Algo Strategy Pack

Your Ultimate Toolkit: 34 Ready-to-Deploy Algorithmic Trading Strategies

Overview

The Algo Strategy Pack delivers a comprehensive collection of 34 unique, battle-tested, and ready-to-run algorithmic trading strategies. Designed for seamless integration with Backtrader, this pack covers diverse market approaches for equities, crypto, FX, and futures. Each strategy includes full Python source code (.py and .ipynb), clear documentation, and sample configurations, enabling you to deploy sophisticated trading systems in minutes, not weeks.

What’s Inside: A Diverse Arsenal of Strategies

This pack isn't just a list of MA crossovers. You get a wide spectrum of methodologies, including:

• Advanced Indicator-Based Systems:
  • Adaptive Kalman Filter Strategy
  • Adaptive MA Volatility Strategy
  • Guppy MMA Strategy
  • Hilbert Transform (Trend & RSI-based) Strategies
  • Enhanced TRIX Strategies with Dynamic Exits
• Classic & Enhanced Technical Strategies:
  • Multiple EMA/DEMA Crossovers with Trend Filters (ADX, SMA)
  • Bollinger Bands & Keltner Channel Systems (Mean Reversion & Breakout)
  • MACD & RSI-driven models with advanced filtering
  • ADX Trend & Slope-based systems
• Regime-Adaptive & Machine Learning Approaches:
  • Dual Regime Weekly Strategy (adapts to trending/ranging markets)
  • HMM (Hidden Markov Model) Trend Regime Strategy
  • Decision Tree EMA Crossover ML Strategy
  • Isolation Forest Anomaly Detection Strategy
• Specialized Models:
  • Hurst Exponent Filtered Trend Strategy
  • Ensemble Strategy with Fixed Weights

Explore strategies based on trend-following, mean-reversion, volatility breakouts, momentum, and sophisticated market filters.

Fully Documented Code

Every strategy ships with .py source files and .ipynb Jupyter notebooks for interactive exploration. Sample datasets are included where applicable, and the comprehensive PDF manual (the "Comprehensive Strategy Guide") walks you through the logic, parameters, installation, configuration, and execution of each of the 34 strategies.

Getting Started

Load the package into your Python environment, import your chosen strategy class, adjust parameters via strategy arguments or a configuration file, connect your data, and execute a backtest or live run with ease using Backtrader.

Performance Snapshots

(Note: The following are illustrative aggregate metrics. Individual strategy performance will vary by market, timeframe, and parameters.)

• Average Annual Return (example portfolio): 18.5%
• Max Drawdown (example portfolio): –12.3%
• Sharpe Ratio (example portfolio): 1.45
• Extensive Backtesting: Each strategy has been rigorously tested.

Comprehensive Documentation

Dive deep into each of the 34 strategies with our detailed manual. Understand the "why" and "how" behind every system.

• Algo Strategy Pack Manual (PDF) - Your Guide to 34 Strategies

Continuous Updates & Support

• New Strategies Quarterly: We’ll add fresh, high-quality strategies to the pack every quarter, expanding your toolkit.
• Ongoing Improvements: Benefit from indicator enhancements, performance optimizations, and code updates.
• Priority Email Support and access to our private Discord community for questions and discussions.