Algo-Trader· Systems Architect· Signal Hunter
I don't predict
the market.
I listen to it.
Automated trading strategies and systems that run while the world sleeps — from raw hypothesis to live pipeline, nothing left to chance.
From C‑64
to live markets.
Born 1976. First machine: a Commodore 64 — eight kilobytes of RAM and the early certainty that logic could describe anything worth describing. Decades of software followed: production systems, the discipline of code that runs in conditions you didn't plan for. Markets came later — not as speculation, but as a pattern recognition problem with immediate feedback and no margin for wishful thinking.
Patterns the
chart can't show.
Markets produce more information per millisecond than any analyst can process. Machine learning doesn't replace judgment — it extends what's observable: statistical structures invisible in isolation but consistent across thousands of bars, correlations invisible to the eye that persist across market regimes. The hypothesis is human. The validation is relentless.
Rules.
Not hunches.
Every automated trading strategy starts with a hypothesis and ends with a backtest across multiple market regimes. Entry logic is explicit. Position size is calculated from risk — not conviction. Stop-losses are defined before the trade opens, not adjusted after the price moves against you. Human judgment belongs in the design. Not the execution.
Origin
Software forged
the instinct.
Markets tested it.
Born 1976. A Commodore 64 arrived early — eight kilobytes of RAM and a BASIC prompt. Enough to understand that a computer did exactly what you told it: no interpretation, no guessing. That precision became non-negotiable.
Decades of software followed. Production systems, failure modes, the discipline of writing code that holds under conditions you didn't plan for. When markets entered the picture, the vocabulary was already there: signal, noise, latency, risk.
Automated trading is where those threads converge — systematic rules that hold under pressure, risk defined before the position opens, algorithmic execution while the operator sleeps. That's the only rigorous way to trade.
Built on solid tools.
Frequently Asked Questions
What is an automated trading strategy?
An automated trading strategy is a systematic set of rules that defines exactly when to enter a trade, how large the position should be, where to place the stop-loss, and when to exit — executed by a computer without human intervention. Unlike discretionary trading, every decision is based on pre-defined logic validated through backtesting across multiple market regimes.
How are automated trading strategies validated?
Validation requires backtesting across multiple market conditions — trending, ranging, and high-volatility periods. A strategy that only works in one environment is not robust. Walk-forward testing on out-of-sample data is mandatory to prevent curve-fitting. Minimum thresholds: Profit Factor > 1.3, Sharpe Ratio > 0.8, Maximum Drawdown < 20%.
What tools power the live trading pipeline?
The stack: Pine Script on TradingView for strategy logic and signal generation, Node.js for custom backtesting and statistical analysis, Docker for reproducible containerized environments, n8n as webhook middleware routing alerts to broker APIs, and a Linux VPS for always-on, 24/7 execution.
What is the difference between algorithmic and automated trading?
Algorithmic trading uses mathematical models and defined rules to decide when and how to trade. Automated trading executes those decisions without human input. In a well-built system, both overlap completely: the algorithm determines the trade, automation fires it instantly. Discretionary trading — acting on instinct rather than rules — is the opposite of both.
How long before an automated strategy goes live?
The minimum responsible timeline: written hypothesis, backtesting across at least two distinct market periods, then paper trading for a minimum of 6 months before any live capital is deployed. Skipping paper trading to go faster is the most common — and most expensive — mistake in systematic trading.
Why Node.js for backtesting and pre-trade tools?
Node.js's event-driven, non-blocking architecture processes historical price data and technical indicator calculations with minimal latency — the same code that runs backtests is reused directly in the live execution layer, keeping dev and prod logic in sync and eliminating an entire class of deployment risk. Custom tools built in Node.js crunch through millions of candles, calculate walk-forward windows, and output validated parameters before a single live trade fires.
What does Docker solve in the trading pipeline?
Docker packages the entire pipeline — data fetchers, strategy engines, signal routers — with identical OS libraries and dependencies, so the environment that validated a strategy in backtest is pixel-perfect identical in production. The "works on my machine" failure mode is eliminated entirely. Spinning up or tearing down the whole system takes one command, which makes disaster recovery and version rollback a non-event.
How does n8n route signals to brokers reliably?
n8n acts as a webhook gateway between TradingView strategy alerts and broker APIs — no hand-coded REST integration required per broker. Built-in retry logic with exponential backoff handles transient API failures so signals are never silently dropped. A single alert can simultaneously place orders, log trades to a spreadsheet, and fire a Telegram notification in milliseconds.
Why a dedicated VPS instead of a local machine?
A VPS Linux server provides 99.9% uptime with redundant power and connectivity in a professional data center — a home machine faces power cuts, OS restarts, and ISP interruptions that will miss live signals and violate the execution guarantees validated in backtest. Running 24/7 near exchange infrastructure also reduces latency, ensuring the timing assumptions baked into every strategy hold in production.