Jim Simons, born in 1938, is often referred to as “the most successful hedge fund manager of all time” and the “greatest investor on Wall Street”. The Financial Times named him “the world’s smartest billionaire” in 2006. Simons is the founder of Renaissance Technologies, a quantitative trading hedge fund based out of New York, that boasts an incredible 66.1% average annual return since 1988. Simons’ approach to the markets is fully systematic and there is so much to glean from his work as Jim Simons launched the quant revolution. This article will examine Simons’ many accomplishments and a few takeaways that can help us become better system traders.
If you are not interested in his origins and rather skip ahead to the systematic trading parts choose below
Simons began as a mathematician and arguably had a full career before leaving to start Renaissance Technologies. He studied mathematics at Massachusetts Institute of Technology (MIT) and later got his Ph.D. from the University of California, Berkeley.
Simons put his mathematics skills to the test working with the National Security Agency (NSA) to break codes during the Cold War while simultaneously teaching at MIT and later Harvard University. After his public opposition to the Vietnam War, Simons was forced out but later appointed chairman of the math department at Stony Brook University.
Simons is known for his studies in pattern recognition and developed the Chern-Simons form with Shing-Shen Chern and is credited with contributions toward the development of String Theory. His theoretical framework combined geometry, topology, and quantum field theory.
Simons has formally received recognition in mathematics, geometry, and topology before shifting his focus to finance in the late 1970s
AMS Oswald Veblen Prize in Geometry 1976
Elected to National Academy of Sciences 2014
The Greatest Trader on Wall Street
Jim Simons founded Renaissance Technologies in 1982 as a 40-year old retired mathematics professor. However, he left his job/academia to start his first hedge fund Monemetrics in 1978. This fund was moderately successful and employed both fundamental and technical approaches to the market, but Simons felt “gut wrenched” by the emotional swings.
Simons decided to use a purely systematic approach to avoid emotional rollercoasters and avoid common trading biases that trip up most traders. Simons staffed the new fund, Renaissance Technologies, with mathematicians, computer scientists, and physicists to pioneer a new approach to algorithmic trading.
Since 1988, Jim Simons’ Renaissance Technologies flagship fund, the Medallion Fund, has returned an average of 66.1% per year which shatters any other publicly available returns over the same horizon. Later I will dissect what little information is available about Simons’ (and RenTec) strategies, approach and success. Skip ahead
Simons is quoted as saying his best algorithm has always been
“you get smart people together. You give them a lot of freedom. Create an atmosphere where everyone talks to everyone else. They’re not hiding in a corner with their own little thing. They talk to everybody else. And you provide the best infrastructure, the best computers and so on that people can work with. And make everyone partners. So that was the model that we used in Renaissance. So we would bring in smart folks and they didn’t know anything about finance, but they learned.”
Philanthropist
Jim Simons has given over $2.7 billion to philanthropic causes ranging from education and health to scientific research. In 1994, Simons and his wife Marilyn Hawrys Simons co-founded the Simons Foundation which later established the Simons Foundation Autism Research Initiative in 2003.
This foundation founded Math for America in 2004 and Simons later doubled the initial $25 million pledge in 2006.
Simons’ alma maters and former academic employers have been benefactors from Simons’ trading successes with large contributions to University of California Berkley, MIT and Stony Brook. His most recent contributions have been aimed to advance computational science and mathematics.
Jim Simons Renaissance Technologies Medallion Fund
Medallion Fund Beginnings
Simons started in the late 1970s and was met with some initial success. However, Simons questions whether the early success was more luck than skill. Primarily focused on commodity futures using fundamental and technical analysis, Simons never achieved the emotional clarity or systematic approach to have full trust.
In 1988, Simons set up the Medallion Fund with a focus on huge amounts of data diversifying across timeframe, asset class, and pattern. Adding the stock market how Jim Simons and team gained additional success. However, the big success did not come until the early 1990s when he brought Bob Mercer and Peter Brown on board from IBM.
Mercer was a large donor in the 2016 US Presidential election (Trump) and eventually left the firm in 2017 after political differences.
Medallion Fund Fees
Most hedge funds charge “2 and 20” which is shorthand for an annual 2% management fee and a 20% rake on performance. Simons knew he had something special and offered investors “5 and 20” in the early days of Medallion but has since moved toward a “5 and 44” structure. This %5 management fee and 44% profit take may be the most aggressive fees in the industry, but Simons and the Medallion Fund have earned them. Due to the large fees, the net returns are more in line with a 39.1% average annual return instead of the earlier stated 66.1%. However, 39.1% is still remarkable with such a large capital base.
Now most of the investors and owners in the fund are employees as Medallion takes no outside capital.
Assuming the net returns column over the most recent 20-year span then an investor would have experienced the growth of a $1,000 investment into a whopping $906,933.26.
