### Getting Started in Python

Dr. Antony Jackson is lecturer in Financial Economics in the School of Economics at University of East Anglia. He talks about statistical significance in algorithmic trading. Antony is an active researcher of algorithmic trading strategies and finished 2nd in Quantiacs' recent algorithmic trading competition. You can find the example code on Github.

You are ready to write your first trading algorithm, the only thing you are missing is a great trading idea? Henry Carstens is quant and author of the brand new book '101 Trading Ideas'. He will talk about the creative part of trading algorithm development. You can find the example code on Github.

### Practical Tips For Algorithmic Trading using Machine Learning

Evgeny “Jenia” Mozgunov (Caltech) won our Q3 algorithmic trading competition. Jenia used machine learning tools to write his trading algorithm that now trades an initial \$1M investment. He is talking about his approach and his main learnings. Jenia's algorithm currently has a live Sharpe Ratio of 2.66.

In this webinar Ernie Chan talks about the main difference between algorithmic and discretionary trading - the possibility of backtesting a strategy. However, a poorly conducted backtest will give rise to false positives. Ernie discusses typical pitfalls and the many ways in which false positives can be avoided.

### Introduction to the Quantiacs Toolbox

This is a quick overview over the functionality of the Quantiacs Toolbox. It's free and open source and enables you to build and test algorithmic trading systems. The supported languages are Matlab and Python.

### From Zero to System

This is a step-by-step guid for how to build a basic algorithmic trading system. It should help you as a starting point so that you learn how to implement your own trading ideas. If you are new to algorithmic trading you might want to watch the Basic 'Concepts of Quantitative Trading' first.

### Basic Concepts of Quantitative Trading

As a beginner in algorithmic trading learning about a few basic concepts can be really helpful starting point. In this video we talk about concepts like the Relative Strength Index (RSI), Sharpe Ratio (SR), and the Average True Range (ATR).

### Machine Learning for Financial Forecasting

Guest speaker S. Burc Eryilmaz talks about how he approached the problem of financial forecasting using machine learning tools. Burc took the machine learning class of Andrew Ng at Stanford. He used the Quantiacs framework and data to model trading algorithms as a class project.

### NYC Quant Club: Quant Trading With Futures

Algorithmic Trading and Futures: Learn the basics about Futures, definitions, mechanics, and how to trade them. Martin from Quantiacs explains Futures starting with the definition and ending with details about how to trade them using the Quantiacs toolbox.