Successful Algorithmic Trading + source – Quant Start

Salepage link: At HERE. Archive: http://archive.is/2HqxI

Struggling To Make Profitable Algo Trading Strategies?

You didn’t set out to lose money when trading, but lots of small mistakes along the way meant that your strategy performance in backtests didn’t pan out when you went live.

We’ve been involved in algorithmic trading for over eight years and in that time we’ve seen some big trading mistakes.

After a lot of trial and error, we eventually discovered that hard work, discipline and a scientific approach are the key to profitability with quantitative trading.

In Successful Algorithmic Trading we’ll teach you a process to identify profitable strategies from the outset, backtest them, reduce your transaction costs and efficiently execute your trades in a fully automated manner.

No matter how far along you are in your quantitative trading career, you can apply these ideas to make a profitable algorithmic trading business. Buy Now

• 200+ pages of algorithmic trading techniques
• How to implement an end-to-end equities backtester with Python libraries
• Download the Table Of Contents
• Instant PDF ebook download – no waiting for delivery
• Lifetime no-quibble 100% money back guarantee – no risk to you!
• Download a Sample Chapter

Creating profitable trading strategies is hard. 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. Nothing could be further from the truth. There is no path to easy riches with algo trading.

However, if you break down the problem, into small easy-to-handle constituent parts and make consistent progress on improving your system every day it can eventually become very successful.

At the beginning it is a struggle to make money consistently with trading.

Now we’ve built up the habit of creating a strategy pipeline which constantly provides us with new trading strategy ideas with which to test. It doesn’t matter if a strategy begins to perform poorly because we have plenty more to choose from – and so will you.

Slow consistent progress on research, testing and execution is the key to achieving algorithmic trading profitability.

Make a commitment to work hard on your strategy components, with a disciplined approach, and you will see success much sooner than you expect.

What if you’re not an expert at algorithmic trading?

Actually, neither were we when we first started! We didn’t know market orders from limit orders, the buy-side from sell-side or what a stop loss was! But we have practised over the last eight years and have learned a huge amount about algorithmic trading in the process.

It is well within your capability to learn what we know about quant finance and trading. We’re certainly not the top of our field, but we have been involved in the development of profitable trading strategies and are extremely keen to show you how to do the same.

We imagine that there is a topic you know a great deal about and we bet there are many who know less about the area than you do. Being an expert comes through practice, discipline and hard work. So does forming a consistent set of profitable algorithmic trading strategies.

Every successful person we know in algorithmic trading started before they knew much about the markets.

Use the fact that you aren’t yet comfortable with algorithmic trading to push yourself harder and learn to become an expert.

What Topics Are Included In The Book?

Strategy Research

You’ll learn how to find new trading strategy ideas and objectively assess them for your portfolio.

Securities Master Databases

We’ll teach you how to create a robust securities master database to store all of your asset pricing information.

Successful Backtesting

We will apply the scientific method to rigourously backtest our strategy ideas before we consider trading them.

Performance Measurement

Our strategies will be tested extensively against industry-grade performance measures.

Statistical Testing

We will utilise time series statistical methods to test for mean reversion and momentum.

Mean-Reversion Strategies

We’ll discuss profitable mean-reverting strategy templates for equities and futures – which you can trade yourself.

Risk Management

You’ll learn about investment grade risk management techniques such as Variance-at-Risk (VaR)

Position Sizing

We will extensively discuss position sizing and money management techniques such as the Kelly Criterion.

Execution Systems

We will create and deploy a robust automated execution system based on our trading portfolio system.

What Technical Skills Will You Learn?

Python Scientific Tools

You will be introduced to the Python scientific toolset, which is used heavily in quantitative trading. We will make use of NumPy, SciPy, pandas, scikit-learn and IPython.

Historical Data

You will learn how to obtain financial data from both free and paid sources. We will tackle equities and futures data, by cleaning it and creating continuous futures contracts.

Backtesting Research

You will learn how to backtest strategy performance using pandas and calculate quantities such as the Sharpe Ratio, max drawdown, drawdown duration and avg win/loss.

Parameter Optimisation Analysis

You will learn how to mathematically optimise a strategy using parameter sensitivity analysis and visually inspect the results. For this we will use pandas and matplotlib with IPython.

Advanced Trading Strategies

You will learn about predictive classifiers and intraday equities pair-trading. We’ll use scikit-learn to perform regression, random forest ensembles and non-linear SVM.

Strategy Execution

You will connect to the Interactive Brokers API with Python to trade. You’ll calculate realistic transaction costs, accounting for them in your performance metrics.