Medallion Fund vs. S&P500
Assuming the past 20 years S&P 500 returns then the same $1,000 invested in the S&P500 would return an impressive $2,039.12. This is a far cry (about 500x smaller) than the return generated by the Medallion fund. There is beating the market, capturing alpha, and then the universe that Medallion and Simons are from. Here are the side-by-side returns:
Medallion Fund Takeaways
Fully systematic
Data-driven
Diversify across timeframe, asset class
Hire smart, collaborative people
Win rate is not as important as trading edge
Compounding creates wealth
Jim Simons Net Worth and Personal Life
Billionaire Status and Forbes List
Jim Simons is the current richest trader in the world with a $28.1 billion dollar net worth ranking him on the Forbes list. He has the highest net worth of any trader or money manager on the Forbes wealthiest list. For example, Simons has a wealth more than three times that of George Soros.
Archimedes Yacht
Jim Simons owns a $100 million yacht with an annual running cost of $8 to $10 million. The 68-meter (222 ft) yacht is appropriately named Archimedes after the famous mathematician. Archimedes the superyacht sleeps 12 in 6 cabins and has room for 18 crew members on board with a dining hall for 20 guests. The yacht was built at the Dutch yacht builder Royal Van Lent and delivered to Simons in 2008. Let’s face it, these big boy toys are half the allure of trading.
Private Jet
Simons also owns a $70 million Gulfstream G650 private jet. You can charter one of these bad boys for about $10,500 per hour, if interested.
Tragedy
Unfortunately, Simons’ 84-plus years have not been without tragedy. In 1996, his son Paul was struck and killed by a car while riding his bicycle on Long Island. Paul was 34 at the time. In 2003, Simons’ son Nicholas, 24 at the time, drowned while on a trip to Bali, Indonesia.
Bonus Fact
Simons does not like, nor does he wear socks. Not sure if there is any correlation to his trading success but perhaps if you are struggling remove the feet prisons. I don’t know.
Jim Simons Quant Trading Insights
As all traders, I am always reading, learning, and trying to find a new piece that can help improve performance, limit drawdown or push me forward. The aforementioned book about Simons came out in 2019 and I devoured it in a weekend. I originally posted some thoughts on a See It Market blog here: 5 Lessons From One of the Greatest Traders of All Time (Jim Simons) – See It Market but want to elaborate on the insights and their application to system trading, Build Alpha and my own musings.
Edge is Important – Not Why It Exists
Simons does not care to explain the hypothesis or explanation of why a predictor or model works. If a predictor has edge and is statistically significant then why bother with some explanation for why it must work.
If the edge can be explained, then others are probably aware of the edge and others will soon trade it away.
In other words, data mining is ok. I’ve long defended this approach since the launch of Build Alpha and is nice to hear Simons echo similar ideas.
In my opinion, it is possible we cannot comprehend why a pattern or edge exists because it exists in a dimension too complex for our current understanding. Discarding an edge because we cannot explain it is a mistake.
Remove human bias and let the data show you where, when, and how to trade. Let others overlook these “unexplainable” patterns.
Excerpts to drive the point home
“Simons and his researchers didn’t believe in spending much time proposing and testing their own intuitive trade ideas. They let the data point them to the anomalies signaling opportunity. They also didn’t think it made sense to worry about why these phenomena existed. All that mattered was that they happened frequently enough to include in their updated trading system, and that they could be tested to ensure they weren’t statistical flukes”. (pg 109)
“Simons and his colleagues hadn’t spent too much time wondering why their growing collection of algorithms predicted prices so presciently. They were scientists and mathematicians, not analysts or economists. If certain signals produced results that were statistically significant, that was enough to include them in the trading model” (pg 150)
“I don’t know why the planets orbit the sun. That doesn’t mean I can’t predict them” – Simons (pg 151)
“More than half of the trading signals Simons’s team was discovering were non-intuitive, or those they couldn’t fully understand. Most quant firms ignore signals if they can’t develop a reasonable hypothesis to explain them, but Simons and his colleagues never liked spending too much time searching for the causes of market phenomena. If their signals met various measures of statistical strength, they were comfortable wagering on them.” (pg 204)
“Volume divided by price change three days earlier, yes, we’d include that” – Simons (pg 204)
To read on how to quantify trading edge check this out
You cannot run a trading business that relies on your emotional state or gut-instincts. There are too many days where you may be sick, tired, hungover, dealing with personal issues and what happens when these days line up with the most opportunistic market days?
Simons is 100% systematic and preaches the importance of treating trading like a business that can be backtested, modelled and followed. Here’s a quick minute long video where he explains why:
You Need a Great Team
Simons is no doubt successful on his own right, but Medallion’s performance really skyrocketed when Simons started building his team. Jim doubled salaries to hire people away from prestigious positions in tech, science, and academia.
Being around other smart, successful, and innovative people will only push you farther. The old saying “if you want to go fast go alone but if you want to go far go together” applies here.
Seek out other like-minded individuals and be open to sharing ideas. This is one of the greatest reasons I keep Build Alpha open to other traders. The ideas, inputs, and feedback help me create better software which allows all of us to create better portfolios. So, thank you for all the contributions, ideas, sound boards, etc.
