National Stock Exchange of India Limited: Index: NIFTY Commodities data was reported at 8,549.250 NA in 13 May 2025. This records a decrease from the previous number of 8,644.150 NA for 12 May 2025. National Stock Exchange of India Limited: Index: NIFTY Commodities data is updated daily, averaging 3,413.100 NA from Jan 2012 (Median) to 13 May 2025, with 3309 observations. The data reached an all-time high of 9,827.550 NA in 27 Sep 2024 and a record low of 1,868.500 NA in 06 Aug 2013. National Stock Exchange of India Limited: Index: NIFTY Commodities data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under High Frequency Database’s Financial and Futures Market – Table IN.EDI.SE: National Stock Exchange of India Limited.

The NSE Futures and Options (F&O) dataset is a collection of data related to derivatives traded on the National Stock Exchange of India. Derivatives, such as futures and options, are financial instruments whose value is derived from an underlying asset, such as stocks, indices, commodities, or currencies. The F&O segment allows traders and investors to speculate on or hedge against future price movements of these assets.

Key Components of the NSE Futures and Options Dataset: 1. Futures Data: Futures Contracts: Agreements to buy or sell an underlying asset at a predetermined price at a future date. Underlying Asset: The asset on which the contract is based (e.g., individual stocks, stock indices like NIFTY, commodities). Contract Specifications: Expiry Date: The date on which the contract will expire. Contract Price: The agreed-upon price for the asset. Lot Size: The quantity of the underlying asset that each contract represents. Open Interest: The total number of outstanding (unsettled) contracts. Volume: The number of contracts traded during a specific period. Settlement Price: The final price of the contract upon expiry.

1. Options Data: Options Contracts: These give the buyer the right (but not the obligation) to buy (Call Option) or sell (Put Option) an underlying asset at a predetermined price before or at a certain expiration date. Option Types: Call Option: Gives the holder the right to buy the asset. Put Option: Gives the holder the right to sell the asset. Strike Price: The price at which the holder of the option can buy/sell the underlying asset. Expiry Date: The date by which the option must be exercised. Premium: The price paid by the option buyer to acquire the option contract. Implied Volatility: A measure of the market’s expectation of the underlying asset's volatility. Greeks: Quantities representing the sensitivity of the option’s price to various factors: Delta: Sensitivity to price changes in the underlying asset. Theta: Sensitivity to time decay (as the option approaches expiry). Vega: Sensitivity to changes in the asset's volatility. Gamma: The rate of change in Delta. Open Interest: Total number of outstanding options contracts. Volume: The number of option contracts traded during a specific period.

2. Option Chain: An option chain is a table showing all available option contracts for a particular stock or index. It includes strike prices, premiums (call and put), open interest, and volume for different expiry dates.

3. Index Derivatives: Futures and options on stock indices like NIFTY 50, Bank NIFTY, etc. These contracts track the performance of the index as the underlying asset.

Key Metrics in F&O Data: Open Interest (OI): The total number of open contracts (both bought and sold) that have not been settled. This helps gauge market participation and liquidity. Price (Premium): In options, the premium is the cost of buying the contract. In futures, the price reflects the contract value. Strike Price: Particularly important for options, it is the price at which the option can be exercised. Expiry Date: Futures and options contracts have specific expiration dates, typically the last Thursday of the month for monthly contracts. Trading Volume: The number of contracts traded within a given period, which can indicate the level of activity in a particular contract.

Use of NSE F&O Data: Speculation: Traders use F&O to speculate on future price movements of stocks, indices, or commodities. Hedging: Investors use F&O to hedge against adverse price movements in their portfolio (for example, buying put options to protect against a market downturn). Arbitrage: Taking advantage of price differences between the underlying asset and its derivative (futures or options).

Data Types: Historical Data: Contains past data on prices, volumes, open interest, etc. for futures and options contracts. Traders use this to analyze trends, patterns, and volatility. Real-time Data: Provides live updates on the price, open interest, and trading volume of contracts. This data is crucial for day traders and high-frequency traders.

How Traders and Analysts Use This Data: Price Action Analysis: Studying how the price of the futures or options contracts changes over time. Open Interest Analysis: A rising OI indicates new money coming into the market, while falling OI can indicate exiting positions. Option Greeks: Traders analyze the Greeks to manage risk and position sizing in options trading. Volatility Analysis: By analyzing implied and historical volatility, traders can gauge market sentiment and potential price swings.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Nifty's Next Move: Bull or Bear?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data