Using Indices-API to Fetch Tel Aviv TA-35 Price Time-Series Data for Market Sentiment Studies

Introduction

In the world of financial analytics, the ability to fetch and analyze time-series data is crucial for understanding market sentiment. One of the most prominent indices in the Middle East is the Tel Aviv TA-35, which represents the 35 largest companies listed on the Tel Aviv Stock Exchange. By utilizing the Indices-API, developers can access real-time and historical price data for the TA-35, enabling them to conduct predictive analytics and derive insights into market trends.

About Tel Aviv TA-35 (TA-35)

The Tel Aviv TA-35 index is a key indicator of the Israeli economy, reflecting the performance of major sectors such as technology, finance, and healthcare. Understanding the fluctuations in this index can provide valuable insights into investor sentiment and economic health. By analyzing the TA-35's price movements, developers can create applications that forecast market trends, assess risk, and inform investment strategies.

API Description

The Indices-API offers a robust set of features that empower developers to build next-generation applications leveraging real-time index data. This API provides seamless access to various endpoints that deliver comprehensive market data, including the latest rates, historical rates, and time-series data. The transformative potential of this API lies in its ability to provide developers with the tools needed to analyze market sentiment and make informed decisions.

Key Features and Endpoints

The Indices-API offers several endpoints that cater to different data needs:

Latest Rates Endpoint: This endpoint provides real-time exchange rate data, updated based on your subscription plan. Developers can access the latest rates for the TA-35 and other indices, allowing for immediate analysis of market conditions.
Historical Rates Endpoint: Access historical rates for the TA-35 dating back to 1999. This endpoint allows developers to analyze past performance and identify trends over time.
Convert Endpoint: Easily convert amounts between different indices or currencies. This feature is particularly useful for applications that require cross-index comparisons.
Time-Series Endpoint: Query daily historical rates between two dates of your choice. This endpoint is essential for conducting in-depth analyses of price movements over specific periods.
Fluctuation Endpoint: Retrieve information about how the TA-35 fluctuates on a day-to-day basis, providing insights into volatility and market sentiment.
Open/High/Low/Close (OHLC) Price Endpoint: Get detailed OHLC data for the TA-35, which is crucial for technical analysis and forecasting.
API Key: Each user is assigned a unique API key that must be included in requests to authenticate access to the API.
API Response: The API returns exchange rates relative to USD, ensuring consistency in data interpretation.
Supported Symbols Endpoint: Access a constantly updated list of all available indices, including the TA-35, to ensure your application is using the correct symbols.

List of Symbols

The Indices-API provides access to a diverse range of index symbols. For a complete list of all supported symbols and their specifications, refer to the Indices-API Supported Symbols page.

API Endpoint Examples and Responses

Latest Rates Endpoint

To fetch the latest rates for the TA-35, you can use the following API call:

{
"success": true,
"timestamp": 1781744046,
"base": "USD",
"date": "2026-06-18",
"rates": {
"TA-35": 0.00029,
"DOW": 0.00029,
"NASDAQ": 0.00039,
"S&P 500": 0.00024
},
"unit": "per index"
}

This response indicates a successful request, providing the latest exchange rates for the TA-35 and other indices.

Historical Rates Endpoint

To access historical rates for the TA-35, you can use the following API call:

{
"success": true,
"timestamp": 1781657646,
"base": "USD",
"date": "2026-06-17",
"rates": {
"TA-35": 0.00028,
"DOW": 0.00028,
"NASDAQ": 0.00038,
"S&P 500": 0.00023
},
"unit": "per index"
}

This response provides historical data, allowing developers to analyze past performance and trends.

Time-series Endpoint

To retrieve exchange rates for a specific time period, you can use the time-series endpoint:

{
"success": true,
"timeseries": true,
"start_date": "2026-06-11",
"end_date": "2026-06-18",
"base": "USD",
"rates": {
"2026-06-11": {
"TA-35": 0.00028
},
"2026-06-13": {
"TA-35": 0.00029
},
"2026-06-18": {
"TA-35": 0.00029
}
},
"unit": "per index"
}

This response provides daily rates for the specified period, essential for time-series analysis.

