QuantDinger

The open-source AI infrastructure layer for quant trading

Turn trading ideas into Python strategies, backtests, paper trading, and live execution - all in one self-hosted stack.

QuantDinger is a product of Open Byte Inc.

AI research -> Strategy code -> Backtest -> Paper/Live execution -> Monitoring

English / Chinese

SaaS  ·  API Docs  ·  Video Demo  ·  Website

     

Try in 2 minutes · Why QuantDinger · Safety model · Technical highlights · Repositories · AI agents & MCP · Overview · Features · Architecture · Install · Docs · FAQ · License

Fastest path: one command. No git clone, no npm, no Vue source tree. The installer asks for the admin account, writes secure secrets, pulls GHCR images, and starts Docker Compose.

Prerequisites: Docker with Compose v2 (Docker Desktop on Windows/macOS). Node.js is not required.

curl -fsSL https://raw.githubusercontent.com/brokermr810/QuantDinger/main/install.sh | bash

Windows PowerShell:

irm https://raw.githubusercontent.com/brokermr810/QuantDinger/main/install.ps1 | iex

Installs to ~/quantdinger on Linux/macOS and $HOME\quantdinger on Windows by default. Override with ... | bash -s -- /opt/quantdinger or $env:QUANTDINGER_INSTALL_DIR="C:\QuantDinger" before the PowerShell one-liner.

Then open http://localhost:8888 and sign in with the admin username/password you entered during installation. The same stack also serves the mobile H5 client at http://localhost:8889.

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 }
(Get-Content backend_api_python\.env) -replace '^SECRET_KEY=.*$', "SECRET_KEY=$key" | Set-Content backend_api_python\.env -Encoding utf8
# Edit backend_api_python\.env before first start:
#   ADMIN_USER=your_admin_user
#   ADMIN_PASSWORD=your_secure_password
docker compose pull
docker compose up -d

git clone https://github.com/brokermr810/QuantDinger.git
cd QuantDinger
cp backend_api_python/env.example backend_api_python/.env
./scripts/generate-secret-key.sh
# Edit backend_api_python/.env before first start:
#   ADMIN_USER=your_admin_user
#   ADMIN_PASSWORD=your_secure_password
docker compose pull
docker compose up -d

For step-by-step detail and troubleshooting, see Installation & first-time setup.

QuantDinger is a self-hosted, local-first quantitative infrastructure layer — not a chatbot with a buy button. It unifies multi-LLM research, Python-native strategy engines, server-side backtesting, and multi-broker live execution across crypto venues, IBKR, and Alpaca in one production-grade stack you fully control.

• Agent tokens are paper-only by default — live trading requires explicit server-side unlock.
• Live execution requires explicit permission — token scope + AGENT_LIVE_TRADING_ENABLED on self-hosted stacks.
• Exchange keys stay inside the user's own deployment — not held by QuantDinger SaaS operators on self-hosted installs.
• Every agent call is audit-logged — append-only audit trail for automation and compliance review.
• QuantDinger does not provide investment advice — software for lawful research and execution only; you are responsible for compliance and risk.

_{From zero to running stack — charting, AI research, and strategy workflow in minutes.}

AI research -> Strategy code -> Backtest -> Paper/Live execution -> Monitoring

curl -O https://raw.githubusercontent.com/brokermr810/QuantDinger/main/docker-compose.ghcr.yml
curl -o backend.env https://raw.githubusercontent.com/brokermr810/QuantDinger/main/backend_api_python/env.example
# Edit backend.env before first start:
#   ADMIN_USER=your_admin_user
#   ADMIN_PASSWORD=your_secure_password
docker compose -f docker-compose.ghcr.yml pull
docker compose -f docker-compose.ghcr.yml up -d

This repo ships the backend, Docker Compose stack, and documentation. The web UI and mobile H5 images are published independently to GHCR by sibling repos. Use the sibling repos when you need source-level UI changes or native mobile builds:

Note: Node.js is only required if you build the web or mobile UI from source; the default Docker quick start pulls the published images and does not need it. For source builds, use Node 22 LTS as the shared local version: the mobile H5 repo requires Node 20.19+ or 22.12+ (Vite 7), and the desktop web repo also works on Node 22.

