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);
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 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 $100,000 worth of shares purchased by insiders in the last month
Classic Algorithm Example
In this example, we would 100% long AAPL if there was any insider buying event. Otherwise, we liquidate it.
from AlgorithmImports import *
from QuantConnect.DataSource import *
class QuiverInsiderTradingAlgorithm(QCAlgorithm):
def initialize(self):
self.set_start_date(2022, 2, 1) #Set Start Date
self.set_end_date(2022, 2, 28) #Set End Date
self.aapl = self.add_equity("AAPL", Resolution.DAILY).symbol
# Subscribe to insider trade data for AAPL to generate trade signal
self.dataset_symbol = self.add_data(QuiverInsiderTrading, self.aapl).symbol
# history request
history = self.history(self.dataset_symbol, 10, Resolution.DAILY)
self.debug(f"We got {len(history)} items from historical data request of {self.dataset_symbol}.")
def on_data(self, slice: Slice) -> None:
# Trade only base on insider trade data
for insider_trades in slice.Get(QuiverInsiderTrading).values():
for insider_trade in insider_trades:
# Any buy insider trade will result in buying, assuming insider have confidence in stock price with more informed information and projection
if insider_trade.shares > 0:
self.set_holdings(self.symbol, 1)
# Any sell insider trade will result in liquidation, assuming insiders believe the stock price has reached maximum or poor future confidence
else:
self.liquidate(self.symbol) public class QuiverInsiderTradingAlgorithm : QCAlgorithm
{
private Symbol _symbol, _datasetSymbol;
public override void Initialize()
{
SetStartDate(2022, 2, 1); //Set Start Date
SetEndDate(2022, 2, 28); //Set End Date
_symbol = AddEquity("AAPL").Symbol;
// Subscribe to insider trade data for AAPL to generate trade signal
_datasetSymbol = AddData<QuiverInsiderTrading>(_symbol).Symbol;
// history request
var history = History<QuiverInsiderTrading>(new[] {_datasetSymbol}, 10, Resolution.Daily);
Debug($"We got {history.Count()} items from historical data request of {_datasetSymbol}.");
}
public override void OnData(Slice slice)
{
// Trade only base on insider trade data
foreach (var kvp in slice.Get<QuiverInsiderTrading>())
{
foreach (QuiverInsiderTrading insiderTrade in kvp.Value)
{
// Any buy insider trade will result in buying, assuming insider have confidence in stock price with more informed information and projection
if (insiderTrade.Shares > 0)
{
SetHoldings(_symbol, 1);
}
// Any sell insider trade will result in liquidation, assuming insiders believe the stock price has reached maximum or poor future confidence
else
{
Liquidate(_symbol);
}
}
}
}
}
Framework Algorithm Example
In this example, we long a equal-sized portfolio with equities that having insider trading for over $100000 USD in the previous day.
from AlgorithmImports import *
from QuantConnect.DataSource import *
class QuiverInsiderTradingDataAlgorithm(QCAlgorithm):
def initialize(self) -> None:
self.set_start_date(2021, 1, 1)
self.set_end_date(2021, 6, 1)
self.set_cash(100000)
# To hold the insider trade dataset symbol for managing subscription
self.data_symbols = {}
# Filter universe using insider trade data
self.add_universe(QuiverInsiderTradingUniverse, self.universe_selection)
self.add_alpha(ConstantAlphaModel(InsightType.PRICE, InsightDirection.UP, timedelta(1)))
# Invest equally to evenly dissipate the capital concentration risk
self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
def universe_selection(self, data: List[QuiverInsiderTradingUniverse]) -> List[Symbol]:
symbol_data = {}
for datum in data:
symbol = datum.symbol
if symbol not in symbol_data:
symbol_data[symbol] = []
symbol_data[symbol].append(datum)
# Select the ones with insider trading dollar amount above 100k, assuming insider have confidence in stock price with more informed information and projection
return [symbol for symbol, d in symbol_data.items()
if sum([x.shares * x.price_per_share for x in d if x.shares and x.price_per_share]) >= 100000]
def on_securities_changed(self, changes: SecurityChanges) -> None:
for security in changes.added_securities:
# Requesting insider trade data for trading
symbol = security.symbol
dataset_symbol = self.add_data(QuiverInsiderTrading, symbol).symbol
self.data_symbols[symbol] = dataset_symbol
# Historical Data
history = self.history(dataset_symbol, 10, Resolution.DAILY)
self.debug(f"We got {len(history)} items from our history request on {dataset_symbol}.")
