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Add Sentiment Investor API integration (#562)
* Add SentimentInvestor core and social metrics * Added `metrics` and `social` commands to behaviour analysis menu * `metrics` fetches the core metrics for a given ticker from SentimentInvestor: AHI, RHI, SGP and sentiment score. * `social` fetches the aggregated data from several social media networks, i.e. the number of mentions and the sentiment score associated with a specific ticker on each network Signed-off-by: Timothy Langer <zeevox.dev@gmail.com> * Format code to conform to PEP8 style guide Signed-off-by: Timothy Langer <zeevox.dev@gmail.com> * Use `argparse` to handle command-line arguments Signed-off-by: Timothy Langer <zeevox.dev@gmail.com> * Use `multiprocessing` to reduce wait time * Reduce `metrics` from 7.2s to 2.9s * Reduce `social` from 13.5s to 5.6s * NB: These times could be reduced further but that would require changes to `sentipy` itself Signed-off-by: Timothy Langer <zeevox.dev@gmail.com> * Add `historical` command to plot past week of data Signed-off-by: Timothy Langer <zeevox.dev@gmail.com> * Add three new commands to discovery mode * `popular` for stocks with the highest AHI * `emerging` for stocks with the highest RHI * `positive` for stocks with the highest sentiment scores Signed-off-by: Timothy Langer <zeevox.dev@gmail.com> * Aggregate data for past week when showing table Signed-off-by: Timothy Langer <zeevox.dev@gmail.com> * Fix `-t` flag not working for `social` command Signed-off-by: Timothy Langer <zeevox.dev@gmail.com> * Handle lacking or incomplete historical data Signed-off-by: Timothy Langer <zeevox.dev@gmail.com> * Improve error handling for unsupported stocks Signed-off-by: Timothy Langer <zeevox.dev@gmail.com> * Remove `positive` command * Sorting SentimentInvestor data by sentiment does not provide much value, seeing as it simply returns stocks that have very few comments, giving it a sentiment score of 1.0, which is inaccurate and misleading Signed-off-by: Timothy Langer <zeevox.dev@gmail.com> * Reformat code per project guidelines Signed-off-by: Timothy Langer <zeevox.dev@gmail.com> * Remove `positive` from list of valid commands Signed-off-by: Timothy Langer <zeevox.dev@gmail.com> * `poetry add sentiment-investor` Signed-off-by: Timothy Langer <zeevox.dev@gmail.com> * Add SentimentInvestor to "About us" section Signed-off-by: Timothy Langer <zeevox.dev@gmail.com> * Use `typing.List` annotation instead of `builtins.list` * This is to support Python versions older than 3.9 Signed-off-by: Timothy Langer <zeevox.dev@gmail.com> * Add `sentiment-investor` to `requirements-full.txt` Signed-off-by: Timothy Langer <zeevox.dev@gmail.com> * Add SentimentInvestor API key check Signed-off-by: Timothy Langer <zeevox.dev@gmail.com> * Show correct program name in help message Signed-off-by: Timothy Langer <zeevox.dev@gmail.com> * Add README documentation for newly added commands Signed-off-by: Timothy Langer <zeevox.dev@gmail.com> * Make changes as requested by code review * Coloring applied to the whole row in `metrics` and `social` commands * Convert `--ticker` flag into an optional positional argument for single-argument commands such as `metrics` and `social` * Make help menus and command descriptions far more detailed * Explain what AHI, RHI, SGP and sentiment score are and how they are calculated by Sentiment. * `historical` command is now sorted with the most recent date first * `historical` command output table can be sorted using new `--sort` and `--direction` arguments * Non-fatal warning and error messages have been suppressed Signed-off-by: Timothy Langer <zeevox.dev@gmail.com> * Wrap command methods in try-except Signed-off-by: Timothy Langer <zeevox.dev@gmail.com> * Add SentimentInvestor API key help * Add to the top-level README * Adjust environment variable name to conform with standard set by others, start it with GT_ Signed-off-by: Timothy Langer <zeevox.dev@gmail.com> * Comment `coinpaprika` erroring line * https://github.com/GamestonkTerminal/GamestonkTerminal/pull/562#issuecomment-887842888 Signed-off-by: Timothy Langer <zeevox.dev@gmail.com>
This commit is contained in:
Timothy Langer
2
README.md
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2
README.md
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@@ -239,6 +239,7 @@ These are the ones where a key is necessary:
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* Polygon: https://polygon.io
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* Quandl: https://www.quandl.com/tools/api
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* Reddit: https://www.reddit.com/prefs/apps
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* SentimentInvestor: https://sentimentinvestor.com
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* Tradier: https://developer.tradier.com/getting_started
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* Twitter: https://developer.twitter.com
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@@ -260,6 +261,7 @@ Alternatively, you can also set them to the following environment variables:
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| [Polygon](https://polygon.io) | GT_API_POLYGON_KEY |
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| [Quandl](https://www.quandl.com) | GT_API_KEY_QUANDL |
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| [Reddit](https://www.reddit.com) | GT_API_REDDIT_CLIENT_ID <br> GT_API_REDDIT_CLIENT_SECRET <br/> GT_API_REDDIT_USERNAME <br/> GT_API_REDDIT_USER_AGENT <br/> GT_API_REDDIT_PASSWORD|
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| [SentimentInvestor](https://sentimentinvestor.com) | GT_API_SENTIMENTINVESTOR_TOKEN <br> GT_API_SENTIMENTINVESTOR_KEY |
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| [Tradier](https://developer.tradier.com) | GT_TRADIER_TOKEN |
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| [Twitter](https://developer.twitter.com) | GT_API_TWITTER_KEY <br/> GT_API_TWITTER_SECRET_KEY <br/> GT_API_TWITTER_BEARER_TOKEN |
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110
gamestonk_terminal/behavioural_analysis/README.md
vendored
110
gamestonk_terminal/behavioural_analysis/README.md
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@@ -45,6 +45,13 @@ This menu aims to extrapolate behavioural analysis regarding a pre-loaded ticker
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* [rise](#rise)
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- top rising related queries with stock
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[SENTIMENT INVESTOR](#SENTIMENTINVESTOR)
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* [metrics](#metrics)
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- core social sentiment metrics for this stock
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* [social](#social)
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- social media figures for stock popularity
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* [historical](#historical)
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- plot the past week of data for a selected metric
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## finbrain <a name="finbrain"></a>
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```
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@@ -251,3 +258,106 @@ Print top rising related queries with this stock's query. [Source: Google]
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* -n : number of top rising related queries to print. Default 10.
