English Articles

Machine-translated and localized editions of Quantide's quantitative research articles.

Nasdaq 100 Rises >20% 4 Years; Citi Overweights Gold

Nasdaq 100 sees 4th consecutive year >20% gain. Citi Wealth advises underweighting duration and overweighting gold amid sticky PCE and rate hikes.

📅 2026-10-11Image: Diego Delso / Wikimedia Commons (CC BY-SA 4.0)

AI Threatens ECDSA; WorldQuant Championship Hits 156K

Justin Drake warns ECDSA may fail within months as AI advances; WorldQuant's 6th Championship draws 156K registrants. Meanwhile, Python 3.15 launches with JIT and new types.

📅 2026-10-10Image: J. Paul Getty Museum / Wikimedia Commons (CC0)

Vnpy 4.5.0 Upgrade & DSTNet: Outperforming Random Walk

Vnpy 4.5.0 brings major backtesting upgrades. DSTNet, a causal multi-step forecast network, outperforms random walk on stock indices and gold.

📅 2026-10-09

Factor Investing FAQ: From CAPM to Multi-Factor Models

Explore 7 key questions on factor investing origins, CAPM, Alpha, and multi-factor evolution. Understand why machine learning is replacing linear models in quantitative trading.

📅 2026-10-08

Factor Preprocessing FAQ: Outliers, Missing Values, Standardization, Neutralization

Learn the correct order for factor preprocessing: outlier clipping, missing value handling, distribution adjustment, standardization, and sector/market-cap neutralization.

📅 2026-10-08

Factor Testing FAQ: Regression, IC, and Layered Backtests

Compare regression, IC analysis, and layered backtests for single-factor testing. Learn thresholds for t-values and IC, and how to validate factor predictive power.

📅 2026-10-08

Hedge Fund Leverage Crisis & Quant Opportunities

IMF warns hedge fund leverage tripled in a decade, rising to 9% of US Treasuries. Eisler Capital’s collapse highlights high-cost risks. New research explores election-driven shorting and compression-based investing.

📅 2026-10-08

Polars 2.0, AI Tax, and Quant Insights: Oct 6, 2026

Polars 2.0 enables SQL and streaming by default, boosting quant research speed. Bridgewater proposes an AI tax, while new research uncovers meme-stock alpha.

📅 2026-10-07

Millennium Hires Jera Trader; Kalshi Sparks Quant Arbitrage

Millennium recruits Jera's Japan power chief to expand Tokyo operations. Kalshi's prediction markets create new data sources for quant arbitrage. AMM dynamic fees address liquidity provision risks.

📅 2026-10-06

2026 Q3 Quant Hiring: Python, C++, and ML Dominance

Analysis of 747 quant roles in Q3 2026 reveals Python (53%) and C++ as core skills. Core quant roles demand ML and stats, while engineering dominates volume. Rare skills like KDB+ and DPDK command premium value.

📅 2026-10-05

US Jobs Miss, AI Twins, and Quant Research: Asset Growth Anomaly

US September NFP missed expectations with only 29k jobs added. Meanwhile, quant research explores asset growth anomalies, robust trading strategies, and signal feasibility thresholds.

📅 2026-10-03

Optiver AI Contest Evaluates Real-Time Earnings Reaction

Optiver co-hosts a contest to test AI agents' real-time interpretation of earnings reports and stock price reactions. Meanwhile, AKShare v1.19.1 fixes futures warehouse receipts parsing.

📅 2026-10-01

Why Good News Doesn't Lift Stocks: AI Earnings Prediction Contest

Optiver and UChicago test AI systems predicting stock reactions to earnings. Using R², they reveal that understanding market expectations, not just model strength, is key to explaining why good news fails to lift prices.

📅 2026-10-01

From Engineering to WorldQuant: Repo Microstructure & Rate Shocks

This article explores the intersection of macroeconomic policy shifts and quantitative career paths, analyzing how rising interest rates and inflation impact equity markets while detailing a successful transition from industrial engineering to quantitative finance.

📅 2026-09-30

Duan Yongping Buys Moutai; AI Agents, Quant Hires & Research

Duan Yongping adds Moutai position; OpenAI and Anthropic unveil new agents; Citadel and Millennium recruit AI talent. Quant researchers review microstructure phase transitions and tax AI benchmarks.

📅 2026-09-29

Vance Harwood’s Passing, AI in Finance, and Dynamic Thresholds

Quant community mourns Vance Harwood. CFTC launches AI forum. LightGBM dynamic thresholds boost Sharpe to 1.34. CMU market-making game begins.

📅 2026-09-28

US Yields Break 5%, AI Chaos: A Quant's Guide to Unpredictable Markets

With US 10Y yields surpassing 5% and AI capital expenditures looming, this article analyzes market risks and introduces a zero-prediction, rule-based quantitative strategy for navigating volatility without constant monitoring.

📅 2026-09-27

Jev Model Enters Quant World: 56GB China A-Share Dataset

Jev-trader achieves 80ms inference on Monad blockchain. QuantDB releases 56GB China A-share dataset with 329 factor columns.

📅 2026-09-26

Quantide Loop 2026-09-21: Kimi 3.1 Rumors, AQR on HFT & EMH

Kimi hints at 3.1 release; ChatGPT cross-site tracking exposed. AQR challenges HFT misconceptions and EMH vs. behavioral finance divide. Essential reading for quants.

📅 2026-09-21

Quantide Loop: Step 5 MoE, SHFE Options, and AQR Factor Insights

StepFun launches 600B sparse MoE; SHFE expands options to 23 products; AQR re-examines classic factor persistence and covered call risks.

📅 2026-09-20

Fed Hike, MNQ Signal Failure, Variance Swap Thresholds

Fed rate hike accelerates yen carry unwinds; MNQ momentum signals fail cost tests; variance swap entry/exit thresholds derived; SEC exempts tokenized stocks.

📅 2026-09-18

Can LLMs Trade? Three Experiments on Principles

LLM trading fails at alpha but excels at risk control. Three experiments on CSI 1000 reveal that while LLMs lack directional edge, their attention mechanisms effectively manage volatility and drawdowns through position sizing.

📅 2026-09-14

Can Outsiders Profit From Insider Trading?

We test whether retail investors can profit from insider buying disclosures in China A-shares. Using a rigorous event study with placebo controls, we find the alpha is likely embedded in momentum, not a standalone edge.

📅 2026-09-09

Can Vibe Coding Build Large Quant Projects? A Developer’s Test

This article evaluates whether AI-driven \"vibe coding\" can handle complex quantitative trading frameworks. It explores the gap between rapid prototyping and production-ready code, highlighting the need for formal verification in large-scale systems.

📅 2026-09-07

Is a $934 Quant Course Worth It? GPT Evaluates LLM Trading

GPT rates Quantinsti’s $934 LLM Trading course low (4/10) for using outdated 2019 FinBERT. In contrast, GPT praises Quantide’s curriculum for rigorous factor research and backtesting, scoring it near top-tier institutional standards.

📅 2026-08-31

Microsoft RD-Agent: 4 Agents Automate Factor Mining

Microsoft’s RD-Agent(Q) automates factor mining via four LLM agents. Accepted at NeurIPS 2025, it integrates with QLib for robust, backtest-validated quantitative research.

📅 2026-04-30

31k-Star Open Source: Turning Market Noise into Systematic Decisions

This 31k-star GitHub project automates stock analysis by transforming fragmented data into structured decision dashboards, prioritizing robust workflows and fail-safe mechanisms over simple AI predictions.

📅 2026-04-23

20 Months of Self-Taught Quant: From Retail to Probabilities

A retail investor’s 20-month journey into quantitative trading reveals that success relies not on coding or prediction, but on mastering probability, statistics, and risk management to navigate market realities.

📅 2026-04-17

The Sound of Risk: Acoustic Signals for Volatility Forecasting

This article analyzes a novel multimodal model, PIAM, which leverages physics-informed acoustic features from earnings call Q&A sessions to predict future market volatility, offering a unique risk radar beyond traditional text analysis.

📅 2026-04-17

2026 Hermes Agent Live Test: From Install Fail to Eureka vs OpenClaw

A hands-on 2026 review of Hermes Agent vs OpenClaw. From installation pitfalls to autonomous multi-agent evolution, this article dissects real-world pain points and selection advice for quant strategy automation.

📅 2026-04-13

How to Get 75M Free Tokens Daily for OpenClaw

Unlock 75M free daily tokens for OpenClaw via OpenRouter and NVIDIA’s AI Playground. Learn step-by-step configuration for GLM-5 and other top-tier models without cost.

📅 2026-04-11

Bypass Paywalls: Free WSJ & Bloomberg Access for Quants

This guide outlines a three-layer financial reading framework to filter noise, then provides a free browser extension to bypass paywalls at WSJ, Bloomberg, and other major financial media.

📅 2026-04-03

TCN for Quant: Clearer Time-Series Deconstruction

TCN replaces black-box RNNs with transparent causal and dilated convolutions. This article dissects its residual architecture, explaining how it builds multi-scale feature pyramids while maintaining training stability in noisy financial data.

📅 2026-03-30

Why Causal Convolution for Quant? Avoiding Look-Ahead Bias

Why fixed parameters fail. Causal convolution learns adaptive weights from K-line data without look-ahead bias, enabling robust multi-horizon feature extraction for quantitative strategies.

