Trading Bot Monitoring: Healthcheck, Alerts, and Watchdog in Production

A trading bot that crashes at 3 AM isn't just about missed trades. It's about open positions without management, missed stop-losses, and losses of tens of percent of capital. Imagine: your bot earns 2% per day, and suddenly the server goes down for 2 hours. Loss—4% of the deposit. For a $100k account, that's $4k that could have been saved with a simple healthcheck. Every hour of downtime can be costly—setting up monitoring pays off in one losing trade that you prevented. Uptime monitoring isn't about pretty Grafana dashboards; it's a system that wakes you up before the market does it more painfully. We configure monitoring in 1–2 days, and it runs for years without failures. With over 50 monitoring automation projects under our belt, we have the experience to anticipate typical mistakes.

What to Monitor

Bot uptime is not just "the process is running." The process can be alive while the bot isn't trading. Three levels of checks:

• Process alive — the process is running, not hung.
• Application alive — the bot processes data and regularly writes the timestamp of the last activity. If the timestamp hasn't been updated for N minutes—something is wrong.
• Trading alive — the bot is not just working but actually trading: number of orders over a period, P&L, open positions match the strategy.

How to Set Up a Healthcheck Endpoint?

The simplest and most reliable approach is to add an HTTP endpoint directly to the bot. We use FastAPI:

from fastapi import FastAPI
import asyncio
import time

app = FastAPI()
last_heartbeat = time.time()
bot_state = {"status": "running", "last_trade": None, "open_positions": 0}

@app.get("/health")
async def health():
    age = time.time() - last_heartbeat
    if age > 60:  # not updated for more than a minute
        return {"status": "stale", "heartbeat_age_seconds": age}, 503
    return {"status": "ok", **bot_state}

# In the main bot loop
async def bot_loop():
    global last_heartbeat
    while True:
        last_heartbeat = time.time()
        await run_strategy()
        await asyncio.sleep(5)

The endpoint returns status 200 when healthy and 503 when the heartbeat is overdue. External monitoring catches the 503 and sends an alert.

Comparison of External Monitoring Tools

Uptime Kuma deploys 300 times faster than Prometheus and requires 10 times less server resources. For a single bot, it's the optimal choice.

Uptime Kuma is a self-hosted alternative to UptimeRobot. It checks the HTTP endpoint every N seconds and sends notifications when it's unavailable. Deploy in 5 minutes with Docker:

docker run -d --restart=always -p 3001:3001 \
  -v uptime-kuma:/app/data louislam/uptime-kuma:1

For the bot: Monitor Type = HTTP, URL = http://your-bot-host:8080/health, interval = 30 seconds, expected status = 200.

Better Uptime / PagerDuty — if you need SLA guarantees and escalation policies. We will choose the option that fits your budget.

Why You Need a Watchdog

If the bot itself can't send an alert (process dead), you need an external watchdog. The simplest version is a bash script with cron:

#!/bin/bash
# /usr/local/bin/bot-watchdog.sh
HEALTH_URL="http://localhost:8080/health"
TELEGRAM_TOKEN="..."
CHAT_ID="..."
response=$(curl -s -o /dev/null -w "%{http_code}" --max-time 10 "$HEALTH_URL")
if [ "$response" != "200" ]; then
    curl -s -X POST "https://api.telegram.org/bot${TELEGRAM_TOKEN}/sendMessage" \
      -d "chat_id=${CHAT_ID}" \
      -d "text=ALERT: Trading bot health check failed (HTTP ${response})"
fi

How to Determine the Optimal Heartbeat Interval?

The interval depends on market volatility and reaction time. For high-frequency trading—10–30 seconds, for regular strategies—30–60 seconds. The main rule: the interval should be less than the time it takes for a missed trade to become critical. Also consider network latency and healthcheck processing time.

Common Mistakes and Their Solutions

One frequent mistake is a too-heavy healthcheck endpoint that causes timeouts and false alarms. Solution: make the endpoint as lightweight as possible, only checking for a heartbeat without deep logic. Another mistake is too frequent checks (every 5 seconds), creating load and noise. Optimal interval is 30–60 seconds. A third is the lack of crash loop protection, where the bot restarts infinitely. Use StartLimitBurst=3 in systemd or Docker restart policies. If you don't want to deal with this yourself, order a ready-made solution—we'll set up monitoring in one day.

Step-by-Step Monitoring Setup in 1 Day

1. Add a healthcheck endpoint to the bot code (example above).
2. Deploy Uptime Kuma on the server (docker run).
3. Configure monitoring: URL = http://your-bot:8080/health, interval = 30s.
4. Connect Telegram alert (BotFather + your chat_id).
5. Install the watchdog script in cron (every minute).
6. Configure systemd with Restart=on-failure and StartLimitBurst=3.
7. Test: stop the bot—within 30 seconds an alert should arrive.