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<img width="934" alt="Captura de ecrã 2021-02-22, às 22 21 21" src="https://user-images.githubusercontent.com/25267873/108777814-4f21fa80-755c-11eb-96da-0327c9a0da57.png">
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# SENTIMENTINVESTOR
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> **[Sentiment Investor](https://sentimentinvestor.com)** analyzes data from four major social media platforms to
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> generate hourly metrics on over 2,000 stocks. Sentiment provides volume and
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> sentiment metrics powered by proprietary NLP models.
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## Metric definitions
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### AHI (Absolute Hype Index) <a name="AHI"></a>
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AHI is a measure of how much people are talking about a stock on social media.
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It is calculated by dividing the total number of mentions for the chosen stock
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on a social network by the mean number of mentions any stock receives on that
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social medium.
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### RHI (Relative Hype Index) <a name="RHI"></a>
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RHI is a measure of whether people are talking about a stock more or less than
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usual, calculated by dividing the mean AHI for the past day by the mean AHI for
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for the past week for that stock.
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### Sentiment Score <a name="sentiment"></a>
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Sentiment score is the percentage of people talking positively about the stock.
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For each social network the number of positive posts/comments is divided by the
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total number of both positive and negative posts/comments.
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### SGP (Standard General Perception) <a name="SGP"></a>
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SGP is a measure of whether people are more or less positive about a stock than
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usual. It is calculated by averaging the past day of sentiment values and then
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dividing it by the average of the past week of sentiment values.
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## metrics <a name="metrics"></a>
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```
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usage: metrics [-h] [ticker]
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```
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The `metrics` command presents the four core metrics provided by Sentiment Investor,
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including AHI, RHI, sentiment and SGP, as described above.
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Optional arguments:
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- [optional, positional] ticker
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ticker to use instead of the loaded one
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- `-h`, `--help`
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show this help message
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Example output:
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## social <a name="social"></a>
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```
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usage: social [-h] [ticker]
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```
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The `social` command prints the raw data for a given stock, including the number
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of mentions it has received on social media in the last hour and the sentiment
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score of those comments.
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Optional arguments:
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- [optional, positional] ticker
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ticker to use instead of the loaded one
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- `-h`, `--help`
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show this help message
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Example output:
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## historical <a name="historical"></a>
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```
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usage: historical [-t TICKER] [-s [{date,value}]] [-d [{asc,desc}]] [-h] [{sentiment,AHI,RHI,SGP}]
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```
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The `historical` command plots the past week of data for a selected
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SentimentInvestor core metric, one of:
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[AHI](#ahi), [RHI](#ahi), [sentiment](#sentiment) or [SGP](#SGP).
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It also outputs a sorted table of the mean value for that metric for each day.
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Positional arguments:
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- one of `sentiment`, `AHI`, `RHI` or `SGP`
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(optional) the metric to plot (default: `sentiment`)
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Optional arguments:
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- `-t`, `--ticker TICKER`
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ticker for which to fetch data
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- `-s`, `--sort [{date,value}]`
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the parameter to sort output table by
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- `-d`, `--direction [{asc,desc}]`
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the direction to sort the output table
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- `-h`, `--help`
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show this help message
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Example output:
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@@ -16,6 +16,7 @@ from gamestonk_terminal.behavioural_analysis import (
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stocktwits_view,
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finbrain_view,
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finnhub_view,
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sentimentinvestor_view,
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)
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@@ -46,6 +47,9 @@ class BehaviouralAnalysisController:
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"rise",
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"finbrain",
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"stats",
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"metrics",
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"social",
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"historical",
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]
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def __init__(self, ticker: str, start: datetime):
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@@ -102,6 +106,11 @@ class BehaviouralAnalysisController:
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print(" queries top related queries with this stock")
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print(" rise top rising related queries with stock")
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print("")
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print("SentimentInvestor:")
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print(" metrics core social sentiment metrics for this stock")
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print(" social social media figures for stock popularity")
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print(" historical plot the past week of data for a selected metric")
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print("")
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def switch(self, an_input: str):
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"""Process and dispatch input
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@@ -246,6 +255,18 @@ class BehaviouralAnalysisController:
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"""Process stats command"""
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finnhub_view.sentiment_stats(other_args, self.ticker)
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def call_metrics(self, other_args: List[str]):
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"""Process metrics command"""
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sentimentinvestor_view.metrics(self.ticker, other_args)
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def call_social(self, other_args: List[str]):
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"""Process social command"""
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sentimentinvestor_view.socials(self.ticker, other_args)
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def call_historical(self, other_args: List[str]):
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"""Process historical command"""
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sentimentinvestor_view.historical(self.ticker, other_args)
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def menu(ticker: str, start: datetime, context: str = ""):
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"""Behavioural Analysis Menu"""
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@@ -0,0 +1,611 @@
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import argparse
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import dataclasses
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import datetime
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import logging
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import multiprocessing
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import os
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import statistics
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import textwrap
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import time
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from typing import Union, Optional, List, Tuple
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import matplotlib.dates as mdates
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import pandas as pd
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import seaborn as sns
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from colorama import Fore, Style
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from matplotlib import pyplot as plt
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from sentipy.sentipy import Sentipy
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import tabulate
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from gamestonk_terminal import config_terminal as cfg
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from gamestonk_terminal.config_plot import PLOT_DPI
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from gamestonk_terminal.helper_funcs import (
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parse_known_args_and_warn,
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plot_autoscale,
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)
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sentipy: Sentipy = Sentipy(
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token=cfg.API_SENTIMENTINVESTOR_TOKEN, key=cfg.API_SENTIMENTINVESTOR_KEY
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)
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"""Initialise SentiPy with the user's API token and key"""
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pd.plotting.register_matplotlib_converters()
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# suppress warning messages for a clean interface
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logging.getLogger().setLevel(logging.CRITICAL)
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@dataclasses.dataclass
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class _Boundary:
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"""Represents a strong or weak bounding inequality for categorising a value"""
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min: Union[float, int]
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"Minimum value that this metric could take"
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max: Union[float, int]
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"Maximum value that this metric could take"
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strong: bool = False
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"Whether this a strongly bounded inequality"
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def categorise(self, num: Union[float, int]) -> Tuple[str, str]:
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"""
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Categorise a given number using this bounding inequality
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Parameters
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----------
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num: the number to bucket
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Returns
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-------
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A color string and a string with low / medium / high as appropriate
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"""
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if num is None or self.min is None or self.max is None:
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return Style.DIM + Fore.WHITE, "N/A"
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boundaries = [self.min + (self.max - self.min) / 5 * i for i in range(1, 5)]
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if (num <= self.min or num >= self.max) and self.strong:
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return Fore.WHITE, "Extreme (anomaly?)"
