43 skills found · Page 1 of 2
JJJerome / Mbt Gymmbt_gym is a module which provides a suite of gym environments for training reinforcement learning (RL) agents to solve model-based high-frequency trading problems such as market-making and optimal execution. The module is set up in an extensible way to allow the combination of different aspects of different models. It supports highly efficient implementations of vectorized environments to allow faster training of RL agents.
apolanco3225 / Deep Reinforcement Learning For Optimal Execution Of Portfolio Transactions Using DDPGPerforming a trading strategy using deep deterministic policy gradients to know when to buy, hold or sell stocks in a virtual environment that simulates stock prices.
lakshmiDRIP / DRIPFixed Income Analytics, Portfolio Construction Analytics, Transaction Cost Analytics, Counter Party Analytics, Asset Backed Analytics
arturbeg / Reinforcement Learning OeThe work aims to explore Value based, Deep Reinforcment Learning (Deep Q-Learning and Double Deep Q-Learning) for the problem of Optimal Trade Execution. The problem of Optimal Trade Execution aims to find the the optimal "path" of executing a stock order, or in other words the number of shares to be executed at different steps given a time constraint, such that the price impact from the market is minimised and consequently revenue from executing a stock order maximised.
cvxgrp / Vwap Opt ExecVolume Weighted Average Price Optimal Execution
huangzz119 / OptimalExecution Stochastic ControlThis is for the capstone project "Optimal Execution of a VWAP order".
joshuapjacob / Almgren Chriss Optimal ExecutionAn optimal trading trajectory solver.
sduprey / Optimal Transaction ExecutionThis entry contains two topics The first item is entirely based on the following paper: http://sfb649.wiwi.hu-berlin.de/papers/pdf/SFB649DP2011-056.pdf It contains 2 MATLAB demonstrating script : DATA_preprocessing.m & VAR_modeling_script.m DATA_preprocessing.m uses the LOBSTER framework (https://lobster.wiwi.hu-berlin.de/) to preprocess high frequency data from the NASDAQ Total View ITCH (csv files) allowing us to reconstruct exactly at each time the order book up to ten depths. Just look at the published script ! VAR_modeling_script.m contains the modeling of the whole order book as VEC/VAR process. It uses the great VAR/VEC Joahnsen cointegration framework. After calibrating your VAR model, you then assess the impact of an order using shock scenario (sensitivity analysis) to the VAR process. We deal with 3 scenarii : normal limit order, aggressive limit order & normal market order). Play section by section the script (to open up figures which contain a lot of graphs). It contains a power point to help you present this complex topic. The second item is entirely based on the following paper : http://www.courant.nyu.edu/~almgren/papers/optliq.pdf It contains a mupad document : symbolic_demo.mn I did struggle to get something nice with the symbolic toolbox. I was not able to drive a continuous workflow and had to recode some equations myself. I nevertheless managed to get a closed form solution for the simplified linear cost model. It contains a MATLAB demonstrating script : working_script.m For more sophisticated cost model, there is no more closed form and we there highlighted MATLAB numerical optimization abilities (fmincon). It contains an Optimization Apps you can install. Just launch the optimization with the default parameters. And then switch the slider between volatility risk and liquidation costs to see the trading strategies evolve on the efficient frontier. It contains a power point to help you present this complex topic.
rshariffdeen / CPRCPR: A new automated program repair technique based on concolic execution which works on patch abstraction with the sub-optimal goal of refining the patch to less over-fit the initial test cases.
danolivo / Pg Track OptimizerExplore query plan and execution statistics to find signs of non-optimal optimization
FernandoDeMeer / RL Optimal ExecutionNo description available
DuarteAbracos / Statistical Arbitrage KalmanIntraday pairs trading engine using Kalman Filters for dynamic beta estimation and Avellaneda-Stoikov optimal execution.
