mirror of
https://github.com/OpenBB-finance/OpenBB.git
synced 2026-11-01 15:01:18 +00:00
Remove latest news and add Customised news (#923)
* remove disc/latest feature from seeking alpha * Add cnews to stocks/discovery * Improve cnews command * update hugo server * remove old READMEs * fix typo * Improve code to remove d_ and l_ * add *.env to gitignore
This commit is contained in:
DidierRLopes
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vendored
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vendored
@@ -1,7 +1,7 @@
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__pycache__/
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*.pyc
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.DS_Store
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.env
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*.env
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.venv
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.vscode
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*.ipynb
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# BEHAVIOURAL ANALYSIS
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This menu aims to extrapolate behavioural analysis regarding a pre-loaded ticker, and the usage of the following
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commands along with an example will be exploited below.
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* [finbrain](#finbrain)
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* sentiment from 15+ major news headlines
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* [stats](#stats)
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* sentiment stats including comparison with sector
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[REDDIT](#REDDIT)
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* [wsb](#wsb)
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* show what WSB gang is up to in subreddit wallstreetbets
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* [watchlist](#watchlist)
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* show other users watchlist
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* [popular](#popular)
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* show popular tickers
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* [spac_c](#spac_c)
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* show other users spacs announcements from subreddit SPACs community
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* [spac](#spac)
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* show other users spacs announcements from other subs
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[STOCKTWITS](#STOCKTWITS)
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* [bullbear](#bullbear)
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* estimate quick sentiment from last 30 messages on board
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* [messages](#messages)
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* output up to the 30 last messages on the board
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* [trending](#trending)
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* trending stocks
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* [stalker](#stalker)
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* stalk stocktwits user's last messages
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[TWITTER](#TWITTER)
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* [infer](#infer)
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* infer about stock's sentiment from latest tweets
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* [sentiment](#sentiment)
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* in-depth sentiment prediction from tweets over time
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[GOOGLE](#GOOGLE)
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* [mentions](#mentions)
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* interest over time based on stock's mentions
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* [regions](#regions)
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* regions that show highest interest in stock
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* [queries](#queries)
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* top related queries with this stock
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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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```text
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usage: finbrain
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```
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FinBrain collects the news headlines from 15+ major financial news sources on a daily basis and analyzes them to
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generate sentiment scores for more than 4500 US stocks. FinBrain Technologies develops deep learning algorithms for
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financial analysis and prediction, which currently serves traders from more than 150 countries all around the world.
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[Source: See <https://finbrain.tech>]
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<img width="986" alt="Finbrain" src="https://user-images.githubusercontent.com/25267873/111629542-4fbf5100-87e9-11eb-8d5d-96faa8d3fca4.png">
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## stats <a name="stats"></a>
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```text
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usage: stats
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```
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Sentiment stats which displays buzz, news score, articles last week, articles weekly average, bullish vs bearish
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percentages, sector average bullish percentage, and sector average news score. [Source: <https://finnhub.io>]
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<img width="1003" alt="Stats" src="https://user-images.githubusercontent.com/25267873/116888922-e9b94c80-ac23-11eb-959e-be24c79488a3.png">
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# REDDIT <a name="REDDIT"></a>
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## wsb <a name="wsb"></a>
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```text
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usage: wsb [-l N_LIMIT] [-n]
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```
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Print what WSB gang are up to in subreddit wallstreetbets. [Source: Reddit]
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* -l : limit of posts to print. Default 10.
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* -n : new flag, if true the posts retrieved are based on being more recent rather than their score. Default False.
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<img width="947" alt="wsb" src="https://user-images.githubusercontent.com/25267873/108612074-d5afce00-73dc-11eb-80ca-8ecfad12020c.png">
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## watchlist <a name="watchlist"></a>
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```text
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usage: watchlist [-l N_LIMIT]
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```
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Print other users watchlist. [Source: Reddit]
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|
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* -l : limit of posts to print. Default 5.
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|
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<img width="941" alt="watchlist" src="https://user-images.githubusercontent.com/25267873/108920576-caeb7800-762c-11eb-937c-94ecbb65d119.png">
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## popular <a name="popular"></a>
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```text
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usage: popular [-l N_LIMIT] [-s S_SUBREDDIT] [-d N_DAYS]
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```
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Print latest popular tickers. [Source: Reddit]
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* -l : limit of posts retrieved per sub reddit. Default 50.
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* -s : subreddits to look for tickers, e.g. pennystocks,stocks. Default: pennystocks, RobinHoodPennyStocks, Daytrading,
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StockMarket, stocks, investing, wallstreetbets.
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<img width="948" alt="popular" src="https://user-images.githubusercontent.com/25267873/108917846-4dbe0400-7628-11eb-821e-9fda97a6d9cd.png">
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## spac_c <a name="spac_c"></a>
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```text
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usage: spac_c [-l N_LIMIT] [-p]
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```
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Print other users SPACs announcement under subreddit 'SPACs'. [Source: Reddit]
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|
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* -l : limit of posts with SPACs retrieved. Default 10.
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* -p : popular flag, if true the posts retrieved are based on score rather than time. Default False.
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|
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<img width="936" alt="spac_c" src="https://user-images.githubusercontent.com/25267873/108920571-c8891e00-762c-11eb-8da8-e2b775bc7109.png">
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## spac <a name="spac"></a>
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```text
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usage: spac [-h] [-l N_LIMIT] [-d N_DAYS]
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```
|
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|
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Print other users SPACs announcement under subreddit 'SPACs'. [Source: Reddit]
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|
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* -l : limit of posts with SPACs retrieved. Default 5.
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|
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<img width="939" alt="spac" src="https://user-images.githubusercontent.com/25267873/108920696-04bc7e80-762d-11eb-896b-1b1c19903584.png">
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# STOCKTWITS <a name="STOCKTWITS"></a>
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## bullbear <a name="bullbear"></a>
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```text
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usage: bullbear [-t S_TICKER]
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```
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Print bullbear sentiment based on last 30 messages on the board. Also prints the watchlist_count. [Source: Stocktwits]
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* -t : ticker to gather sentiment from.
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<img width="934" alt="sentiment" src="https://user-images.githubusercontent.com/25267873/108612307-42c46300-73df-11eb-9cec-253c8fb6d62f.png">
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## messages <a name="messages"></a>
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```text
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usage: messages [-t S_TICKER] [-l N_LIM]
|
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```
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Print up to 30 of the last messages on the board. [Source: Stocktwits]
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* -t : get board messages from this ticker. Default pre-loaded.
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* -l : limit messages shown. Default 30.