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if num < boundaries[0]:
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return Style.BRIGHT + Fore.RED, "Much Lower"
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if num < boundaries[1]:
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return Fore.RED, "Lower"
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if num < boundaries[2]:
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return Fore.YELLOW, "Same"
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if num < boundaries[3]:
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return Fore.GREEN, "Higher"
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return Style.BRIGHT + Fore.GREEN, "Much Higher"
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def _get_past_week_average(ticker: str, metric: str) -> float:
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historical_data = [
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dp
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for dp in sentipy.historical(
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ticker, metric, int(time.time() - 60 * 60 * 24 * 7), int(time.time())
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).values()
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if dp is not None
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]
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return statistics.mean(historical_data) if historical_data else None
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@dataclasses.dataclass
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class _MetricInfo:
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name: str
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"""Metric attribute name"""
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title: str
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"""Human-friendly attribute name"""
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description: str
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"""Metric attribute description"""
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format: str
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"""Format string for presenting the value"""
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class _Metric(_MetricInfo):
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boundary: _Boundary
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"""A contextual range to for this metric"""
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ticker: str
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"""Ticker symbol for which this metric relates"""
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value: Union[float, int]
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"""Value"""
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def __init__(
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self,
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metric_info: _MetricInfo,
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ticker: str,
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value: Union[float, int],
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boundary: Optional[_Boundary] = None,
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):
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super().__init__(**dataclasses.asdict(metric_info))
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weekly_mean = _get_past_week_average(ticker, self.name)
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self.boundary = (
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_Boundary(0, None if weekly_mean is None else 2 * weekly_mean)
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if boundary is None
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else boundary
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)
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self.ticker = ticker
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self.value = value
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def visualise(self) -> Tuple[str, str, str, str]:
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color, category = self.boundary.categorise(self.value)
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return (
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color + self.title,
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category,
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"N/A" if self.value is None else f"{self.value:{self.format}}",
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self.description + Style.RESET_ALL,
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)
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core_metrics = [
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_MetricInfo(
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"sentiment",
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"Sentiment Score",
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"the percentage of people that are talking positively about a stock",
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".0%",
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),
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_MetricInfo(
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"AHI",
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"Average Hype Index",
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"how much people are talking about a stock on social media",
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".3f",
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),
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_MetricInfo(
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"RHI",
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"Relative Hype Index",
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"whether people are talking about a stock more or less than usual",
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".3f",
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),
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_MetricInfo(
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"SGP",
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"Standard General Perception",
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"whether people are more or less positive about a stock than usual",
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".3f",
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),
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]
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social_metrics = [
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_MetricInfo(
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"reddit_comment_mentions",
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"Reddit Comment Mentions",
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"the number of mentions this stock has received in Reddit comments",
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"n",
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),
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_MetricInfo(
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"reddit_comment_sentiment",
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"Reddit Comment Sentiment",
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"percentage of Reddit comments mentioning this stock in a positive light",
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".0%",
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),
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_MetricInfo(
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"reddit_post_mentions",
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"Reddit Post Mentions",
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"the number of mentions this stock has received in Reddit posts",
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"n",
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),
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_MetricInfo(
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"reddit_post_sentiment",
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"Reddit Post Sentiment",
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"percentage of Reddit posts mentioning this stock in a positive light",
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".0%",
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),
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_MetricInfo(
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"tweet_mentions",
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"Twitter Mentions",
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"the number of mentions this stock has received on Twitter",
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"n",
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),
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_MetricInfo(
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"tweet_sentiment",
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"Twitter Sentiment",
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"percentage of tweets mentioning this stock in a positive light",
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".0%",
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),
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_MetricInfo(
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"stocktwits_post_mentions",
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"Stocktwits Mentions",
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"the number of mentions this stock has received in Stocktwits posts",
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"n",
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),
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_MetricInfo(
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"stocktwits_post_sentiment",
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"Stocktwits Sentiment",
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"percentage of Stocktwits posts mentioning this stock in a positive light",
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".0%",
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),
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_MetricInfo(
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"yahoo_finance_comment_mentions",
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"Yahoo! Finance Comment Mentions",
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"the number of mentions this stock has received in Yahoo! Finance comments",
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"n",
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),
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_MetricInfo(
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"yahoo_finance_comment_sentiment",
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"Yahoo! Finance Comment Sentiment",
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"percentage of Yahoo! Finance comments mentioning this stock in a positive light",
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".0%",
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),
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]
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||||
def _contextualise_metrics(
|
||||
data: object, ticker: str, metric_infos: List[_MetricInfo]
|
||||
) -> Optional[List[_Metric]]:
|
||||
arguments = []
|
||||
for metric_info in metric_infos:
|
||||
if not hasattr(data, metric_info.name):
|
||||
logging.error(
|
||||
"data for %s stock is incomplete. If you believe this is an error, "
|
||||
"please contact hello@sentimentinvestor.com",
|
||||
ticker,
|
||||
)
|
||||
return None
|
||||
arguments.append((metric_info, ticker, data.__getattribute__(metric_info.name)))
|
||||
|
||||
with multiprocessing.Pool(os.cpu_count()) as pool:
|
||||
return pool.starmap(_Metric, arguments)
|
||||
|
||||
|
||||
def _tabulate_metrics(ticker: str, metrics_list: List[_Metric]):
|
||||
table_data = []
|
||||
table_headers = [
|
||||
f"{Style.BRIGHT}{ticker}{Style.RESET_ALL} Metrics",
|
||||
"vs Past 7 Days",
|
||||
"Value",
|
||||
"Description",
|
||||
]
|
||||
|
||||
for metric in metrics_list:
|
||||
table_data.append(metric.visualise())
|
||||
|
||||
return tabulate.tabulate(table_data, table_headers, tablefmt="grid")
|
||||
|
||||
|
||||
def _customise_plot() -> None:
|
||||
sns.set(
|
||||
font="Arial",
|
||||
style="darkgrid",
|
||||
rc={
|
||||
"axes.axisbelow": False,
|
||||
"axes.edgecolor": "lightgrey",
|
||||
"axes.facecolor": "None",
|
||||
"axes.grid": False,
|
||||
"axes.labelcolor": "dimgrey",
|
||||
"axes.spines.right": False,
|
||||
"axes.spines.top": False,
|
||||
"figure.facecolor": "white",
|
||||
"lines.solid_capstyle": "round",
|
||||
"patch.edgecolor": "w",
|
||||
"patch.force_edgecolor": True,
|
||||
"text.color": "dimgrey",
|
||||
"xtick.bottom": False,
|
||||
"xtick.color": "dimgrey",
|
||||
"xtick.direction": "out",
|
||||
"xtick.top": False,
|
||||
"ytick.color": "dimgrey",
|
||||
"ytick.direction": "out",
|
||||
"ytick.left": False,
|
||||
"ytick.right": False,
|
||||
},
|
||||
)
|
||||
sns.despine(left=True, bottom=True)
|
||||
sns.color_palette("pastel")
|
||||
plt.legend(frameon=False)
|
||||
|
||||
|
||||
def _parse_args_for_ticker(other_args: List[str], ticker: str, command: str, desc: str):
|
||||
parser = argparse.ArgumentParser(
|
||||
add_help=False,
|
||||
prog=command,
|
||||
formatter_class=argparse.RawDescriptionHelpFormatter,
|
||||
description=desc,
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"ticker",
|
||||
action="store",
|
||||
type=str,
|
||||
nargs="?",
|
||||
default=ticker,
|
||||
help="ticker to use instead of the loaded one",
|
||||
)
|
||||
|
||||
return parse_known_args_and_warn(parser, other_args)
|
||||
|
||||
|
||||
def metrics(ticker: str, other_args: List[str]) -> None:
|
||||
command_description = f"""
|
||||
{Style.BRIGHT}Sentiment Investor{Style.RESET_ALL} analyzes data from four major social media platforms to
|
||||
generate hourly metrics on over 2,000 stocks. Sentiment provides volume and
|
||||
sentiment metrics powered by proprietary NLP models.
