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Stock-Prediction-Models - Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations
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Stock-Prediction-Models, Gathers machine learning and deep learning models for Stock forecasting, included trading bots and simulations. I code LSTM Recurrent Neural Network and Simple signal rolling agent inside Tensorflow JS, you can try it here, huseinhouse.com/stock-forecasting-js, you can download any historical CSV and upload dynamically.
https://github.com/huseinzol05/Stock-Prediction-Models
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StockPricePrediction - Stock Price Prediction using Machine Learning Techniques
To examine a number of different forecasting techniques to predict future stock returns based on past returns and numerical news indicators to construct a portfolio of multiple stocks in order to diversify the risk. We do this by applying supervised learning methods for stock price forecasting by interpreting the seemingly chaotic market data. Download the Dataset needed for running the code from here.
machine-learning supervised-learning stock-price-forecasting forecasting rnn lstm lstm-neural-networks video concept-video analysisDeep-Reinforcement-Learning-for-Automated-Stock-Trading-Ensemble-Strategy-ICAIF-2020 - Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy
This ensemble strategy is reimplemented in a Jupiter Notebook at FinRL. Stock trading strategies play a critical role in investment. However, it is challenging to design a profitable strategy in a complex and dynamic stock market. In this paper, we propose a deep ensemble reinforcement learning scheme that automatically learns a stock trading strategy by maximizing investment return. We train a deep reinforcement learning agent and obtain an ensemble trading strategy using the three actor-critic based algorithms: Proximal Policy Optimization (PPO), Advantage Actor Critic (A2C), and Deep Deterministic Policy Gradient (DDPG). The ensemble strategy inherits and integrates the best features of the three algorithms, thereby robustly adjusting to different market conditions. In order to avoid the large memory consumption in training networks with continuous action space, we employ a load-on-demand approach for processing very large data. We test our algorithms on the 30 Dow Jones stocks which have adequate liquidity. The performance of the trading agent with different reinforcement learning algorithms is evaluated and compared with both the Dow Jones Industrial Average index and the traditional min-variance portfolio allocation strategy. The proposed deep ensemble scheme is shown to outperform the three individual algorithms and the two baselines in terms of the risk-adjusted return measured by the Sharpe ratio.
deep-reinforcement-learning openai-gym sharpe-ratio ddpg stock-trading ppo a2c-algorithm ensemble-strategy stock-trading-strategy automated-stock-tradingPersonae - 📈 Personae is a repo of implements and environment of Deep Reinforcement Learning & Supervised Learning for Quantitative Trading
Personae is a repo that implements papers proposed methods in Deep Reinforcement Learning & Supervised Learning and applies them to Financial Market. It will start from 2018-08-24 to 2018-09-01 a timestamp that I successfully found a job.
reinforcement-learning supervised-learning stock-data trading paper stock time-series-prediction stock-price-predictionClairvoyant - Software designed to identify and monitor social/historical cues for short term stock movement
Using stock historical data, train a supervised learning algorithm with any combination of financial indicators. Rapidly backtest your model for accuracy and simulate investment portfolio performance.During the testing period, the model signals to buy or sell based on its prediction for price movement the following day. By putting your trading algorithm aside and testing for signal accuracy alone, you can rapidly build and test more reliable models.
machine-learning support-vector-machines portfolio-simulation backtesting-trading-strategies stock-marketpredicting_stock_prices - This is the coding challenge for "Predicting Stock Prices" by @Sirajology on Youtube
#predicting_stock_prices Stock Prediction Challenge by @Sirajology on Youtube. This is the code for the Stock Price Prediction challenge for 'Learn Python for Data Science #3' by @Sirajology on YouTube. The code uses the scikit-learn machine learning library to train a support vector regression on a stock price dataset from Google Finance to predict a future price. In the video, I use scikit-learn to build an ML model, but for the challenge you'll use the Keras library.