📅 2026-03-27

Skills Marketplace: Installing AI Tools for Quant Workflows

Skills Marketplace lets quants integrate A-share context from Tushare and XtQuant into AI workflows, offering more value than simple prompts.

📅 2026-03-26

Supervised vs. Reinforcement Learning: AI Trading Strategies

Compare supervised and reinforcement learning for quantitative trading. Learn when to use predictive models versus adaptive strategy optimization for robust alpha generation.

📅 2026-03-23

LOF Arbitrage: Pricing, Engineering, and Code

This guide details LOF fund arbitrage mechanics, the T+2 execution workflow, and risk factors like liquidity and limits. It includes a Python script using Akshare to monitor cross-market premiums and automate alerts.

📅 2026-01-24

Pandas 3.0: The Arrow-Native Revolution for Quantitative Finance

Pandas 3.0 shifts from NumPy to PyArrow, enabling zero-copy memory sharing, deterministic Copy-on-Write semantics, and massive performance gains for high-frequency factor mining and backtesting.

📅 2026-01-19

Wang Yiping: Why Logic Beats Black-Box ML in Quant

Evolutionary Assets’ Wang Yiping explains how logic-driven factors, not black-box ML, control drawdowns in China A-shares. Discover his path from subjective investing to robust, interpretable quantitative strategies.

📅 2026-01-17

Quant Factor Investing: Core Logic, Neutralization, and Alpha

Decoding quantitative factor investing: from defining robust factors and neutralizing industry/market-cap biases to constructing long-short portfolios that isolate pure excess returns.

📅 2026-01-16

Max Dama: The Unconventional Path to HFT Mastery

Max Dama’s journey from surfer to Headlands Technologies core member reveals HFT’s vertical integration, transparency, and rapid feedback loops. He shares insights on AI limitations in trading and the evolving mindset of quant recruits.

📅 2026-01-15

Kronos: Turning K-Line Data into Market Language

Tsinghua’s Kronos uses binary spherical quantization to treat K-lines as semantic tokens, enabling zero-shot generalization across global markets and synthetic stress testing for robust strategy validation.

📅 2026-01-13

Quantum Computing: Reshaping Quant Finance Beyond the Flash Crash

This article explores how quantum computing overcomes classical bottlenecks in extreme risk assessment. It analyzes HSBC and IBM’s algorithmic trading breakthroughs, demonstrating quantum advantages in portfolio optimization, risk management, and derivatives pricing.

📅 2026-01-13

LuoShu Investment: A Priori Factors and Volatility-Equal Weighting

LuoShu Investment distinguishes itself by prioritizing economics-driven alpha models over statistical arbitrage, using Bayesian logic to validate factors and volatility-equal weighting for robust portfolio construction.

📅 2026-01-12

FastHTML: The 2026 Frontend Standard for Quant Systems

FastHTML replaces Streamlit’s limitations with HTMX and Starlette, enabling high-performance, interactive quant frontends using pure Python. This article details the architecture and benefits for 2026 quant infrastructure.

📅 2026-01-11

2026 Quant Infra: SQLite + sqlite-utils for High-Concurrency Trading

SQLite’s WAL mode enables safe multi-process concurrency for trade databases. sqlite-utils simplifies operations with Pythonic syntax, replacing verbose sqlite3 for lightweight, high-performance quant infrastructure.

📅 2026-01-01

UV & Pydantic 2.0: The 2026 Python Engineering Foundation for Quant Systems

Astral’s UV and Pydantic 2.0 redefine Python engineering for 2026. UV accelerates dependency management with Rust, while Pydantic ensures robust data validation, forming the critical foundation for high-performance quantitative systems.

📅 2025-12-23

IPO Lottery: 5 Quant Strategies for Retail Investors

When IPO lottery odds are low, retail investors face missed opportunities. This article uses machine learning to predict IPO valuations and optimal selling prices, helping you decide whether to buy or sell.

📅 2025-12-18

Sharpe >4? A-Share Data Standardization Pitfalls

This case study exposes the \"look-ahead bias\" in data standardization. Global Z-score or Min-Max normalization leaks future data, inflating model performance. Rolling windows are essential for Point-in-Time accuracy.

📅 2025-11-28

TCN Postscript: Why High Win Rates Fail in Live Trading

This article explores why deep learning models like TCN and XGBoost often fail in live trading despite excellent backtest results, highlighting issues like non-stationarity, calendar misalignment, and overfitting.

📅 2025-11-26

Tushare vs East Money: The Hidden Bias in Daily Returns

A deep dive into why Tushare and East Money (via AkShare) report different daily returns for the same stock.

📅 2025-11-24

Static vs Dynamic Adjustments: Why Your Factors Fail

Static adjustments break time-stationarity, rendering factor statistics unreliable. This article exposes the root cause of strategy failure and provides a mathematically sound, high-performance solution for dynamic forward adjustment.

📅 2025-11-21

Moonshot Is All You Need: Finalizing a High-Sharpe Dividend Strategy

This final article completes a dividend strategy using the Moonshot backtest framework. The 5-year backtest shows 11.6% annualized return and a 4.55 Sharpe ratio, significantly outperforming the CSI 300.

📅 2025-11-09

Political Alpha: Quantifying Congressional Trading Patterns

Analyzing public congressional trading records reveals consistent alpha generation. This article explores the data sources, tools, and quantitative implications of political trading signals.

📅 2025-11-03

ESG Long-Short Strategy: Does High ESG Score Mean Alpha? (With Backtest Code)

Systematic backtest of Huazheng and Wind ESG scores reveals weak alpha but significant downside protection. High ESG acts as a risk buffer, not an offensive signal.

📅 2025-10-26

7 Questions to Ask Before Joining a Quant Trading Firm

Max Dama outlines seven critical structural questions for new quant analysts to ask before joining a firm, covering non-compete clauses, code access, and feedback loops to avoid career pitfalls.

📅 2025-10-23

ESG Factor Investing: Data Sourcing & Quant Implementation

This article explores ESG factor investing, comparing data sources like Sina, MSCI, and Huazheng to build a quant strategy. It details data acquisition via Akshare and demonstrates calculating fund-level ESG scores for backtesting.

📅 2025-10-22

Buy vs Sell Side in Finance: Profit, Stability, and Career Paths

This article dissects the structural differences between buy-side and sell-side roles in finance, analyzing revenue models, risk exposure, and career trajectories to guide quantitative finance professionals in their job choices.

📅 2025-10-21

AI-Powered Mispriced Options: Skew Trading Without Code

Discover how to use AI to identify mispriced options via volatility skew and trade them using vertical spreads, achieving asymmetric returns without writing code.

📅 2025-10-17

Alligator Indicator: Trend Following with AO, Fractals, and MACD

This article builds an Alligator Indicator strategy integrating AO, fractals, and MACD to filter noise and capture trends. Backtests on CSI 300 show significant drawdown reduction and improved Sharpe ratio.

📅 2025-10-13

China Customs Probe: The End of the Hardware Race in HFT

Three foreign HFT giants face customs probes, exposing the hardware race. As latency gains vanish, firms pivot to hybrid models, blending machine learning with fundamental value discovery.

📅 2025-10-11

Why ARMA/GARCH Fail in Quant Trading: A Time-Series Reality Check

ARMA and GARCH models require stationarity, which stock prices lack. This article explains why time-series models used in other fields often fail in quantitative trading due to market reflexivity and structural breaks.

📅 2025-10-10

How Master’s Students Can Break Into Quant Trading

A practical guide for master’s students on securing interviews, passing technical assessments, and surviving probation in China’s competitive quantitative trading industry.

📅 2025-10-05

Smart Money Concepts: A Price Action Strategy for Quant Traders?

Smart Money Concepts (SMC) rebrands institutional order flow into retail trading signals. This article dissects SMC’s core mechanics—order blocks, liquidity, and fair value gaps—contrasting them with traditional price action and evaluating their viability in live markets.

📅 2025-10-04

HSBC & IBM: Quantum Computing Reshapes Bond Trading

HSBC and IBM demonstrate a quantum-powered algorithmic trading system, boosting bond execution probability by 34%. This milestone signals a paradigm shift in quantitative finance, mirroring the impact of mainframes in the 1970s.

📅 2025-09-28

Picking Nickels in Front of a Steamroller: Quant Risks

High-frequency, low-risk strategies often mask tail risks. This article dissects the negative skewness of momentum, carry, and arbitrage, using historical blowups to illustrate why diversification and protection are essential for sustainable quant investing.

📅 2025-09-26

XGBoost Time-Series Attribution: Diagnosing Outages

Learn to diagnose system failures using XGBoost. This guide demonstrates converting time-series logs into features for global and local attribution, revealing how lagged indicators drive outage risks.

📅 2025-09-20

Reproducing Research: Screening for Consecutive 2-Year Dividends

This article details the implementation of a dividend screening strategy from a CICC research report, focusing on robust data handling and pandas techniques for identifying stocks with consecutive two-year dividend payments.

