Quiver Quantitative
Insider Trading
Introduction
Corporate insiders are required to disclose purchases or sales of their own stock within two business days of when they occur. Using these disclosures, we collect data on insider trading activity, which can give hints on whether executives are bullish or bearish on their own companies. Here is a blog that we did on this dataset: https://www.quiverquant.com/blog/081121
This dataset depends on the US Equity Security Master dataset because the US Equity Security Master dataset contains information on splits, dividends, and symbol changes.
For more information about the Insider Trading dataset, including CLI commands and pricing, see the dataset listing.
About the Provider
Quiver Quantitative was founded by two college students in February 2020 with the goal of bridging the information gap between Wall Street and non-professional investors. Quiver allows retail investors to tap into the power of big data and have access to actionable, easy to interpret data that hasn’t already been dissected by Wall Street.
Getting Started
The following snippet demonstrates how to request data from the Insider Trading dataset:
self.aapl = self.add_equity("AAPL", Resolution.DAILY).symbol
self.dataset_symbol = self.add_data(QuiverInsiderTrading, aapl).symbol
self._universe = self.add_universe(QuiverInsiderTradingUniverse, self.universe_selection) _symbol = AddEquity("AAPL", Resolution.Daily).Symbol;
_datasetSymbol = AddData<QuiverInsiderTrading>(aapl).Symbol;
_universe = AddUniverse<QuiverInsiderTradingUniverse>(UniverseSelection);
Example Applications
The Quiver Quantitative Insider Trading dataset enables researchers to create strategies using the latest information on insider trading activity. Examples include:
- Taking a short position in securities that have had the most insider selling over the last 5 days
- Buying any security that has had over $100,000 worth of shares purchased by insiders in the last month
For more example algorithms, see Examples.
Requesting Data
To add Insider Trading data to your algorithm, call the AddDataadd_data method. Save a reference to the dataset Symbol so you can access the data later in your algorithm.
class QuiverInsiderTradingDataAlgorithm(QCAlgorithm):
def initialize(self) -> None:
self.set_start_date(2019, 1, 1)
self.set_end_date(2020, 6, 1)
self.set_cash(100000)
symbol = self.add_equity("AAPL", Resolution.DAILY).symbol
self.dataset_symbol = self.add_data(QuiverInsiderTrading, symbol).symbol namespace QuantConnect.Algorithm.CSharp.AltData
{
public class QuiverInsiderTradingDataAlgorithm: QCAlgorithm
{
private Symbol _datasetSymbol;
public override void Initialize()
{
SetStartDate(2019, 1, 1);
SetEndDate(2020, 6, 1);
SetCash(100000);
var symbol = AddEquity("AAPL", Resolution.Daily).Symbol;
_datasetSymbol= AddData<QuiverInsiderTrading>(symbol).Symbol;
}
}
}
Accessing Data
To get the current Insider Trading data, index the current Slice with the dataset Symbol. Slice objects deliver unique events to your algorithm as they happen, but the Slice may not contain data for your dataset at every time step. To avoid issues, check if the Slice contains the data you want before you index it.
def on_data(self, slice: Slice) -> None:
if slice.contains_key(self.dataset_symbol):
data_points = slice[self.dataset_symbol]
for data_point in data_points:
self.log(f"{self.dataset_symbol} shares at {slice.time}: {data_point.shares}") public override void OnData(Slice slice)
{
if (slice.ContainsKey(_datasetSymbol))
{
var dataPoints = slice[_datasetSymbol];
foreach (QuiverInsiderTrading dataPoint in dataPoints)
{
Log($"{_datasetSymbol} shares at {slice.Time}: {dataPoint.Shares}");
}
}
}
To iterate through all of the dataset objects in the current Slice, call the Getget method.
def on_data(self, slice: Slice) -> None:
for dataset_symbol, data_points in slice.get(QuiverInsiderTrading).items():
for data_point in data_points:
self.log(f"{dataset_symbol} shares at {slice.time}: {data_point.shares}")
public override void OnData(Slice slice)
{
foreach (var kvp in slice.Get<QuiverInsiderTrading>())
{
var datasetSymbol = kvp.Key;
var dataPoints = kvp.Value;
foreach(QuiverInsiderTrading dataPoint in dataPoints)
{
Log($"{datasetSymbol} shares at {slice.Time}: {dataPoint.Shares}");
}
}
}
Historical Data
To get historical Insider Trading data, call the Historyhistory method with the dataset Symbol. If there is no data in the period you request, the history result is empty.
# DataFrame history_df = self.history(self.dataset_symbol, 100, Resolution.DAILY) # Dataset objects self.history[QuiverInsiderTrading](self.dataset_symbol, 100, Resolution.DAILY)
var history = History<QuiverInsiderTrading>(_datasetSymbol, 100, Resolution.Daily);
For more information about historical data, see History Requests.
