Resources & Articles

Search Posts:
April 18, 2022
Combining Strategies with AmiBroker

I’m a big fan of AmiBroker, particularly the speed and flexibility it provides for backtesting trading strategies. However, one thing that is not particularly straightforward in AmiBroker is combining multiple strategies into a single system. Many quantitative (a.k.a. systematic) traders, myself included, use more than one strategy to help smooth their portfolio returns. The idea […]

Read More
February 13, 2020
When Past Performance IS Indicative of Future Returns

Finally someone who gets it, past performance CAN be indicative of future returns! For starters, what else is there? More importantly, the only thing that really could be indicative of future results is the demonstration of a repeatable process - and that would be reflected in the past performance revealed by the reliable back-test of a quantitative […]

Read More
January 10, 2020
Podcasts, Books & Other Trading Resources

We frequently get asked about 'getting started in trading'.  The truth is, it's a long journey, don't believe the hype (and certainly don't pay for any of it until you know what you're doing), but you do need to start somewhere and it's a big head-start if you can go directly to resources that represent genuine […]

Read More
December 10, 2019
The Art of Building a Quantitative Trading System

This is a placeholder for the next exciting blog I'd like to write on getting into the detail of building an algo.

Read More
October 8, 2019
Introduction to Auto Trading

My colleague Dave Di Marcantonio runs several web sites, including AmiBroker Courses where I sell some of my training courses. Dave recently published an introduction to trading automation, sometimes referred to as auto trading. Dave’s course also provides details about Alera Portfolio Manager, a new trading automation solution. APM can be integrated directly with AmiBroker but also […]

Read More
October 8, 2019
Beat the Market with a Simple SuperTrend Strategy

Recently I gave a presentation describing the process of creating and validating a simple trading strategy using AmiBroker. In this case, the instruments traded were the NIFTY and Bank NIFTY indices from the NSE in India, and the primary indicator used was the SuperTrend indicator. Although the performance results of the strategy are quite respectable, […]

Read More
July 8, 2019
Strategy Tuning With Market Types

In a previous post we examined how back test results could be summarized for different market types. In today’s post, we’ll look at how we can use that information to tune our strategy for live trading. As the basis for this exercise, we will use a modified version of the short mean reversion strategy known […]

Read More
April 15, 2019
Reporting By Market Type

One of the many interesting ideas put forth by Dr. Van K. Tharp is that you should not try to find a trading strategy that works in every type of market. Instead, he advises trading only those strategies that have performed well in the past under conditions similar to the current ones. If you choose to adopt […]

Read More
December 27, 2018
Adaptive Strategy Presentation

Last night I revisited the topic of creating adaptive strategies for an audience from the AmiBroker Canada User Group and a local chapter of the Canadian Society of Technical Analysts. The presentation was well-received, and the audience asked thoughtful questions at the end. If you want to listen in, the recording can be found here.

Read More
September 27, 2018
SuperTrend Indicator

I recently completed a client project that utilizes the SuperTrend indicator. The indicator is basically a variation on other types of volatility bands, using a multiple of ATR to define bands above and below the current average price. The SuperTrend line follows the lower band when the price is in an up trend (has most […]

Read More
Copyright 2022 Quant Alpha – ‘Quant Alpha’ and ‘Quant Alpha Tech’ are Trade Marks of Quant Alpha – all rights reserved

DISCLAIMER – READ FULL DISCLAIMER HERE

All the information contained on this website is general in nature and does not constitute personal or investment advice. Quant Alpha produces algorithms and software only and does not trade or arrange any trading on your behalf. Quant Alpha will not accept liability for any loss or damage, including without limitation, any loss which may arise directly or indirectly from the use of, or reliance on: its algorithms; the information on this site; or information provided by its managers, partners or affiliates. You should seek independent financial advice and conduct your due diligence prior to acquiring any Quant Alpha technology. Quant Alpha is neither a registered investment advisor nor an investment advisory service and does not provide any recommendations to buy or sell particular financial products. 
Before engaging in any trading activities, you should understand the nature and extent of your rights and obligations and be aware of the risks involved. Don’t trade with money you can’t afford to lose. Your trading and investing decisions are entirely your own responsibility. All securities and financial product transactions involve risks. Where Quant Alpha provides hypothetical representations of what the technology has achieved in the past, this has been done with the greatest know-how, data and expert technology that is available, but still, Quant Alpha cannot guarantee that these results have any likelihood whatsoever of being achieved in future. Where records have been provided of how the software has performed on management’s own accounts, whilst these are an accurate and true record of what has taken place in the past, they are not necessarily indicative of future results – the future is as unknown to Quant Alpha management as it is to anyone else. The past performance of any trading system or methodology is not necessarily indicative of future results.