Forward-Looking Hedging for Quant Portfolio Managers
Quantitative TradingReplacing three separate manual processes with one system for forward-looking hedging signals, full options-chain access, and live position-level risk.
Overview
A quantitative research and trading operation needed forward-looking hedging signals, systematic access to the full options chain, and live position-level risk, running as three separate manual processes on three different schedules. Canvas Labs Partners built a regime-forecasting model, an options data warehouse, and live portfolio tracking, replacing each manual process with a single system.
30pp lower drawdown exposure
to the S&P 500, via regime-based hedging
15pp lower tail-risk drawdown
via the options data warehouse
Challenge
Portfolio managers needed three things that did not exist as one system: forward-looking hedging signals, systematic access to the full options chain, and live position-level risk. Each ran as its own manual process on its own schedule, so a hedging decision, an options-chain lookup, and a risk check never drew on the same up-to-date picture of the portfolio at once.
Solution
Canvas Labs Partners built a macroeconomic regime-forecasting model, drawn from academic research, to support institutional hedging decisions ahead of drawdown events rather than after one has already begun. The model gives portfolio managers a forward-looking signal to act on, which is exactly what the old manual process never had.
A multi-terabyte options data warehouse, architected on a Medallion Bronze/Silver/Gold pipeline in BigQuery, gives systematic access to the full options chain instead of pulling it together by hand for each decision. Raw data moves through Bronze, gets cleaned and structured in Silver, and reaches Gold as the trusted layer the trading workflow queries directly, every single time.
Live tracking of portfolio Beta, P&L and exposure runs directly inside the trading workflow, built in Python on top of the same BigQuery warehouse that holds the options data. A portfolio manager sees position-level risk update as positions change, instead of waiting for whatever schedule the manual risk check used to run on.
Results & benefits
Regime-based hedging cut S&P 500 drawdown exposure by 30 percentage points, giving portfolio managers a forward-looking signal ahead of drawdown events rather than a reaction after one had already started. The options data warehouse cut tail-risk drawdown by a further 15 percentage points, once systematic access to the full options chain replaced pulling that data together by hand. Live tracking of portfolio Beta, P&L and exposure now runs inside the trading workflow itself, so what used to be three separate manual processes on three different schedules is now one system a portfolio manager checks in one place.
Customer
Quantitative trading operation
Services
- Trading & quantitative infrastructure engineering
- Risk modeling & hedging systems
- Data warehouse architecture
Tech stack
About us
Canvas Labs Partners is a software consulting team that designs and ships production agentic AI systems: multi-agent pipelines, tool-calling architectures, and human-governed automation, together with the trading infrastructure, blockchain, mobile, and product engineering those agents plug into. We publish our work as a case ledger rather than a portfolio, so every claim on this site links to the engagement behind it. Engagements follow the same four-step discipline, Map, Bound, Instrument and Govern, whatever the stack, and every entry in the ledger carries a result we are willing to be measured against.
Delivered individually, in a prior role, by one of Canvas Labs Partners’ founding team members. Not by the firm itself.
Have a system like this one?
We built this one end to end. If you are facing something similar in quantitative trading, book a free discovery call and we will tell you what it would take.