What is algo trading?
Algorithmic (“algo”) trading means expressing a trading idea as an explicit, repeatable set of rules — for example, buy when a fast moving average crosses above a slow one, and sell when a stop-loss or target is hit — and having software evaluate and, optionally, execute those rules instead of a human deciding trade by trade in real time.
What our platform does
MyAlgoAgent lets a user connect market data, define entry and exit rules using technical indicators and price conditions, simulate that strategy against historical data, validate it in a risk-free paper-trading mode, and — only with explicit authorization and a connected broker account — run it against live markets with configurable risk limits.
Who it is for
- Individual traders who want to systematize a trading idea instead of trading on discretion alone.
- Developers and quantitative researchers who want a structured environment for strategy iteration.
- Anyone who wants to test a trading idea against history before risking capital on it.
The complete workflow
Market data → strategy creation → backtesting → validation → paper trading → risk controls → broker connection → live execution. Each stage is a distinct, explicit step; the platform never moves a strategy into live trading automatically.
Strategy builder
A no-code interface for composing entry and exit conditions from technical indicators (moving averages, RSI, MACD, Bollinger Bands, VWAP, ATR and others), price action, volume and time-based rules, combined with AND/OR logic — or write the same rules as code, in a purpose-built DSL. See Features.
Backtesting engine
Strategies are simulated against historical price data with configurable capital, brokerage, fees and a slippage model, so results reflect realistic execution rather than a simplified price-only calculation. See Backtesting.
Paper trading
Paper trading runs a strategy against current market data using virtual capital only. No real orders are placed and no real money is at risk. It exists to validate a strategy’s live behavior before any capital is committed. See Paper Trading.
Live trading & broker integration
Live trading requires the user to explicitly connect a supported broker account and confirm risk settings before any strategy can place real orders. The platform does not custody funds; it connects to broker APIs on the user’s behalf, with credentials handled through secure secret management rather than stored in application code. See Live Trading.
Risk management
Configurable daily loss limits, maximum position size, maximum exposure and a global kill switch are enforced independently of the user interface, so a strategy can be stopped even if a user is offline or unresponsive. See Risk Management.
Alerts, portfolio monitoring, order tracking and reporting
Users receive alerts for signals, fills, rejected orders and risk-limit breaches, and can review positions, P&L, open orders and a full trade history, with exports for further analysis.
AI functionality
Where implemented, AI features assist with translating a natural-language strategy description into explicit rules, summarizing backtest results and flagging signs of possible overfitting. AI output is informational only — never a guarantee of future performance or personalized financial advice.
Important disclosure
Product stage
MyAlgoAgent is in active development. Public marketing and documentation pages describe the target product architecture; features are being built out in stages, starting with the strategy, backtesting and risk-management core described above.