.rest import ApiException
intrinio.ApiClient().set_api_key('YOUR_API_KEY')
intrinio.ApiClient().allow_retries(True)
identifier = 'AAPL'
start_date = '2018-01-01'
end_date = '2019-01-01'
page_size = 100
next_page = ''
response = intrinio.TechnicalApi().get_security_price_technicals_fi(identifier, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
print(response)
# Note: For a Pandas DataFrame, import Pandas and use pd.DataFrame(response.property_name_dict) | Name | Type | Description | Notes |
|---|---|---|---|
| identifier | str | A Security identifier (Ticker, FIGI, ISIN, CUSIP, Intrinio ID) | |
| start_date | str | Return technical indicator values on or after the date | [optional] |
| end_date | str | Return technical indicator values on or before the date | [optional] |
| page_size | int | The number of results to return | [optional] [default to 100] |
| next_page | str | Gets the next page of data from a previous API call | [optional] |
View Intrinio API Documentation
ApiResponseSecurityIchimokuKinkoHyo get_security_price_technicals_ichimoku(identifier, low_period=low_period, medium_period=medium_period, high_period=high_period, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
The Ichimoku Kinko Hyo was designed to be an all-in-one trading indicator that could help traders determine momentum, support, and resistance.
from __future__ import print_function
import time
import intrinio_sdk as intrinio
from intrinio_sdk.rest import ApiException
intrinio.ApiClient().set_api_key('YOUR_API_KEY')
intrinio.ApiClient().allow_retries(True)
identifier = 'AAPL'
low_period = 9
medium_period = 26
high_period = 52
start_date = '2018-01-01'
end_date = '2019-01-01'
page_size = 100
next_page = ''
response = intrinio.TechnicalApi().get_security_price_technicals_ichimoku(identifier, low_period=low_period, medium_period=medium_period, high_period=high_period, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
print(response)
# Note: For a Pandas DataFrame, import Pandas and use pd.DataFrame(response.property_name_dict) | Name | Type | Description | Notes |
|---|---|---|---|
| identifier | str | A Security identifier (Ticker, FIGI, ISIN, CUSIP, Intrinio ID) | |
| low_period | int | The number of observations, per period, to calculate Tenkan Sen (Conversion Line) of Ichimoku Kinko Hyo | [optional] [default to 9] |
| medium_period | int | The number of observations, per period, to calculate Kijun Sen (Base Line), Senkou Span A (Leading Span A), and Chikou Span (Lagging Span) of Ichimoku Kinko Hyo | [optional] [default to 26] |
| high_period | int | The number of observations, per period, to calculate Senkou Span B (Leading Span B) of Ichimoku Kinko Hyo | [optional] [default to 52] |
| start_date | str | Return technical indicator values on or after the date | [optional] |
| end_date | str | Return technical indicator values on or before the date | [optional] |
| page_size | int | The number of results to return | [optional] [default to 100] |
| next_page | str | Gets the next page of data from a previous API call | [optional] |
ApiResponseSecurityIchimokuKinkoHyo
View Intrinio API Documentation
ApiResponseSecurityKeltnerChannel get_security_price_technicals_kc(identifier, period=period, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
The Keltner Channel is a volatility based signal, with upper, middle, and lower bands. It is often used at market open, when the largest moves tend to occur. In general, traders tend to buy if the price breaks up above the upper band or sell short if the price drops below the lower band.
from __future__ import print_function
import time
import intrinio_sdk as intrinio
from intrinio_sdk.rest import ApiException
intrinio.ApiClient().set_api_key('YOUR_API_KEY')
intrinio.ApiClient().allow_retries(True)
identifier = 'AAPL'
period = 10
start_date = '2018-01-01'
end_date = '2019-01-01'
page_size = 100
next_page = ''
response = intrinio.TechnicalApi().get_security_price_technicals_kc(identifier, period=period, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
print(response)
# Note: For a Pandas DataFrame, import Pandas and use pd.DataFrame(response.property_name_dict) | Name | Type | Description | Notes |
|---|---|---|---|
| identifier | str | A Security identifier (Ticker, FIGI, ISIN, CUSIP, Intrinio ID) | |
| period | int | The number of observations, per period, to calculate Kelter Channel | [optional] [default to 10] |
| start_date | str | Return technical indicator values on or after the date | [optional] |
| end_date | str | Return technical indicator values on or before the date | [optional] |
| page_size | int | The number of results to return | [optional] [default to 100] |
| next_page | str | Gets the next page of data from a previous API call | [optional] |
ApiResponseSecurityKeltnerChannel
View Intrinio API Documentation
ApiResponseSecurityKnowSureThing get_security_price_technicals_kst(identifier, roc1=roc1, roc2=roc2, roc3=roc3, roc4=roc4, sma1=sma1, sma2=sma2, sma3=sma3, sma4=sma4, price_key=price_key, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
The Know Sure Thing indicator (KST) is a momentum based oscillator that is calculated by measuring the momentum of four separate price cycles. KST fluctuates above and below a zero line and is used to identify overbought and oversold conditions, and is often used with additional indicators to boost signal strength.
