QuantDinger
Open-source AI-driven quantitative trading platform for crypto, stocks, and forex with backtesting, live trading, market data, and multi-agent research.vibe-trading and trading agents
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QuantDinger
Your Private AI Quant Operating System
One deployable stack for charting, AI market research, Python indicators & strategies, backtests, and live execution—on your own servers and your own keys.
Self-hosted quantitative platform: from idea and AI-assisted coding to paper-style workflows and exchange-connected live trading, with optional multi-user and billing primitives for operators.
English · 简体中文 · 日本語 · 한국어 · ไทย · Tiếng Việt · العربية
SaaS · Video Demo · Website · AWS Marketplace
Contents
Quick start · Repositories · AI agents & MCP · Overview · Features · Visual tour · Architecture · Install · Docs · FAQ · License
QuantDinger is a self-hosted, local-first quantitative platform: AI-assisted research, Python-native strategies, backtesting, and live trading (crypto, IBKR stocks, MT5 forex) in one product—not a loose collection of scripts and SaaS tabs.
End-to-end architecture: market data feeds the five-layer engine and exits to live execution, closing the quant loop from idea to monitoring.
Try in 2 minutes
Prerequisites: Docker with Compose (Docker Desktop on Windows/macOS, or Docker Engine + Compose plugin on Linux), and Git. Node.js is not required (prebuilt UI is in frontend/dist).
macOS / Linux (Bash)
One line (or run the same steps separately):
git clone https://github.com/brokermr810/QuantDinger.git && cd QuantDinger && cp backend_api_python/env.example backend_api_python/.env && chmod +x scripts/generate-secret-key.sh && ./scripts/generate-secret-key.sh && docker-compose up -d --build
If ./scripts/generate-secret-key.sh fails with “Permission denied”, run chmod +x scripts/generate-secret-key.sh and retry. If docker-compose is not found, try docker compose (Compose V2).
Windows (PowerShell)
Use PowerShell (not CMD) in a folder where you want the project. Docker Desktop must be running (WSL2 backend recommended).
git clone https://github.com/brokermr810/QuantDinger.git
Set-Location QuantDinger
Copy-Item backend_api_python\env.example -Destination backend_api_python\.env
$key = & python -c "import secrets; print(secrets.token_hex(32))" 2>$null
if (-not $key) { $key = & py -c "import secrets; print(secrets.token_hex(32))" 2>$null }
if (-not $key) { $key = & python3 -c "import secrets; print(secrets.token_hex(32))" 2>$null }
if (-not $key) { Write-Error "Install Python 3 from python.org (tick 'Add to PATH') or use Git Bash with the macOS/Linux block above." }
(Get-Content backend_api_python\.env) -replace '^SECRET_KEY=.*$', "SECRET_KEY=$key" | Set-Content backend_api_python\.env -Encoding utf8
docker-compose up -d --build
If docker-compose is not recognized, use docker compose (space, no hyphen). If Git is missing, install Git for Windows.
Windows alternative: Git Bash
If you installed Git for Windows, open Git Bash and you can use the macOS / Linux one-liner above (Bash + chmod + ./scripts/generate-secret-key.sh).
Then open http://localhost:8888, sign in with quantdinger / 123456, and change the default admin password before any real use. For prerequisites, configuration details, first-run checks, and troubleshooting, continue to Installation & first-time setup below.
Related repositories
This monorepo ships the backend, Docker Compose stack, documentation, and a prebuilt web UI under frontend/dist. Use the sibling repos when you need source-level UI changes or the mobile app:
| Repository | What it is |
|---|---|
| QuantDinger (this repo) | Backend (Flask/Python), deployment, docs, bundled web assets |
| QuantDinger-Vue | Web frontend source (Vue)—themes, forks, npm run build → replace frontend/dist |
| QuantDinger-Mobile | Open-source mobile client—pairs with your self-hosted or SaaS backend |
Note: Node.js is only required if you build the web UI from QuantDinger-Vue; the default Docker quick start does not need it.
Use it from an AI agent (Cursor / Claude Code / Codex / MCP)
QuantDinger ships an Agent Gateway at /api/agent/v1 and a small MCP server that wraps it as Model Context Protocol tools. Once you sign in once and issue a token, your AI client can read markets, manage strategies, run backtests, and (paper-only by default) place trades — without ever seeing your exchange keys or your admin JWT.
