Algo Trading Backtesting & Optimization | Educational Video Tutorials

This FREE video tutorial series takes a journey to educate traders about best-practice backtesting & optimization. Must-watch videos for all algo traders.

These "must-watch" videos are tagetted at algo traders who are serious about investing their time and research into their own success. They were produced by Trade Like A Machine's Martyn Tinsley for Darwinex.

2. Finding parameter values with the best possible edge using Statistical Power Analysis

Martyn TinsleyMartyn Tinsley

This tutorial looks at how the required sample size can be determined to ensure both a robust optimization, and the effective extraction of the parameters with the best possible edge, using a technique called ‘Statistical Power Analysis’.

3. Have you over-fitted your trading system to 'noise' in the price action, and 'news events'?

Martyn TinsleyMartyn Tinsley

Over-fitting in optimizations happens for two primary reasons: Over-fitting to noise in the price action, and over-fitting to market news events. By attempting to model the overall price action too closely with a large number of parameters means that the model is forced to over-fit.

4. Practical Steps to avoid Over-Fitting by increasing sample size (number of trades) in optimizations

Martyn TinsleyMartyn Tinsley

This episode starts to look at 6 practical steps that can be taken to improve the reliability of your own trading system optimizations. This starts by improving statistical significance by increasing sample size (the number of uncorrelated trades), and the use of multi-asset and multi-timeframe testing.

5. Avoiding over-fitting due to market news events when you perform your trading optimizations

Martyn TinsleyMartyn Tinsley

This video looks at practical ways that you can avoid the effects of over-fitting due to Economic News Events. Over-fitting here results in large losing or winning trades that can skew your results significantly. Solutions include enhancing your performance metric and avoiding news events by exiting trades before significant market news is released.

6. Avoiding over-fitting in optimizations due to random noise in the price action

Martyn TinsleyMartyn Tinsley

The video considers how over-fitting to the noise component of price action can destroy a trading system’s ability to work in live trading. It shows how the ‘degrees of freedom’ (or number of parameters being optimized) in a trading optimization process, can cause over-fitting to noise in the price action. A real life illustration of this damaging effect is shown.

7. Best-Practice Design of the 'Walk Forward Phase' in your trading optimizations

Martyn TinsleyMartyn Tinsley

Determining the ratio between the duration of the 'optimization phase' to that of the 'walk forward phase' is critical to a successful optimization process. This episode explains how getting that ratio right, is essential to ensure both robust optimal parameter extraction and robust validation of results. The importance of a ‘Pre-Live’ optimization is also explained, in order to ensure your parameters are in tune with current market dynamics/regimes.

8. Using Optimization Profiles and Surfaces for Effective Parameter Value Selection

Martyn TinsleyMartyn Tinsley

Simply selecting the best performing set of parameters in an algorithmic trading optimization is often not the best approach. Just because a set of parameter values appeared to perform best in the optimization does not necessarily mean they will perform best in live trading. This episode shows you how to avoid 3 major pitfalls in the selection process by using optimization profiles to best effect.

9. The Importance of Choosing a robust Performance Metric / Criteria

Martyn TinsleyMartyn Tinsley

Choosing suitable performance metrics/criteria for effective selection of algorithmic trading parameters is essential for backtesting and trading optimizations. Because the choice of performance criteria will impact which parameters you choose to trade with, getting this right is almost as important as the rules of your trading system itself.

10. The best performance metrics to use to measure Trading System Performance

Martyn TinsleyMartyn Tinsley

This video covers three techniques to measure system performance: 1. CAGR / ”MEAN” DD (or ‘average’ drawdown), 2. Pearson's Correlation Coefficient, and 3. The Coefficient of Determination. Furthermore, the importance of matching the position sizing methodology (‘scaled’ or 'fixed') to each of these is also fully explained.

