Znder Inc

Options &amp; Portfolio Strategy Analyst — AI &amp; PythonAbout Us
We are an investment firm focused on U.S. equities, with fundamental research and long-term value creation at the core of our investment approach.
We complement fundamental investing with options strategies, disciplined trade execution, portfolio risk management, and technology-driven research tools to improve portfolio outcomes.
We are seeking an Options &amp; Portfolio Strategy Analyst who combines strong investment judgment with practical options expertise and Python/AI capabilities.
You will work closely with our fundamental research team to translate company research, valuation ranges, and investment theses into actionable portfolio and trading strategies.
This is an investment and portfolio strategy role first. Python, data analysis, and AI are important tools for improving research, monitoring, risk analysis, and execution—not substitutes for sound investment judgment.
What You Will Do
You will help answer practical portfolio questions such as:
<ul>
<li>For a stock we want to own, should we buy immediately, build the position gradually, wait for a better entry point, or sell a cash-secured put?</li>
<li>For an existing position, should we hold, add, reduce exposure, write covered calls, hedge, or exit?</li>
<li>How should strike prices, expirations, position sizes, and coverage ratios be selected?</li>
<li>How can we generate incremental portfolio income without taking inappropriate risk or sacrificing too much upside?</li>
<li>When is the best decision simply to wait and not trade?</li>
<li>How can Python and AI make our investment research, monitoring, risk management, and review processes more efficient and reliable?</li>
</ul>
Key Responsibilities1. Portfolio Entry &amp; Exit Strategy
Partner with fundamental analysts to understand investment theses, valuation ranges, target entry prices, reduction/exit levels, and thesis-break conditions.
Evaluate market price, valuation, liquidity, volume, upcoming events, volatility, and option pricing to recommend appropriate entry, add, reduce, or exit strategies.
Compare alternatives including:
<ul>
<li>Direct stock purchases</li>
<li>Limit orders</li>
<li>Staged position building</li>
<li>Cash-secured puts</li>
<li>Holding cash and waiting</li>
</ul>
For each recommendation, clearly communicate expected outcomes, capital requirements, key risks, opportunity costs, and exit conditions.
You should also be comfortable recommending no trade when market conditions or risk/reward do not justify action.
<ol start="2">
<li>Options Strategy &amp; Position Management</li>
</ol>
Evaluate and implement appropriate options strategies, including:
<ul>
<li>Covered Calls</li>
<li>Cash-Secured Puts</li>
<li>Protective Puts</li>
<li>Collars</li>
<li>Basic option spreads</li>
</ul>
Analyze appropriate strike prices, expiration dates, contract quantities, and position coverage.
Distinguish between long-term core holdings and positions where active options management is appropriate.
Evaluate the trade-off between option premium income and potential limitations on portfolio upside.
Develop clear plans for expiration, assignment, closing positions, and rolling contracts. Any roll recommendation should clearly identify its cost, incremental risk, and rationale.
Where appropriate, evaluate securities-lending opportunities, including availability, lending rates, broker revenue sharing, utilization, and net portfolio income.
<ol start="3">
<li>Options Pricing &amp; Market Analysis</li>
</ol>
Analyze option chains and market liquidity, including:
<ul>
<li>Bid/ask spreads</li>
<li>Trading volume</li>
<li>Open interest</li>
<li>Market depth</li>
<li>Implied volatility</li>
<li>Historical volatility</li>
<li>Term structure and strike-level pricing differences</li>
</ul>
Apply Delta, Gamma, Theta, and Vega to understand how price, time, and volatility changes may affect portfolio positions.
Monitor earnings announcements, dividends, major corporate events, and macroeconomic events that could materially affect option pricing or portfolio decisions.
Analyze unusual options activity carefully while recognizing the limitations of what options-flow data can actually demonstrate.
<ol start="4">
<li>Portfolio Risk Management</li>
</ol>
Monitor portfolio-level and single-name risks, including:
<ul>
<li>Directional exposure</li>
<li>Position concentration</li>
<li>Volatility exposure</li>
<li>Liquidity risk</li>
<li>Option assignment risk</li>
<li>Capital requirements</li>
</ul>
Evaluate potential cash requirements if multiple short puts are assigned simultaneously and the portfolio impact if covered calls are exercised.
Perform scenario and stress analysis for significant price declines, rapid rallies, volatility shocks, and deteriorating liquidity.
Compare risk-management alternatives such as reducing exposure, protective puts, collars, and index hedges.
Establish appropriate position-size limits, risk triggers, exit rules, and exception-handling procedures.
Maintain complete and auditable records of investment recommendations, assumptions, and outcomes.
<ol start="5">
<li>Python, Data &amp; AI Automation</li>
</ol>
Use Python and financial-data APIs to collect, clean, analyze, and organize equity, options, portfolio, and transaction data.
Develop automated monitoring tools for:
<ul>
<li>Stock price and valuation ranges</li>
<li>Option pricing and volatility</li>
<li>Portfolio risk</li>
<li>Earnings and corporate events</li>
<li>Option expirations</li>
<li>Positions requiring review or action</li>
</ul>
Use AI tools to improve information extraction, coding, research summarization, anomaly investigation, and report generation.
AI-generated outputs must be independently validated. Critical market data, calculations, contract specifications, and trading parameters should always be verified before being used in investment decisions.
