From trading plan to automated system

Evidence that the idea holds. It does not have to be code: a written trading plan and a hundred setups backtested by hand will do. Applying the rules yourself is how you find out which of them are not yet stated precisely enough. A quick throwaway prototype can help with the evaluation. It does not replace the precise implementation.

What this stage covers

• A trading plan written down
• Rules stated precisely enough to apply by hand
• Around a hundred setups tested by hand, or a quick throwaway prototype
• A first indication of whether building it is worth it.

If skipped you pay to build an idea nobody has checked.

The clean version: entry and exit logic, risk and position management, precise alerts. No repainting, no look-ahead, no survivorship bias. Clean code, documented so it reads, and still legible and safely adjustable in six months. Once written, it runs on any instrument.

What this stage covers

• No repainting, look-ahead or survivorship bias
• Clean code and documentation
• Implementation from research papers
• Smart Money Concepts
• Harmonic patterns
• Multi-timeframe logic
• ICT setups
• ORB setups
• Liquidity sweeps
• Dynamic position sizing
• Solutions outside the Pine Script sandbox, where the requirements run past its limits

If skipped a prototype goes live. Errors and imprecision creep in, and they cost you money systematically.

Before large sums are at stake, we want transparency and confidence that the strategy works. Backtests against a wider set of data get us there, and where you want it, a Monte Carlo simulation comparing configurations of the same strategy and its variants. The results are what the input parameters, and with them the outcomes, are optimised against; that needs at least an implementation in Python. Where it is wanted, an audit of third-party code for repainting, look-ahead and survivorship bias, and the fixes for whatever it turns up.

What this stage covers

• Backtests against a wider set of data
• Monte Carlo simulation to optimise the strategy (parameters and variants); needs an implementation in Python
• Repainting (chart ≠ live)
• Look-ahead bias (the backtest cheated)
• Survivorship bias (optimised against the wrong results)
• Faulty multi-timeframe implementation
• Alert timing problems (too early, too late, too often … or not at all)
• Contradictions in the logic
• Inefficient code
• Unrealistic backtest assumptions
• Weaknesses in risk management
• Dissection, fix and reassembly
• Detailed documentation

If skipped the account balance is where you find out whether your edge delivers.

The system is connected to a broker, by way of a detour, and goes live. Alert webhooks leave Pine Script and a third-party platform turns them into an order at your broker. Quick to set up. What is possible is decided by the platform in between.

What this stage covers

• Alerts and notifications
• Platform and gateway integration, for instance PineConnector, TradersPost, 3Commas, Tradovate, or any other service that takes orders over a webhook
• What is possible depends on the platform: position sizing from account equity, order updates and cancellation, error handling
• Optional: position management, tracked virtually in Pine Script

If skipped you keep placing your orders yourself, and the system stays an indicator until there is more confidence in it.

Your own pipeline from signal to broker, with no third-party software in the path. Needed as soon as you want the critical path shorter, a broker offers only REST and WebSocket APIs, or orders have to be authenticated. That is most of them outside the retail world.

What this stage covers

• Order fill confirmation
• Live position monitoring
• Risk filters
• Automatic retries
• Position sizing, based on account equity
• Direct API connection, for instance Interactive Brokers, OANDA, Binance, Bybit, OKX, Alpaca, Hyperliquid, TradeStation
• Required with any broker that offers only REST or WebSocket APIs, or wants orders authenticated

If skipped your execution depends on somebody else’s uptime, rate limits and roadmap.

One account becomes several, one signal source becomes several, and the script becomes a system several people can use. Built modularly, so it stays flexible.

What this stage covers

• A dashboard for the system, every metric at a glance
• A signal and risk filter engine
• Modular design, full flexibility
• Several signal sources
• Several broker connections
• Paper trading of your own
• Signal broadcast and copy trading
• Multi-tenancy, where other users come along
• Cloud deployment and scaling

If skipped the system works for one scenario and leaves its biggest potential on the table.

The last stage. It starts with measurement: where the time actually goes, before anything is rewritten. Sometimes a complete reimplementation is the answer — a strategy that has to respond in under a millisecond will not do it in the runtime it was prototyped in.

What this stage covers

• Bottleneck analysis
• Performance work on CPU, memory and network
• Native FIX and WebSocket connections
• Server colocation with the broker
• Slippage cut to the minimum the venue allows
• Optionally, the strategy reimplemented in Rust or Python
• Signal runtimes under a millisecond, where the path allows it

If skipped nothing measurable. This stage only pays once latency demonstrably costs money.