from __future__ import print_function
import time
import intrinio_sdk as intrinio
from intrinio_sdk.rest import ApiException
intrinio.ApiClient().set_api_key('YOUR_API_KEY')
intrinio.ApiClient().allow_retries(True)
identifier = 'AAPL'
roc1 = 10
roc2 = 15
roc3 = 20
roc4 = 30
sma1 = 10
sma2 = 10
sma3 = 10
sma4 = 15
price_key = 'close'
start_date = '2018-01-01'
end_date = '2019-01-01'
page_size = 100
next_page = ''
response = intrinio.TechnicalApi().get_security_price_technicals_kst(identifier, roc1=roc1, roc2=roc2, roc3=roc3, roc4=roc4, sma1=sma1, sma2=sma2, sma3=sma3, sma4=sma4, price_key=price_key, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
print(response)
# Note: For a Pandas DataFrame, import Pandas and use pd.DataFrame(response.property_name_dict) | Name | Type | Description | Notes |
|---|---|---|---|
| identifier | str | A Security identifier (Ticker, FIGI, ISIN, CUSIP, Intrinio ID) | |
| roc1 | int | The number of observations, per period, to calculate the rate-of-change for RCMA1 | [optional] [default to 10] |
| roc2 | int | The number of observations, per period, to calculate the rate-of-change for RCMA2 | [optional] [default to 15] |
| roc3 | int | The number of observations, per period, to calculate the rate-of-change for RCMA3 | [optional] [default to 20] |
| roc4 | int | The number of observations, per period, to calculate the rate-of-change for RCMA4 | [optional] [default to 30] |
| sma1 | int | The number of observations, per period, to calculate the Simple Moving Average of the rate-of-change for RCMA1 | [optional] [default to 10] |
| sma2 | int | The number of observations, per period, to calculate the Simple Moving Average of the rate-of-change for RCMA2 | [optional] [default to 10] |
| sma3 | int | The number of observations, per period, to calculate the Simple Moving Average of the rate-of-change for RCMA3 | [optional] [default to 10] |
| sma4 | int | The number of observations, per period, to calculate the Simple Moving Average of the rate-of-change for RCMA4 | [optional] [default to 15] |
| price_key | str | The Stock Price field to use when calculating Know Sure Thing | [optional] [default to close] |
| start_date | str | Return technical indicator values on or after the date | [optional] |
| end_date | str | Return technical indicator values on or before the date | [optional] |
| page_size | int | The number of results to return | [optional] [default to 100] |
| next_page | str | Gets the next page of data from a previous API call | [optional] |
ApiResponseSecurityKnowSureThing
View Intrinio API Documentation
ApiResponseSecurityMovingAverageConvergenceDivergence get_security_price_technicals_macd(identifier, fast_period=fast_period, slow_period=slow_period, signal_period=signal_period, price_key=price_key, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
Moving average convergence divergence (MACD) is a trend-following momentum oscillator that consists of three indicators: (1) a 12 period short-term exponential moving average (EMA) a 26 period long-term EMA and a 9 period EMA signal line. Traders using MACD often look for signal line crossovers, centerline crossovers, and EMA divergences to indicate the momentum and underlying trend of a security`s price.
