Algorithmic Trading Python Algo Trading Quantitative Finance

Algorithmic Trading Python Algo Trading Quantitative Finance

Start speaking the language of Hedge Funds in quantitative finance. Build Multiple Machine Learning Models in Python.

Start Learning Trading  Now =>  Algorithmic Trading Python Algo Trading Quantitative Finance

What you'll learn

• Transform Yourself Into a Stock Market Quant in Less Than 1 Hour
• Learn & Build Downside Protection & Drawdown Control Systems Save Your Capital
• Have complete understanding and confidence when investing in the Stock Market.
• Learn About Portfolio Construction using Machine Learning
• Learn How to Use The Value Factor to Outperform the Market and Get Higher Returns
• How to build quantitative trading models.
• Apply best practices and techniques to build better stock portfolios.
• Learn About Diversification, Tracking Error, and Discipline
• Learn About Cutting Edge Quantitative Investing Strategies
• Learn How to Use The Momentum Factor to Outperform the Market and Get Higher Returns
• Learn How to Use The Quality Factor to Outperform the Market and Get Higher Returns
• Learn How to Use The Size Factor to Outperform the Market and Get Higher Returns
• Learn How to Use The Trend Factor to Outperform the Market and Get Higher Returns
• Learn How to Use Advanced Machine learning Techniques to Outperform the Market and Get Higher Returns
• Learn How to Use Advanced Machine learning Techniques to Build Better More Robust Portfolios
• Examples of Superior Trading Models with Full Code Shared

Algorithmic trading is a quantitative investment strategy that uses software code to try to take advantage of things that aren't normal on the market, to lower risk by effectively and efficiently diversify a portfolio, and ultimately making more money.

In this course, we discuss advanced concepts after building our students with strong foundational knowledge about the basic building blocks of algorithmic trading in quantitative finance, Factor Investing. Although Factor Investing topics will cover the first half of the course, they are crucial in understanding advanced concepts. Moreover, they form a building block to be optimized using machine learning algorithms for quantitative trading.

We share Python code with out students to help them build strong financial machine-learning algorithms using cutting-edge research in Graph Theory, Hierarchical Clustering, Cross Validation with K-folds, Šidák correction, and deep learning regression on top of proven factor investing strategies (or smart beta) portfolios, such as Value, Momentum, Size, Quality, Profitability, Attention, Trend, TERM, and more.

Quant strategies, as they are usually called, are made to find and target the factors that make some financial assets do better than others. This is done by creating models that explain the factors, back-testing the models to see which ones work, and then putting strategies based on a set of rules that find and screen assets for a portfolio. Highly experienced quants run this process, which is much more complicated than can be explained in a short paragraph (quantitative analysts). In short, quants try to find the most critical factors and plan how to get the most out of them. Because of this, this way of investing is also called "factor investing."

Quat strategies are made, tested, and put into action with the help of computers. These strategies are based on deep analysis. Because of this and the fact that they follow the rules, they are pretty objective in their search for alpha returns. Before computers, it took a lot of work to implement quantitative strategies because they required a lot of information and data.

In many ways, quantitative strategies don't follow the theory of "efficient markets," on which they are based. Pricing models like the capital asset pricing model (CAPM) say that the expected return on an investment depends on its relationship to the market and the market alone. This is because financial theory assumes that investors price securities correctly. The standard CAPM model doesn't explain why some stocks do better than others. To measure expected returns, expanded versions of CAPM now consider how exposed a stock is to different factors.

In this course, we'll answer questions and learn powerful techniques in depth.

What are factors?

A factor is a property shared by a group of financial assets and explains why these investments have different risk and return metrics than the market. Value, small size, low volatility, quality, high yield, liquidity, and momentum are some of the most common things investors look for. In the past, these factors earned a long-term risk premium, and many can be found in multiple sectors and asset classes, such as equity, bond, commodity, and currency markets. The value factor is the tendency for cheap stocks to do better than expensive ones, while the size factor is why small-cap stocks do better over the long term than large-cap stocks (one explanation is the relative lack of information about small stocks available to traders). On the other hand, low-volatility stocks are often used to limit risks, though many investors say they tend to give higher returns, especially when the market is down. The momentum factor shows why stocks that are going up tend to keep going up, at least in the short term. Other factors include quality and high yield, but quants keep finding out more as they study the market. It's essential to keep in mind that not all factors earn a risk premium over time. For example, the risk premium with momentum and growth stocks tends to be short-lived. Investors can focus on a single factor or build portfolios with several factors. Instead of diversifying across asset classes, as they have always done, many investors are starting to spread their money across different factors. This is because different asset classes are more linked than was thought before, while some factors are not linked to others and, at least in theory, offer better diversification benefits.