📅 2025-09-18

AlphaSuite: A Lean Quant Framework with CANSLIM & Risk Prompts

AlphaSuite offers a lightweight, open-source quantitative framework for individual developers. It integrates CANSLIM models and LLM-driven risk prompts, prioritizing rapid prototyping over production-grade complexity.

📅 2025-09-17

Moonshot Backtest: Dividend Yield Factor Alpha in China A-Shares

This article demonstrates how to efficiently fetch dividend yield data via Tushare and backtest its alpha using Moonshot. Results show high-dividend stocks offer resilience in downturns and significant excess returns over time.

📅 2025-08-28

PEPE’s Warning: How High Can the Market Go?

Market volatility triggers system overflows. We analyze PE trends to assess if valuations are sustainable or if profit growth must accelerate to justify current levels.

📅 2025-08-26

Moonshot 02: Mastering Monthly Backtests with Tushare & Local Caching

Learn to efficiently fetch, adjust, and cache monthly stock data using Tushare. This guide solves Alphalens’ limitations for monthly factor testing via local caching and precise price adjustment techniques.

📅 2025-08-15

PDF Is All You Need (3): From Galton Board to Expected Value

Visualizing binomial distributions via Galton boards reveals the transition from discrete to continuous variables. This article derives PDFs, CDFs, and expected values, applying them to quantitative finance scenarios like downside risk assessment.

📅 2025-08-08

Moonshot: A Minimalist Python Framework for Quant Backtesting

Build a modular, chain-callable backtesting framework from scratch. This article demonstrates how to handle monthly rebalancing, factor mining, and performance evaluation using Python and Polars for high-performance data resampling.

📅 2025-08-06

AI for Quants: Beyond Coding to Autonomous Trading Teams

Explore how AI transforms quant workflows: from Grok-curated news and AI-generated research podcasts to TradingAgents, a multi-agent framework boosting annualized returns by 30% through rational, team-based decision-making.

📅 2025-08-05

Arithmetic vs. Geometric Mean: Why QuantStats’ compsum Reveals the Truth

QuantStats’ compsum and expected_return functions clarify why geometric mean outperforms arithmetic mean in backtesting. Learn to compare strategy efficiency across different time horizons using Python.

📅 2025-08-02

Probability Theory Axiomatized: From Gambling to Kolmogorov

Kolmogorov’s 1933 axioms transformed probability from intuitive gambling odds into rigorous measure theory, unifying discrete and continuous domains via Lebesgue integration.

📅 2025-07-31

Quant Interview Trap: Probability of Random Points on a Circle

This article dissects a classic quant interview problem involving random points on a circle, demonstrating elementary and advanced probabilistic solutions to clarify statistical depth requirements.

📅 2025-07-24

Fixing Quantstats: Backtest Bugs, Overfitting, and Recovery

We revived Quantstats after an 8-month hiatus, fixing critical Python 3.12 bugs and correcting lookahead bias in vectorized backtests. We then validate the strategy using backtrader, optimizing parameters while rigorously testing for overfitting.

📅 2025-07-23

LLT Strategy Backtest: 25% Annualized or Fatal Flaws?

This article dissects a high-performing LLT strategy, exposing hidden backtest traps like look-ahead bias and signal misalignment. It demonstrates how rigorous parameter optimization and realistic cost assumptions reveal the true viability of quantitative trading strategies.

📅 2025-07-21

Z-Transformed MA: Why a 12-Year-Old Strategy Still Beats the Market

This article analyzes the Low-Latency Trend (LLT) indicator, derived via Z-transforms, which reduces lag while maintaining smoothness. Backtests from 2013-2024 show it generates significant alpha against the CSI 300.

📅 2025-07-16

Python 3.14: Free Threading, Numpy 2.3, and the Rust Revolution

Python 3.14 beta3 introduces free threading, ending the GIL era. This article reviews compatible updates in Numpy, scikit-learn GPU support, and the rising dominance of Rust-based developer tools like Ruff and uv.

📅 2025-07-15

Dragon Taming: Engineering Candlestick Factors for Monthly Backtests

This article exposes Alphalens limitations for monthly rebalancing and introduces Moonshot, a specialized library for testing candlestick-based factors like shadow standard deviation and Williams averages.

📅 2025-07-04

Dragon Taming: Engineering a Candlestick Factor from Folklore to Quant

This article dissects a Chinese stock proverb about upper shadows, translating it into a rigorous multi-factor model. It details the engineering challenges of implementing candlestick-based signals, handling missing values, and standardizing factors for live trading.

📅 2025-06-29

Can Reinforcement Learning Evolve Trading Wisdom?

This article explores using RL for quantitative trading, contrasting it with supervised learning. It details a hands-on implementation using Gymnasium and Stable-Baselines3, bypassing FinRL for domestic market compatibility.

📅 2025-06-25

Quantstats Reloaded: Fixing Python 3.12 Compatibility

Quantstats is a popular Python library for trading strategy performance analysis, but it has become nearly unusable on recent Python versions due to lack of maintenance. We introduce quantstats-reloaded to resolve critical compatibility issues.

📅 2025-06-16

Build a 10x Faster QMT Real-Time Data System with Multi-Client Support

Transform a fragile prototype into a high-performance, 10x faster real-time minute-bar data subscription system using QMT’s whole-market API and Redis Streams for robust multi-client consumption.

📅 2025-06-15

21 Days to AI Trader: Optimizing System Logic and Minute-Level Data Synthesis

This article details the architecture of a quantitative trading system that synthesizes tick data into minute-level bars, optimizing data flow across Windows, Mac, and Client endpoints for real-time and historical queries.

📅 2025-06-15

Translating Research into Code: 80% of the Work Explained

Learn to decode quant jargon, implement statistical concepts like RSRS and Z-Score, and leverage libraries like Empyrical to efficiently translate research papers into production-ready code.

📅 2025-06-11

Augment Remote Agent: Why Local Agents Aren't Enough

Augment's Remote Agent offers isolated, parallel development environments. It enables concurrent coding, CI/CD automation, and safer refactoring by executing changes in remote sandboxes, streamlining modern software workflows.

📅 2025-06-10

RSRS Timing Factor: Backtest, Replication, and Alpha Analysis

This article replicates the RSRS timing factor, analyzing its construction via linear regression on support/resistance levels and backtesting performance on the CSI 300 index from 2005 to 2018.

📅 2025-06-09

Monte Carlo: A Brutally Simple Tool for Quant Risk

Monte Carlo simulation is often dismissed as brute-force computing, yet it remains a cornerstone of quantitative risk management. By simulating millions of potential market paths, it provides a statistically robust estimate of maximum drawdown and Value at Risk (VaR), offering peace of mind where simpler models fail.

📅 2025-06-05

Fixing A-Share Data: Limit Prices, ST Status, and Adjustments

Learn to repair critical missing fields in China A-shares daily data—limit prices, ST status, and adjustment factors—using Tushare API for robust quantitative backtesting.

📅 2025-06-03

Automating Daily Data Ingestion for Quant Systems

A modular, scheduled system for automatic daily bar data retrieval from Tushare, featuring integrity checks, smart skipping, batch processing, and robust exception handling.

📅 2025-06-01

SQEP Performance: JSON vs CSV and Batch Size Tuning

Benchmarking SQEP data transfer reveals CSV outperforms JSON in size and deserialization. Optimal batch size is 10,000 records, balancing network overhead and memory pressure for maximum throughput.

📅 2025-05-18

SQEP & Symbol Encoding: 21-Day AI Quant Performance Test

This article benchmarks string vs. int64 stock code encoding in ClickHouse, demonstrating a ~25% query performance gain using the SQEP standard for high-frequency quant systems.

📅 2025-05-18

Hot-Backup DuckDB with Augment: Zero-Downtime Backups

Learn how to hot-backup DuckDB without stopping services using checkpointing and file copying. This case study demonstrates safe, automated backups for live applications.

📅 2025-05-17

How to Store 1B Symbols: SQEP Protocol & Encoding Benchmarks

Designing a universal data exchange format (SQEP) and optimizing stock code encoding can significantly boost query performance. This article details the protocol, implementation, and benchmarking results for high-frequency quant systems.

📅 2025-05-14

DuckDB RETURNING Clause: The Hidden Foreign Key Trap

DuckDB’s RETURNING clause in UPDATE statements can unexpectedly trigger foreign key violations. This article details the root cause—internal DELETE/INSERT logic—and provides verified workarounds for safe usage.

📅 2025-05-14

Optimizing Quant Data Pipelines: DB Design for Factors & Intraday Bars

This article details optimizing a quant data pipeline by integrating daily adj factors and intraday minute bars into ClickHouse via Redis, enhancing strategy decision-making.

📅 2025-05-13

Day 2: Deploying ClickHouse for Quant Data Storage

Day 2 of building a quantitative trading system: installing ClickHouse and DBeaver, creating OHLC tables, and fixing Redis-to-ClickHouse data ingestion errors.

📅 2025-05-11

21-Day AI Quant Build: Data Pipeline with Redis & Tushare

A 21-day challenge to build a quantitative trading system. This first step implements a robust data ingestion pipeline using Redis message queues and Tushare API for OHLC and adjustment factors.

📅 2025-05-10

How I Built a Complex Quant Project with Augment AI

A developer details using Augment’s Agent Auto mode to debug a complex, multi-container quant platform, proving AI’s capability in intricate system architecture.