from __future__ import print_function
import time
import intrinio_sdk as intrinio
from intrinio_sdk.rest import ApiException
intrinio.ApiClient().set_api_key('YOUR_API_KEY')
intrinio.ApiClient().allow_retries(True)
identifier = 'AAPL'
fast_period = 12
slow_period = 26
signal_period = 9
price_key = 'close'
start_date = '2018-01-01'
end_date = '2019-01-01'
page_size = 100
next_page = ''
response = intrinio.TechnicalApi().get_security_price_technicals_macd(identifier, fast_period=fast_period, slow_period=slow_period, signal_period=signal_period, price_key=price_key, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
print(response)
# Note: For a Pandas DataFrame, import Pandas and use pd.DataFrame(response.property_name_dict) | Name | Type | Description | Notes |
|---|---|---|---|
| identifier | str | A Security identifier (Ticker, FIGI, ISIN, CUSIP, Intrinio ID) | |
| fast_period | int | The number of observations, per period, to calculate the fast moving Exponential Moving Average for Moving Average Convergence Divergence | [optional] [default to 12] |
| slow_period | int | The number of observations, per period, to calculate the slow moving Exponential Moving Average for Moving Average Convergence Divergence | [optional] [default to 26] |
| signal_period | int | The number of observations, per period, to calculate the signal line for Moving Average Convergence Divergence | [optional] [default to 9] |
| price_key | str | The Stock Price field to use when calculating Moving Average Convergence Divergence | [optional] [default to close] |
| start_date | str | Return technical indicator values on or after the date | [optional] |
| end_date | str | Return technical indicator values on or before the date | [optional] |
| page_size | int | The number of results to return | [optional] [default to 100] |
| next_page | str | Gets the next page of data from a previous API call | [optional] |
ApiResponseSecurityMovingAverageConvergenceDivergence
View Intrinio API Documentation
ApiResponseSecurityMoneyFlowIndex get_security_price_technicals_mfi(identifier, period=period, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
The Money Flow Index (MFI) is a technical oscillator that incorporates both price and volume, moving between 0 and 100. Traders often consider a MFI above 80 as overbought conditions and below 20 as oversold conditions.
from __future__ import print_function
import time
import intrinio_sdk as intrinio
from intrinio_sdk.rest import ApiException
intrinio.ApiClient().set_api_key('YOUR_API_KEY')
intrinio.ApiClient().allow_retries(True)
identifier = 'AAPL'
period = 14
start_date = '2018-01-01'
end_date = '2019-01-01'
page_size = 100
next_page = ''
response = intrinio.TechnicalApi().get_security_price_technicals_mfi(identifier, period=period, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
print(response)
# Note: For a Pandas DataFrame, import Pandas and use pd.DataFrame(response.property_name_dict) | Name | Type | Description | Notes |
|---|---|---|---|
| identifier | str | A Security identifier (Ticker, FIGI, ISIN, CUSIP, Intrinio ID) | |
| period | int | The number of observations, per period, to calculate Money Flow Index | [optional] [default to 14] |
| start_date | str | Return technical indicator values on or after the date | [optional] |
| end_date | str | Return technical indicator values on or before the date | [optional] |
| page_size | int | The number of results to return | [optional] [default to 100] |
| next_page | str | Gets the next page of data from a previous API call | [optional] |
ApiResponseSecurityMoneyFlowIndex
View Intrinio API Documentation
ApiResponseSecurityMassIndex get_security_price_technicals_mi(identifier, ema_period=ema_period, sum_period=sum_period, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
The mass index (MI) is a technical indicator used by traders to predict trend reversals. A trend reversal signal is said to occur when the 25-day MI reaches 27.0 and then falls below 26.0.
from __future__ import print_function
import time
import intrinio_sdk as intrinio
from intrinio_sdk.rest import ApiException
intrinio.ApiClient().set_api_key('YOUR_API_KEY')
intrinio.ApiClient().allow_retries(True)
identifier = 'AAPL'
ema_period = 9
sum_period = 25
start_date = '2018-01-01'
end_date = '2019-01-01'
page_size = 100
next_page = ''
response = intrinio.TechnicalApi().get_security_price_technicals_mi(identifier, ema_period=ema_period, sum_period=sum_period, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
print(response)
# Note: For a Pandas DataFrame, import Pandas and use pd.DataFrame(response.property_name_dict) | Name | Type | Description | Notes |
|---|---|---|---|
| identifier | str | A Security identifier (Ticker, FIGI, ISIN, CUSIP, Intrinio ID) | |
| ema_period | int | The number of observations, per period, to calculate the single Exponential Moving Average and the Double Exponential Moving Average for Mass Index | [optional] [default to 9] |
| sum_period | int | The number of observations, per period, to calculate the sum of the Exponetinal Moving Average Ratios for Mass Index | [optional] [default to 25] |
| start_date | str | Return technical indicator values on or after the date | [optional] |
| end_date | str | Return technical indicator values on or before the date | [optional] |
| page_size | int | The number of results to return | [optional] [default to 100] |
| next_page | str | Gets the next page of data from a previous API call | [optional] |
View Intrinio API Documentation
ApiResponseSecurityNegativeVolumeIndex get_security_price_technicals_nvi(identifier, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
The negative volume index (NVI) is often referred to as the smart money indicator. It works by the assumption that smart money (institutional money) is at work when volume decreases and vice versa when volume increases. NVI starts at 1000 and increases in regard to the percentage price change when volume decreases over a 255-day EMA period. Traders often use this technical indicator when researching broder markets and indices.
