Pipeline | Tick to Position
Signal Pipeline
Plate 01 | Core Engine
The Feature Matrix
Five modules. Select one to inspect its spec sheet.
Level 7 Trail
Basket-aware trailing that raises the floor under winners in 7 steps. Avg% for market-relative management, points for hard precision.
Advanced Trailing
Seven steps, two formats. Gains protected without choking the move.
Level 7 Trail
Partial Closes
Trim on milestones. Edge compounds, exposure falls.
Dynamic Trimming
Risk Modules
Daily caps, equity guard, lot throttling on margin health.
Equity Guard
Time and News Filters
Session windows plus macro pause and resume. No chaos trading.
Session Sovereignty
Prop-Firm Tooling
Daily and total drawdown caps, stealth execution, multi-account mirror.
Evaluation Rails
Multi-Asset and Market
Forex, crypto, stocks, commodities. MT4, MT5, Rust terminals.
Cross-Platform
Plate 02 | Grid Doctrine
The Core Grid Engine
Non-predictive by design. Noise cannot be forecast; mean reversion, exhaustion and volatility expansion can be measured. The engine buys dips in small steps instead of one big bet.
01
Counter-Trend Edge
Absorbs expansion liquidity, profits on pullback and rotation.
02
Structured Progression
Spacing, ceilings and throttles cap exposure across 20 domains.
03
Basket Life-Cycle Exits
Trims and 7-stage trail drag break-even toward price.
Precision Grid Orchestration
Each entry recalculates the basket target, pulling the required exit closer to price. Capital spreads over 20 domains so no single swing concentrates margin.
Fill noise, slippage, spread bursts, tick delays - edge verified before live capital. Full protocol in Plate 05.
Plate 03 | Survival Systems
Risk, Time, Compliance
Built to survive margin pressure, news spikes, thin sessions and evaluation rules.
Trigger state persists through restarts and session transitions. The system never forgets.
Hard algorithmic limits enforcing prop firm drawdown rules and equity integrity in real time with stealth low-friction execution.
Counter-exposure coupling that dynamically flips net bias or scales opposite lots on volatility breach.
Plate 04 | Proof, Not Promises
Backtesting and Robustness Lab
The algorithm is the hypothesis. The pipeline is the answer: 10,000+ verified backtests, an untouched out-of-sample era, Monte-Carlo stress gates, and a fleet of gradient-boosted tree models - including XGBoost - that reads 42 internal logics to decide which candidate deserves silicon next.
Every claim carries a run ID. An append-only ledger of 10,500+ real backtest runs with deterministic run identity and epoch rebases when market data changes. Nothing is anecdote - every published number traces to a specific, re-runnable run.
Not a cherry-picked record - honest statistics. 200+ ranges spanning 3 independent regimes: institutional 2004-2015, 2016-2019, and the modern tick era - and the catalog keeps growing as new data lands. A fully untouched out-of-sample holdout is never optimized against and serves as the champion ceremony's final judge.
Champions are not declared - they are survivors. Staged promotion: clean, latency, in-EA Monte-Carlo stress, cost stress, forward paper. Fail any gate and the candidate is blocked. Vacuous stress axes were identified and documented instead of quietly ignored.
Latency modeled as a distribution, not a single number. Per-range gates: strict profitability at 150ms, survival at 300ms, an ideal ceiling, and a hard veto on jitter. Ranking uses probability-weighted expectation per range - never a single average across eras. A measured live latency anchors the ladder.
The machine that reads the machine. Per-range surrogates predict profit, drawdown and profit factor before a burn; feature importance surfaces which of the EA's 42 logics drive outcomes; dead-knob detection kills wasted sweeps. Full fleet detail in Plate 04A. Bayesian optimization plus low-discrepancy mass-sweeps propose candidates; active learning ranks the next experiment by insight per compute dollar.
A backtest factory, not a laptop with a terminal open. Parallel terminals driven through a typed control protocol, orchestrator manifests, campaign chunking and clean resume after mid-flight stops. One driver per manifest, orchestrated like a data center - not hobby scripts.
The compounding asset. A structured knowledge base: full map of 42 logics across 20 groups, per-range learnings, distilled lessons queryable by agents, and champion doctrine. Explicitly built as training fuel for a future model - today it is the agents' shared memory; tomorrow it is the fine-tune.
Plate 04A | Model Fleet
The Models Trading The Algorithm
The EA is not a black box - it is a 42-logic machine. A second machine reads the first one. Four gradient-boosted tree model systems (a fleet that includes XGBoost) sit between the algorithm and raw market history. The strongest result so far is blunt: the models proved some parameters do nothing, and we removed them from the search space. This is not "AI trading." This is machine learning as infrastructure - the EA trades; the ML decides what to test next.
The per-range surrogate scored every knob against baseline. On any range where the model cannot separate a setting from baseline, the parameter is dead - it does nothing. Those knobs were cut from the optimization search space. Falsifiable, concrete, and the reason wasted simulation cycles drop.
XGBoost panel model: per logic, per market regime - who makes money where. The EA runs 42 independent logics across 20 engine groups. The map says which earn in a 2004-2015 squeeze, which die, which only work in modern tick regimes. Refreshed on every retrain.
Meta-labeling in the Lopez de Prado pattern: not "does this strategy make money" but "does this exact trade right now make money." Trained on 20,522 real extracted deals; emits win-probability and magnitude per closing deal. The actual technique big-fund quants use - built on retail hardware. Proof-of-concept stage, labeled honestly.
Classifier for drawdown cascades before they happen: family concentration, unrealized pain, regime condition -> P(cascade next N bars). Early warning most retail stacks do not have. Still in development; shown as lab work, not as a live gate.
Gradient-boosted trees from knob settings straight to profit, drawdown, and profit-factor per range - so simulation burns only on ideas that forecast well. Also the engine behind dead-knob detection. Agents call it daily through the MCP predict-range tool.
Plate 04B | Agentic Floor
The Agentic Pipeline
One laptop. That is the whole stack. The rest is the same cloud and local LLMs everyone can rent - the difference is a factory floor. Agents read the same bootstrap doc, follow the same laws, append to the same ledger, and ask the same knowledge base before acting. Cloud models reason; local models recall. The pattern is not "agent generates code." Agents are the researchers; the backtester is the lab; the ledger is the truth.
Agents form hypotheses, burn compute on evidence, reject their own intuition when the ledger disagrees, and hand institutional memory to successors in a format built for a future fine-tune. A fund has departments: research, backtesting, validation, domain expertise. This stack has lanes - the workflows are equally formal.
A custom MCP server gives every agent the same toolkit. Lessons are distilled from measured intervention outcomes - knock out logic X, watch range profit move - not vibes.
The 42-logic algorithm trades gold. The system around it is the actual edge. Machine-learning infrastructure and agent lanes close the department gap a one-person shop would otherwise carry alone - without pretending the lab models are production when they are not.
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