|
||||
|
||||
The {Style.BRIGHT}metrics{Style.RESET_ALL} command prints the following realtime metrics:
|
||||
|
||||
{Style.BRIGHT}AHI (Absolute Hype Index){Style.RESET_ALL}
|
||||
---
|
||||
AHI is a measure of how much people are talking about a stock on social media.
|
||||
It is calculated by dividing the total number of mentions for the chosen stock
|
||||
on a social network by the mean number of mentions any stock receives on that
|
||||
social medium.
|
||||
|
||||
{Style.BRIGHT}RHI (Relative Hype Index){Style.RESET_ALL}
|
||||
---
|
||||
RHI is a measure of whether people are talking about a stock more or less than
|
||||
usual, calculated by dividing the mean AHI for the past day by the mean AHI for
|
||||
for the past week for that stock.
|
||||
|
||||
{Style.BRIGHT}Sentiment Score{Style.RESET_ALL}
|
||||
---
|
||||
Sentiment score is the percentage of people talking positively about the stock.
|
||||
For each social network the number of positive posts/comments is divided by the
|
||||
total number of both positive and negative posts/comments.
|
||||
|
||||
{Style.BRIGHT}SGP (Standard General Perception){Style.RESET_ALL}
|
||||
---
|
||||
SGP is a measure of whether people are more or less positive about a stock than
|
||||
usual. It is calculated by averaging the past day of sentiment values and then
|
||||
dividing it by the average of the past week of sentiment values.
|
||||
"""
|
||||
|
||||
ns_parser = _parse_args_for_ticker(
|
||||
other_args=other_args,
|
||||
ticker=ticker,
|
||||
command="metrics",
|
||||
desc=textwrap.dedent(command_description),
|
||||
)
|
||||
|
||||
try:
|
||||
|
||||
if not ns_parser:
|
||||
logging.error("There was an error in parsing the command-line arguments.")
|
||||
return
|
||||
|
||||
if not sentipy.supported(ns_parser.ticker):
|
||||
print("This stock is not supported by the SentimentInvestor API.")
|
||||
return
|
||||
|
||||
data = sentipy.parsed(ns_parser.ticker)
|
||||
|
||||
metric_values = _contextualise_metrics(data, ns_parser.ticker, core_metrics)
|
||||
|
||||
if not metric_values:
|
||||
logging.error("No data available or an error occurred.")
|
||||
return
|
||||
|
||||
print(_tabulate_metrics(ns_parser.ticker, metric_values))
|
||||
print()
|
||||
|
||||
except Exception as e:
|
||||
logging.error(e)
|
||||
print(e, "\n")
|
||||
|
||||
|
||||
def socials(ticker: str, other_args: List[str]) -> None:
|
||||
command_description = f"""
|
||||
{Style.BRIGHT}Sentiment Investor{Style.RESET_ALL} analyzes data from four major social media platforms to
|
||||
generate hourly metrics on over 2,000 stocks. Sentiment provides volume and
|
||||
sentiment metrics powered by proprietary NLP models.
|
||||
|
||||
The {Style.BRIGHT}social{Style.RESET_ALL} command prints the raw data for a given stock, including the number
|
||||
of mentions it has received on social media in the last hour and the sentiment
|
||||
score of those comments.
|
||||
"""
|
||||
|
||||
try:
|
||||
|
||||
ns_parser = _parse_args_for_ticker(
|
||||
other_args=other_args,
|
||||
ticker=ticker,
|
||||
command="social",
|
||||
desc=textwrap.dedent(command_description),
|
||||
)
|
||||
|
||||
if not ns_parser:
|
||||
logging.error("There was an error in parsing the command-line arguments.")
|
||||
return
|
||||
|
||||
if not sentipy.supported(ns_parser.ticker):
|
||||
print("This stock is not supported by the SentimentInvestor API.")
|
||||
return
|
||||
|
||||
data = sentipy.raw(ns_parser.ticker)
|
||||
|
||||
metric_values = _contextualise_metrics(data, ns_parser.ticker, social_metrics)
|
||||
|
||||
if not metric_values:
|
||||
logging.error("No data available or an error occurred.")
|
||||
return
|
||||
|
||||
print(_tabulate_metrics(ns_parser.ticker, metric_values))
|
||||
print()
|
||||
|
||||
except Exception as e:
|
||||
logging.error(e)
|
||||
print(e, "\n")
|
||||
|
||||
|
||||
def historical(ticker: str, other_args: List[str]) -> None:
|
||||
command_description = f"""
|
||||
{Style.BRIGHT}Sentiment Investor{Style.RESET_ALL} analyzes data from four major social media platforms to
|
||||
generate hourly metrics on over 2,000 stocks. Sentiment provides volume and
|
||||
sentiment metrics powered by proprietary NLP models.
|
||||
|
||||
The {Style.BRIGHT}historical{Style.RESET_ALL} command plots the past week of data for a selected metric, one of:
|
||||
|
||||
{Style.BRIGHT}AHI (Absolute Hype Index){Style.RESET_ALL}
|
||||
---
|
||||
AHI is a measure of how much people are talking about a stock on social media.
