The open-source, AI-native quant trading OS. Research markets, build & backtest Python strategies, deploy live across stocks and crypto. @Dingerquant
Open Source · Worldwide
Joined July 2024
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QuantDinger v5.7.1 is here.
Turn a trading idea into a complete, testable workflow—from AI-assisted research and editable Python strategies to backtesting, execution, monitoring, and customer delivery.
QuantDinger brings the entire quantitative trading lifecycle into one connected platform:
AI-assisted market research
Editable Python strategies
Reproducible backtesting
Paper and live trading workflows
Orders, positions, risk, and performance monitoring
Multi-user platform operations
Memberships, credits, and commercial services
Private deployment for quant teams
One idea. One connected workflow. From strategy to service.
Explore QuantDinger: quantdinger.com
GitHub: github.com/OpenByteInc/Quant…
#QuantDinger #AlgorithmicTrading #QuantTrading #AITrading #OpenSource #PythonTrading #FinTech
Most AI market tools give you a confident paragraph. We wanted a visible research process.
QuantDinger cross-checks market data, fundamentals, filings, news and macro context—then builds bull/base/bear cases, risk triggers and an execution plan.
Research you can inspect, not just trust.
ai.quantdinger.com/#/report/…
Your indicator shouldn’t stop at a signal.
In QuantDinger, indicators run directly on the chart—and Quick Trade opens beside them with live depth, bid/ask pressure, liquidity walls, slippage and order impact.
Read the signal. Check liquidity. Execute in one view. ⚡
quantdinger.com
#QuantTrading #TradingIndicators #OpenSourceHiggsfield
Idea → AI → running trading bot. No code. 🤖📈
Open source, runs on your own server. Link in bio.
#algotrading #aitools #tradingbot #opensource #crypto
Most AI trading tools stop at analysis.
QuantDinger closes the loop:
→ AI research
→ Python strategies
→ Cost-aware backtests
→ Paper & live execution
→ Deterministic risk controls
Open-source. Self-hosted. Built for real workflows.
github.com/OpenByteInc/Quant…
AI can research markets and write strategies. The harder part is what comes next: risk, permissions, execution, and accountability.
That’s why we’re building QuantDinger—an open-source AI quant trading OS for traders, teams, and agents.
github.com/OpenByteInc/Quant…
【INSTALLATION GUIDE】How to Install QuantDinger on Linux | Self-Hosted AI Quant Trading OS
youtu.be/mSdJiSz9VMo
What exactly is QuantDinger? If we were to list the most important project that the quantitative finance community should not overlook in 2026, it would undoubtedly be QuantDinger.
youtu.be/y9EiCZSaIt0
Thrilled that OpenByte Inc. has been accepted into the NVIDIA Inception Program.
More GPU acceleration coming for QuantDinger. 🚀
QuantDinger is now on AWS Marketplace 🚀
Turn trading ideas into strategies with AI research, backtesting and live execution—all in your own AWS account.
$0 software fees. AWS infrastructure charges apply.
Get started 👇
aws.amazon.com/marketplace/p…
#OpenSource #QuantTrading
Security and compliance claims you can inspect.
From open-source security to compliance governance, every engineering claim is backed by public evidence. Open Byte Inc. applies internationally recognized open-source standards and maintains traceable procedures, reviews and release controls for reliable QuantDinger delivery.
github.com/OpenByteInc/Quant…
QuantDinger has achieved the OpenSSF Best Practices Passing badge.
This is not a “zero-risk” certification, but a public signal that we have established basic security governance for the project: automated dependency scanning, coordinated vulnerability disclosure, secure release workflow, and community contribution rules.
For a quant trading infrastructure project, security is not a one-time audit. It’s a continuous process.
QuantDinger v5.6.1 is live 🚀
✨ Refined brand and interface
⚡ Richer Quick Trade position data
🌙 Better dark mode consistency
🌍 Multilingual AI Skills
🛡️ Stronger registration abuse protection
💳 Redesigned membership experience
Build smarter. Trade with clarity.