Two safety properties are non-negotiable: every agent call is audit-logged, and trading-class tokens are paper-only by default. Live execution requires both
paper_only=falseon the token ANDAGENT_LIVE_TRADING_ENABLED=trueon the server.
Step 1 — Get an agent token (two paths, your choice)
The MCP client and the wiring in Step 2 are identical for both paths — only the value of QUANTDINGER_BASE_URL changes.
Path A · Hosted (ai.quantdinger.com) — try it in 30 seconds. Sign up → open Sidebar → Agent Tokens → Issue Token. The hosted instance is locked to paper_only=true and the T (Trading) scope is rejected at issuance — agents can read markets, manage strategies in your tenant, and run backtests, but never route real-money orders. Set QUANTDINGER_BASE_URL=https://ai.quantdinger.com. Best for: trying QuantDinger from Cursor / Claude Code without installing anything; demos; research notebooks against shared datasets.
Path B · Self-hosted (this repo) — production / private data / live trading. After the Try in 2 minutes Docker bring-up, log in as admin and open Sidebar → Agent Tokens (or http://localhost:8888/#/agent-tokens). You decide scopes (incl. T), market/instrument allowlists, rate limits, and whether AGENT_LIVE_TRADING_ENABLED=true is ever flipped. Set QUANTDINGER_BASE_URL=http://localhost:8888 (or your LAN URL). Best for: anyone with their own exchange keys, anyone with private strategies/data, teams behind a VPN, or anyone who eventually wants live execution.
In either path:
- Click Issue Token → name it (
cursor-mcp,claude-research, …). - Pick scopes — start with R + B (read + backtest); add W to let the agent create/edit strategies.
- Copy the token once — the dialog shows the full string once; the server only keeps a SHA-256 hash.
Prefer the CLI? See docs/agent/AGENT_QUICKSTART.md for the equivalent curl.
Step 2 — Wire the MCP server into your AI client
The MCP server lives in mcp_server/. Two transports work everywhere:
A. Local stdio (Cursor, Claude Code, Codex desktop, etc.) — the server is published on PyPI as quantdinger-mcp. Drop this into .cursor/mcp.json, ~/.config/claude/claude_desktop_config.json, or your client's equivalent (template: docs/agent/cursor-mcp.example.json):
{
"mcpServers": {
"quantdinger": {
"command": "uvx",
"args": ["quantdinger-mcp"],
"env": {
"QUANTDINGER_BASE_URL": "http://localhost:8888",
"QUANTDINGER_AGENT_TOKEN": "qd_agent_xxxxxxxx"
}
}
}
}
uvx (install uv) downloads + caches the package on first run; no virtualenv setup. If you prefer pip:
pip install quantdinger-mcp
# then use {"command": "quantdinger-mcp", "args": []}
For Claude Code's CLI helper:
claude mcp add quantdinger \
--env QUANTDINGER_BASE_URL=http://localhost:8888 \
--env QUANTDINGER_AGENT_TOKEN=qd_agent_xxxxxxxx \
-- uvx quantdinger-mcp
B. Remote HTTP (cloud agents like OpenClaw / NanoBot, browser IDEs, anything that can't spawn subprocesses) — run the MCP server as a long-lived service, then point clients at the URL:
QUANTDINGER_BASE_URL=https://your-host \
QUANTDINGER_AGENT_TOKEN=qd_agent_xxxxxxxx \
QUANTDINGER_MCP_TRANSPORT=streamable-http \
QUANTDINGER_MCP_HOST=0.0.0.0 \
QUANTDINGER_MCP_PORT=7800 \
quantdinger-mcp
# clients connect to http://your-host:7800
Use QUANTDINGER_MCP_TRANSPORT=sse instead for clients that only speak the older SSE transport. Put a reverse proxy in front for TLS and IP allowlisting.
Step 3 — Talk to your agent
Restart the IDE, then ask things like:
- "Pull the last 90 daily candles for BTC/USDT and tell me what the regime detector says."
- "Backtest the 20/60 SMA crossover on ETH/USDT 4h between 2024-01-01 and 2024-06-30 and stream the result as it runs."
- "Create a strategy named eth-trend-bot, use the indicator I just designed, leave it in
stoppedstate."
Long-running jobs (/api/agent/v1/jobs/{id}/stream) are exposed as SSE so the agent can react to partial results without polling. Every call shows up under Agent Tokens → Audit log with route, scope class, status code, and duration.
Want to use QuantDinger as a coding agent context too?
