Jesse

---

Jesse is an advanced algorithmic trading framework in Python that aims to simplify researching and defining YOUR OWN trading strategies for backtesting, optimizing, and live trading.

What is Jesse?

Watch this video to get a quick overview of Jesse:

Why Jesse?

In short, Jesse is more accurate than other solutions, and way more simple. In fact, it is so simple that in case you already know Python, you can get started today, in matter of minutes, instead of weeks and months.

Key Features

• 📝 Simple Syntax: Define both simple and advanced trading strategies with the simplest syntax in the fastest time.
• 📊 Comprehensive Indicator Library: Access a complete library of technical indicators with easy-to-use syntax.
• 📈 Smart Ordering: Supports market, limit, and stop orders, automatically choosing the best one for you.
• ⏰ Multiple Timeframes and Symbols: Backtest and livetrade multiple timeframes and symbols simultaneously without look-ahead bias.
• 🔒 Self-Hosted and Privacy-First: Designed with your privacy in mind, fully self-hosted to ensure your trading strategies and data remain secure.
• 🛡️ Risk Management: Built-in helper functions for robust risk management.
• 📋 Metrics System: A comprehensive metrics system to evaluate your trading strategy's performance.
• 🔍 Debug Mode: Observe your strategy in action with a detailed debug mode.
• 🔧 Optimize Mode: Search strategy parameters efficiently with Optuna and parallel processing powered by Ray.
• 📈 Leveraged and Short-Selling: First-class support for leveraged trading and short-selling.
• 🔀 Partial Fills: Supports entering and exiting positions in multiple orders, allowing for greater flexibility.
• 🔔 Advanced Alerts: Create real-time alerts within your strategies for effective monitoring.
• 🔌 Jesse MCP: Connect Claude, Codex, Cursor, VS Code, Zed, and other MCP-compatible AI assistants directly to your local Jesse project.
• 🔧 Built-in Code Editor: Write, edit, and debug your strategies with a built-in code editor.
• 📊 Interactive Trading Charts: Inspect candles, strategy indicators, horizontal levels, orders, and completed trades across backtest, paper, and live sessions.
• 🔬 Rule Significance Testing: Test whether an entry rule shows genuine historical edge or could have appeared by chance.
• 🎲 Monte Carlo Analysis: Stress-test your strategies with trade-order shuffling and candles-based simulations to distinguish skill from luck and guard against overfitting.
• 🧠 Machine Learning: A built-in ML pipeline — gather labelled training data from backtests, train scikit-learn models (binary, multiclass, or regression), and deploy predictions directly inside your strategies.
• 🧪 Research API and Jupyter: Run backtests, optimization, significance tests, Monte Carlo analysis, candle workflows, and machine learning from Python scripts or notebooks.
• 🦀 Rust-Powered Indicators: Native Rust implementations make indicator-heavy strategies and large research runs substantially faster.
• 🤖 Reinforcement Learning — Coming Soon: First-class reinforcement-learning workflows built on Jesse's simulation and research stack are on the way.
• 📺 Youtube Channel: Jesse has a Youtube channel with screencast tutorials that go through example strategies step by step.

Dive Deeper into Jesse's Capabilities

Stupid Simple

Craft complex trading strategies with remarkably simple Python. Access 300+ indicators, multi-symbol/timeframe support, spot/futures trading, partial fills, and risk management tools. Focus on logic, not boilerplate.

class GoldenCross(Strategy):
    def should_long(self):
        # go long when the EMA 8 is above the EMA 21
        short_ema = ta.ema(self.candles, 8)
        long_ema = ta.ema(self.candles, 21)
        return short_ema > long_ema

    def go_long(self):
        entry_price = self.price - 10        # limit buy order at $10 below the current price
        qty = utils.size_to_qty(self.balance*0.05, entry_price) # spend only 5% of my total capital
        self.buy = qty, entry_price                 # submit entry order
        self.take_profit = qty, entry_price*1.2  # take profit at 20% above the entry price
        self.stop_loss = qty, entry_price*0.9   # stop loss at 10% below the entry price

Backtest

Execute highly accurate and fast backtests without look-ahead bias. Utilize debugging logs, interactive charts with indicator support, and detailed performance metrics to validate your strategies thoroughly.

Interactive Trading Charts

Inspect your strategy where its decisions happened. Jesse combines candlesticks, strategy-added indicators and levels, executed orders, and completed trades in synchronized interactive charts. The same charting workflow is available for backtests and for running or completed paper/live sessions.

Expand a trade to inspect every execution, collapse or isolate indicator panes, follow OHLC and indicator values under the cursor, reset the view, use fullscreen mode, or export the chart as an image.

This is the beginning of the README. Read the rest at the original. Read the full README ↗