infer and sentiment from twitter

gamestonk_terminal/due_diligence/reddit_view.py

@@ -99,7 +99,6 @@ def due_diligence(other_args: List[str], ticker: str):
             "investing",
             "wallstreetbets",
             "forex",
-            "Daytrading",
             "Forexstrategy",
         ]
 

gamestonk_terminal/forex/README.md

@@ -38,6 +38,10 @@ This menu utilizes oanda to enable support for trading forex.
 	* Output up to the 30 last messages on the board
 * [reddit](#reddit)
 	* Gets due diligence from another user's post [Reddit]
+* [infer](#infer)
+	* Infer about stock's sentiment from latest tweets
+* [sentiment](#sentiment)
+	* In-depth sentiment prediction from tweets over time
 * [edasummary](#edasummary)
 	* Brief summary statistics using exploratory data analysis
 * [edarolling](#edarolling)
@@ -206,6 +210,23 @@ Print top stock's due diligence from other users. [Source: Reddit]
 * -d : number of prior days to look for
 * -a : "search through all flairs (apart from Yolo and Meme). Default False (i.e. use flairs: DD, technical analysis, Catalyst, News, Advice, Chart, Charts and Setups, Fundamental Analysis, Forex, Trade Idea)
 
+## infer <a name="infer"></a>
+```
+usage: infer [-n N_NUM]
+```
+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. Inspired by https://towardsdatascience.com/sentiment-analysis-for-stock-price-prediction-in-python-bed40c65d178. [Source: Twitter]
+
+* -n : num of latest tweets to infer from. Default 100.
+
+## sentiment <a name="sentiment"></a>
+```
+usage: sentiment [-n N_NUM] [-d N_DAYS_PAST]
+```
+Plot in-depth sentiment extracted 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. Inspired by https://towardsdatascience.com/sentiment-analysis-for-stock-price-prediction-in-python-bed40c65d178. [Source: Twitter]
+
+* -n : num of tweets to extract per hour. Default 100.
+* -d : num of days in the past to extract tweets. Default 7.
+
 ## edasummary <a name="edasummary"></a>
 
 ```text

gamestonk_terminal/forex/fx_controller.py

@@ -7,7 +7,7 @@ from gamestonk_terminal.menu import session
 from gamestonk_terminal.forex import fx_view
 from gamestonk_terminal import config_terminal as cfg
 from gamestonk_terminal.due_diligence import news_view, reddit_view
-from gamestonk_terminal.behavioural_analysis import stocktwits_view
+from gamestonk_terminal.behavioural_analysis import stocktwits_view, twitter_view
 from gamestonk_terminal.exploratory_data_analysis import eda_api
 
 account = cfg.OANDA_ACCOUNT
@@ -37,12 +37,14 @@ class ForexController:
         "news",
         "bullbear",
         "messages",
+        "reddit",
+        "infer",
+        "sentiment",
         "edasummary",
         "edarolling",
         "edadecompose",
         "edacusum",
         "edahist",
-        "reddit",
     ]
 
     def __init__(self):
@@ -222,6 +224,12 @@ class ForexController:
         instrument = fx_view.format_instrument(self.instrument, " ")
         reddit_view.due_diligence(other_args, instrument)
 
+    def call_infer(self, other_args: List[str]):
+        twitter_view.inference(other_args, self.instrument)
+
+    def call_sentiment(self, other_args: List[str]):
+        twitter_view.sentiment(other_args, self.instrument)
+
 
 def menu():
     """Oanda Menu"""