ideas4u / Trading PlatformThis project is the most awaited project in open source community where every user who belongs to Stock Trading always wanted to develop its own software. This project has been developed specifically for Indian Market Stock Trading. It encompasses end to end trading cycle for intraday trading but the design would be such that it can be easily extended for delivery trading. During the lifecycle of this project we will be using most advance technologies but the base code will always be C/C++. Development Methodology: ======================== We use "Incremental Life Cycle Model" along with Cross-Platform Development (Portable). Project Priorities and Assumptions: =================================== 1) Low Latency, High Performance all the time. 2) Wherever choice has to be made between memory and execution speed, we give preference to speed. 3) Every module devloped will be exhaustively tested. How the work Proceed: ===================== Before the beginning of any new project, we should know the "PROBLEM STATEMENT", so here it is "Problem Statement" ------------------- To Build a high performance, low latency, end to end Trading Platform for Indian Stock Market but not limited to which home users should be able use for trading which guarantees (99% of the times) the profit but does not guarantees maximized profit for intraday trading. First Step: ----------- To provide the optimal solution to any problem is "UNDERSTAING THE PROBLEM". To understand the above problem statement you need to really extract the explicit and implcit requirements from the statement. Here is the List of requirements: Explicit: --------- 1) High Performance 2) Low-Latency 3) End-to-End Trading Platform 4) Focus on Indian Stock Market but not limited to it. 5) Guarantees (99% of the times) the profit but does not guarantees maximized profit. 6) Only for Intraday Trading. Implicit: --------- 1) Book Keeping of the order and trade (Order Management System). 2) Availability of Market Data to End-Users on Demand for identifying the stock and placing the order. 3) User Account Management. Might be I missed something please suggest and after reveiw we add it here. Second Step: ------------ To understand the above Explicit/Implicit requirements, you should have the "KNOWLEDGE OF VARIOUS TECHNOLOGIES" and indepth undertstanding of the "PROBLEM DOMAIN" i.e. Stock Market. Once this is achieved we need to architect the solution in terms of Software and Hardware nodes and their integration. Third Step: ----------- To solve the problem statement, the above requirements should be "DECOMPOSED IN MODULES" and map to them with technolgoies/software/hardware used. Below is the list of modules we are able to identify: Modules Included: ================= Core Modules: -------------- 1) Core Libraries 2) Manual Order Entry System 3) Auto Order Entry System 4) Artificial Exchange 5) Algorithmic Trading Platform 6) Smart Order Router 7) Direct Trading Platform (Ooptional) Utility Modules: ---------------- 8) Logger Server 9) HeartBeat Server Technologies Used: ================= Software: --------- We always use freeware, Open Source Softwares or APIs which are the part of GPL, LGPL.xx licence. Any special requirement for building/using the modules will be detailed in specific module. For development, we generally use: ---------------------------------- Windows-7 for Operating System but any other OS ca be used. Our Code is Platform Indepandant. Visual Studio 2013 in built compiler for build or Intel@ Compilers which can be easily integrated with Visual Studio IDE. For real time, we generally use: -------------------------------- Linux-susse 10 or above with real time extensions. gcc 4.4.1 for build. vi editor Hardware: --------- No special requirement for development purpose. For real time use, it depands how much Stock you are interested in and the various configuration of modules. We prefer generally the below configuration for any number of Stock Trading: 256 GB RAM 16 core processor 1 TB of HDD/SDD Programming Languages and other Technologies: --------------------------------------------- C, C++99/c++11, Lua, ZeroMq, nanodbc, Lock-Free Data Structures, Intel TBB, Boost, Google Protobuf, MySql, Python. Fourth Step: ------------ Dcompose each module till it becomes entity to provide the useful functionality. We are going to explain this in each module detailed section. Fifth Step: ------------ We do design/develop/benchmark/unit test/integration testing of the above modules. Sixth Step: ------------ We deploy the delivered software on various hardware nodes as per the deployment architecture and integrate them. Seventh Step: ------------ Observe the behaviour of deployed software on live traffic and cut two branches at this level : 1st branch continue to do incremental development and 2nd branch fix the issues reported which can be later merged with 1st branch for another release. Any suggestions for improvement are most welcome.
roswelly / Solana Amm Dex ContractSolana AMM DEX Smart Contract – A high-performance decentralized exchange (DEX) on Solana, combining an on-chain order book with Raydium’s AMM aggregator for optimal liquidity, best price execution, and low slippage trading. Built with Rust & Anchor, it offers fast, cost-efficient, and transparent decentralized trading.
maxlamberti / Optimal Order Execution:arrows_counterclockwise: Exploring Optimal Order Execution in Simulated Limit Order Books
strawberryPunch / Vram Optimizer"An optimizer custom node for ComfyUI that ensures each queue execution starts in an optimal state by clearing unused VRAM and unnecessary resources between runs."
yc3566 / Optimal Trade Execution Algorithm Market Impact Model2 algorithms of optimal trade execution: 1) Dynamic Programming 2) Frank-Wolfe Algorithm (Python & C++)
awilliea / Risk Based RL For Optimal Trading ExecutionNo description available
Weichong515 / Algo Trading 14Momentum following strategies and optimal execution cost upon Implement Shortfall algorithm
luca-nik / Mev Agent🤖 This project involves developing a Maximal Extractable Value (MEV) agent designed to optimize order execution by matching a set of order intents with various potential liquidity sources. The goal is to maximize the order's surplus through optimal execution strategies.
