Beginner
Introduction to Time Series Data in Financial Markets
A ground-up introduction to time series data for engineering graduates, coders, and traders who want to systematize their process. Starts with what makes market data a time series, how an OHLCV bar is built, and where Indian market data actually comes from (NSE bhavcopy, broker APIs, free sources). Moves to the one transformation every quant does first, prices to returns, including log returns, adjusted prices, and Indian corporate actions. Then teaches how to describe a series honestly: rolling windows, volatility, return distributions, stationarity, autocorrelation, seasonality, and the cleaning work that real NSE data demands. Every concept is applied to Nifty 50 and NSE stock data with short Python examples.
Time Series DataOHLCV DataReturns and Log ReturnsAdjusted PricesRolling StatisticsVolatilityStationarityAutocorrelationData CleaningPython for Finance
MODULES
4
DURATION
~3.5 hrs
TRACK
Quantitative Finance
What You'll Master
Explain what makes financial data a time series and why ordering and sampling frequency change what you can conclude from it
Read an OHLCV bar, pick the right frequency for a question, and source Indian market data from NSE, BSE, and broker APIs
Convert prices to simple and log returns, handle dividends, splits, and bonus issues, and rebuild a price path from returns
Compute rolling means, rolling volatility, and annualised volatility for Nifty 50 and NSE stocks in Python
Describe a return distribution using mean, standard deviation, skewness, and kurtosis, and recognise fat tails in Indian data
Test whether a series is stationary and understand why prices fail the test while returns nearly pass
Measure autocorrelation, decompose a series into trend, seasonality, and residual, and spot Indian calendar effects
Clean a real NSE series: holidays, missing bars, outliers, and timestamp mistakes
Access Level
LEARNER
Everything included
Full Text Playbooks
Actionable Exercises
Mobile Reading Mode
Lifetime Updates
Curriculum Breakdown
Chapter 1: What Time Series Data Is and Why Markets Are Made of It
5 Lessons▶
What Makes Data a Time Series: Order, Timestamps, and Why It Changes Everything9 min read
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The Anatomy of an OHLCV Bar: Reading a Nifty 50 Candle as Data10 min read
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Frequencies and Sampling: Tick, Minute, Daily, Weekly, and What Changes Between Them10 min read
▶
Where Indian Market Data Comes From: NSE Bhavcopy, Broker APIs, and Free Sources11 min read
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Case Study: Loading and Inspecting Five Years of Nifty 50 Daily Data in Python12 min read
Chapter 2: From Prices to Returns: The First Transformation Every Quant Makes
5 Lessons▶
Why We Work With Returns, Not Prices9 min read
▶
Simple Returns vs Log Returns: The Formulas and When Each One Is Right11 min read
▶
Cumulative Returns and Compounding: Rebuilding a Price Path From Returns10 min read
▶
Adjusted Prices: Dividends, Splits, and Bonus Issues in Indian Stock Data11 min read
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Case Study: Comparing the Returns of Reliance, Infosys, and the Nifty 5012 min read
Chapter 3: Describing a Series: Rolling Windows, Volatility, and Distributions
5 Lessons▶
Rolling Windows: Moving Averages and Rolling Statistics Done Properly10 min read
▶
Volatility as a Time Series: Rolling Standard Deviation and Annualisation11 min read
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The Distribution of Returns: Mean, Standard Deviation, Skewness, and Fat Tails11 min read
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Stationarity: Why Prices Are Not Stationary and Returns Nearly Are12 min read
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Case Study: Nifty 50 Volatility Across 2020, 2022, and a Calm Year12 min read
Chapter 4: Structure Inside the Series: Autocorrelation, Seasonality, and Noise
5 Lessons▶
Autocorrelation: Does Yesterday's Return Tell You Anything About Today's?11 min read
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Decomposing a Series: Trend, Seasonality, and Residual11 min read
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Calendar Effects in Indian Markets: Expiry Days, Budget Day, and Muhurat Trading10 min read
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Missing Data, Outliers, and Holidays: Cleaning a Real NSE Series11 min read
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Case Study: Testing the Random Walk on Nifty 50 Daily Returns12 min read