2284 Open source projects that are alternatives of or similar to Darwinexlabs

Succinct Machine Learning algorithm implementations from scratch in Python, solving real-world problems (Notebooks and Book). Examples of Logistic Regression, Linear Regression, Decision Trees, K-means clustering, Sentiment Analysis, Recommender Systems, Neural Networks and Reinforcement Learning.

Educational notebooks on quantitative finance, algorithmic trading, financial modelling and investment strategy

Use unsupervised and supervised learning to predict stocks

Introducing neural networks to predict stock prices

A Python-based development platform for automated trading systems - from backtesting to optimisation to livetrading.

51bitquant Python数字货币量化交易视频 CCXT框架 爬取交易所数据 比特币量化交易 交易机器人51bitquant tradingbot cryptocurrency quantitative trading btc trading

Courses, Articles and many more which can help beginners or professionals.

Amacımız Türkçe NLP literatüründeki birçok farklı sorunu bir arada çözebilen, eşsiz yaklaşımlar öne süren ve literatürdeki çalışmaların eksiklerini gideren open source bir yazım destekleyicisi/denetleyicisi oluşturmak. Kullanıcıların yazdıkları metinlerdeki yazım yanlışlarını derin öğrenme yaklaşımıyla çözüp aynı zamanda metinlerde anlamsal analizi de gerçekleştirerek bu bağlamda ortaya çıkan yanlışları da fark edip düzeltebilmek.

Tutorials, assignments, and competitions for MIT Deep Learning related courses.

Smart Algorithms to predict buying and selling of stocks on the basis of Mutual Funds Analysis, Stock Trends Analysis and Prediction, Portfolio Risk Factor, Stock and Finance Market News Sentiment Analysis and Selling profit ratio. Project developed as a part of NSE-FutureTech-Hackathon 2018, Mumbai. Team : Semicolon

An open-source platform for automating tasks using machine learning models

Python-based framework for backtesting trading strategies & analyzing financial markets [GUI ]

Using python and scikit-learn to make stock predictions

a delightful machine learning tool that allows you to train, test, and use models without writing code

Code for Machine Learning for Algorithmic Trading, 2nd edition.

Applying Machine Learning and AI Algorithms applied to Trading for better performance and low Std.

Machine Learning tutorials with TensorFlow 2 and Keras in Python (Jupyter notebooks included) - (LSTMs, Hyperameter tuning, Data preprocessing, Bias-variance tradeoff, Anomaly Detection, Autoencoders, Time Series Forecasting, Object Detection, Sentiment Analysis, Intent Recognition with BERT)

Quantitative analysis, strategies and backtests

The Open-Source Backtesting Engine/ Market Simulator by Bertram Solutions.

Deep Learning Computer Vision Algorithms for Real-World Use

💫 Industrial-strength Natural Language Processing (NLP) in Python

Support content for my blog

This repositary is a combination of different resources lying scattered all over the internet. The reason for making such an repositary is to combine all the valuable resources in a sequential manner, so that it helps every beginners who are in a search of free and structured learning resource for Data Science. For Constant Updates Follow me in …

Codera Quant is a Java framework for algorithmic trading strategies development, execution and backtesting via Interactive Brokers TWS API or other brokers API

modular quant framework.

Python quantitative trading strategies including VIX Calculator, Pattern Recognition, Commodity Trading Advisor, Monte Carlo, Options Straddle, Shooting Star, London Breakout, Heikin-Ashi, Pair Trading, RSI, Bollinger Bands, Parabolic SAR, Dual Thrust, Awesome, MACD

A curated list of practical financial machine learning tools and applications.

AI-related tutorials. Access any of them for free → https://towardsai.net/editorial

Practice and tutorial-style notebooks covering wide variety of machine learning techniques

Notes & exercise solutions of Part I from the book: "Hands-On ML with Scikit-Learn, Keras & TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems" by Aurelien Geron

The Cutest Deep Learning Framework which is also a wonderful Declarative Language

🚀 Websocket based trading bot for 💰cryptocurrencies 📈

🛠 All-in-one web-based IDE specialized for machine learning and data science.

Collection of Artificial Intelligence projects.