Edge Does Not Have to be Big
Renaissance searched for “overlooked” edges and joked about a 50.75%-win rate while utilizing the law of large numbers to win in the long-run. Seeking the perfect entry or exit or the one strategy is often a failed approach. Ren Tech generated astronomical returns with a nearly 50%-win rate. Much more can be gained combining unique smaller edges together than wasting time hunting for the holy grail.
Some of the trading signals they identified weren’t especially novel or sophisticated. But many traders had ignored them. (Page 112)
“We’re right 50.75 percent of the time… but we’re 100 percent right 50.75 percent of the time. You can make billions that way” (pg 272)
The Man Who Solved the Market – Gregory Zuckerman
Most of the quotes in this article are from this tremendous book. The book released in November 2019 and does such a great job covering Simons. Simons and Renaissance are very secretive about their strategies but there are a few insights (if you read between the lines) in the book.
Jim Simons Interviews and Videos
James Simons Full Length Numberphile Interview
The Mathematician who cracked Wall Street
James Simons – Mathematics, Common Sense and Good Luck
Famous Jim Simons Quotes
These quotes come from Zuckerman’s book along with page number. You can read into the lines and see why Simons is such a staunch supporter of the systematic trading approach.
Early on, he traded like others, relying on intuition and instinct, but the ups and downs left Simons sick to his stomach. (Page 2)
Simons and his colleagues used mathematics to determine the set of states best fitting the observed pricing data; their model then made its bets accordingly. The why’s didn’t matter, Simons and his colleagues seemed to suggest, just the strategies to take advantage of the inferred states. (Page 29)
“I don’t want to have to worry about the market every minute. I want models that will make money while I sleep”, Simons said. “A pure system without humans interfering.” (Page 56)
If a currency went down three days in a row, what were the odds of it going down a fourth day? Do gold prices lead silver prices? Might wheat prices predict gold and other commodity prices? Simons even explored whether natural phenomena affected prices. (Page 57)
Their goal remained the same: scrutinize historic price information to discover sequences that might repeat, under the assumption that investors will exhibit similar behavior in the future. Simon’s team viewed the approach as sharing some similarities with technical trading. The Wall Street establishment generally viewed this type of trading as something of a dark art, but Berlekamp and his colleagues were convinced it could work, if done in a sophisticated and scientific manner – but only if their trading focused on short-term shifts rather than longer-term trends. (Page 108)
Berlekamp also argued that buying and selling infrequently magnifies the consequences of each move. Mess up a couple of times, and your portfolio could be doomed. Make a lot of trades, however, and each individual move is less important, reducing a portfolio’s overall risk. (Page 108)
Humans are most predictable in times of high stress – they act instinctively and panic. Our entire premise was that human actors will react the way humans did in the past….we learned to take advantage.” (Page 153)
“Any time you hear financial experts talking about how the market went up because of such and such – remember it’s all nonsense”, Brown later would say. (Page 199)
By 1997, though, more than half of the trading signals Simon’s team was discovering were nonintuitive, or those they couldn’t fully understand. (Page 203)
“If there were signals that made a lot of sense that were very strong, they would have long-ago been traded out”, Brown explained. “There are signals that you can’t understand, but they’re there, and they can be relatively strong.” (Page 204)
The gains on each trade were never huge, and the fund only got it right a bit more than half the time, but that was more than enough. (Page 272)
his larger point was that Renaissance enjoyed a slight advantage in it collection of thousands of simultaneous trades, one that was large and consistent enough to make an enormous fortune. (Page 272)
The inefficiencies are so complex they are, in a sense, hidden in the markets in code,” a staffer says. “RenTec decrypts them. we find them across time, across risk factors, across sectors and industries.” (Page 273)
For all the unique data, computer firepower, special talent, and trading and risk-management expertise Renaissance has gathered, the firm only profits on barely more than 50 percent of its trades, a sign of how challenging it is to try to beat the market (Page 317)
To skip down to the best automated trading strategies click here.
What is an automated trading system?
An automated trading system is a set of rules that can be programmed for a computer to automatically execute trades whenever the rules occur in a given financial market. An automated trading system is the same thing as an automated trading strategy, an algo strategy, a trading algorithm, a trading robot or an algorithmic trading strategy.
In short, any pre-determined set of buy and sell rules that can execute trades automatically can be considered an automated trading system. Trading rules can be any set of if then scenarios and do not need to be complicated mathematical models.
Simple Automated Trading System Example
The simplest automated trading strategy is a moving average cross strategy. This system buys when the stock price rises above the moving average and sells when the stock price falls below the moving average.
Algorithmic traders have long known this strategy to lose its luster in financial markets, but this system is a great example of how simple buy or sell decisions can be made using technical indicators or market prices. If you can think it, then it can be coded.
Later we will get into much more complex algorithmic trading strategies and trading algorithms. However, I will not go into black box trading where strategies are known to the computer but not known to the human in charge of the computer.
How Does Algorithmic Trading Work?
Algorithmic trading works over the internet allowing one computer or server to receive market data and send trading instructions to another computer or server. For instance, a high frequency trading firm may have their servers co-located in the exchange’s data center. This allows for lower latency in receiving new market prices and issuing new orders.
