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godzilla.dev is an open-source framework for running self-hosted crypto trading strategies, with a focus on funding rate arbitrage and ultra low-latency market making. It pairs a C++ execution core (microsecond-level tick-to-trade latency, designed for co-located deployment) with a Python strategy layer for fast iteration. Unlike hosted platforms or retail bots, godzilla.dev is local software: API keys, private keys, and strategy configuration never leave machines you control. The codebase is maintained by the Godzilla Foundation under the Apache 2.0 license, with enterprise private deployment available for institutional funding rate arbitrage teams.
godzilla.dev ๆฏไธไธชๅผๆบ็ๅ ๅฏ่ดงๅธไบคๆๅบ็ก่ฎพๆฝๆกๆถ๏ผไธปๆ่ต้่ดน็ๅฅๅฉๅ่ถ ไฝๅปถ่ฟๅๅธใๆถๆไธๆฏ C++ ๆง่กๅ ๆ ธ๏ผๅพฎ็ง็บง tick-to-trade ๅปถ่ฟ๏ผไธบไบคๆๆๅๆบๆฟ้จ็ฝฒ่ฎพ่ฎก๏ผๅ Python ็ญ็ฅๅฑ๏ผๅ ผ้กพๆง่ฝๅ่ฟญไปฃ้ๅบฆใๅฎไธๆฏๆ็ฎกๅนณๅฐไนไธๆฏ้ถๅฎๆบๅจไบบ๏ผ่ๆฏๆฌๅฐ้จ็ฝฒ่ฝฏไปถโโAPI keyใ็ง้ฅๅ็ญ็ฅ้ ็ฝฎๅ จ็จไธ็ฆปๅผไฝ ่ชๅทฑ็ๆบๅจใไปฃ็ ็ฑ Godzilla Foundation ไปฅ Apache 2.0 ๅ่ฎฎๅผๆบ๏ผๅฆ้ขๅๆบๆๅฅๅฉๅข้ๆไพไผไธ็บง็งๆๅ้จ็ฝฒใ
Links Website: https://godzilla.dev Documentation: https://godzilla.dev/documentation/ Installation guide: https://godzilla.dev/documentation/installation/ Source code: https://github.com/godzilla-foundation/godzilla-community PyPI (CLI): https://pypi.org/project/godzilla.dev/ Enterprise private deployment: https://godzilla.dev/enterprise/ License Apache 2.0 โ free for personal, research, and commercial use. Maintained by the Godzilla Foundation.
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godzilla.dev's answer:
godzilla.dev pairs a C++ execution core (microsecond-scale tick-to-trade latency on the local software path, designed for co-located deployment) with a Python strategy layer for fast iteration. It is fully self-hosted: API keys, private keys, and strategy configuration never leave machines you control. Most open-source trading frameworks optimize for connector breadth and ease of use; godzilla.dev optimizes for latency-critical, production-grade execution.
godzilla.dev's answer:
Choose godzilla.dev if execution latency and self-custody of infrastructure matter more to you than connector breadth. Frameworks like Hummingbot are excellent for connecting many venues quickly with ready-made strategies. godzilla.dev focuses on a different problem: running funding rate arbitrage and market making with a native C++ hot path, a journal-based event architecture with deterministic replay, and no cloud backend. If you need dozens of exchange connectors out of the box, a breadth-focused framework may serve you better โ the two design goals are genuinely different.
godzilla.dev's answer:
Quantitative trading teams, individual quants with systems programming experience, and institutions (exchanges, market makers, arbitrage desks) that need self-hosted trading infrastructure. Users typically run delta-neutral funding rate arbitrage, low-latency market making, or custom strategies on centralized crypto exchanges, and prefer local deployment over hosted platforms for security and latency reasons.
godzilla.dev's answer:
godzilla.dev grew out of production trading infrastructure built for exchange-scale liquidity provision, inventory hedging, and cross-market funding rate arbitrage. The architecture โ separate market-data, strategy, and trade processes communicating over a shared-memory event journal โ was shaped by years of operating latency-sensitive strategies in production, then open-sourced by the Godzilla Foundation under Apache 2.0 so that the same infrastructure is auditable and available to independent traders and institutions alike.
godzilla.dev's answer:
C++ (execution core: market data, order routing, shared-memory journal), Python (strategy layer via pybind11), CMake, running on Linux. Distributed via GitHub, PyPI (pip install godzilla.dev), and Docker Hub.
godzilla.dev's answer:
A top-10 derivatives exchange (by trading volume) โ multi-year production deployment covering liquidity provision, inventory hedging, and cross-market funding rate arbitrage across 1,000+ trading pairs (identity confidential)
Independent quantitative traders and small arbitrage teams running self-hosted deployments
Based on our record, Django seems to be more popular. It has been mentiond 16 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
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Let's dive into a quick implementation of this using AWS and Django. We will be using a couple of ideas from the AWS Official Blog. - Source: dev.to / almost 2 years ago
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