What is equity factor investing?

Equity factor investing is a way to look at companies systematically. Companies are ranked against each other based on how attractive they are based on one or more factors. Companies with better rankings may offer more chances for alpha.

Types of quant strategies

Quant strategies can be put together differently, each of which has a different way of getting the factor risk premium. Smart beta and risk premia are the most common quantitative strategies. Smart beta is a long-only strategy based on indices built in different ways and lean toward one or more factors. This can be done by reweighting stock-based benchmark indices like the S&P 500 Index, the Russell 2000, or the MSCI index. A smart beta version of the index aims to get better risk-adjusted returns than the benchmark. One way to do this is to reweight the benchmark to give more weight to stocks with low volatility. The benchmark index, which stands for exposure to the stock market, is a cheap and passive way to get the equity risk premium or the extra return over risk-free assets like government bonds.

The matching smart beta fund captures most of the risk premium of the equity market as well as the risk premium of the factor it is trying to target. Smart beta funds, sometimes called "custom indices," can also be built from the bottom up. For example, a basket of high-yielding or high-quality assets could be used to make a smart beta fund. Stocks are chosen based on the rules of the strategy, so smart-beta funds are clear and follow the rules to the letter. These passive indices, which have a small amount of active management, are becoming popular alternatives to mutual funds because they are cheap ways to get exposure to risk factors. Smart beta funds have a strong beta component, which means that they are closely related to the market and that their performance is heavily influenced by how the market moves.

On the other hand, risk premia strategies try to make absolute returns by trading long and short on factors. This means they can eliminate a lot of the beta factor and offer positive returns even when the market is decreasing. Risk premia strategies, like hedge funds, can use tools like leverage and derivatives to boost returns or protect against certain risks. For example, a long-short value strategy could involve buying the least expensive stocks in a portfolio and selling the most expensive ones short (on a price-to-book value basis). This means there are more chances to get alpha returns since a smart beta fund that only buys undervalued stocks can only try to get extra returns by buying undervalued stocks. Risk premia can also be made by selling short overpriced stocks, like getting the risk premium from both sides of the same coin. Risk premia can also eliminate a lot of the risks that come with being exposed to the market, unlike smart beta funds, which are heavily affected by changes in the market. One problem with shorting is that it has high transaction costs, partly because it requires borrowing assets. The longer a short position is held open, the more these costs add up. Also, the prices of shorting small-cap stocks are higher than those of shorting large-cap stocks. This means that when a long-short risk premia strategy is used, the benefits of the size factor are somewhat lessened.

Accessibility of Quant Strategies

Smart beta indices are easy to access for most investors, including individual retail investors. This is mostly because they can be sold as exchange-traded funds (ETFs). Large institutional clients have more choices (ETFs). Risk premia strategies are more complex to get into than smart beta funds but easier than hedge funds. So far, the primary market for risk premia has been the typical hedge fund client, who is looking for cheaper and more open ways to get absolute returns from factors. Long-short risk premia products can't be sold as ETFs, which means that individual investors can't buy them directly – at least for now. In a report on the outlook for 2020, the professional services firm PwC said that factor investing would move from active managers to sophisticated institutional passive investors and then to the mass market. The growth of passive strategies would also be driven by factor investing. Most asset managers agree that quantitative strategies can work well with traditional fund management models instead of replacing them completely. In a 2016 report, FTSE Russell said that nearly half of the asset owners it had surveyed said they were now looking to combinations of factor strategies to help them reach their future asset allocation goals. Disclaimer: This course is only meant to teach and give information. No specific investments, like stocks or mutual funds, will be suggested because only you know what is best for your portfolio and how comfortable you are with risk and volatility. For specific advice, talk to a professional. The course is only for learning, and the teacher is not responsible for any direct or indirect loss or damage.

Who should take this course:

By the end of the course, a beginner, new, or traditional investor who wants to learn about the latest tools in the field of finance will learn practical things about investing in stocks and become fluent in quantitative finance.

Great for investors with more experience who want to improve their skills, gain new insights, or feel more confident about investing in stocks.