FinRL - A Deep Reinforcement Learning Library for Automated Trading in Quantitative Finance
FinRL is an open source library that provides practitioners a unified framework for pipeline strategy development. In reinforcement learning (or Deep RL), an agent learns by continuously interacting with an environment, in a trial-and-error manner, making sequential decisions under uncertainty and achieving a balance between exploration and exploitation. The open source community AI4Finance (to efficiently automate trading) provides educational resources about deep reinforcement learning (DRL) in quantitative finance. To contribute? Please check the end of this page.
finance deep-reinforcement-learning openai-gym fintech stock-trading multi-agent-learning stock-markets pythorch tensorflow2 drl-trading-agents drl-algorithms finrl-library drl-framework trading-tasksGekko-Strategies - Strategies to Gekko trading bot with backtests results and some useful tools.
Gekko Trading Bot. Repository of strategies which I found at Git and Google, orginal source is in README or .js file. Strategies was backtested, results are in backtest_database.csv file. I used ForksScraper and Gekko BacktestTool to create content of this repository.
gekko strategies gekko-strategies gekko-backtest backtest-database cryptocurrency cryptocurrencies crypto cryptocurrency-exchanges trading-bot trading-strategies trading-algorithms trading-platform stock stock-price-prediction hodl rsi technical-analysis trading-simulator tradingHow-to-Predict-Stock-Prices-Easily-Demo - How to Predict Stock Prices Easily - Intro to Deep Learning #7 by Siraj Raval on Youtube
This is the code for this video on Youtube by Siraj Raval part of the Udacity Deep Learning nanodegree. We use an LSTM neural network to predict the closing price of the S&P 500 using a dataset of past prices. Install Keras from here and Tensorflow from here.
deepstock - Technical experimentations to beat the stock market using deep learning :chart_with_upwards_trend:
Technical experimentations to beat the stock market using deep learning.
deep-learning stock-market stock technical-experimentationstensorflow-lstm-regression - Sequence prediction using recurrent neural networks(LSTM) with TensorFlow
The objective is to predict continuous values, sin and cos functions in this example, based on previous observations using the LSTM architecture. This example has been updated with a new version compatible with the tensrflow-1.1.0. This new version is using a library polyaxon that provides an API to create deep learning models and experiments based on tensorflow.
lstm tensorflow recurrent-networks deep-learning sequence-prediction tensorflow-lstm-regression jupyter time-series recurrent-neural-networksLSTM---Stock-prediction - A long term short term memory recurrent neural network to predict stock data time series
The model can be trained on daily or minute data of any forex pair. The data can be downloaded from here. The lstm-rnn should learn to predict the next day or minute based on previous data.
NowTrade - Algorithmic trading library with a focus on creating powerful strategies
NowTrade is an algorithmic trading library with a focus on creating powerful strategies using easily-readable and simple Python code. With the help of NowTrade, full blown stock/currency trading strategies, harnessing the power of machine learning, can be implemented with few lines of code. NowTrade strategies are not event driven like most other algorithmic trading libraries available. The strategies are implemented in a sequential manner (one line at a time) without worrying about events, callbacks, or object overloading.
trading technical-indicators neural-network random-forest stock currency algorithmic-trading-library machine-learning algorithmic-tradingIEX-API - The IEX API provides any individual or academic, public or private institution looking to develop applications that require stock market data to access near real-time quote and trade data for all stocks trading on IEX
After this date, you will no longer be able to access IEX Exchange market data through this API. IEX Exchange market data will continue to be available via the TOPS and DEEP feeds, as well as through commercial vendors such as IEX Cloud, which is operated separately from IEX Exchange. If you have any questions, please reach out to api@iextrading.com. This github repository is not a support channel for IEX Cloud. We are looking to migrate to a more centralized, scalable community support site in 2021. In the mean time, please consult the following links for information and support.
api finance real-time fintech stock-market iex market-data stocks stock-prices stock-exchange iex-api iextradingNLP-Models-Tensorflow - Gathers machine learning and Tensorflow deep learning models for NLP problems, 1
NLP-Models-Tensorflow, Gathers machine learning and tensorflow deep learning models for NLP problems, code simplify inside Jupyter Notebooks 100%. I will attached github repositories for models that I not implemented from scratch, basically I copy, paste and fix those code for deprecated issues.