If you're editing this repo with Cursor / Claude Code / Codex, the repo also ships a Cursor Skill at .cursor/skills/quantdinger-agent-workflow/SKILL.md that explains the Agent Gateway internals, red lines (no real keys, paper-only by default), and where to verify changes. Read docs/agent/AGENT_ENVIRONMENT_DESIGN.md for the full layered-contracts model.
Deeper links: AI Integration design · Quickstart with curl · OpenAPI 3.0 spec · MCP server README
Product overview
QuantDinger is a self-hosted quantitative OS: AI-assisted research, Python-native strategies (IndicatorStrategy + ScriptStrategy), backtesting, and live trading (crypto, IBKR, MT5)—with optional multi-user roles, notifications, credits, and USDT billing. It replaces a patchwork of charts, notebooks, bots, and disconnected LLM chats with one Compose stack and your credentials in Postgres + .env.
| Typical DIY stack | QuantDinger |
|---|---|
| Chat AI separate from execution | Analysis, NL→code, backtests, and execution in one product |
| Many tools wired by hand | Nginx + Vue UI, Flask API, workers, exchange/LLM adapters |
| Opaque SaaS keys | Your infra, your exchange keys, your LLM keys |
Audience: traders and quants, Python strategy authors, small teams building internal or commercial trading products.
Visual Tour
▶ Watch Product Demo on YouTube Click the preview card above to open the full video walkthrough. | |
![]() Indicator IDE, charting, backtest, and quick trade | ![]() AI asset analysis and opportunity radar |
![]() Trading bot workspace and automation templates | ![]() Strategy live operations, performance, and monitoring |
Features at a glance
- Research & AI — Multi-LLM analysis, watchlists, analysis history; optional ensemble/calibration; NL→indicator/strategy; post-backtest AI hints; Polymarket as a research workflow. Agent Gateway + MCP for Cursor / Claude Code / Codex.
- Build —
IndicatorStrategy(dataframe signals, chart overlays) andScriptStrategy(on_bar, explicit orders); professional chart UI. - Validate — Server-side backtests, metrics, equity curves, strategy snapshots.
- Operate — Crypto execution, quick trade, IBKR / MT5, notifications (Telegram, email, SMS, Discord, webhooks).
- Platform — Docker Compose, Postgres, Redis, OAuth, multi-user patterns, credits / membership / USDT billing toggles.
Architecture
Stack: Nginx serves the prebuilt Vue app (frontend/dist); Flask API runs strategy/AI/billing services; PostgreSQL holds state; Redis backs workers. Exchanges, brokers, LLMs, and payments plug in through env-driven adapters. Crypto market data and order execution paths are separated by design.
Runtime (short): data feeds → backtest/strategy engine → live runtime → exchange adapters; pending orders dispatched per venue.
System diagram
flowchart LR
U[Trader / Operator / Researcher]
subgraph FE[Frontend Layer]
WEB[Vue Web App]
NG[Nginx Delivery]
end
subgraph BE[Application Layer]
API[Flask API Gateway]
AI[AI Analysis Services]
STRAT[Strategy and Backtest Engine]
EXEC[Execution and Quick Trade]
BILL[Billing and Membership]
end
subgraph DATA[State Layer]
PG[(PostgreSQL 16)]
REDIS[(Redis 7)]
FILES[Logs and Runtime Data]
end
subgraph EXT[External Integrations]
LLM[LLM Providers]
EXCH[Crypto Exchanges]
BROKER[IBKR / MT5]
MARKET[Market Data / News]
PAY[TronGrid / USDT Payment]
NOTIFY[Telegram / Email / SMS / Webhook]
end
U --> WEB
WEB --> NG --> API
API --> AI
API --> STRAT
API --> EXEC
API --> BILL
AI --> PG
STRAT --> PG
EXEC --> PG
BILL --> PG
API --> REDIS
API --> FILES
AI --> LLM
AI --> MARKET
EXEC --> EXCH
EXEC --> BROKER
BILL --> PAY
API --> NOTIFY
Installation & first-time setup (Docker Compose)
Fast path: Try in 2 minutes first. The steps below are the full checklist (same outcome, more detail).
This section mirrors a typical “local deploy” path: prepare the host → obtain the code → configure secrets → start the stack → verify → harden → optionally wire AI. Node.js is not required: the repo ships a prebuilt UI under frontend/dist and Nginx serves it inside the frontend container.
