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Code Issues Packages Projects Releases Wiki Activity

Adding identification of exceptions

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This commit is contained in:
Artem Veremey
2021-02-28 19:23:48 -08:00
parent dd82f8f285
commit 55825c2e64
5 changed files with 63 additions and 36 deletions
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4
.pylintrc
View File

@@ -54,7 +54,7 @@ confidence=
# disable=import-error,print-statement,parameter-unpacking,unpacking-in-except,old-raise-syntax,backtick,long-suffix,old-ne-operator,old-octal-literal,import-star-module-level,raw-checker-failed,bad-inline-option,locally-disabled,locally-enabled,file-ignored,suppressed-message,useless-suppression,deprecated-pragma,apply-builtin,basestring-builtin,buffer-builtin,cmp-builtin,coerce-builtin,execfile-builtin,file-builtin,long-builtin,raw_input-builtin,reduce-builtin,standarderror-builtin,unicode-builtin,xrange-builtin,coerce-method,delslice-method,getslice-method,setslice-method,no-absolute-import,old-division,dict-iter-method,dict-view-method,next-method-called,metaclass-assignment,indexing-exception,raising-string,reload-builtin,oct-method,hex-method,nonzero-method,cmp-method,input-builtin,round-builtin,intern-builtin,unichr-builtin,map-builtin-not-iterating,zip-builtin-not-iterating,range-builtin-not-iterating,filter-builtin-not-iterating,using-cmp-argument,eq-without-hash,div-method,idiv-method,rdiv-method,exception-message-attribute,invalid-str-codec,sys-max-int,bad-python3-import,deprecated-string-function,deprecated-str-translate-call
# disable=too-many-instance-attributes,len-as-condition,too-few-public-methods,anomalous-backslash-in-string,no-else-return,simplifiable-if-statement,too-many-arguments,duplicate-code,no-name-in-module,no-member,print-statement,parameter-unpacking,unpacking-in-except,old-raise-syntax,backtick,long-suffix,old-ne-operator,old-octal-literal,import-star-module-level,raw-checker-failed,bad-inline-option,locally-disabled,locally-enabled,file-ignored,suppressed-message,useless-suppression,deprecated-pragma,apply-builtin,basestring-builtin,buffer-builtin,cmp-builtin,coerce-builtin,execfile-builtin,file-builtin,long-builtin,raw_input-builtin,reduce-builtin,standarderror-builtin,unicode-builtin,xrange-builtin,coerce-method,delslice-method,getslice-method,setslice-method,no-absolute-import,old-division,dict-iter-method,dict-view-method,next-method-called,metaclass-assignment,indexing-exception,raising-string,reload-builtin,oct-method,hex-method,nonzero-method,cmp-method,input-builtin,round-builtin,intern-builtin,unichr-builtin,map-builtin-not-iterating,zip-builtin-not-iterating,range-builtin-not-iterating,filter-builtin-not-iterating,using-cmp-argument,eq-without-hash,div-method,idiv-method,rdiv-method,exception-message-attribute,invalid-str-codec,sys-max-int,bad-python3-import,deprecated-string-function,deprecated-str-translate-call,import-error,missing-docstring,invalid-name,bad-whitespace,consider-using-enumerate,unexpected-keyword-arg
disable=useless-return,missing-docstring,invalid-name,import-error,bad-continuation
disable=useless-return,missing-docstring,invalid-name,import-error,bad-continuation,broad-except
# Enable the message, report, category or checker with the given id(s). You can
# either give multiple identifier separated by comma (,) or put this option
@@ -74,7 +74,7 @@ evaluation=10.0 - ((float(5 * error + warning + refactor + convention) / stateme
# Template used to display messages. This is a python new-style format string
# used to format the message information. See doc for all details
msg-template='{path} {line}: {msg} ({symbol})'
msg-template='{path} {line}: {msg_id} {msg} ({symbol})'
# Set the output format. Available formats are text, parseable, colorized, json
# and msvs (visual studio).You can also give a reporter class, eg

5
gamestonk_terminal/due_diligence/reddit_api.py
View File

@@ -107,7 +107,7 @@ def due_diligence(l_args, s_ticker):
s_datetime = datetime.utcfromtimestamp(
submission.created_utc
).strftime("%d/%m/%Y %H:%M:%S")
s_link = f"https://www.reddit.com{submission.permalink}"
s_link = f"https://old.reddit.com{submission.permalink}"
s_all_awards = ""
for award in submission.all_awardings:
s_all_awards += f"{award['count']} {award['name']}\n"
@@ -172,5 +172,6 @@ def due_diligence(l_args, s_ticker):
# df_submissions = pd.DataFrame.from_dict(d_submission, orient='index', columns=list(d_submission[next(iter(d_submission.keys()))].keys()))
# df_submissions.sort_values(by=['created_utc'], inplace=True, ascending=True)
except:
except Exception as e:
print(e)
print("")

30
gamestonk_terminal/fundamental_analysis/financial_modeling_prep_api.py
View File

