Historic Market Knowledge Sources

By Manusha Rao

A superb buying and selling or funding technique is barely pretty much as good as the info behind it. Excessive-quality knowledge is important in case you are backtesting a quant mannequin, analyzing market developments, or constructing an algorithmic buying and selling system.

Conditions:To benefit from this weblog, it’s important to have a powerful basis in market knowledge sources, knowledge dealing with methods, and monetary knowledge processing.

Begin with Market Knowledge FAQ to grasp the basics of economic knowledge sources, codecs, and purposes in buying and selling. This weblog covers frequent queries relating to knowledge suppliers, entry strategies, and integration into buying and selling fashions.For these considering a structured studying strategy, the Getting Market Knowledge course gives a step-by-step information on the right way to fetch, course of, and use monetary knowledge for algorithmic buying and selling.

On this weblog, we’ll discover the next:

1. High monetary knowledge sources

2. How to decide on the best knowledge supplier?

3. Widespread knowledge high quality points and the right way to deal with them

4. The way to deal with time zone and knowledge synchronization?

High monetary knowledge sources

Some platforms present intraday knowledge (superb for high-frequency and short-term methods), whereas others concentrate on end-of-day (EOD) knowledge for long-term evaluation. Relying on the supplier, knowledge will be accessed by way of APIs, CSV downloads, or software program terminals.

The desk beneath breaks down the highest monetary knowledge sources, highlighting whether or not they’re free or paid, the kind of knowledge they provide, and how one can entry it.

Responsive Knowledge Sources Desk

How to decide on the best knowledge supplier?

Listed here are a couple of factors to contemplate:

Accuracy and reliability – How reliable is the info?

Monetary knowledge have to be clear, correct, and free from inconsistencies. Errors in value feeds, lacking knowledge factors, or incorrect changes for company actions (e.g., inventory splits, dividends) distort backtesting outcomes and result in incorrect buying and selling selections.

Instance:

A dealer utilizing Yahoo Finance could discover discrepancies in adjusted shut costs attributable to inconsistent dividend changes. She’ll discover {that a} paid supplier like Bloomberg would guarantee changes are accurately utilized.

Latency and velocity – How briskly do you get the info?

Low-latency, real-time knowledge is essential for high-frequency buying and selling (HFT) and intraday methods. A delay in receiving market costs can result in slippage (executing trades at worse costs than anticipated).

Instance:

A dealer utilizing Interactive Brokers (IB API) receives real-time bid-ask quotes, which is right for algorithmic execution. In distinction, if she makes use of Yahoo Finance, she’s going to expertise delayed costs, making it unsuitable for energetic buying and selling.

Historic knowledge availability – How a lot previous knowledge is accessible?

Backtesting a technique requires long-term historic knowledge. A dataset with just one–2 years of knowledge is inadequate for testing efficiency throughout completely different market circumstances (e.g., bull and bear markets).

Instance:

A quant researcher backtesting a technique on Nifty 50 shares could discover NSE India gives 10+ years of each day knowledge however lacks intraday historic knowledge. In distinction, Bloomberg gives tick-level historical past for institutional customers.

Price and subscription plans – Is a free supplier enough, or is a paid plan essential?

Monetary knowledge suppliers supply completely different pricing tiers, from free restricted entry to enterprise-level subscriptions costing hundreds of {dollars} monthly. Your alternative is determined by your price range and buying and selling wants.

Instance:

A retail investor monitoring long-term developments could discover Yahoo Finance and NSE India enough. In the meantime, a hedge fund operating real-time execution algorithms would require a Bloomberg terminal or Reuters Refinitiv.

Widespread knowledge high quality points and the right way to deal with them

Monetary knowledge is usually messy, incomplete, or inconsistent, resulting in inaccurate evaluation and poor buying and selling selections. Listed here are a number of the commonest knowledge high quality points and the right way to deal with them successfully.

1. Lacking Knowledge – The way to deal with gaps in knowledge?

Lacking knowledge can happen attributable to buying and selling holidays, change downtime, incomplete API responses, or knowledge supplier limitations. Gaps in knowledge can distort technical indicators, backtests, and mannequin predictions.

Instance:

A inventory has lacking closing costs attributable to a buying and selling halt. As an alternative of leaving gaps, we are able to:

Use ahead fill: Copy the final recognized value.Use sector index actions as an estimate.Exclude these days from the backtesting calculation

Python Instance for Filling Lacking Knowledge:

2. Changes for company actions – Dealing with inventory splits, dividends, and mergers

Company actions like inventory splits, dividends, and spin-offs affect inventory costs and have to be dealt with accurately for correct evaluation.

Widespread Company Actions & Their Results

Inventory splits – Modify the worth and quantity proportionally.Dividends – Money dividends cut back the inventory value; they have to be accounted for in whole return calculations.Mergers & acquisitions – Might trigger value discontinuities; use adjusted costs.

The way to Deal with Company Actions?

Use adjusted costs – Most knowledge suppliers (Yahoo Finance, Bloomberg) supply adjusted closing costs, which account for company actions.Manually alter splits – If solely uncooked costs can be found, divide previous costs and multiply volumes by the cut up ratio.Whole Return Index (TRI) – If analyzing efficiency, think about using whole return knowledge that features dividends.

Instance:

A 2-for-1 inventory cut up means:

The inventory value is halved.The variety of shares doubles.Unadjusted value knowledge would incorrectly present a 50% drop.

Python Instance for Adjusting Inventory Splits:

3. Knowledge Synchronization – Aligning time zones and completely different knowledge sources

Market knowledge usually comes from a number of exchanges, sources, or time zones, resulting in misaligned timestamps, lacking knowledge, or incorrect comparisons.

Widespread Knowledge Synchronization Points:

Time Zone Variations – NYSE operates in Japanese Time, whereas NSE follows Indian Commonplace Time (IST).Asynchronous Knowledge Feeds – Basic knowledge updates quarterly, however value knowledge updates in actual time.Mismatched Knowledge Granularity – One dataset is likely to be minute-level, whereas one other is daily-level.

The way to deal with time zone and knowledge synchronization?

Convert time zones—Earlier than evaluation, guarantee all timestamps are in the identical time zone. Use pytz in Python for conversions.Resample knowledge – If combining intraday and each day knowledge, convert them to a typical frequency.Align knowledge from completely different sources – If merging two datasets, use pd.merge() with the suitable time alignment.

Instance:

If merging intraday foreign exchange knowledge (UTC) with inventory knowledge (EST), convert the whole lot to UTC.

Python Instance for Time Zone Conversion:

Conclusion

To sum up, this weblog lined:

A comparability of high free and paid monetary knowledge sources based mostly on asset protection, entry kind, and availability of intraday, each day, and elementary knowledge.Key elements to contemplate when selecting an information supplier, embody accuracy, latency, historic depth, and value.Widespread knowledge high quality points corresponding to lacking knowledge, company actions, and synchronisation challenges—and the right way to deal with them successfully.

Deciding on the best monetary knowledge supplier is essential for merchants, buyers, and researchers who depend on quantitative evaluation. Components corresponding to accuracy, reliability, latency, historic depth, and value play a key position in figuring out which supplier most accurately fits your wants. Whereas free knowledge sources could also be enough for primary evaluation, skilled merchants and establishments usually require premium knowledge with decrease latency and higher high quality management.

Subsequent steps

Here’s a record of assets you employ to develop your data with superior methods in knowledge retrieval, processing, and monetary evaluation.

To discover completely different libraries and instruments for working with monetary knowledge, learn Python Buying and selling Library, which introduces Python-based options for monetary knowledge extraction, evaluation, and visualisation.

Moreover, The way to Use Monetary Market Knowledge for Basic and Quantitative Evaluation gives insights into quantitative buying and selling fashions, sentiment evaluation, and data-driven decision-making.

For those who’re considering elementary and sentiment evaluation, the Basic and Sentiment Evaluation Knowledge weblog gives steering on extracting and processing different datasets for higher market predictions.

For merchants seeking to retrieve futures, cryptocurrency, and foreign exchange value knowledge, think about these hands-on tutorials:

Obtain Futures Knowledge Utilizing Yahoo Finance Library in Python

Obtain Cryptocurrency Knowledge Utilizing CryptoCompare API in Python

Obtain Foreign exchange Value Knowledge Utilizing YFinance Library in Python

Since knowledge high quality and preprocessing are essential for monetary modelling, discover Knowledge Cleansing to study finest practices for dealing with lacking values, outliers, and inconsistencies in buying and selling datasets.

For a structured and hands-on strategy to making ready monetary knowledge for machine studying and algorithmic buying and selling, think about the Knowledge and Function Engineering for Buying and selling course. This course covers important subjects corresponding to characteristic choice, dataset transformation, and optimizing predictive fashions utilizing monetary knowledge.

All knowledge and data supplied on this article are for informational functions solely. QuantInsti® makes no representations as to accuracy, completeness, currentness, suitability, or validity of any data on this article and won’t be chargeable for any errors, omissions, or delays on this data or any losses, accidents, or damages arising from its show or use. All data is supplied on an as-is foundation.