This server can also calculate the trading strategies and send buy and sell orders directly to the exchange. The exchange parses the message and executes the market makers’ desired actions.
Most trading systems communicate with the exchange through Financial Information Exchange protocol or FIX protocol. However, many independent traders have brokers that provide this communication connection built into the platform and most traders never have to worry about how algorithmic trading works.
Most platforms, brokers, and software provide application programming interfaces or APIs that make it easy to connect custom code to the exchange or broker. However, software like Build Alpha or TradeStation make it possible to connect directly with data providers, brokers, and the exchanges so traders only have to worry about entries, exits and risk management.
What are different types of automated trading systems?
There are various different types of trading strategies but let’s cover the five most popular strategies below. These strategies can apply for longer term investors or day traders. For a more extensive look please check out my Algorithmic Trading Guide.
Momentum or Trend Following
Arguably the simplest and most widely used – especially amongst commodity traders. Trend following strategies aim to capture prolonged price movement in a single direction or a “trend”.
Trend strategies tend to have lower win percentages as these strategies have outsized winners in comparison to more frequent but smaller losers. The typical returns from a trend strategy may be a series of small losses and then one large win when the system captures the large trend move.
These strategies are best implemented by traders with strong resolve and the ability to withstand frequent losses. Periods of chop and noise are much more common than big trend moves thus leading to your trading account not making much progress most of the time and then making large leaps “at once”.
Mean Reversion
Mean reversion trading strategies tend to have higher win percentages as they have many frequent small wins and the infrequent large loss. The typical returns from a mean reversion strategy may be a series of small wins and then one large loss when the trend breaks.
The simplest mean reversion strategy is one that aims to buy and sell large deviations from a moving average or volume weighted average price. As price moves away from the mean, however calculated, the strategy looks to enter a position with the intention of price returning to the average price.
Many traders refer to these extremes as overbought or oversold and attempt to quantify them with technical analysis, technical indicators, mathematical models or statistical concepts.
In theory, traders that employ mean reversion strategies can experience profit and loss of their trading account to inch higher most of the time with periodic large set backs. Many market makers and dealers experience this.
Statistical Arbitrage
Typically, statistical arbitrage is looking for short-lived opportunities between two securities. Many traders will isolate two correlated or related stocks such as Coca-Cola and Pepsi and monitor the spread or difference between the two. Whenever the difference between the two becomes large enough the trader places a trade buying the cheaper and shorting the expensive until the pairing comes back into normal ranges.
An obvious simplification, but many traders cannot execute pair trading or statistical arbitrage with retail brokerage accounts and software. There is also steep competition from professionals, market makers and hedge funds.
Market Making and High Frequency Trading
A market maker can be thought of as a liquidity provider that quotes both bids and offers regardless of market conditions. The market maker will hold inventory and aims to profit on the bid-ask spread or the difference between the highest price someone is willing to buy and the lowest price someone is willing to sell.
Market making is arguably the most complex form of algorithmic trading and automated trading as it requires tons of price data, quote information, data from multiple stock exchanges, active trading activities and trading decisions and is often completely automated with zero room for discretion.
High frequency trading (HFT) is market making or trading in fractions of a second based on powerful computer programs executing large numbers of orders almost simultaneously. The depths, pros and cons, and nuances of HFT and market making far exceed the scope of this post.
Pattern Trading
In the previously mentioned Algorithmic Trading Guide, I mention pattern trading as being one of the most common strategy types for algorithmic traders. This style was popularized by Jaffray Woodriff of Quantitative Investment Management in Jack Schwager’s Hedge Fund Market Wizards book (still one of my favorites).
This style looks for repeatable patterns that shift the odds one way or the other. For example, based on historical data, whenever this candlestick formation or these two events have occurred there has been a X% chance price is higher over the next N days. Again, a gross simplification, but a trading style most traders are familiar.
Is Automated Trading Profitable?
Yes. The leading 12 investment banks earned about $2 billion from portfolio and algorithmic trading in 2020 according to Coalition Greenwich. Additionally, the greatest hedge fund of all time – Renaissance Technologies – is strictly algorithmic. Listen to James Simons, the founder, speak from 36:48 to 38:00.
Furthermore, I have posted tons of algo trading success stories on the Build Alpha blog. Successful automated trading requires rigorous testing, robustness tests, stress testing, considering multiple market conditions, contextual data and more. Nothing manual trading does not require. I wrote a bit about strategy reliability here: Robustness Testing for Trading Strategies.
Can I automate my trading strategy?
Yes. We live in the golden age of algo trading as nearly 75% of US equity volume now comes from algorithms while algorithmic trading is expected to grow at 11.23% CAGR over the next five years. Brokers and data platforms have made trade automation incredibly simple.
My goal with Build Alpha has been to connect those with a desire to automate trading strategies with the tools to do so without having to learn or write any code. Now anyone can test, build and automate their trading strategies without any code.
What’s the best automated trading software?
