Using Indices-API to Fetch Tel Aviv TA-35 Price Time-Series Data for Quantitative Models

Introduction

In the realm of quantitative finance, accessing reliable and real-time data is paramount for developing predictive models. One such valuable resource is the Indices-API, which provides comprehensive access to various financial indices, including the Tel Aviv TA-35. This blog post will delve into how to fetch the Tel Aviv TA-35 price time-series data using the Indices-API, focusing on its capabilities, endpoints, and practical applications in predictive analytics.

About Tel Aviv TA-35 (TA-35)

The Tel Aviv TA-35 index is a benchmark index that represents the performance of the 35 largest companies listed on the Tel Aviv Stock Exchange. This index is crucial for investors and analysts as it reflects the overall health of the Israeli economy and provides insights into market trends. Understanding the TA-35 is essential for anyone looking to engage in quantitative analysis or algorithmic trading strategies.

API Description

The Indices-API is a powerful tool designed to provide developers with real-time and historical data on various financial indices, including the TA-35. This API empowers users to build innovative applications that can analyze market trends, forecast future movements, and enhance trading strategies. With its robust infrastructure, the Indices-API enables seamless integration into existing systems, allowing for the development of next-generation financial applications.

For more information, visit the Indices-API Website or check out the Indices-API Documentation for detailed guidance on implementation.

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

The Latest Rates Endpoint provides real-time exchange rate data for various indices, updated every 60 minutes, 10 minutes, or even more frequently, depending on your subscription plan. This endpoint is essential for applications that require up-to-the-minute data for trading decisions.

Historical Rates Endpoint

Accessing historical rates is crucial for backtesting trading strategies and understanding market trends. The Historical Rates Endpoint allows users to query historical data for most currencies dating back to October 2024. By appending a specific date in the required format, developers can retrieve past data to analyze trends over time.

Convert Endpoint

The Convert Endpoint is a valuable feature that enables users to convert any amount from one currency to another. This is particularly useful for applications that require real-time currency conversion as part of their functionality.

Time-Series Endpoint

The Time-Series Endpoint allows users to query daily historical rates between two dates of their choice. This feature is particularly beneficial for predictive analytics, as it enables developers to analyze trends over specific periods, facilitating more accurate forecasting models.

Fluctuation Endpoint

With the Fluctuation Endpoint, users can track how currencies fluctuate on a day-to-day basis. This information is vital for understanding market volatility and can be used to inform trading strategies.

Open/High/Low/Close (OHLC) Price Endpoint

The OHLC Price Endpoint provides detailed price data, including open, high, low, and close prices for a specified time period. This data is essential for technical analysis and can help traders identify potential entry and exit points in the market.

API Key and Response

To access the Indices-API, users must obtain an API Key, which is passed into the API base URL's access_key parameter. The API delivers exchange rates relative to USD by default, ensuring consistency across different queries.

Available Endpoints

The Indices-API comprises multiple endpoints, each designed to provide specific functionalities. For a complete list of supported symbols and their specifications, refer to the Indices-API Supported Symbols page.

API Endpoint Examples and Responses

Understanding the structure of API responses is crucial for effective data handling. Below are examples of responses from various endpoints:

Latest Rates Endpoint

To retrieve real-time exchange rates for all available indices, you can use the Latest Rates Endpoint. Here’s an example response:

{
"success": true,
"timestamp": 1781312180,
"base": "USD",
"date": "2026-06-13",
"rates": {
"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"
}

Historical Rates Endpoint

Accessing historical exchange rates for any date since 1999 can be done through the Historical Rates Endpoint. Below is an example response:

{
"success": true,
"timestamp": 1781225780,
"base": "USD",
"date": "2026-06-12",
"rates": {
"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"
}

Time-Series Endpoint

The Time-Series Endpoint allows users to obtain exchange rates for a specific time period. Here’s an example response:

{
"success": true,
"timeseries": true,
"start_date": "2026-06-06",
"end_date": "2026-06-13",
"base": "USD",
"rates": {
"2026-06-06": {
"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
},
"2026-06-08": {
"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
},
"2026-06-13": {
"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"
}

Convert Endpoint

The Convert Endpoint allows for currency conversion. Here’s an example response:

{
"success": true,
"query": {
"from": "USD",
"to": "DOW",
"amount": 1000
},
"info": {
"timestamp": 1781312180,
"rate": 0.00029
},
"result": 0.29,
"unit": "per index"
}

Fluctuation Endpoint

To track rate fluctuations between two dates, the Fluctuation Endpoint can be utilized. Here’s an example response:

