Algorithm Reference

Initializing Algorithms


The Initialize method is called to setup your strategy. Here you can request data, set starting cash or warm up periods. It is called just once at the start of your algorithm.

Setting Cash

In backtests you can set your starting capital using the SetCash(decimal cash)self.SetCash(decimal cash)this.SetCash(decimal cash) method. In live trading this is ignored and your brokerage cash is used instead. In paper trading we set the cash to a fictional $100,000 USD.

SetCash(decimal cash)
self.SetCash(decimal cash)
this.SetCash(decimal cash)

Setting Dates

Backtesting uses the SetStartDate(int year, int month, int day) self.SetStartDate(int year, int month, int day) this.SetStartDate(int year, int month, int day) and SetEndDate(int year, int month, int day) self.SetEndDate(int year, int month, int day) this.SetEndDate(int year, int month, int day) methods to configure the backtest time range. If unspecified the end date defaults to yesterday. In .NET languages you can also use a DateTime object to set the dates.

SetStartDate(2013, 1, 1);         // Set start date to specific date.
SetEndDate(2015, 1, 1);         // Set end date to specific date.
SetEndDate(DateTime.Now.Date.AddDays(-1)); // Or use a relative date.
self.SetEndDate( - timedelta(1)) # Or use a relative date.
this.SetStartDate(2013, 10, 7)
this.SetEndDate(2013, 10, 11)

Selecting Asset Data

Algorithms can manually subscribe to data for specific assets they need, or use universes to choose groups of assets based on filtering criteria (e.g. all stocks with volumes greater than $10M/day). See more about Universes here.

To manually subscribe to a specific asset you can call the AddEquity(), AddForex(), AddCrypto(), AddCfd() and AddOption() methods in your Initialize() method. There is no official limit to how much data you can ask for; but there are practical resource limitations.

QuantConnect supports international trading across multiple timezones and markets. The Market parameter is used to distinguish between the same tickers on different exchanges (e.g. FXCM and OANDA both offer EURUSD, but have different rates).

QuantConnect provides 40TB of data. Check out more information about our data in our data library. The resolution of the data available depends on the type of data. For Equities, Futures, Forex and Crypto we provide Tick, Second, Minute, Hourly and Daily resolution. For Options we only have minute bar data. These are specified by the Resolution enum.

If there is a gap in the data (e.g. because there are no trades), by default the data is still pumped into your strategy on each time step. This behavior is called "fillForward" and defaults to true. You can disable this by setting fillForward to false.

By default equity data in QuantConnect is Split and Dividend adjusted backwards in time to give smooth continuous prices. This allows easy use for indicators. Some algorithms need raw or partially adjusted price data. You can control this with the SetDataNormalizationMode() method. The DataNormalizationMode enum has the values Adjusted (default), Raw, SplitAdjusted, and TotalReturn. When data is set to Raw mode the dividends are paid as cash into your algorithm and the splits are directly applied to your holding quantity.

Data Adjustment Modes

Splits and dividends are backwards adjusted into the price of the asset. The price today is identical to current market price. For more information on this see Investopedia's article Adjusted Pricing.


No modifications to the asset price at all. Dividends are paid in cash; splits are applied directly to your portfolio quantity.


Only equity splits are applied to the price adjustment, while dividends are still paid in cash to your portfolio. This allows for management of the dividend payments (e.g. reinvestment) while still giving a smooth curve for indicators.


Return of the investment adding the dividend sum to the initial asset price. For more information on this see Investopedia's article Total Returns.

If you have your own custom data you'd like to backtest against, check out the custom data section.

