1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
|
"""Seeking Alpha Forward Sales Estimates Model."""
# pylint: disable=unused-argument
from typing import Any, Dict, List, Literal, Optional
from warnings import warn
from openbb_core.provider.abstract.fetcher import Fetcher
from openbb_core.provider.standard_models.forward_sales_estimates import (
ForwardSalesEstimatesData,
ForwardSalesEstimatesQueryParams,
)
from openbb_core.provider.utils.helpers import amake_request
from openbb_seeking_alpha.utils.helpers import HEADERS, get_seekingalpha_id
from pydantic import Field, field_validator
class SAForwardSalesEstimatesQueryParams(ForwardSalesEstimatesQueryParams):
"""Seeking Alpha Forward Sales Estimates Query.
Source: https://seekingalpha.com/earnings/earnings-calendar
"""
__json_schema_extra__ = {"symbol": {"multiple_items_allowed": True}}
period: Literal["annual", "quarter"] = Field(
default="quarter",
description="The reporting period.",
json_schema_extra={"choices": ["annual", "quarter"]},
)
@field_validator("symbol", mode="before", check_fields=False)
@classmethod
def check_symbol(cls, value):
"""Check the symbol."""
if not value:
raise RuntimeError("Error: Symbol is a required field for Seeking Alpha.")
return value
class SAForwardSalesEstimatesData(ForwardSalesEstimatesData):
"""Seeking Alpha Forward Sales Estimates Data."""
actual: Optional[int] = Field(
default=None,
description="Actual sales (revenue) for the period.",
)
period_growth: Optional[float] = Field(
default=None,
description="Estimated (or actual if reported) EPS growth for the period.",
json_schema_extra={"x-unit_measurement": "percent", "x-frontend_multiply": 100},
)
class SAForwardSalesEstimatesFetcher(
Fetcher[
SAForwardSalesEstimatesQueryParams,
List[SAForwardSalesEstimatesData],
]
):
"""Seeking Alpha Forward Sales Estimates Fetcher."""
@staticmethod
def transform_query(params: Dict[str, Any]) -> SAForwardSalesEstimatesQueryParams:
"""Transform the query."""
return SAForwardSalesEstimatesQueryParams(**params)
@staticmethod
async def aextract_data(
query: SAForwardSalesEstimatesQueryParams,
credentials: Optional[Dict[str, str]],
**kwargs: Any,
) -> Dict:
"""Return the raw data from the Seeking Alpha endpoint."""
tickers = query.symbol.split(",")
fp = query.period if query.period == "annual" else "quarterly"
url = "https://seekingalpha.com/api/v3/symbol_data/estimates"
payload = ""
querystring = {
"estimates_data_items": "revenue_actual,revenue_consensus_low,revenue_consensus_mean,"
"revenue_consensus_high,revenue_num_of_estimates",
"period_type": fp,
"relative_periods": "-4,-3,-2,-1,0,1,2,3,4,5,6,7,8,9,10,11,12",
}
ids = {ticker: await get_seekingalpha_id(ticker) for ticker in tickers}
querystring["ticker_ids"] = (",").join(list(ids.values()))
response = await amake_request(
url, data=payload, headers=HEADERS, params=querystring
)
estimates = response.get("estimates", {}) # type: ignore
if not estimates:
raise RuntimeError(f"No estimates data was returned for: {query.symbol}")
output: Dict = {"ids": ids, "estimates": estimates}
return output
@staticmethod
def transform_data(
query: SAForwardSalesEstimatesQueryParams,
data: Dict,
**kwargs: Any,
) -> List[SAForwardSalesEstimatesData]:
"""Transform the data to the standard format."""
tickers = query.symbol.split(",")
ids = data.get("ids")
estimates = data.get("estimates")
results: List[SAForwardSalesEstimatesData] = []
for ticker in tickers:
sa_id = str(ids.get(ticker, ""))
if sa_id == "" or sa_id not in estimates:
warn(f"Symbol Error: No data found for, {ticker}")
seek_object = estimates.get(sa_id)
items = len(seek_object["revenue_num_of_estimates"])
for i in range(0, items - 4):
rev_estimates: Dict = {}
rev_estimates["symbol"] = ticker
num_estimates = seek_object["revenue_num_of_estimates"].get(str(i))
if not num_estimates:
continue
period = num_estimates[0].get("period", {})
if period:
period_type = period.get("periodtypeid")
rev_estimates["calendar_year"] = period.get("calendaryear")
rev_estimates["calendar_period"] = (
"Q" + str(period.get("calendarquarter", ""))
if period_type == "quarterly"
else "FY"
)
rev_estimates["date"] = period.get("periodenddate").split("T")[0]
rev_estimates["fiscal_year"] = period.get("fiscalyear")
rev_estimates["fiscal_period"] = (
"Q" + str(period.get("fiscalquarter", ""))
if period_type == "quarterly"
else "FY"
)
rev_estimates["number_of_analysts"] = num_estimates[0].get(
"dataitemvalue"
)
mean = seek_object["revenue_consensus_mean"].get(str(i))
if mean:
mean = mean[0].get("dataitemvalue")
rev_estimates["mean"] = int(float(mean))
actual = (
seek_object["revenue_actual"][str(i)][0].get("dataitemvalue")
if i < 1
else None
)
if actual:
rev_estimates["actual"] = int(float(actual))
low = seek_object["revenue_consensus_low"].get(str(i))
if low:
low = low[0].get("dataitemvalue")
rev_estimates["low_estimate"] = int(float(low))
high = seek_object["revenue_consensus_high"].get(str(i))
if high:
high = high[0].get("dataitemvalue")
rev_estimates["high_estimate"] = int(float(high))
# Calculate the estimated growth percent.
this = float(mean) if mean else None
prev = None
percent = None
try:
prev = float(
seek_object["revenue"][str(i - 1)][0].get("dataitemvalue")
)
except KeyError:
prev = float(
seek_object["revenue_consensus_mean"][str(i - 1)][0].get(
"dataitemvalue"
)
)
if this and prev:
percent = (this - prev) / prev
rev_estimates["period_growth"] = percent
results.append(
SAForwardSalesEstimatesData.model_validate(rev_estimates)
)
return results
|