for security in changes.removed_securities:
dataset_symbol = self.data_symbols.pop(security.symbol, None)
if dataset_symbol:
# Remove insider trade data subscription to release computation resources
self.remove_security(dataset_symbol) public class QuiverInsiderTradingDataAlgorithm : QCAlgorithm
{
// To hold the insider trade dataset symbol for managing subscription
private Dictionary<Symbol, Symbol> _dataSymbols = new();
public override void Initialize()
{
SetStartDate(2021, 1, 1);
SetEndDate(2021, 6, 1);
SetCash(100000);
// Filter universe using insider trade data
AddUniverse<QuiverInsiderTradingUniverse>(data =>
{
var symbolData = new Dictionary<Symbol, List<QuiverInsiderTradingUniverse>>();
foreach (var datum in data.OfType<QuiverInsiderTradingUniverse>())
{
var symbol = datum.Symbol;
if (!symbolData.ContainsKey(symbol))
{
symbolData.Add(symbol, new List<QuiverInsiderTradingUniverse>());
}
symbolData[symbol].Add(datum);
}
// Select the ones with insider trading dollar amount above 100k, assuming insider have confidence in stock price with more informed information and projection
return from kvp in symbolData
where kvp.Value.Sum(x => x.Shares * x.PricePerShare) >= 100000
select kvp.Key;
});
AddAlpha(new ConstantAlphaModel(InsightType.Price, InsightDirection.Up, TimeSpan.FromDays(1)));
// Invest equally to evenly dissipate the capital concentration risk
SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
}
public override void OnSecuritiesChanged(SecurityChanges changes)
{
foreach (var security in changes.AddedSecurities)
{
// Requesting insider trade data for trading
var symbol = security.Symbol;
var datasetSymbol = AddData<QuiverInsiderTrading>(symbol).Symbol;
_dataSymbols.Add(symbol, datasetSymbol);
// History request
var history = History<QuiverInsiderTrading>(datasetSymbol, 10, Resolution.Daily);
Debug($"We get {history.Count()} items in historical data of {datasetSymbol}");
}
foreach (var security in changes.RemovedSecurities)
{
var symbol = security.Symbol;
if (_dataSymbols.ContainsKey(symbol))
{
// Remove insider trade data subscription to release computation resources
_dataSymbols.Remove(symbol, out var datasetSymbol);
RemoveSecurity(datasetSymbol);
}
}
}
}
Research Example
The following example lists US Equities having insider trading for over $10,000,000.
#r "../QuantConnect.DataSource.QuiverInsiderTrading.dll"
using QuantConnect.DataSource;
var qb = new QuantBook();
// Requesting data
var aapl = qb.AddEquity("AAPL", Resolution.Daily).Symbol;
var symbol = qb.AddData<QuiverInsiderTrading>(aapl).Symbol;
// Historical data
var history = qb.History<QuiverInsiderTrading>(symbol, 60, Resolution.Daily);
foreach (var insiders in history)
{
foreach (QuiverInsiderTrading insider in insiders)
{
Console.WriteLine($"{insider.Symbol} shares at {insider.EndTime}: {insider.Shares}");
}
}
// Add Universe Selection
IEnumerable<Symbol> UniverseSelection(IEnumerable<BaseData> altCoarse)
{
return from d in altCoarse.OfType<QuiverInsiderTradingUniverse>()
where d.Shares * d.PricePerShare >= 10000000 select d.Symbol;
}
var universe = qb.AddUniverse<QuiverInsiderTradingUniverse>(UniverseSelection);
// Historical Universe data
var universeHistory = qb.UniverseHistory(universe, qb.Time.AddDays(-5), qb.Time);
foreach (var insiders in universeHistory)
{
foreach (QuiverInsiderTradingUniverse insider in insiders)
{
Console.WriteLine($"{insider.Symbol} volume at {insider.EndTime}: {insider.Shares * insider.PricePerShare}");
}
} qb = QuantBook()
# Requesting Data
aapl = qb.add_equity("AAPL", Resolution.DAILY).symbol
symbol = qb.add_data(QuiverInsiderTrading, aapl).symbol
# Historical data
history = qb.history(QuiverInsiderTrading, symbol, 60, Resolution.DAILY)
for (symbol, time), insiders in history.items():
for insider in insiders:
print(f"{insider.symbol} shares at {insider.end_time}: {insider.shares}")
# Add Universe Selection
def universe_selection(alt_coarse: List[QuiverInsiderTradingUniverse]) -> List[Symbol]:
return [d.symbol for d in alt_coarse if d.price_per_share and d.shares * d.price_per_share >= 10000000]
universe = qb.add_universe(QuiverInsiderTradingUniverse, universe_selection)
# Historical Universe data
universe_history = qb.universe_history(universe, qb.time-timedelta(5), 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}")
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