📅 2025-04-21

Pandas Performance: Memory & Speed Hacks for Quant Data

Reduce memory by 90%+ using category types and compact dtypes. Boost iteration speed 6x with itertuples. Achieve C-level performance in numerical calculations via Numba JIT compilation for high-frequency trading data.

📅 2025-04-05

Pandas Alternatives: Modin, Polars, Dask for Quant Data

Compare Modin, Polars, and Dask as high-performance Pandas alternatives for quantitative finance, covering parallel processing, memory efficiency, and distributed computing for large-scale data analysis.

📅 2025-04-05

Pandas for Quant: Alphalens Data Prep & TDX Rolling Functions

Master Pandas techniques for quantitative finance: converting data for Alphalens factor analysis and replicating TongDaXin rolling window calculations.

📅 2025-04-04

Pandas Styler & Plotting: Data Visualization for Quants

Master Pandas Styler for Excel-like conditional formatting and leverage built-in plotting tools for efficient financial data visualization and analysis.

📅 2025-04-03

Pandas Essentials: Datetime & String Ops for Quant Data

Master Pandas datetime parsing, timezone handling, and string manipulation for robust quantitative data preprocessing and factor engineering workflows.

📅 2025-04-02

Pandas Core Syntax 5: Data Preprocessing & High-Performance IO

Master Pandas data preprocessing with fillna, clip, and winsorize. Optimize IO operations for CSV, Parquet, and SQL using chunksize, dtype, and pyarrow for faster, memory-efficient quantitative data pipelines.

📅 2025-04-01

Pandas for Quant: Logic, Grouping, Indexing, and Stats

Master Pandas for quantitative finance: logical filtering, groupby, MultiIndex, window functions, and statistical operations for factor analysis and data processing.

📅 2025-03-31

Pandas Core Syntax Part 3: DataFrame Creation, Merging, and Manipulation

Master DataFrame creation from diverse sources, master merging strategies (concat/merge/join), and learn essential indexing, slicing, and time-series resampling techniques for quantitative data analysis.

📅 2025-03-30

Pandas Series Essentials: Indexing, Alignment, and Ranking

Master Pandas Series operations including reindexing, dropping items, boolean filtering, arithmetic alignment, and ranking methods for robust quantitative data preprocessing.

📅 2025-03-29

Pandas Core Syntax for Quant: Series, Indexing, and Financial Data

Pandas is foundational in quantitative trading, powering data pipelines from Python SDKs to libraries like Alphalens. This guide covers Series construction, indexing, and financial calculations like moving averages and RSI.

📅 2025-03-28

Numpy Vectorization in Quant: Outlier Clipping, Rolling MDD, and Adaptive Signals

Master Numpy vectorization for quantitative preprocessing. Learn efficient multi-asset outlier clipping, rolling max drawdown, and adaptive thresholding to replace slow loops in factor mining.

📅 2025-03-27

Vectorize Linear Regression: 100x Speedup with Numpy

Learn to accelerate quantitative factor computation by replacing Python loops with Numpy vectorization. This guide demonstrates how to implement sliding-window linear regression using matrix operations and `as_strided`, achieving a 100x performance boost for large-scale data analysis.

📅 2025-03-26

Numpy Vectorization for Quant: Counting Consecutive Runs

Learn to use Numpy vectorization to efficiently count consecutive events in quantitative finance, such as limit-ups or streaks, using mask arrays and find_runs for high-performance backtesting.

📅 2025-03-25

NumPy Masked Arrays & ufuncs: Vectorized Quant Finance

Master NumPy’s Masked Array for robust data handling and ufuncs for high-performance vectorized calculations in quantitative analysis.

📅 2025-03-24

NumPy Date/Time and String Ops for Quant Data

Master NumPy datetime64 conversions and char array operations to handle messy market data from sources like AkShare and QMT efficiently.

📅 2025-03-23

NumPy for Quant: Random Sampling, RNG, and IO

Master NumPy’s random modules for generating price series and balancing datasets. Learn legacy vs. modern RNGs, and efficient CSV/binary I/O for quantitative workflows.

📅 2025-03-22

Numpy Structured Arrays for Tabular Data in Quant Finance

Learn to handle heterogeneous tabular data in Numpy using structured arrays. Covers field access, logical comparisons, set operations, and matrix algebra for quantitative analysis.

📅 2025-03-19

NumPy Core Syntax: Arrays, Indexing, and Quant Preprocessing

A practical guide to NumPy’s ndarray, covering creation, indexing, slicing, and view/copy semantics, with quant-specific examples for factor preprocessing and data manipulation.

📅 2025-03-18

NumPy Core Syntax 3: Type Conversion, Typing, and NaN Handling

Master NumPy type conversion, Python interoperability, and static typing. Learn to handle NaN/inf in quantitative data using specialized aggregation functions and the bottleneck library for performance optimization.

📅 2025-03-09

GBDT Regression Secrets: Beyond DeepSeek

Decision trees transform hard-coded logic into data-driven models. This article unpacks GBDT regression, explaining how tree structures enable flexible, scalable predictive modeling for quantitative finance.

📅 2025-03-09

Numpy and Pandas: The Native Language of Quant Developers

This course introduces Numpy and Pandas as the essential native language for quantitative finance. It covers core data structures, high-performance algorithms, and practical applications in factor analysis and backtesting.

📅 2025-03-08

Automated Triangle Pattern Detection for Quant Trading

This article details an algorithmic approach to detecting technical triangle patterns in candlestick charts. It demonstrates how to identify support and resistance trends using slope analysis and applies this logic to real-time trading scenarios.

📅 2025-02-25

DeepSeek’s Pit, Not the Grave: Why Junior Devs Can’t Escape

This case study benchmarks factor data retrieval using pandas, Polars, and DuckDB. It reveals how DeepSeek’s parallelization assumptions fail against simple type optimization, proving AI still lacks practical testing intuition.

📅 2025-02-10

Validating HDBSCAN for Stock Selection via Cointegration

This article validates HDBSCAN clustering for stock selection by constructing stationary time series from cointegrated pairs to generate mean-reversion trading signals.

📅 2025-01-11

HDBSCAN Clustering for Pairs Trading: 99x Speed Boost

Leverage HDBSCAN clustering to reduce pairs trading backtests by 99x. This guide demonstrates how to identify cointegrated asset pairs efficiently using statistical methods and machine learning.

📅 2025-01-07

ta-lib v0.6.1: A 17-Year Update for Quant Developers

After 17 years, ta-lib releases v0.6.1 with automated builds and cross-platform support. This guide covers installation on Windows, Mac, and Linux, plus ecosystem updates for Python, Polars, and Rust.

📅 2024-12-31

Is an 85% Accurate ML Model a Money Printer?

This article demonstrates building a decision tree classifier for quantitative trading using factor data, achieving ~85% F1-score through careful feature engineering and class balancing.

📅 2024-12-06

Word-Count Factor: News Sentiment via TF-IDF

Leverage TF-IDF to build a word-count factor from news data. By comparing stock mention frequency against moving averages, this approach captures early sentiment signals before volume spikes.

📅 2024-12-04

Best Day to Buy Stocks: Sharpe 22.5 on Friday!

This article analyzes China A-shares CSI 1000 index data to determine the optimal weekday for entry. By backtesting buy-and-hold strategies across different days, we reveal a high-Sharpe timing signal hidden in the calendar.

📅 2024-11-24

AI’s Awakening: 70 Years of Machine Learning’s Turbulent Rise

Tracing AI’s 70-year journey from the 1956 Dartmouth workshop to AGI, this article details the pivotal role of data-driven machine learning and key Chinese scientists in overcoming early setbacks.

📅 2024-11-23

CSI 1000 Bottom-Fishing: 95% Win Rate on Sharp Drawdowns

Backtesting CSI 1000 reveals that buying after a 7.5% continuous bearish candle drawdown yields a 95% win rate. This data-driven approach filters noise to identify high-probability mean reversion opportunities in China A-shares.

📅 2024-11-20

Max Volume Factor: Mining Alpha via Smart Money Footprints

Discover the Max Volume Factor, a novel quantitative strategy leveraging herd behavior and smart money flows to identify high-probability trend reversals in China A-shares.

📅 2024-11-13

Rocket Science for Trading: Derivatives Detect Elliott Waves & Factor Alpha

Leverage John Ehlers’ DSP principles to detect Elliott waves and double tops via derivative analysis. This article demonstrates how derivative-based signals predict price extrema, enabling the construction of a high-alpha factor with 20% annualized returns after optimization.

📅 2024-11-05

Quantide Weekly: UO Indicator & FFT Factor Insights

Nvidia and Sherwin-Williams join Dow Jones; manufacturing PMI rebounds. Larry Williams’ Ultimate Oscillator and an FFT-based DC-diff factor show strong alpha in backtests.

📅 2024-11-03

Larry Williams’ Ultimate Oscillator: A 10x Quant Strategy

Explore Larry Williams’ Ultimate Oscillator, a multi-timeframe technical indicator. Backtests show strong alpha in futures, particularly via short positions, with no overfitting on out-of-sample data.

