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Canli Capital

Research

Execution realism and model boundary

This page describes what AlphaForge actually models as of 2026-08-18. It is an engineering capability statement, not evidence of investment performance. The compatibility default remains NextOpenFill; research that claims bar-liquidity realism must explicitly select ParticipationCappedFill and publish that choice in its run config.

Implemented and tested

Risk Current contract Evidence boundary
Commissions and spreads Venue/asset-aware fees and half-spread in one shared cost model Unit-tested exact arithmetic
Nonlinear impact Square-root impact with a 1% ADV build cap and hard 5% validity edge Out-of-regime use fails loudly
Latency Configured adverse price add-on Placeholder until calibrated from paper fills
Partial fills Optional next-bar participation cap, lot-flooring, explicit canceled residual Missing/zero quote volume fails closed
Rejected/unfilled orders Missing bars, missing cost inputs, missing liquidity and final-bar orders are counted and labeled; broker recovery ingests full or partial venue executions No synthetic fallback fill
Holidays Equity D1 execution follows exchange sessions Weekend/holiday hopping is tested
Corporate actions PIT split and cash-dividend replay, lake NaN-to-null normalization for no-cash splits with non-finite dividends still rejected, queued-order split rescaling, and metadata-confirmed delisting force-flat No complex reorganizations, full historical coverage, dedicated delisting returns, or live broker path
Delistings Point-in-time membership and metadata-confirmed conservative administrative force-flat Dedicated delisting-return data remains a promotion requirement
Borrow and financing General-collateral equity borrow and stored perp funding accrue; optional PIT locates, dynamic fees, recall buy-ins, cash credit, margin debit, and segregated short-sale-proceeds rates No broad historical lending/rate coverage, multicurrency collateral, margin-call liquidation, broker reservation, restart persistence, auction replay or live broker path
Stale/missing data Signal staleness holds, price collars, missing-bar drops and feed-quality checks No bundled historical venue-outage coverage
Market status/outages Optional PIT OPEN/HALTED/OUTAGE/AUCTION_ONLY/CLOSE_ONLY replay blocks impossible fills; enabling it now requires full run-interval coverage for every instrument before bars are read, while strict source-byte-bound manifests, exact official/vendor reconciliation, and audit-hash-bound reviewed providers preserve PIT lineage and expose every missing/future-known millisecond No bundled historical status corpus, production feed adapter, empirical broad-market coverage evidence, auction-price model, smart routing or live status polling
Crowding and liquidation Optional PIT ownership, short-interest, borrow-utilization and flow gate; ADV-haircut liquidation stress in shared pre-trade No broad historical ownership/flow coverage, empirical unwind calibration or correlated-depth model
Operational controls Pre-trade limits, reconciliation, risk-event storage, drawdown ladder and absorbing kill switch; three consecutive fill-outcome reconciliation failures persistently engage the kill and block submission Live effectiveness still requires prospective paper evidence
Dated futures lifecycle PIT contract metadata, session-counted first-notice/last-trade exits, immediate-next rolls, locked-limit classification and variation margin Domain primitives only; no futures data, product calendar, cost schedule, backtest or broker path
Options lifecycle and surface integrity PIT terms/quotes/official settlement/assignment notices; premium-currency-local static-arbitrage checks; single-underlying active surfaces; displayed bid/ask-bound strike monotonicity and convexity; ratio-preserving multi-leg IOC/FOK replay with homogeneous premium currency, side-specific displayed-size caps and net debit/credit limits; optional per-leg OPEN/HALTED/OUTAGE/AUCTION_ONLY/CLOSE_ONLY replay with proven reduce-only integrity; exact PIT side/liquidity/event-scoped fee revisions with declared rounding, minima, caps and rebates; complete-matrix internal scenario margin with cross-leg netting, locked model/input hashes, short floors and concentration add-ons; signed cash/physical expiry; source-bound adjusted baskets; immutable SHA-256 OCC memo archive; strict reviewed manifests and exact vendor reconciliation Domain/data primitives only; no historical option-status corpus, complex-order auction model or live status polling/failover; internal margin is not broker/OCC/regulatory equivalent and excludes opening premium, margin calls and liquidation; no validated stress repricer/calibrated scenario corpus or verified historical fee corpus/production adapter; package replay assumes an atomic cross of independently displayed legs and has no fill probability, queue, price-improvement or beyond-size impact model; live OCC acquisition is Cloudflare-challenge-blocked; no historical fitted surface or reviewed adjustment corpus, parity/rate-dividend inputs, exercise model, backtest or broker path

Participation-capped fill semantics

For an order presented to a bar with open price P, observed quote volume V, lot size L, and participation ceiling p, executable quantity is:

min(order_qty, floor((p × V) / (P × L)) × L)

The executed slice is priced by the same commission, spread, impact and latency model used by the default fill. If the slice is smaller than the order, the engine records partial_fill_residual_canceled; it does not silently assume the remainder traded. If quote volume is absent/non-positive or the cap cannot support one valid minimum lot/notional, the engine records dropped_no_bar_liquidity and executes nothing.

Residual cancellation is intentionally conservative and deterministic. Multi-bar child-order scheduling, queue position and venue-specific rejection probabilities remain future work.

Local engineering benchmark

The reproducible synthetic microbenchmark executes 100,000 fill-model calls per repeat across seven repeats on the recorded CPython 3.12.13/arm64 Darwin runtime. In the 2026-08-17 snapshot, median call time was 887 ns for the compatibility full-fill path and 2,004 ns for the participation-capped path (2.26× the baseline). Deterministic checksums accompany every timing sample.

These figures measure isolated Python fill-call overhead on one local machine. They are not end-to-end backtest throughput, capacity, latency-to-market, or return evidence. The complete machine-readable samples, workload, runtime metadata, guardrails and source hashes are published at /glassbox/execution_models_benchmark.json.

Explicitly not implemented

  • End-to-end securities lending: historical quote/locate/recall ingestion, broker reservations, restart persistence, buy-in auction replay, and live reconciliation.
  • End-to-end financing: historical rate schedules, multicurrency cash/collateral, broker-specific haircuts and rehypothecation, margin calls/liquidation, and live reconciliation.
  • End-to-end futures ingestion and trading: product calendars, dated quotes/settlements, fees and margin schedules, continuous-series attribution, broker routing, and historical limit/outage replay.
  • End-to-end options ingestion and trading: historical fitted surfaces, put-call-parity inputs, operational unattended OCC acquisition (direct and headless-browser clients received a Cloudflare HTTP 403 managed challenge on 2026-08-18), automated PDF text extraction, reviewed historical adjustment coverage, a production vendor adapter, calibrated early-exercise/ assignment modeling, quote repair, a verified historical fee corpus, broker-equivalent margin, validated scenario repricing, opening-premium collateral, margin calls/liquidation, complex-order-book execution, queue and beyond-displayed-size impact, historical option-status ingestion, complex-order auction execution, live outage polling/failover, backtest ledger, and broker reconciliation.
  • Calibrated queue position, partial-fill continuation, auction imbalance/price formation, a content-verified historical status corpus with production adapters, and live outage failover.
  • Historical crowding coverage and calibration: point-in-time ownership/flows, cross-manager overlap, and correlated liquidation-depth feedback.

No sleeve may claim these risks are modeled merely because the platform has a generic cost or staleness control. Candidates requiring one of these capabilities remain data- or engine-gated.