QuantDinger ships an Agent Gateway at /api/agent/v1 plus a small MCP server (quantdinger-mcp on PyPI) that wraps it as Model Context Protocol tools. Issue a token once and 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.

Every agent call is audit-logged, and trading-class tokens are paper-only by default. Live execution requires both paper_only=false on the token AND AGENT_LIVE_TRADING_ENABLED=true on the server.

Two backends, same client config — only QUANTDINGER_BASE_URL differs:

• Hosted (30 s try-out) — sign up at ai.quantdinger.com → Profile → My Agent Token → Issue Token. T (Trading) scope is available; paper-only by default. Live execution still requires paper_only=false on the token, explicit risk acknowledgment at issuance, and AGENT_LIVE_TRADING_ENABLED=true on the server. On multi-tenant SaaS, opening T scope increases shared infrastructure load and platform operational risk — see the in-app risk disclosure.
• Self-hosted (this repo) — after the Try in 2 minutes Docker bring-up, open Profile → My Agent Token (or the admin-only /agent-tokens page for cross-tenant audit). You control scopes, allowlists, rate limits, and the live-trading flag.

Then point Cursor / Claude Code / Codex at the MCP server (.cursor/mcp.json 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" }
} } }

Full setup recipe — local stdio config, remote HTTP transport, Claude Code CLI helper, example agent prompts, audit-log walkthrough: docs/agent/MCP_SETUP.md.

Deeper references: AI Integration design · Quickstart with curl · OpenAPI 3.0 spec · MCP server README

Audience: independent quants, Python strategy authors, prop/small teams, and operators building white-label quant products on private infrastructure — without handing API keys to a black-box SaaS.

• Research & AI — Multi-LLM ensemble analysis, watchlists, opportunity radar, NL→Indicator/strategy, post-backtest AI hints; optional confidence calibration. Agent Gateway + MCP for Cursor / Claude Code / Codex with scoped tokens and SSE job streaming.
• Build — Professional KLine chart UI; IndicatorStrategy (four-way dataframe signals: open_long, close_long, open_short, close_short) and ScriptStrategy (on_bar, ctx.buy() / ctx.sell()); AI code generation as a starting point, Python as source of truth.
• Validate — Server-side backtests with equity curves, drawdown metrics, trade logs, and strategy snapshots — no client-side-only backtest theater.
• Operate — Live strategy bots, quick trade, crypto spot/swap execution through direct exchange adapters, IBKR / Alpaca workflows for traditional markets; unified Broker Accounts page; notifications (Telegram, email, SMS, Discord, webhooks).
• Platform — Docker Compose + GHCR images, PostgreSQL 16, Redis 7, OAuth, multi-user RBAC, credits / membership / USDT billing toggles, an 11-language web UI, and multilingual documentation.

Design principle: separate market data ingestion, strategy/backtest compute, and order execution so research never shares a code path with live capital unless you explicitly promote a strategy.

Stack: Nginx serves the prebuilt Vue SPA (ghcr.io/brokermr810/quantdinger-frontend); Flask + Gunicorn API hosts strategy, AI, billing, and agent services; PostgreSQL 16 is the system of record; Redis 7 backs cache and worker coordination. Exchanges, brokers, LLMs, and payment rails plug in through env-driven adapters — swap providers without forking core code.

Runtime flow: market feeds → indicator/signal layer → strategy engine → backtest or live runtime → venue-specific execution adapters; pending orders dispatched by background workers with health checks and retry semantics.

Deploy surfaces: one-line install.sh, zero-repo GHCR Compose, full-repo Compose (local backend build), and SaaS at ai.quantdinger.com for trials.