from __future__ import print_function
import time
import intrinio_sdk as intrinio
from intrinio_sdk.rest import ApiException
intrinio.ApiClient().set_api_key('YOUR_API_KEY')
intrinio.ApiClient().allow_retries(True)
identifier = 'AAPL'
start_date = '2018-01-01'
end_date = '2019-01-01'
page_size = 100
next_page = ''
response = intrinio.TechnicalApi().get_security_price_technicals_nvi(identifier, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
print(response)
# Note: For a Pandas DataFrame, import Pandas and use pd.DataFrame(response.property_name_dict) | Name | Type | Description | Notes |
|---|---|---|---|
| identifier | str | A Security identifier (Ticker, FIGI, ISIN, CUSIP, Intrinio ID) | |
| start_date | str | Return technical indicator values on or after the date | [optional] |
| end_date | str | Return technical indicator values on or before the date | [optional] |
| page_size | int | The number of results to return | [optional] [default to 100] |
| next_page | str | Gets the next page of data from a previous API call | [optional] |
ApiResponseSecurityNegativeVolumeIndex
View Intrinio API Documentation
ApiResponseSecurityOnBalanceVolume get_security_price_technicals_obv(identifier, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
On-balance volume (OBV) is a leading momentum indicator that uses the increase/decrease flow in volume to predict upcoming stock price changes. When both OBV and a security`s price are making higher highs, it is presumed the upward trend is likely to continue and vice versa.
from __future__ import print_function
import time
import intrinio_sdk as intrinio
from intrinio_sdk.rest import ApiException
intrinio.ApiClient().set_api_key('YOUR_API_KEY')
intrinio.ApiClient().allow_retries(True)
identifier = 'AAPL'
start_date = '2018-01-01'
end_date = '2019-01-01'
page_size = 100
next_page = ''
response = intrinio.TechnicalApi().get_security_price_technicals_obv(identifier, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
print(response)
# Note: For a Pandas DataFrame, import Pandas and use pd.DataFrame(response.property_name_dict) | Name | Type | Description | Notes |
|---|---|---|---|
| identifier | str | A Security identifier (Ticker, FIGI, ISIN, CUSIP, Intrinio ID) | |
| start_date | str | Return technical indicator values on or after the date | [optional] |
| end_date | str | Return technical indicator values on or before the date | [optional] |
| page_size | int | The number of results to return | [optional] [default to 100] |
| next_page | str | Gets the next page of data from a previous API call | [optional] |
ApiResponseSecurityOnBalanceVolume
View Intrinio API Documentation
ApiResponseSecurityOnBalanceVolumeMean get_security_price_technicals_obv_mean(identifier, period=period, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
On-balance volume mean (OBVM) is a leading momentum indicator that uses the increase/decrease flow in volume to predict upcoming stock price changes. The difference between OBV and OBVM is that OBVM takes the mean average of a provided period. When both OBVM and a security`s price are making higher highs, it is presumed the upward trend is likely to continue and vice versa.
from __future__ import print_function
import time
import intrinio_sdk as intrinio
from intrinio_sdk.rest import ApiException
intrinio.ApiClient().set_api_key('YOUR_API_KEY')
intrinio.ApiClient().allow_retries(True)
identifier = 'AAPL'
period = 10
start_date = '2018-01-01'
end_date = '2019-01-01'
page_size = 100
next_page = ''
response = intrinio.TechnicalApi().get_security_price_technicals_obv_mean(identifier, period=period, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
print(response)
# Note: For a Pandas DataFrame, import Pandas and use pd.DataFrame(response.property_name_dict) | Name | Type | Description | Notes |
|---|---|---|---|
| identifier | str | A Security identifier (Ticker, FIGI, ISIN, CUSIP, Intrinio ID) | |
| period | int | The number of observations, per period, to calculate On-balance Volume Mean | [optional] [default to 10] |
| start_date | str | Return technical indicator values on or after the date | [optional] |
| end_date | str | Return technical indicator values on or before the date | [optional] |
| page_size | int | The number of results to return | [optional] [default to 100] |
| next_page | str | Gets the next page of data from a previous API call | [optional] |
ApiResponseSecurityOnBalanceVolumeMean
View Intrinio API Documentation
ApiResponseSecurityRelativeStrengthIndex get_security_price_technicals_rsi(identifier, period=period, price_key=price_key, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
Relative strength index (RSI) is a momentum oscillator that ranges between 0 and 100. Traders believe that an RSI value over 70 indicates that a security is overbought and an RSI under 30 indicates that a security is oversold.
