A cryptocurrency trading environment using deep reinforcement learning and OpenAI's gym

Predict Cryptocurrency Price with Deep Learning

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

CryptoCurrency prediction using machine learning and deep learning

First Version.. Cryptocurrency Prediction with Artificial Intelligence (Deep Learning via LSTM Neural Networks)- Emirhan BULUT

ChartScanAI is an advanced app for detecting patterns in stock and cryptocurrency charts using deep learning and YOLOv8. It automates chart pattern recognition, providing traders with a powerful tool for making informed decisions. Key features include real-time analysis, high accuracy for Buy/Sell signals, and support for various charts.

TradeAI: Empowering Algorithmic Trading with Deep Learning for Cryptocurrency Data. Explore the potential of deep learning in cryptocurrency trading through our full-stack algorithmic trading system, fueled by state-of-the-art transformer models

Python Bitcoin is widely used cryptocurrency for digital market. It is decentralised that means it is not own by government or any other company.Transactions are simple and easy as it doesn’t belong to any country.Records data are stored in Blockchain.Bitcoin price is variable and it is widely used so it is important to predict the price of it for making any investment.This project focuses on the accurate prediction of cryptocurrencies price using neural networks. We’re implementing a Long Short Term Memory (LSTM) model using keras; it’s a particular type of deep learning model that is well suited to time series data (or any data with temporal/spatial/structural order e.g. movies, sentences, etc.).We have used different activation function for analysing the efficiency of the system.Instead of historical data we are using live streaming data for better accuracy.

Predict any cryptocurrency with any pair (USDT/ETH/BTC/BNB/ETC) on Binance through Deep Learning (commonly known as Artificial Intelligence for those who do not know the difference between AI/Ml/Dl).

TraderNet-CRv2 - Combining Deep Reinforcement Learning with Technical Analysis and Trend Monitoring on Cryptocurrency Markets

Evaluation of novel deep learning models for cryptocurrency price forecasting

This repository is for the code of paper "Automated Cryptocurrency Trading Approach Using Ensemble Deep Reinforcement Learning: Learn to Understand Candlesticks"

Identify the software project, create business case, arrive at a problem statement. REQUIREMENT: Window XP, Internet, MS Office, etc. Problem Description: - 1. Introduction of AI and Machine Learning: - Artificial Intelligence applies machine learning, deep learning and other techniques to solve actual problems. Artificial intelligence (AI) brings the genuine human-to-machine interaction. Simply, Machine Learning is the algorithm that give computers the ability to learn from data and then make decisions and predictions, AI refers to idea where machines can execute tasks smartly. It is a faster process in learning the risk factors, and profitable opportunities. They have a feature of learning from their mistakes and experiences. When Machine learning is combined with Artificial Intelligence, it can be a large field to gather an immense amount of information and then rectify the errors and learn from further experiences, developing in a smarter, faster and accuracy handling technique. The main difference between Machine Learning and Artificial Intelligence is , If it is written in python then it is probably machine learning, If it is written in power point then it is artificial intelligence. As there are many existing projects that are implemented using AI and Machine Learning , And one of the project i.e., Bitcoin Price Prediction :- Bitcoin (₿ ) (founder - Satoshi Nakamoto , Ledger start: 3 January 2009 ) is a digital currency, a type of electronic money. It is decentralized advanced cash without a national bank or single chairman that can be sent from client to client on the shared Bitcoin arrange without middle people's requirement. Machine learning models can likely give us the insight we need to learn about the future of Cryptocurrency. It will not tell us the future but it might tell us the general trend and direction to expect the prices to move. These machine learning models predict the future of Bitcoin by coding them out in Python. Machine learning and AI-assisted trading have attracted growing interest for the past few years. this approach is to test the hypothesis that the inefficiency of the cryptocurrency market can be exploited to generate abnormal profits. the application of machine learning algorithms to the cryptocurrency market has been limited so far to the analysis of Bitcoin prices, using random forests , Bayesian neural network , long short-term memory neural network , and other algorithms. 2. Applications/Scope of AI and Machine Learning :- a) Sentiment Analysis :- It is the classification of subjective opinions or emotions (positive, negative, and neutral) within text data using natural language processing. b) It is Characterized as a use of computerized reasoning where accessible data is utilized through calculations to process or help the handling of factual information. BITCOIN PRICE PREDICTION USING AI AND MACHINE LEARNING: - The main aim of this is to find the actual Bitcoin price in US dollars can be predicted. The chance to make a model equipped for anticipating digital currencies fundamentally Bitcoin. # It works the prediction by taking the coinMarkup cap. # CoinMarketCap provides with historical data for Bitcoin price changes, keep a record of all the transactions by recording the amount of coins in circulation and the volume of coins traded in the last 24-hours. # Quandl is used to filter the dataset by using the MAT Lab properties. 3. Problem statement: - Some AI and Machine Learning problem statements are: - a) Data Privacy and Security: Once a company has dug up the data, privacy and security is eye-catching aspect that needs to be taken care of. b) Data Scarcity: The data is a very important aspect of AI, and labeled data is used to train machines to learn and make predictions. c) Data acquisition: In the process of machine learning, a large amount of data is used in the process of training and learning. d) High error susceptibility: In the process of artificial intelligence and machine learning, the high amount of data is used. Some problem statements of Bitcoin Price Prediction using AI and Machine Learning: - a) Experimental Phase Risk: It is less experimental than other counterparts. In addition, relative to traditional assets, its level can be assessed as high because this asset is not intended for conservative investors. b) Technology Risks: There is a technological risk to other cryptocurrencies in the form of the potential appearance of a more advanced cryptocurrency. Investors may simply not notice the moment when their virtual assets lose their real value. c) Price Variability: The variability of the value of cryptocurrency are the large volumes of exchange trading, the integration of Bitcoin with various companies, legislative initiatives of regulatory bodies and many other, sometimes disregarded phenomena. d) Consumer Protection: The property of the irreversibility of transactions in itself has little effect on the risks of investing in Bitcoin as an asset. e) Price Fluctuation Prediction: Since many investors care more about whether the sudden rise or fall is worth following. Bitcoin price often fluctuates by more than 10% (or even more than 30%) at some times. f) Lacks Government Regulation: Regulators in traditional financial markets are basically missing in the field of cryptocurrencies. For instance, fake news frequently affects the decisions of individual investors. g) It is difficult to use large interval data (e.g., day-level, and month-level data) . h) The change time of mining difficulties is much longer. Moreover, do not consider the news information since it is hard to determine the authenticity of a news or predict the occurrence of emergencies.