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 / Alpaca]
        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

Already ran Try in 2 minutes? Skip this section — it's the same outcome, just expanded into a step-by-step checklist for first-time deployers and operations folks who want to understand every knob.

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 frontend service pulls ghcr.io/brokermr810/quantdinger-frontend directly, so Nginx serves the SPA without any local build step.

git clone https://github.com/brokermr810/QuantDinger.git
cd QuantDinger

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).

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.

Set the bootstrap administrator before the first start:

ADMIN_USER=your_admin_user
ADMIN_PASSWORD=your_secure_password
ADMIN_EMAIL=optional@example.com

If you leave ADMIN_PASSWORD=123456, the system will treat it as an unsafe bootstrap password and the UI will remind the user to change it. Password changes intentionally still require email verification, so local operators should set a real admin password during installation.

docker compose pull
docker compose up -d

• frontend — pulls ghcr.io/brokermr810/quantdinger-frontend:latest (no local Vue tree required).
• backend — built from ./backend_api_python on first start if no local image exists yet.
• For UI development from Vue source, clone QuantDinger-Vue into ./QuantDinger-Vue/ and add -f docker-compose.build.yml to the command (see Build the frontend from Vue source below).

Services: postgres, redis, backend, frontend, mobile (see docker-compose.yml).

Prebuilt multi-arch (amd64/arm64) images for both backend and frontend — no git clone:

curl -O https://raw.githubusercontent.com/brokermr810/QuantDinger/main/docker-compose.ghcr.yml
curl -o backend.env https://raw.githubusercontent.com/brokermr810/QuantDinger/main/backend_api_python/env.example
docker compose -f docker-compose.ghcr.yml pull
docker compose -f docker-compose.ghcr.yml up -d

The backend entrypoint auto-generates a random SECRET_KEY on first start and applies the schema (migrations/init.sql) idempotently. Edit backend.env for persistent overrides (API keys, OAuth, broker credentials). Compose orchestration knobs go in a separate .env (optional) — e.g. pin a version:

# Common case: lockstep both sides to one tag
IMAGE_TAG=4.0.4

# Advanced (opt-in): decouple sides. Either var alone overrides only
# that side; the other still follows IMAGE_TAG.
# BACKEND_TAG=4.0.4
# FRONTEND_TAG=4.0.4
# MOBILE_TAG=4.0.3

# BACKEND_IMAGE=ghcr.io/<your-fork>/quantdinger-backend     # optional, for forks
# FRONTEND_IMAGE=ghcr.io/<your-fork>/quantdinger-frontend
# MOBILE_IMAGE=ghcr.io/<your-fork>/quantdinger-mobile

Tag resolution: BACKEND_TAG / FRONTEND_TAG / MOBILE_TAG → IMAGE_TAG → compose default (latest). Without a root .env, compose pulls ghcr.io/brokermr810/quantdinger-{backend,frontend,mobile}:latest. Pin a specific release by setting IMAGE_TAG (lockstep) or a per-side tag — see GitHub Releases for available tags.

Published backend images are stamped from the Git release tag automatically. A v4.0.4 tag becomes APP_VERSION=4.0.4, which is what OpenAPI metadata and the UI brand config expose. Local source runs fall back to git describe and then the repo-root VERSION file; local Docker builds can override explicitly:

APP_VERSION=$(git describe --tags --abbrev=0 | sed 's/^v//') docker compose up -d --build backend

If you have access to the QuantDinger-Vue repo and want to iterate on UI source (theme tweaks, forks, debugging) instead of pulling the published image, clone it into the ./QuantDinger-Vue/ slot at the repo root (gitignored) and let Compose build from there:

git clone https://github.com/brokermr810/QuantDinger-Vue.git QuantDinger-Vue
docker compose -f docker-compose.yml -f docker-compose.build.yml up -d --build

The same override can build the mobile H5 client from ./QuantDinger-Mobile/ or MOBILE_SRC_PATH=/abs/path/to/QuantDinger-Mobile.

