investpy.technical
¶
-
investpy.technical.
moving_averages
(name, country, product_type, interval='daily')¶ This function retrieves the moving averages values calculated by Investing.com for every financial product available (stocks, funds, etfs, indices, currency crosses, bonds, certificates and commodities) for different time intervals. So on, the user must provide the product_type name and the name of the product (unless product_type is ‘stock’ which name value will be the stock’s symbol) and the country if required (mandatory unless product_type is either ‘currency_cross’ or ‘commodity’, where it must be None). Additionally, the interval can be specified which defines the update frequency of the calculations of the moving averages (both simple and exponential). Note that the specified interval is not the moving average’s interval, since all the available time frames used on the calculation of the moving averages are retrieved.
- Parameters
name (
str
) – name of the product to retrieve the moving averages table from (if product_type is stock, its value must be the stock’s symbol not the name).country (
str
) – country name of the introduced product if applicable (if product_type is either currency_cross or commodity this parameter should be None, unless it can be specified just for commodity product_type).product_type (
str
) – identifier of the introduced product, available ones are: stock, fund, etf, index, currency_cross, bond, certificate and commodity.interval (
str
) – time interval of the resulting calculations, available values are: 5mins, 15mins, 30mins, 1hour, 5hours, daily, weekly and monthly.
- Returns
The resulting
pandas.DataFrame
contains the table with the results of the calculation of the moving averages made by Investing.com for the introduced financial product. So on, if the retrieval process succeed its result will look like:period | sma_value | sma_signal | ema_value | ema_signal --------|-----------|------------|-----------|------------ xxxxxx | xxxxxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxxxxxxx
- Return type
pandas.DataFrame
- moving_averages- Raises
ValueError – raised if any of the introduced parameters is not valid or errored.
ConnectionError – raised if the connection to Investing.com errored or could not be established.
Examples
>>> data = investpy.moving_averages(name='bbva', country='spain', product_type='stock', interval='daily') >>> data.head() period sma_value sma_signal ema_value ema_signal 0 5 4.615 buy 4.650 buy 1 10 4.675 sell 4.693 sell 2 20 4.817 sell 4.763 sell 3 50 4.859 sell 4.825 sell 4 100 4.809 sell 4.830 sell 5 200 4.822 sell 4.867 sell
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investpy.technical.
pivot_points
(name, country, product_type, interval='daily')¶ This function retrieves the pivot points values calculated by Investing.com for every financial product available (stocks, funds, etfs, indices, currency crosses, bonds, certificates and commodities) for different time intervals. Pivot points are calculated on different levels: three support levels (S) and three resistance ones (R). So on, the user must provide the product_type name and the name of the product (unless product_type is ‘stock’ which name value will be the stock’s symbol) and the country if required (mandatory unless product_type is either ‘currency_cross’ or ‘commodity’, where it must be None). Additionally, the interval can be specified which defines the update frequency of the calculations of the technical indicators (mainly momentum indicators).
- Parameters
name (
str
) – name of the product to retrieve the technical indicators table from (if product_type is stock, its value must be the stock’s symbol not the name).country (
str
) – country name of the introduced product if applicable (if product_type is either currency_cross or commodity this parameter should be None, unless it can be specified just for commodity product_type).product_type (
str
) – identifier of the introduced product, available ones are: stock, fund, etf, index, currency_cross, bond, certificate and commodity.interval (
str
) – time interval of the resulting calculations, available values are: 5mins, 15mins, 30mins, 1hour, 5hours, daily, weekly and monthly.
- Returns
The resulting
pandas.DataFrame
contains the table with the results of the calculation of the pivot points made by Investing.com for the introduced financial product. So on, if the retrieval process succeed its result will look like:name | s3 | s2 | s1 | pivot_points | r1 | r2 | r3 ------|----|----|----|--------------|----|----|---- xxxx | xx | xx | xx | xxxxxxxxxxxx | xx | xx | xx
- Return type
pandas.DataFrame
- pivot_points- Raises
ValueError – raised if any of the introduced parameters is not valid or errored.
ConnectionError – raised if the connection to Investing.com errored or could not be established.
Examples
>>> data = investpy.pivot_points(name='bbva', country='spain', product_type='stock', interval='daily') >>> data.head() name s3 s2 s1 pivot_points r1 r2 r3 0 Classic 4.537 4.573 4.620 4.656 4.703 4.739 4.786 1 Fibonacci 4.573 4.605 4.624 4.656 4.688 4.707 4.739 2 Camarilla 4.645 4.653 4.660 4.656 4.676 4.683 4.691 3 Woodie's 4.543 4.576 4.626 4.659 4.709 4.742 4.792 4 DeMark's NaN NaN 4.639 4.665 4.721 NaN NaN
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investpy.technical.
technical_indicators
(name, country, product_type, interval='daily')¶ This function retrieves the technical indicators values calculated by Investing.com for every financial product available (stocks, funds, etfs, indices, currency crosses, bonds, certificates and commodities) for different time intervals. So on, the user must provide the product_type name and the name of the product (unless product_type is ‘stock’ which name value will be the stock’s symbol) and the country if required (mandatory unless product_type is either ‘currency_cross’ or ‘commodity’, where it must be None). Additionally, the interval can be specified which defines the update frequency of the calculations of the technical indicators (mainly momentum indicators).
- Parameters
name (
str
) – name of the product to retrieve the technical indicators table from (if product_type is stock, its value must be the stock’s symbol not the name).country (
str
) – country name of the introduced product if applicable (if product_type is either currency_cross or commodity this parameter should be None, unless it can be specified just for commodity product_type).product_type (
str
) – identifier of the introduced product, available ones are: stock, fund, etf, index, currency_cross, bond, certificate and commodity.interval (
str
) – time interval of the resulting calculations, available values are: 5mins, 15mins, 30mins, 1hour, 5hours, daily, weekly and monthly.
- Returns
The resulting
pandas.DataFrame
contains the table with the results of the calculation of the technical indicators made by Investing.com for the introduced financial product. So on, if the retrieval process succeed its result will look like:technical_indicator | value | signal ---------------------|-------|-------- xxxxxxxxxxxxxxxxxxx | xxxxx | xxxxxx
- Return type
pandas.DataFrame
- technical_indicators- Raises
ValueError – raised if any of the introduced parameters is not valid or errored.
ConnectionError – raised if the connection to Investing.com errored or could not be established.
Examples
>>> data = investpy.technical_indicators(name='bbva', country='spain', product_type='stock', interval='daily') >>> data.head() technical_indicator value signal 0 RSI(14) 39.1500 sell 1 STOCH(9,6) 33.2340 sell 2 STOCHRSI(14) 67.7390 buy 3 MACD(12,26) -0.0740 sell 4 ADX(14) 55.1150 sell 5 Williams %R -66.6670 sell 6 CCI(14) -77.1409 sell 7 ATR(14) 0.0939 less_volatility 8 Highs/Lows(14) -0.0199 sell 9 Ultimate Oscillator 43.0010 sell 10 ROC -6.6240 sell 11 Bull/Bear Power(13) -0.1590 sell