Cross-asset strategy, macro positioning, and execution intelligence.

Engine 01 · Liquidity & Execution · Analytics & Automation

Cross-Asset Liquidity & Positioning Intelligence Engine: signal to execution, automated.

A desk-level execution intelligence framework that ranks when market-state changes should alter trade timing, order routing, and clip sizing; signals escalate only when magnitude, speed, crowding, and liquidity all converge simultaneously.

Architecture: Four fragility dimensions govern every signal: MOVE percentile (Normal <50, Watch 50–80, Escalate >80), depth change, spread state, and flow-risk classification (balanced / crowded / forced). The engine's decision outputs span UST/GoC, USD/CAD, WTI, and rates-vol; cross-asset interaction scores weight each signal for transmission risk before any execution decision is made. Single-indicator signals are filtered out as explicit false positives.

Workflow value: A four-phase implementation roadmap moves the desk from manual morning pack to an auto-refreshing Python/SQL pipeline with intraday alert routing, dashboard escalation, and a backtest-feedback loop for threshold tuning. Historical fragility reference points (Mar-2020 UST stress, UK LDI shock, vol-control unwind) calibrate the escalation ladder. The automation cuts the time between a market-state change and the desk response, and it puts the execution rule in writing before liquidity gaps out.

Engine 02 · Market Data · Quality & Monitoring

Market Data Health Monitor: a market-data feed, validated before the desk trusts it.

A data-quality monitor on a PySpark pipeline that validates a market-data feed, models it into a star-schema lakehouse, and publishes SLAs with column-level lineage, so regressions get caught before downstream use.

What it does: the pipeline ingests the feed, runs validation checks against the expected schema and value ranges, and models the clean output into a star-schema lakehouse. Each published table carries an SLA and column-level lineage, so a consumer can see where every field came from and whether the feed met its freshness and quality bar.

Why a desk cares: trade reporting, reconciliation, and position monitoring all inherit the quality of the data underneath them. The monitor applies the same discipline a desk applies to orders and positions, continuous checks with clear escalation when something breaks upstream.

Study 03 · US Equities · Factor Research

The US Value Premium, Measured: decay tested against the source data.

A factor study of US equities that tests whether the value premium has weakened, built from the Ken French Data Library and published as a live dashboard that recomputes from the source file on refresh.

Method: the study works from the monthly HML factor series in the Ken French Data Library and estimates the premium across rolling windows and subperiods. Inference uses Newey-West (HAC) standard errors, so the conclusions survive the autocorrelation in monthly factor returns instead of overstating significance.

Why a desk cares: factor premia sit underneath equity positioning, and the evidence quality matters as much as the estimate. The dashboard pulls the same public source the study used and recomputes the analysis, so the numbers on screen stay current rather than freezing at publication.

Engine 04 · Fixed Income · Rates & Portfolio Analytics

Tenor: Canadian rates, risk, and portfolio construction in code.

A fixed-income analytics engine, built from first principles in pure Python, that constructs the Government of Canada curve, prices a provincial portfolio, and produces the duration, key-rate risk, relative value, scenario P&L, and ESG allocation a rates desk runs on.

What it computes: a GoC par curve, then bond-level risk straight from the cash flows, modified and Macaulay duration, convexity, DV01, and key-rate durations at the 2y / 5y / 10y / 30y nodes, so the desk sees where on the curve the interest-rate risk actually sits rather than a single parallel number. Provincial bonds (Ontario, Quebec, BC, Alberta) are priced at their spread to Canada, with a relative-value screen flagging each issuer rich, fair, or cheap against the provincial sector.

Portfolio and positioning: it aggregates a roughly $212mm book to a weighted yield, modified duration, convexity, and total DV01 (about $181k per basis point), then fully reprices the book under parallel, bear-flattener, and bull-steepener curve moves rather than a linear estimate. A duration rebalance to target and a green-bond overlay complete it; the positioning note recommends trimming long-end duration, adding the cheap provincial on spread, and rotating long-end holdings into green 10s to cut duration and lift the ESG sleeve at once. North-star: a desk-ready positioning call an analyst can defend.

Note 05 · Cross-Asset Strategy · Macro Positioning

Cross-Asset Regime Transition: rates, oil, CAD, and liquidity positioning.

A probability-weighted institutional strategy note mapping regime transmission across US rates, USD/CAD, WTI crude, and volatility, built for desk-ready implementation at CIBC Global Markets FICC.

Regime read: Late-cycle inflation shock with policy divergence and fragile liquidity. Markets underprice both rates-vol persistence and liquidity fragility. This is not a clean CAD/oil bullish setup. The same oil move can be simultaneously CAD-positive (terms-of-trade), CAD-negative (USD/rates-vol channel), and duration-negative (inflation credibility) depending on which transmission channel dominates.