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<img width="958" alt="messages" src="https://user-images.githubusercontent.com/25267873/108612310-448e2680-73df-11eb-8fa8-b619c6269742.png">
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## trending <a name="trending"></a>
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```text
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usage: trending
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```
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Stocks trending. [Source: Stocktwits]
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<img width="956" alt="trending" src="https://user-images.githubusercontent.com/25267873/108612311-4526bd00-73df-11eb-840b-18829fcecdd3.png">
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|
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## stalker <a name="stalker"></a>
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```text
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usage: stalker [-u S_USER] [-l N_LIM]
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```
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Print up to the last 30 messages of a user. [Source: Stocktwits]
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* -u : username. Default newsfilter.
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* -l : limit messages shown. Default 30.
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|
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<img width="943" alt="stalker" src="https://user-images.githubusercontent.com/25267873/108612309-435cf980-73df-11eb-92c9-e9f15f966d8e.png">
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# TWITTER <a name="TWITTER"></a>
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Uses VADER sentiment. VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment
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analysis tool that is specifically attuned to sentiments expressed in social media.
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The output of VADER analysis :
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```text
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The compound score is computed by summing the valence scores of each word in the lexicon, adjusted according to the
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rules, and then normalized to be between -1 (most extreme negative) and +1 (most extreme positive). This is the most
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useful metric if you want a single unidimensional measure of sentiment for a given sentence. Calling it a 'normalized,
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weighted composite score' is accurate.
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```
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A sample tweet is shown below along with what the VADER polarity scores are.
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```text
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Have some $SENS 🚀🏆\n👉FDA approval coming very soon, 👉News is out, amazing test results. \n👉Very high short
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interest rate. \n👉Major short squeeze coming. \n\n$amc $gme $nakd $ctrm $tsla $aapl $oeg $aht $bbby $bb $fsr $spce
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https://t.co/B0cPhufpA2'
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```
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This gives the following scores:
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```python
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created_at Thu Jun 03 20:37:54
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text Have some $SENS 🚀🏆\n👉FDA approval coming very...
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sentiment 0.872
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positive 0.177
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negative 0
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neutral 0.823
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```
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Note that both functions use twitters v2 endpoint, which only gives 7 days of historical data. It also requires at
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least 10 tweets be pulled.
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## infer <a name="infer"></a>
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```text
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usage: infer [-n N_NUM]
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```
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Print quick sentiment inference from last tweets that contain the ticker. This model splits the text into
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character-level tokens and uses theVADER model to make predictions.
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* -n : num of latest tweets to infer from. Default 100.
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## sentiment <a name="sentiment"></a>
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```text
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usage: sentiment [-n N_NUM] [-d N_DAYS_PAST]
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```
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Plot in-depth sentiment extracted from tweets from last days that contain pre-defined ticker. This model splits the
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text into character-level tokens and uses the VADER model to make predictions.
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* -n : num of tweets to extract per hour. Default 15.
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* -d : num of days in the past to extract tweets. Default 6. Max 6
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# GOOGLE
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## mentions <a name="mentions"></a>
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```text
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usage: mentions [-s S_START]
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```
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Plot weekly bars of stock's interest over time. other users watchlist. [Source: Google]
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* -s : starting date (format YYYY-MM-DD) from when we are interested in stock's mentions. Default: the one provided
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in main menu.
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## regions <a name="regions"></a>
|
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|
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```text
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usage: regions [-n N_NUM]
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```
|
||||
|
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Plot bars of regions based on stock's interest. [Source: Google]
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|
||||
* -n : number of regions to plot that show highest interest. Default 10.
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||||
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||||
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||||
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## queries <a name="queries"></a>
|
||||
|
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```text
|
||||
usage: queries [-n N_NUM]
|
||||
```
|
||||
|
||||
Print top related queries with this stock's query. [Source: Google]
|
||||
|
||||
* -n : number of top related queries to print. Default 10.
|
||||
|
||||
<img width="937" alt="Queries" src="https://user-images.githubusercontent.com/25267873/108777341-91970780-755b-11eb-985e-819285688eea.png">
|
||||
|
||||
## rise <a name="rise"></a>
|
||||
|
||||
```text
|
||||
usage: rise [-n N_NUM]
|
||||
```
|
||||
|
||||
Print top rising related queries with this stock's query. [Source: Google]
|
||||
|
||||
* -n : number of top rising related queries to print. Default 10.
|
||||
|
||||
<img width="934" alt="Rise" src="https://user-images.githubusercontent.com/25267873/108777814-4f21fa80-755c-11eb-96da-0327c9a0da57.png">
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|
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# SENTIMENTINVESTOR
|
||||
|
||||
> **[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.
|
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|
||||
## Metric definitions
|
||||
|
||||
### 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.
|
||||
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.
|
||||
|
||||
### RHI (Relative Hype Index) <a name="RHI"></a>
|
||||
|
||||
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 Score <a name="sentiment"></a>
|
||||
|
||||
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.
|
||||
|
||||
### SGP (Standard General Perception) <a name="SGP"></a>
|
||||
|
||||
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.
|
||||
|
||||
## metrics <a name="metrics"></a>
|
||||
|
||||
```text
|
||||
usage: metrics [-h] [ticker]
|
||||
```
|
||||
|
||||
The `metrics` command presents the four core metrics provided by Sentiment Investor,
|
||||
including AHI, RHI, sentiment and SGP, as described above.
|
||||
|
||||
Optional arguments:
|
||||
|
||||
* [optional, positional] ticker
|
||||
ticker to use instead of the loaded one
|
||||
* `-h`, `--help`
|
||||
show this help message
|
||||
|
||||
Example output:
|
||||
|
||||
|
||||
|
||||
## social <a name="social"></a>
|
||||
|
||||
```text
|
||||
usage: social [-h] [ticker]
|
||||
```
|
||||
|
||||
The `social` 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.
|
||||
|
||||
Optional arguments:
|
||||
|
||||
* [optional, positional] ticker
|
||||
ticker to use instead of the loaded one
|
||||
* `-h`, `--help`
|
||||
show this help message
|
||||
|
||||
Example output:
|
||||
|
||||
|
||||
|
||||
## historical <a name="historical"></a>
|
||||
|
||||
```text
|
||||
usage: historical [-t TICKER] [-s [{date,value}]] [-d [{asc,desc}]] [-h] [{sentiment,AHI,RHI,SGP}]
|
||||
```
|
||||
|
||||
The `historical` command plots the past week of data for a selected
|
||||
SentimentInvestor core metric, one of:
|
||||
[AHI](#ahi), [RHI](#ahi), [sentiment](#sentiment) or [SGP](#SGP).
|
||||
It also outputs a sorted table of the mean value for that metric for each day.
