证券、期货市场量化交易相关业务和技术知识笔记
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Updated
May 5, 2025 - Python
证券、期货市场量化交易相关业务和技术知识笔记
Python package that enables access to the entire Darwinex Data Offering (DARWIN, FX, Stock, Commodity, Index and Cryptocurrency assets) from one Wrapper Library.
Trading Evolved book code
Nautilus_Trader_Jerry_fall_2023 is a customized verision of Nautilus trader by Zhuoran "Jerry" Li on Fall 2023
A set of personal trading and quant research projects in the crypto markets.
Generates returns of Mutual Funds and comparison them
End-to-end quantitative finance portfolio demonstrating skills in financial modeling, risk analysis, and data-driven investment research.
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LLM-powered Quantitative research assistant which combines financial data, quantitative models, and natural laungage generation to produce insightful market research reports. AI/ML + FinTech Engineering
Reinforcement Learning for Execution & Portfolio Allocation -- open research repo using PPO/SAC with classical baselines (TWAP, VWAP, Almgren-Chriss). LGPL-3.0 Licensed. Contributions welcome.
RBI-grade market regime detection & liquidity stress modelling using volatility, yield curves, and ensemble HMMs.
Explore how Bitcoin market sentiment influences trader behavior and performance using real trading data and emotional indicators like the Fear & Greed Index. This project applies data science, clustering, and visualization techniques to uncover actionable insights for crypto trading strategies.
A quant research-backed tool designed to visualize and analyze the interplay between key macroeconomic indicators and financial assets performance.
A comprehensive list of quantitative finance portfolio/strategy performance measures.
Agent B: cloned from the v1.0 DEV baseline. Future specialization track for analytical experiments, statistical tooling, and cross-evaluation inside the Black Box Lab cluster.
High-performance C++ limit order book engine built for quantitative research, with microsecond-level matching, realistic order types, and strict price-time priority.
End-to-end quant trading: QuantCore (research) + QuantArena (simulation) + QuantPod (AI agents)
Minimal PEAD (post-earnings announcement drift) backtest using Wharton Research Data Services (IBES + CRSP) — Python pipeline for research & plots.
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