Corporate Actions – TheUniBit

Corporate Listings, Relistings, and Price Series Continuity

This Python-centric deep dive explains how corporate listings, relistings, mergers, suspensions, and symbol changes reshape historical price series in Indian equities. It shows how to engineer continuity-safe data pipelines, mathematically valid indicators, and robust backtests by treating corporate events as structural breaks, not cosmetic adjustments.

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Price-Based Market Data

Adjusted vs Non-Adjusted Historical Series for Index Constituents

This article uniquely examines adjusted versus non-adjusted historical price series through the lens of index-constituent data engineering, not trading signals. By focusing on corporate-action restatement logic, index maintenance rules, and Python-driven data workflows, it reveals how subtle dataset choices materially alter analytics, backtests, and long-term market interpretation.

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Price-Based Market Data

Adjustment Factors and Backward Price Restatement Logic

Indian equity market price history cannot be analyzed correctly without mathematically adjusting for corporate actions. This Python-centric guide explains how stock splits, bonuses, and rights issues reshape historical prices, detailing precise adjustment formulas, backward restatement algorithms, and production-grade data engineering workflows for building reliable Indian market data systems.

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Price-Based Market Data

Adjusted vs Unadjusted Prices in Indian Historical Data

Price Truth, Economic Continuity, and the Foundations of Indian Market Data In Indian equity markets, price data is not merely a time series of numbers—it is a historical record shaped by regulation, corporate decisions, and exchange mechanics. For Python developers building analytics platforms, data pipelines, or financial products, the distinction between adjusted and unadjusted prices

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Price-Based Market Data