from __future__ import print_function
import time
import intrinio_sdk as intrinio
from intrinio_sdk.rest import ApiException
intrinio.ApiClient().set_api_key('YOUR_API_KEY')
intrinio.ApiClient().allow_retries(True)
identifier = 'AAPL'
period = 14
price_key = 'close'
start_date = '2018-01-01'
end_date = '2019-01-01'
page_size = 100
next_page = ''
response = intrinio.TechnicalApi().get_security_price_technicals_rsi(identifier, period=period, price_key=price_key, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
print(response)
# Note: For a Pandas DataFrame, import Pandas and use pd.DataFrame(response.property_name_dict) | Name | Type | Description | Notes |
|---|---|---|---|
| identifier | str | A Security identifier (Ticker, FIGI, ISIN, CUSIP, Intrinio ID) | |
| period | int | The number of observations, per period, to calculate Relative Strength Index | [optional] [default to 14] |
| price_key | str | The Stock Price field to use when calculating Relative Strength Index | [optional] [default to close] |
| start_date | str | Return technical indicator values on or after the date | [optional] |
| end_date | str | Return technical indicator values on or before the date | [optional] |
| page_size | int | The number of results to return | [optional] [default to 100] |
| next_page | str | Gets the next page of data from a previous API call | [optional] |
ApiResponseSecurityRelativeStrengthIndex
View Intrinio API Documentation
ApiResponseSecuritySimpleMovingAverage get_security_price_technicals_sma(identifier, period=period, price_key=price_key, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
A simple moving average (SMA) adds recent prices for a specified period and divides the total by that same number of periods. SMA is typically used to indicate whether a security is in an uptrend or downtrend and can also be combined with a long-term moving average to improve the signal`s abilities.
from __future__ import print_function
import time
import intrinio_sdk as intrinio
from intrinio_sdk.rest import ApiException
intrinio.ApiClient().set_api_key('YOUR_API_KEY')
intrinio.ApiClient().allow_retries(True)
identifier = 'AAPL'
period = 20
price_key = 'close'
start_date = '2018-01-01'
end_date = '2019-01-01'
page_size = 100
next_page = ''
response = intrinio.TechnicalApi().get_security_price_technicals_sma(identifier, period=period, price_key=price_key, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
print(response)
# Note: For a Pandas DataFrame, import Pandas and use pd.DataFrame(response.property_name_dict) | Name | Type | Description | Notes |
|---|---|---|---|
| identifier | str | A Security identifier (Ticker, FIGI, ISIN, CUSIP, Intrinio ID) | |
| period | int | The number of observations, per period, to calculate Simple Moving Average | [optional] [default to 20] |
| price_key | str | The Stock Price field to use when calculating Simple Moving Average | [optional] [default to close] |
| start_date | str | Return technical indicator values on or after the date | [optional] |
| end_date | str | Return technical indicator values on or before the date | [optional] |
| page_size | int | The number of results to return | [optional] [default to 100] |
| next_page | str | Gets the next page of data from a previous API call | [optional] |
ApiResponseSecuritySimpleMovingAverage
View Intrinio API Documentation
ApiResponseSecurityStochasticOscillator get_security_price_technicals_sr(identifier, period=period, signal_period=signal_period, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
The Stochastic Oscillator (SO) is a range-bound momentum indicator that ranges from 0 to 100 and follows the velocity of the momentum itself, not the underlying price or volume. When SO is above 80 it indicates that a security is trading at the high end of its period`s high-low range and vice versa if the reading is below 20.
