Developing an order execution environment that slices the big incoming orders into smaller orders for trade execution.
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Updated
Sep 17, 2022 - Jupyter Notebook
Developing an order execution environment that slices the big incoming orders into smaller orders for trade execution.
Portfolio execution strategy based on the Almgren-Chriss model, focusing on trade cost optimization in Python
Parses MetaTrader 5 trade journal logs to extract and analyse order execution times across terminals, accounts, and symbols.
A deterministic agent that evaluates trade execution discipline against a defined trading plan and market regime.
Market-microstructure pipeline for predicting cryptocurrency trade execution cost from slippage, liquidity, volatility, spread, and order-book depth.
An intelligent Reinforcement Learning based trade execution engine trained on real SPY 1-minute data to minimize market impact and cost. Uses PPO in a custom Gym environment to dynamically decide execution quantities and outperforms traditional TWAP/VWAP strategies.
Quantitative Finance Paper Adaptations | 量化金融领域论文创作 — 9 full conference papers adapting AI/ML conference methods (CVPR/ICML/ICLR) to quantitative finance, with complete runnable Python implementations | 将9篇AI顶会论文的核心方法迁移至量化金融领域,含完整可运行Python实现
mt5 mql5 linked-order automation
Free MT4 and MT5 position sizer, lot-size calculator and manual trade execution utility for demo accounts.
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