High-performance derivatives valuation, Markowitz frontier optimisation, signal backtesting, and stochastic risk simulations.
This tool helps you calculate the fair price of an options contract. It uses famous mathematical models (like Black-Scholes) to figure out what an option should cost today, and shows exactly how sensitive that price is to things like time passing, stock price changes, and market volatility.
Awaiting calculation...
Next step
Calculate an option to unlock payoff, break-even and risk interpretation.
Position Interpretation
Run a calculation to translate the option price into break-even, payoff and risk intuition.
Break-even at expiry
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Underlying price needed to recover premium.
Intrinsic value
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Value from exercising immediately.
Time value
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Premium paid for remaining uncertainty.
Moneyness
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Relationship between spot and strike.
Profit after subtracting the theoretical premium paid today.
What to take away
Run an option calculation to generate a plain-English interpretation.
Greek translation
This section takes a list of stocks and figures out the smartest way to mix them. By looking at how the stocks move together over time, the algorithm calculates the exact percentage of your money you should put into each one to get the highest expected return for the lowest possible risk.
Enter 2 to 5 ticker symbols to find the mathematically optimal portfolio weighting.
Before risking real money, you need to know if a trading idea actually works. This tool lets you test out different trading strategies on years of historical market data. It calculates exactly how much money you would have made (or lost) and compares it to simply buying and holding the stock normally.
Return earned per unit of total volatility.
Return relative to downside volatility only.
Cumulative return difference over this sample.
Next step
Analyse a strategy to unlock drawdowns, trade quality and risk-adjusted diagnostics.
Backtest Diagnostics
Run a strategy to reveal drawdowns, trade quality, market exposure and risk-adjusted performance.
Completed Trades
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Trade Win Rate
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Time In Market
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Worst Drawdown
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Shows how far the strategy sits below its previous equity peak.
Latest completed long-position segments.
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This engine simulates thousands of correlated market scenarios to project extreme portfolio losses. It calculates Value at Risk (VaR) to establish a baseline loss threshold, alongside Expected Shortfall (CVaR) to estimate the magnitude of severe tail-risk events.
MC 99% VaR
MC Expected Shortfall
Historical VaR
Historical ES
Same portfolio, different risk assumptions.
Losses beyond the VaR threshold form the tail.
Run an analysis to decompose portfolio VaR.
Interpretation Guide
The models answer different questions. Read them together rather than treating any single number as a complete measure of risk.
VaR is the loss threshold at the chosen confidence level. Expected Shortfall asks how severe losses are on average once that threshold is breached.
Component VaR shows which holdings are driving portfolio risk after correlations are considered. A large weight does not always mean a large risk contribution.
Delta measures directional option exposure, Gamma measures curvature and Vega measures sensitivity to volatility. These explain how derivatives reshape the portfolio's P&L.
Stress tests ask “what if markets move sharply?” They are scenario analyses, not forecasts. Compare the equity, options and total P&L to see where protection or extra downside comes from.
Important Legal Notice: All mathematical models, derivatives pricing, and algorithmic backtesting simulations provided on SethiQuant are strictly for educational and portfolio demonstration purposes. This terminal does not provide financial, investment, or trading advice. Algorithmic outputs (including Markowitz allocations and Black-Scholes valuations) rely on historical data and theoretical formulas, which do not guarantee future market behaviour. Always conduct independent fundamental research and consult a licensed professional before executing real capital allocation.
SethiQuant is continuously evolving. Let me know what quant model, pricing engine, or backtest feature we should build next.
Add one instantaneous market shock alongside the four preset scenarios. Options are fully repriced under the shocked spot and volatility state.
Add up to three European options. VaR uses a Delta–Gamma–Vega approximation with simulated spot and volatility shocks.
Positive contracts are long; negative contracts are short. Contract multiplier is 100. The approximation also includes Black–Scholes theta over the selected VaR horizon.
Student Guide
The theoretical option value is treated as the premium paid today for one share of option exposure. The expiry P&L chart subtracts this premium from the option's payoff at expiry.
