chrispyroberts

chrispyroberts / imc-prosperity-4

Public

Prosperity 4 Monte Carlo backtester, Rust simulator, and dashboard visualizer.

37
15
100% credibility
Found Apr 04, 2026 at 21 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A Monte Carlo backtesting suite for IMC Prosperity 4 trading strategies, featuring Rust simulation, Python CLI, and interactive dashboard for profitability analysis.

How It Works

1
🔍 Discover reliable strategy tester

You find a free tool to test your trading ideas against thousands of simulated market days without risking real money.

2
📥 Get it ready on your computer

Download and prepare the tester with simple one-time setup steps.

3
🚀 Test with ready example

Run a quick test using the built-in sample strategy to see profits, charts, and dashboard in action.

4
✏️ Add your trading rules

Drop your own strategy file into the tool – no changes needed if it follows the standard format.

5
⚡ Launch Monte Carlo runs

Start hundreds of simulated market sessions to measure your strategy's strength across many scenarios.

6
📊 Explore the dashboard

Open the colorful charts showing profit ranges, best/worst cases, and path bands.

✅ Master your strategy's risks

Spot weaknesses, tune your rules, and build confidence before live trading.

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AI-Generated Review

What is imc-prosperity-4?

This repo delivers a Monte Carlo backtester for IMC Prosperity 4 strategies, simulating tutorial-round markets for EMERALDS and TOMATOES with Rust speed and Python CLI ease. Drop in your Trader.run(state) code via `prosperity4mcbt your_trader.py --quick --vis`, and it spits out parallel sessions with PnL distributions, path bands, profitability/stability metrics, plus a local dashboard. Builds on github prosperity 3 tools for imc prosperity 4 backtester needs, skipping official sim waits.

Why is it gaining traction?

Rust parallelism cranks 1000 sessions in ~55s on a laptop, way faster than pure Python replays, while the dashboard auto-opens with histograms, quantiles, and cross-product correlations—zero setup for deep robustness checks. Presets like --quick (100 runs) hook devs for rapid iteration, and it matches tutorial book/trade stats without rewriting strategies. Stands out from basic github prosperity 2 replays by generative sims calibrated to imc prosperity 4 dates.

Who should use this?

Quant devs prepping IMC Prosperity 4 entries, testing algos against tutorial volatility before imc prosperity 4 discord shares or reddit posts. Ideal for local PnL stress-testing on EMERALDS/TOMATOES without official timelines, or comparing baselines like the IMC starter trader.

Verdict

Grab it for Prosperity 4 research—CLI and dashboard nail fast feedback loops. 1.0% credibility and 12 stars flag early maturity (solid README, no broad tests), but it's battle-ready for tutorial rounds now.

(198 words)

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