This Year’s Portfolios.

Systematic trading strategy for natural gas calendar spreads. The primary driver is quantifying storage stress in salt caverns.

Seasonal storage stress patterns are typically already priced into the market. This portfolio aims to identify abnormally large stress events to find an edge.

Pulls data from national and institutional databases that report on storage flow and regional pricing. Trades execute through IBKR.

Great for analysts who want to understand energy markets, commodities pricing, and data engineering.

When the spread between an interest-rate swap and a Treasury bond of the same maturity becomes unusually large, a relative-value position can seek to profit as the spread converges.

This portfolio hopes to test various sophisticated risk management strategies and techniques (DV01 hedging, volatility targeting, etc.) with the goal of maximizing risk-adjusted returns.

Analysts will lead R&D work that extends this strategy through backtesting, machine-learning methods, and practical implementation. Through this work, you will develop a deeper understanding of derivative instruments and interest-rate markets.

Systematically selling options volatility around earnings announcements. Option prices carry a "fear premium" before earnings; the moment the news drops, that premium collapses (the IV crush). We test whether harvesting it is profitable after realistic costs.

Not every earnings event is worth selling. We filter using the options term structure: how elevated near-expiry implied volatility is versus longer-dated options, relative to each name's own history. Only the richest setups get traded.

A regime-switching long/short gamma strategy on SPY options that exploits the volatility risk premium — going long gamma via ATM straddles when implied vol is cheap, short gamma via OTM strangles when it's rich, with hourly delta hedging to isolate pure vol exposure.

What you'll do: build a realized volatility forecast engine using machine learning to feed signal generation, connected to live paper execution through IBKR's API.

Test out different methods of going long/short gamma such as straddles and strangles.

Research, build, and test deep hedging as the primary risk management technique.

Retail prop firm futures trading has ballooned in popularity recently due to the interesting payout convexity attributes that these online firms offer. Most retail traders choose to use guru strategies that don't have any live market edge nor prop firm edge.

This portfolio is about finding algorithmic edges that exploit the nature of the prop firm account structure.

This portfolio will aim to fully automate simulated trading and create a mathematically positive EV strategy that is optimized dependent on account rules, prop firm, and account size.

Building a systematic trading strategy based on momentum — the concept that assets that have been rising tend to continue rising for a period — incorporating game theory to account for the behavior of other investors chasing the same trends, which can lead to crowded trades and diminished returns.

The objective is to create a strategy that captures the upside of strong markets while significantly minimizing losses during market crashes compared to the broader market.

The strategy is tested across multiple asset classes, sectors, countries, styles, bonds, and commodities to ensure its effectiveness is a genuine edge rather than in-sample noise.

This portfolio is focused on bonds and credit trading.

Our system trades US Treasury and corporate bond ETFs around moments when big players like index funds and insurers are forced to buy or sell at predictable times.

As an analyst you will help refine how these strategies operate and dig into the data we own to find new sources of edge.