investfly.models.strategy.TradingStrategy

Initialize strategy with optional configuration.

Args: config: Dictionary of strategy-specific parameters. Can contain any structure (numeric, list, string, nested dictionaries, etc.). Defaults to None if no configuration is provided.

Note: The dataService and context attributes are set by the execution engine before strategy methods are called. They should not be set manually in the constructor.

Set the data service for accessing market data and indicators.

This method is called by the execution engine to inject the data service into the strategy instance. The data service provides access to:

• Indicator computation (SMA, RSI, MACD, etc.)
• Market data (quotes, bars, financials, news)

Args: dataService: The strategy data service instance. This is a singleton service shared across all strategy executions.

Note: This method is typically called automatically by the execution engine. Strategies should not call this method directly.

Set the execution context for the strategy instance.

This method is called by the execution engine to inject the execution context into the strategy instance. The context contains:

• Portfolio: Current portfolio state with positions and balances
• Universe securities: List of securities in the strategy's trading universe

Args: context: The execution context containing portfolio and universe data.

Note: This method is typically called automatically by the execution engine. Strategies should not call this method directly.

Inject the runtime implementation of the public strategy helper contract.

Get the portfolio from the execution context.

Returns: The current portfolio state containing: - Open positions - Account balances - Portfolio performance metrics

Example:

portfolio = self.getPortfolio()
portfolio_value = portfolio.balances.currentValue
for position in portfolio.openPositions:
    print(f"{position.security.symbol}: {position.quantity} shares")

Get the universe securities from the execution context.

Returns: List of Security objects representing all securities in the strategy's trading universe. This is the resolved list based on the security universe selector returned by getSecurityUniverseSelector().

Example:

universe = self.getUniverseSecurities()
for security in universe:
    quote = self.dataService.getQuote(security)
    if quote:
        print(f"{security.symbol}: {quote.lastPrice}")

Get the state value for the given key.

Args: key: The key to get the state value for.

Returns: The state value for the given key.

Return the security universe selector for this strategy.

This method must be implemented by all strategy subclasses. It defines which securities the strategy will evaluate and trade.

Returns: A SecurityUniverseSelector object that defines the strategy's trading universe. The selector can specify: - A single security - A standard predefined list (e.g., S&P 500, NASDAQ 100) - A custom list of symbols - A fundamental query-based selection

Example:

# Single stock
return SecurityUniverseSelector.singleStock("AAPL")

# Standard list
return SecurityUniverseSelector.fromStandardList(StandardSymbolsList.SP_500)

# Custom list
return SecurityUniverseSelector.fromSymbols(
    SecurityType.STOCK, ["AAPL", "MSFT", "GOOGL"]
)

# Fundamental query
query = FinancialQuery(
    SecurityType.STOCK,
    FinancialCondition(FinancialField.MARKET_CAP, ComparisonOperator.GREATER_THAN, 1000000000)
)
return SecurityUniverseSelector.fromFinancialQuery(query)

Handle market data updates (bars/ticks).

This optional method is called whenever market data is updated for securities in the strategy's universe. To enable this callback, override this method and decorate it with @data_trigger.

The @data_trigger decorator specifies when this method should be called:

• type (DataType): The type of market data to trigger on. Currently supported: DataType.BARS (bar/candlestick data).
• barInterval (BarInterval, optional): Required for BARS type. Options: ONE_MINUTE, FIVE_MINUTE, FIFTEEN_MINUTE, THIRTY_MINUTE, SIXTY_MINUTE, ONE_DAY.

Args: updatedSecurities: List of Security objects that received market data updates triggering this callback. Strategies should iterate over this list instead of the full universe to process only securities with new data.

Returns: Optional list of TradeOrder objects to execute. Return None or an empty list if no trades should be placed.

Note: - If your logic requires Quote data (e.g., LastPrice), use @data_trigger(type=DataType.BARS, barInterval=BarInterval.ONE_MINUTE) to trigger on every 1-minute bar update, then access quotes using self.dataService.getQuote(security). - The method is called only for securities that have received updates, not for the entire universe.

Example:

@data_trigger(type=DataType.BARS, barInterval=BarInterval.ONE_DAY)
def onMarketData(self, updatedSecurities):
    orders = []
    for security in updatedSecurities:
        # Compute indicators
        sma = self.dataService.computeIndicatorSeries(
            "SMA", security, {"period": 20, "barInterval": BarInterval.ONE_DAY}
        )
        # Generate trade orders based on indicator values
        if sma.last.value > threshold:
            orders.append(TradeOrder(security, TradeType.BUY, OrderType.MARKET_ORDER))
    return orders if orders else None

Handle scheduled time-based events.

Custom strategies may decorate any number of scheduled methods with @scheduled(TriggerSchedule...). The execution engine discovers all decorated callbacks through getScheduledCallbacks() and passes a ScheduleEvent to each callback.

Returns: Optional list of TradeOrder objects to execute. Return None or an empty list if no trades should be placed.

Note: - Multiple methods can be decorated with @scheduled to run distinct jobs at distinct schedule patterns. - The event argument includes the callback name, the schedule, the scheduled time, the actual evaluation time, and whether the callback is executing inside a backtest.

Example:

@scheduled(TriggerSchedule.daily("10:00"))
def rebalanceDaily(self, event: ScheduleEvent):
    orders = []
    portfolio = self.getPortfolio()
    return orders

@scheduled(TriggerSchedule.interval(5, days=ScheduleDayMode.EVERYDAY, window=ScheduleWindow("09:00", "13:00")))
def checkPositions(self, event: ScheduleEvent):
    return None

Return all scheduled callbacks exposed by this strategy instance.

Return runtime-managed position, lifecycle, and portfolio behavior.

The same contract models used by configuration-driven strategies are accepted here. The runtime evaluates the policy in live trading and backtests; user strategy code supplies inputs and consumes planned orders without owning the algorithms.