|
||||
|
||||
Positional arguments:
|
||||
|
||||
* one of `sentiment`, `AHI`, `RHI` or `SGP`
|
||||
(optional) the metric to plot (default: `sentiment`)
|
||||
|
||||
Optional arguments:
|
||||
|
||||
* `-t`, `--ticker TICKER`
|
||||
ticker for which to fetch data
|
||||
* `-s`, `--sort [{date,value}]`
|
||||
the parameter to sort output table by
|
||||
* `-d`, `--direction [{asc,desc}]`
|
||||
the direction to sort the output table
|
||||
* `-h`, `--help`
|
||||
show this help message
|
||||
|
||||
Example output:
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -1,308 +0,0 @@
|
||||
# PREDICTION TECHNIQUES
|
||||
|
||||
This menu aims to predict the share price of a pre-loaded stock, and the usage of the following commands along with an
|
||||
example will be exploited below. See [How to fune-tuning a NN model](FUNE-TUNING-NN-models.md).
|
||||
|
||||
* [ets](#ets)
|
||||
* Exponential Smoothing (e.g. Holt-Winters)
|
||||
* [knn](#knn)
|
||||
* k-Nearest Neighbors
|
||||
* [linear](#linear)
|
||||
* linear regression (polynomial 1)
|
||||
* [quadratic](#quadratic)
|
||||
* quadratic regression (polynomial 2)
|
||||
* [cubic](#cubic)
|
||||
* cubic regression (polynomial 3)
|
||||
* [regression](#regression)
|
||||
* regression (other polynomial)
|
||||
* [arima](#arima)
|
||||
* autoregressive integrated moving average
|
||||
* [mlp](#mlp)
|
||||
* MultiLayer Perceptron
|
||||
* [rnn](#rnn)
|
||||
* Recurrent Neural Network
|
||||
* Contains a [looping example](#looping)
|
||||
* [lstm](#lstm)
|
||||
* Long-Short Term Memory
|
||||
* Contains a [backtesting example](#backtesting)
|
||||
* [conv1d](#conv1d)
|
||||
* 1D Convolutional Neural Net
|
||||
|
||||
**Note:** _Use this at your own discretion. All of these prediciton techniques rely solely on the closing price of the
|
||||
stock. This means that there are several factors that the models aren't aware of at the time of prediction, and may -
|
||||
drastically - move the price up or down. Examples are: news, analyst price targets, reddit post, tweets from Elon Musk,
|
||||
and so on._
|
||||
|
||||
**Note 2:** _[Enabling GPU acceleration for TensorFlow requires CUDA setup](README-gpu-accel.md) and will probably not
|
||||
provide any speedup unless you are building large custom models._
|
||||
|
||||
## ets <a name="ets"></a>
|
||||
|
||||
```text
|
||||
usage: ets [-t TREND] [-s SEASONAL] [-p SEASONAL_PERIODS] [-d N_DAYS]
|
||||
```
|
||||
|
||||
Exponential Smoothing (based on trend+seasonality, see <https://otexts.com/fpp2/taxonomy.html>):
|
||||
|
||||
* -t : trend component: N: None, A: Additive, Ad: Additive Damped. Default N.
|
||||
* -s : seasonality component: N: None, A: Additive, M: Multiplicative. Default N.
|
||||
* -p : seasonal periods. Default 5.
|
||||
* -d : prediction days. Default 5.
|
||||
* -e : end date (format YYYY-MM-DD) of the stock - Backtesting. Default None.
|
||||
|
||||
|
||||
|
||||
## knn <a name="knn"></a>
|
||||
|
||||
```text
|
||||
usage: knn [-i N_INPUTS] [-d N_DAYS] [-n N_NEIGHBORS]
|
||||
```
|
||||
|
||||
k-Nearest Neighbors:
|
||||
|
||||
* -i : number of days to use for prediction. Default 40.
|
||||
* -d : prediction days. Default 5.
|
||||
* -n : number of neighbors to use on the algorithm. Default 20.
|
||||
* -e : end date (format YYYY-MM-DD) of the stock - Backtesting. Default None.
|
||||
|
||||
|
||||
|
||||
## linear <a name="linear"></a>
|
||||
|
||||
```text
|
||||
usage: linear [-i N_INPUTS] [-d N_DAYS]
|
||||
usage: regression -p 1 [-i N_INPUTS] [-d N_DAYS]
|
||||
```
|
||||
|
||||
Linear Regression (p=1):
|
||||
|
||||
* -i : number of days to use for prediction. Default 40.
|
||||
* -d : prediction days. Default 5.
|
||||
* -e : end date (format YYYY-MM-DD) of the stock - Backtesting. Default None.
|
||||
|
||||
|
||||
|
||||
## quadratic <a name="quadratic"></a>
|
||||
|
||||
```text
|
||||
usage: quadratic [-i N_INPUTS] [-d N_DAYS]
|
||||
usage: regression -p 2 [-i N_INPUTS] [-d N_DAYS]
|
||||
```
|
||||
|
||||
Quadratic Regression (p=2):
|
||||
|
||||
* -i : number of days to use for prediction. Default 40.
|
||||
* -d : prediction days. Default 5.
|
||||
* -e : end date (format YYYY-MM-DD) of the stock - Backtesting. Default None.
|
||||
|
||||
|
||||
|
||||
## cubic <a name="cubic"></a>
|
||||
|
||||
```text
|
||||
usage: cubic [-i N_INPUTS] [-d N_DAYS]
|
||||
usage: regression -p 3 [-i N_INPUTS] [-d N_DAYS]
|
||||
```
|
||||
|
||||
Cubic Regression (p=3):
|
||||
|
||||
* -i : number of days to use for prediction. Default 40.
|
||||
* -d : prediction days. Default 5.
|
||||
* -e : end date (format YYYY-MM-DD) of the stock - Backtesting. Default None.
|
||||
|
||||
|
||||
|
||||
## regression <a name="regression"></a>
|
||||
|
||||
```text
|
||||
usage: regression -p N_POLYNOMIAL [-i N_INPUTS] [-d N_DAYS]
|
||||
```
|
||||
|
||||
Regression:
|
||||
|
||||
* -i : number of days to use for prediction. Default 40.
|
||||
* -d : prediction days. Default 5.
|
||||
* -p : polynomial associated with regression. Required.
|
||||
* -e : end date (format YYYY-MM-DD) of the stock - Backtesting. Default None.
|
||||
|
||||
|
||||
|
||||
## arima <a name="arima"></a>
|
||||
|
||||
```text
|
||||
usage: arima [-d N_DAYS] [-i {aic,aicc,bic,hqic,oob}] [-s] [-r] [-o S_ORDER]
|
||||
```
|
||||
|
||||
Auto-Regressive Integrated Moving Average:
|
||||
|
||||
* -d : prediciton days. Default 5.
