Stranum's Market Making Algorithmic software uses long calendar spreads to trade index fund options.

Hedge stock portfolio holdings by profiting from volatility and time decay while maintaining low-cost exposure to market movements.

Utilize long calendar spreads on liquid index options (e.g., SPY, QQQ) to minimize risks associated with market downturns or volatility spikes.

Use the software as a stand-alone tool to generate incredible returns with a minimal risk profile.

Stranum works through a software licensing agreement, so we don't even handle your money.

Key Features

• Trades option on highly liquid index ETFs like SPY and QQQ

• Ensures tight bid-ask spread and efficient pricing.

• Allocates hedging exposure proportionately across portfolio holdings, targeting specific sectors or indices relevant to the portfolio's composition.

• Buys longer-term options and sells shorter-term options.

• Matches strike prices to create calendar spreads at or near the current market price (ATM).

Inputs and Parameters

• The algorithm optimizes the Theta decay differential between long and short options to maximize profitability.

• Sets rules for rolling over short-term options to the next expiry to maintain the spread.

Risk Tolerance

Defines maximum capital allocation per trade and overall hedge exposure as a percentage of portfolio value.

Volatility Metrics

Tracks implied volatility (IV) levels to identify favorable entry points, e.g. low IV for long options purchase trigger.

Trade Execution

Algorithm initiates trades when IV skew is favorable, indicating potential profitability from time decay.

Calculates optimal contract size based on portfolio size, delta, and risk limits.

Uses historical IV percentiles to determine underpriced options.

Monitors short-term options nearing expiry and rolls them forward to maintain the spread and manage exposure.

Performance Monitoring

Integration with Portfolio Management

• Separately tracks realized and unrealized gains from calendar spreads.

• Measures the algorithm's impact on overall portfolio volatility and hedge effectiveness.

• Continuously backtests using historical data to refine entry/exit criteria and improve returns.

• The algorithm adjusts hedging based on changes in portfolio composition, size, and market conditions.

• Uses real-time market data feeds to adapt to changes in volatility, pricing, and liquidity.