profitviews

profitviews / merton-market-maker

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Demo algo showing Python with C++ bindings using compile-time reflection

26
2
89% credibility
Found Feb 23, 2026 at 17 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Jupyter Notebook
AI Summary

An engineering demonstration of a high-speed math model integrated into a Python trading script for real-time cryptocurrency market making using the Merton Jump Diffusion approach.

How It Works

1
📖 Discover the idea

You come across this clever demo showing how to blend quick math with easy trading scripts for smarter crypto decisions.

2
🛠️ Get your tools ready

You grab a few simple helpers like a container toolbox and a recipe runner to prepare your computer.

3
⚡ Build the speed wizard

You create a hidden super-fast calculator that handles complex price predictions without slowing down.

4
📝 Paste into your trader

You copy the ready-made script into your trading bot platform and tweak a few starting numbers.

5
🔗 Connect to Bitcoin trading

You link it to the crypto exchange for Bitcoin perpetuals and set it as a market maker.

🎉 Bot comes alive

Your bot starts watching prices, calculating fair values, and quoting smart buy-sell offers automatically.

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AI-Generated Review

What is merton-market-maker?

This algorithm demo builds a market-making bot for BitMEX XBTUSDT perpetuals, computing theoretical fair value from a Merton Jump Diffusion model and quoting around it in real-time. It solves the tension between Python's rapid iteration for trading logic and C++'s low-latency math for online model calibration, using PyBind11 bindings to expose C++ performance directly in Python scripts. Deploy it in a platform like ProfitView to stream quotes and dashboard metrics from live ticks.

Why is it gaining traction?

The killer hook is C++26 compile-time reflection automating Python bindings—no manual boilerplate when tweaking C++ models, making hybrid algo trading workflows practical for daily use. As a demo algo trading repo with Dockerized experimental Clang builds, it lowers the barrier to test high-perf pricing in Python environments. Devs dig the end-to-end example, from tick ingestion to fair-value signals, with QuantLib validation baked in.

Who should use this?

Algo traders or quants prototyping jump-diffusion strategies for crypto perps, especially those bridging Python backtests to C++ execution. Teams at high-frequency shops evaluating C++26 for Python extensions in live trading bots. Not for pure Python shops avoiding experimental compilers.

Verdict

Grab it as a demo github repo for C++-Python algo bindings if you're into cutting-edge reflection—10 stars signal early days, but solid README and container setup make it runnable fast. Credibility score of 0.9% reflects niche maturity; fork and extend for production.

(187 words)

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