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NICK JAVOS's Projects

agentkit icon agentkit

Starter-kit to build constrained agents with Nextjs, FastAPI and Langchain

awesome-quant icon awesome-quant

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

docs icon docs

The October CMS Documentation

luminol icon luminol

Anomaly Detection and Correlation library

market-emotion-indicator icon market-emotion-indicator

Built market emotional indicators using variables like PE, PB, PCF and dividend yield; developed model to predict return of different index and different time span.

nlp.js icon nlp.js

An NLP library for building bots, with entity extraction, sentiment analysis, automatic language identify, and so more

pattern icon pattern

Web mining module for Python, with tools for scraping, natural language processing, machine learning, network analysis and visualization.

pdftabextract icon pdftabextract

A set of tools for extracting tables from PDF files helping to do data mining on (OCR-processed) scanned documents.

quantstats icon quantstats

Portfolio analytics for quants, written in Python

remix icon remix

Build Better Websites. Create modern, resilient user experiences with web fundamentals.

stock-analysis icon stock-analysis

Simple to use interfaces for basic technical analysis of stocks.

stock-prediction-models icon stock-prediction-models

Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations

stockpredictionai icon stockpredictionai

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.

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