Prop Firm Monte Carlo Simulator
Monte Carlo simulator for prop-firm challenge and funded-account rules
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1 0 copies · 7 days MITupdated 14 Mar '26
Overview
Prop Firm Monte Carlo Simulator estimates whether a zero-edge strategy can pass prop-firm challenge and funded-account rules through variance alone.
- Fortraders evaluating whether a prop firm's challenge economics favor the firm even against a coin-flip strategy.
- NeedsPython and pip; everything else is configured in a local config.yaml, no account or key.
- RunsPython scripts (main.py, funded_analysis.py, unified_analysis.py) run from a local clone, simulating thousands of trade sequences per run.
- Limitthe README's own clone command points at the maintainer's old GitHub username — use the current repo URL instead.
Vibe it
Paste this into your AI agent. Write the first line, or take a suggestion; the copy carries your words.
Source: https://github.com/gabrielee5/prop-firm-simulator — clone it and read its README and examples before writing anything; its documented setup beats what you remember. Stack: Python scripts driven by a config.yaml (firm rules, trading assumptions, fee model), running thousands of simulated trade sequences per pass/fail estimate. Needs: Python and `pip install -r requirements.txt`; no account or API key. Start: `git clone https://github.com/gabrielee5/prop-firm-simulator.git`, edit config.yaml with the firm's actual loss limits and profit target, then run `python3 main.py`. Limit: the README's own clone command points at the maintainer's old GitHub username (gabrielefabietti) — use the URL above instead. Done means: you show me the pass rate and estimated funded-account value for the firm and R:R I gave you, and the config.yaml you edited. Never place a live order. Paper or testnet until I say live in this chat. Before you start, ask me: which prop firm's rules to model (loss limits, profit target, challenge cost, payout percentage) and which R:R ratios to test.