investfly.models.TradingStrategy

class TradingStrategy(abc.ABC):

This is the main interface (abstract class) used to implement a trading strategy.

state: Dict[str, int | float | bool]

The persisted state of the strategy.

@abstractmethod
def getSecurityUniverseSelector( self) -> investfly.models.SecurityUniverseSelector.SecurityUniverseSelector:

This function is used to narrow down the set of securities (i.e stocks) against which to run your logic. You can pick one of the standard list (e.g SP100) that we provide, provide your own list with comma separated symbols list, or provide a query based on fundamental metrics like MarketCap, PE Ratio etc. See docs on investfly.models.SecurityUniverseSelector.SecurityUniverseSelector for more details. Returns

Returns

investfly.models.SecurityUniverseSelector.SecurityUniverseSelector

@abstractmethod
def evaluateOpenTradeCondition( self, security: investfly.models.MarketData.Security, data: Dict[str, Any]) -> investfly.models.StrategyModels.TradeSignal | None:

This function must be annotated with @DataParams to indicate what data (i.e indicator values) are needed as shown below. The function is called whenever a new data is available based on the subscribed data. This function is called separately for each security.

@DataParams({
    "sma2":             {"datatype": DataType.INDICATOR, "indicator": "SMA", "barinterval": BarInterval.ONE_MINUTE,  "period": 2, "count": 2},
    "sma3":             {"datatype": DataType.INDICATOR, "indicator": "SMA", "barinterval": BarInterval.ONE_MINUTE, "period": 3, "count": 2},
    "allOneMinBars":    {"datatype": DataType.BARS, "barinterval": BarInterval.ONE_MINUTE},
    "latestDailyBar":   {"datatype": DataType.BARS, "barinterval": BarInterval.ONE_DAY, "count":1},
    "quote":            {"datatype": DataType.QUOTE},
    "lastprice":        {"datatype": DataType.QUOTE, "field": QuoteField.LASTPRICE},
    "allFinancials":    {"datatype": DataType.FINANCIAL},
    "revenue":          {"datatype": DataType.FINANCIAL, "field": FinancialField.REVENUE}

})
Parameters
  • security: The stock security against which this is evaluated. You use it to construct TradeSignal
  • data: Dictionary with the requested data based on @DataParams annotation.

The keys of the data param dictionary match the keys specified in @DataParams annotation. The value depends on the datatype parameter. datatype=INDICATOR, value type = investfly.models.CommonModels.DatedValue datatype=QUOTE, field is specified, value type = investfly.models.CommonModels.DatedValue datatype=QUOTE, field is not specified, value type is investfly.models.MarketData.Quote object (has dayOpen, dayHigh, dayLow, prevOpen etc) datatype=BARS, value type is investfly.models.MarketData.Bar

Further, if the count is specified and greater than 1, value is returned as a List

Returns

investfly.model.StrategyModels.TradeSignal if open condition matches and to signal open trade, None if open trade condition does not match

def processOpenTradeSignals( self, portfolio: investfly.models.PortfolioModels.Portfolio, tradeSignals: List[investfly.models.StrategyModels.TradeSignal]) -> List[investfly.models.PortfolioModels.TradeOrder]:

This method is used to convert TradeSignals into TradeOrders. You must do this for couple reasons:

  1. Assume 1000 stocks match the open trade condition and so you have 1000 TradeSignals, but that does not mean that you want to open position for 1000 stocks in your portfolio. You may want to order those trade signals by strength and limit to top 10 trade signals
  2. Your portfolio may already have open position for a stock corresponding to particular trade signal. In that case, you may wan to skip that trade signal, and prioritize opening new position for other stocks
  3. Here, you also set TradeOrder speficiations such as order type, quantity etc
  4. You may want to fully rebalance portfolio baseed on these new trade signals
Parameters
  • portfolio: Current portfolio state
  • tradeSignals: Trade Signals correspoding to stocks matching open trade condition
Returns

List of TradeOrders to execute

def getStandardCloseCondition(self) -> investfly.models.StrategyModels.StandardCloseCriteria | None:

TargetProfit, StopLoss, TrailingStop and Timeout are considered as standard close criteria. This function is created so that you can specify those standard close/exit conditions easily

Returns

investfly.models.StrategyModels.StandardCloseCriteria

def evaluateCloseTradeCondition( self, openPos: investfly.models.PortfolioModels.OpenPosition, data) -> investfly.models.PortfolioModels.TradeOrder | None:

Implementing this method is optional. But when implemented, it should be implemented similar to evaluateOpenTradeCondition

Parameters
  • openPos: The open position
  • data: Requested data that corresponds to the open position's security symbol
Returns

TradeOrder if the position is supposed to be closed, None otherwise

def getState(self) -> Dict[str, int | float | bool]:
def restoreState(self, state: Dict[str, int | float | bool]) -> None: