sevenpen Goto Github PK

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sevenpen's Projects

Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.

asknews-python-sdk

Infusing news into any LLM with a single line of code

backtrader

Python Backtesting library for trading strategies

bayesianoptimization

A Python implementation of global optimization with gaussian processes.

catalyst

An Algorithmic Trading Library for Crypto-Assets in Python

chronos-forecasting

Chronos: Pretrained (Language) Models for Probabilistic Time Series Forecasting

cointanalysis

Python library for cointegration analysis. It carries out cointegration test and evaluates spread between cointegrated time-series based on scikit-learn API.

crypto-rl

Deep Reinforcement Learning toolkit: record and replay cryptocurrency limit order book data & train a DDQN agent

cryptocurrencyprediction

Predict Cryptocurrency Price with Deep Learning

datasieve

Adding coherence to the SKLearn pipeline

deep_rl_trader

Trading Environment(OpenAI Gym) + DDQN (Keras-RL)

deeplearningzerotoall

TensorFlow Basic Tutorial Labs

elliottwaveanalyzer

Tools to find Elliot Wave count in financial data

fcore

Fcore Is an AI Framework for Financial Markets Analysis (Active Development).

feature_engine

Feature engineering package with sklearn like functionality

flowdapt-cryptocast-plugin

$fracdiff icon$ fracdiff

Compute fractional differentiation super-fast. Processes time-series to be stationary while preserving memory. cf. "Advances in Financial Machine Learning" by M. Prado.

freqai

An interface that lets users build, test, and deploy custom adaptive machine learning models in freqtrade. Mirrored development on github.com/freqtrade/freqtrade