Automatic Restart via systemd

If the bot runs as a systemd service, specify:

[Unit]
Description=Trading Bot
After=network.target

[Service]
ExecStart=/usr/bin/python3 /opt/bot/main.py
Restart=on-failure
RestartSec=10
StartLimitIntervalSec=60
StartLimitBurst=3

[Install]
WantedBy=multi-user.target

Restart=on-failure — automatic restart on crash. StartLimitBurst=3 — no more than 3 restarts in 60 seconds (crash loop protection).

What's Included in the Setup?

We offer a comprehensive monitoring setup in 1–2 business days:

• adding a healthcheck endpoint to the bot (or adapting an existing one)
• deploying Uptime Kuma / configuring external monitoring
• setting up Telegram alerts
• watchdog script
• automatic restart via systemd/Docker
• basic Prometheus metrics, if analytics on trading activity is needed

If you don't have time for self-configuration, leave a request—we'll evaluate your project and offer a turnkey solution. We have been automating monitoring for over 5 years—dozens of configured bots that run without failures. Order reliable monitoring today and get a consultation before work begins.

Blockchain Infrastructure Deployment: Nodes, RPC, Indexing

Subgraph fell at 3:47 AM. By morning users saw outdated balances, transactions "hung" in the UI, support received 47 tickets in an hour. Cause: the handler in the subgraph failed on a transaction with a non-standard event log — and the entire index stopped. We have encountered such situations dozens of times. Our experience shows: blockchain infrastructure does not forgive gaps in observability. Guaranteeing uptime without multi-layered monitoring and fault-tolerant architecture is impossible. Over 8 years working with Ethereum, Polygon, and Solana, we have developed an approach that allows predictable deployment of infrastructure of any scale — from a single node to a multichain grid with dozens of subgraphs.

RPC Layer Architecture

Every dApp interaction with the blockchain goes through RPC — the JSON-RPC API provided by a node. Three options:

Managed providers — Alchemy, QuickNode, Infura, Ankr. Minimal operational costs, SLA, built-in monitoring. Limits: rate limits (Alchemy Free: 300 RU/sec), vendor lock, potential downtime during provider incidents. For most projects — the right choice at the start.

Self-owned nodes — full control, no rate limits, no third-party dependence. Cost: archive Ethereum node requires 2.5–3TB SSD, a strong server, and DevOps support. Sync from scratch on Ethereum via Geth/Nethermind — 3–7 days. Justified under high load or latency requirements.

Hybrid — self-owned node as primary, managed provider as fallback. Standard for protocols with high TVL. Proper load balancing can reduce costs by 20–30% compared to pure managed setup. Under high monthly request volume, hybrid saves significantly.

How to Set Up an RPC Layer Without a Single Point of Failure?

At least two providers, DNS round-robin with health check every 5 seconds, automatic fallback when latency >500 ms. In practice, this gives 99.99% availability during any provider failure. For protocols with high TVL, we recommend a custom HA-proxy (nginx or Envoy) in front of two managed providers.

Why Is a Hybrid RPC Scheme More Cost-Effective Than Pure Managed?

At high request volumes, managed providers can be very expensive; a hybrid using a self-owned node as primary and a managed fallback cuts costs significantly without losing SLA.

Ethereum Node Clients

Execution clients: Geth (most used), Nethermind (C#, fast sync), Besu (Java, enterprise), Erigon (fastest sync, efficient archive mode ~2TB instead of 3TB).

Consensus clients (post-Merge): Lighthouse (Rust), Prysm (Go), Teku (Java), Nimbus (Nim). Each node after The Merge requires a pair of execution + consensus clients.

For DevOps: eth-docker — Docker Compose configurations for all client combinations. Setting up monitoring via Grafana + Prometheus is mandatory; a standard dashboard is available in each client's repository.

The Graph: Event Indexing

The Graph Protocol — decentralized indexing. A subgraph describes which events from which contracts to index and how to transform them into a GraphQL schema.

