Engineering applied to investing
InfoMax Analytics is a quantitative research and analytics practice. It treats portfolio decisions as engineering problems — measured, modelled, and stress-tested in code rather than asserted — across these connected areas.
Portfolio Construction
Building efficient portfolios from first principles — mean–variance optimization, covariance estimation, and disciplined rebalancing rules that turn capital-market assumptions into a concrete, defensible asset allocation.
Factor & Return Research
Decomposing performance into its sources — market, size, value, momentum — to separate genuine skill (alpha) from exposure. CAPM and multi-factor attribution applied to real return streams.
Derivatives & Hedging
Valuing optionality and managing convexity — Black–Scholes–Merton pricing with full Greeks, protective-put and covered-call structures, and implied-volatility analysis for hedging a live book.
Risk & Position Sizing
Measuring the downside that matters — Value-at-Risk, Expected Shortfall, and risk-adjusted ratios — and sizing each position for long-run compounding with growth-optimal (Kelly) discipline.
Quant Tooling in Python
Everything is implemented, not asserted — Python with NumPy and pandas for the mathematics, backtesting for validation, and live interactive models so any result can be reproduced and stress-tested.
Market Intelligence
Tracking the forces that move model inputs — a rolling 30-day archive of global, Indian and Asian market news, plus the macro, policy and volatility drivers that shape returns before they reach prices.
Six instruments, no hand-waving
Each tool implements a named result from the canonical finance literature. Change the inputs; the outputs recompute from the closed-form mathematics, not a lookup table.
Mean–Variance Optimizer
CAPM — Security Market Line
Black–Scholes–Merton Option Pricer
Value-at-Risk & Expected Shortfall
Risk-Adjusted Performance Suite
Growth-Optimal Position Sizing
The market doesn't move in a vacuum
Models price risk; news and macro forces move the inputs. Each panel below keeps a rolling 30-day archive — scroll within any column to browse up to a month of headlines across global, Indian, and Asian markets, plus the macro and commodity drivers behind them.
Global Markets
GLOBAL · USIndia Markets
INDIAAsia Markets
ASIAMacro & Policy
MACRO · POLICYCommodities & Currency
COMMODITIES · FXThe forces behind the headlines
News reports what already happened. These are the structural factors that cause it — the variables a disciplined process monitors before the move shows up in prices. Each maps directly to inputs in the models above.
Monetary Policy
Central-bank rates and liquidity set the risk-free rate — the anchor of CAPM, option pricing, and discounting. The RBI and the Fed move everything downstream.
Macro Indicators
Inflation, GDP growth, employment, and PMI shape expected returns and the equity risk premium. Surprises versus consensus are what actually move markets.
Commodities & Energy
Crude oil, gold, and industrial metals feed input costs, inflation, and the import bill — a primary swing factor for India's deficit and corporate margins.
Currency & Flows
The rupee, dollar index, and FII/DII flows drive returns for foreign-exposed sectors and the cost of imported capital. Capital movement front-runs the news.
Sentiment & Volatility
The India VIX and global fear gauges measure expected turbulence — the σ in Black-Scholes and VaR. Spiking volatility reprices every risk asset at once.
Geopolitics
Wars, trade deals, tariffs, and sanctions inject discrete jumps that no smooth model anticipates — the fat-tail risk that VaR and stress testing exist to bound.
Earnings & Fundamentals
Corporate results, guidance, and sector rotation determine realised returns versus the CAPM baseline — the raw material of alpha and factor attribution.
Regulation & Policy
Budget, taxation, SEBI rules, and sectoral policy reshape the opportunity set and risk limits — the boundary conditions every portfolio must respect.
How a thesis becomes a position
Research discipline, not prediction. The edge is in measurement, sizing, and the willingness to say when the model breaks.
Measure before you forecast
Estimate the moments — returns, covariances, tail behavior — with honest error bars. A confident point estimate on a noisy covariance matrix is the most common way to lose money quietly.
cf. Ledoit & Wolf (2004), shrinkage estimatorsDecompose the return
Attribute performance to known factors — market, size, value, momentum, quality — before claiming skill. What survives factor exposure is the part worth paying for.
cf. Fama & French (1993, 2015); Carhart (1997)Size for survival
Position sizing dominates entry timing over long horizons. Log-optimal growth, drawdown control, and fractional Kelly keep the compounding engine alive through regime changes.
cf. Kelly (1956); Thorp; MacLean–ZiembaPrice the optionality
Convexity is everywhere — in equity, in credit, in real decisions. Closed-form and lattice pricing make the hidden options on a balance sheet explicit and hedgeable.
cf. Black–Scholes–Merton; Hull, Options, Futures & DerivativesRespect the tail
VaR tells you a threshold; Expected Shortfall tells you what happens beyond it. We model both and stress them, because the loss that matters is the one the normal distribution underweights.
cf. Rockafellar–Uryasev; Taleb, Dynamic HedgingFalsify relentlessly
Every signal is out-of-sample tested, deflated for multiple testing, and retired the moment its premise stops holding. The portfolio of beliefs is rebalanced like any other.
cf. Harvey–Liu–Zhu (2016), “…and the Cross-Section of Expected Returns”The shelf the work is built on
The models above are not invented here — they are implementations of results from a literature spanning seven decades. The standing reading list:
Ashutosh Goel
Quantitative Research and Analytics · Gurugram, India
InfoMax Analytics is led by Ashutosh Goel, and built on the conviction that decisions deserve the same mathematical rigor as any other engineering problem. An engineer by training, he brings a systems mindset to capital — the practice spans portfolio construction, factor research, pricing, and risk measurement, implemented in code rather than asserted in slides.
The approach is deliberately transparent: open models, cited methods, and tools you can stress-test yourself. Where the literature gives a closed form, it is used; where it gives a method, it is implemented; where it gives only a warning, it is heeded.
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