This page explains the QuantEngineClient Class
QuantEngineClient class is the main class, which you derive your Algo strategy class from. QuantEngineClient class provides all the functionality required by your strategy class to receive real-time/historic tick data, place orders, receive order status, log messages, publish stats of your strategy, get and set strategy state, create and use indicators, etc.,
PlaceOrder method allows you to place an order to Algorum, which in turn will direct the order through appropriate API of the Brokerage Platform selected while starting the strategy.
public async Task<string> PlaceOrderAsync( PlaceOrderRequest placeOrderRequest )
def place_order(self, place_order_request: PlaceOrderRequest):
|placeOrderRequest||PlaceOrderRequest||Place order request object.|
Backtest method initiates the backtesting of your strategy in the Algorum Cloud.
public virtual async Task<string> BacktestAsync( BacktestRequest backtestRequest )
def backtest(self, backtest_request: BacktestRequest):
|backtestRequest||BacktestRequest||Backtest request object with properties required for backtesting.|
Log method logs a message to Algorum Cloud, which you can download using Algorum CLI
public async Task LogAsync( LogLevel logLevel, string message )
In Python programs, log_level is a string. You should pass one of the LogLevel enum values to this parameter.
def log(self, log_level: str, message: str):
|logLevel||LogLevel||Log level of the message|
|message||String||Message to log|
CreateIndicatorEvaluator method will create a remote instance of an indicator evaluator in Algorum Cloud in your Algorum Quant Engine. This object keeps track of incoming data during live/paper or backtesting of the strategy, and will provide methods to get various indicator values.
public async Task<RemoteIndicatorEvaluator> CreateIndicatorEvaluatorAsync( CreateIndicatorRequest createIndicatorRequest )
def create_indicator_evaluator(self, create_indicator_request: CreateIndicatorRequest) \ -> RemoteIndicatorEvaluator:
Method Return Value
SetData method allows your strategy to store data in Algorum Cloud, which can be retrieved any time, even across strategy restarts, using GetData method. This should be used as the primary mechanism by your strategy to save strategy state, which can be accessed across strategy restarts.
public async Task SetDataAsync<T>( string key, T value )
|key||String||Unique key for the data within the strategy|
|value||Type||Object of the Type that you want to store in Algorum Cloud.|
GetData method allows you to get the data from Algorum Cloud, which your strategy stored using SetData method.
public async Task<T> GetDataAsync<T>( string key )
|key||String||Unique key within the strategy that is used to store the data in Algorum Cloud.|
Method Return Value
|Type object.||You can specify the type of the data originally stored in Algorum Cloud using SetData method. The same type object will be returned.|
SubscribeSymbols method allows your strategy to subscribe to a list of stock symbols. The tick data from these symbols will be streamed into your strategy, calling your strategy on_tick method. The streaming happens both in live/paper trading mode and backtesting mode.
public async Task SubscribeSymbolsAsync( List<Symbol> symbols )
def subscribe_symbols(self, symbols):
|symbols||List of Symbol objects||List of Symbol objects to subscribe.|
UnsubscribeSymbols method allows your strategy to unsubscribe from a lost of stock symbols. After unsubscribe, your strategy will not receive the ticks related to the symbols, until they are subscribed to again.
public async Task UnsubscribeSymbolsAsync( List<Symbol> symbols )
|symbols||List of Symbol objects||List of Symbol objects to unsubscribe.|
StartTrading method allows your strategy to start trading either in live or paper mode. Based on how you started your strategy using Algorum CLI, your strategy main program will receive the launch mode configuration parameter, using which you can determine if you are running in backtesting mode or live/paper mode. If the launch mode is live or paper, you should call StartTrading method to start the trading and start receiving the real-time tick data into your strategy (which calls the on_tick method of your strategy).
public virtual async Task StartTradingAsync( TradingRequest tradingRequest )
def start_trading(self, trading_request: TradingRequest):
StopTrading method allows you to stop your strategy at any point of time. This will stop sending real-time tick data into your strategy. You can start the trading in your strategy after this method call using StartTrading method call.
public virtual async Task StopTradingAsync()
Wait method allows your strategy to wait until it is stopped by the user using Algorum CLI or automatically closed when the connection with your Algorum Quant Engine is terminated or lost due to network disconnect. The Wait method is key to keep your strategy program running and processing until the termination condition occurs as mentioned above.
public void Wait()
OnTick Override Method
OnTick method is a mandatory override method, which you should implement in your strategy class. This method is called for the real-time and historic tick data coming into your strategy. This is where you will write your primary trading logic (entry and exit conditions) of your strategy.
public override async Task OnTickAsync( TickData tickData )
def on_tick(self, tick_data: TickData):
|tickData||TickData||TickData object with tick details.|
OnOrderUpdate Override Method
OnOrderUpdate method is a mandatory override method, which you should implement in your strategy class. This method is called when there is an update in the order flow for the order placed by your strategy.
public override async Task OnOrderUpdateAsync( Order order )
def on_order_update(self, order: Order):
|order||Order||Order object with order details.|
Updated over 1 year ago