nlp machine-learning embedded deep-learning chatbot language-detection lstm summarization attention speech-to-text neural-machine-translation optical-character-recognition pos-tagging lstm-seq2seq-tf dnc-seq2seq luong-apiHands-On-Reinforcement-Learning-With-Python - Master Reinforcement and Deep Reinforcement Learning using OpenAI Gym and TensorFlow
Reinforcement Learning with Python will help you to master basic reinforcement learning algorithms to the advanced deep reinforcement learning algorithms. The book starts with an introduction to Reinforcement Learning followed by OpenAI and Tensorflow. You will then explore various RL algorithms and concepts such as the Markov Decision Processes, Monte-Carlo methods, and dynamic programming, including value and policy iteration. This example-rich guide will introduce you to deep learning, covering various deep learning algorithms. You will then explore deep reinforcement learning in depth, which is a combination of deep learning and reinforcement learning. You will master various deep reinforcement learning algorithms such as DQN, Double DQN. Dueling DQN, DRQN, A3C, DDPG, TRPO, and PPO. You will also learn about recent advancements in reinforcement learning such as imagination augmented agents, learn from human preference, DQfD, HER and many more.
reinforcement-learning deep-reinforcement-learning sarsa q-learning policy-gradients deep-q-network deep-learning-algorithms asynchronous-advantage-actor-critic deep-deterministic-policy-gradient deep-recurrent-q-network double-dqn dueling-dqn hindsight-experience-replay drqn trpo ppoJStock - Free Stock Market Software
JStock is a free stock market software for 26 countries. It provides Stock watchlist, Intraday stock price snapshot, Stock indicator editor, Stock indicator scanner and Portfolio management. Free SMS/email alert supported.
Ticker - Terminal stock ticker with live updates and position tracking
Ticker is a terminal stock watcher and stock position tracker. It helps to track value of your stock positions, Support for multiple cost basis lots, Live stock price quotes and lot more.
quotes terminal tui symbols stock-market ticker cryptocurrencies stocks terminal-app golang-application financial-markets stock-positions stocks-appStock Data
This library provides APIs to get the stock data such as trade price, the history price data, volume and so on. It can be used to get the real time stock data or the history sotck data. Please feel free to add your questions or ideas in the Discussions Tab Created by CB
stock stock-history stock-quotesHands-On-Deep-Learning-Algorithms-with-Python - Master Deep Learning Algorithms with Extensive Math by Implementing them using TensorFlow
Deep learning is one of the most popular domains in the artificial intelligence (AI) space, which allows you to develop multi-layered models of varying complexities. This book is designed to help you grasp things, from basic deep learning algorithms to the more advanced algorithms. The book is designed in a way that first you will understand the algorithm intuitively, once you have a basic understanding of the algorithms, then you will master the underlying math behind them effortlessly and then you will learn how to implement them using TensorFlow step by step. The book covers almost all the state of the art deep learning algorithms. First, you will get a good understanding of the fundamentals of neural networks and several variants of gradient descent algorithms. Later, you will explore RNN, Bidirectional RNN, LSTM, GRU, seq2seq, CNN, capsule nets and more. Then, you will master GAN and various types of GANs and several different autoencoders.
tensorflow word-embeddings gru autoencoder gans doc2vec skip-thoughts adagrad cyclegan deep-learning-mathematics capsule-network few-shot-learning quick-thought deep-learning-scratch nadam deep-learning-math lstm-math cnn-math rnn-derivation contractive-autonencodersTechAn - Technical Analysis Library for Golang
TechAn is a technical analysis library for Go! It provides a suite of tools and frameworks to analyze financial data and make trading decisions. Techan is heavily influenced by the great ta4j. It provides Basic and advanced technical analysis indicators, Profit and trade analysis and Strategy building.
technical-analysis stock stock-market bitcoin trading-strategies trading-bot cryptocurrency
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