|
||||
* -i : information criteria - used if auto_arima library is invoked. Default aic.
|
||||
* -s : weekly seasonality flag. Default False.
|
||||
* -r : results about ARIMA summary flag. Default False.
|
||||
* -o : arima model order. If the model order is defined, auto_arima is not invoked, deeming information criteria useless.
|
||||
<br />Example: `-o 5,1,4` where:
|
||||
* p = 5 : order (number of time lags) of the autoregressive model.
|
||||
* d = 1 : degree of differencing (the number of times the data have had past values subtracted).
|
||||
* q = 4 : order of the moving-average model.
|
||||
* -e : end date (format YYYY-MM-DD) of the stock - Backtesting. Default None.
|
||||
|
||||
|
||||
|
||||
## mlp <a name="mlp"></a>
|
||||
|
||||
```text
|
||||
usage: mlp [-d N_DAYS] [-i N_INPUTS] [--epochs N_EPOCHS] [-e S_END_DATE] [--loops N_LOOPS] [-v VALID]
|
||||
[--lr LEARNING_RATE] [--no_shuffle]
|
||||
```
|
||||
|
||||
MulitLayer Perceptron:
|
||||
|
||||
* -d : prediciton days. Default 5.
|
||||
* -i : number of days to use for prediction. Default 40.
|
||||
* --epochs : number of training epochs. Default 200.
|
||||
* -v : validation split. Default 0.1
|
||||
* -e : end date (format YYYY-MM-DD) of the stock - Backtesting. Default None.
|
||||
* --xla_cpu: if present, will enable XLA for CPU (overrides environment variables during run).
|
||||
* --xla_gpu: if present, will enable XLA for GPU (overrides environment variables during run).
|
||||
* --force_allow_gpu_growth: if true, will force TensorFlow to allow GPU memory usage to grow as needed. Otherwise will
|
||||
allocate 100% of available GPU memory when CUDA is set up. Default true.
|
||||
* --batch_size: batch size for model training, should not be used unless advanced user. Default None.
|
||||
* --lr : learning rate for optimizer
|
||||
* --loops: number of loops to iterate and train models. Default 1.
|
||||
* --no_shuffle : split validation data in time_ordered way instead of random.
|
||||
|
||||
Due to the complexity of defining a model through command line, one can define it in:
|
||||
[config_neural_network_models.txt](/config_neural_network_models.py)
|
||||
|
||||
```python
|
||||
MultiLayer_Perceptron \
|
||||
= [ {'Dense':
|
||||
{'units':50, 'activation':'relu'} },
|
||||
{'Dense':
|
||||
{'units':100, 'activation':'relu'} },
|
||||
{'Dense':
|
||||
{'units':80, 'activation':'relu'} },
|
||||
{'Dense':
|
||||
{'units':30, 'activation':'relu'} },
|
||||
{'Dense':
|
||||
{'activation':'relu'} }]
|
||||
```
|
||||
|
||||
|
||||
|
||||
## rnn <a name="rnn"></a>
|
||||
|
||||
```text
|
||||
usage: rnn [-d N_DAYS] [-i N_INPUTS] [--epochs N_EPOCHS] [-p {normalization,standardization,none}]
|
||||
[-l {mae,mape,mse,msle}] [-e S_END_DATE] [--loops N_LOOPS] [-v VALID] [--lr LEARNING_RATE] [--no_shuffle]
|
||||
```
|
||||
|
||||
Recurrent Neural Network:
|
||||
|
||||
* -d : prediciton days. Default 5.
|
||||
* -i : number of days to use for prediction. Default 40.
|
||||
* --epochs : number of training epochs. Default 200.
|
||||
* -v : validation split. Default 0.1
|
||||
* -e : end date (format YYYY-MM-DD) of the stock - Backtesting. Default None.
|
||||
* --xla_cpu: if present, will enable XLA for CPU (overrides environment variables during run).
|
||||
* --xla_gpu: if present, will enable XLA for GPU (overrides environment variables during run).
|
||||
* --force_allow_gpu_growth: if true, will force TensorFlow to allow GPU memory usage to grow as needed.
|
||||
Otherwise will allocate 100% of available GPU memory when CUDA is set up. Default true.
|
||||
* --batch_size: batch size for model training, should not be used unless advanced user. Default None.
|
||||
* --loops: number of loops to iterate and train models. Default 1.
|
||||
* --lr : learning rate for optimizer
|
||||
* --no_shuffle : split validation data in time_ordered way instead of random.
|
||||
|
||||
Due to the complexity of defining a model through command line, one can define it in:
|
||||
[config_neural_network_models.txt](/config_neural_network_models.py)
|
||||
|
||||
```python
|
||||
Recurrent_Neural_Network \
|
||||
= [ {'SimpleRNN':
|
||||
{'units':100, 'activation':'linear', 'return_sequences':True} },
|
||||
{'SimpleRNN':
|
||||
{'units':50, 'activation':'linear', 'return_sequences':True} },
|
||||
{'Dropout':
|
||||
{'rate':0.2} },
|
||||
{'SimpleRNN':
|
||||
{'units':21, 'activation':'linear', 'return_sequences':False} },
|
||||
{'Dense':
|
||||
{'activation':'relu'} }]
|
||||
```
|
||||
|
||||
|
||||
|
||||
### Looping Example <a name="looping"></a>
|
||||
|
||||
|
||||
|
||||
<img width="1006" alt="Captura de ecrã 2021-03-22, às 02 14 38" src="https://user-images.githubusercontent.com/25267873/111932420-436f1880-8ab5-11eb-831c-ebb88f1c5473.png">
|
||||
|
||||
## lstm <a name="lstm"></a>
|
||||
|
||||
```text
|
||||
usage: lstm [-d N_DAYS] [-i N_INPUTS] [--epochs N_EPOCHS] [-e S_END_DATE] [--loops N_LOOPS] [-v VALID]
|
||||
[--lr LEARNING_RATE] [--no_shuffle]
|
||||
```
|
||||
|
||||
Long-Short Term Memory:
|
||||
|
||||
* -d : prediction days. Default 5.
|
||||
* -i : number of days to use for prediction. Default 40.
|
||||
* --epochs : number of training epochs. Default 200.
|
||||
* -v : validation split. Default 0.1
|
||||
* -e : end date (format YYYY-MM-DD) of the stock - Backtesting. Default None.
|
||||
* --xla_cpu: if present, will enable XLA for CPU (overrides environment variables during run).
|
||||
* --xla_gpu: if present, will enable XLA for GPU (overrides environment variables during run).