Build reusable analytical tools with appropriate logging, error handling, alerts, historical records, and documentation.
<ol start="6">
<li>Backtesting &amp; Performance Review</li>
</ol>
Conduct historical backtests, out-of-sample testing, and simulated portfolio tracking.
Backtests should appropriately account for factors such as:
<ul>
<li>Bid/ask spreads</li>
<li>Transaction costs</li>
<li>Execution assumptions</li>
<li>Option assignment</li>
<li>Capital utilization</li>
<li>Data quality</li>
</ul>
Avoid look-ahead bias and excessive parameter optimization, and clearly document assumptions and limitations.
Compare strategies against appropriate benchmarks, including direct stock ownership, the original investment plan, or holding cash.
Track both realized and unrealized P&amp;L and distinguish among:
<ul>
<li>Equity gains/losses</li>
<li>Option premium income</li>
<li>Hedging costs</li>
<li>Transaction costs</li>
<li>Opportunity cost from foregone upside</li>
</ul>
Regularly review which strategies created incremental value, where assumptions or decisions were incorrect, and how portfolio rules should be improved.
Key Deliverables
Daily Monitoring Summary Key market developments, portfolio risks, upcoming expirations, significant events, and positions requiring decisions.
Trade Recommendation Memo Underlying security, strategy, contracts, target prices, quantities, capital requirements, investment rationale, scenario P&amp;L, risks, and exit conditions.
Weekly Portfolio Review Equity and options performance, changes in portfolio risk, transaction costs, major decisions, and priorities for the following week.
Monthly Strategy Review Incremental performance relative to appropriate benchmarks, drawdowns, opportunity costs, execution quality, and recommended improvements.
Automation &amp; Analytical Tools Reusable code and tools for data processing, options analysis, portfolio monitoring, alerts, and reporting, with appropriate documentation.
QualificationsRequired
<ul>
<li>Experience in U.S. equities, options research/trading, portfolio analysis, or investment management.</li>
<li>Strong understanding of options strategies, including Covered Calls, Cash-Secured Puts, Protective Puts, Collars, and basic spreads.</li>
<li>Strong understanding of option pricing, volatility, Greeks, assignment/exercise mechanics, and margin/capital requirements.</li>
<li>Fundamental investment and valuation knowledge, including the ability to understand earnings expectations, valuation changes, and company-specific events.</li>
<li>Strong Python skills for financial analysis; experience with pandas, NumPy, or comparable analytical libraries.</li>
<li>Experience working with APIs, data-quality validation, backtesting, and data visualization.</li>
<li>Practical experience using AI tools to improve research or analytical workflows while independently validating outputs.</li>
<li>Ability to read and analyze English-language financial statements, market research, and technical documentation.</li>
<li>Strong analytical judgment, risk awareness, and ability to explain complex investment strategies clearly.</li>
<li>Highly detail-oriented, self-directed, and capable of maintaining auditable research and decision records.</li>
</ul>
Preferred Qualifications
Experience in one or more of the following areas is highly desirable:
<ul>
<li>Asset management, family office, proprietary trading, hedge fund, or brokerage environment</li>
<li>Direct management of a live options portfolio</li>
<li>Portfolio performance and drawdown attribution</li>
<li>Brokerage APIs, order-management systems, or automated monitoring</li>
<li>Securities lending and borrow-rate analysis</li>
<li>SQL, Git, databases, and scheduled data pipelines</li>
<li>AI agents, financial-data engineering, or investment-research automation</li>
<li>Understanding of U.S. equity and options tax considerations and the ability to identify matters requiring professional tax review</li>
<li>Bilingual Mandarin Preferred</li>
</ul>
Work Authorization :H1B sponsorship is available for qualified candidates
Performance will be evaluated across several dimensions:
<ul>
<li>Net incremental portfolio value relative to established benchmarks</li>
<li>Risk identification and drawdown management</li>
<li>Compliance with portfolio risk limits</li>
<li>Quality and practicality of investment recommendations</li>
<li>Quality of post-trade analysis and continuous improvement</li>
<li>Accuracy and reliability of data, code, and automated processes</li>
<li>Collaboration with the fundamental research team</li>
<li>Ability to identify, explain, and learn from unsuccessful decisions</li>
</ul>
Performance will be evaluated over an appropriate investment horizon rather than based solely on short-term trading results.
Application &amp; Interview Process
Please submit your resume along with at least one of the following:
<ul>
<li>An options or portfolio-strategy research report</li>
<li>A trading or portfolio review showing returns, risk, drawdown, and benchmark comparison</li>
<li>A Python, financial-data, or AI automation project, including code or a demonstration</li>
</ul>
Selected candidates may be given a practical investment case.
For example, you may receive an existing portfolio, available cash balance, and fundamental valuation ranges and be asked to compare direct stock transactions, holding the existing position, options strategies, and securities lending opportunities.
We are primarily evaluating your decision-making process, understanding of risk, analytical accuracy, and ability to communicate a clear and actionable investment recommendation.
Pay: From $3,000.00 per month
Benefits:
<ul>
<li>Employee discount</li>
<li>Flexible schedule</li>
<li>Health insurance</li>
<li>Paid time off</li>
</ul>
Language:
<ul>
<li>Mandarin (Required)</li>
</ul>
Work Location: In person

Options & Portfolio Strategy Analyst — AI & Python

Options & Portfolio Strategy Analyst — AI & Python

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