from __future__ import print_function
import time
import intrinio_sdk as intrinio
from intrinio_sdk.rest import ApiException
intrinio.ApiClient().set_api_key('YOUR_API_KEY')
intrinio.ApiClient().allow_retries(True)
identifier = 'AAPL'
period = 14
signal_period = 3
start_date = '2018-01-01'
end_date = '2019-01-01'
page_size = 100
next_page = ''
response = intrinio.TechnicalApi().get_security_price_technicals_sr(identifier, period=period, signal_period=signal_period, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
print(response)
# Note: For a Pandas DataFrame, import Pandas and use pd.DataFrame(response.property_name_dict) | Name | Type | Description | Notes |
|---|---|---|---|
| identifier | str | A Security identifier (Ticker, FIGI, ISIN, CUSIP, Intrinio ID) | |
| period | int | The number of observations, per period, to calculate %K of Stochastic Oscillator | [optional] [default to 14] |
| signal_period | int | The number of observations, per period, to calculate the %D (the Simple Moving Average of %K) as a signal line for Stochastic Oscillator | [optional] [default to 3] |
| start_date | str | Return technical indicator values on or after the date | [optional] |
| end_date | str | Return technical indicator values on or before the date | [optional] |
| page_size | int | The number of results to return | [optional] [default to 100] |
| next_page | str | Gets the next page of data from a previous API call | [optional] |
ApiResponseSecurityStochasticOscillator
View Intrinio API Documentation
ApiResponseSecurityTripleExponentialAverage get_security_price_technicals_trix(identifier, period=period, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
The Triple Exponential Average (TEA) is a momentum indicator used to identify when a security is oversold and overbought. By exponentially smoothing out the underlying security`s moving average, the TEA filters out insignificant price movements. A positive TEA is often believed to indicate momentum is increasing and a negative TEA indicates that momentum is decreasing.
from __future__ import print_function
import time
import intrinio_sdk as intrinio
from intrinio_sdk.rest import ApiException
intrinio.ApiClient().set_api_key('YOUR_API_KEY')
intrinio.ApiClient().allow_retries(True)
identifier = 'AAPL'
period = 15
start_date = '2018-01-01'
end_date = '2019-01-01'
page_size = 100
next_page = ''
response = intrinio.TechnicalApi().get_security_price_technicals_trix(identifier, period=period, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
print(response)
# Note: For a Pandas DataFrame, import Pandas and use pd.DataFrame(response.property_name_dict) | Name | Type | Description | Notes |
|---|---|---|---|
| identifier | str | A Security identifier (Ticker, FIGI, ISIN, CUSIP, Intrinio ID) | |
| period | int | The number of observations, per period, to calculate Exponential Moving Average for Triple Exponential Average | [optional] [default to 15] |
| start_date | str | Return technical indicator values on or after the date | [optional] |
| end_date | str | Return technical indicator values on or before the date | [optional] |
| page_size | int | The number of results to return | [optional] [default to 100] |
| next_page | str | Gets the next page of data from a previous API call | [optional] |
ApiResponseSecurityTripleExponentialAverage
View Intrinio API Documentation
ApiResponseSecurityTrueStrengthIndex get_security_price_technicals_tsi(identifier, low_period=low_period, high_period=high_period, price_key=price_key, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
The True Strength Index (TSI) is a momentum oscillator used to identify building trends and trend reversals, typically by signalling overbought and oversold conditions. TSI fluctuates between positive and negative values, and traders typically combine its signal with other momentum oscillators to increase its strength. When TSI crosses the signal line into positive territory it is presumed to be an entrance opportunity and vice versa when the TSI crosses into negative territory.
from __future__ import print_function
import time
import intrinio_sdk as intrinio
from intrinio_sdk.rest import ApiException
intrinio.ApiClient().set_api_key('YOUR_API_KEY')
intrinio.ApiClient().allow_retries(True)
identifier = 'AAPL'
low_period = 13
high_period = 25
price_key = 'close'
start_date = '2018-01-01'
end_date = '2019-01-01'
page_size = 100
next_page = ''
response = intrinio.TechnicalApi().get_security_price_technicals_tsi(identifier, low_period=low_period, high_period=high_period, price_key=price_key, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
print(response)
# Note: For a Pandas DataFrame, import Pandas and use pd.DataFrame(response.property_name_dict) | Name | Type | Description | Notes |
|---|---|---|---|
| identifier | str | A Security identifier (Ticker, FIGI, ISIN, CUSIP, Intrinio ID) | |
| low_period | int | The number of observations, per period, to calculate low period Exponential Moving Average for smoothing in True Strength Index | [optional] [default to 13] |
| high_period | int | The number of observations, per period, to calculate high period Exponential Moving Average for smoothing in True Strength Index | [optional] [default to 25] |
| price_key | str | The Stock Price field to use when calculating True Strength Index | [optional] [default to close] |
| start_date | str | Return technical indicator values on or after the date | [optional] |
| end_date | str | Return technical indicator values on or before the date | [optional] |
| page_size | int | The number of results to return | [optional] [default to 100] |
| next_page | str | Gets the next page of data from a previous API call | [optional] |
ApiResponseSecurityTrueStrengthIndex
View Intrinio API Documentation
ApiResponseSecurityUltimateOscillator get_security_price_technicals_uo(identifier, short_period=short_period, medium_period=medium_period, long_period=long_period, short_weight=short_weight, medium_weight=medium_weight, long_weight=long_weight, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
The Ultimate Oscillator (UO) is a range bound technical indicator that moves between 0 and 100 and is calculated with 3 timeframes, typically 7, 14, and 28 day periods. When UOs value is above 70 a security is categorized as overbought and when UOs value is below 30 a security is categorized as oversold.
