Pinned Repositories
AQTrader
AQTrader (AutoQuantTrader) is tool that gather a sub-penny coin fast (in front of roadroller).
liquibook
Modern C++ order matching engine
OrderManagerTest
QuantTraderEd's Repositories
QuantTraderEd/AQTrader
AQTrader (AutoQuantTrader) is tool that gather a sub-penny coin fast (in front of roadroller).
QuantTraderEd/liquibook
Modern C++ order matching engine
QuantTraderEd/algorithm-trading-webapp
Algorithm Trading web application with Python, Django, PyQt5 and Javascript
QuantTraderEd/AlphaGo
A replication of DeepMind's 2016 Nature publication, "Mastering the game of Go with deep neural networks and tree search," details of which can be found on their website.
QuantTraderEd/anomaliesinoptions
In this notebook we will explore a machine learning approach to find anomalies in stock options pricing.
QuantTraderEd/bok_mp_minutes
BoK 금통위 의사록 / 기준금리 분석
QuantTraderEd/chatgpt_stock_report
그날의 증권사 리포트를 챗 gpt를 활용해 요약하는 레포
QuantTraderEd/ChosunYepJeon
ChosunYepJeon (Korea Ancient Cryptocurrency technology)
QuantTraderEd/data-course
QuantTraderEd/ETC
etc
QuantTraderEd/exchange-core
Ultra-fast exchange engine
QuantTraderEd/FedWatchTool_Test
FedWatchTool 분석 및 테스트
QuantTraderEd/ficc_report_scrapping
네이버 금융 / 채권 리포트 스크랩핑 / 텍스트 추출
QuantTraderEd/Financial-Models-Numerical-Methods
Collection of notebooks about quantitative finance, with interactive python code.
QuantTraderEd/FinQuant
A program for financial portfolio management, analysis and optimisation.
QuantTraderEd/github-actions-test-workflow
QuantTraderEd/hftbacktest
A high-frequency trading and market-making backtesting tool accounts for limit orders, queue positions, and latencies, utilizing full tick data for trades and order books.
QuantTraderEd/pycoinone
Python wrapper for Coinone API
QuantTraderEd/pykiwoom
Python Wrapper for Kiwoom OpenAPI+
QuantTraderEd/real_estate_ml
Machine Learning for Real Estate
QuantTraderEd/ruruki
QuantTraderEd/stock_report_gpt_analytics
QuantTraderEd/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.
QuantTraderEd/switch-config-render
QuantTraderEd/Test
QuantTraderEd/tf_pjt_test
TensorFlow 테스트 프로젝트
QuantTraderEd/trump2cash
A stock trading bot powered by Trump tweets
QuantTraderEd/VisualHFT
GUI for enterprise level high frequency trading systems, making focus on visualizing market microstructure analytics, such Limit Order Book dynamic, latencies, execution quality, and other analytics. WPF & C#
QuantTraderEd/vnpy_crypto
QuantTraderEd/zmq-protobuf
communication between python and cpp using zeromq and protobuf