What You Actually Need to Build a Signals Engine

Let me show you exactly what goes into a professional trading signals system. Spoiler: it is more than a Python script and a Binance API key.

Most tutorials make it look easy. "Connect to an API, calculate RSI, place orders." They show you the 5% that is fun and skip the 95% that is hard. Then you try to build something real and wonder why it falls apart.

In this lesson, I am going to break down every component of a production trading signals system. Not the toy version—the real thing. By the end, you will understand exactly what you are signing up for if you decide to build your own.

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The Five Layers of a Signals Engine

A complete trading signals system has five distinct layers. Miss any one of them and the whole thing breaks.

1. Data Layer — Getting the information you need 2. Processing Layer — Turning data into signals
3. Execution Layer — Acting on signals 4. Monitoring Layer — Knowing what is happening 5. Infrastructure Layer — Keeping it all running

Let me walk you through each one.

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Layer 1: The Data Layer

Your signals are only as good as your data. Garbage in, garbage out.

What you need:

• •Price data feeds — Real-time and historical OHLCV from exchanges
• •Alternative data — Open interest, funding rates, liquidations, order flow
• •Data storage — Database or file system to store historical data
• •Data validation — Checks for gaps, outliers, and corruption

The hidden complexity:

Exchanges have different data formats. Binance returns timestamps in milliseconds, others use seconds. Some include the current candle (incomplete), others do not. Funding rates are calculated differently across exchanges.

You need normalization layers to make everything consistent. You need gap detection to know when data is missing. You need deduplication because APIs sometimes return the same data twice.

Realistic costs:

• •Free tier exchange data: Limited, delayed, rate-limited
• •Professional data (HyBlock, Coinglass, etc.): $200-600/month
• •Storage (cloud database): $50-200/month
• •Total: $250-800/month just for data

Time to build: 2-4 weeks for a basic pipeline, 2-3 months for production-grade

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Layer 2: The Processing Layer

This is where your trading logic lives. It takes data and outputs signals.

What you need:

• •Indicator calculations — RSI, funding z-scores, OI changes, etc.
• •Signal generation — Logic that combines indicators into buy/sell decisions
• •Backtesting engine — Test signals against historical data
• •Walk-forward validation — Proper out-of-sample testing

The hidden complexity:

Calculating indicators sounds simple until you realize:

• •You need to handle missing data gracefully
• •Real-time calculations must match backtest calculations exactly
• •Some indicators need warm-up periods (200 candles for a 200 MA)
• •Time alignment across different data sources is tricky

Your backtesting engine needs to account for:

• •Slippage and spread
• •Exchange fees
• •Funding payments (for perpetuals)
• •Position sizing and leverage
• •The difference between signal time and execution time

Common mistake: Building a backtester that assumes perfect execution. Your backtest shows 80% win rate, but live trading shows 55% because you did not model reality.

Time to build: 1-2 months for basic backtesting, 3-6 months for robust validation

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Layer 3: The Execution Layer

A signal means nothing if you cannot act on it.

What you need:

• •Exchange connections — API integrations with your target exchanges
• •Order management — Place, modify, cancel orders
• •Position tracking — Know what you own at all times
• •Risk controls — Maximum position size, daily loss limits, etc.

The hidden complexity:

Exchange APIs are... fun.

• •Rate limits vary by endpoint and change without notice
• •WebSocket connections drop and need reconnection logic
• •Order status updates can be delayed or arrive out of order
• •Each exchange has different order types and parameters

Position tracking sounds simple but:

• •Your local state can desync from the exchange
• •Partial fills create complexity
• •Funding payments change your effective position value
• •Multiple accounts or exchanges multiply the complexity

The scary part: Bugs in execution code lose real money. A misplaced decimal point, a wrong side (buy vs sell), or a loop that places orders repeatedly can drain an account in seconds.

Realistic costs:

• •Exchange fees: 0.02-0.1% per trade (adds up fast)
• •VPS for low latency: $20-100/month
• •Total variable: Depends on trading volume

Time to build: 1-2 months for single exchange, 3-4 months for multi-exchange

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Layer 4: The Monitoring Layer

If you cannot see what your system is doing, you are flying blind.

What you need:

• •Real-time dashboards — Current positions, P&L, signal status
• •Alerting — Notifications when things go wrong
• •Logging — Detailed records of every decision and action
• •Performance tracking — Win rate, drawdown, Sharpe over time

The hidden complexity:

What counts as "wrong" that deserves an alert?

• •System offline (obvious)
• •No signals for X hours (maybe normal, maybe broken)
• •Unusual slippage (market conditions or bug?)
• •Drawdown exceeding threshold (stop trading or ride it out?)