|
||||
* --force_allow_gpu_growth: if true, will force TensorFlow to allow GPU memory usage to grow as needed. Otherwise will
|
||||
allocate 100% of available GPU memory when CUDA is set up. Default true.
|
||||
* --batch_size: batch size for model training, should not be used unless advanced user. Default None.
|
||||
* --loops: number of loops to iterate and train models. Default 1
|
||||
* --lr : learning rate for optimizer
|
||||
* --no_shuffle : split validation data in time_ordered way instead of random.
|
||||
|
||||
Due to the complexity of defining a model through command line, one can define it in:
|
||||
[config_neural_network_models.py](/config_neural_network_models.py)
|
||||
|
||||
```python
|
||||
Long_Short_Term_Memory \
|
||||
= [ {'LSTM':
|
||||
{'units':25, 'activation':'tanh', 'return_sequences':True} },
|
||||
{'LSTM':
|
||||
{'units':15, 'activation':'tanh', 'return_sequences':False} },
|
||||
{'Dense':
|
||||
{'activation':'relu'} }]
|
||||
```
|
||||
|
||||
|
||||
|
||||
### Backtesting Example <a name="backtesting"></a>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
<img width="992" alt="Captura de ecrã 2021-03-14, às 00 06 51" src="https://user-images.githubusercontent.com/25267873/111053158-420c7300-8459-11eb-993b-6d9c26f98af9.png">
|
||||
|
||||
## conv1d <a name="conv1d"></a>
|
||||
|
||||
```text
|
||||
usage: lstm [-d N_DAYS] [-i N_INPUTS] [--epochs N_EPOCHS] [-e S_END_DATE] [--loops N_LOOPS] [-v VALID]
|
||||
[--lr LEARNING_RATE] [--no_shuffle]
|
||||
```
|
||||
|
||||
1D Convolutional Neural Net:
|
||||
|
||||
* -d : prediction days. Default 5.
|
||||
* -i : number of days to use for prediction. Default 40.
|
||||
* --epochs : number of training epochs. Default 50.
|
||||
* -v : validation split. Default 0.1
|
||||
* -e : end date (format YYYY-MM-DD) of the stock - Backtesting. Default None.
|
||||
* --xla_cpu: if present, will enable XLA for CPU (overrides environment variables during run).
|
||||
* --xla_gpu: if present, will enable XLA for GPU (overrides environment variables during run).
|
||||
* --force_allow_gpu_growth: if true, will force TensorFlow to allow GPU memory usage to grow as needed. Otherwise will
|
||||
allocate 100% of available GPU memory when CUDA is set up. Default true.
|
||||
* --batch_size: batch size for model training, should not be used unless advanced user. Default None.
|
||||
* --loops: number of loops to iterate and train models. Default 1.
|
||||
* --lr : learning rate for optimizer
|
||||
* --no_shuffle : split validation data in time_ordered way instead of random.
|
||||
@@ -1,240 +0,0 @@
|
||||
# TECHNICAL ANALYSIS
|
||||
|
||||
This menu aims to perform a technical analysis on a pre-loaded ticker chart, and the usage of the following commands
|
||||
along with an example will be exploited below.
|
||||
|
||||
* [view](#view)
|
||||
* view historical data and trendlines [Finviz]
|
||||
* [summary](#summary)
|
||||
* technical summary report [FinBrain API]
|
||||
* [recom](#recom)
|
||||
* recommendation based on Technical Indicators [Tradingview API]
|
||||
* [pr](#pr)
|
||||
* pattern recognition [Finnhub]
|
||||
|
||||
[OVERLAP](#OVERLAP)
|
||||
|
||||
* [ema](#ema)
|
||||
* exponential moving average
|
||||
* [sma](#sma)
|
||||
* simple moving average
|
||||
* [vwap](#vwap)
|
||||
* volume weighted average price
|
||||
|
||||
[MOMENTUM](#MOMENTUM)
|
||||
|
||||
* [cci](#cci)
|
||||
* commodity channel index
|
||||
* [macd](#macd)
|
||||
* moving average convergence/divergence
|
||||
* [rsi](#rsi)
|
||||
* relative strength index
|
||||
* [stoch](#stoch)
|
||||
* stochastic oscillator
|
||||
|
||||
[TREND](#TREND)
|
||||
|
||||
* [adx](#adx)
|
||||
* average directional movement index
|
||||
* [aroon](#aroon)
|
||||
* aroon indicator
|
||||
|
||||
[VOLATILITY](#VOLATILITY)
|
||||
|
||||
* [bbands](#bbands)
|
||||
* bollinger bands
|
||||
|
||||
[VOLUME](#VOLUME)
|
||||
|
||||
* [ad](#ad)
|
||||
* Accumulation/distribution line values
|
||||
* [obv](#obv)
|
||||
* on balance volume
|
||||
|
||||
[CUSTOM](#CUSTOM)
|
||||
|
||||
* [fib](#fib)
|
||||
* Shows fibonocci levels
|
||||
|
||||
**S/O to <https://github.com/twopirllc/pandas-ta>** _Owing to this library, it is fairly easy to add other technical
|
||||
indicators. So, let us know if there's any other that you would like._
|
||||
|
||||
## view <a name="view"></a>
|
||||
|
||||
```text
|
||||
usage: view
|
||||
```
|
||||
|
||||
View historical price with trendlines. [Source: Finviz]
|
||||
|
||||
|
||||
|
||||
## summary <a name="summary"></a>
|
||||
|
||||
```text
|
||||
usage: summary
|
||||
```
|
||||
|
||||
Technical summary report provided by FinBrain's API. FinBrain Technologies develops deep learning algorithms for
|
||||
financial analysis and prediction, which currently serves traders from more than 150 countries all around the world.
|
||||
[Source: <https://finbrain.tech>]
|
||||
|
||||
<img width="976" alt="Captura de ecrã 2021-03-29, às 00 31 29" src="https://user-images.githubusercontent.com/25267873/112772089-db758080-9026-11eb-93d5-9fd7a4b40380.png">
|
||||
|
||||
## recom <a name="recom"></a>
|
||||
|
||||
```text
|
||||
usage: recom [-s {crypto,forex,cfd}] [-e EXCHANGE] [-i {1M,1W,1d,4h,1h,15m,5m,1m}]
|
||||
```
|
||||
|
||||
Print tradingview recommendation based on technical indicators. [Source: <https://pypi.org/project/tradingview-ta/>]
|
||||
|
||||
* -s : screener. Default america.
|
||||
* -e : exchange, e.g. NASDAQ, NYSE. Default uses Alpha Vantage to get this information from ticker
|
||||
* -i : interval. Default is showing all of different intervals.