#QuantDinger #AlgoTrading #FinTech
Big upgrade shipped for QuantDinger today 🚀
✅ TA-Lib 0.8.1 with new indicators across Factor Library, AI Research, and multilingual UI
✅ Unified factor engine with skewness, kurtosis, and multi-dimensional risk analysis
✅ Point-in-time computation to eliminate look-ahead bias
✅ Stronger multi-exchange position reconciliation to prevent “ghost positions”
✅ Cleaner architecture, dependencies, and PR backlog
3,430 backend tests + 389 frontend tests passed, along with CodeQL and security scans.
QuantDinger is evolving from a quant toolkit into a complete AI-powered system for research, validation, and execution.
#QuantDinger #AITrading #QuantTrading #FinTech #OpenSource
A quick breakdown between two top open-source quant projects:
NautilusTrader is a high-performance trading engine building block. Built in Rust with a single-threaded event-driven actor core. It eliminates race conditions and guarantees research-to-live consistency. It’s designed for quant engineers to build their own trading systems from scratch. No native web UI, no built-in cluster orchestration or strategy marketplace in the open-source release.
QuantDinger is a full distributed multi-cluster quant hosting platform, a ready-to-deploy product.
It decouples workloads into independent workers over Kafka event backbone, supports horizontal scaling to host up to 100,000 concurrent strategies. It ships with web/H5 frontend, native MCP AI Agent gateway, distributed safety primitives (fencing tokens, leases, checkpoints), audit logs, and a built-in strategy marketplace.
Simply put:
NautilusTrader = engine components for custom in-house trading infrastructure.
QuantDinger = end-to-end distributed quant platform with UI, multi-cluster & AI Agent, ready to run a large-scale strategy marketplace.
#QuantTrading #OpenSourceQuant #NautilusTrader #QuantDinger #SystemArchitecture #AIQuant
QuantDinger’s trading runtime just received a major architectural upgrade 🚀
• Shared, venue-scoped market data
• Event-driven strategy scheduling
• Kafka dispatch + hot evaluator runtimes
• Durable inboxes, leases, fencing & idempotency
• Persistent grid actors
• Native exchange clients
Single-node ready. Horizontally scalable. Built for the path toward 100K+ strategies.
#OpenSource #QuantTrading #Kafka #FinTech
QuantDinger Mobile is now live. 📱
Discover strategies, monitor live runs, research markets with AI, explore indicators, and trade across exchanges—all from your phone.
Try it: m.quantdinger.com
Source: github.com/OpenByteInc/Quant…
#AITrading #AlgoTrading #FinTech
QuantDinger retweeted
Jev builds the BEST trading agents
and someone JUST open sourced QuantDinger, a full AI Trading OS with Jev
your strategy no longer fires blind, every entry gets reviewed by Jev first and bad trades die before they cost you
here is how you set it up:
1. run one install command, it asks for your admin login, generates every secret, and boots the whole stack in docker
2. start in paper mode, backtest your Python strategy on server side data before a single real dollar moves
3. drop your Jev API key into System Settings, AI / LLM, and flip on AI Decision Filter
4. now every entry gets scored across evidence quality, signal consistency, market regime, account risk, and execution quality
5. Jev returns a typed decision with full probabilities and confidence, not a paragraph of text you have to parse
6. rejected entries NEVER reach the exchange, and every decision lands in an audit timeline with the reason and latency
the whole point is this repo hands you the HARDEST part for FREE
> the risk gate between your strategy and your money
Bookmark it, then read my step by step guide below on making money from your own agent team for free
Everyone is asking:
“Can Jev trade?”
We asked a different question:
“Can Jev stop you from making a bad trade?”
There are two ways AI is entering trading:
① AI as the Trader
Feed it market data, technical rules and context.
AI decides BUY / SELL / HOLD and sends the trade.
② AI as the Gatekeeper**
Your quantitative strategy still generates the signal.
But before the order reaches the market, AI gets one final decision:
ALLOW or REJECT?
QuantDinger chose the second path.
Strategy → Signal → Jev Decision Gate → Execution
Why?
Because sometimes the best trade
is the one you never opened.
AI doesn't have to replace quantitative trading.
It can become the layer between *a signal and a mistake.*
We integrated Jev into QuantDinger as a pre-trade decision layer from the start.
Open source. Auditable. Built for real trading workflows.
**Don't let AI trade more.
Let AI help you trade less — but better.**