A call must finish above strike plus premium to break even. A put must finish below strike minus premium. Crossing the strike only creates intrinsic value; it does not automatically create a profit.
Intrinsic value is what immediate exercise would produce. Any premium above intrinsic value is time value: the price of having remaining time for the underlying to move favourably.
In-the-money means the option already has intrinsic value; at-the-money means spot is close to strike; out-of-the-money means exercise would currently produce no value.
Delta, Gamma, Vega and Theta are local sensitivities of the option's current theoretical value. The payoff chart is different: it shows the contractual outcome at expiry after the premium is paid.
This dynamic pricing engine automatically routes your parameters to the correct quantitative model based on the selected exercise style.
European (Black-Scholes): Options that can only be exercised on the exact expiration date. The engine uses the closed-form Black-Scholes-Merton differential equation to calculate the precise theoretical premium and the real-time Greek risk sensitivities.
American (Binomial Lattice): Options that can be exercised early at any point before expiration. The engine uses the Cox-Ross-Rubinstein (CRR) recursive tree, stepping backwards from maturity to calculate the absolute maximum value between holding the contract and exercising it early at every single node.
This engine uses SciPy's Sequential Least Squares Programming (SLSQP) algorithm to mathematically calculate the "Tangency Portfolio"—the exact asset allocation that maximizes risk-adjusted returns.
This module tests rules-based trading strategies against 5 years of historical market data. All signals are shifted by 1 day to strictly eliminate look-ahead bias (you cannot execute a trade at the closing price of the signal day).
A momentum strategy. The algorithm generates a "Buy" signal when the Fast SMA crosses above the Slow SMA (Golden Cross), and sells into cash when it crosses below (Death Cross).
A volatility-based strategy. Calculates a 20-day SMA with bands at 2 standard deviations. The algorithm buys when the asset drops below the lower band (statistically oversold) and sells when it touches the upper band (overbought).
Student Guide
This lab combines several risk models because each one answers a different question. The aim is not to find one “correct” risk number, but to understand how assumptions, correlations and nonlinear positions change the loss profile.
Historical VaR replays realised market moves from the selected lookback. Monte Carlo VaR creates new correlated scenarios from estimated return behaviour. At 99% confidence, VaR marks the loss level exceeded by roughly the worst 1% of scenarios. Expected Shortfall then averages those tail losses.
Takeaway: compare the two models. A meaningful gap tells you the estimated tail risk is sensitive to modelling assumptions.
The Contribution view uses a parametric Euler decomposition. Component VaR allocates total equity-book VaR across holdings after accounting for covariance. Marginal VaR estimates how much portfolio VaR changes for an additional unit of exposure.
Takeaway: focus on concentration of risk, not just concentration of capital. Correlation can make a smaller holding a larger risk contributor.
Delta captures first-order exposure to the underlying price. Gamma captures curvature, meaning Delta itself changes as the underlying moves. Vega measures sensitivity to a one volatility-point change. The chart expresses each sensitivity as an approximate £ P&L impact.
Takeaway: options can hedge directional risk while adding convexity or volatility exposure, so their risk cannot be understood from Delta alone.
Stress testing deliberately asks what happens outside normal model conditions. Equity positions are marked directly to the scenario shock, while European options are fully repriced with Black–Scholes at the stressed spot and volatility rather than extrapolating local Greeks across a large move.
Takeaway: the worst scenario is not a forecast. It is a vulnerability check: where would the portfolio lose money, and do the options cushion or amplify that loss?
The correlation matrix shows how the assets have historically moved together. High positive correlation reduces diversification; low or negative correlation can offset risk. Correlations are estimates and can change sharply during stressed markets.
Takeaway: diversification is a property of relationships between positions, not simply the number of positions held.
This engine models extreme portfolio tail-risk by running 10,000 distinct price simulations using Geometric Brownian Motion (GBM), a stochastic calculus model used by institutional trading desks to forecast asset behaviour.