---
title: "Requesting Data"
description: "Request India Equity data in your algorithm to receive a feed of asset prices in the OnData method."
canonical: https://www.quantconnect.com/docs/v2/drafts/india-equity/requesting-data
markdown: https://www.quantconnect.com/docs/v2/drafts/india-equity/requesting-data.md
language: python
source: https://github.com/QuantConnect/Documentation/tree/master/08%20Drafts/99%20India%20Equity/01%20Requesting%20Data
modified: 2026-10-31
index: https://www.quantconnect.com/llms.txt
---

# Requesting Data

[Documentation](https://www.quantconnect.com/docs/v2.md) > Drafts > [India Equity](https://www.quantconnect.com/docs/v2/drafts/india-equity.md) > Requesting Data

> Code examples are shown in Python. Append `?lang=csharp` to this URL for the C# version of this page.

## Introduction

Request India Equity data in your algorithm to receive a feed of asset prices in the `on_data` method. Historical data for backtesting is unavailable. To trade India Equities live, you can use one of the [brokerage data providers](https://www.quantconnect.com/docs/v2/cloud-platform/datasets.md).

## Create Subscriptions

To create an India Equity subscription, in the `initialize` method, call the `add_equity` method. The `add_equity` method returns an `Equity` object, which contains a `symbol` property. Save a reference to the `symbol` so you can use it in `on_data` to access the security data in the `Slice`.

```python
self._symbol = self.add_equity("YESBANK", market=Market.INDIA).symbol
```

If you set the [brokerage model](https://www.quantconnect.com/docs/v2/writing-algorithms/reality-modeling/brokerages/key-concepts.md) to an India brokerage, you don't need to pass a `market` argument. To view the integrated brokerages that offer India Equities, see [Brokerages](https://www.quantconnect.com/docs/v2/cloud-platform/live-trading/brokerages.md).

## Resolutions

The following table shows the available resolutions and data formats for India Equity subscriptions:

| Resolution | TradeBar | QuoteBar | Trade Tick | Quote Tick |
| --- | --- | --- | --- | --- |
| `TICK` | Not supported | Not supported | Not supported | Not supported |
| `SECOND` | Not supported | Not supported | Not supported | Not supported |
| `MINUTE` | Supported✓ | Not supported | Not supported | Not supported |
| `HOUR` | Supported✓ | Not supported | Not supported | Not supported |
| `DAILY` | Supported✓ | Not supported | Not supported | Not supported |

The default resolution for India Equity subscriptions is `Resolution.MINUTE`. To change the resolution, pass a `resolution` argument to the `add_equity` method.

```python
self._symbol = self.add_equity("YESBANK", Resolution.DAILY, Market.INDIA).symbol
```

To create custom resolution periods, see [Consolidating Data](https://www.quantconnect.com/docs/v2/writing-algorithms/consolidating-data/getting-started.md).

## Supported Markets

LEAN groups all of the India Equity exchanges under `Market.INDIA`. To set the market for a security, pass a `market` argument to the `add_equity` method.

```python
self._symbol = self.add_equity("YESBANK", market=Market.INDIA).symbol
```

The [brokerage models](https://www.quantconnect.com/docs/v2/writing-algorithms/reality-modeling/brokerages/key-concepts.md) have a default market for each asset class. If you set a brokerage model, you may not need to specify the market to use.

## Fill Forward

Fill forward means if there is no data point for the current [slice](https://www.quantconnect.com/docs/v2/writing-algorithms/key-concepts/time-modeling/timeslices.md), LEAN uses the previous data point. Fill forward is the default data setting. If you disable fill forward, you may get [stale fills](https://www.quantconnect.com/docs/v2/writing-algorithms/reality-modeling/trade-fills/key-concepts.md#06-Stale-Fills) or you may see trade volume as zero.

To disable fill forward for a security, set the `fill_forward` argument to false when you create the security subscription.

```python
self._symbol = self.add_equity("YESBANK", market=Market.INDIA, fill_forward=False).symbol
```

## Margin and Leverage

LEAN models buying power and margin calls to ensure your algorithm stays within the margin requirements. The amount of margin that's available depends on the [brokerage model](https://www.quantconnect.com/docs/v2/writing-algorithms/reality-modeling/brokerages/key-concepts.md) you use. For more information about the margin requirements of each brokerage, see the **Margin** section of the [brokerage guides](https://www.quantconnect.com/docs/v2/cloud-platform/live-trading/brokerages.md). To change the amount of leverage you can use for a security, pass a `leverage` argument to the `add_equity` method.

```python
self._symbol = self.add_equity("YESBANK", market=Market.INDIA, leverage=3).symbol
```

## Extended Market Hours

By default, your security subscriptions only cover regular trading hours. To subscribe to pre and post-market trading hours for a specific asset, enable the `extended_market_hours` argument when you create the security subscription.

```python
self._symbol = self.add_equity("YESBANK", market=Market.India, extended_market_hours=True).Symbol
```

You only receive extended market hours data if you create the subscription with minute, second, or tick resolution. If you create the subscription with daily or hourly resolution, the bars only reflect the regular trading hours.

To view the schedule of regular and extended market hours, see [Market Hours](https://www.quantconnect.com/docs/v2/writing-algorithms/securities/asset-classes/india-equity/market-hours.md).

## Data Normalization

The data normalization mode defines how historical data is adjusted for [corporate actions](https://www.quantconnect.com/docs/v2/writing-algorithms/securities/asset-classes/india-equity/corporate-actions.md). The data normalization mode affects the data that LEAN passes to `on_data` and the data from [history request](https://www.quantconnect.com/docs/v2/writing-algorithms/historical-data/history-requests.md). By default, LEAN adjusts India Equity data for splits and dividends to produce a smooth price curve, but the following data normalization modes are available:

*Members of `QuantConnect.DataNormalizationMode` (JSON): https://www.quantconnect.com/services/inspector?type=T:QuantConnect.DataNormalizationMode&language=python*

Fields: `Raw`, `Adjusted`, `SplitAdjusted`, `TotalReturn`, `ScaledRaw`, `RAW`, `ADJUSTED`, `SPLIT_ADJUSTED`, `TOTAL_RETURN`, `SCALED_RAW`

If you use `ADJUSTED`, `SPLIT_ADJUSTED`, or `TOTAL_RETURN`, we use the entire split and dividend history to adjust historical prices. This process ensures you get the same adjusted prices, regardless of the backtest end date.

To set the data normalization mode for a security, pass a `data_normalization_mode` argument to the `add_equity` method.

```python
self._symbol = add_equity("YESBANK", market=Market.INDIA, data_normalization_mode=DataNormalizationMode.RAW).symbol
```

The `SCALED_RAW` data normalization is only for [history requests](https://www.quantconnect.com/docs/v2/writing-algorithms/historical-data/history-requests.md). When you use `SCALED_RAW`, we use the split and dividend history before the algorithm's current time to adjust historical prices. The `SCALED_RAW` data normalization model enables you to warm up indicators with adjusted data when you subscribe to `RAW` security data.

## Properties

The `add_equity` method returns an `Equity` object, which have the following properties:

*Members of `QuantConnect.Securities.Equity.Equity` (JSON): https://www.quantconnect.com/services/inspector?type=T:QuantConnect.Securities.Equity.Equity&language=python*