OHLC Average Prediction of Apple Inc. Using LSTM Recurrent Neural Network

Powerful financial charting library based on R's Quantmod | http://py-quantmod.readthedocs.io/en/latest/

Statistical and Algorithmic Investing Strategies for Everyone

Avalanche: a End-to-End Library for Continual Learning.

Stock indicator technical analysis library package for .NET. Send in historical price quotes and get back desired technical indicators. Nothing more. It can be used in any market analysis software using standard OHLCV price quotes for equities, commodities, forex, cryptocurrencies, and others. We had private trading algorithms, machine learning, and charting systems in mind when originally creating this community library. Current indicators include: Accumulation/Distribution Line (ADL), Aroon Oscillator, Arnaud Legoux Moving Average (ALMA), Average Directional Index (ADX), Average True Range (ATR), Awesome Oscillator (AO), Balance of Power (BOP), Beta Coefficient, Bollinger Bands®, Chaikin Money Flow (CMF), Chaikin Oscillator, Chandelier Exit, Choppiness Index (CHOP), Commodity Channel Index (CCI), ConnorsRSI, Correlation Coefficient, Donchian Channels, Double Exponential Moving Average (DEMA), Elder-ray Index, Exponential Moving Average (EMA), Force Index, Fractal Chaos Bands (FCB), Gator Oscillator, Heikin-Ashi, Hull Moving Average (HMA), Ichimoku Cloud, Kaufman's Adaptive Moving Average (KAMA), KDJ Index, Keltner Channels, Momentum Oscillator, Money Flow Index (MFI), MESA Adaptive Moving Averages (MAMA), Moving Average Convergence/Divergence (MACD), Moving Average Envelopes, On-balance Volume (OBV), Parabolic SAR (stop and reverse), Percentage Volume Oscillator (PVO), Pivot Points and Rolling Pivot Points, Price Channels, Price (Comparative) Relative Strength (PRS), Price Momentum Oscillator (PMO), Rate of Change (ROC), Relative Strength Index (RSI), R-Squared (Coefficient of Determination), Simple Moving Average (SMA), Slope and Linear Regression, Smoothed Moving Average (SMMA), Standard Deviation, Stoller Average Range Channel (STARC) Bands, Stochastic Oscillator, Stochastic RSI, SuperTrend, Tillson T3 Moving Average, Triple Exponential Moving Average (TEMA), Triple EMA Oscillator (TRIX), True Strength Index (TSI), Ulcer Index, Ultimate Oscillator, Volume Simple Moving Average, Volume Weighted Average Price (VWAP), Vortex Indicator (VI), Weighted Moving Average (WMA), Williams %R, Williams Alligator, Williams Fractal, and Zig Zag.

A powerful and intuitive WYSIWYG interface that allows anyone to create Machine Learning models!

Financial portfolio optimisation in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity

💹 openAlgo is a public repository for various work product relavant to algorithms and the high frequency low latency electronic trading space with a bias toward market microstructure as well as exchange traded futures and options.

A Python toolkit for Reservoir Computing and Echo State Network experimentation based on pyTorch. EchoTorch is the only Python module available to easily create Deep Reservoir Computing models.

Autodifferentiation package in Rust.

A curated list of artificial intelligence resources (Courses, Tools, App, Open Source Project)

Improved Residual Networks (https://arxiv.org/pdf/2004.04989.pdf)

Slide show presentations regarding data driven investing.

An automatic ML model optimization tool.

Concise deep learning for JAX

🍳 Recipes for the Prodigy, our fully scriptable annotation tool

Spying using Smartwatch and Deep Learning

A cleaner way to build neural networks for PyTorch.

The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate.

VIP cheatsheets for Stanford's CS 221 Artificial Intelligence

Library to scrape and clean web pages to create massive datasets.

👨🏽‍🏫You can learn about what data science is and why it's important in today's modern world. Are you interested in data science?🔋

A hyperparameter optimization and data collection toolbox for convenient and fast prototyping of machine-learning models.

Gender recognition by voice and speech analysis

Here you can find all the quantitative finance algorithms that I've worked on and refined over the past year!

A simple from-scratch neural net written in Go