|
||||
It is calculated by dividing the total number of mentions for the chosen stock
|
||||
on a social network by the mean number of mentions any stock receives on that
|
||||
social medium.
|
||||
|
||||
{Style.BRIGHT}RHI (Relative Hype Index){Style.RESET_ALL}
|
||||
---
|
||||
RHI is a measure of whether people are talking about a stock more or less than
|
||||
usual, calculated by dividing the mean AHI for the past day by the mean AHI for
|
||||
for the past week for that stock.
|
||||
|
||||
{Style.BRIGHT}Sentiment Score{Style.RESET_ALL}
|
||||
---
|
||||
Sentiment score is the percentage of people talking positively about the stock.
|
||||
For each social network the number of positive posts/comments is divided by the
|
||||
total number of both positive and negative posts/comments.
|
||||
|
||||
{Style.BRIGHT}SGP (Standard General Perception){Style.RESET_ALL}
|
||||
---
|
||||
SGP is a measure of whether people are more or less positive about a stock than
|
||||
usual. It is calculated by averaging the past day of sentiment values and then
|
||||
dividing it by the average of the past week of sentiment values.
|
||||
"""
|
||||
|
||||
parser = argparse.ArgumentParser(
|
||||
add_help=False,
|
||||
prog="historical",
|
||||
formatter_class=argparse.RawDescriptionHelpFormatter,
|
||||
description=textwrap.dedent(command_description),
|
||||
)
|
||||
parser.add_argument(
|
||||
"-t",
|
||||
"--ticker",
|
||||
action="store",
|
||||
dest="ticker",
|
||||
type=str,
|
||||
default=ticker,
|
||||
help="ticker for which to fetch data",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-s",
|
||||
"--sort",
|
||||
action="store",
|
||||
type=str,
|
||||
default="date",
|
||||
help="the parameter to sort output table by",
|
||||
dest="sort_param",
|
||||
nargs="?",
|
||||
choices=["date", "value"],
|
||||
)
|
||||
parser.add_argument(
|
||||
"-d",
|
||||
"--direction",
|
||||
action="store",
|
||||
type=str,
|
||||
default="desc",
|
||||
help="the direction to sort the output table",
|
||||
dest="sort_dir",
|
||||
nargs="?",
|
||||
choices=["asc", "desc"],
|
||||
)
|
||||
parser.add_argument(
|
||||
"metric",
|
||||
type=str,
|
||||
action="store",
|
||||
default="sentiment",
|
||||
nargs="?",
|
||||
choices=["sentiment", "AHI", "RHI", "SGP"],
|
||||
help="the metric to plot",
|
||||
)
|
||||
|
||||
try:
|
||||
|
||||
ns_parser = parse_known_args_and_warn(parser, other_args)
|
||||
if not ns_parser:
|
||||
logging.error("There was an error in parsing the command-line arguments.")
|
||||
return
|
||||
|
||||
if not sentipy.supported(ns_parser.ticker):
|
||||
print("This stock is not supported by the SentimentInvestor API.")
|
||||
return
|
||||
|
||||
data = sentipy.historical(
|
||||
ns_parser.ticker,
|
||||
ns_parser.metric,
|
||||
int(time.time() - 60 * 60 * 24 * 7),
|
||||
int(time.time()),
|
||||
)
|
||||
df = pd.DataFrame.from_dict(
|
||||
{
|
||||
"date": map(datetime.datetime.utcfromtimestamp, data.keys()),
|
||||
ns_parser.metric: data.values(),
|
||||
}
|
||||
)
|
||||
|
||||
df.sort_index(ascending=True, inplace=True)
|
||||
|
||||
if df.empty:
|
||||
logging.error("The dataset is empty, something must have gone wrong")
|
||||
return
|
||||
|
||||
_customise_plot()
|
||||
|
||||
# use seaborn to lineplot
|
||||
ax = sns.lineplot(
|
||||
data=df,
|
||||
x="date",
|
||||
y=ns_parser.metric,
|
||||
legend=False,
|
||||
label=[ns_parser.metric],
|
||||
)
|
||||
|
||||
# always show zero on the y-axis
|
||||
plt.ylim(bottom=0)
|
||||
|
||||
# set the x-axis date formatting
|
||||
ax.xaxis.set_major_formatter(mdates.DateFormatter("%x"))
|
||||
|
||||
# scale the plot appropriately
|
||||
plt.gcf().set_size_inches(plot_autoscale())
|
||||
plt.gcf().set_dpi(PLOT_DPI)
|
||||
plt.gcf().autofmt_xdate()
|
||||
|
||||
# fill below the line
|
||||
plt.fill_between(df.date, df[ns_parser.metric], alpha=0.3)
|
||||
|
||||
# add title e.g. AAPL sentiment since 22/07/21
|
||||
plt.title(
|
||||
f"{ns_parser.ticker} {ns_parser.metric} since {min(df.date).strftime('%x')}"
|
||||
)
|
||||
|
||||
plt.show()
|
||||
|
||||
###
|
||||
|
||||
boundary = _Boundary(
|
||||
0, max(df[ns_parser.metric].max(), 2 * df[ns_parser.metric].mean())
|
||||
)
|
||||
|
||||
# average for each day
|
||||
aggregated = df.resample("D", on="date").mean()
|
||||
|
||||
# reverse the ordering if requested
|
||||
aggregated.sort_values(
|
||||
ns_parser.metric
|
||||
if ns_parser.sort_param == "value"
|
||||
else ns_parser.sort_param,
|
||||
axis=0,
|
||||
ascending=ns_parser.sort_dir == "asc",
|
||||
inplace=True,
|
||||
)
|
||||
|
||||
# format the date according to user's locale
|
||||
aggregated.index = aggregated.index.strftime("%x")
|
||||
|
||||
# apply coloring to every value
|
||||
aggregated[ns_parser.metric] = [
|
||||
boundary.categorise(value)[0] + str(value) + Style.RESET_ALL
|
||||
for value in aggregated[ns_parser.metric]
|
||||
]
|
||||
|
||||
print(
|
||||
tabulate.tabulate(
|
||||
aggregated,
|
||||
headers=["Day", f"average {ns_parser.metric}"],
|
||||
tablefmt="psql",
|
||||
floatfmt=".3f",
|
||||
)
|
||||
)
|
||||
print()
|
||||
|
||||
except Exception as e:
|
||||
logging.error(e)
|
||||
print(e, "\n")
|
||||
@@ -70,3 +70,9 @@ API_FINNHUB_KEY = os.getenv("GT_API_FINNHUB_KEY") or "REPLACE_ME"
|
||||
|
||||
# https://iexcloud.io
|
||||
API_IEX_TOKEN = os.getenv("GT_API_IEX_KEY") or "REPLACE_ME"
|
||||
|
||||
# https://www.sentimentinvestor.com
|
||||
API_SENTIMENTINVESTOR_KEY = os.getenv("GT_API_SENTIMENTINVESTOR_KEY") or "REPLACE_ME"
|
||||
API_SENTIMENTINVESTOR_TOKEN = (
|
||||
os.getenv("GT_API_SENTIMENTINVESTOR_TOKEN") or "REPLACE_ME"
|
||||
)
|
||||
|
||||
