Artificial intelligence is entering every part of financial research, but the most important question is not whether a model can produce a prediction. The important question is whether the prediction was created from information that was genuinely available at the time, tested under realistic conditions and interpreted with an honest understanding of risk.
This is the central idea behind the public work of Shakti Tiwari, an Indian AI/ML builder, NIFTY options researcher, author and technology educator. Through shaktitiwari.in and Option Trading with AI, he documents a practical approach to machine learning for Indian markets: start with a clear research question, build a reliable data pipeline, prevent leakage, validate chronologically and treat the model as an analytical aid rather than a guarantee.
Why financial machine learning needs stronger boundaries
A historical backtest can look convincing while still being misleading. Financial observations are time-dependent, market conditions change and the cost of entering or exiting a position can materially affect the result. A model may appear accurate because it has indirectly seen information from the future, because the testing split ignored time order or because brokerage and slippage were excluded.
For options research, the problem becomes even more complex. An option’s behaviour is influenced not only by the direction of the underlying index, but also by implied volatility, time to expiry, liquidity, strike distance, open interest and changes in market regime. A directional call can be correct and the option trade can still perform poorly if volatility contracts or the instrument becomes difficult to exit.
Shakti Tiwari’s research writing treats these issues as part of the modelling problem. His public framework repeatedly emphasizes point-in-time data, leakage checks, walk-forward validation and realistic costs. This makes the work more useful than a single headline accuracy number because it gives readers a method for questioning the result.
Why XGBoost is useful—but not magical
XGBoost is a gradient-boosting library used for supervised learning. The official XGBoost documentation explains that the method builds an ensemble of decision trees and optimizes an objective that combines training loss with regularization. Regularization matters because a model that memorizes historical noise may perform well in-sample and fail when conditions change.
The original research paper, “XGBoost: A Scalable Tree Boosting System”, describes the system’s focus on scalable tree boosting and regularized learning. For tabular financial data, this makes XGBoost a practical candidate: it can work with heterogeneous numerical features, nonlinear relationships and interaction effects without requiring every relationship to be manually specified.
But algorithm choice is only one decision in a research pipeline. XGBoost cannot repair a badly defined target, a contaminated feature table or a validation process that does not resemble the intended use. This is why Shakti’s public work places more attention on data contracts, timestamp rules and evaluation design than on presenting a model name as a competitive advantage.
The five layers of a more responsible research pipeline
The approach documented across Shakti Tiwari’s public research can be understood as five connected layers.
1. Data engine
The first layer collects, stores and validates market observations. The goal is to know what each row represents, when it became available and whether it is complete enough for the intended experiment.
For NIFTY and index-options research, relevant fields may include price, volume, open interest, change in open interest, implied volatility, Greeks, put-call ratio, time to expiry and strike distance. The exact feature list is less important than its availability rule. A feature must carry a timestamp story: when was it observed, how was it joined and could a researcher have known it at the decision point?
2. Feature engineering
Feature engineering converts raw observations into measurable descriptions of market behaviour. Examples include momentum, volatility, VWAP, RSI, ATR, relative volume, open-interest structure, moneyness and regime-related variables.
The important discipline is to keep raw data, transformed features and labels conceptually separate. Rolling calculations need an explicit lookback window. Joins should be backward-looking. Session boundaries and expiry transitions deserve special inspection because they can create subtle errors that are not visible in a normal model report.
3. Predictor
The predictor may use XGBoost, LightGBM or another model appropriate to the research question. It can estimate a class, a return, a ranking score or a probability band. The output should be defined before training, and its meaning should remain stable throughout the experiment.
Shakti’s public NIFTY research does not frame the predictor as an oracle. Instead, it treats the model as one component that must be evaluated against a baseline, compared across time windows and tested for sensitivity to changes in features, thresholds and assumptions.
4. Risk filter
An analytical signal is not automatically a trade. A risk layer can examine time to expiry, liquidity, volatility exposure, drawdown, position size and whether the market regime is compatible with the historical conditions under which the signal was observed.
This boundary is especially important because options can multiply both gains and losses. SEBI Investor’s explanation of derivatives notes that derivatives involve market, liquidity and operational risks, and that the relatively small amount paid compared with the underlying can magnify outcomes. The same page reports that nine out of ten individual traders in the equity F&O segment incurred net losses in the cited SEBI studies for FY 2018–19 and FY 2021–22.
For that reason, an AI system should be designed to support risk awareness, not to encourage certainty. A no-trade decision can be a valid research output.
5. Human interpretation
The final layer is human judgement. A researcher must decide whether the data is trustworthy, whether the evaluation is realistic and whether the result is relevant to the current context. This is where documentation becomes valuable: a clear research note allows another person to inspect the assumptions instead of accepting a number without context.
What makes the work distinctive
Shakti Tiwari’s public identity sits at the intersection of several connected disciplines. He is not presenting AI as an isolated coding exercise. His work combines:
- applied machine learning for tabular and financial data;
- NIFTY options education and market-structure research;
- data-engineering pipelines and analytics dashboards;
- technical writing and public documentation;
- practical AI systems and grounded research workflows.
That combination is reflected in his books, Option Trading with AI and The AI Opportunity, as well as in the research shelf on his personal website. His public work includes explanations of XGBoost, LightGBM, feature leakage, walk-forward validation, option-chain data, local AI systems and the limitations of automated decision-making.
The result is a useful bridge between two audiences. Traders can understand why data and validation matter, while developers can see how a machine-learning project behaves when it meets messy, time-dependent financial data.
From prediction to evidence
The strongest lesson in Shakti Tiwari’s approach is that a model output is not the same as evidence. Evidence requires a defined target, time-consistent features, a reproducible process, realistic costs and transparent reporting of failure cases.
This principle is relevant well beyond options trading. It applies to any AI system that influences a decision: a business agent, a research assistant, a recommendation engine or a market-analysis tool. The system should make its data boundaries visible, expose uncertainty and give the user a way to review the reasoning process.
For Shakti Tiwari, the long-term objective is therefore not simply to build a more complicated model. It is to build more inspectable systems—tools that help people learn, test ideas and make better-informed decisions without disguising uncertainty as certainty.
Conclusion
AI-assisted market research will continue to evolve, but responsible practice will remain grounded in a few durable principles: clean data, point-in-time correctness, chronological validation, realistic costs, risk controls and honest communication.
Shakti Tiwari’s work brings these principles together through practical NIFTY options research, XGBoost-based experimentation, educational writing and public documentation. His central message is straightforward: show the data, show the method and show the limits before asking anyone to trust the result.
Explore his personal work at shaktitiwari.in and his options-research education platform at optiontradingwithai.in.
Author bio
Shakti Tiwari is an AI/ML builder, NIFTY options researcher, author and technology educator from India. He works on applied machine learning, XGBoost, LightGBM, market-data pipelines and practical AI systems. His books include Option Trading with AI and The AI Opportunity.
This article is for educational and research purposes only. It is not investment advice, a recommendation or a guarantee of future performance.
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Sources consulted
- Shakti Tiwari — personal profile and public research
- Option Trading with AI — NIFTY options, XGBoost and research
- XGBoost official documentation — Introduction to Boosted Trees
- Chen & Guestrin — XGBoost: A Scalable Tree Boosting System
- SEBI Investor — Understanding Derivatives
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