def create_twitter_kw_search() -> Action:
test_action = Action()
test_action.act_id = "twitter_kw_search_action"
test_action.creator_uuid = generate_uuid()
test_action.act_time = datetime.now()
test_action.act_description = "kw search in twitter"
test_action.act = [
ActionItem(
"browser_kw_search_actions/act_multi_nlp_task",
para={
"site":
"twitter",
"function":
"kw_search",
"pages":
1000,
"ls_paras": [
"(from:realDonaldTrump)",
# "oil (from:realDonaldTrump)",
# "luckin fraud min_retweets:10",
# "gsx fraud min_retweets:10"
]
})
]
return test_action
def create_azure_txt_ana() -> Action:
test_action = Action()
test_action.act_id = "azure_txt_ana_action"
test_action.creator_uuid = generate_uuid()
test_action.act_time = datetime.now()
test_action.act_description = "text analytics use azure"
test_action.act = [
ActionItem(
"browser_kw_search_actions/act_multi_nlp_task",
para={
"site":
"azure",
"function":
"txt_ana",
"ls_paras": [{
"doc":
"Article",
"pk_field":
"uuid",
"pk_val":
"BD9341B99C800299E8302C60294F01A1",
"txt_field":
"title",
"txt_value":
"""The spectacular implosion of Luckin Coffee in an accounting fraud has renewed a push in the United States to cut Chinese companies off from Wall Street"""
}, {
"doc":
"Article",
"pk_field":
"uuid",
"pk_val":
"04CF9288835D237EAAF8ACF179BB4ACE",
"txt_field":
"abstract",
"txt_value":
"""GSX Stock: Berger Montague Investigates Securities Fraud Class Action Claims Against GSX Techedu...
PHILADELPHIA, April 29, 2020 /PRNewswire via COMTEX/ -- PHILADELPHIA, April 29, 2020 /PRNewswire/ -- Berger Montague is investigating securities fraud claims...
marketwatch.com"""
}, {
"doc":
"UserInTwitter",
"pk_field":
"user_id",
"pk_val":
"Yubico",
"txt_field":
"intro",
"txt_value":
"""Yubico sets new world standards for simple, secure login, preventing unauthorized access to computers, servers, and internet accounts."""
}]
})
]
return test_action
def create_detect_monitors() -> Action:
test_action = Action()
test_action.act_id = "detect_monitors"
test_action.creator_uuid = generate_uuid()
test_action.act_time = datetime.now()
test_action.act_description = "detect all monitors "
test_action.act = [
ActionItem("DesktopRPA/act_desktop_action_dispatch",
para={"action": "detect_monitors"})
]
test_action.result_save_mongo = False
test_action.no_output = True
return test_action
def create_test_action(uuid: str) -> str:
action = Action()
action.act_id = uuid
action.creator_uuid = generate_uuid()
action.act_time = datetime.now()
action.act_description = "action for test"
action.act = [
ActionItem("browser_kw_search_actions/act_batch_google_news_search",
para=["Trump Speech", "Trump Hiring"])
]
test_file = f"D:\\gs_uipath\\p2u\\act_{uuid}.js"
with open(test_file, "w") as json_file:
json.dump(action.to_dict(), json_file)
return test_file
def create_seeking_alpha_earning_calendar() -> Action:
test_action = Action()
test_action.act_id = "seeking_alpha_earning_calendar"
test_action.creator_uuid = generate_uuid()
test_action.act_time = datetime.now()
test_action.act_description = "earning_calendar in seeking alpha"
test_action.act = [
ActionItem("browser_kw_search_actions/act_multi_nlp_task",
para={
"site": "seeking_alpha",
"function": "earning_calendar"
})
]
return test_action
def create_seeking_alpha_kw_search() -> Action:
test_action = Action()
test_action.act_id = "seeking_alpha_kw_search_action"
test_action.creator_uuid = generate_uuid()
test_action.act_time = datetime.now()
test_action.act_description = "kw search in seeking alpha"
test_action.act = [
ActionItem("browser_kw_search_actions/act_multi_nlp_task",
para={
"site": "seeking_alpha",
"function": "kw_search",
"pages": 3,
"ls_paras": ["trump oil", "luckin fraud", "gsx fraud"]
})
]
return test_action
def create_cleanup_one_monitor() -> Action:
test_action = Action()
test_action.act_id = "cleanup_one_monitor"
test_action.creator_uuid = generate_uuid()
test_action.act_time = datetime.now()
test_action.act_description = "cleanup_one_monitor"
test_action.act = [
ActionItem("DesktopRPA/act_desktop_action_dispatch",
para={
"action": "cleanup_one_monitor",
"monitor": "DispA2"
})
]
test_action.result_save_mongo = False
test_action.no_output = True
return test_action
def create_twitter_following() -> Action:
