We are ready to demo our new experimental package for Algorithmic Trading,
flyingfox, which uses
reticulate to to bring Quantopian’s open source algorithmic trading Python library,
Zipline, to R. The
flyingfox library is part of our NEW Business Science Labs innovation lab, which is dedicated to bringing experimental packages to our followers early on so they can test them out and let us know what they think before they make their way to CRAN. This article includes a long-form code tutorial on how to perform backtest optimizations of trading algorithms via grid search and parallel processing. In this article, we’ll show you how to use the combination of
tibbletime (time-based extension of
furrr (a parallel-processing compliment to
Zipline in R) to develop a backtested trading algorithm that can be optimized via grid search and parallel processing. We are releasing this article as a compliment to the R/Finance Conference presentation “A Time Series Platform For The Tidyverse”, which Matt will present on Saturday (June 2nd, 2018). Enjoy!
New: Business Science Labs
I (Davis) am excited to introduce a new open source initiative called Business Science Labs. A lot of the experimental work we do is done behind the scenes, and much of it you don’t see early on. What you do see is a “refined” version of what we think you need based on our perception, which is not always reality. We aim to change this. Starting today, we have created Business Science Labs, which is aimed at bringing our experimental software to you earlier so you can test it out and let us know your thoughts!
Our first initiative is to bring Quantopian’s Open Source algorithmic trading Python library,
Zipline, to R via an experimental package called
flyingfox (built using the awesome
What We’re Going To Learn
Introducing Business Science Labs is exciting, but we really want to educate you on some new packages! In this tutorial, we are going to go over how to backtest algorithmic trading strategies using parallel processing and Quantopian’s Zipline infrastructure in R. You’ll gain exposure to a
furrr, and our experimental
flyingfox package. The general progression is:
tibbletime: What it is and why it’s essential to performing scalable time-based calculations in the
furrr: Why you need to know this package for speeding up code by processing
flyingfox: The story behind the package, and how you can use it to test algorithmic trading strategies
flyingfox: Putting it all together to perform parallelized algorithmic trading strategies and analyze time-based performance
Here’s an example of the grid search we perform to determine which are the best combinations of short and long moving averages for the stock symbol JPM (JP Morgan).
Here’s an example of the time series showing the order (buy/sell) points determined by the moving average crossovers, and the effect on the portfolio value.
Algorithmic Trading Strategies And Backtesting
Algorithmic trading is nothing new. Financial companies have been performing algorithmic trading for years as a way of attempting to “beat” the market. It can be very difficult to do, but some traders have successfully applied advanced algorithms to yield significant profits.
Using an algorithm to trade boils down to buying and selling. In the simplest case, when an algorithm detects an asset (a stock) is going to go higher, a buy order is placed. Conversely, when the algorithm detects that an asset is going to go lower, a sell order is placed. Positions are managed by buying and selling all or part of the portfolio of assets. To keep things simple, we’ll focus on just the full buy/sell orders.
One very basic method of algorithmic trading is using short and long moving averages to detect shifts in trend. The crossover is the point where a buy/sell order would take place. The figure below shows the price of Halliburton (symbol “HAL”), which a trader would have an initial position in of say 10,000 shares. In a hypothetical case, the trader could use a combination of a 20 day short moving average and a 150 day long moving average and look for buy/sell points at the crossovers. If the trader hypothetically sold his/her position in full on the sell and bought the position back in full, then the trader would stand to avoid a delta loss of approximately $5/share during the downswing, or $50,000.
Backtesting is a strategy that is used to detect how a trading strategy would have performed in the past. It’s impossible to know what the future will bring, but using trading strategies that work in the past helps to instill confidence in an algorithm.
Quantopian is a platform designed to enable anyone to develop algorithmic trading strategies. To help its community, Quantopian provides several open source tools. The one we’ll focus on is
Zipline for backtesting. There’s one downside: it’s only available in Python.