📅 2024-10-29

Quantide Weekly: Washout Detection via Numpy

This week’s QuanTide Weekly covers fiscal policy shifts and market updates, alongside a technical deep-dive into detecting \"violent washout\" patterns in A-shares using Numpy-based quantitative methods.

📅 2024-10-27

Why PE Fails as a Quant Factor: A Noise-Reduction Approach

This article exposes the pitfalls of using PE as a quantitative factor in China A-shares, demonstrating how to denoise it by dividing by price to reveal cyclical trading signals.

📅 2024-10-25

Rescuing CCI: Factor Purification Reveals a Powerful Technical Signal

Raw CCI signals fail factor testing due to bimodal distribution. Purifying the factor by discarding negative values unlocks significant alpha, validating CCI as a potent technical indicator in quantitative trading.

📅 2024-10-25

Python 3.13 Released: Free Threading, JIT, and REPL

Python 3.13 introduces experimental free-threading, JIT compilation, and a new REPL. This release marks a major leap in performance and developer experience, aligning with the global celebration of programmers on October 24.

📅 2024-10-24

Pandas: Counting Consecutive Daily Limits in China A-Shares

Learn to efficiently identify consecutive price-limit hits in China A-shares using vectorized Pandas operations, replacing slow loops with elegant, high-performance code for quantitative research.

📅 2024-10-23

Live Trading Interfaces: Easytrader, Ptrade, QMT & EMC

A comparative guide to live trading interfaces for China A-shares, evaluating Easytrader, Ptrade, QMT, and EMC based on stability, latency, and deployment flexibility.

📅 2024-10-22

QuanTide Weekly: Hedge Funds Cut Positions, Flag Pattern Detection

Huaxing Quant reduces hedge positions to zero; China’s CPI and GDP data miss expectations. Learn to automate flag pattern detection using Numpy for quantitative trading.

📅 2024-10-20

Factor 42: 17.6% Annualized, 10x in 15 Years

We backtest WorldQuant’s Alpha42 on China A-shares (2008-2022), achieving 16.1% annualized return and 7x cumulative gain. Refactored, it yields superior quantile performance and robust alpha even during bear markets.

📅 2024-10-18

Low Volume, Low Price? Backtesting Shanghai Index Data

Validating the stock market adage \"low volume signals low price\" using one year of Shanghai Composite data. We analyze volume patterns to identify potential reversal points and backtest the strategy's effectiveness.

📅 2024-10-13

Numpy Quant: Median Clipping, VWAP, Rolling MDD

Explore advanced Numpy techniques for quant finance: robust median-based outlier clipping, volume-weighted average price (VWAP) calculation, and efficient rolling max drawdown. Includes adaptive parameter strategies using percentiles.

📅 2024-10-13

Sharpe 12.8%: Arbitrage Pricing Theory in Practice

This article explores implementing linear regression for momentum factors in Python, using Numpy to optimize performance through vectorization and matrix operations for quantitative trading strategies.

📅 2024-09-29

QuanTide Weekly: China Market Updates & Numpy Quant Techniques

This week, China’s economic working group met, regulators cracked down on market rumors, and CSRC optimized broker risk metrics. Learn Numpy techniques for detecting consecutive price limits and calculating Connor's RSI streaks.

📅 2024-09-22

A-Share Valuation: PE Percentiles vs. The Low-PE Trap

Analyzing CSI 300 PE percentiles reveals apparent undervaluation, yet diverging price and earnings trends signal a potential low-PE trap. This article explores whether current A-share levels offer genuine opportunities or hidden risks.

📅 2024-09-16

QuanTide Weekly: Market Lows, Fed Decision, and Numpy for Quants

Major indices hit 5-year lows as Fed and PBOC decisions loom. This week’s quant tutorial covers handling NaNs in Numpy and generating balanced datasets for machine learning.

📅 2024-09-15

The Small-Cap Factor: Banz’s Thesis, Criticism, and Legacy

Rolf Banz discovered the small-cap factor in his PhD thesis. Despite Nobel laureates’ skepticism, his work proved robust. This article explores the factor’s history, academic pushback, and enduring relevance in quantitative investing.

📅 2024-09-12

QuanTide Weekly: Macro Signals, Factor Papers, and NumPy Core

PBOC signals room for reserve cuts amid rate constraints. Buffett trims Bank of America. CSI 300 breaks 2800. Essential quant papers and NumPy structured arrays, logic, and linear algebra covered.

📅 2024-09-08

How Finance Masters Pick Quant Research Topics

A guide for finance and econometrics master’s students on selecting quantitative trading research topics, covering industry history, key papers, and networking strategies.

📅 2024-09-04

PCA, DWT, and XGBoost: A Machine Learning Trading Strategy

This paper reviews a 2019 study combining PCA, DWT, and XGBoost for stock trading. The strategy outperforms buy-and-hold benchmarks across multiple markets, though practical implementation faces challenges in data labeling and normalization.

📅 2024-09-03

FAQ: Factor Machine Learning Course

Comprehensive FAQ for the Factor Machine Learning course, covering enrollment, environment setup, prerequisites, and instructor background.

📅 2024-09-02

FFT for Stock Prediction: Decoding Market Cycles

Decomposes stock price time series into frequency domains using FFT to identify dominant capital cycles. Demonstrates how to isolate main force trading patterns and predict short-term trends through signal energy analysis.

📅 2024-09-01

Factor Analysis & ML Strategy: Course Guide

A comprehensive guide to mastering factor analysis and machine learning for quantitative research. Covers Alphalens, XGBoost, and advanced strategy construction.

📅 2024-08-28

Factor ML Course Syllabus: From Alpha Mining to Live Trading

A comprehensive syllabus for quantitative researchers covering factor mining, Alphalens analysis, and machine learning strategies using LightGBM and CNNs for systematic trading.

📅 2024-08-27

NumPy & Pandas Syllabus for Quant Data Processing

Comprehensive course outline covering NumPy and Pandas for quantitative trading, including core syntax, performance optimization, and real-world quant scenarios.

📅 2024-08-27

FFT for Stock Prediction: Decomposing Market Cycles

This article applies Fast Fourier Transform to decompose stock price time series into frequency domains, identifying dominant capital cycles and their operational rhythms to forecast market trends.

📅 2024-08-26

Efficient Quant Coding: Mask Arrays and find_runs for Streak Detection

Learn to detect consecutive events like limit-ups or N-day rallies in quantitative trading using NumPy’s find_runs and mask arrays for efficient streak calculation.

📅 2024-08-25

Mastering Pandas MultiIndex for Quant Factor Analysis

Learn to leverage Pandas MultiIndex for efficient factor analysis and backtesting. This guide covers creation, renaming, and querying hierarchical data structures essential for quant workflows.

📅 2024-08-25

QuanTide Weekly: Powell Signals Rate Cut, JPMark Reallocates HK Stocks

Fed Chair Powell signals rate cut timing is right. JPMorgan shifts >HK$1.1T HK stock positions. Also: Alpha 101 implementation, overfitting detection, and efficient quant coding with mask arrays.

📅 2024-08-25

Beyond Out-of-Sample: Advanced Overfitting Detection for Quant Strategies

This article explores overfitting detection methods beyond standard out-of-sample testing, including k-fold and rolling cross-validation, noise testing, and parameter plateau analysis to ensure strategy robustness.

📅 2024-08-19

OpenBB & HybridRAG: Free Market Data and AI for Quant Research

This week: Monkeypox impacts markets, China's M2/M1 diverges, and US inflation cools. Deep dive: OpenBB for free global data and BlackRock's HybridRAG for financial document extraction.

📅 2024-08-18

DuckDB 1.0: A Milestone for Local Data Analytics

DuckDB 1.0 stabilizes its storage format, ensuring backward compatibility and eliminating breaking updates. This release marks a significant milestone for local, columnar data processing, competing directly with Polars and ClickHouse in performance benchmarks.

📅 2024-08-16

OpenBB Tutorial: Free Global Market Data Access

Learn to use OpenBB for free global market data. This guide covers CLI and Python integration, resolving API key issues, and accessing equity prices, fundamentals, and index constituents for quantitative research.

📅 2024-08-13

How to Land a Citadel Quant Role via Datathon

Discover the fastest path to a Citadel quantitative research internship by mastering their Datathon. This guide covers preparation, team strategy, and resources from 2017–2024 competitions.

📅 2024-08-11

Citadel Quant Internship: Datathon Strategy & Market Recap

Master Citadel’s Datathon for quant roles. Analyze July CPI/PPI, Fed data, and US equity trends. Explore tsfresh for time-series feature extraction in quantitative trading.

📅 2024-08-11

First Movers: Why Early Earnings Announcements Signal Alpha

Research confirms that early earnings announcements signal positive news, while delays indicate bad news. This article explores the \"first-mover\" strategy in China A-shares, adapting academic findings on announcement timing to live trading for potential excess returns.

📅 2024-08-08

Mining CSI Index Constituents for Quant Edge

This article analyzes CSI index constituent changes, revealing the growing inclusion of STAR Market stocks and their performance dynamics relative to the broader market.

📅 2024-08-06

How to Get Papers for Free: Download Channels

A practical guide to accessing academic papers without paying subscription fees, focusing on Sci-Hub and FreeFullPDF for researchers and individual investors.

