---
title: "OTC Transparency"
description: "OTC Transparency dataset from Financial Industry Regulatory Authority."
canonical: https://www.quantconnect.com/docs/v2/writing-algorithms/datasets/financial-industry-regulatory-authority/otc-transparency
markdown: https://www.quantconnect.com/docs/v2/writing-algorithms/datasets/financial-industry-regulatory-authority/otc-transparency.md
language: python
source: https://github.com/QuantConnect/Documentation/tree/master/03%20Writing%20Algorithms/14%20Datasets/20%20Financial%20Industry%20Regulatory%20Authority/01%20OTC%20Transparency
modified: 2026-10-26
index: https://www.quantconnect.com/llms.txt
---

# OTC Transparency

[Documentation](https://www.quantconnect.com/docs/v2.md) > [Writing Algorithms](https://www.quantconnect.com/docs/v2/writing-algorithms.md) > Datasets > [Financial Industry Regulatory Authority](https://www.quantconnect.com/docs/v2/writing-algorithms/datasets/financial-industry-regulatory-authority.md) > OTC Transparency

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

## Introduction

The OTC Transparency dataset by the Financial Industry Regulatory Authority (FINRA) tracks short interest and off-exchange trading volume for US Equities. The data covers 59,712 US Equities, starts in January 2017, and is delivered on a daily frequency. This dataset is created by processing the short interest and off-exchange volume reports that broker-dealers file with FINRA.

For more information about the OTC Transparency dataset, including CLI commands and pricing, see the [dataset listing](https://www.quantconnect.com/datasets/finra-otc-transparency).

## About the Provider

The Financial Industry Regulatory Authority (FINRA) is a not-for-profit organization that oversees broker-dealers in the United States, under the supervision of the SEC. It was formed in 2007 when NASD merged with the member regulation arm of the New York Stock Exchange. Its mission is to protect investors and safeguard market integrity.

## Getting Started

The following snippet demonstrates how to request data from the FINRA OTC Transparency dataset:

```python
self._symbol = self.add_equity("AAPL", Resolution.DAILY).symbol
self.short_interest = self.add_data(FINRAShortInterest, self._symbol).symbol
self.weekly_summary = self.add_data(FINRAWeeklySummary, self._symbol).symbol
self.monthly_summary = self.add_data(FINRAMonthlySummary, self._symbol).symbol
```

## Data Summary

The following table describes the dataset properties:

| Property | Value |
| --- | --- |
| Start Date | January 2017 |
| Asset Coverage | 59,712 US Equities |
| Data Density | Sparse |
| Resolution | Daily* |
| Timezone | New York |

* Weekly, twice-monthly, and monthly data we fetch daily.

## Requesting Data

To add FINRA OTC Transparency data to your algorithm, call the `add_data` method. Save a reference to the dataset `Symbol` so you can access the data later in your algorithm.

```python
class FINRAOtcTransparencyDataAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self.set_start_date(2022, 1, 1)
        self.set_end_date(2023, 1, 1)
        self.set_cash(100000)

        self._symbol = self.add_equity("AAPL", Resolution.DAILY).symbol
        self.short_interest = self.add_data(FINRAShortInterest, self._symbol).symbol
        self.weekly_summary = self.add_data(FINRAWeeklySummary, self._symbol).symbol
        self.monthly_summary = self.add_data(FINRAMonthlySummary, self._symbol).symbol
```

## Accessing Data

To get the current FINRA OTC Transparency data, index the current [`Slice`](https://www.quantconnect.com/docs/v2/writing-algorithms/key-concepts/time-modeling/timeslices.md) with the dataset `Symbol`. `Slice` objects deliver unique events to your algorithm as they happen, but the `Slice` may not contain data for your dataset at every time step. To avoid issues, check if the `Slice` contains the data you want before you index it.

```python
def on_data(self, slice: Slice) -> None:
    if slice.contains_key(self.short_interest):
        data_point = slice[self.short_interest]
        self.log(f"{self.short_interest} days to cover at {slice.time}: {data_point.days_to_cover}")
```

