Arne  Denesik

Arne Denesik

1603494000

Algorithmic Trading with Technical Indicators in R

Feature engineering is one of the fun, creative, and essential steps in machine learning. It transforms raw data into a form that very meaningful information for a model to forecast the future. The predictability of a model relies on good features, which in turn relies on your domain knowledge.

Many experienced stock market traders who evaluate trading rules or charts have already engaged in some forms of feature engineering — whether they realized it or not. For example, a moving average is a feature that characterizes the movement of a stock price. All the technical indicators (RSI, MACD, stochastic oscillators, Bollinger Bands, etc.) are some forms of features too. These features can be fed into a machine learning model, or used as trading signals. There can be hundreds, if not thousands, of trading strategies to capture the market anomalies or predict future trends.

In this post I will walk you gently to build your algorithmic trading code in R. R has several powerful quantitative finance libraries because of its long development history including QuantmodTTRPerformanceAnalytics. If you are new to algorithmic trading, you will be ready to start your algorithmic trading. The code github is made available in the end for download.

(0) The Four-Decade Debates on Market Efficiency

_As we discuss the efficacy of algorithmic trading or technical rules, the iconic debates on the “efficiency market hypothesis” (EMH) always come to our minds. Although this article does not plan to address the debate, it is worthwhile to mention it here. It is believed that prices should already fully reflect all available information. There is no way to “beat the market” to obtain abnormal returns. This is the famous “”. According to EMH, prices already reflect at least all past publicly available information — so-called the weak form; or prices change __instantly _to reflect all publicly available information — so-called the semi-strong form; or prices have also reflected any hidden insider information — so-called the strong form. Eugene Fama, the 2013 Nobel Prize winner, believes anomalies are consistent with rational pricing.

Do all traders subscribe to the EMH? Not really. Many traders believe there exist opportunities that prices are mis-priced and they can obtain abnormal returns. An “anomaly” is the situation that a price does not fully reflect all the public information, thus provides a trading opportunity. Although “beating the market” is something many novice or experienced traders try to do, few actually succeed. Robert Shiller –also a 2013 Nobel Prize winner — believes that markets are irrational and subject to behavioral biases in investors’ expectations. If that is the case, investment performance can be improved by advanced predictive models that are always in place or yet to come. Readers who are interested in the philosophical debates and empirical verifications, please see my post “Stock Market Anomalies” and “Stock Market Anomaly Detection” Are Two Different Things.

Okey. Now is time to enjoy the coding for algorithmic trading.

#stock-market #technical-analysis #algorithmic-trading #data-analysis

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Algorithmic Trading with Technical Indicators in R
August  Larson

August Larson

1624372980

Algorithmic Trading with Williams %R in Python

Learn to build a killer trading strategy with a powerful technical indicator in python

Introduction

While having a look at the list of most popular momentum indicators that consists of the Relative Strength Index, and the Stochastic Oscillator, the one we are going to discuss today also joins the list when considering its usage and efficiency in the real world market. It’s none other than the Williams %R.

In this article, we are going to explore what Williams %R is all about, the math behind this indicator, and how a trading strategy based on it can be built with the help of python. As a bonus step, we will compare the returns of our Williams %R strategy returns with the returns of SPY ETF (an ETF specifically designed to track the movement of the S&P 500 Index) to get an idea of how well our strategy performs in the real-world market and can be considered as a step to evaluate the strategy. Considering your curiosity piqued, let’s dive into the article!

#python #algorithmic trading with williams %r in python #algorithmic trading with williams %r #algorithmic trading #williams %r

How I Made a 65% ROI with this Boeing Trading Algorithm

Since the market crashed in March of 2020 the rebound has been swift and irrational.

Boeing, for example, is in many ways worse off than it was in March.

It’s clear air travel has plummeted and that airlines will be impacted for

years. Where will airlines get the money to purchase planes?

An example of one headline in April: “Boeing customers cancel staggering 150 Max plane orders”.

Buy the dip?

One thing I’ve noticed is that since the end of March you can

basically just buy every dip and expect a pop, selling the next day. I

mentioned this to a friend on Friday and decided to backtest it.

Well, sure enough it works!

I’ll mention I made one modification. Originally I wrote the system like so:

1. Check if Boeing is down more than 3% 15 minutes from close

2. If yes, buy with 100% of portfolio

3. The next day, 15 minutes from open liquidate the portfolio.

This worked OK. Great actually! It returned about 25%. But want to know what really kicked it up a notch?