📅 2024-08-04

Second-Derivative Momentum Factor: 61.5% Alpha in China A-Shares

We explore a second-derivative momentum factor that captures price acceleration. Backtesting on China A-shares reveals an annualized alpha of 61.5% for long-only portfolios, outperforming traditional first-derivative approaches.

📅 2024-08-04

Second-Derivative Momentum Factor: 61% Annualized Alpha on China A-Shares

We introduce a second-derivative momentum factor that achieves 61.5% annualized alpha on long-only China A-shares. The article details factor optimization, Alphalens layering, and mathematical principles for trend reversal prediction.

📅 2024-08-03

A-Share Slope Momentum Factor: 10-Day Regression Backtest

This article backtests a 10-day regression slope momentum factor on China A-shares. While the raw signal acts as a contrarian indicator, its negative version yields ~15% cumulative return with low drawdown. The analysis highlights the factor's sensitivity to short-term mean reversion and short-selling constraints.

📅 2024-08-02

Connecting Home Servers from Starbucks Over IPv6

With a 256GB, 192-core teaching cluster stuck behind residential broadband with no public IPv4, I enabled native IPv6 with free DDNS for direct remote access, eliminating port forwarding.

📅 2024-07-29

Top 6 Color Palette Tools for Web Designers

Discover six essential color palette tools for web designers, from Adobe Color’s theory-based combinations to Coolors’ random generation and Chinese traditional hues.

📅 2024-07-28

Top 7 Journals Every Quant Finance Professional Should Read

A curated list of seven top-tier academic journals essential for quantitative finance, covering foundational theories like EMH to emerging crypto research.

📅 2024-07-28

QuanTide Weekly: HFT Fees, Olympic Plays, and RSI Mean Reversion

Regulators may hike HFT fees 10x; Paris Olympics spark new A-share themes; Guangdong quant self-audit intensifies. This week also covers Nasdaq drops, RSI mean-reversion strategies, and Alphalens factor analysis techniques for non-linear relationships.

📅 2024-07-28

Mastering Alphalens: 12 Parameters for Factor Analysis

The get_clean_factor_and_forward_returns function automates return calculation, layering, missing value handling, and standardization. This article demystifies its 12 parameters to help quants avoid common pitfalls in factor testing.

📅 2024-07-26

RSI Mean Reversion: Validating a Quantitative Trading Strategy

This article validates a short-term RSI mean reversion strategy, demonstrating how iterative optimization—adjusting for liquidity, market cap, and position sizing—can transform a simple signal into a robust, high-Sharpe live trading system.

📅 2024-07-22

QuanTide Weekly: CSI 300 Total Return Index & Quant Sentiment

Shanghai Stock Exchange launches total return index; private funds deny causing market drops; regulators tighten oversight on convertible bonds and short selling.

📅 2024-07-21

Global Windows Outage: Lessons for Building Robust Quant Systems

The CrowdStrike faulty update caused a global Windows outage hitting airlines, banks and exchanges. Here's what quants must learn to keep live trading systems stable and resilient.

📅 2024-07-20

Stat Arb’s 75-Page Quant Roadmap: The Ultimate Self-Study Guide

A comprehensive 75-page roadmap by Stat Arb for aspiring quants, covering machine learning, data resources, career paths, and specific strategy guides.

📅 2024-07-19

Loss vs. Metrics: Why MAPE Fails as XGBoost Objective

XGBoost needs twice-differentiable objectives, so MAPE can only evaluate, not train. We unpack loss vs. metrics, SMAPE alternatives, and why tree models need no standardization.

📅 2024-07-16

8 IPython Tricks You Probably Don't Know

IPython is a lightweight yet powerful alternative to Jupyter for interactive Python work. These 8 practical tricks — magic commands, debugging, bookmarks and more — will boost your productivity.

📅 2024-07-16

XGBoost Portfolio Strategy: Basic Framework Explained

We review an XGBoost-based portfolio framework from a PhD thesis, explain its two-stage selection and weighting process, and argue why classification outperforms regression for cross-sectional stock picking.

📅 2024-07-15

Pandas Tricks: Monthly Alignment and Top-N Selection

Pandas is essential for quant research. This guide shows two practical tricks for factor testing: aligning dates to month-end and extracting top-N stocks per period with groupby.

📅 2024-07-15

Millennium’s 2024 Brainteaser: Decoding the 3^2025 Puzzle

Millennium Management’s latest brain teaser challenges you to find the highest power of 3 dividing a number composed of 3^2024 threes. The answer reveals a clever Python syntax trick.

📅 2024-07-10

5-Minute ORB on Hot Stocks: 36% Annualized Alpha

Carlo Zarattini's Quantpedia 2023 third-place entry found a 5-minute ORB on Stocks in Play delivered 1600%+ net return in six years, with 2.81 Sharpe and 36% annualized alpha.

📅 2024-06-15

Top Programming Languages in Finance: SQL, Python Lead

Revelio Labs data shows SQL is required in 25% of finance job postings, ahead of Python, Java and R. We break down why SQL and Python dominate finance and what proficiency employers expect.

📅 2024-06-10

Rebels Rise: Fawce, Quantopian and Democratizing Quant

From Harvard materials science to $70M exit to founding Quantopian, John Fawcett tried to democratize quantitative investing for 120,000 quants — and failed commercially while changing quant forever.

📅 2024-06-01

Complete Guide to Building the Barra Risk Model

The Barra Risk Model Handbook is the authoritative 204-page guide to building Barra's equity multi-factor model for risk attribution, not return prediction.

📅 2024-05-21

How to Detect Violent Washout Patterns Quantitatively

Violent shakeouts often precede fast rallies as weak hands are flushed out. This article defines a 4-bar washout pattern and shows how to detect it quantitatively in Python.

📅 2024-05-17

Look-Ahead Bias: Easy to Understand, Easy to Get Wrong

Using zigzag for labeling doesn't cause look-ahead bias, but using it for backtesting does. This article explains why and reviews common look-ahead pitfalls in quant research.

📅 2024-05-16

Be Guided by Beauty: Remembering Jim Simons

Mathematician and Renaissance Technologies founder James Simons died in May 2024. He asked to be remembered as a mathematician and philanthropist guided by beauty, not just a quant legend.

📅 2024-05-11

Why RSI Top Divergence Happens: A Multi-Timeframe Hypothesis

Daily RSI top divergence may occur because weekly RSI still has upside room; once weekly RSI hits its prior high, a pullback follows. We test this multi-timeframe hypothesis on CSI 1000 data.

📅 2024-04-25

Numpy & Pandas for Quant: Essential Data Processing

Master Numpy and Pandas through real-world quantitative finance scenarios. This course covers core algorithms for factor analysis, backtesting, and data manipulation, tailored for quant developers and researchers.

📅 2024-04-24

Predicting Market Tops and Bottoms with XGBoost

Building on our labeled tops and bottoms for the CSI 1000, this article uses XGBoost with upper-shadow and Williams %R features to predict market turning points and explains the core ML principles.

📅 2024-04-23

Low-Volatility Factor Rises in Importance After New Rules

Low-volatility stocks have historically outperformed despite contradicting CAPM. We review the evidence, explain why it works, and show how to build a low-volatility screen.

📅 2024-04-22

Don't Fly Solo: How Quants Use AI Tools

From grilling GPT-4 on Q-Q plots to learning new Python tricks with GitHub Copilot, this post shows how quants can use AI as a co-pilot for research, intuition checks, and coding.

📅 2024-04-19

Labeling 20K Market Bars to Train ML for Tops and Bottoms

Predicting next prices with LSTM fails on noisy data. This piece shows how to label swing tops and bottoms for supervised learning, using zigzag plus manual fixes to build 20K training bars.

📅 2024-04-18

The Moon and Pandas: Wes McKinney's Legendary Story

Wes McKinney created pandas at AQR to replace tedious Excel workflows, later co-created Apache Arrow. His story shows solving industry tooling pain points can change the world.

📅 2024-04-10

CSI 1000 Bottom-Fishing: How a 10-to-1 Payoff Emerges

After a 6.94% consecutive drop in the CSI 1000, statistics show a 91.4% rebound probability with 10-to-1 expected payoff. This analysis quantifies dip-buying odds, expected return, and downside risk.

📅 2024-04-08

Remembering Daniel Kahneman, Founder of Behavioral Economics

Daniel Kahneman, founder of behavioral economics, died on March 27. His Prospect Theory formalized psychology in economics and underpins behavioral finance in quantitative investing.

📅 2024-04-01

Delivery-Day Curse: Why A-Shares Fell on March 27

After the Moscow attack, China A-shares fell for 3 days, with the Shanghai Index down 1.8% and CSI 1000 down 5.89%. We unpack delivery-day effects, RSI divergence and trend signals behind the selloff.

📅 2024-03-27

Fung & Hsieh 7-Factor Model: Beyond Market, Size and Momentum

Fung and Hsieh's 7-factor model extends beyond market, size, value and momentum with trend-following and bond factors. This article breaks down all seven factors and China's adapted 8-factor version.

📅 2024-03-26

How to Search Stock Codes by Pinyin Initials in Python

Type ZGPA to instantly find Ping An Insurance (中国平安) — a standard terminal trick. This guide shows how to build pinyin-initial search for China A-shares in Python with pypinyin.