Core thesis: Trade the transmission path, not the headline. Best expression is conditional: CAD crosses over outright USD/CAD shorts, conditional steepeners over blunt duration, and event gamma over carry-only exposure. Asymmetry favors CAD short-squeeze (positive convexity if oil and BoC repricing align), belly duration convexity, and oil tail hedges via call-spread tails. A probability-weighted five-scenario matrix anchors the positioning framework: Sticky Inflation (30%), Growth Slowdown (25%), Commodity Shock (20%), Liquidity Tightening (15%), Risk-On Reflation (10%).

Framework 06 · Cross-Asset Strategy · Market Structure

Markets Thinking Framework: reflexive transmission, regime evolution, execution-aware decision-making.

The foundational mental model: how recursive feedback loops connect price, positioning, liquidity, and volatility, and what separates institutional-quality thinking from naive directional conviction.

Core standard: A view becomes institutional once it specifies confidence decay, failure modes, execution path, and expression quality. Modern markets are dominated by a recursive loop: price moves change positioning, positioning shifts change liquidity depth, liquidity changes bind volatility limits, which alter execution capacity and ultimately drive price discovery. The loop is self-reinforcing and must be anticipated, not reacted to.

Structure: Regime migration is mapped across four states (Calm, Transition, Fragile, Stress) with scoring across volatility, liquidity, crowding, and reflexivity dimensions; the Fragile quadrant is maximum on all four. An execution-aware decision tree sequences every trade thesis through signal validity, falsification conditions, expression choice (option / RV / delta), and execution mode (passive / aggressive). Three failure-mode overlays (policy reaction shifts, liquidity vacuum, reflexive reversal) each carry explicit adaptive responses. The output is a view on what the market underprices and on which structure survives an adverse path, read through the way the shock propagates.

Engine 07 · Cross-Asset · Event & Macro Pipeline

Corroborate: a macro event pipeline read against price.

A cross-asset event record ingesting rates, policy and filings on a 15-minute cadence, plotted behind price and market cap so a move and the news around it sit on the same vertical.

Architecture: three lanes, macro, markets and micro, over 87,000 stored events. The macro layer pulls the 10-year and 2-year, fed funds, CPI and unemployment from FRED and ingests Federal Reserve policy releases directly. The micro layer reads SEC EDGAR, where the 8-K item codes arrive inline at no extra request cost and turn a filing from “Apple filed an 8-K” into a stated reason.

Ticker tagging is case-sensitive on symbols and case-insensitive on names, which is not cosmetic: thirteen S&P constituents have tickers that are ordinary English words, so matching without regard to case tags Allstate on the phrase “all eyes are on the Fed”. Symbol matching is skipped entirely when text runs more than 60% uppercase. The coverage universe expands as companies recur in events beside covered names.

Paper 08 · Options & Volatility · Payoff Decomposition · CFA Society Toronto / Hillsdale

The Covered-Call Yield Illusion: an option overlay, decomposed.

A covered-call fund sells volatility and reports the proceeds as income. The paper decomposes the distribution against fund data and quantifies how much of the advertised yield is the investor's own capital coming back.

The structure: the overlay is long the underlying and short a call, so the holder is paid a premium for capping their own upside. That premium is compensation for selling volatility, and it is not the same thing as yield. Where the declared distribution runs above what the overlay actually earns, the balance is funded out of capital, and the marketing reports both halves as a single figure.

Why it sits on this site: reading a payoff for what it actually pays, and for who ends up carrying which risk, is the discipline structuring runs on. The paper was submitted for the CFA Society Toronto and Hillsdale award.

Engine 09 · Market Microstructure · Execution Cost

Order Book Mechanics and the Cost of Execution: what four ways of working the same order actually cost.

A limit order book built with strict price-time priority, and a 200-path study of one 50,000-share parent worked four ways, measured as Perold implementation shortfall with the unfilled remainder marked at the terminal mid.

The book: matching across levels at resting prices, so a taker earns price improvement where it exists and pays the next level once the touch is exhausted. Resting limits, market orders, IOC and FOK, partial fills and cancels. Every resting order records the volume queued ahead of it and drains that figure only as the orders in front of it trade, which is how queue position is earned and lost. Self-tested on priority, level-walking, IOC residue, all-or-nothing FOK, and queue drain.

The finding: the ranking flips depending on where you look. Posting passively is the cheapest strategy at the median, 3.3bp, and the most expensive at the 90th percentile, 39.4bp, because the 35% of the parent it never completes is marked at the terminal mid. Crossing the spread is the most expensive on average, 6.6bp, and carries by far the tightest distribution at 13.5bp of 90th-percentile cost. Aggression buys a narrow outcome, and the adaptive rule that posts on schedule and crosses when behind takes the lowest mean of the strategies that finish.

Why the measurement matters: pricing only the shares that filled makes an incomplete passive order look free. Charging the unfilled tail at the terminal mid is what exposes the trade-off, and it is the standard the industry measures against.