Build Alpha is the best automated trading software. I am biased because as I set out to find the best automated trading software, I could not find one that gave me the end-to-end tools necessary. That is what prompted Build Alpha’s development oh so many years ago. That being said, I will review the three most popular software for automated trading strategies.
Build Alpha
Build Alpha enables traders to create, test, and automate trading strategies with no code. There are 5,000+ built-in entry and exit signals as well as the ability for users to add their own signals with a drag and drop builder. The signal library includes everything from seasonality, price action, candlesticks, chart patterns, technical indicators, market breadth, options flows, economic data, time of day and more.
Build Alpha works in two ways. First, the trader can select specific entry and exit signals from the point-and-click interface to test a specific strategy. On the other hand, Build Alpha’s genetic algorithm can take thousands of inputs such as potential entries, exits, filters, risk management, position sizing, account size and will build the best possible strategies.
The trader can also specify certain thresholds that each strategy must pass such as the Monte Carlo simulation must be profitable or the profit factor must be above 1.7, etc. These automated workflows allow for the fast creation of strategies that fit any trader’s desired risk tolerance and performance thresholds.
Build Alpha then connects to live market prices for real-time strategy monitoring or can produce fully automatable code for every strategy. This code can be copy and pasted into various broker platforms for live trading.
Take a look at these to see step-by-step walkthroughs
TradeStation makes automating trading strategies very simple as TradeStation is a broker and has a really reliable platform which is a favorite among independent algorithmic traders. TradeStation also created Easy Language, a proprietary programming language aimed to make trading strategy development much simpler for traders.
Traders can write their strategy logic using Easy Language inside TradeStation’s Development Environment. Once the strategy’s code is complete, the trader can easily add the strategy to a chart to enable simulated or live trading.
A simple moving average crossover strategy may only require two lines of code using Easy Language. Here is an example below:
If Close crosses above average(close,10) then buy 100 shares next bar open;
If Close crosses below average(close,10) then sell 100 shares next bar open;
For those of you interested in a TradeStation account, email me to find out how TradeStation will pay for your Build Alpha license.
Python
Python is undoubtedly the fastest growing programming language due to its easy-to-read syntax and clean scripting style. Many traders have flocked to python as the vast number of public libraries that have already built trading functionality such as technical indicators and other position sizing logic coded continues to grow.
If you are interested in learning about using python to build automated trading strategies, then I highly recommend checking out a few other resources I have created.
How to create a trading algorithm in 3 Steps with Build Alpha
Build Alpha is the simplest way to create algorithmic trading strategies as it does not require any coding. Let’s take the moving average cross strategy above and build it in 3 clicks.
Step 1 – Select Signals
Search for SMA signals and select an entry and exit. Let’s enter when the close crosses above the 10-period SMA and exit when the close crosses above the 10-period SMA.
Yes, you can optimize parameters. I am showing the lazy hardcoded settings. Traders can create any range of parameters to test and optimize.
Step 2 – Select Dates and Symbol(s)
Select your test dates, symbol(s), and any risk management. I will select SPY and test from 2006 to 2022.
Step 3 – Simulate
Hit Simulate and view your results. If the results are suitable (they should not be) then hit one of the “Generate Code” buttons to get fully automatable code for this (or any) strategy.
It is important to note that Build Alpha permits automated trading strategy monitoring in two ways. First, you can export generated code. Second, you can connect your broker’s data feed with existing connections to TradeStation, Interactive Brokers, QuoteMedia, Binance, and more.
Example Automated Trading Strategy
Later in this post I will share a table that automatically updates with the best technical analysis strategies for all financial markets. Each strategy uses one rule for entry and one rule for exit. These are meant to give insights to what is working now, building blocks for your own strategies, and general free information.
This strategy below has two rules: one for entry and one for exit. The trading strategy is applied to CSCO or Cisco Systems Inc which is a large multinational technology conglomerate.
This strategy enters when the closing price crosses below the 5-period exponential moving average (EMA) and exits when the closing price crosses above the 5-period exponential moving average.
At the time of publishing, you can see a sample of this strategy’s performance on the far right over the past 1,809 trades earning $18,138.17 per 100 shares traded over the last 12 months. This strategy also would have made $1,883.81 over the last 30 days.
What are the best automated trading strategies right now?
First, how do we define best? The simplest way is total P&L. Second, what is a trading strategy? In this case, let’s look at all possible combinations of technical indicators and only use one rule for entry and one rule for exit.
Can we build more advanced systems? Yes. However, this results table serves to show us what parameter settings, lookback ranges and indicators have been performing well lately. Perhaps this info can serve as building blocks for new automated trading strategies or just free information for my fellow system traders.
In the table below, you can sort by asset class, timeframe, ticker, and minimum trade count. All strategies are sorted by their total profit and loss over the last 30 days and last 12 months. All strategies use the same position sizing.
Key Points
Automated trading systems automatically execute buy and sell orders based on pre-defined rules.