Prerequisites
| Item | Notes |
|---|---|
| Docker + Docker Compose v2 | Used for Postgres, Redis, API, and static UI. |
git | To clone this repository. |
| Ports (defaults) | 8888 (web), 5000 (API, bound to 127.0.0.1), 5432 / 6379 (DB/Redis, loopback by default). Change via root .env if they collide. |
| Disk | Postgres volume grows with users, strategies, and logs; plan a few GB minimum for serious use. |
1) Clone the repository
git clone https://github.com/brokermr810/QuantDinger.git
cd QuantDinger
2) Create backend configuration (mandatory)
cp backend_api_python/env.example backend_api_python/.env
Almost all runtime behavior is driven by backend_api_python/.env (database URL, admin user, LLM keys, workers, billing toggles, etc.). The optional repository root .env only adjusts Compose-level concerns such as ports and image mirrors (IMAGE_PREFIX).
3) Set SECRET_KEY before the first boot (mandatory)
The API refuses to start if SECRET_KEY is still the placeholder from env.example. This blocks accidental insecure deployments.
Linux / macOS (recommended):
./scripts/generate-secret-key.sh
The script overwrites the SECRET_KEY= line in backend_api_python/.env using Python’s secrets module.
Manual (any OS): generate a long random string (for example 64 hex chars) and set SECRET_KEY=... in backend_api_python/.env.
4) Start the stack
docker-compose up -d --build
Services: postgres, redis, backend, frontend (see docker-compose.yml for healthchecks and port mappings).
5) Verify and sign in
| Check | URL / command |
|---|---|
| Web UI | http://localhost:8888 (override host/port with FRONTEND_HOST / FRONTEND_PORT in root .env if needed). |
| API health | http://localhost:5000/api/health |
| Logs | docker-compose logs -f backend |
Default admin (change immediately in production):
- User:
quantdinger - Password:
123456(fromenv.example; override withADMIN_USER/ADMIN_PASSWORDin.envbefore first use if you prefer).
Also set FRONTEND_URL in backend_api_python/.env to the URL users actually use (including https:// behind a reverse proxy); it affects redirects, CORS-related settings, and some generated links.
6) Optional: enable AI features
AI analysis, NL→code, and related flows need at least one LLM provider configured. Open backend_api_python/env.example, find the AI / LLM block, copy the relevant keys into your .env (for example LLM_PROVIDER + OPENROUTER_API_KEY, or another supported provider). Restart the backend after edits.
7) Windows notes
Use Docker Desktop (WSL2 backend recommended). From PowerShell in the repo root:
git clone https://github.com/brokermr810/QuantDinger.git
cd QuantDinger
Copy-Item backend_api_python\env.example -Destination backend_api_python\.env
$key = py -c "import secrets; print(secrets.token_hex(32))"
(Get-Content backend_api_python\.env) -replace '^SECRET_KEY=.*$', "SECRET_KEY=$key" | Set-Content backend_api_python\.env -Encoding UTF8
docker-compose up -d --build
If py is not on PATH, use python or python3 in the one-liner that generates $key. Line endings should remain UTF-8; avoid editors that strip newlines from .env.
Troubleshooting (first boot)
| Symptom | What to check |
|---|---|
| Backend exits immediately | SECRET_KEY still default, or invalid .env syntax. Read docker-compose logs backend. |
| Blank page or API errors from browser | FRONTEND_URL / origins mismatch; API not reachable from the host you opened. |
| Port already in use | Another Postgres, Redis, or local service on 5432 / 6379 / 5000 / 8888. Adjust variables in root .env per docker-compose.yml. |
| Many live strategies, “start denied” | Raise STRATEGY_MAX_THREADS in backend_api_python/.env and restart API (see comments in env.example). |
Common Docker commands
docker-compose ps
docker-compose logs -f backend
docker-compose restart backend
docker-compose up -d --build
docker-compose down
Optional root .env (Compose only)
For custom ports or mirror/prefix for base images (slow Docker Hub pulls), create a file named .env in the repository root (same directory as docker-compose.yml):
FRONTEND_PORT=3000
BACKEND_PORT=127.0.0.1:5001
IMAGE_PREFIX=docker.m.daocloud.io/library/
Production-style TLS, domain, and reverse-proxy placement are covered in Cloud deployment.
