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Matt Dancho – Backtesting Algorithmic Trading Strategies with Python

Original price was: $ 497.0.Current price is: $ 27.0.

Published on: July 9, 2025

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Category: Product For Sale Tag: Matt Dancho - Backtesting Algorithmic Trading Strategies with Python

IMPORTANT

Product is NOT a part of the Membership

Sold separately

Description

✅ Stable Website

✅ We Are Not Resellers

✅ Automatic Access

✅ Stream or Download

✅ No Subscription

✅ Working Request System

✅ Great Support

✅ No Partnerships with Other Sites

Here’s What You Get:

Set up an trading portfolio project and Quant Lab software and get started (even if you’re a beginner with no prior algorithmic trading experience)
Get 4 algorithmic portfolio trading strategies. Plan your way to success, minimize risk, and protect your money (using volatility targeting, portfolio construction optimization, and setting minimum return thresholds so your portfolio grows in a risk-managed way).
Professionally backtest your portfolio trading strategy so you can test how your strategy would have performed under different market conditions.
Make trading for growing your investment portfolio a reality without losing money, sleep, or your mind!

Step 1: Trading Project and Python Quant Lab Setup ($500 Value)

Get the Quant Stack Python Software installed
Set up your algorithmic trading project
Create your Python environment
Everything you need to begin building and backtesting portfolio trading strategies

Step 2: How to Create a Profitable Algorithmic Portfolio Trading Strategy ($2,500 Value)

Get our top portfolio-based trading strategy: Volatility targeting with auto-rebalancing ($2,500 Value)
Get our code template for how to construct a risk-managed portfolio with the Riskfolio-Lib Python library
Discover how to increase returns using the “Ray Dalio Bridgewater Cheat Code”

Step 3: Learn how to Backtest the right way ($2,500 Value)

Detailed walkthrough of event-based backtesting ($2,500 Value)
Backtested portfolio strategies with Zipline Reloaded
How to avoid mistakes in backtesting portfolios
How to include rebalancing, slippage, and trading commissions

Bonus #1: Code to Backtest 21,000+ US Equities using Premium Data ($1,500 Value)

Solves the “I need professional market data for high-quality backtesting” Problem
Get code templates for how to ingest professional data for 21,000+ US Equites ($1,500 value)
Get code to covert the data to Zipline Bundles
Requires a $50/month Premium Market Data Subscription (only needed for this section of the course)

Bonus #2: Code to Use Free Market Data for Backtesting ($1,500 Value)

Solves the “I need free market data for when I am first beginning to backtest non-professionally” Problem
Get code templates for how to ingest free market data
Get code to covert the data to Zipline Bundles ($1,500 value)
Does NOT require a data subscription (it’s free data)

Bonus #3: Top 3 Variations of Volatility Targeting Strategy ($3,000 Value)

Solves the “I need more portfolio trading strategies for different market conditions” problem
Get 3 different variants of the portfolio-based algorithmic trading strategy
Variant #1: Hierarchical Risk Parity ($1,000 value)
Variant #2: CVaR Risk Measure ($1,000 value)
Variant #3: Risk Factor with Principal Component Regression ($1,000 value)

Matt Dancho – Backtesting Algorithmic Trading Strategies with Python

Here’s What You Get:

Set up an trading portfolio project and Quant Lab software and get started (even if you’re a beginner with no prior algorithmic trading experience)

Get 4 algorithmic portfolio trading strategies. Plan your way to success, minimize risk, and protect your money (using volatility targeting, portfolio construction optimization, and setting minimum return thresholds so your portfolio grows in a risk-managed way).

Professionally backtest your portfolio trading strategy so you can test how your strategy would have performed under different market conditions.

Make trading for growing your investment portfolio a reality without losing money, sleep, or your mind!

