Pinned Repositories
Attention-CLX-stock-prediction
Attention-based CNN-LSTM and XGBoost hybrid model for stock prediction
Automated-Cryptocurrency-trading-using-Deep-RL
Training and evaluation of Deep Reinforcement Learning cruptocurrency trading agent.
Bitcoin-Price-Prediction
Analysis of LSTM and Deep-Learning for machine-learning guided Bitcoin Trading.
Bitcoin-Scalping-using-Neural-Network
bkersbergen.github.io
carkit
crypto-algorithmic-trading
LSTM neural network predicting price movements of Bitcoin, backtesting and visualisations.
etudelib
FreqAI-Marcos-Lopez-De-Prado
Implementation of "Advances in Financial Machine Learning" quant trading strategies in python (building upon Freqtrade / FreqAI open source project)
hbase-kerberos-mapreduce-hadoop2
example hbase with hadoop2 and kerberos
bkersbergen's Repositories
bkersbergen/etudelib
bkersbergen/bkersbergen.github.io
bkersbergen/FreqAI-Marcos-Lopez-De-Prado
Implementation of "Advances in Financial Machine Learning" quant trading strategies in python (building upon Freqtrade / FreqAI open source project)
bkersbergen/Attention-CLX-stock-prediction
Attention-based CNN-LSTM and XGBoost hybrid model for stock prediction
bkersbergen/Automated-Cryptocurrency-trading-using-Deep-RL
Training and evaluation of Deep Reinforcement Learning cruptocurrency trading agent.
bkersbergen/carkit
bkersbergen/crypto-algorithmic-trading
LSTM neural network predicting price movements of Bitcoin, backtesting and visualisations.
bkersbergen/Cryptocurrency_Trading_Bot
A strategy and system for automated trading of cryptocurrencies
bkersbergen/pytorch_onnx_runtime_error
bkersbergen/trials
Tiny Bayesian A/B testing library
bkersbergen/crypto-forecasting-benchmark
This repository contains the codebase used in the research conducted for the paper titled "Benchmarking Cryptocurrency Forecasting Models in the Context of Data Properties and Market Factors." The study involved a rigorous assessment of thirteen different time series forecasting models over twenty-one cryptocurrencies and four distinct time frames.
bkersbergen/darts
A python library for user-friendly forecasting and anomaly detection on time series.
bkersbergen/DataShapley
Data Shapley: Equitable Valuation of Data for Machine Learning
bkersbergen/freqAI-LSTM
A Trading Model Utilizing a Dynamic Weighting and Aggregate Scoring System with LSTM Networks
bkersbergen/freqst-strategies
bkersbergen/freqtrade
Free, open source crypto trading bot
bkersbergen/freqtrade-gym
A customized gym environment for developing and comparing reinforcement learning algorithms in crypto trading.
bkersbergen/freqtrade-strategies
Free trading strategies for Freqtrade bot
bkersbergen/freqtrade-stuff
Strategies for Freqtrade
bkersbergen/intelligent-trading-bot
Intelligent Trading Bot: Automatically generating signals and trading based on machine learning and feature engineering
bkersbergen/Large-Time-Series-Model
Official code, datasets and checkpoints for "Timer: Generative Pre-trained Transformers Are Large Time Series Models" (ICML 2024)
bkersbergen/NostalgiaForInfinity
Trading strategy for the Freqtrade crypto bot
bkersbergen/OctoBot
Open source crypto trading bot
bkersbergen/Price-Forecaster
Forecasting the future prices of BTC and More using Machine and Deep Learning Models
bkersbergen/refreshpairlist
Dynamic Pairlist for FreqAI (freqtrade)
bkersbergen/RL-Bitcoin-trading-bot
Trying to create Reinforcement Learning powered Bitcoin trading bot
bkersbergen/stumpy
STUMPY is a powerful and scalable Python library for modern time series analysis
bkersbergen/TEMPO
The official code for "TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting (ICLR 2024)". TEMPO is one of the very first open source Time Series Foundation Models for forecasting task v1.0 version.
bkersbergen/tensortrade
This repository contains my TensorTrade-focused code, including the core program and supplemental tools used in my bachelor's thesis on trading low market capitalization cryptocurrencies using reinforcement learning.
bkersbergen/Time-LLM
[ICLR 2024] Official implementation of " 🦙 Time-LLM: Time Series Forecasting by Reprogramming Large Language Models"