Arithmetic of Alpha. No Camouflage.

This page is not a marketing dashboard — it is the public audit log of the Ordertune algorithmic trading system. Nine systematic strategies trade the Nasdaq 100 universe, and every closed trade, monthly return, drawdown, and equity-curve datapoint you see below is rendered directly from the live signal database. There are no curated screenshots, no cherry-picked windows, no smoothed lines. The data refreshes daily after the U.S. equity market close.

The cockpit lets you re-slice that data four ways: by strategy, by position size, by time window, and by benchmark comparison. Below is what each lever does and why it matters when you are evaluating a quantitative trading signal service.

The headline chart plots the cumulative growth of the Ordertune long-only portfolio against the unleveraged Nasdaq 100 benchmark (QQQ ETF) on a logarithmic scale. The log scale matters: it makes a 2% return look the same regardless of whether your account is at $10,000 or $1,000,000, so you can read structural outperformance instead of being deceived by the visual exponential of late-stage compounding. The curve includes compounding effects, trading costs, and slippage; it is not a clean theoretical line. Periods where the curves cross are the moments where the system was simply matching the index — periods where they diverge are where systematic edge translated into real basis points.

A single trading signal can be sized prudently or aggressively, and the realized portfolio return scales accordingly. The Performance Matrix exposes three position-size scenarios so you can map the system to your own risk appetite without taking our word for it:

• 3.0% Conservative — A defensive sizing for traders whose primary objective is capital preservation. Lower CAGR, lower max drawdown, more stable monthly distribution.
• 5.0% Standard — The default Ordertune sizing. This is the configuration referenced in the headline KPIs and the published Sharpe and MAR ratios.
• 7.5% Progressive — A growth sizing for traders accepting larger drawdowns in exchange for compounding velocity. Use only if your risk tolerance and capital base support double-digit equity drawdowns without behavioral failure.

Switching between scenarios is non-destructive: it does not re-run a backtest, it re-weights the closed-trade history that already exists. The signals were identical across scenarios — only the dollar exposure per trade changed.

The Performance Matrix is a year-by-month grid covering 2015 to the current trading day. Each cell is the realized portfolio return for that calendar month at the selected position-size scenario, anchored to the closing date of each trade. Reading the matrix top to bottom reveals annual consistency — how often the system delivered a positive year. Reading it left to right within a year reveals seasonality — whether the alpha is concentrated in specific months (e.g. earnings-season heavy months in Q1/Q3) or distributed. Annual totals on the right margin let you compare year-over-year against the QQQ benchmark in the same row.

Underneath the aggregate statistics sits the full trading ledger — every closed trade across all nine strategies. Each row contains the ticker, the entry date, the exit date, the position direction (long or short), the executed open and close prices, the percentage return, the holding period in days, and the originating strategy model. This is the most aggressive transparency posture a signal service can adopt: there is no way to hide a losing month if every losing trade is enumerated in the ledger. Use the table to verify spot prices against your own data vendor, to inspect concentration (is the alpha coming from five trades or five hundred?), and to confirm that the headline CAGR is the arithmetic sum of what actually happened. For a deeper machine-readable view see the complete trading ledger.

The strategy filter isolates a single model out of the nine-strategy portfolio so you can audit one engine at a time. The full Ordertune portfolio is a deliberate ensemble of orthogonal edges: mean reversion (The Weekly Pulse, The Trend Quality Rebound, The Selective Sniper, The Deep Dip Strategy, The Peak Reloader), intraday liquidity capture (The Intraday Liquidity Hunter), panic-phase contrarian entries (The Precision Panic Predator), long-term momentum (The Momentum Powerhouse), and risk-on/risk-off rotation (The Risk-Flow Arbitrage). Filtering by one strategy lets you see its standalone equity curve, drawdown profile, and trade frequency — which is exactly what you would need to underweight or overweight individual models inside your own capital allocation. The full methodology for each strategy is documented on the Trading Strategies page.

The view selector controls two independent axes: which benchmark you compare against (the absolute Ordertune curve, the QQQ benchmark, or the Ordertune outperformance spread) and which lookback window the chart renders (the full 2015-onwards history, the last 5 years, year-to-date, etc.). The reason these are separated: a strategy that beats the index over 11 years can still lose to it over any rolling 3-year window, and you should be able to see that. We do not hide negative spread periods. If the Ordertune curve underperformed QQQ during the 2023 mega-cap melt-up, that period is fully visible at the appropriate lookback — and the system also captures the longer arc where systematic risk control pays off in regime changes.