from __future__ import print_function
import time
import intrinio_sdk as intrinio
from intrinio_sdk.rest import ApiException
intrinio.ApiClient().set_api_key('YOUR_API_KEY')
intrinio.ApiClient().allow_retries(True)
identifier = 'AAPL'
short_period = 7
medium_period = 14
long_period = 28
short_weight = 4.0
medium_weight = 2.0
long_weight = 1.0
start_date = '2018-01-01'
end_date = '2019-01-01'
page_size = 100
next_page = ''
response = intrinio.TechnicalApi().get_security_price_technicals_uo(identifier, short_period=short_period, medium_period=medium_period, long_period=long_period, short_weight=short_weight, medium_weight=medium_weight, long_weight=long_weight, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
print(response)
# Note: For a Pandas DataFrame, import Pandas and use pd.DataFrame(response.property_name_dict) | Name | Type | Description | Notes |
|---|---|---|---|
| identifier | str | A Security identifier (Ticker, FIGI, ISIN, CUSIP, Intrinio ID) | |
| short_period | int | The number of observations, per period, to calculate the short period for Ultimate Oscillator | [optional] [default to 7] |
| medium_period | int | The number of observations, per period, to calculate the medium period for Ultimate Oscillator | [optional] [default to 14] |
| long_period | int | The number of observations, per period, to calculate the long period for Ultimate Oscillator | [optional] [default to 28] |
| short_weight | float | The weight of short Buying Pressure average for Ultimate Oscillator | [optional] [default to 4.0] |
| medium_weight | float | The weight of medium Buying Pressure average for Ultimate Oscillator | [optional] [default to 2.0] |
| long_weight | float | The weight of long Buying Pressure average for Ultimate Oscillator | [optional] [default to 1.0] |
| start_date | str | Return technical indicator values on or after the date | [optional] |
| end_date | str | Return technical indicator values on or before the date | [optional] |
| page_size | int | The number of results to return | [optional] [default to 100] |
| next_page | str | Gets the next page of data from a previous API call | [optional] |
ApiResponseSecurityUltimateOscillator
View Intrinio API Documentation
ApiResponseSecurityVortexIndicator get_security_price_technicals_vi(identifier, period=period, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
The Vortex Indicator (VI) is composed of an uptrend line (VI+) and a downtrend line (VI-). When VI+ crosses VI- from below it typically indicates an entry into a given security. When VI- crosses VI+ from below it typically triggers an exit and that the current trend is reversing course.
from __future__ import print_function
import time
import intrinio_sdk as intrinio
from intrinio_sdk.rest import ApiException
intrinio.ApiClient().set_api_key('YOUR_API_KEY')
intrinio.ApiClient().allow_retries(True)
identifier = 'AAPL'
period = 14
start_date = '2018-01-01'
end_date = '2019-01-01'
page_size = 100
next_page = ''
response = intrinio.TechnicalApi().get_security_price_technicals_vi(identifier, period=period, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
print(response)
# Note: For a Pandas DataFrame, import Pandas and use pd.DataFrame(response.property_name_dict) | Name | Type | Description | Notes |
|---|---|---|---|
| identifier | str | A Security identifier (Ticker, FIGI, ISIN, CUSIP, Intrinio ID) | |
| period | int | The number of observations, per period, to calculate Vortex Indicator | [optional] [default to 14] |
| start_date | str | Return technical indicator values on or after the date | [optional] |
| end_date | str | Return technical indicator values on or before the date | [optional] |
| page_size | int | The number of results to return | [optional] [default to 100] |
| next_page | str | Gets the next page of data from a previous API call | [optional] |
ApiResponseSecurityVortexIndicator
View Intrinio API Documentation
ApiResponseSecurityVolumePriceTrend get_security_price_technicals_vpt(identifier, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
The volume price trend (VPT) is a technical indicator that uses price & volume to determine whether a trend is established. Typically, when a security is trending upwards, there is more volume on positive days than negative ones, and as a result VPT should be increasing on these days as well. However, if VPT fails to increase past its previous high during an outbreak, this is suggested to indicate the rally is losing strength.