There are at least 5 different types of algorithmic trading strategies
Trend Following
Mean Reversion
Statistical Arbitrage
Market Making / High Frequency Trading
Pattern Trading
It has never been easier to automate trading strategies – even with no programming
Build Alpha, TradeStation and Python are easier routes to algo trading
The best trading strategies are changing all the time, but the table above is free to all
Summary
Automated trading strategies continue to grow in popularity as traders with no programming background can now turn their strategies into trading algorithms. Automated trading strategies are just pre-defined rules that instruct a computer when and how much to buy and sell in financial markets. I have spent the past decade involved in professional trading and can honestly answer there is no best automated trading strategy. However, a combination of robustness testing and portfolio construction can help any trader gain an edge in algorithmic trading over the trader that fails to grasp these algorithmic trading concepts.
Author
David Bergstrom – the guy behind Build Alpha. I have spent a decade-plus in the professional trading world working as a market maker and quantitative strategy developer at a high frequency trading firm with a Chicago Mercantile Exchange (CME) seat, consulting for Hedge Funds, Commodity Trading Advisors (CTAs), Family Offices and Registered Investment Advisors (RIAs). I am a self-taught programmer utilizing C++, C# and python with a statistics background specializing in data science, machine learning and trading strategy development. I have been featured on Chatwithtraders.com, Bettersystemtrader.com, Desiretotrade.com, Quantocracy, Traderlife.com, Seeitmarket.com, Benzinga, TradeStation, NinjaTrader and more. Most of my experience has led me to a series of repeatable processes to find, create, test and implement algorithmic trading ideas in a robust manner. Build Alpha is the culmination of this process from start to finish. Please reach out to me directly at any time.
Automate Trading with No Coding | Complete Guide
Can you automate your trading?
Yes, you can automate your trading! In fact, according to BusinessWire, algorithmic trading is responsible for 60-73% of all U.S. equity trading. Most brokers support automated trading and even provide easy to learn programming languages to build your first automated trading system.
Three Ways to Automate Your Trading
Most broker platforms support automated trading with two options for automation. Build Alpha provides a third.
A development environment to add your own code and will execute your trades when your trading rules are true.
An application programming interface or API which allows your code to speak directly with the broker’s platform.
Build Alpha – a no code algo trading platform.
Both of the first two solutions require tedious hours learning to code, intricacies of the platform’s development environment or API, and introduces fragile breakpoints that can harm your trading results if you are not an experienced programmer.
Later in this article, I will introduce how you can still automate your trading with no coding using Build Alpha and a variety of popular brokers. The benefits of automated trading are still possible without the ability to program!
Is Automated Trading Profitable?
Absolutely. Automated trading is simply having a computer execute trades instead of you manually clicking the mouse buttons. The largest banks, hedge funds, and the best trader of all time are staunch supporters and users of automated trading.
I also highlighted several Build Alpha success stories (with statements) in my Algorithmic Trading Guide.
Many fear automated trading is not profitable because they fall for snake oil salesmen selling overfit trading strategies that were not developed to last but developed to sell. Automated trading requires robustness testing or stress testing to break the strategy before the market does. I wrote more about Robustness Testing here.
What is an automated trading system?
An automated trading system is a set of pre-defined entry and exit rules executed by a computer program. Many proper trading systems contain risk management and position sizing as well.
Simple Moving Average Example
The simplest example of an automated trading system, and probably the most popular technical analysis system, is the moving average crossover. In the moving average crossover system, a trading system would enter a long position when the faster moving average (shorter length) crosses above the slower moving average (longer length). The system would exit a long position when the faster moving average crosses below the slower moving average.
How do I automate my trading with no coding?
Build Alpha is no code algo trading software that allows traders to create hundreds of algorithmic trading strategies on historical data at the click of a button. No algo trading experience needed.
There are thousands of built-in entry and exit signals to choose from and the ability to create your own with a drag and drop builder or using python. The built-in library covers everything from
seasonality
price action
candlestick patterns
chart patterns
technical analysis indicators
volume
volatility
pre- and post-market
market breadth
options flows
economic data
dark pools
and more
Simply search and select your desired entry signals, exit signals, risk management and position sizing then hit simulate and Build Alpha will generate the best results.
From the results window, select your desired trading system then click on one of the code generators in the lower right. The code generator will produce fully automatable code that can be copy and pasted into any of the supported broker platforms.
This strategy enters when the 2-period RSI is below 10 and price is above the 200-period simple moving average. On the other hand, this strategy sells when price is above the 5-period simple moving average.
Step 2 – Set account size and position sizing
We will use a fixed size of $10,000 per position. We achieve this by setting the account size to $10,000 and the position sizing to Fixed. Both are found in the settings menu.
Step 3 – Select Symbol(s)
Next, we need to select our symbol as SPY.
Step 4 – Select Entry and Exit rules
Finally, let’s select our trading decisions or entry and exit rules. The entry requires the following two conditions to be true
2-Period RSI is below 10
Price is above the 200-period simple moving average
Alternatively, the exit signal closes the trade when price is above the 5-day moving average.
Here is a quick gif searching and selecting the following entry and exit signals in Build Alpha.
Click on the gif to view/watch
Now that we see our results, we can highlight the strategy, review its performance metrics and equity curve, and ultimately generate code for it.