@@ -70,7 +70,8 @@ CURRENCIES = [
|
||||
PLATFORMS = paprika.get_all_contract_platforms()["platform_id"].tolist()
|
||||
COINS = paprika.get_list_of_coins()
|
||||
COINS_DCT = dict(zip(COINS.id, COINS.symbol))
|
||||
EXCHANGES = paprika.get_list_of_exchanges()
|
||||
# see https://github.com/GamestonkTerminal/GamestonkTerminal/pull/562#issuecomment-887842888
|
||||
# EXCHANGES = paprika.get_list_of_exchanges()
|
||||
|
||||
|
||||
def global_market(other_args: List[str]):
|
||||
|
||||
56
gamestonk_terminal/discovery/README.md
vendored
56
gamestonk_terminal/discovery/README.md
vendored
@@ -46,6 +46,10 @@ This menu aims to discover new stocks, and the usage of the following commands a
|
||||
* dark pool short position [Stockgrid]
|
||||
* [shortvol](#shortvol)
|
||||
* short interest and days to cover [Stockgrid]
|
||||
* [popular](#popular)
|
||||
* most popular stocks on social media right now [SentimentInvestor.com]
|
||||
* [emerging](#emerging)
|
||||
* stocks that are being talked about more than usual [SentimentInvestor.com]
|
||||
|
||||
## ipo <a name="ipo"></a>
|
||||
|
||||
@@ -327,3 +331,55 @@ Print short interest and days to cover. [Source: Stockgrid]
|
||||
|
||||
<img width="949" alt="shortvol" src="https://user-images.githubusercontent.com/25267873/122323861-af7be400-cf1f-11eb-9de2-5c7f2debddf0.png">
|
||||
|
||||
## popular <a name="popular"></a>
|
||||
|
||||
```
|
||||
usage: popular [-l LIMIT] [-h]
|
||||
```
|
||||
|
||||
The `popular` command prints the top `n` (default=10) stocks with the highest Average Hype Index right now.
|
||||
|
||||
### AHI (Absolute Hype Index)
|
||||
|
||||
AHI is a measure of how much people are talking about a stock on social media.
|
||||
It is calculated by dividing the total number of mentions for the chosen stock
|
||||
on a social network by the mean number of mentions any stock receives on that
|
||||
social medium.
|
||||
|
||||
> **[Sentiment Investor](https://sentimentinvestor.com)** analyzes data from four major social media platforms to
|
||||
> generate hourly metrics on over 2,000 stocks. Sentiment provides volume and
|
||||
> sentiment metrics powered by proprietary NLP models.
|
||||
|
||||
Optional arguments:
|
||||
- `-l`, `--limit LIMIT`
|
||||
the maximum number of stocks to retrieve
|
||||
- `-h`, `--help`
|
||||
show this help message
|
||||
|
||||
|
||||
|
||||
## emerging <a name="emerging"></a>
|
||||
|
||||
```
|
||||
usage: emerging [-l LIMIT] [-h]
|
||||
```
|
||||
|
||||
The `emerging` command prints the top `n` (default=10) stocks with the highest Relative Hype Index right now.
|
||||
|
||||
### RHI (Relative Hype Index)
|
||||
RHI is a measure of whether people are talking about a stock more or less than
|
||||
usual, calculated by dividing the mean AHI for the past day by the mean AHI for
|
||||
for the past week for that stock.
|
||||
|
||||
> **[Sentiment Investor](https://sentimentinvestor.com)** analyzes data from four major social media platforms to
|
||||
> generate hourly metrics on over 2,000 stocks. Sentiment provides volume and
|
||||
> sentiment metrics powered by proprietary NLP models.
|
||||
|
||||
|
||||
Optional arguments:
|
||||
- `-l`, `--limit LIMIT`
|
||||
the maximum number of stocks to retrieve
|
||||
- `-h`, `--help`
|
||||
show this help message
|
||||
|
||||
|
||||
|
||||
@@ -24,6 +24,7 @@ from gamestonk_terminal.discovery import (
|
||||
finra_ats_view,
|
||||
finnhub_view,
|
||||
stockgrid_view,
|
||||
sentimentinvestor_view,
|
||||
)
|
||||
|
||||
|
||||
@@ -59,6 +60,8 @@ class DiscoveryController:
|
||||
"darkpool",
|
||||
"darkshort",
|
||||
"shortvol",
|
||||
"popular",
|
||||
"emerging",
|
||||
]
|
||||
|
||||
def __init__(self):
|
||||
@@ -112,6 +115,12 @@ class DiscoveryController:
|
||||
)
|
||||
print(" darkshort dark pool short position [Stockgrid.io]")
|
||||
print(" shortvol short interest and days to cover [Stockgrid.io]")
|
||||
print(
|
||||
" popular show most popular stocks on social media right now [SentimentInvestor.com]"
|
||||
)
|
||||
print(
|
||||
" emerging show stocks that are being talked about more than usual [SentimentInvestor.com]"
|
||||
)
|
||||
print("")
|
||||
|
||||
def switch(self, an_input: str):
|
||||
@@ -255,6 +264,14 @@ class DiscoveryController:
|
||||
"""Process shortvol command"""
|
||||
stockgrid_view.shortvol(other_args)
|
||||
|
||||
def call_popular(self, other_args: List[str]):
|
||||
"""Process popular command"""
|
||||
sentimentinvestor_view.sort("AHI", other_args, "popular")
|
||||
|
||||
def call_emerging(self, other_args: List[str]):
|
||||
"""Process popular command"""
|
||||
sentimentinvestor_view.sort("RHI", other_args, "emerging")
|
||||
|
||||
|
||||
def menu():
|
||||
"""Discovery Menu"""
|
||||
|
||||
91
gamestonk_terminal/discovery/sentimentinvestor_view.py
Normal file
91
gamestonk_terminal/discovery/sentimentinvestor_view.py
Normal file
@@ -0,0 +1,91 @@
|
||||
import argparse
|
||||
import logging
|
||||
import textwrap
|
||||
from typing import Any, List
|
||||
|
||||
from colorama import Style
|
||||
from sentipy.sentipy import Sentipy
|
||||
from tabulate import tabulate
|
||||
|
||||
from gamestonk_terminal import config_terminal as cfg
|
||||
from gamestonk_terminal.helper_funcs import parse_known_args_and_warn
|
||||
|
||||
sentipy: Sentipy = Sentipy(
|
||||
token=cfg.API_SENTIMENTINVESTOR_TOKEN, key=cfg.API_SENTIMENTINVESTOR_KEY
|
||||
)
|
||||
"""Initialise SentiPy with the user's API token and key"""
|
||||
|
||||
|
||||
__command_descriptions = {
|
||||
"popular": f"""
|
||||
The {Style.BRIGHT}popular{Style.RESET_ALL} command prints the stocks with highest Average Hype Index right now.
