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wtf is shadow-options-trading-lab?

csnyder256/shadow-options-trading-lab — explained in plain English

Analysis updated 2026-05-18

8PythonAudience · researcherComplexity · 5/5LicenseSetup · hard

TL;DR

A research tool that runs about 20 options trading strategies against live market data on paper only, grading each one honestly using worst case fills and rigorous statistics, and never places real trades.

Mindmap

mindmap
  root((shadow-options-trading-lab))
    What it does
      Paper trades 20 strategies
      Worst case grading
      No broker orders
      Live market data
    Tech stack
      Python
      Statistics
    Use cases
      Test strategy edge
      Compare fill assumptions
      Nightly valid checks
    Audience
      Quant researchers
      Options traders

Code map

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Why would anyone build with this?

REASON 1

Test whether an options trading strategy has a real edge before ever risking money on it.

REASON 2

Compare a strategy's worst case, middle case, and optimistic fill outcomes on live market data.

REASON 3

Check nightly whether a strategy should be paused, using statistics that stay valid even when checked repeatedly.

What's in the stack?

Python

How it stacks up

csnyder256/shadow-options-trading-lab100/awesome-machine-learningadam-s/car-diagnosis
Stars888
LanguagePythonPythonPython
Last pushed2024-08-07
MaintenanceStale
Setup difficultyhardeasymoderate
Complexity5/51/53/5
Audienceresearcherdeveloperresearcher

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

How do you spin it up?

Difficulty · hard Time to first run · 1day+

Needs a live 1-minute market data feed and Python 3.14, no broker connection exists at all.

Use freely for any purpose, including commercial use, as long as you keep the copyright notice.

Wtf does this do

Shadow Options Trading Lab, also called ATLAS, is a research tool for testing options trading strategies without ever placing a real trade. It watches live market prices, decides what position about 20 different strategies would have opened, tracks how that pretend position's value moves, decides when it would have closed, and writes every step to a log file. It never sends an order to a broker. The project's author was explicit that the code capable of placing real orders was removed entirely, not just switched off, so there is no way for this system to accidentally start trading with real money. The bigger goal is not to build a trading bot but to build an honest way of grading whether a strategy actually works. The author points out that most simple backtests get this wrong in a few common ways: they assume you always get a perfect price, they let you peek at results and stop testing once numbers look good, they let a tuned strategy quietly inherit credit from before it was changed, and they mishandle strategies whose losses are not capped at the amount of money put in. To fix these problems, every trade is recorded under three different assumptions about the price you actually got, a worst case, a middle case, and an optimistic case, and only the worst case number is used for real grading. Each strategy is tested using a statistical method that lets the author check results every night without invalidating the test, unlike ordinary significance testing. If a strategy's parameters change, it is treated as a brand new strategy so it cannot carry over an old track record. A strategy only gets labeled a loser once both the statistics and a clear explanation of why it is losing line up. The project is written in Python, uses about 726 passing automated tests, and is licensed under MIT. It is explicitly a work in progress and a snapshot of ongoing research rather than a finished product.

Yoink these prompts

Prompt 1
Explain how the worst, base, and optimistic fill ledgers work in this options trading lab.
Prompt 2
Walk me through why this project statistically retires a losing options strategy.
Prompt 3
Help me understand the e-process based testing method this repo uses to avoid peeking bias.

Frequently asked questions

wtf is shadow-options-trading-lab?

A research tool that runs about 20 options trading strategies against live market data on paper only, grading each one honestly using worst case fills and rigorous statistics, and never places real trades.

What language is shadow-options-trading-lab written in?

Mainly Python. The stack also includes Python.

What license does shadow-options-trading-lab use?

Use freely for any purpose, including commercial use, as long as you keep the copyright notice.

How hard is shadow-options-trading-lab to set up?

Setup difficulty is rated hard, with roughly 1day+ to a first successful run.

Who is shadow-options-trading-lab for?

Mainly researcher.

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