AlgoSeek
US Equities
Introduction
The US Equities dataset by AlgoSeek is survivorship bias-free daily coverage of every stock traded in the US Securities Information Processors (SIP) CTA/UTP feed since 1998. The dataset covers approximately 27,500 securities, starts in January 1998, and is delivered in any resolution from tick to daily. The Data is collected from the full SIP feed via our Equinix co-located servers, including all trades and quotes published to every exchange as well as FINRA. Over-the-Counter (OTC) trades are not included.
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 US Equities dataset, including CLI commands and pricing, see the dataset listing.
About the Provider
AlgoSeek is a leading historical intraday US market data provider offering the most comprehensive and detailed market data and analytics products in the financial industry covering Equities, Futures, Options, cash FOREX, and Cryptocurrencies. AlgoSeek data is built for quantitative trading and machine learning. For more information about AlgoSeek, visit algoseek.com.
Data Summary
The following table describes the dataset properties:
| Property | Value |
|---|---|
| Start Date | January 1998 |
| Asset Coverage | 27,500 US Equities |
| Data Density | Dense |
| Resolution | Tick, Second, Minute, Hourly, & Daily |
| Timezone | New York |
| Market Hours | Regular and Extended |
Requesting Data
To add US Equities data to your algorithm, call the AddEquityadd_equity method. Save a reference to the Equity Symbol so you can access the data later in your algorithm.
class USEquityDataAlgorithm(QCAlgorithm):
def initialize(self) -> None:
self.set_start_date(2018, 1, 1)
self.set_end_date(2021, 6, 1)
self.set_cash(100000)
# Subscribe to AAPL data
self.aapl = self.add_equity("AAPL", Resolution.MINUTE).symbol public class USEquityDataAlgorithm : QCAlgorithm
{
private Symbol _symbol;
public override void Initialize()
{
SetStartDate(2018, 1, 1);
SetEndDate(2021, 6, 1);
SetCash(100000);
// Subscribe to AAPL data
_aapl = AddEquity("AAPL", Resolution.Minute).Symbol;
}
}
For more information about creating US Equity subscriptions, see Requesting Data or US Equity Universes.
Accessing Data
To get the current US Equities data, index the Barsbars, QuoteBarsquote_bars, or Ticksticks properties of the current Slice with the Equity Symbol. Slice objects deliver unique events to your algorithm as they happen, but the Slice may not contain data for your security 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:
# Access data: TradeBar data
if self.aapl in slice.bars:
trade_bar = slice.bars[self.aapl]
self.log(f"{self.aapl} close at {slice.time}: {trade_bar.close}")
# Access data: QuoteBar data
if self.aapl in slice.quote_bars:
quote_bar = slice.quote_bars[self.aapl]
self.log(f"{self.aapl} bid at {slice.time}: {quote_bar.bid.close}")
# Access data: Ticks data
if self.aapl in slice.ticks:
ticks = slice.ticks[self.aapl]
for tick in ticks:
self.log(f"{self.aapl} price at {slice.time}: {tick.price}") public override void OnData(Slice slice)
{
// Access data: TradeBar data
if (slice.Bars.ContainsKey(_symbol))
{
var tradeBar = slice.Bars[_symbol];
Log($"{_symbol} price at {slice.Time}: {tradeBar.Close}");
}
// Access data: QuoteBar data
if (slice.QuoteBars.ContainsKey(_symbol))
{
var quoteBar = slice.QuoteBars[_symbol];
Log($"{_symbol} bid at {slice.Time}: {quoteBar.Bid.Close}");
}
// Access data: Ticks data
if (slice.Ticks.ContainsKey(_symbol))
{
var ticks = slice.Ticks[_symbol];
foreach (var tick in ticks)
{
Log($"{_symbol} price at {slice.Time}: {tick.Price}");
}
}
}
You can also iterate through all of the data objects in the current Slice.
def on_data(self, slice: Slice) -> None:
# Iterate all TradeBar received
for symbol, trade_bar in slice.bars.items():
self.log(f"{symbol} close at {slice.time}: {trade_bar.close}")
# Iterate all QuoteBar received
for symbol, quote_bar in slice.quote_bars.items():
self.log(f"{symbol} bid at {slice.time}: {quote_bar.bid.close}")
# Iterate all Ticks received
for symbol, ticks in slice.ticks.items():
for tick in ticks:
self.log(f"{symbol} price at {slice.time}: {tick.price}") public override void OnData(Slice slice)
{
// Iterate all TradeBar received
foreach (var kvp in slice.Bars)
{
var symbol = kvp.Key;
var tradeBar = kvp.Value;
Log($"{symbol} price at {slice.Time}: {tradeBar.Close}");
}
// Iterate all QuoteBar received
foreach (var kvp in slice.QuoteBars)
{
var symbol = kvp.Key;
var quoteBar = kvp.Value;
Log($"{symbol} bid at {slice.Time}: {quoteBar.Bid.Close}");
}
// Iterate all Ticks received
foreach (var kvp in slice.Ticks)
{
var symbol = kvp.Key;
var ticks = kvp.Value;
foreach (var tick in ticks)
{
Log($"{symbol} price at {slice.Time}: {tick.Price}");
}
}
}
For more information about accessing US Equities data, see Handling Data.