Convert Endpoint

To convert an amount from one index to another, you can use the convert endpoint:

{
"success": true,
"query": {
"from": "USD",
"to": "TA-35",
"amount": 1000
},
"info": {
"timestamp": 1781744046,
"rate": 0.00029
},
"result": 0.29,
"unit": "per index"
}

This response shows the conversion of 1000 USD to the TA-35 index, providing a clear understanding of the conversion rate.

Fluctuation Endpoint

To track rate fluctuations between two dates, you can use the fluctuation endpoint:

{
"success": true,
"fluctuation": true,
"start_date": "2026-06-11",
"end_date": "2026-06-18",
"base": "USD",
"rates": {
"TA-35": {
"start_rate": 0.00028,
"end_rate": 0.00029,
"change": 1.0e-5,
"change_pct": 3.57
}
},
"unit": "per index"
}

This response provides insights into how the TA-35 fluctuated over the specified period, which is crucial for understanding market volatility.

OHLC (Open/High/Low/Close) Endpoint

To get OHLC data for the TA-35, you can use the following API call:

{
"success": true,
"timestamp": 1781744046,
"base": "USD",
"date": "2026-06-18",
"rates": {
"TA-35": {
"open": 0.00028,
"high": 0.00029,
"low": 0.00027,
"close": 0.00029
}
},
"unit": "per index"
}

This response provides the open, high, low, and close prices for the TA-35, which are essential for technical analysis.

Bid/Ask Endpoint

To get current bid and ask prices for the TA-35, you can use the bid/ask endpoint:

{
"success": true,
"timestamp": 1781744046,
"base": "USD",
"date": "2026-06-18",
"rates": {
"TA-35": {
"bid": 0.00028,
"ask": 0.00029,
"spread": 1.0e-5
}
},
"unit": "per index"
}

This response provides the current bid and ask prices, which are crucial for traders looking to execute orders.

Data Processing Steps

Once you have fetched the data using the Indices-API, the next step is to process this data for predictive analytics. Here are some key steps to consider:

1. Data Cleaning

Before conducting any analysis, ensure that the data is clean and free from inconsistencies. This may involve handling missing values, removing duplicates, and ensuring that the data types are correct.

2. Data Transformation

Transform the data into a suitable format for analysis. This may include normalizing the data, creating new features, or aggregating data over specific time intervals.

3. Exploratory Data Analysis (EDA)

Conduct EDA to understand the underlying patterns in the data. Visualizations can help identify trends, seasonal patterns, and anomalies that may impact predictive modeling.

4. Model Selection

Choose appropriate predictive models based on the nature of the data and the specific goals of your analysis. Common models include time-series forecasting models, regression models, and machine learning algorithms.

5. Model Training and Evaluation

Train your selected models using historical data and evaluate their performance using metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared.

6. Deployment

Once satisfied with the model's performance, deploy it in a production environment where it can provide real-time predictions based on incoming data from the Indices-API.

Examples of Predictive Model Applications

There are numerous applications for predictive models using the TA-35 price time-series data:

1. Market Trend Forecasting

By analyzing historical price movements, developers can create models that predict future trends in the TA-35, helping investors make informed decisions.

2. Risk Assessment

Predictive models can assess the risk associated with investing in the TA-35 by analyzing volatility and historical performance, allowing investors to adjust their portfolios accordingly.

3. Algorithmic Trading

Developers can create algorithmic trading strategies that leverage real-time data from the Indices-API to execute trades based on predefined criteria, optimizing trading performance.

Conclusion

The Indices-API provides a powerful toolset for developers looking to fetch and analyze time-series data for the Tel Aviv TA-35 index. By leveraging the various endpoints, developers can access real-time and historical data, enabling them to conduct predictive analytics and derive valuable insights into market sentiment. With the ability to clean, transform, and analyze this data, developers can create applications that forecast market trends, assess risk, and inform investment strategies. For more information on how to get started, visit the Indices-API Documentation and explore the Indices-API Supported Symbols page for a complete list of available indices.

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