AlgoDeploy

AlgoDeploy is algorithmic trading software written in Python that handles backtesting, optimization, and live execution of trading strategies. The platform runs entirely on your local machine, meaning your broker credentials and trading logic never leave your infrastructure. This stands in contrast to cloud-based trading platforms where you must trust external servers with your API keys and proprietary strategies. AlgoDeploy positions itself around a one-time purchase model rather than ongoing subscriptions, so you buy a license once and keep using it indefinitely without monthly fees eating into your trading profits.

The backtesting engine operates in two modes. A vectorized mode enables fast iteration when you need quick feedback on strategy variations. An event-driven mode handles more complex logic where order flow and timing matter. Both modes support long and short positions with configurable slippage and commission assumptions. Analysis tools include Monte Carlo simulation with confidence intervals, parameter sweep grid searches with visual heatmaps, and walk-forward optimization to detect when a strategy has been overfit to historical data. Interactive HTML reports present equity curves and drawdown charts without requiring separate visualization tools. These reports make it easy to share results with collaborators or review your own strategy performance over time.

Signal generation draws from eleven built-in technical indicators, with a composable signal scanner that combines ten different indicators according to your rules. Four position-sizing algorithms let you control how capital allocates across trades, from fixed amounts to volatility-adjusted sizing. Eight pre-trade risk checks validate orders before execution, catching potential problems before money is at risk. A runtime drawdown monitor can trigger circuit breakers if losses exceed thresholds you define, automatically halting trading when things go wrong. Regime filters overlay market conditions using VIX levels or simple moving average classifications, letting you see how strategies perform in different environments and optionally adjust behavior when regimes shift.

Live trading connects to Alpaca Markets and Interactive Brokers. Alpaca handles US equities, ETFs, and cryptocurrency with automatic symbol routing that sends crypto orders to crypto execution and stock orders to stock execution. Interactive Brokers adds support for single-leg equity options alongside stocks and crypto through TWS or IB Gateway. A real-time WebSocket dashboard displays position status and fills as they happen. Native notifications push alerts to Telegram, Discord, or custom webhooks so you know what your strategies are doing even when you are away from your desk. Paper trading mode lets you validate strategies against live market data without risking capital, and an immutable audit log records every trade for compliance and review.

Strategy definition accommodates different skill levels. A dashboard UI provides a no-code path for assembling strategies visually. YAML configuration files offer a structured middle ground for those who prefer text-based configuration. Full Python API access gives advanced users complete control over every aspect of strategy logic. The same execution engine runs in backtesting and live trading, so behavior matches between simulation and production. Six timeframes from one-minute bars to daily candles cover intraday scalping through swing trading approaches. This unified engine eliminates the common problem of strategies that look great in backtests but behave differently in production due to execution differences.

Pricing uses perpetual licenses across four tiers. Students with verified education email addresses get free access to learn algorithmic trading without financial barriers. The Starter tier at ninety-nine dollars includes backtesting, live trading, and the dashboard. The Pro tier at two hundred forty-nine dollars adds regime filters, parameter sweeps, and Monte Carlo analysis for more sophisticated strategy development. The Quant tier at four hundred ninety-nine dollars includes multi-strategy portfolio backtesting, white-label reports for professional presentation, and API access for programmatic integration. An optional maintenance subscription at ten dollars per month or one hundred per year covers updates, new broker integrations, priority support, and Discord community access. Canceling maintenance keeps your purchased version working forever.

Data sources include Yahoo Finance for free daily data, Alpaca Markets for free intraday data from one-minute to daily resolution with historical lookback, and CSV imports for custom datasets. The DataSource interface supports custom implementations if you have proprietary data feeds from professional vendors. Python 3.10 or higher is required, and the software runs on Windows, macOS, and Linux without additional subscriptions beyond your broker account and optional data services. Schwab integration appears on the roadmap for future releases.