Historical Data
To get historical US Equity data, call the Historyhistory method with the Equity Symbol. If there is no data in the period you request, the history result is empty.
# DataFrame history_df = self.history(self.aapl, 100, Resolution.DAILY) # TradeBar objects history_trade_bars = self.history[TradeBar](self.aapl, 100, Resolution.DAILY) # QuoteBar objects history_quote_bars = self.history[QuoteBar](self.aapl, 100, Resolution.MINUTE) # Tick objects history_ticks = self.history[Tick](self.aapl, timedelta(seconds=10), Resolution.TICK)
// TradeBar objects var historyTradeBars = History(_symbol, 100, Resolution.Daily); // QuoteBar objects var historyQuoteBars = History<QuoteBar>(_symbol, 100, Resolution.Minute); // Tick objects var historyTicks = History<Tick>(_symbol, TimeSpan.FromSeconds(10), Resolution.Tick);
For more information about historical data, see History Requests.
Universe Selection
To select a universe of US Equities, see Equity Universes.
Remove Subscriptions
To unsubscribe from a US Equity that you added with the AddEquityadd_equity method, call the RemoveSecurityremove_security method.
self.remove_security(self.aapl)
RemoveSecurity(_symbol);
The RemoveSecurityremove_security method cancels your open orders for the security and liquidates your holdings.
Supported Assets
To view the supported assets in the US Equities dataset, see the Data Explorer. This dataset doesn't include Over-the-Counter (OTC) stocks.
Example Applications
The US Equities dataset enables you to accurately design Equity trading strategies. Examples include the following strategies:
- Momentum strategies using historical returns on the premise that the momentum will continue
- Value strategies using fundamental factors on the premise that the price of undervalued securities will rise
- Factor investing with periodic rebalancing
Classic Algorithm Example
The following example algorithm buys and holds Apple stock:
from AlgorithmImports import *
from QuantConnect.DataSource import *
class USEquityDataAlgorithm(QCAlgorithm):
def initialize(self) -> None:
self.set_start_date(2018, 1, 1)
self.set_end_date(2021, 6, 1)
self.set_cash(100000)
# Requesting single equity data, since we only trade AAPL
self.aapl = self.add_equity("AAPL", Resolution.DAILY).symbol
# Historical data
history = self.history(self.aapl, 60, Resolution.DAILY)
self.debug(f"We got {len(history)} items from our history request")
def on_data(self, slice: Slice) -> None:
# Check if the current slice containing AAPL and if we hold any position
# As we make use of the most updated price data to decide the order size
if slice.contains_key(self.aapl) and slice[self.aapl] is not None and not self.portfolio.invested:
self.set_holdings(self.aapl, 1) public class USEquityDataAlgorithm : QCAlgorithm
{
private Symbol _symbol;
public override void Initialize()
{
SetStartDate(2018, 1, 1);
SetEndDate(2021, 6, 1);
SetCash(100000);
// Requesting single equity data, since we only trade AAPL
_symbol = AddEquity("AAPL", Resolution.Daily).Symbol;
// Historical data
var history = History(_symbol, 60, Resolution.Daily);
Debug($"We got {history.Count()} items from our history request");
}
public override void OnData(Slice slice)
{
// Check if the current slice containing AAPL and if we hold any position
// As we make use of the most updated price data to decide the order size
if (slice.ContainsKey(_symbol) && slice[_symbol] != None && !Portfolio.Invested)
{
SetHoldings(_symbol, 1);
}
}
}
Framework Algorithm Example
The following example algorithm buys and holds Apple stock:
from AlgorithmImports import *
from QuantConnect.DataSource import *
class USEquityDataAlgorithm(QCAlgorithm):
def initialize(self) -> None:
self.set_start_date(2018, 1, 1)
self.set_end_date(2021, 6, 1)
self.set_cash(100000)
self.universe_settings.resolution = Resolution.DAILY
# To select only AAPL, use a manual selection universe
symbols = [Symbol.create("AAPL", SecurityType.EQUITY, Market.USA)]
self.add_universe_selection(ManualUniverseSelectionModel(symbols))
# Constant investment signal
self.add_alpha(ConstantAlphaModel(InsightType.PRICE, InsightDirection.UP, timedelta(days=7), 0.025, None))
# Invest in all members equally
self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel()) public class USEquityDataAlgorithm : QCAlgorithm
{
public override void Initialize()
{
SetStartDate(2018, 1, 1);
SetEndDate(2021, 6, 1);
SetCash(100000);
UniverseSettings.Resolution = Resolution.Daily;
// To select only AAPL, use a manual selection universe
var symbols = new[] {QuantConnect.Symbol.Create("AAPL", SecurityType.Equity, Market.USA)};
AddUniverseSelection(new ManualUniverseSelectionModel(symbols));
// Constant investment signal
AddAlpha(new ConstantAlphaModel(InsightType.Price, InsightDirection.Up, TimeSpan.FromDays(7), 0.025, None));
// Invest in all members equally
SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
}
}
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