Step 5 – Generate Code
In the results window, highlight your strategy and navigate over to the lower right where you will find the Generate Code buttons. Find your preferred broker platform and hit Generate.
The Generate Code button will create complete code for the highlighted strategy that can be copy and pasted into your broker’s platform. Voila! Automated trading with no coding necessary. Steps for each broker below.
How do I set up my automated trading with Generated Code? Best Automated Trading Platforms
Great! How do we take the generated code and start auto trading buy and sell orders? Let’s walk through how you can take the generated code and set it up in the three most popular automated trading platforms.
TradeStation – Automated Trading Platform
TradeStation is arguably the consensus favorite among automated trading platforms. It is very easy to use, reliable and has competitive fees. Now you can partake in TradeStation algorithmic trading without being a tradestation coder.
Step 1 – Open Development Environment
The first step to automating with TradeStation is to open the Development Environment found under View >> Development Environment.
Step 2 – Create new strategy File
Next, create a new strategy File >> New >> Strategy
Step 3 – Copy and Paste
Then copy and paste the Build Alpha generated code into the new Strategy and hit F3 to verify. You can hit CTRL+A to highlight all the Build Alpha generated code then CTRL+C to copy it all. Then inside the new Strategy in TradeStation’s Development Environment you can hit CTRL+V to paste it all.
Step 4 – Add to Chart
Finally, open a new chart and set it with the symbol you wish to trade. Then right-click and select Insert Strategy!
Please note this is an older version of Build Alpha’s results window. There are many more features now!
As part of TradeStation’s confidence in the software, we have partnered to help traders have the best of both worlds. If you open a new TradeStation account and license Build Alpha then TradeStation offers a commission rebate plan to repay you until they have paid for your license. For more information, please check out TradeStation Build Alpha promo.
NinjaTrader – Automated Trading Platform
NinjaTrader8 is a close second and highly favored automated trading platform among futures traders. Below are four simple steps to take Build Alpha generated code and enable inside NinjaTrader8. That’s right, you can generate and trade NT8 scripts without being a ninjatrader coder.
Step 1 – Open Script Editor
Open up the NinjaScript Editor
Step 2 – Create new strategy file
In the NinjaScript Editor, right-click on Strategies to create a new one.
Step 3 – Name your strategy
Name your strategy and hit Generate.
Step 4 – Copy and Paste
In the new NinjaTrader Strategy that appears, please hit CTRL+A to highlight all text and then hit ‘BACKSPACE’ to delete it all. Then inside Build Alpha, hit Generate NT Code. Please highlight and copy all the generated code using CTRL+A, CTRL+C. Then come back to the blank NinjaTrader Strategy and hit CTRL+V to paste it all.
Step 5 – Quick Edit
Go to line 47 and edit the class name to be the same as the designated strategy name. You can go to line 47 by hitting CTRL+G then entering 47. I named the strategy NinjaExample.
After making this change you can verify or compile the strategy by hitting the above button.
Step 6 – Add to Chart
Open up your desired chart, right-click and select Strategies. Alternatively, you can hit CTRL+S.
Step 7 – Enable
Select your strategy and be sure to enable the strategy in the Properties pane.
That’s a wrap. Ninjatrader automated trading systems without writing a single line of code in your ninjatrader scripts.
MetaTrader4 and MetaTrader5 – Automated Trading Platforms
MetaTrader4 and MetaTrader5 are both supported by Build Alpha’s code generators. However, most traders should migrate to MetaTrader5. In a recent Build Alpha survey, MetaTrader was the lowest ranked automated trading platform among the three listed in this article. I highly encourage you to check out the other two if you are currently struggling with MetaTrader.
Step 1 – Open MetaEditor
Open MetaEditor. This can often be done from your search menu or from within MetaTrader.
Step 2 – Create new EA
Create a New Expert Advisor. Expert Advisors are what MetaTrader calls strategies. Give your expert advisor any name you desire. I will use MetaExample.
Step 3 – Copy and Paste
In the new strategy that appears, please highlight and delete all text. You can hit CTRL+A and ‘BACKSPACE’ as we have done before.
In Build Alpha, hit the Generate MT4 Code or Generate MT5 Code button. Then highlight and copy all the text. This can be done using CTRL+A, CTRL+C. Back in the MetaEditor, paste all the generated code using CTRL+V. Then hit the Compile button in the top menu.
Step 4 – Add to Chart
You can now add expert advisors to the tester or a live chart.
Can I automate my day trading?
Absolutely. Build Alpha has time frame selection, time of day filters, and a Force End of Day Exit option that forces any strategy to close open positions at the specified time.
The pre- and post-market signals allow traders to specify signals around volume weighted average price (VWAP), pre- and post-market volume or pre- and post-market highs and lows.
The Quick Test feature even allows you to test a strategy across thousands of symbols and view the optimal times to take profits, stop out, or when highs and lows of the day are made. The test even shows you most profitable and least profitable symbols as well as a summary across all tickers.