Suggested first session (product walkthrough)
After the stack is healthy: (1) run an AI asset / market analysis so LLM and data paths are verified; (2) open the Indicator IDE, load a symbol, and run a signal backtest on a small date range; (3) optionally use AI code generation to draft an indicator, then edit the Python; (4) when ready, attach exchange API keys (profile / credentials), use test connection, then explore live strategy or quick trade with execution mode you intend. This order surfaces configuration issues early before real capital.
Minimal Example: Python Indicator Strategy
This is the kind of Python-native strategy logic QuantDinger is designed for:
# @param sma_short int 14 Short moving average
# @param sma_long int 28 Long moving average
sma_short_period = params.get('sma_short', 14)
sma_long_period = params.get('sma_long', 28)
my_indicator_name = "Dual Moving Average Strategy"
my_indicator_description = f"SMA {sma_short_period}/{sma_long_period} crossover"
df = df.copy()
sma_short = df["close"].rolling(sma_short_period).mean()
sma_long = df["close"].rolling(sma_long_period).mean()
buy = (sma_short > sma_long) & (sma_short.shift(1) <= sma_long.shift(1))
sell = (sma_short < sma_long) & (sma_short.shift(1) >= sma_long.shift(1))
df["buy"] = buy.fillna(False).astype(bool)
df["sell"] = sell.fillna(False).astype(bool)
See full examples:
docs/examples/dual_ma_with_params.pydocs/examples/multi_indicator_composite.pydocs/examples/cross_sectional_momentum_rsi.py
Supported Markets, Brokers, and Exchanges
Crypto Exchanges
| Venue | Coverage |
|---|---|
| Binance | Spot, Futures, Margin |
| OKX | Spot, Perpetual, Options |
| Bitget | Spot, Futures, Copy Trading |
| Bybit | Spot, Linear Futures |
| Coinbase | Spot |
| Kraken | Spot, Futures |
| KuCoin | Spot, Futures |
| Gate.io | Spot, Futures |
| Deepcoin | Derivatives integration |
| HTX | Spot, USDT-margined perpetuals |
Traditional Markets
| Market | Broker / Source | Execution |
|---|---|---|
| US Stocks | IBKR, Yahoo Finance, Finnhub | Via IBKR |
| Forex | MT5, OANDA | Via MT5 |
| Futures | Exchange and data integrations | Data and workflow support |
Prediction Markets
Polymarket is currently supported as a research and analysis workflow, not as direct in-platform live execution. It is useful for market lookup, divergence analysis, opportunity scoring, and AI-assisted review.
Strategy Development Modes
QuantDinger supports two main strategy authoring models:
IndicatorStrategy
- dataframe-based Python scripts
buy/sellsignal generation- chart rendering and signal-style backtests
- best for research, indicator logic, and visual strategy prototyping
ScriptStrategy
- event-driven
on_init(ctx)/on_bar(ctx, bar)scripts - explicit runtime control with
ctx.buy(),ctx.sell(),ctx.close_position() - best for stateful strategies, execution-oriented logic, and live alignment
For the full developer workflow, see:
The example scripts live in docs/examples/ and are kept aligned with the current strategy development guides.
Repository Layout
QuantDinger/
├── backend_api_python/ # Open backend source code
│ ├── app/routes/ # REST endpoints
│ ├── app/services/ # AI, trading, billing, backtest, integrations
│ ├── migrations/init.sql # Database initialization
│ ├── env.example # Main environment template
│ └── Dockerfile
├── frontend/ # Prebuilt web UI (sources: QuantDinger-Vue; mobile app: QuantDinger-Mobile)
│ ├── dist/
│ ├── Dockerfile
│ └── nginx.conf
├── docs/ # Product, strategy, and deployment documentation
├── docker-compose.yml
├── LICENSE
└── TRADEMARKS.md
Configuration Areas
Use backend_api_python/env.example as the primary template. Key areas include:
| Area | Examples |
|---|---|
| Authentication | SECRET_KEY, ADMIN_USER, ADMIN_PASSWORD |
| Database | DATABASE_URL |
| LLM / AI | LLM_PROVIDER, OPENROUTER_API_KEY, OPENAI_API_KEY |
| OAuth | GOOGLE_CLIENT_ID, GITHUB_CLIENT_ID |
| Security | TURNSTILE_SITE_KEY, ENABLE_REGISTRATION |
| Billing | BILLING_ENABLED, BILLING_COST_AI_ANALYSIS |
| Membership | MEMBERSHIP_MONTHLY_PRICE_USD, MEMBERSHIP_MONTHLY_CREDITS |
| USDT Payment | USDT_PAY_ENABLED, USDT_TRC20_XPUB, TRONGRID_API_KEY |
| Optional data APIs | TWELVE_DATA_API_KEY, FINNHUB_API_KEY, TIINGO_API_KEY, ADANOS_API_KEY |