|
||||
|
||||
{Style.BRIGHT}AHI (Absolute Hype Index){Style.RESET_ALL}
|
||||
---
|
||||
AHI is a measure of how much people are talking about a stock on social media.
|
||||
It is calculated by dividing the total number of mentions for the chosen stock
|
||||
on a social network by the mean number of mentions any stock receives on that
|
||||
social medium.
|
||||
|
||||
===
|
||||
|
||||
{Style.BRIGHT}Sentiment Investor{Style.RESET_ALL} analyzes data from four major social media platforms to
|
||||
generate hourly metrics on over 2,000 stocks. Sentiment provides volume and
|
||||
sentiment metrics powered by proprietary NLP models.
|
||||
""",
|
||||
"emerging": f"""
|
||||
The {Style.BRIGHT}emerging{Style.RESET_ALL} command prints the stocks with highest Relative Hype Index right now.
|
||||
|
||||
{Style.BRIGHT}RHI (Relative Hype Index){Style.RESET_ALL}
|
||||
---
|
||||
RHI is a measure of whether people are talking about a stock more or less than
|
||||
usual, calculated by dividing the mean AHI for the past day by the mean AHI for
|
||||
for the past week for that stock.
|
||||
|
||||
===
|
||||
|
||||
{Style.BRIGHT}Sentiment Investor{Style.RESET_ALL} analyzes data from four major social media platforms to
|
||||
generate hourly metrics on over 2,000 stocks. Sentiment provides volume and
|
||||
sentiment metrics powered by proprietary NLP models.
|
||||
""",
|
||||
}
|
||||
|
||||
|
||||
def sort(metric: str, other_args: List[str], command_name: str) -> None:
|
||||
parser = argparse.ArgumentParser(
|
||||
add_help=False,
|
||||
prog=command_name,
|
||||
formatter_class=argparse.RawDescriptionHelpFormatter,
|
||||
description=textwrap.dedent(__command_descriptions[command_name]),
|
||||
)
|
||||
|
||||
parser.add_argument(
|
||||
"-l",
|
||||
"--limit",
|
||||
action="store",
|
||||
dest="limit",
|
||||
type=int,
|
||||
default=10,
|
||||
help="the maximum number of stocks to retrieve",
|
||||
)
|
||||
|
||||
try:
|
||||
ns_parser = parse_known_args_and_warn(parser, other_args)
|
||||
if not ns_parser:
|
||||
return
|
||||
|
||||
data = sentipy.sort(metric, ns_parser.limit)
|
||||
|
||||
table: List[List[Any]] = []
|
||||
for index, stock in enumerate(data):
|
||||
if not hasattr(stock, "symbol") or not hasattr(stock, metric):
|
||||
logging.warning("data for stock %s is incomplete, ignoring", index + 1)
|
||||
table.append([])
|
||||
else:
|
||||
table.append([index + 1, stock.symbol, stock.__getattribute__(metric)])
|
||||
|
||||
print(tabulate(table, headers=["Rank", "Ticker", metric], floatfmt=".3f"))
|
||||
print()
|
||||
except Exception as e:
|
||||
logging.error(e)
|
||||
print(e, "\n")
|
||||
@@ -867,6 +867,20 @@ def check_api_keys():
|
||||
else:
|
||||
key_dict["BINANCE"] = "defined, not tested"
|
||||
|
||||
# SentimentInvestor keys
|
||||
si_keys = [cfg.API_SENTIMENTINVESTOR_KEY, cfg.API_SENTIMENTINVESTOR_TOKEN]
|
||||
if "REPLACE_ME" in si_keys:
|
||||
key_dict["SENTIMENT_INVESTOR"] = "Not defined"
|
||||
else:
|
||||
account = requests.get(
|
||||
f"https://api.sentimentinvestor.com/v4/account"
|
||||
f"?token={cfg.API_SENTIMENTINVESTOR_TOKEN}&key={cfg.API_SENTIMENTINVESTOR_KEY}"
|
||||
)
|
||||
if account.ok and account.json().get("success", False):
|
||||
key_dict["SENTIMENT_INVESTOR"] = "Defined, test passed"
|
||||
else:
|
||||
key_dict["SENTIMENT_INVESTOR"] = "Defined, test unsuccessful"
|
||||
|
||||
print(
|
||||
tabulate(
|
||||
pd.DataFrame(key_dict.items()),
|
||||
@@ -966,6 +980,7 @@ def about_us():
|
||||
f"{Fore.CYAN}FinBrain: {Style.RESET_ALL}https://finbrain.tech\n"
|
||||
f"{Fore.CYAN}Quiver Quantitative: {Style.RESET_ALL}https://www.quiverquant.com\n"
|
||||
f"{Fore.CYAN}Ops.Syncretism: {Style.RESET_ALL}https://ops.syncretism.io/api.html\n"
|
||||
f"{Fore.CYAN}SentimentInvestor: {Style.RESET_ALL}https://sentimentinvestor.com\n"
|
||||
f"\n{Fore.RED}"
|
||||
"DISCLAIMER: Trading in financial instruments involves high risks including the risk of losing some, "
|
||||
"or all, of your investment amount, and may not be suitable for all investors. Before deciding to trade in "
|
||||
|
||||
36
poetry.lock
generated
vendored
36
poetry.lock
generated
vendored
@@ -2380,6 +2380,28 @@ python-versions = "*"
|
||||
[package.extras]
|
||||
win32 = ["pywin32"]
|
||||
|
||||
[[package]]
|
||||
name = "sentiment-investor"
|
||||
version = "2.0.0"
|
||||
description = "Access the Sentiment Investor API through Python"
|
||||
category = "main"
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
|
||||
[package.dependencies]
|
||||
sentipy = "*"
|
||||
|
||||
[[package]]
|
||||
name = "sentipy"
|
||||
version = "0.2.0"
|
||||
description = "Python implementations of Sentinel 2 Toolbox L2 products & spectral indices"
|
||||
category = "main"
