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Add extra methods retrieve_pivot_points and retrieve_moving_averages in SearchObj #516

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Add extra methods retrieve_pivot_points and retrieve_moving_averages in SearchObj #516

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221 changes: 221 additions & 0 deletions investpy/utils/search_obj.py
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Original file line number Diff line number Diff line change
Expand Up @@ -395,6 +395,227 @@ class instance, to later put in into the `SearchObj.technical_indicators` attrib
)

return self.technical_indicators

def retrieve_moving_averages(self, interval="daily"):
"""Class method used to retrieve the moving averages from the class instance of any financial product.

This method retrieves the moving averages from Investing.com for the financial product of the current
class instance, to later put in into the `SearchObj.moving_averages` attribute. This method uses the
previously retrieved data from the `investpy.search_quotes(text, products, countries, n_results)`
function search results to build the request that it is going to be sent to Investing.com so to retrieve and
parse the moving averages, since the product id is required.

Args:
interval (:obj:`str`, optional):
time interval of the moving averages' calculations, available values are: `5mins`, `15mins`,
`30mins`, `1hour`, `5hours`, `daily`, `weekly` and `monthly`. Note that for funds just the intervals:
`daily`, `weekly` and `monthly` are available.

Returns:
:obj:`pd.DataFrame` - moving_averages:
This method retrieves the moving averages from the current class instance of a financial product
from Investing.com. This method not just stores retrieved moving averages table into self.moving_averages
but it also returns it as a normal function will do.

Raises:
ValueError: raised if any of the input parameters is not valid.
ConnectionError: raised if connection to Investing.com could not be established.
RuntimeError: raised if there was any problem while retrieving the data from Investing.com.

"""

if self.pair_type in []:
raise ValueError(
"Investing.com does not provide technical indicators for"
f" {self.pair_type}."
)

if self.pair_type != "funds" and interval not in INTERVAL_FILTERS:
raise ValueError(
"Investing.com just provides the following intervals for"
f" {self.pair_type}' technical indicators:"
f" {', '.join(list(INTERVAL_FILTERS.keys()))}"
)

if self.pair_type == "funds" and interval not in FUNDS_INTERVAL_FILTERS:
raise ValueError(
"Investing.com just provides the following intervals for funds'"
" technical indicators:"
f" {', '.join(list(FUNDS_INTERVAL_FILTERS.keys()))}"
)

params = {
"pairID": self.id_,
"period": INTERVAL_FILTERS[interval],
"viewType": "normal",
}

headers = {
"User-Agent": random_user_agent(),
"X-Requested-With": "XMLHttpRequest",
"Accept": "text/html",
"Accept-Encoding": "gzip, deflate",
"Connection": "keep-alive",
}

url = "https://www.investing.com/instruments/Service/GetTechincalData"

req = requests.post(url, headers=headers, data=params)

if req.status_code != 200:
raise ConnectionError(
f"ERR#0015: error {req.status_code}, try again later."
)

root = fromstring(req.text)
table = root.xpath(".//table[contains(@class, 'movingAvgsTbl')]/tbody/tr")

moving_avgs = list()

for row in table:
for value in row.xpath("td"):
if value.get("class") is not None:
if value.get("class").__contains__("symbol"):
ma_period = value.text_content().strip().replace("MA", "")
sma_signal = (
value.getnext().xpath("span")[0].text_content().strip().lower()
)
sma_value = float(
value.getnext()
.text_content()
.lower()
.replace(sma_signal, "")
.strip()
)
value = value.getnext()
ema_signal = (
value.getnext()
.xpath(".//span")[0]
.text_content()
.strip()
.lower()
)
ema_value = float(
value.getnext()
.text_content()
.lower()
.replace(ema_signal, "")
.strip()
)

moving_avgs.append(
{
"period": ma_period,
"sma_value": sma_value,
"sma_signal": sma_signal,
"ema_value": ema_value,
"ema_signal": ema_signal,
}
)

return pd.DataFrame(moving_avgs)


def retrieve_pivot_points(self, interval="daily"):
"""Class method used to retrieve the pivot_points from the class instance of any financial product.

This method retrieves the pivot_points from Investing.com for the financial product of the current
class instance, to later put in into the `SearchObj.pivot_points` attribute. This method uses the
previously retrieved data from the `investpy.search_quotes(text, products, countries, n_results)`
function search results to build the request that it is going to be sent to Investing.com so to retrieve and
parse the pivot_points, since the product id is required.

Args:
interval (:obj:`str`, optional):
time interval of the pivot_points' calculations, available values are: `5mins`, `15mins`,
`30mins`, `1hour`, `5hours`, `daily`, `weekly` and `monthly`. Note that for funds just the intervals:
`daily`, `weekly` and `monthly` are available.

Returns:
:obj:`pd.DataFrame` - pivot_points:
This method retrieves the pivot_points from the current class instance of a financial product
from Investing.com. This method not just stores retrieved pivot_points table into self.pivot_points
but it also returns it as a normal function will do.

Raises:
ValueError: raised if any of the input parameters is not valid.
ConnectionError: raised if connection to Investing.com could not be established.
RuntimeError: raised if there was any problem while retrieving the data from Investing.com.

"""

if self.pair_type in []:
raise ValueError(
"Investing.com does not provide technical indicators for"
f" {self.pair_type}."
)

if self.pair_type != "funds" and interval not in INTERVAL_FILTERS:
raise ValueError(
"Investing.com just provides the following intervals for"
f" {self.pair_type}' technical indicators:"
f" {', '.join(list(INTERVAL_FILTERS.keys()))}"
)

if self.pair_type == "funds" and interval not in FUNDS_INTERVAL_FILTERS:
raise ValueError(
"Investing.com just provides the following intervals for funds'"
" technical indicators:"
f" {', '.join(list(FUNDS_INTERVAL_FILTERS.keys()))}"
)

params = {
"pairID": self.id_,
"period": INTERVAL_FILTERS[interval],
"viewType": "normal",
}

headers = {
"User-Agent": random_user_agent(),
"X-Requested-With": "XMLHttpRequest",
"Accept": "text/html",
"Accept-Encoding": "gzip, deflate",
"Connection": "keep-alive",
}

url = "https://www.investing.com/instruments/Service/GetTechincalData"

req = requests.post(url, headers=headers, data=params)

if req.status_code != 200:
raise ConnectionError(
f"ERR#0015: error {req.status_code}, try again later."
)

root = fromstring(req.text)
header = root.xpath(".//table[contains(@class, 'crossRatesTbl')]/thead/tr/th")

values = dict()

for index, column in enumerate(header):
values[index] = column.text_content().strip().lower().replace(" ", "_")

table = root.xpath(".//table[contains(@class, 'crossRatesTbl')]/tbody/tr")

pivot_pts = list()

for row in table:
pivot_pt = dict()
elements = row.xpath("td")

for key, value in values.items():
if value != "name":
val = elements[key].text_content().strip()
try:
pivot_pt[value] = float(val)
except:
pivot_pt[value] = None
else:
pivot_pt[value] = elements[key].text_content().strip()

pivot_pts.append(pivot_pt)

return pd.DataFrame(pivot_pts)

def retrieve_currency(self):
"""Class method used to retrieve the default currency from the class instance of any financial product.
Expand Down