To iterate through all of the dataset objects in the current `Slice`, call the `get` method.

```python
def on_data(self, slice: Slice) -> None:
    for symbol, data_point in slice.get(FINRAShortInterest).items():
        self.log(f"{symbol} days to cover at {slice.time}: {data_point.days_to_cover}")

    for symbol, data_point in slice.get(FINRAWeeklySummary).items():
        self.log(f"{symbol} ATS weekly shares at {slice.time}: {data_point.share_quantity}")

    for symbol, data_point in slice.get(FINRAMonthlySummary).items():
        self.log(f"{symbol} non-ATS monthly shares at {slice.time}: {data_point.share_quantity}")
```

## Historical Data

To get historical FINRA OTC Transparency data, call the `history` method with the dataset `Symbol`. If there is no data in the period you request, the history result is empty.

```python
# DataFrames
short_interest_history = self.history(self.short_interest, 100, Resolution.DAILY)
weekly_history = self.history(self.weekly_summary, 100, Resolution.DAILY)
monthly_history = self.history(self.monthly_summary, 100, Resolution.DAILY)
history = self.history([self.short_interest, self.weekly_summary, self.monthly_summary], 100, Resolution.DAILY)

# Dataset objects
short_interest_bars = self.history[FINRAShortInterest](self.short_interest, 100, Resolution.DAILY)
weekly_bars = self.history[FINRAWeeklySummary](self.weekly_summary, 100, Resolution.DAILY)
monthly_bars = self.history[FINRAMonthlySummary](self.monthly_summary, 100, Resolution.DAILY)
```

For more information about historical data, see [History Requests](https://www.quantconnect.com/docs/v2/writing-algorithms/historical-data/history-requests.md).

## Universe Selection

To select a dynamic universe of US Equities based on FINRA OTC Transparency data, call the `add_universe` method with one of the dataset's universe classes and a selection function. The universe classes are `FINRAShortInterestUniverse`, `FINRAWeeklySummaryUniverse`, and `FINRAMonthlySummaryUniverse`.

```python
def initialize(self) -> None:
    self._universe = self.add_universe(FINRAShortInterestUniverse, self.universe_selection)

def universe_selection(self, alt_coarse: List[FINRAShortInterestUniverse]) -> List[Symbol]:
    return [d.symbol for d in alt_coarse
            if d.days_to_cover is not None and d.days_to_cover > 5]
```

For more information about dynamic universes, see [Universes](https://www.quantconnect.com/docs/v2/writing-algorithms/universes/key-concepts.md).

## Remove Subscriptions

To remove a subscription, call the `remove_security` method.

```python
self.remove_security(self.short_interest)
self.remove_security(self.weekly_summary)
self.remove_security(self.monthly_summary)
```

If you subscribe to FINRA OTC Transparency data for assets in a dynamic universe, remove the dataset subscription when the asset leaves your universe. To view a common design pattern, see [Track Security Changes](https://www.quantconnect.com/docs/v2/writing-algorithms/algorithm-framework/alpha/key-concepts.md#05-Track-Security-Changes).

## Example Applications

The OTC Transparency dataset enables you to monitor short interest and off-exchange trading. Examples include the following strategies:

- Finding short squeeze candidates by comparing short interest to average daily volume
- Trading stocks when their short interest rises or falls sharply between reports
- Spotting institutional buying or selling from changes in alternative trading system (ATS) volume