With the advent of the
reticulate package, which enables porting any Python library to R, we took it upon ourselves to test out the viability of porting
Zipline to R. Our experiment is called
RStudio Cloud Experiment Sandbox
In this code-based tutorial, we’ll use an experimental package called
flyingfox. It has several dependencies including Python that require setup time and effort. For those that want to test out
flyingfox quickly, we’ve created a FREE RStudio Cloud Sandbox for running experiments. You can access the Cloud Sandbox here for FREE: https://rstudio.cloud/project/38291
Packages Needed For Backtest Optimization
The meat of this code-tutorial is the section Backtest Optimization Using tibbletime + furrr + flyingfox . However, before we get to it, we’ll go over the three main packages used to do high-performance backtesting optimizations:
tibbletime: What it is, and why it’s essential to performing scalable time-based calculations in the
furrr: Why you need to know this package for speeding up code by processing
flyingfox: How to use it to test algorithmic trading strategies
Putting It All Together:
flyingfox for backtesting optimizations performed using parallel processing and grid search!
Install & Load Libraries
For this post, you’ll need to install development version of
If you are on windows, you should also install the development version of
Other packages you’ll need include
tidyverse. We’ll also load
tidyquant mainly for the
ggplot2 themes. We’ll install
ggrepel to repel overlapping plot labels. You can install these from CRAN using
We’ll cover how a few packages work before jumping into backtesting and optimizations.
tibbletime package is a cornerstone for future time series software development efforts at Business Science. We have major plans for this package. Here are some key benefits:
- Time periods down to milliseconds are supported
- Because this is a
tibbleunder the hood, we are able to leverage existing packages for analysis without reinventing the wheel
- Scalable grouped analysis is at your fingertips because of
collapse_by()and integration with
It’s best to learn now, and we’ll go over the basics along with a few commonly used functions:
First, let’s get some data. We’ll use the FANG data set that comes with
tibbletime, which includes stock prices for FB, AMZN, NFLX, and GOOG. We recommend using the
tidyquant package to get this or other stock data.
Next, you’ll need to convert this
tbl_df object to a
tbl_time object using the
Beautiful. Now we have a time-aware tibble. Let’s test out some functions. First, let’s take a look at
collapse_by(), which is used for grouped operations. We’ll collapse by “year”, and calculate the average price for each of the stocks.
Next, let’s take a look at
rollify(). Remember the chart of Halliburton prices at the beginning. It was created using
rollify(), which turns any function into a rolling function. Here’s the code for the chart. Notice how we create two rolling functions using
mean() and supplying the appropriate
Let’s check out
filter_time(), which enables easier subsetting of time-based indexes. Let’s redo the chart above, instead focusing in on sell and buy signals, which occur after February 2017. We can convert the previously stored
hal_ma_tbl to a
tbl_time object, group by the “key” column, and then filter using the time function format
filter_time("2017-03-01" ~ "end"). We then reuse the plotting code above.
We can use the
as_period() function to change the periodicity to a less granular level (e.g. going from daily to monthly). Here we convert the HAL share prices from daily periodicity to monthly periodicity.
Next, let’s check out a new package for parallel processing using
furrr package combines the awesome powers of
future for parallel processing with
purrr for iteration. Let’s break these up into pieces by
purrr package is used for iteration over a number of different types of generic objects in R, including vectors, lists, and tibbles. The main function used is
map(), which comes in several varieties (e.g.
map_df(), etc). Here’s a basic example to get the
class() of the columns of the
FANG_time variable. Using
map() iterates over the columns of the data frame returning a list containing the contents of the function applied to each column.
future package enables parallel processing. Here are a few important points:
futureis a unified interface for parallel programming in R.
You set a “plan” for how code should be executed, call
future()with an expression to evaluate, and call
value()to retrieve the value. The first
future()call sends off the code to another R process and is non-blocking so you can keep running R code in this session. It only blocks once you call
Global variables and packages are automatically identified and exported for you!
Now, the major point: If you’re familiar with
purrr, you can take advantage of
future parallel processing with
furrr = future + purrr
furrr takes the best parts of
purrr and combines it with the parallel processing capabilities of
future to create a powerful package with an interface instantly familiar to anyone who has used
purrr before. All you need to do is two things:
future_map()(or other compatible
purrr function has a compatible
furrr function. For example,
future_map_df(). Set a plan, and run
future_map_df() and that is all there
is to it!
furrr Example: Multiple Moving Averages
Say you would like to process not a single moving average but multiple moving averages for a given data set. We can create a custom function,
multi_roller(), that uses
map_dfc() to iteratively map a
mean() based on a sequence of windows. Here’s the function and how it works when a user supplies a data frame, a column in the data frame to perform moving averages, and a sequence of moving averages.