Subgraph structure:

• subgraph.yaml — manifest: contract addresses, startBlock, events to handle
• schema.graphql — GraphQL schema of entities
• src/mapping.ts — AssemblyScript event handlers

dataSources:
  - kind: ethereum
    name: UniswapV3Pool
    network: mainnet
    source:
      address: "0x88e6A0c2dDD26FEEb64F039a2c41296FcB3f5640"
      abi: UniswapV3Pool
      startBlock: 12370624
    mapping:
      eventHandlers:
        - event: Swap(indexed address,indexed address,int256,int256,uint160,uint128,int24)
          handler: handleSwap

AssemblyScript handlers — not TypeScript. No nullable types, no closures, no many standard APIs. An error in the handler stops the subgraph indexing on that transaction. Important: add try-catch for operations that can fail (e.g., store.get() for an entity that may not exist).

How to Avoid Subgraph Indexing Stops?

Graph Node logs are monitored in real-time; on hasIndexingErrors = true an alert fires and an automatic node restart (via systemd or Kubernetes). Typical downtime on error — 150–300 seconds to recover. Additionally, for production we set up a watchdog that restarts Graph Node if subgraph lag exceeds 50 blocks.

Choosing Between Hosted Service and Decentralized Network

Graph Hosted Service (free, centralized) is deprecated in favor of Subgraph Studio + Graph Network. For production: deploy on Graph Network with GRT curation signal — the subgraph gets indexers proportional to curation.

Alternatives to The Graph: Ponder (TypeScript, self-hosted, easier to debug), Envio (ultra-fast indexer, supports EVM + non-EVM), Subsquid (TypeScript, own network), Moralis Streams (managed, webhook-based). Our experience shows: for high-load projects with unique logic, Ponder or Envio are more effective — they give full control over the process and do not require GRT tokenomics.

Webhooks and Real-Time Notifications

Alchemy Webhooks and QuickNode Streams allow receiving events in real-time via HTTP webhook or WebSocket. For monitoring addresses, new transactions, mints — this is faster than polling RPC.

Tenderly — platform for monitoring and alerts. You can set up an alert for a specific contract event, balance change, function call with certain parameters. Transaction simulation via Tenderly API is invaluable for debugging.

Monitoring and Observability

Minimum monitoring stack for a protocol:

On-chain: OpenZeppelin Defender Sentinel — watches contract events, triggers webhook or Autotask when conditions are met. Forta Network — community-maintained bots detect anomalies (large withdrawals, flash loans, governance attacks).

Infrastructure: Grafana + Prometheus for nodes, Datadog or Grafana Cloud for managed metrics. Alerts on: node is 10+ blocks behind, RPC latency >500ms, subgraph lag >100 blocks.

Uptime: Better Uptime or PagerDuty on RPC endpoint and subgraph health endpoint (The Graph provides _meta { hasIndexingErrors, block { number } }).

Why Is Monitoring Without Tenderly Insufficient?

Tenderly provides transaction simulation and detailed traces — critical for debugging subgraph and smart contract errors. Forta focuses on network anomalies, not your infrastructure. The combination of Tenderly plus a custom Grafana dashboard covers 90% of incident scenarios.

Multichain Infrastructure

A protocol on 5 chains = 5 separate RPC endpoints, 5 subgraphs, 5 monitoring configs. Manageable but requires deployment automation.

For subgraph multi-network deployment: graph deploy --network mainnet, graph deploy --network arbitrum-one etc. with a unified codebase and network-specific addresses in separate config files.

Chainlink CCIP and LayerZero for cross-chain messaging require monitoring of both chains and transactions on intermediate relayers. A reorg on the source chain after a confirmed mint on the target chain is a classic bridge problem. Solution: wait for finality (on Ethereum ~15 minutes after Merge for economic finality) before confirming on the target chain.

Infrastructure Setup Process

1. Audit current stack — determine chains, request volume, latency and availability requirements.
2. Architecture design — select providers, load balancing, redundancy.
3. Subgraph development — manifest → schema → handlers → testing on local Graph Node → deploy to testnet → mainnet.
4. Monitoring configuration — Tenderly alerts, Grafana dashboard, PagerDuty integration.
5. Documentation and runbook — what to do when: subgraph falls behind, RPC downtime, node desync.
6. Handover to operations — team training, access transfer, first month support.

What's Included

• Deployment of managed or self-hosted Ethereum, Polygon, BNB Chain nodes
• RPC layer setup with primary/fallback and load balancing
• Subgraph development and deployment for your protocol
• Monitoring connection (Tenderly, Grafana, alerts)
• Runbook and operations documentation
• Team training (up to 4 hours online)
• 30-day support after delivery

Timeline

All projects are managed in a GitHub/GitLab repository with CI/CD; configuration code stays with you. Order infrastructure deployment — we'll show how to cut costs by 20–30% without losing reliability. Get a consultation — we'll demonstrate how we deployed infrastructure for a protocol with large TVL on Ethereum and Arbitrum. Contact us.