|
||||
|
||||
<img width="977" alt="Recom" src="https://user-images.githubusercontent.com/25267873/113069531-76ea2b00-91b8-11eb-8934-9f693d3b4ffa.png">
|
||||
|
||||
## pr <a name="pr"></a>
|
||||
|
||||
```text
|
||||
usage: pr [-r {1,5,15,30,60,D,W,M}]
|
||||
```
|
||||
|
||||
Display pattern recognition signals on the data. [Source: <https://finnhub.io>]
|
||||
|
||||
* -r : Plot resolution to look for pattern signals. Default 'D'.
|
||||
|
||||
|
||||
|
||||
<img width="985" alt="PR" src="https://user-images.githubusercontent.com/25267873/116812505-f6b43e00-ab46-11eb-9776-fa469dc3d3f0.png">
|
||||
|
||||
# OVERLAP <a name="OVERLAP"></a>
|
||||
|
||||
## ema <a name="ema"></a>
|
||||
|
||||
```text
|
||||
usage: ema [-l N_LENGTH] [-o N_OFFSET]
|
||||
```
|
||||
|
||||
|
||||
|
||||
## sma <a name="sma"></a>
|
||||
|
||||
```text
|
||||
usage: sma [-l L_LENGTH] [-o N_OFFSET]
|
||||
```
|
||||
|
||||
|
||||
|
||||
## vwap <a name="vwap"></a>
|
||||
|
||||
VWAP is available for intraday data. This plots the most recent day only.
|
||||
|
||||
```text
|
||||
usage: vwap [-o N_OFFSET]
|
||||
```
|
||||
|
||||
|
||||
|
||||
# MOMENTUM <a name="MOMENTUM"></a>
|
||||
|
||||
## cci <a name="cci"></a>
|
||||
|
||||
```text
|
||||
usage: cci [-l N_LENGTH] [-s N_SCALAR] [-o N_OFFSET]
|
||||
```
|
||||
|
||||
|
||||
|
||||
## macd <a name="macd"></a>
|
||||
|
||||
```text
|
||||
usage: macd [-f N_FAST] [-s N_SLOW] [--signal N_SIGNAL] [-o N_OFFSET]
|
||||
```
|
||||
|
||||
|
||||
|
||||
## rsi <a name="rsi"></a>
|
||||
|
||||
```text
|
||||
usage: rsi [-l N_LENGTH] [-s N_SCALAR] [-d N_DRIFT] [-o N_OFFSET]
|
||||
```
|
||||
|
||||
|
||||
|
||||
## stoch <a name="stoch"></a>
|
||||
|
||||
```text
|
||||
usage: stoch [-k N_FASTKPERIOD] [-d N_SLOWDPERIOD] [--slowkperiod N_SLOWKPERIOD] [-o N_OFFSET]
|
||||
```
|
||||
|
||||
|
||||
|
||||
# TREND <a name="TREND"></a>
|
||||
|
||||
## adx <a name="adx"></a>
|
||||
|
||||
```text
|
||||
usage: adx [-l N_LENGTH] [-s N_SCALAR] [-d N_DRIFT] [-o N_OFFSET]
|
||||
```
|
||||
|
||||
|
||||
|
||||
## aroon <a name="aroon"></a>
|
||||
|
||||
```text
|
||||
usage: aroon [-l N_LENGTH] [-s N_SCALAR] [-o N_OFFSET]
|
||||
```
|
||||
|
||||
|
||||
|
||||
# VOLATILITY <a name="VOLATILITY"></a>
|
||||
|
||||
## bbands <a name="bbands"></a>
|
||||
|
||||
```text
|
||||
usage: bbands [-h] [-l N_LENGTH] [-s N_STD] [-m S_MAMODE] [-o N_OFFSET]
|
||||
```
|
||||
|
||||
|
||||
|
||||
# VOLUME <a name="VOLUME"></a>
|
||||
|
||||
## ad <a name="ad"></a>
|
||||
|
||||
```text
|
||||
usage: ad [-o N_OFFSET] [--open]
|
||||
```
|
||||
|
||||
|
||||
|
||||
## obv <a name="obv"></a>
|
||||
|
||||
```text
|
||||
usage: obv [-o N_OFFSET]
|
||||
```
|
||||
|
||||
|
||||
|
||||
# CUSTOM <a name="CUSTOM"></a>
|
||||
|
||||
## fib <a name="fib"></a>
|
||||
|
||||
Shows fibonocci levels
|
||||
|
||||
```python
|
||||
usage: fib [-p PERIOD] [--start] [--end]
|
||||
```
|
||||
|
||||
* -p/--period: Number of days to lookback for retracement. Default = 120.
|
||||
* --start: Allows for custom selection of min point.
|
||||
* --end : Allows for custom selection of max point.
|
||||
|
||||
The fibonocci retracement works by looking at a minimum and maximum point. Between these values, there is typically
|
||||
support/resistance at 23.5 %, 38.2%, 50% and 61.8%. Those values follow the golden ratio from the fibonocci sequence.
|
||||
|
||||
The `--start` and `--end` flags allow the user to select dates for the min and max points as your retracement levels.
|
||||
Both flags are required if using.
