ilbegonia666,github

This is my github repository where I post trading strategies, tutorials and research on quantitative finance with R, C++ and Python. Some of the topics explored include: machine learning, high frequency trading, NLP, technical analysis and more. Hope you enjoy it!

algotrading-example

algorithmic trading backtest and optimization examples using order book imbalances. (bitcoin, cryptocurrency, bitmex, binance futures, market making)

appliedpredictivemodeling

:exclamation: This is a read-only mirror of the CRAN R package repository. AppliedPredictiveModeling — Functions and Data Sets for 'Applied Predictive Modeling'. Homepage: http://appliedpredictivemodeling.com/

awesome-deep-learning

A curated list of awesome Deep Learning tutorials, projects and communities.

bayeshmm

Full Bayesian Inference for Hidden Markov Models

bristolstockexchange

BSE is a simple minimal simulation of a limit order book financial exchange

ccapi

A header-only C++ library for interacting with crypto exchanges. Binding for Python is provided. A spot market making application is also provided as an end-to-end solution for liquidity providers.

ccontrol

Using advanced control techniques in an easy way for embedded - No theory, only practice

chinese-future-ctp-demo

Chinese future ctp arbitrage trade tool.

cplusplus_interview

📚 C/C++ 技术面试基础知识总结，包括语言、程序库、数据结构、算法、系统、网络、链接装载库等知识及面试经验、招聘、内推等信息。

cpp-ml-linearregression

A linear regression implementation in C++

cpptrader

High performance components for building Trading Platform such as ultra fast matching engine, order book processor

data-analysis

金融市场与体育彩券市场 --- 数据分析与量化交易

fann

Official github repository for Fast Artificial Neural Network Library (FANN)

fmzquant_strategies

quantitative trading with Javascript, Python, C++, PineScript, Blockly, MyLanguage(麦语言)

gallery

ui for quant project

gamma-ray

High frequency trading bot for crypto currencies

hft

real high-frequency-trading system based on c++

hft-mm

High Frequency Market Making

hft-orderbook

Limit Order Book for high-frequency trading (HFT), as described by WK Selph, implemented in Python3 and C

hftbacktest

high frequency trading backtesting tool of limit orders and queue position modeling with latencies based on full trade and order book tick data

high-frequency

High Frequency Analysis Based On Level-2 Data（Limit Order Book& Transaction Data)

high-frequency-data-order-book-analyser

If you are professionals, retailers or even organisms trading on a financial market, you know that data given on online platforms is not precise enough. Banks and huge financial institutions use powerful computers to transact a large number of orders at very fast speed. Thus, thousands of buying and selling orders are launched within a fraction of a second. So why can’t you see it? High Frequency Data analyser, is an innovative program helping you to analyze high frequency data in order to increase your understanding of the market. High Frequency Data takes trading to the next level, transforming your order book into a interactive visual map. You can now observe visual trading patterns and create new trading strategies. You can chose the parameters you are interested in. ( Volume/askBid). You can observe executed orders and analyse the impact of these executions on the market. You can pause and save the graph whenever you want in order to study more deeply a situation. It plays real time information and offers you many indicators to get an accurate vision of the market.(market indicator) You can visualize the available shares for each value with a very deep order book. (avancer dans le order book). You can also export your data into Excel and draw your most significant graphs. For a deeper and precise order book analysis, download High Frequency Data analyser now!

high-frequency-trading-simulation-system

HFT, A high-frequency trading simulation package in R

highfrequency

The highfrequency package contains an extensive toolkit for the use of highfrequency financial data in R. It contains functionality to manage, clean and match highfrequency trades and quotes data. Furthermore, it enables users to: calculate easily various liquidity measures, estimate and forecast volatility, and investigate microstructure noise and intraday periodicity.

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