YOUR UNIVERSEChoose supported marketsDefine the instruments to evaluate
From idea to monitored automation
One algorithmic trading platform for every stage of your strategy.
Replace disconnected scripts, testing tools, alerts, and account dashboards with one place to develop and operate the trading logic you choose.
-
01
Build
Define your universe, entry and exit conditions, sizing, schedule, and risk controls.
-
02
Backtest
Run your rules against selected historical data and inspect hypothetical behavior and assumptions.
-
03
Simulate
Observe eligible strategies in a virtual portfolio without treating simulation as actual results.
-
04
Monitor
Review activity and exposure, then pause or revise your logic whenever you decide.
Build your way
Go from trading idea to testable rules—without rebuilding the plumbing.
Use the visual trading bot builder when you want speed and clarity. Use Python when your strategy calls for custom logic. Both use the same strategy testing, simulation, and monitoring tools.
YOUR CONDITIONSBuild entry logicCombine indicators and criteria you select
YOUR SIZINGSet position rulesChoose sizing and allocation inputs
YOUR EXITSDefine exit controlsReview every exit and risk rule
class MyStrategy(TradingStrategy):
def getSecurityUniverseSelector(self):
return SecurityUniverseSelector.fromSymbols(
SecurityType.STOCK, ["AAPL", "MSFT"]
)
@data_trigger(type=DataType.BARS, barInterval=BarInterval.ONE_DAY)
def onMarketData(self, updatedSecurities):
orders = []
# Evaluate signals and append TradeOrder objects
return orders or None
Test before live authorization
See how your rules behave—not just how they read.
Use historical backtesting software and virtual portfolio simulation to inspect activity, uncover unexpected logic, and revise your assumptions before deciding what comes next.
- Replay your rulesSelect the historical range and strategy inputs to test.
- Inspect the assumptionsReview the modeling limitations behind hypothetical results.
- Observe in simulationMonitor virtual activity without confusing it with actual trading.
Backtests are hypothetical and virtual portfolios are simulations. Neither represents or predicts actual trading results.
Strategy test workspace
SIMULATION
TEST RANGEUser selected
RULE SETDraft 04
STATUS Review ready
Inputs
Historical data
Limitations
Review
A connected trading toolkit
Automated trading software that works as one connected toolkit.
Build a trading bot, test the logic, watch the market, and monitor activity without constantly changing systems.
Visual trading bot builder
Translate the rules in your head into a structured strategy you can inspect and revise.
Explore builderPython algo trading
Write custom logic with supported Python strategy tools.
Explore PythonStrategy backtesting
Review hypothetical strategy behavior against historical data you select.
Explore backtestingVirtual trading portfolio
Observe eligible strategy activity in a simulated portfolio before considering live use.
Explore simulationMarket Automation
Monitor a universe and condition you choose, send a notification, and optionally configure an order action for separate review.
Explore automationsScreeners and alerts
Apply your criteria to supported instruments and receive notifications when your conditions are met.
Explore monitoringEligible broker accounts
Connect a supported account, review your strategy, and separately authorize it when you decide.
Explore connections
Start from where you are
A practical route for every kind of strategy builder.
Begin in simulation, translate supported chart rules into visual configuration, or bring your own Python logic.
New to trading
Explore before you automate.
Use virtual portfolios to observe supported orders, positions, and market mechanics without committing real capital.
Trading tools for beginners
Active trader
Make repeatable rules explicit.
Configure supported price, indicator, schedule, sizing, and exit logic in a no-code trading bot you can inspect.
No-code strategy builder
Quant or developer
Code the logic that differentiates you.
Use the Investfly Python SDK and sample strategies with shared data, testing, simulation, and deployment services.
Python algorithmic trading
Multi-asset automation
Build automation for the markets you follow.
Use the same strategy tools across supported asset classes while keeping the universe, instruments, rules, and account decisions in your hands.
Stock & ETF trading bots
Build around shares, universes, indicators, baskets, and sizing rules you specify.
Crypto trading bots
Configure supported spot-pair strategies, scheduled DCA, and basket rebalancing.
Options strategy automation
Define contracts, legs, DTE, strike rules, group exits, and supported lifecycle behavior.
Futures trading bots
Use contracts, ticks, session timing, and expiration-aware controls.
Forex trading bots
Build with currency pairs, pips, directional rules, and session timing.
Asset, market-data, order, and live-account availability varies by provider, account type, jurisdiction, entitlement, and platform support.
Designed around your control
Your strategy stays yours at every stage.
01Your rulesYou choose or adopt every material input.
02Your reviewYou inspect tests, assumptions, and activity.
03Your authorizationYou decide whether eligible live use is appropriate.
Automated trading software FAQ
Questions before you start?
Here is how Investfly's algorithmic trading platform approaches strategy building, testing, simulation, and connected accounts.
Explore all featuresWhat is automated trading software?
Automated trading software lets a user define market conditions and trading rules for a strategy the software can evaluate repeatedly. Investfly combines visual and Python strategy authoring with hypothetical backtesting, virtual portfolio simulation, monitoring, and eligible account connections.
Can I build a trading bot without coding?
Yes. Investfly's no-code trading bot builder lets you configure a supported universe, entry and exit conditions, sizing, scheduling, and risk controls through a visual strategy builder. A Python option is available for users who want custom code.
Can I create a crypto trading bot with Investfly?
You can create user-defined automation for supported crypto instruments and eligible connections. Crypto availability varies by provider, account, jurisdiction, market-data entitlement, instrument, and supported order capability.
How does trading strategy backtesting work?
You select strategy rules, inputs, and a historical range, then review the resulting hypothetical activity. Backtests may omit or simplify fees, slippage, liquidity, latency, taxes, borrowing constraints, and market impact. They are not actual trading and do not predict future performance.
What is the difference between backtesting and a virtual portfolio?
A backtest applies rules to selected historical data. A virtual portfolio simulates eligible strategy activity over time. Both can help you inspect behavior, but neither represents actual execution or predicts real-world results.
Can I use Python for algorithmic trading strategies?
Yes. Investfly supports Python strategy tools for users who want to write and review custom strategy logic using the supported SDK and libraries.
How do connected broker accounts work?
After connecting a supported account, the account owner reviews the strategy and separately authorizes eligible live use. Provider, account, jurisdiction, instrument, market-data, and order restrictions apply.
Build. Test. Observe. Decide.
Turn your trading rules into a strategy you can inspect.
Start with the visual builder or Python, test your logic, and stay in control of what happens next.
No credit card required. Review current trial and plan terms before subscribing.