You need to tune alerts so you catch real problems without alert fatigue. Too many false alarms and you start ignoring them.

Logging needs to be detailed enough to debug issues but not so verbose that you cannot find anything. You need to log signal generation, order placement, fills, errors—and be able to correlate them.

Realistic costs:

• •Monitoring tools (Grafana, etc.): Free to $50/month
• •Alert services (PagerDuty, etc.): $10-50/month
• •Log storage: $20-100/month

Time to build: 2-4 weeks for basic monitoring, ongoing refinement

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Layer 5: The Infrastructure Layer

Everything above needs somewhere to run.

What you need:

• •Servers — Reliable compute that runs 24/7
• •Deployment — Way to update code without downtime
• •Backup systems — What happens when the primary fails
• •Security — API keys, credentials, access control

The hidden complexity:

Crypto markets never close. Your system needs to run 24/7/365. That means:

• •Handling server restarts gracefully
• •Recovering state after crashes
• •Dealing with network partitions
• •Managing updates without losing positions

Security is critical. Your system has API keys that can trade real money. A breach means:

• •Stolen funds (direct theft)
• •Malicious trades (drain account via bad trades)
• •Data theft (your strategies exposed)

Realistic costs:

• •Cloud servers (AWS, GCP): $50-300/month
• •Redundancy (multiple regions): 2-3x base cost
• •Security tools: $20-100/month

Time to build: 1-2 weeks for basic setup, ongoing maintenance

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The Full Picture: Time and Money

Let me add it all up.

Monthly operating costs (realistic range):

Development time (building from scratch):

And this assumes you already know how to code, understand trading, and do not make major architectural mistakes that require rewrites.

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Skills You Need

Building a signals engine requires a surprisingly broad skill set:

Programming:

• •Python or JavaScript (primary language)
• •SQL (data queries)
• •Basic DevOps (deployment, servers)

Trading knowledge:

• •Market microstructure
• •Order types and execution
• •Risk management principles

Data engineering:

• •ETL pipelines
• •Data validation
• •Time series handling

Systems thinking:

• •Distributed systems basics
• •Failure mode analysis
• •State management

Most people are strong in one or two areas and weak in others. The learning curve is steep.

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The Maintenance Burden

Here is what nobody tells you: building is the easy part. Maintenance is forever.

Ongoing work includes:

• •Exchange API changes (they happen constantly)
• •Data source changes (providers modify formats)
• •Strategy decay (edges stop working)
• •Bug fixes (there are always bugs)
• •Security updates (vulnerabilities discovered)
• •Performance optimization (as data grows)

Plan for 5-10 hours per week of maintenance, minimum. More if you are actively developing new strategies.

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What We Built at TargetHit

I am not going to pretend this is easy. Let me tell you what our system looks like:

• •54 coins tracked across 58 data endpoints and 5 timeframes
• •HyBlock Capital data at $600/month for institutional-grade alternative data
• •Millions of indicator combinations tested through our discovery engine
• •Walk-forward validation on every edge before promotion
• •Real-time scanning that fires signals within seconds of conditions being met
• •Auto-execution across 7 exchanges with position management
• •24/7 monitoring with alerts to Discord, Telegram, and email

This took over a year to build and represents thousands of hours of development. We run it so our VIP members do not have to.

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The Honest Assessment

If you are thinking about building your own system, ask yourself:

Do you have 3-6 months of development time? Not weekends—focused development time. More if you are learning as you go.

Do you have $300-1000/month for operating costs? Before you make a single profitable trade.

Do you have the technical skills? Or the time to learn them?

Do you have edges worth automating? A system that automates bad strategies just loses money faster.

Do you enjoy this kind of work? Because maintenance never ends.

For some people, the answer to all of these is yes. Building your own system is rewarding if you enjoy the process. You learn an enormous amount. You have complete control.

For most people, the honest answer is: the time and money would be better spent elsewhere.

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Your Action Items

1. •

   Audit your skills. Which of the five layers could you build today? Which require learning?

2. •

   Estimate your timeline. Be realistic. Double your first estimate—you will probably need it.

3. •

   Calculate your costs. Add up data, infrastructure, and tools. Can you sustain this for 6-12 months while developing?

4. •

   Consider alternatives. Would your time be better spent improving your trading skills? Finding better edges? Or using a service that handles the infrastructure?

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Coming Up Next

Now you understand what it takes to build a signals engine. In Lesson 5: The Build vs Buy Decision, we will do the math together. We will calculate the true cost of building versus subscribing to signals, and help you decide which path makes sense for your situation.

Spoiler: there is no universally right answer. But there is a right answer for you.

See you in the next lesson.