Fixed Income Analytics, Portfolio Construction Analytics, Transaction Cost Analytics, Counter Party Analytics, Asset Backed Analytics
DRIP Asset Allocation is a collection of model libraries for MPT framework, Black Litterman Strategy Incorporator, Holdings Constraint, and Transaction Costs.
Optimal trade execution using the Almgren–Chriss stochastic control framework with illustrative notebooks.Optimal trade execution using the Almgren–Chriss stochastic control framework with illustrative notebooks.Using Stochastic Control especially the Almgren-Chriss framework
Market Microstructure & Liquidity Simulator for B3 using Mean Field Games (MFG). High-performance numerical solver for coupled HJB-Fokker-Planck systems to model price formation and HFT dynamics.
Portfolio execution strategy based on the Almgren-Chriss model, focusing on trade cost optimization in Python
Logistic‑Normal Actor‑Critic für optimale Trade‑Ausführung in einem realistischen Limit‑Order‑Book‑Simulator (Noise/Tactical/Strategic); PyTorch‑Training inkl. TWAP/SL‑Baselines & Evaluation.
Computational framework for Mean-Field Game-based optimal execution with latent market dynamics, endogenous price impact, posterior filtering, and heterogeneous agent equilibrium interactions.
中文高频交易与市场微观结构交互式课程：用可视化实验学习订单簿、订单流、价格发现、做市、执行、风险与实时数据系统。
Reinforcement Learning for Optimal Trade Execution
Optimal trade execution using Deep Q-Networks (DQN) and PyTorch. Simulates an Almgren-Chriss market environment to outperform TWAP benchmarks.
Almgren-Chriss optimal trade execution model with market impact simulation
C++20 limit order book and Almgren-Chriss optimal execution research laboratory.
Execution research lab: realistic L2 replay simulator, classical optimal-execution benchmarks (TWAP/VWAP/POV/Almgren-Chriss), and a from-scratch PPO agent — with honest, ablation-tested findings.
A quantitative framework for optimal trade execution comparing classic Almgren-Chriss dynamics against Heston stochastic volatility models using Monte Carlo simulation and rigorous statistical validation.
MBA dissertation: can reinforcement learning reduce implementation shortfall? PPO and DQN against TWAP, VWAP and Almgren-Chriss in a literature-calibrated multi-venue simulator, with Newey-West HAC t-statistics. No proprietary or real tick data.
Reinforcement learning environment for optimal trade execution — Gymnasium + Stable-Baselines3 + Almgren-Chriss market impact model
Deep Reinforcement Learning for Optimal Trade Execution using DQN and Baseline Strategy Comparison
Optimal execution simulator comparing Almgren-Chriss and GLFT (Guéant et al. 2012) on BTC/USDT L2 order book data
Literature survey of order execution strategies implemented in python
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