|
||||
|
||||
|
||||
@@ -15,8 +15,8 @@ Command|Description|Source
|
||||
`ford` |orders by Fidelity Customers |[Fidelity](https://www.fidelity.com/)
|
||||
`arkord` |orders by ARK Investment Management LLC | [Cathiesark](https://www.cathiesark.com)
|
||||
`upcoming` |upcoming earnings release dates |[Seeking Alpha](https://seekingalpha.com/)
|
||||
`latest` |latest news |[Seeking Alpha](https://seekingalpha.com/)
|
||||
`trending` |trending news |[Seeking Alpha](https://seekingalpha.com/)
|
||||
`cnews` |customized news (buybacks, ipos, spacs, healthcare, politics) |[Seeking Alpha](https://seekingalpha.com/)
|
||||
`low_float` |show low float stocks under 10M shares float |[Low Float](https://www.lowfloat.com/)
|
||||
`hotpenny` |today's hot penny stocks |[hotpenny](https://pennystockflow.com)
|
||||
`fds` |advanced Equities search based on country, sector, industry, name and/or description |[FinanceDatabase](https://github.com/JerBouma/FinanceDatabase)
|
||||
|
||||
@@ -58,12 +58,12 @@ class DiscoveryController:
|
||||
"ford",
|
||||
"arkord",
|
||||
"upcoming",
|
||||
"latest",
|
||||
"trending",
|
||||
"lowfloat",
|
||||
"hotpenny",
|
||||
"rtearn",
|
||||
"fds",
|
||||
"cnews",
|
||||
]
|
||||
|
||||
CHOICES += CHOICES_COMMANDS
|
||||
@@ -105,8 +105,8 @@ cathiesark.com:
|
||||
arkord orders by ARK Investment Management LLC
|
||||
Seeking Alpha:
|
||||
upcoming upcoming earnings release dates
|
||||
latest latest news
|
||||
trending trending news
|
||||
cnews customized news (buybacks, ipos, spacs, healthcare, politics)
|
||||
shortinterest.com
|
||||
lowfloat low float stocks under 10M shares float
|
||||
pennystockflow.com
|
||||
@@ -683,66 +683,6 @@ Finance Database:
|
||||
export=ns_parser.export,
|
||||
)
|
||||
|
||||
@try_except
|
||||
def call_latest(self, other_args: List[str]):
|
||||
"""Process latest command"""
|
||||
parser = argparse.ArgumentParser(
|
||||
add_help=False,
|
||||
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
|
||||
prog="latest",
|
||||
description="""Latest news articles. [Source: Seeking Alpha]""",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-i",
|
||||
"--id",
|
||||
action="store",
|
||||
dest="n_id",
|
||||
type=check_positive,
|
||||
default=-1,
|
||||
help="article ID",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-n",
|
||||
"--num",
|
||||
action="store",
|
||||
dest="n_num",
|
||||
type=check_positive,
|
||||
default=5,
|
||||
help="number of articles being printed",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-d",
|
||||
"--date",
|
||||
action="store",
|
||||
dest="s_date",
|
||||
type=valid_date,
|
||||
default=datetime.now().strftime("%Y-%m-%d"),
|
||||
help="starting date of articles",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--export",
|
||||
choices=["csv", "json", "xlsx"],
|
||||
default="",
|
||||
type=str,
|
||||
dest="export",
|
||||
help="Export dataframe data to csv,json,xlsx file",
|
||||
)
|
||||
if other_args:
|
||||
if "-" not in other_args[0]:
|
||||
other_args.insert(0, "-i")
|
||||
|
||||
ns_parser = parse_known_args_and_warn(parser, other_args)
|
||||
if not ns_parser:
|
||||
return
|
||||
|
||||
seeking_alpha_view.news(
|
||||
news_type="latest",
|
||||
article_id=ns_parser.n_id,
|
||||
num=ns_parser.n_num,
|
||||
start_date=ns_parser.s_date,
|
||||
export=ns_parser.export,
|
||||
)
|
||||
|
||||
@try_except
|
||||
def call_trending(self, other_args: List[str]):
|
||||
"""Process trending command"""
|
||||
@@ -844,6 +784,78 @@ Finance Database:
|
||||
export=ns_parser.export,
|
||||
)
|
||||
|
||||
@try_except
|
||||
def call_cnews(self, other_args: List[str]):
|
||||
"""Process cnews command"""
|
||||
parser = argparse.ArgumentParser(
|
||||
add_help=False,
|
||||
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
|
||||
prog="cnews",
|
||||
description="""Customized news. [Source: Seeking Alpha]""",
|
||||
)
|
||||
l_news_type = [
|
||||
"Top-News",
|
||||
"On-The-Move",
|
||||
"Market-Pulse",
|
||||
"Notable-Calls",
|
||||
"Buybacks",
|
||||
"Commodities",
|
||||
"Crypto",
|
||||
"Issuance",
|
||||
"Global",
|
||||
"Guidance",
|
||||
"IPOs",
|
||||
"SPACs",
|
||||
"Politics",
|
||||
"M-A",
|
||||
"Consumer",
|
||||
"Energy",
|
||||
"Financials",
|
||||
"Healthcare",
|
||||
"MLPs",
|
||||
"REITs",
|
||||
"Technology",
|
||||
]
|
||||
parser.add_argument(
|
||||
"-t",
|
||||
"--type",
|
||||
action="store",
|
||||
dest="s_type",
|
||||
choices=[tnews.lower() for tnews in l_news_type],
|
||||
default="Top-News",
|
||||
help="number of news to display",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-n",
|
||||
"--num",
|
||||
action="store",
|
||||
dest="n_num",
|
||||
type=check_positive,
|
||||
default=5,
|
||||
help="number of news to display",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--export",
|
||||
choices=["csv", "json", "xlsx"],
|
||||
default="",
|
||||
type=str,
|
||||
dest="export",
|
||||
help="Export dataframe data to csv,json,xlsx file",
|
||||
)
|
||||
if other_args:
|
||||
if "-" not in other_args[0]:
|
||||
other_args.insert(0, "-t")
|
||||
|
||||
ns_parser = parse_known_args_and_warn(parser, other_args)
|
||||
if not ns_parser:
|
||||
return
|
||||
|
||||
seeking_alpha_view.display_news(
|
||||
news_type=ns_parser.s_type,
|
||||
num=ns_parser.n_num,
|
||||
export=ns_parser.export,
|
||||
)
|
||||
|
||||
@try_except
|
||||
def call_hotpenny(self, other_args: List[str]):
|
||||
"""Process hotpenny command"""
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
""" Seeking Alpha Model """
|
||||
__docformat__ = "numpy"
|
||||
|
||||
from typing import List
|
||||
from typing import List, Dict
|
||||
from datetime import datetime
|
||||
import requests
|
||||
from bs4 import BeautifulSoup
|
||||
@@ -210,3 +210,66 @@ def get_article_data(article_id: int) -> dict:
|
||||
}
|
||||
|
||||
return article
|
||||
|
||||
|
||||
def get_news_html(news_type: str = "Top-News") -> dict:
|
