Caliber Patterns is Caliber's lead strategy for NQ and MNQ futures — classical chart-pattern detection, session-aware risk, and a development process where nothing gets promoted to live without a logged reference case behind it. Caliber's original structural-level engine (v1.7.1/v1.8) continues running alongside it.
Three principles govern every decision the engine makes — none of them optional, none of them adjustable mid-session.
Every entry is anchored to a real price level — prior day, week, or month highs and lows, Asian session extremes — not an indicator lagging behind price.
London, New York, and Asian sessions are treated as genuinely different markets — each with its own blackout windows, risk posture, and entry rules.
No feature ships without a logged reference case first. Backtest evidence precedes every version. Forward-test evidence precedes every live decision.
Rendered in Caliber's own live dashboard format — the same readout used to monitor every session in production. This is the base MAIN line: the confirmed, live-tested Caliber Patterns product, not an experimental variant.
| Metric | Value | Detail |
|---|---|---|
| Total P&L | +$16,940.00 | — |
| Profit Factor | 1.88 | — |
| Max Drawdown | — | 17.8% |
Caliber's original structural-level engine continues running in production alongside the new lead strategy. Its own schedule-effect finding, shown below, is specific to v1.7.1 — not Caliber Patterns.
The process a feature has to survive before it ever reaches a live account.
Every proposed change starts as a documented, observed instance — not a theory about what might work.
Historical evidence is gathered before a single line of live logic changes, with walk-forward validation against unseen data.
A change earns its place in production only after proving out in real, forward-moving market conditions.
Complete builds ship at defined milestones. No incremental edits to a script that's actively trading.
Backtested and simulated results are a starting point, not a guarantee. Every strategy's internal record and its real, broker-confirmed outcome are two different things — and the gap between them is treated as seriously as any other risk in the system.
Caliber's own monitoring exists to catch that gap in real time, not just to report a return figure and move on.
A standalone classical chart-pattern recognition system — evidence-gated independently of Caliber's original structural-level engine, and now the strategy Caliber leads with. v1.7.1/v1.8 continues running alongside it, not replaced by it.
Head & Shoulders and Inverse H&S, Double Top and Double Bottom, Rising and Falling Wedges, Ascending and Descending Channels, and Symmetrical Triangles — detected from swing-pivot structure, not a lagging indicator.
Its own codebase, its own version line, its own evidence trail. Nothing here touches Caliber's live structural-level files — a failed idea stays contained, a working one doesn't inherit token debt it didn't earn.
Shipped as pure pattern detection before a single order was ever placed. Only after the read was trusted did it become a single-entry bot with a fixed bracket — and now a P1/P2/runner fork under separate test.
| Metric | Value | Detail |
|---|---|---|
| Total P&L | +$38,220.00 | +382.20% |
| Win Rate | 60.8% | 62 / 102 trades |
| Profit Factor | 2.389 | — |
| Max Drawdown | $3,360.00 | 7.0% |
This is the strongest of five variants tested on the same 90-day window — and it's still an experimental trial, not the base "Caliber Patterns Bot" product. It adds a higher-timeframe directional bias filter (a 4H EMA read) on top of the same pattern-detection engine: longs only fire above it, shorts only below. The base MAIN line, running the confirmed asymmetric long/short bracket without this filter, returned +$16,940 (PF 1.88, 17.8% max drawdown) over the identical window — a real, live result, just a smaller one.
Worth knowing before reading too much into the number above: one of the other four variants tested on this same window (a P1/P2/Runner partial-exit fork) showed a max drawdown that took simulated equity negative against its $10,000 starting balance, driven entirely by its long side. Five different exit and filter architectures, tested on identical data, producing results this far apart is itself the evidence for why nothing here gets promoted to the base bot without its own forward-test track record — a strong backtest number is a candidate, not a conclusion.