The main docker-compose.yml only pulls GHCR images; the override file docker-compose.build.yml adds local build: blocks. Without the override, ./QuantDinger-Vue/ and ./QuantDinger-Mobile/ do not need to exist. Point FRONTEND_SRC_PATH / MOBILE_SRC_PATH somewhere else if you keep sources outside this repo, or set COMPOSE_FILE=docker-compose.yml:docker-compose.build.yml in a root .env to skip the long -f -f invocation.

Admin account:

• The one-command installer uses the username/password entered during setup.
• Manual deployments read ADMIN_USER / ADMIN_PASSWORD from backend_api_python/.env or backend.env.
• 123456 is only a fallback for unfinished local setup and should not be used for production or shared environments.

If ADMIN_PASSWORD is set to any value other than 123456, the bootstrap admin is treated as safely initialized and the first-login password reminder is not shown. If an existing database still stores the old default, startup syncs the first admin password to the non-default env value.

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.

In the normal Docker stack, you usually do not need to edit a frontend API URL. The desktop web (8888) and mobile H5 (8889) containers proxy /api/ to the internal backend:5000 service.

For LAN testing on a phone, do not use localhost inside the phone browser. Use the host machine's LAN IP, such as http://192.168.1.10:8889.

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.

AtlasCloud is also supported as an OpenAI-compatible provider. Use the official AtlasCloud LLM API docs and API key guide, then configure:

LLM_PROVIDER=atlascloud
ATLASCLOUD_API_KEY=your_api_key
ATLASCLOUD_MODEL=openai/gpt-5.4
ATLASCLOUD_BASE_URL=https://api.atlascloud.ai/v1

Use Docker Desktop (WSL2 backend recommended). From PowerShell in the repo root:

git clone https://github.com/brokermr810/QuantDinger.git
Set-Location 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
# Edit backend_api_python\.env before first start:
#   ADMIN_USER=your_admin_user
#   ADMIN_PASSWORD=your_secure_password
docker compose pull
docker compose up -d

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.

docker compose ps
docker compose logs -f backend
docker compose restart backend
docker compose pull
docker compose up -d
docker compose up -d --build backend   # backend code changes only
docker compose down

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
MOBILE_PORT=3001
BACKEND_PORT=127.0.0.1:5001
IMAGE_PREFIX=docker.m.daocloud.io/library/

When running the desktop or mobile UI containers by themselves, use BACKEND_URL on that container instead of the root Compose .env port variables:

docker run --rm -p 8889:80 -e BACKEND_URL=http://host.docker.internal:5000 ghcr.io/brokermr810/quantdinger-mobile:4.0.3

Production-style TLS, domain, and reverse-proxy placement are covered in Cloud deployment.

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.

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()

def edge(signal):
    signal = signal.fillna(False).astype(bool)
    return signal & ~signal.shift(1).fillna(False)

open_long = (sma_short > sma_long) & (sma_short.shift(1) <= sma_long.shift(1))
open_short = (sma_short < sma_long) & (sma_short.shift(1) >= sma_long.shift(1))

df["open_long"] = edge(open_long)
df["close_long"] = edge(open_short)
df["open_short"] = edge(open_short)
df["close_short"] = edge(open_long)

output = {
    "name": my_indicator_name,
    "plots": [
        {"name": "SMA Short", "data": sma_short.fillna(0).tolist(), "color": "#FF9800", "overlay": True},
        {"name": "SMA Long", "data": sma_long.fillna(0).tolist(), "color": "#3F51B5", "overlay": True},
    ],
    "signals": [],
}

See full examples:

• docs/examples/dual_ma_with_params.py
• docs/examples/multi_indicator_composite.py
• docs/examples/cross_sectional_momentum_rsi.py

Broker Accounts page (/broker-accounts) — IBKR and Alpaca share a single unified management page: per-broker connect form, account KPIs, positions table and open-order management with one-click cancel. Multi-tenant safe: each user's session is isolated via BrokerSessionRegistry, so one user reconnecting doesn't kick everyone else off.

