A Python algorithmic trading platform for your code.

Start with a runnable SDK strategy

The public SDK version 3.1.2 supports Python 3.11+. This minimal authoring example declares a stock universe, runtime-managed protection and an allocation callback. It is an educational starting point; it has no predictive signal and its example allocation must be reviewed before any use.

from typing import List, Optional
from investfly.models import *


class MyStrategy(TradingStrategy):
    def getSecurityUniverseSelector(self) -> SecurityUniverseSelector:
        return SecurityUniverseSelector.fromSymbols(SecurityType.STOCK, ["AAPL", "MSFT"])

    def getStrategyPolicy(self) -> CustomStrategyPolicy:
        return CustomStrategyPolicy(
            assetType=SecurityType.STOCK,
            positionManagement=PositionManagementRules(
                positionExits=ExitRules(
                    protectiveExits=buildProtectiveExitPlan(
                        targetProfitPct=10.0,
                        stopLossPct=5.0,
                        trailingStopPct=2.0,
                    )
                )
            ),
            portfolioLimits=PortfolioLimits(maxOpenPositions=5),
        )

    @data_trigger(type=DataType.BARS, barInterval=BarInterval.FIFTEEN_MINUTE)
    def onMarketData(self, updatedSecurities: List[Security]) -> Optional[List[TradeOrder]]:
        execution = OpenExecutionSettings(
            positionSize=PositionSizeSpec(PositionSizeMode.PERCENT_OF_EQUITY, 15.0),
            orderSpec=OrderSpec(),
            instrumentSelection=DirectInstrumentSelection(),
        )
        return self.services.planAllocationOrders(AllocationPlan(updatedSecurities, execution)) or None

Download MyStrategy.py and follow installation, type-checking, upload and hosted testing. A local import/type check does not reproduce hosted data or fills. The bounded runtime supports Investfly modules and selected math, statistics, typing, NumPy, pandas and TA-Lib imports; it is not unrestricted package hosting.

Premium/Elite plans provide continued historical testing and automated deployment after the trial. Elite adds eligible broker connections. Use Python templates for worked market-specific logic. Standalone backtesting-library/framework searches can seek a different product; this is a hosted strategy platform.

A complete public Python crypto example

Download the public volatility-adaptive BTC DCA source or inspect its strategy and report. This larger maintained example demonstrates scheduled contributions and volatility-based safety orders; it is separate from MyStrategy.py and is not an investment recommendation.

Observed October 14, 2026. Historical modeled output is not a live or paper-deployment result. Downloaded examples are type-checked locally; the platform still validates uploaded code and returns the current run’s actual status, warnings, dates and trades.

Check the paper-deployment output

After uploading, testing and choosing a compatible Investfly virtual portfolio, inspect the saved strategy’s Deployment status and Execution Logs. A deployed state establishes the running copy/account; it does not establish a filled order. Logs can show startup, evaluation, no-order decisions or errors. Reconcile orders and positions separately. See the labeled actual status/log screen and its interpretation. That fictional SMA demo is separate from MyStrategy.py; no hosted paper run of the minimal snippet is claimed here.

Next: compare no-code and Python build routes.