Historic Market Knowledge Sources

By Manusha Rao

An excellent buying and selling or funding technique is barely pretty much as good as the information behind it. Excessive-quality information is crucial if you’re backtesting a quant mannequin, analyzing market tendencies, or constructing an algorithmic buying and selling system.

Conditions:To take advantage of this weblog, it’s important to have a robust basis in market information sources, information dealing with strategies, and monetary information processing.

Begin with Market Knowledge FAQ to grasp the basics of economic information sources, codecs, and purposes in buying and selling. This weblog covers widespread queries relating to information suppliers, entry strategies, and integration into buying and selling fashions.For these concerned with a structured studying method, the Getting Market Knowledge course offers a step-by-step information on find out how to fetch, course of, and use monetary information for algorithmic buying and selling.

On this weblog, we are going to discover the next:

1. High monetary information sources

2. How to decide on the suitable information supplier?

3. Widespread information high quality points and find out how to deal with them

4. How one can deal with time zone and information synchronization?

High monetary information sources

Some platforms present intraday information (ideally suited for high-frequency and short-term methods), whereas others deal with end-of-day (EOD) information for long-term evaluation. Relying on the supplier, information will be accessed through APIs, CSV downloads, or software program terminals.

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

Responsive Knowledge Sources Desk

How to decide on the suitable information supplier?

Listed below are just a few factors to contemplate:

Accuracy and reliability – How reliable is the information?

Monetary information should be clear, correct, and free from inconsistencies. Errors in value feeds, lacking information 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 resulting from inconsistent dividend changes. She’ll discover {that a} paid supplier like Bloomberg would guarantee changes are appropriately utilized.

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

Low-latency, real-time information 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 good 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 information availability – How a lot previous information is on the market?

Backtesting a method requires long-term historic information. 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 method on Nifty 50 shares could discover NSE India offers 10+ years of day by day information however lacks intraday historic information. In distinction, Bloomberg offers tick-level historical past for institutional customers.

Price and subscription plans – Is a free supplier ample, or is a paid plan needed?

Monetary information suppliers supply completely different pricing tiers, from free restricted entry to enterprise-level subscriptions costing 1000’s of {dollars} monthly. Your alternative is determined by your finances and buying and selling wants.

Instance:

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

Widespread information high quality points and find out how to deal with them

Monetary information is commonly messy, incomplete, or inconsistent, resulting in inaccurate evaluation and poor buying and selling selections. Listed below are a number of the commonest information high quality points and find out how to deal with them successfully.

1. Lacking Knowledge – How one can deal with gaps in information?

Lacking information can happen resulting from buying and selling holidays, trade downtime, incomplete API responses, or information supplier limitations. Gaps in information can distort technical indicators, backtests, and mannequin predictions.

Instance:

A inventory has lacking closing costs resulting from a buying and selling halt. As an alternative of leaving gaps, we will:

Use ahead fill: Copy the final identified 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 impression inventory costs and should be dealt with appropriately for correct evaluation.

Widespread Company Actions & Their Results

Inventory splits – Alter the worth and quantity proportionally.Dividends – Money dividends scale back the inventory value; they should be accounted for in complete return calculations.Mergers & acquisitions – Could trigger value discontinuities; use adjusted costs.

How one can Deal with Company Actions?

Use adjusted costs – Most information 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 break up ratio.Complete Return Index (TRI) – If analyzing efficiency, think about using complete return information that features dividends.

Instance:

A 2-for-1 inventory break up means:

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

Python Instance for Adjusting Inventory Splits:

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

Market information usually comes from a number of exchanges, sources, or time zones, resulting in misaligned timestamps, lacking information, 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 information updates quarterly, however value information updates in actual time.Mismatched Knowledge Granularity – One dataset may be minute-level, whereas one other is daily-level.

How one can deal with time zone and information synchronization?

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

Instance:

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

Python Instance for Time Zone Conversion:

Conclusion

To sum up, this weblog coated:

A comparability of high free and paid monetary information sources primarily based on asset protection, entry sort, and availability of intraday, day by day, and elementary information.Key components to contemplate when selecting a knowledge supplier, embrace accuracy, latency, historic depth, and price.Widespread information high quality points comparable to lacking information, company actions, and synchronisation challenges—and find out how to deal with them successfully.

Deciding on the suitable monetary information supplier is vital for merchants, buyers, and researchers who depend on quantitative evaluation. Components comparable to accuracy, reliability, latency, historic depth, and price play a key position in figuring out which supplier most closely fits your wants. Whereas free information sources could also be ample for primary evaluation, skilled merchants and establishments usually require premium information with decrease latency and higher high quality management.

Subsequent steps

Here’s a listing of sources you employ to develop your data with superior strategies in information retrieval, processing, and monetary evaluation.

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

Moreover, How one can Use Monetary Market Knowledge for Basic and Quantitative Evaluation offers insights into quantitative buying and selling fashions, sentiment evaluation, and data-driven decision-making.

Should you’re concerned with elementary and sentiment evaluation, the Basic and Sentiment Evaluation Knowledge weblog provides steerage on extracting and processing different datasets for higher market predictions.

For merchants seeking to retrieve futures, cryptocurrency, and foreign exchange value information, contemplate these hands-on tutorials:

Obtain Futures Knowledge Utilizing Yahoo Finance Library in Python

Obtain Cryptocurrency Knowledge Utilizing CryptoCompare API in Python

Obtain Foreign exchange Worth Knowledge Utilizing YFinance Library in Python

Since information 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 method to getting ready monetary information for machine studying and algorithmic buying and selling, contemplate the Knowledge and Function Engineering for Buying and selling course. This course covers important matters comparable to characteristic choice, dataset transformation, and optimizing predictive fashions utilizing monetary information.

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