test_action = Action()
test_action.act_id = "twitter_following_action"
test_action.creator_uuid = generate_uuid()
test_action.act_time = datetime.now()
test_action.act_description = "kw search in twitter"
test_action.act = [
ActionItem("browser_kw_search_actions/act_multi_nlp_task",
para={
"site": "twitter",
"function": "following",
"pages": 3,
"ls_paras": ["realDonaldTrump", "TechCrunch"]
})
]
return test_action
def create_tableau_action() -> Action:
test_action = Action()
test_action.act_id = "tableau_action"
test_action.creator_uuid = generate_uuid()
test_action.act_time = datetime.now()
test_action.act_description = "tableau_action"
test_action.act = [
ActionItem("DesktopRPA/act_desktop_action_dispatch",
para={
"action": "tableau_act",
"tb_file_name": "equity_analysis_v2",
"industry": "Telecom Services",
"sector": "Communication Services"
})
]
test_action.result_save_mongo = False
test_action.no_output = True
return test_action
def create_general_desktop_browser_action() -> Action:
test_action = Action()
test_action.act_id = "general_desktop_browser_action"
test_action.creator_uuid = generate_uuid()
test_action.act_time = datetime.now()
test_action.act_description = "general_desktop_browser_action"
test_action.act = [
ActionItem("DesktopRPA/act_desktop_action_dispatch",
para={
"action": "general_web_app_on_monitor",
"monitor": "DispA2",
"tooltip": "hello_world",
"cfg_name": "utility/google_translate",
"kw": "自然语言",
"additional_kw": ""
})
]
test_action.result_save_mongo = False
test_action.no_output = True
return test_action
def create_seeking_alpha_special_column_action() -> Action:
test_action = Action()
test_action.act_id = "seeking_alpha_special_columns_action"
test_action.creator_uuid = generate_uuid()
test_action.act_time = datetime.now()
test_action.act_description = "query some special columns in seeking alpha"
test_action.act = [
ActionItem("browser_kw_search_actions/act_multi_nlp_task",
para={
"site":
"seeking_alpha",
"function":
"special_columns",
"ls_paras": [
"market-outlook/todays-market",
"stock-ideas/long-ideas", "market-outlook/economy"
]
})
]
return test_action
def create_seeking_alpha_symbol_summary() -> Action:
test_action = Action()
test_action.act_id = "seeking_alpha_symbol_summary_action"
test_action.creator_uuid = generate_uuid()
test_action.act_time = datetime.now()
test_action.act_description = "query symbol summary in seeking alpha"
test_action.act = [
ActionItem(
"browser_kw_search_actions/act_multi_nlp_task",
para={
"site": "seeking_alpha",
"function": "symbol_summary",
"pages": 3,
"ls_paras": [
"PDD",
# "NFLX",
# "STX"
]
})
]
return test_action
def create_general_desktop_browser_backend_action(cfg_name: str,
kw: str = "",
additional_kw: str = ""
) -> Action:
test_action = Action()
test_action.act_id = "general_desktop_browser_backend_action"
test_action.creator_uuid = generate_uuid()
test_action.act_time = datetime.now()
test_action.act_description = "general_desktop_browser_backend_action"
test_action.act = [
ActionItem("DesktopRPA/act_desktop_action_dispatch",
para={
"action": "general_desktop_browser_backend_action",
"cfg_name": cfg_name,
"kw": kw,
"additional_kw": additional_kw
})
]
test_action.result_save_mongo = True
test_action.no_output = False
return test_action
def create_seeking_alpha_author_info() -> Action:
test_action = Action()
test_action.act_id = "seeking_alpha_author_info_action"
test_action.creator_uuid = generate_uuid()
test_action.act_time = datetime.now()
test_action.act_description = "query author info in seeking alpha"
test_action.act = [
ActionItem(
"browser_kw_search_actions/act_multi_nlp_task",
para={
"site":
"seeking_alpha",
"function":
"author_info",
"ls_paras": [
"value-kicker",
# "investing-theory",
# "the-value-trend"
]
})
]
return test_action
def create_google_news_search() -> Action:
test_action = Action()
test_action.act_id = "google_news_search_action"
test_action.creator_uuid = generate_uuid()
test_action.act_time = datetime.now()
test_action.act_description = "news search in google"
test_action.act = [
ActionItem(
"browser_kw_search_actions/act_multi_nlp_task",
para={
"site": "google_news",
"function": "kw_search",
"pages": 5,
"ls_paras": [
"拼多多"
# "trump oil",
# "luckin fraud",
# "gsx fraud"
]
})
]
return test_action