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

Introduction

In today's fast-paced financial landscape, the ability to access and analyze real-time market data is crucial for developers and analysts alike. The Tel Aviv TA-35 index, which represents the 35 largest companies listed on the Tel Aviv Stock Exchange, is a key indicator of the Israeli economy. Utilizing the Indices-API to fetch TA-35 price time-series data can empower developers to conduct in-depth sentiment analysis and predictive analytics. This blog post will guide you through the process of leveraging the Indices-API to access TA-35 data, including sample API calls, data processing steps, and practical applications for predictive modeling.

Understanding the Indices-API

The Indices-API is a powerful tool designed for developers seeking to integrate real-time index data into their applications. With its innovative capabilities, the API allows users to access a wide range of financial data, including the latest rates, historical data, and time-series information. This API is particularly valuable for building applications that require up-to-date market insights and analytics.

About Tel Aviv TA-35 (TA-35)

The Tel Aviv TA-35 index is a benchmark for the Israeli stock market, encompassing a diverse array of sectors such as technology, finance, and healthcare. By analyzing the TA-35 index, developers can gain insights into market trends and investor sentiment, which can be crucial for making informed investment decisions. The Indices-API provides comprehensive access to this data, enabling developers to create applications that can predict market movements based on historical trends and real-time data.

API Description

The Indices-API is designed to facilitate the retrieval of financial data with ease and efficiency. Its transformative potential lies in its ability to provide real-time index data, which can be utilized for various applications, including algorithmic trading, market analysis, and sentiment analysis. The API empowers developers to build next-generation applications that can respond to market changes in real-time, enhancing decision-making processes.

For more information, you can refer to the Indices-API Documentation, which provides detailed guidance on how to utilize the API effectively.

Key Features and Endpoints

The Indices-API offers a variety of endpoints that cater to different data needs. Here are some of the key features:

Latest Rates Endpoint: This endpoint provides real-time exchange rate data, updated based on your subscription plan. Depending on the plan, updates can occur every 60 minutes or every 10 minutes, ensuring that you have the most current data at your fingertips.
Historical Rates Endpoint: Access historical rates for the TA-35 index and other indices dating back to 1999. This feature is invaluable for conducting long-term trend analysis and understanding market behavior over time.
Time-Series Endpoint: The time-series endpoint allows you to query daily historical rates between two specified dates. This is particularly useful for analyzing trends and patterns in the TA-35 index over specific periods.
Fluctuation Endpoint: Track how the TA-35 index fluctuates on a day-to-day basis. This endpoint provides insights into market volatility and can help in assessing risk.
Open/High/Low/Close (OHLC) Price Endpoint: Retrieve the open, high, low, and close prices for the TA-35 index over a specified time period. This data is essential for technical analysis and trading strategies.
Convert Endpoint: Use this endpoint to convert amounts between different currencies, which can be useful when analyzing international investments.
API Key: Your unique API key is required to authenticate requests to the Indices-API. This ensures secure access to the data.
API Response: The API returns exchange rates relative to USD by default, providing a consistent basis for comparison.
Supported Symbols Endpoint: This endpoint provides a constantly updated list of all available indices and their specifications, allowing you to easily find the data you need.

List of Symbols

The Indices-API provides access to a diverse range of index symbols, including the TA-35. For a complete list of all supported symbols and their specifications, refer to the Indices-API Supported Symbols page.

API Endpoint Examples and Responses

Understanding how to interact with the Indices-API is crucial for effective data retrieval. Below are examples of various endpoints and their expected responses.

Latest Rates Endpoint

The Latest Rates Endpoint allows you to get real-time exchange rates for all available indices. Here’s an example response:

{
"success": true,
"timestamp": 1781657726,
"base": "USD",
"date": "2026-06-17",
"rates": {
"TA-35": 0.00029,
"DOW": 0.00029,
"NASDAQ": 0.00039,
"S&P 500": 0.00024,
"FTSE 100": 0.00058,
"DAX": 0.00448,
"CAC 40": 0.00137,
"NIKKEI 225": 0.0125
},
"unit": "per index"
}

In this response, the "rates" field contains the latest exchange rates for various indices, including the TA-35. The "success" field indicates whether the request was successful.

Historical Rates Endpoint

Accessing historical exchange rates is essential for trend analysis. Here’s an example response from the Historical Rates Endpoint:

{
"success": true,
"timestamp": 1781571326,
"base": "USD",
"date": "2026-06-16",
"rates": {
"TA-35": 0.00028,
"DOW": 0.00028,
"NASDAQ": 0.00038,
"S&P 500": 0.00023,
"FTSE 100": 0.0124,
"DAX": 0.0126,
"CAC 40": 0.0126,
"NIKKEI 225": 0.0126
},
"unit": "per index"
}

This response provides historical rates for the specified date, allowing developers to analyze past performance and market trends.