📅 2024-03-24

QMT Adjustment Factor Too Slow? 100x Faster Vectorized Method

Still looping with XtQuant's example to compute adjustment factors? This vectorized approach converts dividend data to factors in bulk, running 100x+ faster for market-data storage and backtest prep.

📅 2024-03-10

After 6 Limit-Ups: Finding the Next Winner With Correlation

After two tourism stocks hit limit-up on Jan 2-3, 2024 — one nearly tripling, the other posting six straight limit-ups — we show how correlation analysis could have flagged the second winner.

📅 2024-03-07

How Quants Use Jupyter: JupySQL Queries & Faster EDA

Explore how quants can use JupySQL inside Jupyter to query databases and replace Navicat, DBeaver, or pgAdmin, plus agile EDA tools that cut coding time dramatically.

📅 2024-03-05

How Quants Can Master Jupyter: Magics and VS Code Tips (Part 1)

This guide shows quants how to get more from Jupyter, from displaying all cell outputs and handy magics like %timeit and %precision to debugging and running notebooks in VS Code.

📅 2024-03-04

Zillionare 2.0 Data Sync & Maintenance Guide

Learn how Zillionare initializes and maintains market data. This guide explains the sync pointers, InfluxDB storage, and Redis caching for accurate backtesting.

📅 2024-02-23

Zillionare 2.0: Resolving 'Unclosed Bar' Build Failures

Fix the \"failed to build unclosed bar\" error in Omega logs caused by missing minute-level data during initial post-market startup.

📅 2024-02-23

Why Every Quant Should Use DuckDB

Beyond SQL on DataFrames and AsOf Joins for aligning multi-timeframe bars, DuckDB fully replaces SQLite, adds friendlier SQL, and beats ClickHouse and Polars on joins and group-bys.

📅 2024-02-01

Give Pandas a Partner: Query DataFrames with SQL & DuckDB

If you know SQL, pandas filtering can feel verbose. Learn how DuckDB lets you query DataFrames with SQL, speed up analytics, and even handle larger-than-memory data.

📅 2024-01-29

How Decimalization Changed Spreads and Strategy

When U.S. stocks switched from 1/16 fractions to decimals in 2001, price clustering broke down, shifting support levels and market-maker profits — a key lesson in data consistency for quants.

📅 2024-01-26

Finding Alphas: WorldQuant's Approach to Building Strategies

What does 'alpha seeking' really mean? This review of WorldQuant's Finding Alphas unpacks the definition of alpha, how to design and test trading signals, and where academic research fits in.

📅 2024-01-25

$Left-Digit Effect: Round Numbers and Price Refraction$

Left-Digit Effect: Round Numbers and Price Refraction

Where do new factors and strategies come from? This note traces the left-digit effect from retail pricing to round-number pressure in markets, plus a refraction model for breakouts.

📅 2024-01-24

Dragon-Phoenix Mania: Detecting Name Hype With Quant Methods

Quants never ignore a potential profit opportunity, no matter how irrational it looks. We boldly hypothesize, then carefully verify — including China's magical stock-name speculation.

📅 2024-01-23

Python Best Practices for Quant Researchers: Code Quality

Master Python best practices for quantitative research: resolve dependency hell with Poetry, enforce style with Black, and catch errors early via type hints and unit tests.

📅 2024-01-18

ClickHouse: One Table to Rule Them All!

Our earlier notes covered personal setups for storing massive market data. They are called personal not for lack of speed, but because data lives locally and supports single-machine queries only.

📅 2024-01-17

Alma Maters of China's 10-Billion-Yuan Fund Founders

We mapped the universities of China's 10-billion-yuan private fund founders: math, CS and physics dominate, with Tsinghua, Peking, Zhejiang and Fudan leading.

📅 2024-01-16

China Private Quant Strategies Roundup: Early 2024

After reviewing roadshows from a dozen private funds, we summarize the most effective 2023 quant strategies, led by technical trend and reversal factors combined with machine learning models.

📅 2024-01-14

How to Set Environment Variables in Jupyter Notebook?

Jupyter Notebook doesn't inherit host environment variables by default, complicating secure credential handling. Learn how to expose them via kernel.json for clean, shareable notebooks.

📅 2024-01-14

Alphalens Factor Analysis (4): Information Coefficient

Unlike return analysis and factor alpha, Information Analysis is unaffected by trading costs. It uses the Information Coefficient to measure correlation between factor values and forward returns.

📅 2024-01-12

Alphalens Factor Analysis: Why Your Alpha Calculation Is Wrong

Alphalens alpha and beta look simple, but hidden weighting assumptions can invalidate them. We explain why factor values must correlate positively with returns, using turnover as an example.

📅 2024-01-11

Mastering XtQuant: QMT Live Trading with Third-Party Frameworks

QMT offers a cost-effective live trading solution for China A-shares. This course details integrating XtQuant with third-party frameworks to bypass platform lock-in, enhance backtest speed, and enable advanced strategy development.

📅 2024-01-10

Alphalens Factor Analysis (2): Low Turnover Returns

With factor data ready, we run factor analysis in Alphalens. This guide walks through return reports — by-quantile returns, long-short spreads, and cumulative curves — to judge performance.

📅 2024-01-10

Alphalens Factor Analysis: Low-Turnover Factor Example (1)

Learn single-factor analysis with Alphalens using a low-turnover factor on CSI 300 stocks, covering data prep, cleaning, and quantile grouping for factor testing.

📅 2024-01-09

KS Test, Generalized Hyperbolic Fit, and Buying SSE Dip

When the Shanghai Index drops 4%, should you buy the dip? We run K-S tests to identify its return distribution and use its CDF to estimate the odds of further decline.

📅 2024-01-05

24-Lesson Quant Trading Course: From Data to Live Trading

A comprehensive 40,000-word course with 7,000+ lines of code covering the full quantitative trading lifecycle. Master data sourcing, factor analysis, backtesting, and live execution for China A-shares.

📅 2024-01-04

2025 Quant Investing Course: Timeline, Profitability, and Tech Stack

A 30-hour, 24-chapter course with live server access, 4B+ commercial data, and minute-level backtesting. Ideal for Python/math beginners. Learn how to bridge strategy ideas to live trading with professional tools.

📅 2024-01-04

Monopoly Quant: 24-Lesson Curriculum Guide

A comprehensive quantitative trading course covering data acquisition, preprocessing, factor analysis, backtesting, and live trading using Python libraries like AkShare, Tushare, and Backtrader.

📅 2024-01-04

Z-Score Factor: Signals, Limits, and Mean Reversion

Z-score flags statistically stretched prices by measuring distance from the mean in standard deviations. It works well as a high-win-rate mean-reversion factor, but not as a standalone strategy.

📅 2024-01-04

Storing 50TB: PyArrow + Parquet for Tick Data

HDF5 handles daily bars with ease, but tick and Level-2 data quickly reach terabyte scale. Learn how PyArrow + Parquet keeps storage compact and queries at millisecond speed.

📅 2024-01-03

200x Faster: Storing Securities Data with HDF5

Local market-data storage avoids quota limits and network latency that slow down backtests. This practical guide uses HDF5 and h5py to store, update, and query bars 200x faster than CSV.

📅 2024-01-02

2024 Guide: Monetize Free Static Blogs with MkDocs

Learn to build a zero-cost, static blog using MkDocs and MkDocs-Material. This guide covers dynamic card layouts, cross-platform publishing (Xiaohongshu, WeChat, Zhihu), and automated ad insertion for monetization.

📅 2024-01-01

Conners RSI: My Top Indicator Discovery of the Year

In the multi-factor era, Conners RSI stands out as a single factor powerful enough to build a strategy around — and potentially beat the market.

📅 2023-12-29

Herd Behavior in China A-Shares and How to Factorize It

China A-shares exhibit strong herd behavior. This note explains how retail herding shows up and how to turn signals like new brokerage accounts and popularity rankings into quant factors.

📅 2023-12-28

Sector Data in xtquant: Constituents and Index Prices

This guide maps xtquant's 5,000+ sectors, shows how to list constituents with get_stock_list_in_sector, and explains which index sectors provide downloadable market data.

📅 2023-12-27

Poetry: Python Project Management and Semantic Versioning

A deep dive into Python project management using Poetry, covering semantic versioning, dependency resolution, and reproducible builds to avoid dependency hell.

📅 2023-12-26

Regret-Aversion Factor Hits 5.5 Sharpe in A-Shares

Built on regret-aversion theory, this intraday order-flow factor identifies reluctant sellers in floating loss, delivering a 5.5 Sharpe in CSI 1000 backtests from 2016 to 2022.

📅 2023-12-26

Santa Claus Rally: History, Causes and Exceptions

The Santa Claus Rally refers to the stock market's tendency to rise over the last five trading days of the year and the first two of the new year, a pattern defined by Yale Hirsch in 1972.

📅 2023-12-25

Building the Net-High-NL Factor for Sector Trends

Learn to construct the Net-High-NL (NHNL) factor using anchor effect theory. This guide covers factor definition, signal thresholds, and Python implementation via XtQuant for momentum and reversal strategies.