||||
"""Gets news. [Source: SeekingAlpha]
|
||||
|
||||
Parameters
|
||||
----------
|
||||
news_type : str
|
||||
From: Top-News, On-The-Move, Market-Pulse, Notable-Calls, Buybacks, Commodities, Crypto, Issuance, Global,
|
||||
Guidance, IPOs, SPACs, Politics, M-A, Consumer, Energy, Financials, Healthcare, MLPs, REITs, Technology
|
||||
|
||||
Returns
|
||||
-------
|
||||
dict
|
||||
HTML page of articles
|
||||
"""
|
||||
sa_url = (
|
||||
f"http://seekingalpha.com/api/v3/news?filter%5Bcategory%5D=market-news%3A%3A{news_type}"
|
||||
"&filter%5Bsince%5D=0&filter%5Buntil%5D=0&include=author%2CprimaryTickers%2CsecondaryTickers"
|
||||
"&isMounting=true&page%5Bsize%5D=25&page%5Bnumber%5D=1"
|
||||
)
|
||||
|
||||
articles_html = requests.get(
|
||||
sa_url, headers={"User-Agent": get_user_agent()}
|
||||
).json()
|
||||
|
||||
return articles_html
|
||||
|
||||
|
||||
def get_news(news_type: str = "Top-News", num: int = 5) -> List:
|
||||
"""Gets news. [Source: SeekingAlpha]
|
||||
|
||||
Parameters
|
||||
----------
|
||||
news_type : str
|
||||
From: Top-News, On-The-Move, Market-Pulse, Notable-Calls, Buybacks, Commodities, Crypto, Issuance, Global,
|
||||
Guidance, IPOs, SPACs, Politics, M-A, Consumer, Energy, Financials, Healthcare, MLPs, REITs, Technology
|
||||
num : int
|
||||
Number of news to display
|
||||
|
||||
Returns
|
||||
-------
|
||||
List[dict]
|
||||
List of dict news
|
||||
"""
|
||||
news_articles: Dict = get_news_html(news_type)
|
||||
news_to_display = list()
|
||||
|
||||
if news_articles:
|
||||
for idx, news in enumerate(news_articles["data"]):
|
||||
if idx > num:
|
||||
break
|
||||
|
||||
news_to_display.append(
|
||||
{
|
||||
"publishOn": news["attributes"]["publishOn"].replace("T", " ")[:-6],
|
||||
"id": news["id"],
|
||||
"title": news["attributes"]["title"],
|
||||
"url": news["links"]["canonical"],
|
||||
}
|
||||
)
|
||||
|
||||
return news_to_display
|
||||
|
||||
@@ -1,6 +1,7 @@
|
||||
""" Seeking Alpha View """
|
||||
__docformat__ = "numpy"
|
||||
|
||||
from typing import List
|
||||
import os
|
||||
from datetime import datetime
|
||||
from tabulate import tabulate
|
||||
@@ -149,3 +150,41 @@ def news(news_type: str, article_id: int, num: int, start_date: datetime, export
|
||||
news_type,
|
||||
df_articles,
|
||||
)
|
||||
|
||||
|
||||
def display_news(news_type: str = "Top-News", num: int = 5, export: str = ""):
|
||||
"""Display news. [Source: SeekingAlpha]
|
||||
|
||||
Parameters
|
||||
----------
|
||||
news_type : str
|
||||
From: Top-News, On-The-Move, Market-Pulse, Notable-Calls, Buybacks, Commodities, Crypto, Issuance, Global,
|
||||
Guidance, IPOs, SPACs, Politics, M-A, Consumer, Energy, Financials, Healthcare, MLPs, REITs, Technology
|
||||
num : int
|
||||
Number of news to display
|
||||
export : str
|
||||
Export dataframe data to csv,json,xlsx file
|
||||
"""
|
||||
news_to_display: List = seeking_alpha_model.get_news(news_type, num)
|
||||
|
||||
if not news:
|
||||
print("No news found.", "\n")
|
||||
|
||||
else:
|
||||
for news_element in news_to_display:
|
||||
print(
|
||||
news_element["publishOn"]
|
||||
+ " - "
|
||||
+ news_element["id"]
|
||||
+ " - "
|
||||
+ news_element["title"]
|
||||
)
|
||||
print(news_element["url"])
|
||||
print("")
|
||||
|
||||
export_data(
|
||||
export,
|
||||
os.path.dirname(os.path.abspath(__file__)),
|
||||
"cnews : " + news_type,
|
||||
pd.DataFrame(news_to_display),
|
||||
)
|
||||
|
||||
16
website/content/stocks/discovery/cnews/_index.md
vendored
Executable file
16
website/content/stocks/discovery/cnews/_index.md
vendored
Executable file
@@ -0,0 +1,16 @@
|
||||
```
|
||||
usage: cnews [-t {top-news,on-the-move,market-pulse,notable-calls,buybacks,commodities,crypto,issuance,global,guidance,ipos,spacs,politics,m-a,consumer,energy,financials,healthcare,mlps,reits,technology}] [-n N_NUM] [--export {csv,json,xlsx}] [-h]
|
||||
```
|
||||
|
||||
Customized news. [Source: Seeking Alpha]
|
||||
|
||||
```
|
||||
optional arguments:
|
||||
-t {top-news,on-the-move,market-pulse,notable-calls,buybacks,commodities,crypto,issuance,global,guidance,ipos,spacs,politics,m-a,consumer,energy,financials,healthcare,mlps,reits,technology}, --type {top-news,on-the-move,market-pulse,notable-calls,buybacks,commodities,crypto,issuance,global,guidance,ipos,spacs,politics,m-a,consumer,energy,financials,healthcare,mlps,reits,technology}
|
||||
number of news to display (default: Top-News)
|
||||
-n N_NUM, --num N_NUM
|
||||
number of news to display (default: 5)
|
||||
--export {csv,json,xlsx}
|
||||
Export dataframe data to csv,json,xlsx file (default: )
|
||||
-h, --help show this help message (default: False)
|
||||
```
|
||||
@@ -1,19 +0,0 @@
|
||||
```
|
||||
usage: latest [-i N_ID] [-n N_NUM] [-d S_DATE] [--export {csv,json,xlsx}] [-h]
|
||||
```
|
||||
|
||||
Latest news articles. [Source: Seeking Alpha]
|
||||
|
||||
```
|
||||
optional arguments:
|
||||
-i N_ID, --id N_ID article ID (default: -1)
|
||||
-n N_NUM, --num N_NUM
|
||||
number of articles being printed (default: 5)
|
||||
-d S_DATE, --date S_DATE
|
||||
starting date of articles (default: 2021-09-16)
|
||||
--export {csv,json,xlsx}
|
||||
Export dataframe data to csv,json,xlsx file (default: )
|
||||
-h, --help show this help message (default: False)
|
||||
```
|
||||
|
||||
<img width="1208" alt="latest" src="https://user-images.githubusercontent.com/25267873/115089633-926c6a00-9f0a-11eb-9d0e-1eedfd8ba7ce.png">
|
||||
4
website/data/menu/main.yml
vendored
4
website/data/menu/main.yml
vendored
@@ -40,8 +40,8 @@ main:
|
||||
ref: "/stocks/discovery/arkord"
|
||||
- name: upcoming
|
||||
ref: "/stocks/discovery/upcoming"
|
||||
- name: latest
|
||||
ref: "/stocks/discovery/latest"
|
||||
- name: cnews
|
||||
ref: "/stocks/discovery/cnews"
|
||||
- name: trending
|
||||
ref: "/stocks/discovery/trending"
|
||||
- name: lowfloat
|
||||
|
||||
Reference in New Issue
Block a user