@@ -41,7 +41,8 @@ def profile(l_args, s_ticker):
print("")
except:
except Exception as e:
print(e)
print("")
return
@@ -90,7 +91,8 @@ def quote(l_args, s_ticker):
print("")
except:
except Exception as e:
print(e)
print("")
return
@@ -157,7 +159,8 @@ def enterprise(l_args, s_ticker):
print("")
except:
except Exception as e:
print(e)
print("")
return
@@ -223,7 +226,8 @@ def discounted_cash_flow(l_args, s_ticker):
print("")
except:
except Exception as e:
print(e)
print("")
return
@@ -303,7 +307,8 @@ def income_statement(l_args, s_ticker):
print("")
except:
except Exception as e:
print(e)
print("")
return
@@ -391,7 +396,8 @@ def balance_sheet(l_args, s_ticker):
print("")
except:
except Exception as e:
print(e)
print("")
return
@@ -474,7 +480,8 @@ def cash_flow(l_args, s_ticker):
print("")
except:
except Exception as e:
print(e)
print("")
return
@@ -560,7 +567,8 @@ def key_metrics(l_args, s_ticker):
print("")
except:
except Exception as e:
print(e)
print("")
return
@@ -644,7 +652,8 @@ def financial_ratios(l_args, s_ticker):
print("")
except:
except Exception as e:
print(e)
print("")
return
@@ -726,6 +735,7 @@ def financial_statement_growth(l_args, s_ticker):
print("")
except:
except Exception as e:
print(e)
print("")
return

15
gamestonk_terminal/sentiment/reddit_api.py
View File

@@ -216,7 +216,8 @@ def watchlist(l_args):
print(s_watchlist_tickers[:-2] + "\n")
print("")
except:
except Exception as e:
print(e)
print("")
@@ -379,7 +380,8 @@ def popular_tickers(l_args):
print("No tickers found")
print("")
except:
except Exception as e:
print(e)
print("")
@@ -556,7 +558,8 @@ def spac_community(l_args):
print(s_watchlist_tickers[:-2])
print("")
except:
except Exception as e:
print(e)
print("")
@@ -756,7 +759,8 @@ def spac(l_args):
print(s_watchlist_tickers[:-2])
print("")
except:
except Exception as e:
print(e)
print("")
@@ -880,5 +884,6 @@ def wsb_community(l_args):
else:
break
print("")
except:
except Exception as e:
print(e)
print("")

45
gamestonk_terminal/sentiment/twitter_api.py
View File

@@ -1,24 +1,25 @@
import argparse
from datetime import datetime, timedelta
import requests
import dateutil
import pandas as pd
import re
import numpy as np
import flair
import matplotlib.pyplot as plt
import iso8601
import dateutil
from gamestonk_terminal import config_terminal as cfg
from gamestonk_terminal.helper_funcs import *
from gamestonk_terminal.helper_funcs import get_data, clean_tweet
# ------------------------------------------------- INFERENCE -------------------------------------------------
def inference(l_args, s_ticker):
parser = argparse.ArgumentParser(
prog="infer",
description="""Print quick sentiment inference from last tweets that contain
the ticker. This model splits the text into character-level
tokens and uses the DistilBERT model to make predictions. DistilBERT is a distilled
version of the powerful BERT transformer model. Not only time period of these,
but also frequency. [Source: Twitter]""",
description="""
Print quick sentiment inference from last tweets that contain the ticker.
This model splits the text into character-level tokens and uses the DistilBERT
model to make predictions. DistilBERT is a distilled version of the powerful
BERT transformer model. Not only time period of these, but also frequency. [Source: Twitter]
""",
)
parser.add_argument(
@@ -110,6 +111,12 @@ def inference(l_args, s_ticker):
print(f"From: {dt_from.strftime('%Y-%m-%d %H:%M:%S')}")
print(f"To: {dt_to.strftime('%Y-%m-%d %H:%M:%S')}")
# pd.set_option("display.max_rows", None)
# pd.set_option("display.max_columns", None)
# pd.set_option("display.width", None)
# pd.set_option("display.max_colwidth", None)
# print(df_tweets)
print(f"{len(df_tweets)} tweets were analyzed.")
dt_delta = dt_to - dt_from
n_freq = dt_delta.total_seconds() / len(df_tweets)
@@ -120,7 +127,8 @@ def inference(l_args, s_ticker):
print(f"The sentiment of {s_ticker} is: {s_sen} ({s_conf})")
print("")
except:
except Exception as e:
print(e)
print("")
@@ -128,12 +136,14 @@ def inference(l_args, s_ticker):
def sentiment(l_args, s_ticker):
parser = argparse.ArgumentParser(
prog="sen",
description="""Plot in-depth sentiment predicted from tweets from last days
that contain pre-defined ticker. This model splits the text into character-level
tokens and uses the DistilBERT model to make predictions. DistilBERT is a distilled
version of the powerful BERT transformer model. Note that a big num of tweets extracted per
hour in conjunction with a high number of days in the past, will make the
algorithm take a long period of time to estimate sentiment. [Source: Twitter]""",
description="""
Plot in-depth sentiment predicted from tweets from last days
that contain pre-defined ticker. This model splits the text into character-level
tokens and uses the DistilBERT model to make predictions. DistilBERT is a distilled
version of the powerful BERT transformer model. Note that a big num of tweets extracted per
hour in conjunction with a high number of days in the past, will make the
algorithm take a long period of time to estimate sentiment. [Source: Twitter]
""",
)
# in reality this argument could be 100, but after testing it takes too long to compute which may not be acceptable
@@ -386,5 +396,6 @@ def sentiment(l_args, s_ticker):
plt.xlabel("Time")
plt.show()
except:
except Exception as e:
print(e)
print("")