Universe Selection
To select a dynamic universe of US Equities based on Insider Trading data, call the AddUniverseadd_universe method with the QuiverInsiderTradingUniverse class and a selection function.
class InsiderTradingUniverseAlgorithm(QCAlgorithm):
def initialize(self):
self.set_start_date(2023, 1, 1)
self._universe = self.add_universe(QuiverInsiderTradingUniverse, self._select_assets)
def _select_assets(self, alt_coarse: List[QuiverInsiderTradingUniverse]) -> List[Symbol]:
dollar_volume_by_symbol = {}
for data in alt_coarse:
symbol = data.symbol
if not data.price_per_share:
continue
if symbol not in dollar_volume_by_symbol:
dollar_volume_by_symbol[symbol] = 0
dollar_volume_by_symbol[symbol] += data.shares * data.price_per_share
return [
symbol for symbol, _ in sorted(dollar_volume_by_symbol.items(), key=lambda kvp: kvp[1])[-10:]
] public class InsiderTradingUniverseAlgorithm : QCAlgorithm
{
private Universe _universe;
public override void Initialize()
{
SetStartDate(2023, 1, 1);
_universe = AddUniverse<QuiverInsiderTradingUniverse>(SelectAssets);
}
private IEnumerable<Symbol> SelectAssets(IEnumerable<BaseData> altCoarse)
{
var dollarVolumeBySymbol = new Dictionary<Symbol, decimal?>();
foreach (QuiverInsiderTradingUniverse data in altCoarse)
{
if (data.PricePerShare == 0m)
{
continue;
}
if (!dollarVolumeBySymbol.ContainsKey(data.Symbol))
{
dollarVolumeBySymbol[data.Symbol] = 0;
}
dollarVolumeBySymbol[data.Symbol] += data.Shares * data.PricePerShare;
}
return dollarVolumeBySymbol
.OrderByDescending(kvp => kvp.Value)
.Take(10)
.Select(kvp => kvp.Key);
}
}
Universe History
You can get historical universe data in an algorithm and in the Research Environment.
Historical Universe Data in Algorithms
To get historical universe data in an algorithm, call the Historyhistory method with the Universe object and the lookback period. If there is no data in the period you request, the history result is empty.
var universeHistory = History(universe, 30, Resolution.Daily);
foreach (var insiders in universeHistory)
{
foreach (QuiverInsiderTradingUniverse insider in insiders)
{
Log($"{insider.Symbol} volume at {insider.EndTime}: {insider.Shares * insider.PricePerShare}");
}
}} # DataFrame example where the columns are the QuiverInsiderTradingUniverse attributes:
history_df = self.history(self._universe, 30, Resolution.DAILY, flatten=True)
# Series example where the values are lists of QuiverInsiderTradingUniverse objects:
universe_history = self.history(self._universe, 30, Resolution.DAILY)
for (_, time), insiders in universe_history.items():
for insider in insiders:
if insider.price_per_share:
self.log(f"{insider.symbol} volume at {insider.end_time}: {insider.shares * insider.price_per_share}")
Historical Universe Data in Research
To get historical universe data in research, call the UniverseHistoryuniverse_history method with the Universe object, a start date, and an end date. This method returns the filtered universe. If there is no data in the period you request, the history result is empty.
var universeHistory = qb.UniverseHistory(universe, qb.Time.AddDays(-30), qb.Time);
foreach (var insiders in universeHistory)
{
foreach (QuiverInsiderTradingUniverse insider in insiders)
{
Console.WriteLine($"{insider.Symbol} volume at {insider.EndTime}: {insider.Shares * insider.PricePerShare}");
}
} # DataFrame example where the columns are the QuiverInsiderTradingUniverse attributes:
history_df = qb.universe_history(universe, qb.time-timedelta(30), qb.time, flatten=True)
# Series example where the values are lists of QuiverInsiderTradingUniverse objects:
universe_history = qb.universe_history(universe, qb.time-timedelta(30), qb.time)
for (_, time), insiders in universe_history.items():
for insider in insiders:
if insider.price_per_share:
print(f"{insider.symbol} volume at {insider.end_time}: {insider.shares * insider.price_per_share}")
You can call the Historyhistory method in Research.
Remove Subscriptions
To remove a subscription, call the RemoveSecurityremove_security method.
self.remove_security(self.dataset_symbol)
RemoveSecurity(_datasetSymbol);
If you subscribe to Insider Trading 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.
Example Applications
The Quiver Quantitative Insider Trading dataset enables researchers to create strategies using the latest information on insider trading activity. Examples include:
- Taking a short position in securities that have had the most insider selling over the last 5 days
- Buying any security that has had over $100,000 worth of shares purchased by insiders in the last month
For more example algorithms, see Examples.
You can also see our Videos. You can also get in touch with us via Discord.
Did you find this page helpful?