| Proxy | PROXY_URL |
| Workers | ENABLE_PENDING_ORDER_WORKER, ENABLE_PORTFOLIO_MONITOR, ENABLE_REFLECTION_WORKER |
| AI tuning | ENABLE_AI_ENSEMBLE, ENABLE_CONFIDENCE_CALIBRATION, AI_ENSEMBLE_MODELS |
Documentation
| Doc | Notes |
|---|---|
| Changelog | Releases & migrations |
| README (中文) | Chinese overview |
| JA · KO · TH · VI · AR | Concise localized READMEs (Japanese, Korean, Thai, Vietnamese, Arabic) |
| Cloud deployment | HTTPS, reverse proxy, production |
| Multi-user | Postgres multi-tenant patterns |
| Agent environment · AI integration · Quickstart · OpenAPI · MCP server | Coding agents & MCP (quantdinger-mcp on PyPI) |
Strategy: EN · CN · TW · JA · KO · Cross-sectional EN / CN · Examples
Integrations & alerts: IBKR · MT5 EN / CN · OAuth EN / CN · Telegram / Email / SMS configs under docs/ (NOTIFICATION_*).
FAQ
Is QuantDinger really self-hosted?
Yes. The default deployment model is your own Docker Compose stack with your own database, Redis instance, credentials, and environment configuration.
Is QuantDinger only for crypto trading?
No. Crypto is a major focus, but the platform also includes IBKR workflows for US stocks, MT5 workflows for forex, and Polymarket research support.
Can I write strategies directly in Python?
Yes. QuantDinger supports both dataframe-style IndicatorStrategy development and event-driven ScriptStrategy development. You can also use AI to generate a starting point and then edit it yourself.
Is this a research tool or a live trading platform?
It is both. QuantDinger is built to connect AI research, charting, strategy development, backtesting, quick trade flows, and live execution operations in one system.
Can I use QuantDinger commercially?
The backend is licensed under Apache 2.0. The web frontend source (QuantDinger-Vue) uses a separate source-available license—review both and contact the project for commercial frontend authorization if needed. The mobile app repo is open source under its own license (see that repository).
Is there a mobile app?
Yes—see QuantDinger-Mobile (open source). It connects to the same backend you self-host or to SaaS.
Exchange Partner Links
The following links are available in-app under Profile -> Open account and may qualify users for trading-fee rebates depending on venue policies.
| Exchange | Signup Link |
|---|---|
| Binance | Register |
| Bitget | Register |
| Bybit | Register |
| OKX | Register |
| Gate.io | Register |
| HTX | Register |
License and Commercial Terms
- Backend source code is licensed under Apache License 2.0. See
LICENSE. - This repository distributes the frontend UI here as prebuilt files for integrated deployment.
- The frontend source code is available separately at QuantDinger Frontend under the QuantDinger Frontend Source-Available License v1.0.
- Under that frontend license, non-commercial use and eligible qualified non-profit use are permitted free of charge, while commercial use requires a separate commercial license from the copyright holder.
- Trademark, branding, attribution, and watermark usage are governed separately and may not be removed or altered without permission. See
TRADEMARKS.md.
For commercial licensing, frontend source access, branding authorization, or deployment support:
- Website: quantdinger.com
- Telegram: t.me/worldinbroker
- Email: support@quantdinger.com
Legal Notice and Compliance
QuantDinger is intended for lawful research, education, and compliant trading only—not for fraud, market manipulation, sanctions evasion, money laundering, or other illegal activity. Operators must follow applicable laws, licensing, and exchange rules in every jurisdiction where they deploy. This project does not provide legal, tax, investment, or regulatory advice. You use the software at your own risk; to the extent permitted by law, contributors disclaim liability for trading losses, service interruption, or regulatory enforcement arising from use or misuse.
Community and Support
Support the Project
Crypto donations:
0x96fa4962181bea077f8c7240efe46afbe73641a7
Star History
Acknowledgements
QuantDinger stands on top of a strong open-source ecosystem. Special thanks to projects such as:
If QuantDinger is useful to you, a GitHub star helps the project a lot.