We can test the function out with the FB stock prices from FANG. We’ll ungroup, filter by FB, and select the important columns, then pass the data frame to the
multi_roller() function with
window = seq(10, 100, by = 10). Great, it’s working!
We can apply this
multi_roller() function to a nested data frame. Let’s try it on our
FANG_time data set. We’ll select the columns of interest (symbol, date, and adjusted), group by symbol, and use the
nest() function to get the data nested.
Next, we can perform a rowwise map using the combination of
map(). We apply
multi_roller() as an argument to
map() along with data (variable being mapped), and the additional static arguments, col and window.
Great, we have our moving averages. But…
What if instead of 10 moving averages, we had 500? This would take a really long time to run on many stocks. Solution: Parallelize with
There are two ways we could do this since there are two maps:
- Parallelize the
map()internal to the
- Parallelize the rowwise
map()applied to each symbol
We’ll choose the former (1) to show off
To make the
multi_roller_parallel() function, copy the
multi_roller() function and do 2 things:
plan("multiprocess")at the beginning
In the previous rowwise map, switch out
multi_roller_parallel() and change the
window = 2:500. Sit back and let the function run in parallel using each of your computer cores.
Bam! 500 moving averages run in parallel in fraction of the time it would take running in series.
We have one final package we need to demo prior to jumping into our Algorithmic Trading Backtest Optimization:
What is Quantopian?
Quantopian is a company that has setup a community-driven platform for everyone (from traders to home-gamers) enabling development of algorithmic trading strategies. The one downside is they only use Python.
What is Zipline?
Zipline is a Python module open-sourced by Quantopian to help traders back-test their trading algorithms. Here are some quick facts about Quantopian’s
Zipline Python module for backtesting algorithmic trading strategies:
It is used to develop and backtest financial algorithms using Python.
It includes an event-driven backtester (really good at preventing look-ahead bias)
Algorithms consist of two main functions:
initialize(): You write an
initialize()function that is called once at the beginning of the backtest. This sets up variables for use in the backtest, schedules functions to be called daily, monthly, etc, and let’s you set slippage or commission for the backtest.
handle_data(): You then write a
handle_data()function that is called once per day (or minute) that implements the trading logic. You can place orders, retrieve historical pricing data, record metrics for performance evalutation and more.
Extra facts: You can use any Python module inside the handle_data() function, so you have a lot of flexibility here.
What is reticulate?
The reticulate package from RStudio is an interface with Python. It smartly takes care of (most) conversion between R and Python objects.
Can you combine them?
Yes, and that’s exactly what we did. We used
reticulate to access the
Zipline Python module from R!
What is the benefit to R users?
What if you could write your
handle_data() functions in R utilizing any financial or time series R package for your analysis and then have them called from Python and
Introducing flyingfox: An R interface to Zipline
flyingfox integrates the
Zipline backtesting module in R! Further, it takes care of the overhead with creating the main infrastructure functions
handle_data() by enabling the user to set up:
fly_initialize(): R version of Zipline’s
fly_handle_data(): R version of Zipline’s
flyingfox takes care of passing these functions to Python and
Why “Flying Fox”?
Zipline just doesn’t quite make for a good hex sticker. A flying fox is a synonym for zipliners, and it’s hard to argue that this majestic animal wouldn’t create a killer hex sticker.
Getting Started With flyingfox: Moving Average Crossover
Let’s do a Moving Average Crossover example using the following strategy:
- Using JP Morgan (JPM) stock prices
- If the 20 day short-term moving average crosses above the 150 day long-term moving average, buy 100% into JP Morgan
- If 20 day crosses below the 150 day, sell all of the current JPM position
Setup can take a while and take up some computer space due to ingesting data (which is where
Zipline saves every major asset to your computer). We recommend one of two options:
No weight option (for people that just want to try it out): Use our
flyingfoxsandbox on RStudio Cloud. You can connect here: https://rstudio.cloud/project/38291
Heavy weight option (for people that want to expand and really test it): Follow the instructions on my GitHub page to
fly_ingest()data. The setup and data ingesting process are discussed here: https://github.com/DavisVaughan/flyingfox. Keep in mind that this is still a work in progress. We recommend doing the no weight option as a first start.
First, write the R function for
initialize(). It must take
context as an argument. This is where you store variables used later, which are accessed via
context$variable. We’ll store
context$i = 0L to initialize the tracking of days, and
context$asset = fly_symbol("JPM") to trade the JPM symbol. You can select any symbol that you’d like (provided Quantopian pulls it from Quandl).
