Help shape the Jesse roadmap

Create a step-by-step tutorial covering Jesse’s full workflow—from installation and writing your first strategy to backtesting, understanding results, and optimizing performance. Using a practical example throughout, cover Monte Carlo simulations, the rule significance test, connecting exchange API keys, paper trading, live trading, and more—making it easy for beginners to follow along and use Jesse confidently.

Add a smart order type that attempts to fill a trade using post-only limit orders instead of immediately executing as a taker. Jesse would read the current best bid and best ask from the order book, then place a post-only order at a competitive price. If the order is filled, it receives maker execution and typically pays maker fees. If the market moves before the order fills, Jesse automatically cancels or replaces the order at the new best available price and repeats the process until the requested quantity is filled or a configured limit is reached. This would provide market-order-like convenience while helping traders reduce slippage and avoid taker fees, particularly in markets with limited liquidity or wide spreads.

Add first-class support for real-time order book data, including the best bid and ask prices, market depth, spread, and order book updates. This would enable strategies to make decisions using live liquidity and market microstructure data rather than relying exclusively on candles and trades. It would also allow Jesse strategies to evaluate signals and execute trades within seconds, removing the current one-minute minimum interval. This would provide the foundation for faster execution strategies, market making, order book imbalance signals, and other latency-sensitive trading techniques.

Expose authenticated, permission-controlled MCP tools for managing Jesse live-trading sessions. Compatible AI clients could inspect session status, positions, orders, and errors, as well as start or stop sessions, with explicit confirmation for operations that could affect live trading.

Introduce reinforcement-learning support for training and evaluating Jesse strategies. Strategies could learn from market environments, test learned policies through backtesting, and eventually use those policies during live or paper trading.

Integrate third-party market-data providers so Jesse can access years of high-quality historical stock, forex, and commodity data. Users could backtest and develop strategies against those traditional markets, then run the resulting strategies live through crypto exchanges that offer them.

Add first-class support for Alpaca so Jesse strategies can execute and manage live trades through Alpaca, including order placement, cancellation, position tracking, and account synchronization for supported assets.

Add first-class support for Interactive Brokers so Jesse strategies can execute and manage live trades through IBKR, including order placement, cancellation, position tracking, and account synchronization across supported markets.

Allow a TradingView alert to call a webhook URL assigned to a Jesse strategy. The alert payload would be passed into the strategy, where you could validate the data, perform your own calculations, and decide whether Jesse should execute a trade.