This is a small piece of code to make it easier to store market data from different exchanges and store them into an SQL database to be used for Deep Learning in later steps. It can be deployed into digitalocean dokku server

LSTM deep learning model for cryptocurrency price prediction!

When-to-bull AI is an advanced application designed for detecting patterns in stock and cryptocurrency charts using deep learning techniques. This project extends their approach by implementing the model with YOLOv8 and integrating it into a user-friendly Streamlit app.

This is the GitHub repository complementing the paper "Forecasting Cryptocurrency Prices Using Deep Learning: Integrating Financial, Blockchain, and Text Data"

Deep Q learning algorithm for cryptocurrency trade deployed with Tensorflow (keras) in python

📈 Machine learning for cryptocurrency chart pattern detection and technical analysis using Python and deep learning models

This repository is for my master's project, A Survey of Deep Learning Architectures for Algorithmic Cryptocurrency Trading, delivered on April 22, 2022 for the University of Colorado Denver's M.S. Statistics program.

AI-powered cryptocurrency trading bot built using deep reinforcement learning (DRL). The bot is designed as a research platform for developing and testing advanced trading strategies, emphasizing flexibility in reward function design, data ingestion, and multi-agent experimentation.

Skoltech Deep Learning course 2020 project - Convolutional RNNs for Portfolio Investments of Cryptocurrencies using Order Book Data

This repo contains web application for Cryptocurrency prediction upto 30 days using Python, Tensorflow, LSTM (deep learning), Php, MySQL etc.

Predicting Cryptocurrency prices (Bitcoin & ZCOIN)

Algorithmic Trading on cryptocurrencies using deep learning

Predict prices of BTC and ETH with TensorFlow and Keras

Deep reinforcement learning for algorithmic cryptocurrency trading.

LSTM-based Bitcoin price prediction using PyTorch. Explore deep learning for time-series forecasting with data preprocessing and model training. Ideal for cryptocurrency enthusiasts and those interested in financial forecasting.

This repository contains implementations of Long Short-Term Memory (LSTM) models for predicting the closing prices of three major cryptocurrencies: Bitcoin (BTC), Solana (SOL), and XRP. The project leverages historical OHLC data to train deep learning models capable of forecasting future price trends.

Time Series based analysis of cryptocurrency prediction using deep learning models