Next, write a
This implements the crossover trading algorithm logic
In this example we also use
fly_data_history()to retrieve historical data each day for JP Morgan
fly_order_target_percent()to move to a new percentage amount invested in JP Morgan (if we order
1, we want to move to be 100% invested in JP Morgan, no matter where we were before)
fly_record()to store arbitrary metrics for review later
Run The Algorithm
Finally, run the algorithm from 2013-2016 using
If you got to this point, you’ve just successfully run a single backtest. Let’s review the performance output.
Reviewing The Performance
performance_time to see what the results show. It’s a
tbl_time time series data frame organized by the “date” column, and there is a ton of information. We’ll focus on:
- date: Time stamp for each point in the performance analysis
- JPM: This is the price of the asset
- short_mavg and long_mavg: These are our moving averages we are using for the buy/sell crossover signals
- portfolio_value: The value of the portfolio at each time point
- transactions: Transactions stored as a list column. The tibble contains a bunch of information that is useful in determining what happened. More information below.
First, let’s plot the asset (JPM) along with the short and long moving averages. We can see there are a few crossovers.
Next, we can investigate the transactions. Stored within the
performance_time output are transaction information as nested tibbles. We can get these values by flagging which time points contain tibbles and the filtering and unnesting. A transaction type can be determined if the “amount” of the transaction (number of shares bought or sold) is positive or negative.
Finally, we can visualize the results using
ggplot2. We can see that the ending portfolio value is just under $11.5K.
Last, let’s use
tibbletime to see what happened to our portfolio towards the end. We’ll use the portfolio value as a proxy for the stock price, visualizing the crossover of the 20 and 150-day moving averages of the portfolio. Note that the actual algorithm is run with moving averages based on the adjusted stock price, not the portfolio value.
Now for the main course: Optimizing our algorithm using the backtested performance. To do so, we’ll combine what we learned from our three packages:
Let’s say we want to use backtesting to find the optimal combination or several best combinations of short and long term moving averages for our strategy. We can do this using Cartesian Grid Search, which is simply creating a combination of all of the possible “hyperparameters” (parameters we wish to adjust). Recognizing that running multiple backtests can take some time, we’ll parallelize the operation too.
Before we can do grid search, we need to adjust our
fly_handle_data() function to enable our parameters to be adjusted. The two parameters we are concerned with are the short and long moving averages. We’ll add these as arguments of a new function
Making The Grid
Next, we can create a grid of values from a
list() containing the hyperparameter values. We can turn this into a cross product as a
tibble using the
Now that we have the hyperparameters, let’s create a new column with the function we wish to run. We’ll use the
partial() function to partially fill the function with the hyper parameters.
Running Grid Search In Parallel Using furrr
Now for the Grid Search. We use the
future_map() function to process in parallel. Make sure to setup a
plan() first. The following function runs the
fly_run_algorithm() for each
fly_handle_data() function stored in the “f” column.
Inspecting The Backtest Performance Results
The performance results are stored in the “results” column as
tbl_time objects. We can examine the first result.
We can also get the final portfolio value using a combination of
We can turn this into a function and map it to all of the columns to obtain the “final_portfolio_value” for each of the grid search combinations.
Visualizing The Backtest Performance Results
Now let’s visualize the results to see which combinations of short and long moving averages maximize the portfolio value. It’s clear that short >= 60 days and long >= 200 days maximize the return. But, why?
Let’s get the transaction information (buy/sell) by unnesting the results and determining which transactions are buys and sells.
Finally, we can visualize the portfolio value over time for each combination of short and long moving averages. By plotting the buy/sell transactions, we can see the effect on a stock with a bullish trend. The portfolios with the optimal performance are those that were bought and held rather than sold using the moving average crossover. For this particular stock, the benefit of downside protection via the moving average crossover costs the portfolio during the bullish uptrend.
We’ve covered a lot of ground in this article. Congrats if you’ve made it through. You’ve now been exposed to three cool packages:
tibbletime: For time-series in the tidyverse
furrr: Our parallel processing extension to
flyingfox: Our experimental package brought to you as part of our Business Science Labs initiative
Further, you’ve seen how to apply all three of these packages to perform grid search backtest optimization of your trading algorithm.
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