Step 1: Trading Project and Python Quant Lab Setup ($500 Value)

Get the Quant Stack Python Software installed

Set up your algorithmic trading project

Create your Python environment

Everything you need to begin building and backtesting portfolio trading strategies

Step 2: How to Create a Profitable Algorithmic Portfolio Trading Strategy ($2,500 Value)

Get our top portfolio-based trading strategy: Volatility targeting with auto-rebalancing ($2,500 Value)

Get our code template for how to construct a risk-managed portfolio with the Riskfolio-Lib Python library

Discover how to increase returns using the “Ray Dalio Bridgewater Cheat Code”

Step 3: Learn how to Backtest the right way ($2,500 Value)

Detailed walkthrough of event-based backtesting ($2,500 Value)

Backtested portfolio strategies with Zipline Reloaded

How to avoid mistakes in backtesting portfolios

How to include rebalancing, slippage, and trading commissions

Bonus #1: Code to Backtest 21,000+ US Equities using Premium Data ($1,500 Value)

Solves the “I need professional market data for high-quality backtesting” Problem

Get code templates for how to ingest professional data for 21,000+ US Equites ($1,500 value)

Get code to covert the data to Zipline Bundles

Requires a $50/month Premium Market Data Subscription (only needed for this section of the course)

Bonus #2: Code to Use Free Market Data for Backtesting ($1,500 Value)

Solves the “I need free market data for when I am first beginning to backtest non-professionally” Problem

Get code templates for how to ingest free market data

Get code to covert the data to Zipline Bundles ($1,500 value)

Does NOT require a data subscription (it’s free data)

Bonus #3: Top 3 Variations of Volatility Targeting Strategy ($3,000 Value)

Solves the “I need more portfolio trading strategies for different market conditions” problem

Get 3 different variants of the portfolio-based algorithmic trading strategy

Variant #1: Hierarchical Risk Parity ($1,000 value)

Variant #2: CVaR Risk Measure ($1,000 value)

Variant #3: Risk Factor with Principal Component Regression ($1,000 value)

Matt Dancho – Backtesting Algorithmic Trading Strategies with Python

Here’s What You Get:

Set up an trading portfolio project and Quant Lab software and get started (even if you’re a beginner with no prior algorithmic trading experience)

Get 4 algorithmic portfolio trading strategies. Plan your way to success, minimize risk, and protect your money (using volatility targeting, portfolio construction optimization, and setting minimum return thresholds so your portfolio grows in a risk-managed way).

Professionally backtest your portfolio trading strategy so you can test how your strategy would have performed under different market conditions.

Make trading for growing your investment portfolio a reality without losing money, sleep, or your mind!

Step 1: Trading Project and Python Quant Lab Setup ($500 Value)

Get the Quant Stack Python Software installed

Set up your algorithmic trading project

Create your Python environment

Everything you need to begin building and backtesting portfolio trading strategies

Step 2: How to Create a Profitable Algorithmic Portfolio Trading Strategy ($2,500 Value)

Get our top portfolio-based trading strategy: Volatility targeting with auto-rebalancing ($2,500 Value)

Get our code template for how to construct a risk-managed portfolio with the Riskfolio-Lib Python library

Discover how to increase returns using the “Ray Dalio Bridgewater Cheat Code”

Step 3: Learn how to Backtest the right way ($2,500 Value)

Detailed walkthrough of event-based backtesting ($2,500 Value)

Backtested portfolio strategies with Zipline Reloaded

How to avoid mistakes in backtesting portfolios

How to include rebalancing, slippage, and trading commissions

Bonus #1: Code to Backtest 21,000+ US Equities using Premium Data ($1,500 Value)

Solves the “I need professional market data for high-quality backtesting” Problem

Get code templates for how to ingest professional data for 21,000+ US Equites ($1,500 value)

Get code to covert the data to Zipline Bundles

Requires a $50/month Premium Market Data Subscription (only needed for this section of the course)

Bonus #2: Code to Use Free Market Data for Backtesting ($1,500 Value)

Solves the “I need free market data for when I am first beginning to backtest non-professionally” Problem

Get code templates for how to ingest free market data

Get code to covert the data to Zipline Bundles ($1,500 value)

Does NOT require a data subscription (it’s free data)

Bonus #3: Top 3 Variations of Volatility Targeting Strategy ($3,000 Value)

Solves the “I need more portfolio trading strategies for different market conditions” problem

Get 3 different variants of the portfolio-based algorithmic trading strategy

Variant #1: Hierarchical Risk Parity ($1,000 value)

Variant #2: CVaR Risk Measure ($1,000 value)

Variant #3: Risk Factor with Principal Component Regression ($1,000 value)

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