QuantDinger supports two main strategy authoring models:

• dataframe-based Python scripts
• four-way signal generation with open_long, close_long, open_short, and close_short
• chart rendering and signal-style backtests
• best for research, indicator logic, and visual strategy prototyping

• 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:

• Strategy Development Guide
• Cross-Sectional Strategy Guide
• Strategy Examples

The example scripts live in docs/examples/ and are kept aligned with the current strategy development guides.

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
├── docs/                    # Product, strategy, and deployment documentation
├── install.sh               # One-line GHCR install (curl | bash)
├── docker-compose.yml       # Web UI + Mobile H5 via GHCR; optional local UI builds
├── docker-compose.ghcr.yml  # Zero-repo deploy — app images from GHCR
├── LICENSE
└── TRADEMARKS.md

Use backend_api_python/env.example as the primary template. Key areas include:

Economic calendar data is free-first: QuantDinger uses the no-key AkShare/WallstreetCN calendar fallback by default, can use Trading Economics when credentials are configured, and keeps Finnhub paid-only calendar/social sentiment endpoints disabled unless FINNHUB_FREE_ONLY=false.

Strategy: EN · CN · Cross-sectional EN / CN · Examples

Integrations & alerts: IBKR · OAuth EN / CN · Telegram / Email / SMS configs under docs/ (NOTIFICATION_*).

Yes. The default deployment model is your own Docker Compose stack with your own database, Redis instance, credentials, and environment configuration.

No. Crypto is a major focus, but the platform also includes IBKR and Alpaca workflows for US stocks / ETFs. Alpaca additionally covers crypto.

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.

It is both. QuantDinger is built to connect AI research, charting, strategy development, backtesting, quick trade flows, and live execution operations in one system.

QuantDinger is a product of Open Byte Inc. The backend is licensed under Apache 2.0. The web frontend source (QuantDinger-Vue) and mobile app repo (QuantDinger-Mobile) use separate source-available licenses — review each repository and contact Open Byte Inc for commercial frontend/mobile authorization if needed.

Yes — the Docker stack serves the mobile H5 client at http://localhost:8889. For native Android/iOS shells, see QuantDinger-Mobile.

The following links are available in-app under Profile → Open account or Broker Accounts → Open account, and may qualify users for trading-fee rebates depending on venue policies.

• 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.
• QuantDinger is a product of Open Byte Inc. QuantDinger names, logos, product identity, and commercial licensing are managed by Open Byte Inc.
• The frontend source code is available separately at QuantDinger Frontend under the QuantDinger Frontend Source-Available License v1.0.
• The mobile H5/native client source code is available separately at QuantDinger Mobile under the same source-available license family.
• Under those frontend/mobile licenses, non-commercial use and eligible qualified non-profit use are permitted free of charge, while commercial use requires a separate commercial license from Open Byte Inc.
• 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

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.

• Contributing Guide
• Contributors
• Report Bugs / Request Features
• Email: support@quantdinger.com

Crypto donations:

0x96fa4962181bea077f8c7240efe46afbe73641a7

QuantDinger stands on top of a strong open-source ecosystem. Special thanks to projects such as:

• Flask
• Pandas
• CCXT
• yfinance
• Vue.js
• Ant Design Vue
• KLineCharts
• ECharts
• Capacitor
• bip-utils

QuantDinger is a small tribute to Erwin Schrödinger — the "-dinger" in our name is the tail of "Schrödinger". The cat in the box was a thought experiment; every un-fired strategy is its own little version of it — simultaneously winning and losing until the order actually fills. Backtests open the box. Live trading collapses the wavefunction. Trade carefully.

_{If QuantDinger is useful to you, a GitHub star helps the project a lot.}