jesse 2.2.0

A trading framework for cryptocurrencies

Algo-trading was 😵‍💫, we made it 🤩

Jesse

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Jesse is an advanced crypto trading framework 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: Fine-tune your strategies using AI, without needing a technical background.
• 📈 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.
• 🤖 JesseGPT: Jesse has its own GPT, JesseGPT, that can help you write strategies, optimize them, debug them, and much more.
• 🔧 Built-in Code Editor: Write, edit, and debug your strategies with a built-in code editor.
• 🎲 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.
• 📺 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.

Live/Paper Trading

Deploy strategies live with robust monitoring tools. Supports paper trading, multiple accounts, real-time logs & notifications (Telegram, Slack, Discord), interactive charts, spot/futures, DEX, and a built-in code editor.

Benchmark

Accelerate research using the benchmark feature. Run batch backtests, compare across timeframes, symbols, and strategies. Filter and sort results by key performance metrics for efficient analysis.

AI

Leverage our AI assistant even with limited Python knowledge. Get help writing and improving strategies, implementing ideas, debugging, optimizing, and understanding code. Your personal AI quant.

Monte Carlo Analysis

Stress-test your strategies beyond a single historical path. Jesse's Monte Carlo mode runs hundreds of simulations using trade-order shuffling (tests whether trade timing drove your results) and candles-based (tests robustness under slightly different market conditions) methods. Use it to distinguish skill from luck, understand the range of outcomes you can realistically expect, and catch overfitting early.

Machine Learning

Jesse includes a complete, end-to-end ML pipeline built for trading strategies:

1. Gather data — run a backtest in gather mode; call record_features({...}) at each signal bar and record_label(name, value) when the outcome is known. Data is auto-saved to CSV.
2. Train a model — call train_model() with any scikit-learn–compatible estimator and choose a task type: "binary" classification, "multiclass" classification, or "regression". Get a full report with feature importance, calibration, and metrics.
3. Deploy — switch to deploy mode and call ml_predict() or ml_predict_proba() inside your strategy. Model loading, scaling, and feature ordering are handled automatically.

# Gather phase — inside your strategy
def before(self):
    self.record_features({
        'rsi': ta.rsi(self.candles),
        'adx': ta.adx(self.candles),
    })

# Deploy phase — gate entries with model confidence
def should_long(self):
    proba = self.ml_predict_proba()
    return proba['long'] > 0.65

Optimize Your Strategies

Unsure about optimal parameters? Let the optimization mode decide using simple syntax. Fine-tune any strategy parameter with the Optuna library and easy cross-validation.

@property
def slow_sma(self):
    return ta.sma(self.candles, self.hp['slow_sma_period'])

@property
def fast_sma(self):
    return ta.sma(self.candles, self.hp['fast_sma_period'])

def hyperparameters(self):
    return [
        {'name': 'slow_sma_period', 'type': int, 'min': 150, 'max': 210, 'default': 200},
        {'name': 'fast_sma_period', 'type': int, 'min': 20, 'max': 100, 'default': 50},
    ]

Getting Started

Head over to the "getting started" section of the documentation. The documentation is short yet very informative.

Resources

• ⚡️ Website
• 🎓 Documentation
• 🎥 Youtube channel (screencast tutorials)
• 🛟 Help center
• 💬 Discord community
• 🤖 JesseGPT (Requires a free account)

What's next?

You can see the project's roadmap here. Subscribe to our mailing list at jesse.trade to get the good stuff as soon they're released. Don't worry, We won't send you spam—Pinky promise.

Disclaimer

This software is for educational purposes only. USE THE SOFTWARE AT YOUR OWN RISK. THE AUTHORS AND ALL AFFILIATES ASSUME NO RESPONSIBILITY FOR YOUR TRADING RESULTS. Do not risk money that you are afraid to lose. There might be bugs in the code - this software DOES NOT come with ANY warranty.