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We are dedicated to providing investors with a cutting-edge platform for rapidly creating quant investment strategies. Founded in 2012, we've empowered more than 250,000 quants and engineers to create and trade their ideas.
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What is Boot Camp?
Boot Camp is a great way to improve your skills and learn the QuantConnect API in easily digestible portions.
A collection of courses from independent educators to improve your quant skill base and create better strategies.
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Master algorithmic trading on QuantConnect; backtest and live trade Stocks, Options, Futures, Forex, and Crypto.
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Learn to use Python, Pandas, Matplotlib, and the QuantConnect Lean Engine to perform financial analysis and trading.
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Organization Notes
Get Started with Algorithm Lab
New Research
Optimizing a Gold-SPY Portfolio Using Hidden Markov Models for Market Downtime
Gold-SPY portfolio optimization using Hidden Markov Models for minimizing market downturn risk....
ReadAlgorithm Lab is your playground for developing and refining trading algorithms with QuantConnect. Utilize advanced tools, historical data, and robust backtesting to enhance your trading strategies. Transform your ideas into actionable insights and optimize your trading approach with ease.
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Algorithm Lab is your playground for developing and refining trading algorithms with QuantConnect. Utilize advanced tools, historical data, and robust backtesting to enhance your trading strategies. Transform your ideas into actionable insights and optimize your trading approach with ease.
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QuantConnect Datasets
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Bybit Crypto Price Data
Dataset by CoinAPI
The US Interest Rate dataset provides the primary credit rate from the Federal Open Market Committee (FOMC). The data starts in January 2003 and is updated on a daily frequency. This dataset is created using information from the FOMC meetings.
The Federal Reserve Bank of St. Louis, often referred to as the St. Louis Fed, is one of the 12 regional banks that make up the United States Federal Reserve System. It is responsible for the Eighth Federal Reserve District, which includes the states of Arkansas, Illinois, Indiana, Kentucky, Mississippi, Missouri, and Tennessee, as well as portions of eastern Kansas and southern Illinois.
The St. Louis Fed, like other regional banks, participates in the formulation and implementation of monetary policy in the United States. It contributes to the Federal Open Market Committee (FOMC) meetings, where key decisions regarding interest rates and other monetary policy tools are made.
The following snippet demonstrates how to access data from the US Interest Rate dataset:
interest_rate = self.risk_free_interest_rate_model.get_interest_rate(self.time)
var interestRate = RiskFreeInterestRateModel.GetInterestRate(Time);
To find the average interest rate between two dates, call the GetRiskFreeRate method.
avg_risk_free_rate = RiskFreeInterestRateModelExtensions.get_risk_free_rate(
self.risk_free_interest_rate_model,
self.time-timedelta(365), self.time
)
var avgRiskFreeRate = RiskFreeInterestRateModel.GetRiskFreeRate(Time.AddDays(-365), Time);
The following table describes the dataset properties:
Property | Value |
---|---|
Start Date | January 2003 |
Data Density | Sparse |
Resolution | Daily |
Timezone | New York |
The US Interest Rate dataset provides an important economic indicator. Examples include the following applications:
For more example algorithms, see Examples.
You don't need any special code to request US Interest Rate data. QCAlgorithm automatically subscribes to the data by setting its default risk free interest rate model.
To get the current US Interest Rate data, call the GetInterestRateget_interest_rate method of the RiskFreeInterestRateModel object with the current time.
interest_rate = self.risk_free_interest_rate_model.get_interest_rate(self.time)
var interestRate = RiskFreeInterestRateModel.GetInterestRate(Time);
To get the average risk free interest rate for a window of time, call the GetRiskFreeRateget_risk_free_rate method with the start date and end date.
risk_free_rate = RiskFreeInterestRateModelExtensions.get_risk_free_rate(
self.risk_free_interest_rate_model,
self.time-timedelta(365), self.time
)
var riskFreeRate = RiskFreeInterestRateModel.GetRiskFreeRate(Time.AddDays(-365), Time);
To get the average risk free interest rate for a set of dates, call the GetAverageRiskFreeRateget_average_risk_free_rate method with the list of dates.
risk_free_rate = RiskFreeInterestRateModelExtensions.get_average_risk_free_rate(
self.risk_free_interest_rate_model,
[self.time, self.time-timedelta(180), self.time-timedelta(365)]
)
var riskFreeRate = RiskFreeInterestRateModel.GetAverageRiskFreeRate(
new [] {Time, Time.AddDays(-180), Time.AddDays(-365)}
);
The following example algorithm plots the current interest rate and the last year's average.
from AlgorithmImports import *
class RiskFreeInterestRateModelAlgorithm(QCAlgorithm):
def initialize(self):
self.set_start_date(2022, 5, 21)
self.set_cash(100000)
self.add_equity("SPY", Resolution.DAILY)
def on_end_of_day(self, symbol):
self.set_holdings(symbol, 1)
# Get the average risk free rate of the last year at the current time
risk_free_rate = RiskFreeInterestRateModelExtensions.get_risk_free_rate(self.risk_free_interest_rate_model, self.time - timedelta(365), self.time)
# Plot the current interest rate and the 1-year average rate for comparison
self.plot('Interest', 'EOD', self.risk_free_interest_rate_model.get_interest_rate(self.time))
self.plot('Interest', '1Y-RW', risk_free_rate)
namespace QuantConnect.Algorithm.CSharp
{
public class RiskFreeInterestRateModelAlgorithm : QCAlgorithm
{
public override void Initialize()
{
SetStartDate(2022, 5, 21);
SetCash(100000);
AddEquity("SPY", Resolution.Daily);
}
public override void OnEndOfDay(Symbol symbol)
{
SetHoldings(symbol, 1);
// Plot the current interest rate and the 1-year average rate for comparison
Plot("Interest Rate", "EOD", RiskFreeInterestRateModel.GetInterestRate(Time));
// Get the average risk free rate of the last year at the current time
Plot("Interest Rate", "1-year Window", RiskFreeInterestRateModel.GetRiskFreeRate(Time.AddDays(-365), Time));
}
}
}
US Interest Rate is allowed to be used in the cloud for personal and commercial projects for free. The data is permissioned for use within the licensed organization only
Free | Documentation
US Interest Rate can be downloaded on premise with the LEAN CLI, for a charge per file downloaded. This download is for the licensed organization's internal LEAN use only and cannot be redistributed or converted in any format.
Free | Learn More
LEAN CLI is a cross-platform wrapper on the QuantConnect algorithmic trading engine called LEAN. The CLI makes using LEAN incredibly easy, reducing most of the pain points of developing and managing an algorithmic trading strategy to a few lines of bash.
Using the CLI you can download the same data QuantConnect hosts in the cloud for a small fee. These fees are per file downloaded, and are paid for in QuantConnect-Credits (QCC). We recommend purchasing credits to enable downloading.
The CLI command generator is a helpful tool to generate a copy-paste command to download this dataset from the form below.
lean data download \
--dataset "US Interest Rate"
lean data download `
--dataset "US Interest Rate"
US interest rate for statistics and options modeling.
US interest rate for statistics and options modeling.
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Take extreme care to carefully structure your data TAR package with
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Algorithm Lab is your playground for developing and refining trading algorithms with QuantConnect. Utilize advanced tools, historical data, and robust backtesting to enhance your trading strategies. Transform your ideas into actionable insights and optimize your trading approach with ease.
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