Time-Series Endpoint

The Time-Series Endpoint is particularly useful for analyzing trends over a specified period. Here’s an example response:

{
"success": true,
"timeseries": true,
"start_date": "2026-06-10",
"end_date": "2026-06-17",
"base": "USD",
"rates": {
"2026-06-10": {
"TA-35": 0.00028,
"DOW": 0.00028,
"NASDAQ": 0.00038,
"S&P 500": 0.00023,
"FTSE 100": 0.0124,
"DAX": 0.0126,
"CAC 40": 0.0126,
"NIKKEI 225": 0.0126
},
"2026-06-12": {
"TA-35": 0.00029,
"DOW": 0.00029,
"NASDAQ": 0.00039,
"S&P 500": 0.00024,
"FTSE 100": 0.0124,
"DAX": 0.0126,
"CAC 40": 0.0126,
"NIKKEI 225": 0.0126
},
"2026-06-17": {
"TA-35": 0.00029,
"DOW": 0.00029,
"NASDAQ": 0.00039,
"S&P 500": 0.00024,
"FTSE 100": 0.0124,
"DAX": 0.0126,
"CAC 40": 0.0126,
"NIKKEI 225": 0.0126
}
},
"unit": "per index"
}

This response provides daily rates for the specified date range, allowing developers to visualize trends and fluctuations in the TA-35 index over time.

Convert Endpoint

The Convert Endpoint allows you to convert amounts between different indices. Here’s an example response:

{
"success": true,
"query": {
"from": "USD",
"to": "TA-35",
"amount": 1000
},
"info": {
"timestamp": 1781657726,
"rate": 0.00029
},
"result": 0.29,
"unit": "per index"
}

This response shows the conversion of 1000 USD to the TA-35 index, providing the current rate and the resulting value.

Fluctuation Endpoint

The Fluctuation Endpoint tracks rate fluctuations between two dates. Here’s an example response:

{
"success": true,
"fluctuation": true,
"start_date": "2026-06-10",
"end_date": "2026-06-17",
"base": "USD",
"rates": {
"TA-35": {
"start_rate": 0.00028,
"end_rate": 0.00029,
"change": 1.0e-5,
"change_pct": 3.57
},
"DOW": {
"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 index has changed over the specified period, including both absolute and percentage changes.

OHLC (Open/High/Low/Close) Endpoint

The OHLC Endpoint provides detailed price data for a specific time period. Here’s an example response:

{
"success": true,
"timestamp": 1781657726,
"base": "USD",
"date": "2026-06-17",
"rates": {
"TA-35": {
"open": 0.00028,
"high": 0.00029,
"low": 0.00027,
"close": 0.00029
},
"DOW": {
"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 index, which are essential for technical analysis and trading strategies.

Bid/Ask Endpoint

The Bid/Ask Endpoint retrieves current bid and ask prices for indices. Here’s an example response:

{
"success": true,
"timestamp": 1781657726,
"base": "USD",
"date": "2026-06-17",
"rates": {
"TA-35": {
"bid": 0.00028,
"ask": 0.00029,
"spread": 1.0e-5
},
"DOW": {
"bid": 0.00028,
"ask": 0.00029,
"spread": 1.0e-5
}
},
"unit": "per index"
}

This response provides the current bid and ask prices for the TA-35 index, along with the spread, which is crucial for traders looking to execute orders effectively.

Data Processing Steps for Predictive Analytics

Once you have retrieved the necessary data from the Indices-API, the next step is to process this data for predictive analytics. Here are the key steps involved:

1. Data Collection

Utilize the various endpoints of the Indices-API to collect the required data. Depending on your analysis goals, you may want to gather historical rates, time-series data, and OHLC data for the TA-35 index.

2. Data Cleaning

Ensure that the collected data is clean and free from inconsistencies. This may involve removing duplicates, handling missing values, and standardizing formats. Data cleaning is crucial for accurate analysis and modeling.

3. Data Transformation

Transform the data into a suitable format for analysis. This may include normalizing values, creating new features, or aggregating data over specific time periods. For example, you might want to calculate moving averages or volatility metrics based on the OHLC data.

4. Exploratory Data Analysis (EDA)

Conduct exploratory data analysis to identify trends, patterns, and correlations within the data. Visualization tools can be helpful in this step, allowing you to create charts and graphs that illustrate key insights.

5. Model Selection

Select appropriate predictive models based on your analysis goals. Common models for time-series forecasting include ARIMA, Exponential Smoothing, and machine learning approaches such as Random Forest or Gradient Boosting.

6. Model Training and Evaluation

Train your selected models using the processed data and evaluate their performance using metrics such as Mean Absolute Error (MAE) or Root Mean Squared Error (RMSE). It’s essential to validate your models using a separate test dataset to ensure their robustness.

7. Deployment

Once you have a well-performing model, deploy it into a production environment where it can be used to make real-time predictions based on incoming data from the Indices-API.

Practical Applications of Predictive Models

Predictive models built using TA-35 data can be applied in various ways:

Algorithmic Trading: Use predictive models to automate trading strategies based on forecasted price movements of the TA-35 index.
Market Sentiment Analysis: Analyze sentiment based on historical price movements and news data to gauge investor sentiment towards the Israeli market.
Risk Management: Implement models to assess risk and volatility, helping investors make informed decisions about their portfolios.
Investment Strategy Development: Create investment strategies based on predictive insights, optimizing asset allocation and timing of trades.

Conclusion

In conclusion, the Indices-API provides a robust framework for accessing and analyzing the Tel Aviv TA-35 index data. By leveraging its various endpoints, developers can fetch real-time and historical data, enabling them to conduct in-depth sentiment analysis and predictive modeling. The steps outlined in this blog post, from data collection to model deployment, offer a comprehensive guide for utilizing the API effectively. As the financial landscape continues to evolve, the ability to harness real-time data will be paramount for developers and analysts alike. For further exploration, refer to the Indices-API Documentation and the Indices-API Supported Symbols page to enhance your understanding and application of this powerful tool.

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