{
"success": true,
"fluctuation": true,
"start_date": "2026-06-06",
"end_date": "2026-06-13",
"base": "USD",
"rates": {
"DOW": {
"start_rate": 0.00028,
"end_rate": 0.00029,
"change": 1.0e-5,
"change_pct": 3.57
},
"NASDAQ": {
"start_rate": 0.00038,
"end_rate": 0.00039,
"change": 1.0e-5,
"change_pct": 2.63
},
"S&P 500": {
"start_rate": 0.0124,
"end_rate": 0.0125,
"change": 0.0001,
"change_pct": 0.81
},
"FTSE 100": {
"start_rate": 0.0124,
"end_rate": 0.0125,
"change": 0.0001,
"change_pct": 0.81
},
"DAX": {
"start_rate": 0.0126,
"end_rate": 0.0126,
"change": 0,
"change_pct": 0
},
"CAC 40": {
"start_rate": 0.0126,
"end_rate": 0.0126,
"change": 0,
"change_pct": 0
},
"NIKKEI 225": {
"start_rate": 0.0126,
"end_rate": 0.0126,
"change": 0,
"change_pct": 0
}
},
"unit": "per index"
}

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": 1781312180,
"base": "USD",
"date": "2026-06-13",
"rates": {
"DOW": {
"open": 0.00028,
"high": 0.00029,
"low": 0.00027,
"close": 0.00029
},
"NASDAQ": {
"open": 0.00038,
"high": 0.0004,
"low": 0.00037,
"close": 0.00039
},
"S&P 500": {
"open": 0.0124,
"high": 0.0126,
"low": 0.0123,
"close": 0.0125
},
"FTSE 100": {
"open": 0.0124,
"high": 0.0126,
"low": 0.0123,
"close": 0.0125
},
"DAX": {
"open": 0.0126,
"high": 0.0126,
"low": 0.0126,
"close": 0.0126
}
},
"unit": "per index"
}

Bid/Ask Endpoint

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

{
"success": true,
"timestamp": 1781312180,
"base": "USD",
"date": "2026-06-13",
"rates": {
"DOW": {
"bid": 0.00028,
"ask": 0.00029,
"spread": 1.0e-5
},
"NASDAQ": {
"bid": 0.00038,
"ask": 0.00039,
"spread": 1.0e-5
},
"S&P 500": {
"bid": 0.0124,
"ask": 0.0125,
"spread": 0.0001
},
"FTSE 100": {
"bid": 0.0124,
"ask": 0.0125,
"spread": 0.0001
},
"DAX": {
"bid": 0.0126,
"ask": 0.0126,
"spread": 0
},
"CAC 40": {
"bid": 0.0126,
"ask": 0.0126,
"spread": 0
},
"NIKKEI 225": {
"bid": 0.0126,
"ask": 0.0126,
"spread": 0
}
},
"unit": "per index"
}

Data Processing Steps for Predictive Analytics

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

1. Data Retrieval

Utilize the appropriate endpoints to retrieve the required data. For time-series analysis, the Time-Series Endpoint is particularly useful, as it allows you to specify the date range for your analysis.

2. Data Cleaning

Data cleaning is essential to ensure the accuracy of your predictive models. This involves handling missing values, removing outliers, and ensuring that the data is in the correct format for analysis.

3. Feature Engineering

Feature engineering involves creating new variables that can enhance the predictive power of your models. This could include calculating moving averages, volatility measures, or other technical indicators based on the retrieved data.

4. Model Selection

Choose the appropriate predictive model based on your analysis goals. Common models include linear regression, decision trees, and more complex algorithms like neural networks. The choice of model will depend on the nature of the data and the specific predictions you aim to make.

5. Model Training and Validation

Train your model using historical data and validate its performance using a separate dataset. This step is crucial to ensure that your model generalizes well to unseen data.

6. Deployment

Once validated, deploy your model into a production environment where it can make real-time predictions based on incoming data from the Indices-API.

Examples of Predictive Model Applications

The data retrieved from the Indices-API can be applied in various predictive modeling scenarios:

1. Stock Price Prediction

Using historical price data from the TA-35, developers can create models that predict future stock prices based on past trends and patterns. This can be beneficial for traders looking to optimize their investment strategies.

2. Market Trend Analysis

By analyzing fluctuations in the TA-35 index, developers can identify broader market trends and make informed decisions about asset allocation and risk management.

3. Algorithmic Trading

Integrating the Indices-API data into algorithmic trading systems allows for automated trading strategies that react to real-time market conditions, enhancing trading efficiency and profitability.

Conclusion

The Indices-API offers a robust solution for accessing real-time and historical data on the Tel Aviv TA-35 index, making it an invaluable resource for developers engaged in quantitative finance and predictive analytics. By leveraging the various endpoints and features of the API, developers can build sophisticated applications that analyze market trends, forecast future movements, and optimize trading strategies.

For further exploration of the API's capabilities, refer to the Indices-API Documentation and the Indices-API Supported Symbols page. Embrace the power of real-time data and transform your financial applications today!