Deployment - NOVOSKY Docs

Architecture options

NOVOSKY offers three deployment options. The Go binary option is the simplest for end-users — no Python, no model management, just a single executable.

Go binary (simplest)
Option A — Single machine
Option B — Two machines (recommended)

The compiled binary has all ML models embedded. Customers receive three files.What the customer gets:

File	Description
`novosky_go`	Compiled binary — models, license, and configs embedded
`config.json`	User-editable runtime configuration
`.env`	Secrets (MT5 token, Telegram, Supabase)
`libonnxruntime.so`	ONNX runtime library (provided alongside binary)

Hardware requirements (trading only — no retraining):

Component	Minimum
CPU	1 core
RAM	512 MB
Disk	100 MB
OS	Linux (Ubuntu 20.04+) or Windows (WSL2)

The Go binary is orders of magnitude lighter than the Python bot. No scikit-learn, no LightGBM, no GPU needed.Setup:

# Place all four files in the same directory
chmod +x novosky_go
cp .env.example .env   # fill in MT5 token, Telegram token
./novosky_go --dry     # verify connectivity
./novosky_go           # live

See Go binary for the full reference.

One Linux VPS handles everything: the MT5 API Docker container (via Wine), the trading bot, and weekly retraining.

Component	Minimum	Recommended
CPU	4 cores	8 cores
RAM	6 GB	8 GB
Disk	40 GB SSD	80 GB SSD
OS	Ubuntu 24.04	Ubuntu 24.04
Network	50 Mbps stable	100 Mbps stable
GPU	—	NVIDIA (any CUDA-capable)

Retraining on the minimum spec (CPU-only, 4 cores) takes ~2–2.5 h. With an NVIDIA GPU the full retrain drops to ~30–45 min. The trading bot itself uses under 300 MB RAM at runtime.Cloud equivalents: Hetzner CCX23 (~~€15/mo, 8 vCPU/16GB), DigitalOcean 8 GB Droplet (~~$48/mo), AWS t3.xlarge, Azure D4s v5.For GPU training: AWS g4dn.xlarge (T4, ~$0.50/h spot) or Hetzner GPU server.

Machine 1 — MT5 API server (Windows)

Component	Spec
OS	Windows 10/11 or Windows Server 2019
CPU	2 cores
RAM	4 GB
Disk	40 GB (MT5 stores tick history)
Network	Stable broadband, low latency to broker

Runs: MetaTrader 5 terminal + MT5 REST API Docker container on port 6542. Can be a cheap Windows VPS from Vultr, Contabo, or your broker’s hosted VPS.Machine 2 — Trading + ML VM (Linux)

Component	Minimum	Recommended
CPU	4 cores	8 cores
RAM	6 GB	8 GB
Disk	40 GB SSD	80 GB SSD
OS	Ubuntu 24.04	Ubuntu 24.04
GPU	—	NVIDIA (any CUDA-capable)

Runs: trading.py, weekly_optimize.py, all ML training. Connects to Machine 1 via API_URL in .env.Why split? MT5 only runs on Windows. The training stack (scikit-learn, XGBoost, LightGBM) is much easier on Linux. Splitting also means a model retrain crash never affects live trading.

GPU note

Training uses a GPU when one is available via LightGBM’s CUDA backend and XGBoost’s device=cuda. Without a GPU, training falls back to CPU automatically — no configuration change needed.

Setup	Retrain time (all 4 models)
CPU-only, 4 cores	~2–2.5 hours
CPU-only, 8 cores	~1–1.5 hours
NVIDIA GPU (T4 / RTX 3060+)	~30–45 minutes

If running on Azure or another hypervisor VM, GPU passthrough requires a specific VM type (e.g. Azure NC-series, not the default B/D/E series which have only virtual VGA). CPU-only is perfectly viable for weekly retraining.

MT5 REST API setup

The bot does not use the MetaTrader5 Python package. All broker communication goes through a self-hosted HTTP API running on port 6542.

Install MetaTrader 5 on your Windows VM

Download and install MT5 from your broker’s website. Log in with your trading account credentials.

Install Docker

# Windows PowerShell (run as Administrator)
winget install Docker.DockerDesktop

Or install Docker Desktop from docker.com. Make sure it starts on boot.

Pull and run the MT5 REST API container

docker pull novosky/mt5-api:latest
docker run -d \
  --name mt5-api \
  --restart always \
  -p 6542:6542 \
  -e MT5_LOGIN=<your_account_number> \
  -e MT5_PASSWORD=<your_password> \
  -e MT5_SERVER=<broker_server_name> \
  -e API_TOKEN=<choose_a_secret_token> \
  novosky/mt5-api:latest

Replace <your_account_number>, <your_password>, <broker_server_name> with your MT5 credentials. Set <choose_a_secret_token> to a long random string — this is the API_TOKEN in your .env on the Linux VM.

Verify connectivity

From your Linux VM:

curl http://<windows_vm_ip>:6542/ping
# Expected: {"status": "ok", "time": 1714000000}

If you are using a broker-provided hosted VPS with MT5 pre-installed, skip Docker and configure the API container to connect to the already-running MT5 terminal.

Linux VM setup

# Run the automated VM setup script
./setup-vm.sh

# Or manually:
sudo apt update && sudo apt upgrade -y
sudo apt install -y python3.11 python3.11-venv git htop tmux libgomp1

# Clone repo
git clone <repo> && cd novosky
python3.11 -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt

# Set up .env
cp .env.example .env
# Edit .env with your credentials

Process management (PM2)

PM2 keeps the trading bot running continuously and restarts it if it crashes (but not after a hard halt — sys.exit(99) is intentional).

# Install PM2
npm install -g pm2

# Start the bot
pm2 start ecosystem.config.js
pm2 save
pm2 startup   # install as system service

# Check status
pm2 status
pm2 logs novosky --lines 50

# Restart after config or code changes
pm2 restart ecosystem.config.js --update-env

# Restart after a hard halt (check the reason first)
pm2 restart novosky

ecosystem.config.js is pre-configured for trading.py. It reads .env automatically at startup.

After a hard halt (sys.exit(99)), do not restart automatically. First check the reason in the logs, update starting_balance_usd in config.json to your current balance, then restart manually.

PM2 snapshots the environment at first start. If you change any value in .env — for example rotating a Telegram bot token or updating API_URL — a plain pm2 restart will keep using the old cached values. Always restart with --update-env after any .env change:

pm2 restart ecosystem.config.js --update-env

To verify what PM2 is actually using before debugging a token or connection error:

pm2 env 0 | grep TELEGRAM
pm2 env 0 | grep API_URL

Cron setup (weekly optimize)

The first optimize run installs the cron job automatically. To verify or install manually:

# Check existing cron jobs
crontab -l

# Manual install
crontab -e
# Add:
# 0 2 * * 0 cd /path/to/novosky && .venv/bin/python scripts/weekly_optimize.py --profile balanced >> logs/weekly_optimize.log 2>&1

Firewall

The MT5 REST API on your Windows VM should only be accessible from your Linux trading VM. Restrict port 6542 accordingly:

# Windows Firewall — inbound rule
Allowed source IP: <linux_vm_ip>
Port: 6542 (TCP)

Do not expose port 6542 publicly.

​Architecture options

​GPU note

​MT5 REST API setup

​Linux VM setup

​Process management (PM2)

​Cron setup (weekly optimize)

​Firewall

Architecture options

GPU note

MT5 REST API setup

Linux VM setup

Process management (PM2)

Cron setup (weekly optimize)

Firewall