from __future__ import print_function
import time
import intrinio_sdk as intrinio
from intrinio_sdk.rest import ApiException
intrinio.ApiClient().set_api_key('YOUR_API_KEY')
intrinio.ApiClient().allow_retries(True)
identifier = 'AAPL'
start_date = '2018-01-01'
end_date = '2019-01-01'
page_size = 100
next_page = ''
response = intrinio.TechnicalApi().get_security_price_technicals_vpt(identifier, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
print(response)
# Note: For a Pandas DataFrame, import Pandas and use pd.DataFrame(response.property_name_dict) | Name | Type | Description | Notes |
|---|---|---|---|
| identifier | str | A Security identifier (Ticker, FIGI, ISIN, CUSIP, Intrinio ID) | |
| start_date | str | Return technical indicator values on or after the date | [optional] |
| end_date | str | Return technical indicator values on or before the date | [optional] |
| page_size | int | The number of results to return | [optional] [default to 100] |
| next_page | str | Gets the next page of data from a previous API call | [optional] |
ApiResponseSecurityVolumePriceTrend
View Intrinio API Documentation
ApiResponseSecurityVolumeWeightedAveragePrice get_security_price_technicals_vwap(identifier, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
Volume Weighted Average Price (VWAP) is a lagging technical indicator that is used in combination with a security`s price. When the underlying price rises above its VWAP, it is often interpreted as a bullish signal, and vice versa in the opposite direction.
from __future__ import print_function
import time
import intrinio_sdk as intrinio
from intrinio_sdk.rest import ApiException
intrinio.ApiClient().set_api_key('YOUR_API_KEY')
intrinio.ApiClient().allow_retries(True)
identifier = 'AAPL'
start_date = '2018-01-01'
end_date = '2019-01-01'
page_size = 100
next_page = ''
response = intrinio.TechnicalApi().get_security_price_technicals_vwap(identifier, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
print(response)
# Note: For a Pandas DataFrame, import Pandas and use pd.DataFrame(response.property_name_dict) | Name | Type | Description | Notes |
|---|---|---|---|
| identifier | str | A Security identifier (Ticker, FIGI, ISIN, CUSIP, Intrinio ID) | |
| start_date | str | Return technical indicator values on or after the date | [optional] |
| end_date | str | Return technical indicator values on or before the date | [optional] |
| page_size | int | The number of results to return | [optional] [default to 100] |
| next_page | str | Gets the next page of data from a previous API call | [optional] |
ApiResponseSecurityVolumeWeightedAveragePrice
View Intrinio API Documentation
ApiResponseSecurityWilliamsR get_security_price_technicals_wr(identifier, period=period, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
Williams %R is a momentum indicator used to determine overbought and oversold environments for a security and fluctuates between 0 and -100. When Williams %R is above -20 the security is considered to be overbought and when Williams %R is under -80 the security is considered to be oversold.
from __future__ import print_function
import time
import intrinio_sdk as intrinio
from intrinio_sdk.rest import ApiException
intrinio.ApiClient().set_api_key('YOUR_API_KEY')
intrinio.ApiClient().allow_retries(True)
identifier = 'AAPL'
period = 14
start_date = '2018-01-01'
end_date = '2019-01-01'
page_size = 100
next_page = ''
response = intrinio.TechnicalApi().get_security_price_technicals_wr(identifier, period=period, start_date=start_date, end_date=end_date, page_size=page_size, next_page=next_page)
print(response)
# Note: For a Pandas DataFrame, import Pandas and use pd.DataFrame(response.property_name_dict) | Name | Type | Description | Notes |
|---|---|---|---|
| identifier | str | A Security identifier (Ticker, FIGI, ISIN, CUSIP, Intrinio ID) | |
| period | int | The number of observations, per period, to look-back when calculating Williams %R | [optional] [default to 14] |
| start_date | str | Return technical indicator values on or after the date | [optional] |
| end_date | str | Return technical indicator values on or before the date | [optional] |
| page_size | float | The number of results to return | [optional] [default to 100] |
| next_page | str | Gets the next page of data from a previous API call | [optional] |