📅 2023-12-24

Teams vs. Coins Factor: Sports Betting Meets Asset Pricing

The Teams and Coins factor comes from Yale's Tobias Moskowitz and his September 2021 paper 'Asset Pricing and Sports Betting,' linking sports-betting behavior to momentum and reversals in markets.

📅 2023-12-23

QMT/XtQuant Setup: Installation Pitfalls & Remote Dev Guide

Learn to set up QMT/XtQuant: install the source package in a Conda venv, understand its Socket proxy to QMT, avoid broker version pitfalls, and develop remotely with VSCode.

📅 2023-12-22

Free Quant Data with QMT: XtQuant Setup Guide

XtQuant is QMT's standalone SDK for free market data and trading. This guide shows how to cache history with download_history_data and read bars with get_market_data.

📅 2023-12-21

Genetic Algorithms in Quantitative Trading

Genetic algorithms use natural selection to find optimal solutions, tuning strategy parameters like MACD and RSI to maximize profit in quantitative trading.

📅 2023-12-20

Brilliant Code in Just a Few Lines

From a zero-line project with 50k stars to Quake's fast inverse square root, this tour celebrates tiny code snippets with outsized ingenuity in games and quant trading.

📅 2023-12-19

How to Normalize Factors for Deep Learning Strategies

Learn the difference between normalization, standardization and regularization, why min-max and sigmoid often fail on price data, and how to build monotonic, sensitive transforms for quant factors.

📅 2023-12-16

How to Restart MiniQMT Without Manual Login

The xtquant package lacks auto-reconnect, so an unstable MiniQMT must be restarted with a new session ID. This guide shows how to enable password-free restart by preserving the linkMini auth file.

📅 2023-12-14

CAPM: Theory, Beta Calculation, and Python Implementation

This article explains the Capital Asset Pricing Model (CAPM), detailing how to calculate beta via regression and covariance using Python and AkShare data for China A-shares.

📅 2023-12-13

Modern Portfolio Theory: Basics, Efficient Frontier, and Sharpe Calculation

This article introduces Modern Portfolio Theory (MPT), the efficient frontier, and practical implementation using Python to calculate portfolio returns, volatility, and Sharpe ratios via Monte Carlo simulation.

📅 2023-12-13

Portfolio Theory & Practice (2): Monte Carlo Optimization

This article explores Monte Carlo simulations for portfolio optimization, analyzing Sharpe ratios and volatility to identify efficient frontiers. It discusses computational limits and vectorization strategies for scaling factor investing models.

📅 2023-12-13

Portfolio Optimization: Convex Optimization for Efficient Frontiers

Leverage SciPy’s minimize function to solve constrained portfolio optimization problems, constructing efficient frontiers by balancing target returns against volatility and Sharpe ratio constraints.

📅 2023-12-13

Portfolio Theory & Practice (4): PyPortfolioOpt Toolkit

This article introduces PyPortfolioOpt, a specialized library for asset allocation. It covers efficient frontier visualization, Sharpe ratio optimization, and integrating CAPM with Modern Portfolio Theory for robust quantitative strategies.

📅 2023-12-13

Live Trading Integration: Easytrader, Ptrade, QMT, EMC

Comprehensive guide to live trading integration in China A-shares, covering Easytrader, Ptrade, QMT, and East Money EMC. Compares latency, security, and setup requirements for quantitative investors.

📅 2023-12-13

Persisting Quant Data: Storage Strategies for Backtesting

Backtesting requires long-term, high-frequency data. This article evaluates storage solutions—relational, time-series, and columnar—to handle massive factor libraries and ensure low-latency access.

📅 2023-12-13

Tulip Mania: The Birth of Options Trading

Exploring the 17th-century Dutch Tulip Mania, this article traces the origins of futures and options contracts. It reveals how a speculative bubble inadvertently created the first legal framework for options, laying the groundwork for modern quantitative finance.

📅 2023-12-13

How the Dutch East India Company Built the First Stock Exchange

Tracing the origins of modern equity markets from 17th-century spice trade monopolies to the Amsterdam Stock Exchange, revealing how liquidity and secondary trading enabled global capital formation.

📅 2023-12-13

East Money Quant Interface: Installation & Config Guide

Step-by-step guide to installing the East Money GM adapter, configuring accounts, and testing live trading simulations with conda and gmadaptor.

📅 2023-12-13

Zillionare 2.0 Docker Setup: Deploying a Quant Research Environment

A step-by-step guide to deploying Zillionare 2.0 via Docker, covering container orchestration, data synchronization, and live trading integration.

📅 2023-12-13

Why Python? The Case for Quantitative Finance

Python dominates quantitative finance due to its readability, vast ecosystem, and AI integration. This article explores its history, design philosophy, and addresses common misconceptions about performance and scalability in large-scale applications.

📅 2023-12-13

Chapter 2: Setting Up Your Python Quant Dev Environment

This chapter guides you through selecting the optimal OS, configuring WSL/Docker on Windows, and mastering VS Code extensions for efficient quantitative development.

📅 2023-12-13

Chapter 3: Setting Up Python Virtual Environments

Learn to isolate Python dependencies using virtual environments to resolve dependency conflicts. This guide covers Anaconda, pip, and VS Code configuration for robust quantitative development workflows.

📅 2023-12-13

Standard Python Project Layout & Automated Generation

Learn the standard Python project structure and use the Python Project Wizard to automate configuration, testing, and CI/CD workflows for reproducible, high-quality codebases.

📅 2023-12-13

10x Coding: AI, Type Hints, and Linting for Python

Master efficient Python development with AI assistants, strict type annotations, and automated linting. Learn to leverage tools like Copilot, Mypy, and Flake8 to write robust, maintainable code.

📅 2023-12-13

Chapter 7: Python Unit Testing with Pytest and Mock

A practical guide to unit testing in Python using pytest, covering fixtures, assertions, and advanced mocking techniques. Learn to manage test coverage with coverage.py and automate matrix testing with Tox for robust quantitative code.

📅 2023-12-13

Git Version Control Best Practices for Quant Devs

Master Git workflows, branching strategies, and CLI tools to streamline quantitative development, ensure code integrity, and prevent catastrophic deployment errors.

📅 2023-12-13

Chapter 9: Continuous Integration & Automation with GitHub Actions

Learn to implement robust CI/CD pipelines using GitHub Actions. This chapter covers workflow definitions, matrix builds, and automated publishing for Python libraries, ensuring code quality and streamlined releases.

📅 2023-12-13

Writing Technical Docs: Sphinx vs MkDocs

A practical guide to Python technical documentation, comparing Sphinx and MkDocs workflows, formats, and deployment strategies for modern software projects.

📅 2023-12-13

Packaging and Publishing Python Applications

Covers Python library distribution via PyPI, wheel/sdist formats, and application deployment strategies including desktop, mobile, and cloud containerization with Docker.

📅 2023-12-13

Scaling and Normalization in Quant Strategies: Beyond ML

Scaling and normalization are critical in quantitative trading, not just machine learning. This article explores min-max pitfalls, non-linear mappings, and custom sigmoid functions for robust signal processing.

📅 2023-12-13

Mastering Matplotlib GridSpec: Advanced Layouts and Subplots

Learn advanced matplotlib layout techniques using GridSpec to create complex, merged-cell grids. This guide covers precise axis positioning and cross-axis drawing for professional data visualization.

📅 2023-12-13

Mastering Matplotlib Layouts: Subgridspec and Mosaic

Learn advanced matplotlib layout techniques using subgridspec and subplot_mosaic. This guide covers top-down grid creation, semantic axis naming, and complex nested layouts for precise figure design.

📅 2023-12-13

Using Q-Q Plots for Statistical Inference

Q-Q plots visually verify if a random variable follows a target distribution by comparing sample quantiles against theoretical ones. This guide explains the underlying principles and implementation in Python.

📅 2023-12-13

Dash Web Apps: Routing, Auth, and Pitfalls

Explore Dash’s core concepts, routing strategies, and authentication patterns. Learn to build scalable, multi-page Python web apps while avoiding common pitfalls in layout management and state handling.

📅 2023-07-22

24-Lesson Quantitative Investing: From Data to Live Trading

A comprehensive, hands-on course for quants and developers covering data pipelines, factor mining, backtesting, and live trading execution in China A-shares.

📅 2023-05-13

24 Lessons in Quantitative Investing: Complete Syllabus

Comprehensive syllabus for quantitative trading, covering data sources, strategy development, factor analysis, backtesting frameworks, and live trading integration for China A-shares.

📅 2023-05-13

Why Peak Detection Fails in Live Trading: A Look at SciPy

SciPy’s argrelextrema detects relative extrema but lags, making it a look-ahead function unsuitable for live trading. This article explains the lag, shows a workaround, and demonstrates how to use these signals for ML labeling.

📅 2021-11-14

Zillionare: Open-Source Quant Framework for Large-Scale Data

Zillionare is a deployable open-source quantitative framework supporting massive data storage (3.5B+ records). It features unified backtest/live APIs, InfluxDB integration, and a modular architecture for robust quantitative investing.

📅 2021-03-20

Zillionare 1.0: A High-Performance Distributed Quant Framework

Zillionare is a distributed, high-speed quantitative trading framework built on Python 3.8+, leveraging async I/O and microservices for low-latency, scalable factor investing and AI-driven strategy development.

📅 2020-11-30