What is quant-research?

hilothefunnydog123-coder/quant-research — explained in plain English

Analysis updated 2026-07-26

★ 1PythonAudience · researcherComplexity · 3/5Setup · moderate

Mindmap

mindmap
  root((repo))
    What it does
      Tests trading claims
      Publishes failed results
      Out of Sample Registry
    Research notes
      Lookahead bias demo
      Volatility prediction
      Momentum vs holding
    Methodology
      Pre registered hypothesis
      Reserved data
      Realistic trading costs
    Audience
      Finance researchers
      Quants
      Vibe coders learning finance
    Contributing
      Submit a research note
      Peer reviewed

mindmap root((repo)) What it does Tests trading claims Publishes failed results Out of Sample Registry Research notes Lookahead bias demo Volatility prediction Momentum vs holding Methodology Pre registered hypothesis Reserved data Realistic trading costs Audience Finance researchers Quants Vibe coders learning finance Contributing Submit a research note Peer reviewed

What do people build with it?

USE CASE 1

Verify whether a trading strategy you discovered actually holds up on unseen future data.

USE CASE 2

Learn why backtests can be misleading and how lookahead bias inflates results.

USE CASE 3

Submit a pre-registered trading strategy to a public registry for honest evaluation.

USE CASE 4

Read research notes on whether volatility, momentum, or liquidity patterns predict returns.

What is it built with?

Python

How does it compare?

	hilothefunnydog123-coder/quant-research	0xustaz/streamgate	a-bissell/unleash-lite
Stars	1	1	1
Language	Python	Python	Python
Setup difficulty	moderate	hard	hard
Complexity	3/5	4/5	4/5
Audience	researcher	developer	researcher

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · moderate Time to first run · 30min

Requires Python and access to historical market price data for running the research scripts.

So what is it?

Martingale is a monthly quantitative finance research project by Neil Gilani. Instead of trying to find trading strategies that beat the market, this project tests whether claimed trading advantages actually hold up, and reports the honest answer even when the answer is that something does not work. The repository follows a strict methodology for every experiment. Each study must state its hypothesis before running, use only past data for decisions, reserve data that was never touched during analysis, count how many strategies were tried, charge realistic trading costs, publish failed results, and be fully reproducible from a single script. The goal is rigor over hype. A standout feature is the Out-of-Sample Registry, a public scoreboard where anyone can submit a trading strategy locked at today's date. The system then evaluates that strategy only on future data, feeding it prices one day at a time so it can never peek ahead. This is designed to be a more honest test than a backtest, because the only thing you cannot optimize against is time itself. The repository contains four completed research notes so far. Note 001 shows how a single line of lookahead code can inflate performance numbers, and how testing 337 strategies on random data can fake results that collapse later. Note 002 finds that daily volatility does not meaningfully predict next-day returns. Note 003 shows that neither momentum nor mean reversion beats simply buying and holding after costs, though each works better in different market conditions. Note 004 finds no statistical edge in popular liquidity-grab trading patterns. A fifth note on overnight versus intraday returns is planned. The project is open to contributors. Anyone can submit a research note, which gets published under their name after peer review against the methodology standard. The full README is longer than what was shown.

Copy-paste prompts

Prompt 1

Help me design a quantitative trading experiment that follows the Martingale methodology: pre-state a hypothesis, use only past data, reserve untouched data, count strategies tried, charge realistic costs, and be reproducible from one script.

Prompt 2

I want to submit a strategy to the Out-of-Sample Registry. Help me write the code to lock in my trading rules today so they can only be evaluated on future price data, one day at a time, with no peeking ahead.

Prompt 3

Explain the concept of lookahead bias using the example from Martingale Note 001, where a single line of lookahead code inflates performance numbers. Then show me how to make sure my Python backtest avoids this mistake.

Prompt 4

Using the findings from Martingale Note 003, help me understand why neither momentum nor mean reversion beats buy-and-hold after costs, and write a Python script that compares all three approaches on S&P 500 data.

Frequently asked questions