Successful Algorithmic Trading + source

Our experience in algorithmic trading has been over ten years. We have seen many trading errors.

We found that hard work, discipline, and a scientific approach were the keys to quantitative trading profitability after a lot of trial-and-error.

In Successful Algorithmic Trading We’ll show you how to identify profitable strategies, backtest them, lower transaction costs, and execute trades efficiently in an automated way.

You can use these ideas no matter where you are at the beginning of your quantitative trading career to create a profitable algorithmic trading company.

More than 300 pages of algorithmic trading strategies
How to set up a Python end-to-end equity backtester
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It’s hard to develop profitable trading strategies. It is really hard.

Despite all these benefits we wouldn’t want you to get the wrong idea and think developing an algorithmic trading system is easy. You could not be more wrong. Trading algos is not the way to make quick money.

If you can break the problem down into smaller, manageable parts, and work consistently to improve your system each day, it can become very successful.

It is difficult to consistently make money trading at first.

We have a habit of building a strategy pipeline that constantly gives us new ideas for trading strategies to test. It doesn’t really matter if a strategy fails to perform because we have many more options – and so will yours.

To achieve algorithmic trading profitability, slow and steady progress in research, testing, and execution is key.

With a disciplined approach and a strong commitment, you’ll see success sooner than you think.

What if your trading skills are not up to par?

We weren’t even aware of these things when we started. We didn’t know what stop loss was, market orders from limit order, buy-side and sell-side. However, we have practiced algorithmic trading over the past eight years.

You are well equipped to learn about quant finance and trading. Although we are not experts in our field, we have worked on profitable trading strategies and would be happy to share them with you.

Imagine that you are an expert on a particular topic. We bet you don’t know as much about it. It takes discipline, practice and hard work to be an expert. The same goes for forming profitable trading strategies.

We know of no one who is more successful in algorithmic trading than someone who didn’t know much about the markets.

You don’t know much about algorithmic trading, so push yourself to learn more and be better.

What topics are included in the book?
Strategy Research

You will learn how to evaluate new trading strategies for your portfolio and how to spot them.

Securities Master Databases

We’ll show you how create a secure securities master database to keep all of your asset pricing information.

Successful Backtesting

Before trading our strategy ideas, we will rigorously backtest them using the scientific method.

Performance measurement

The strategies will be extensively tested against industry-grade performance metrics.

Statistical Testing

To test for momentum or mean reversion, we will use time series statistical techniques.

Mean-Reversion Strategies

We will discuss profitable means-reverting strategies for futures and equities – which you can trade.

Risk Management

Learn about investment-grade risk management techniques like Variance-at Risk (VaR).

Position Sizing

We will be discussing position sizing as well as money management techniques, such the Kelly Criterion.

Execution Systems

Based on our trading portfolio system, we will design and deploy an automated execution system.

What technical skills will you learn?
Python Scientific Resources

This course will introduce you to the Python scientific toolkit, which is heavily used for quantitative trading. We will use SciPy and pandas as well as NumPy and IPython.

Historical Data

Learn how to get financial data from paid and free sources. We will address futures and equity data, cleaning it up and creating ongoing futures contracts.

Backtesting Research

You’ll learn how to backtest strategy performance with pandas and calculate quantities, such as the Sharpe ratio, maximum drawdown, drawdown length and average win/loss.

Parameter Optimisation

This course will teach you how to mathematically optimize a strategy by using parameter sensitivity analysis. You can also visually inspect the results. This will be done using pandas and matplotlib, IPython.

Advanced Trading Strategies

Learn about intraday equities pair trading and predictive classifiers. Scikit-learn will be used to perform regression, random forest ensembles, and non-linear SVM.

Strategy Execution

To trade you will connect to Interactive Brokers API via Python. Calculate realistic transaction costs and account for them in your performance metrics.

Frequently Asked Question
Find out more about us.

QuantStart.com hosts over 200 articles covering quant trading and quant careers. To learn more about our trading strategies and methodology, you can browse the archives.

Which package should I buy?

It mostly depends on what your budget is. You get the book with all extras source If you are looking for code, code is the best. But the book also contains a lot of code snippets to help with your quant trading process.

What if you aren’t happy with the book?

We think you’ll find them, however. Successful Algorithmic Trading It is an excellent resource for quantitative trading education. We also believe you can return the book without questions for a full refund.

We can be reached

You can! Please let us know if you have any further questions. Please take a look at this page.

Do you want a hardcopy?

No. No. “Book + Software” option.

Are you going to need a math degree?

Most of the book is easy to follow without needing to know complicated mathematics. Some sections of the book require basic algebra and linear calculus.

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