Instead of just selling the next day, I only sell if the position is

sitting at a realized gain. So e.g. if the next day its flat or drops

another 1%, don’t sell it, just keep holding on until its up and THEN

sell. Of course, this is completely insane and you would have to expect

the market to only go up, but that’s what has been happening.

Guess what? This simple system returned a whopping 65% in two-ish months. Yeah, I know, crazy.

Check out the backtest screenshot:

And here are the raw trading logs for those that want to see the dates the trades were made:

Here is the code!

Before we look at the code, I’ll just mention here are the details of the backtest:

  • start with 100k in cash

  • start at April first and go until last Friday (June 19th 2020)

  • end up with about 165k or a 65% return.

I wrote this little script on Quant Connect. The screenshot at the

top of the page is the backtest result, and the code below is everything you need to try this out.

Note the place I mentioned in the code you should comment if you want this to be a little bit less insane.

#algorithmic-trading #trading #python #algorithms #trading-algorithms

Beth  Nabimanya

Beth Nabimanya

1624867080

Algorithm trading backtest and optimization examples

Algorithm trading backtest and optimization examples

Algorithmic trading backtests

Algorithm trading backtest and optimization examples.

xbtusd-vanila-market-making-backtest-hedge

xbtusd-vanila-market-making-backtest-hedge

#algorithms #optimization examples #algorithm trading backtest #algorithm #trading backtest

Implementing a Trading Algorithm with R

This story explains how to implement the moving average trading algorithm with R. If you’re interested in setting up your automated trading pipeline, you should first read this article. This story is a purely technical guide focusing on programming and statistics, not financial advice.

Throughout this story, we will build an R function which takes historical stock data and arbitrary threshold as…

#programming #statistics #algorithmic-trading #trading #data-science #algorithms

Arne  Denesik

Arne Denesik

1603494000

Algorithmic Trading with Technical Indicators in R

Feature engineering is one of the fun, creative, and essential steps in machine learning. It transforms raw data into a form that very meaningful information for a model to forecast the future. The predictability of a model relies on good features, which in turn relies on your domain knowledge.

Many experienced stock market traders who evaluate trading rules or charts have already engaged in some forms of feature engineering — whether they realized it or not. For example, a moving average is a feature that characterizes the movement of a stock price. All the technical indicators (RSI, MACD, stochastic oscillators, Bollinger Bands, etc.) are some forms of features too. These features can be fed into a machine learning model, or used as trading signals. There can be hundreds, if not thousands, of trading strategies to capture the market anomalies or predict future trends.

In this post I will walk you gently to build your algorithmic trading code in R. R has several powerful quantitative finance libraries because of its long development history including QuantmodTTRPerformanceAnalytics. If you are new to algorithmic trading, you will be ready to start your algorithmic trading. The code github is made available in the end for download.

(0) The Four-Decade Debates on Market Efficiency

_As we discuss the efficacy of algorithmic trading or technical rules, the iconic debates on the “efficiency market hypothesis” (EMH) always come to our minds. Although this article does not plan to address the debate, it is worthwhile to mention it here. It is believed that prices should already fully reflect all available information. There is no way to “beat the market” to obtain abnormal returns. This is the famous “”. According to EMH, prices already reflect at least all past publicly available information — so-called the weak form; or prices change __instantly _to reflect all publicly available information — so-called the semi-strong form; or prices have also reflected any hidden insider information — so-called the strong form. Eugene Fama, the 2013 Nobel Prize winner, believes anomalies are consistent with rational pricing.

Do all traders subscribe to the EMH? Not really. Many traders believe there exist opportunities that prices are mis-priced and they can obtain abnormal returns. An “anomaly” is the situation that a price does not fully reflect all the public information, thus provides a trading opportunity. Although “beating the market” is something many novice or experienced traders try to do, few actually succeed. Robert Shiller –also a 2013 Nobel Prize winner — believes that markets are irrational and subject to behavioral biases in investors’ expectations. If that is the case, investment performance can be improved by advanced predictive models that are always in place or yet to come. Readers who are interested in the philosophical debates and empirical verifications, please see my post “Stock Market Anomalies” and “Stock Market Anomaly Detection” Are Two Different Things.

Okey. Now is time to enjoy the coding for algorithmic trading.

#stock-market #technical-analysis #algorithmic-trading #data-analysis