11. How to ensure backtest results represent future live-trading results

Martyn TinsleyMartyn Tinsley

This video uncovers common issues with the use of price data models used in the backtesting process, which can cause significant differences between the results you see in your backtests compared with what you obtain in live trading. The solution to the problems is also explained.

12. How 'Walk Forward Analysis' Solves Major Trading System Optimization Issues

Martyn TinsleyMartyn Tinsley

Walk Forward Analysis provides an excellent alternative to the standard ‘optimization’ followed by a ‘Walk Forward validation’ phase, which is the most common approach taken by algorithmic traders. However, this latter method suffers from a number of major issues. This episode shows how Walk Forward Analysis provides a solution.

13. Best-practice Walk Forward Optimization and how to avoid pitfalls

Martyn TinsleyMartyn Tinsley

This video talks you through a series of best-practice techniques to help make your Walk Forward Analysis/Optimization process run smoothley. It also warns against a number of common mistakes and pitfalls.

14. Using Logical Parameter Values to Improve Backtesting Results

Martyn TinsleyMartyn Tinsley

This video uses two examples of algorithmic trading systems - the first based on a price action strategy and the second based on an indicator system - to illustrate how you can improve your optimization results by choosing a logical range of parameters.

15. Research Study Part 1 | Which Optimization Performance Measure is best?

Martyn TinsleyMartyn Tinsley

Ever wondered which metrics work best for measuring algorithmic trading performance in an optimization? If so then these research results will help to answer that question. In scope are the Sharpe Ratio, Profit Factor, Expected Payoff, Recovery Factor, CAGR/Max Drawdown, CAGR/Mean Drawdown, r and R-Squared.

16. Research Study Part 2 | Which Optimization Performance Measure is best?

Martyn TinsleyMartyn Tinsley

This research concludes that the optimal way to ascertain the best performance criterion in trading system optimizations is to compare the metric with the Correlation Coefficient of the walk forward equity curve.

17. Research Study | Does Walk Forward Analysis work better than standard Optimizations?

Martyn TinsleyMartyn Tinsley

This research study compares the effectiveness of a standard optimization with a multi-stage Walk Forward Analysis methodology in a trading backtest. Additionally, the number of stages used in the WFA is studied to determine how this relates to effectiveness.

More comments taken from the YouTube Darwinex channel:

"That's really awesome content!... Very, VERY useful to see if there's an edge or it is just because of luck and/or overfitting. He's a very good source of valuable knowledge."

Marty Castany

"Thanks Martyn. Very informative. Looking forward to more content on this."

Ezalor Investments

"These series of videos are really interesting for two reasons: 1) they address a real and serious problem for algorithmic trading. 2) they are absolutely clear (which is not a common thing). I am eager to see the next videos of this series."

Leon Jaime Bendayán Rios

"These videos are purely gold"

yeison wilchez

"This is fantastic. Finally, some real value being added to the retail space. Keep it up"

Mohammad Ahmad

"This is amazing, and although I understood this subconsciously, its the first time I've heard it academically."


"Martyn your whole series is excellent. thank u!"

Ivan Hudec

"Thank you very much for sharing your knowledge, you're awesome man!"

Eduard Querol

"Thank you for your hard work and dedication of putting this series together for the Algo Trading community!"

Michael Williams

Thank you everyone for your kind comments. It makes it all worthwhile, Martyn Tinsley

About The Creator

Martyn Tinsley - Algorithmic Trader

A passion for all things analytical, and in particular for automated algorithmic trading using artificial intelligence and reinforcement learning.

YouTube Comments

"Wow. Amazingly enlightening. I have read so much on proper ways to back test and [these videos] blew my mind. Thank you so much."

Jason Smith

"Martyn, you should seriously consider writing a book... Your content is top notch."


"What a unique level of great quality, useful content"

Bigote Blanco

"This is great!... You are a talented educator Martyn!"

Krisjanis Berzins

See more comments on the YouTube playlist or at the bottom of this page

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