### Classic Algorithm Example

The following example algorithm short sells AAPL when its open short interest increases. Otherwise, it buys AAPL.

```python
from AlgorithmImports import *

class FINRAOtcTransparencyDataAlgorithm(QCAlgorithm):
    def initialize(self):
        self.set_start_date(2022, 1, 1)
        self.set_end_date(2023, 1, 1)
        self.set_cash(100000)

        aapl = self.add_equity("AAPL", Resolution.DAILY).symbol
        self.short_interest = self.add_data(FINRAShortInterest, aapl).symbol
        self.weekly_summary = self.add_data(FINRAWeeklySummary, aapl).symbol
        self.monthly_summary = self.add_data(FINRAMonthlySummary, aapl).symbol

        history = self.history(FINRAShortInterest, self.short_interest, 30, Resolution.DAILY)
        self.debug(f"We got {len(history)} items from our history request")

    def on_data(self, slice):
        # Log the monthly non-ATS share volume.
        for symbol, point in slice.get(FINRAMonthlySummary).items():
            self.debug(f"{symbol} non-ATS monthly shares at {slice.time}: {point.share_quantity}")

        for symbol, point in slice.get(FINRAShortInterest).items():
            if point.change is None:
                continue

            # Short sell when open short interest increases. Otherwise, buy.
            if point.change > 0:
                self.set_holdings(symbol.underlying, -1)
            else:
                self.set_holdings(symbol.underlying, 1)
```

### Framework Algorithm Example

The following example algorithm emits a down insight for AAPL when its open short interest increases. Otherwise, it emits an up insight.

```python
from AlgorithmImports import *

class FINRAOtcTransparencyFrameworkAlgorithm(QCAlgorithm):

    def initialize(self) -> None:
        self.set_start_date(2022, 1, 1)
        self.set_end_date(2023, 1, 1)
        self.set_cash(100000)

        self.universe_settings.resolution = Resolution.DAILY

        symbols = [Symbol.create("AAPL", SecurityType.EQUITY, Market.USA)]
        self.set_universe_selection(ManualUniverseSelectionModel(symbols))

        self.add_alpha(FINRAShortInterestAlphaModel())

        self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
        self.set_execution(ImmediateExecutionModel())

class FINRAShortInterestAlphaModel(AlphaModel):

    def __init__(self) -> None:
        self._symbols_by_security = {}

    def update(self, algorithm: QCAlgorithm, data: Slice) -> List[Insight]:
        insights = []
        for security_symbol, short_interest_symbol in self._symbols_by_security.items():
            if short_interest_symbol not in data:
                continue
            change = data[short_interest_symbol].change
            if change is None:
                continue
            # Emit a down insight when short interest increases, otherwise an up insight.
            direction = InsightDirection.DOWN if change > 0 else InsightDirection.UP
            insights.append(Insight.price(security_symbol, timedelta(days=14), direction))
        return insights

    def on_securities_changed(self, algorithm: QCAlgorithm, changes: SecurityChanges) -> None:
        for security in changes.added_securities:
            symbol = security.symbol
            short_interest_symbol = algorithm.add_data(FINRAShortInterest, symbol).symbol
            self._symbols_by_security[symbol] = short_interest_symbol
            history = algorithm.history(FINRAShortInterest, short_interest_symbol, 10, Resolution.DAILY)
            algorithm.debug(f"Got {len(history)} historical short interest rows for {symbol}")
        for security in changes.removed_securities:
            short_interest_symbol = self._symbols_by_security.pop(security.symbol, None)
            if short_interest_symbol is not None:
                algorithm.remove_security(short_interest_symbol)
```

## Data Point Attributes

The FINRA OTC Transparency dataset provides `FINRAShortInterest`, `FINRAWeeklySummary`, and `FINRAMonthlySummary` objects, each with a matching universe class.

### FINRAShortInterest

`FINRAShortInterest` objects have the following attributes:

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

### FINRAShortInterestUniverse

`FINRAShortInterestUniverse` objects have the following attributes:

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

### FINRAWeeklySummary

`FINRAWeeklySummary` objects have the following attributes:

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

### FINRAWeeklySummaryUniverse

`FINRAWeeklySummaryUniverse` objects have the following attributes:

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

### FINRAMonthlySummary

`FINRAMonthlySummary` objects have the following attributes:

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

### FINRAMonthlySummaryUniverse

`FINRAMonthlySummaryUniverse` objects have the following attributes:

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