|
||||
optional = false
|
||||
python-versions = ">=3.6,<4.0"
|
||||
|
||||
[package.dependencies]
|
||||
numpy = ">=1.18.4,<2.0.0"
|
||||
|
||||
[[package]]
|
||||
name = "six"
|
||||
version = "1.15.0"
|
||||
@@ -3030,7 +3052,7 @@ prediction = ["tensorflow", "pmdarima"]
|
||||
[metadata]
|
||||
lock-version = "1.1"
|
||||
python-versions = "^3.6.8"
|
||||
content-hash = "47ea04f7495d8f1e468fcf36dc988d67aacd436a44149261bd77c631b5fc4a72"
|
||||
content-hash = "f0f4648cb473e92ab7e49380ac8a8b7f0ec9ec05e8cf1c8c8bc2ffb3a390e599"
|
||||
|
||||
[metadata.files]
|
||||
absl-py = [
|
||||
@@ -3247,8 +3269,8 @@ chardet = [
|
||||
{file = "chardet-4.0.0.tar.gz", hash = "sha256:0d6f53a15db4120f2b08c94f11e7d93d2c911ee118b6b30a04ec3ee8310179fa"},
|
||||
]
|
||||
charset-normalizer = [
|
||||
{file = "charset-normalizer-2.0.3.tar.gz", hash = "sha256:c46c3ace2d744cfbdebceaa3c19ae691f53ae621b39fd7570f59d14fb7f2fd12"},
|
||||
{file = "charset_normalizer-2.0.3-py3-none-any.whl", hash = "sha256:88fce3fa5b1a84fdcb3f603d889f723d1dd89b26059d0123ca435570e848d5e1"},
|
||||
{file = "charset-normalizer-2.0.1.tar.gz", hash = "sha256:ad0da505736fc7e716a8da15bf19a985db21ac6415c26b34d2fafd3beb3d927e"},
|
||||
{file = "charset_normalizer-2.0.1-py3-none-any.whl", hash = "sha256:b68b38179052975093d71c1b5361bf64afd80484697c1f27056e50593e695ceb"},
|
||||
]
|
||||
click = [
|
||||
{file = "click-8.0.1-py3-none-any.whl", hash = "sha256:fba402a4a47334742d782209a7c79bc448911afe1149d07bdabdf480b3e2f4b6"},
|
||||
@@ -4698,6 +4720,14 @@ send2trash = [
|
||||
{file = "Send2Trash-1.7.1-py3-none-any.whl", hash = "sha256:c20fee8c09378231b3907df9c215ec9766a84ee20053d99fbad854fe8bd42159"},
|
||||
{file = "Send2Trash-1.7.1.tar.gz", hash = "sha256:17730aa0a33ab82ed6ca76be3bb25f0433d0014f1ccf63c979bab13a5b9db2b2"},
|
||||
]
|
||||
sentiment-investor = [
|
||||
{file = "sentiment-investor-2.0.0.tar.gz", hash = "sha256:ff97986fbd414e8166f92e4503b3b9cb8c583bff860aaba1dc9a3536666b57a4"},
|
||||
{file = "sentiment_investor-2.0.0-py3-none-any.whl", hash = "sha256:e5744dd6e591d52d44d2ea51b1c5483e434affc598ea1a4b0cabab02f0d455ec"},
|
||||
]
|
||||
sentipy = [
|
||||
{file = "sentipy-0.2.0-py3-none-any.whl", hash = "sha256:ca80a3cc284c08de8c7df0ee9bccff27fb2f4b07e8aa7f0dc52683e765855287"},
|
||||
{file = "sentipy-0.2.0.tar.gz", hash = "sha256:dfcb4d5caeee6606714bc6e00966692ebf637616ce9215c69768583ad757efac"},
|
||||
]
|
||||
six = [
|
||||
{file = "six-1.15.0-py2.py3-none-any.whl", hash = "sha256:8b74bedcbbbaca38ff6d7491d76f2b06b3592611af620f8426e82dddb04a5ced"},
|
||||
{file = "six-1.15.0.tar.gz", hash = "sha256:30639c035cdb23534cd4aa2dd52c3bf48f06e5f4a941509c8bafd8ce11080259"},
|
||||
|
||||
1
pyproject.toml
vendored
1
pyproject.toml
vendored
@@ -76,6 +76,7 @@ GitPython = "^3.1.17"
|
||||
fear-greed-index = "^0.1.3"
|
||||
pyEX = "^0.5.0"
|
||||
charset-normalizer = "2.0.1"
|
||||
sentiment-investor = "^2.0.0"
|
||||
|
||||
[tool.poetry.dev-dependencies]
|
||||
pytest = "^6.2.2"
|
||||
|
||||
1
requirements-full.txt
vendored
1
requirements-full.txt
vendored
@@ -178,6 +178,7 @@ scs==2.1.4; python_full_version >= "3.6.1" and python_full_version < "4.0.0" and
|
||||
seaborn==0.11.1; python_version >= "3.6"
|
||||
selenium==3.141.0
|
||||
send2trash==1.7.1; python_version >= "3.6"
|
||||
sentiment-investor==2.0.0
|
||||
six==1.15.0; python_full_version >= "3.6.1" and python_version >= "3.5" and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0") and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.6") and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.5") and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.6") and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.6.0")
|
||||
smmap==4.0.0; python_version >= "3.6"
|
||||
snowballstemmer==2.1.0; python_full_version >= "3.6.8" and python_full_version < "4.0.0" and python_version >= "3.6"
|
||||
|
||||
1
requirements.txt
vendored
1
requirements.txt
vendored
@@ -163,6 +163,7 @@ scs==2.1.4; python_full_version >= "3.6.1" and python_full_version < "4.0.0" and
|
||||
seaborn==0.11.1; python_version >= "3.6"
|
||||
selenium==3.141.0
|
||||
send2trash==1.7.1; python_version >= "3.6"
|
||||
sentiment-investor==2.0.0
|
||||
six==1.15.0; python_full_version >= "3.6.1" and python_version >= "3.5" and (python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0") and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.6") and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.5") and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.6")
|
||||
smmap==4.0.0; python_version >= "3.6"
|
||||
snowballstemmer==2.1.0; python_full_version >= "3.6.8" and python_full_version < "4.0.0" and python_version >= "3.6"
|
||||
|
||||
Reference in New Issue
Block a user