// Complete Add Equity API - Including Default Parameters:
AddEquity(string ticker, Resolution resolution = Resolution.Minute, string market = Market.USA, bool fillDataForward = true, decimal leverage = 0m, bool extendedMarketHours = false)

//Complete Add Forex API - Including Default Parameters:
AddForex(string ticker, Resolution resolution = Resolution.Minute, string market = Market.FXCM, bool fillDataForward = true, decimal leverage = 0m)
AddEquity("AAPL"); //Add Apple 1 minute bars (minute by default).
AddForex("EURUSD", Resolution.Second); //Add EURUSD 1 second bars.
this.AddEquity("SPY", Resolution.Minute) |> ignore
this.AddForex("EURUSD", Resolution.Second) |> ignore
self.AddEquity("SPY")  # Default to minute bars
self.AddForex("EURUSD", Resolution.Second) # Set second bars.
//Setting the data normalization mode for the MSFT security to raw (pay dividends as cash)
# Setting the data normalization mode for the MSFT security to raw (pay dividends as cash)
(* Setting MSFT security to use raw prices *)

Setting Warm Up Period

Often algorithms need some historical data to prime technical indicators, or populate historical data arrays. Using the SetWarmUp(TimeSpan period) or SetWarmUp(int barCount) methods you can specify a warm up period for your algorithm which pumps data in from before the start date. During the warm up period you cannot place a trade.

Algorithms can use the bool IsWarmingUp property to determine if the warm up period has completed.

See more about using historical data in the History section.

SetWarmUp(200); //Warm up 200 bars for all subscribed data.
SetWarmUp(TimeSpan.FromDays(7)); //Warm up 7 days of data.
self.SetWarmUp(200) # Warm up 200 bars for all subscribed data.
self.SetWarmUp(timedelta(7)) # Warm up 7 days of data.

Cash and Brokerage Models

In QuantConnect by default we model your algorithm with margin-modeling but you can select a cash account type. Cash accounts do not allow leveraged trading, whereas Margin accounts support 2-4x leverage on your account value. You can set your brokerage account type in your initialization with SetBrokerageModel(BrokerageName brokerage, AccountType account).

The BrokerageName enum supports values of Default, TradierBrokerage, InteractiveBrokersBrokerage, FxcmBrokerage and OandaBrokerage. When setting the brokerage name we also set the trading fee structures for that brokerage.

The AccountType enum supports values of Cash and Margin. When using cash leverage is disabled by default, and the cash settlement period is set to 3 days for Equity securities. Margin accounts are settled immediately and have a leverage of 2.

Margin accounts with more than $25,000 in equity are eligible for pattern day trading margin limits. This increases your available leverage to 4x while the market is open and 2x overnight. To model this behavior in your algorithm you must set your security MarginModel to PatternDayTradingMarginModel.

See more about brokerage models in the Reality Modelling section.

//Brokerage model and account type:
SetBrokerageModel(BrokerageName.InteractiveBrokersBrokerage, AccountType.Margin);

//Add securities and if required set custom margin models
var spy = AddEquity("SPY"); //Defaults to minute bars.
spy.MarginModel = new PatternDayTradingMarginModel();
#Brokerage model and account type:
self.SetBrokerageModel(BrokerageName.InteractiveBrokersBrokerage, AccountType.Cash)

//Add securities and if required set custom margin models
spy = self.AddEquity("SPY") # Default to minute bars
spy.MarginModel = PatternDayTradingMarginModel()
(* In Initialize method, set the brokerage models *)
this.SetBrokerageModel(BrokerageName.InteractiveBrokersBrokerage, AccountType.Cash);
this.AddEquity("SPY", Resolution.Second) |> ignore
(* Then if required, you can set specific margin models for your secutities *)
this.Securities.Item("SPY").MarginModel <- new PatternDayTradingMarginModel()
// You can also create your own brokerage model: IBrokerageModel
class MyBrokerage: DefaultBrokerage {
   // Custom implementation of brokerage here.

SetBrokerageModel(new MyBrokerage());

Setting Benchmark

You can set a custom benchmark for your algorithm using the SetBenchmark() method. This should be called in your Initialize() function.

// Defaults to Equity market

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