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Awesome Quant

A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)

wilsonfreitas/awesome-quant #algorithmic-trading-engine #algorithmic-trading-library #algotrading #arbitrage-bot #awesome #awesome-list #finance #finance-api #financial-data #financial-instruments #google-finance #quant #quantitative-finance #quantitative-trading #stock-data #technical-analysis #trading-algorithms #trading-bot #trading-strategies #yahoo-finance 11 repo(s) failed to sync.
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3 repos matching these filters.

Topic: prediction
Latest repo push 2026-06-20

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statsmodels/statsmodels

Statsmodels: statistical modeling and econometrics in Python

Stack
Python Jupyter pytest Meson PEP 517 pip
GitHub topics
#count-model #data-analysis #data-science #econometrics #forecasting #generalized-linear-models
Updated
2026-06-18
Lists
4 list mentions
First commit
2006-03-03
License
BSD-3-Clause
Issues
2,973 open
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11,469
stars
Forks
3,358
Commits
16,123 commits
Star growth, last 7 days
0 0.0%
Commit velocity, last 7 days
0 0.0%
Website GitHub
achillesrasquinha/bulbea

:boar: :bear: Deep Learning based Python Library for Stock Market Prediction and Modelling

Stack
Python Flask Jupyter pytest pip
GitHub topics
#deep-learning #finance #machine-learning #prediction #python-library #quantitative-finance
Updated
2021-01-17
Lists
1 list mention
First commit
2017-03-09
License
NOASSERTION
Issues
37 open
Sign in to like achillesrasquinha/bulbea
2,297
stars
Forks
487
Commits
49 commits
Star growth, last 7 days
0 0.0%
Commit velocity, last 7 days
0 0.0%
Website GitHub
LastAncientOne/Deep_Learning_Machine_Learning_Stock

Deep Learning and Machine Learning stocks represent promising opportunities for both long-term and short-term investors and traders.

Stack
Jupyter Notebook
GitHub topics
#algorithms #data-science #deep-learning #feature-engineering #feature-extraction #feature-selection
Updated
2024-03-01
Lists
1 list mention
First commit
2018-09-29
License
MIT
Issues
4 open
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1,755
stars
Forks
366
Commits
526 commits
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0 0.0%
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0 0.0%
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