Please note it is best that day traders do not use excessive leverage or size as there is a high risk of losing money rapidly due to leverage.
Automated Trading Pros
What are the benefits of automated trading?
More Markets
Market data moves so quickly, and manual trading can rarely keep up. There are often missed trades during fast moving market conditions. Automated strategies can be one of many trading solutions to help.
Trading more markets, more systems can often mean getting to the law of large numbers faster. If your systems have edge, then this means a rising account.
Quantified Risk
Experienced traders know that having a set of rules with a risk you can afford to take is the key to financial longevity. New traders seeking massive upside often take the high risk and lose money because they do not have a system or have properly quantified their trading edge.
Computer Never Sleeps
Certain financial markets like futures, forex and cryptocurrencies trade 24 hours per day. There are tons of trading account opportunities in off market hours and auto trading and automated trading strategies can help.
Fewer Mistakes
Trading is hard. Losing your money sucks. Losing your money from human errors like fat fingering an order or entering the wrong symbol suck even more.
Automated systems help avoid these blunders. Consider whether you understand how big of an impact this can have on your emotional state. No more calling customer support to see if they will cancel a trade.
Less Emotions
Backtesting trading strategies and understanding how a system can make money or lose money over the next N trades is crucial to reducing emotions. If you have a plan, you can stick to it.
If you do not have a plan, then losing your money heightens stress, which heightens emotions, which raises your probabilities to harm your trading account.
Known Systems
Automated trading is much more than automatic order entry. Advanced trading work is understanding edge, setting proper expectations, and quantifying your risk. Many investors do not know their systems and cannot properly answer these above questions. How do you think these traders wind up?
Automated Trading Cons
Where to Start with Automated Trading?
There are so many markets, symbol, timeframes and potential signal combinations. New traders often do not know how to start. The simplest solution is to start quantifying what you do know. For example, does price need to be above a moving average? What moving average? Does yesterday need to be a bullish or bearish day? What about volume?
I wrote here about quantifying simple ideas:
Solution: Build Alpha does not require you to have a trading idea. You can of course test your own ideas, but if you do not have an idea you can select thousands of input signals at once and Build Alpha will find the best strategies for you. The genetic algorithm learns from your inputs and creates the best combinations quickly.
Computer needs to be on 24 hours?
Yes, if your computer is not connected to the internet or turned off then the automated trading systems will not send orders to the broker’s trading platform.
Solution: Rent a virtual private server (VPS) which is a computer that is on 24-7 which you can remotely log into. Amazon, Microsoft and other smaller companies have been reliably offering this service for years and often you can rent a VPS for only a few bucks per month. All PCs have remote monitoring software built-in.
What if my trading signal is not included in the built-in signals?
There are nearly 5,000-plus signals built-in, and I have done my very best to include those with above average e-ratios, but it is possible that your trading idea requires something unique.
Solution: Build Alpha has two ways to add custom signals that can be passed to the strategy builder engine. First, there is a drag and drop signal builder. It allows the trader to combine any technical indicator, math operator, and custom parameter setting.
Anyone can automate their trading even without programming
Automated trading pros outweigh the cons
Most brokerage platforms support automated trading now
Summary
Automated trading is growing every year with now nearly three quarters of U.S. stock market volume being attributed to automated trading. In the past, creating automated trading systems required access to historical market data, live market data, broker connections, and the ability to program it all. However, now, Build Alpha provides professional automated trading software to all traders. This enables all traders to create, test, and automate any trading strategy on a variety of automated trading platforms.
Automated trading platforms support both demo account and live account implementations. Cryptocurrencies, Forex, CFDS are complex instruments and come with a steeper learning curve and intricate nuances, but traders can focus on futures, stocks and ETFs as all markets are automatable.
Author
David Bergstrom – the guy behind Build Alpha. I have spent a decade-plus in the professional trading world working as a market maker and quantitative strategy developer at a high frequency trading firm with a Chicago Mercantile Exchange (CME) seat, consulting for Hedge Funds, Commodity Trading Advisors (CTAs), Family Offices and Registered Investment Advisors (RIAs). I am a self-taught programmer utilizing C++, C# and python with a statistics background specializing in data science, machine learning and trading strategy development. I have been featured on Chatwithtraders.com, Bettersystemtrader.com, Desiretotrade.com, Quantocracy, Traderlife.com, Seeitmarket.com, Benzinga, TradeStation, NinjaTrader and more. Most of my experience has led me to a series of repeatable processes to find, create, test and implement algorithmic trading ideas in a robust manner. Build Alpha is the culmination of this process from start to finish. Please reach out to me directly at any time.
What is a Stop Loss?
A stop loss is an order to close an existing position to limit losses when the market reaches a certain price. A stop loss is a form of risk management and intends to serve as protection from more severe trading or investing losses. The purpose of a stop loss is to automatically exit a trade when the trader cannot stomach more pain in the trade or the set up has been invalidated. Before we get into all the types and variations of stop losses including the trailing stop loss, let’s become masters of understanding stop losses as a money management tool.
How Does a Stop Loss work?
A simple stop loss example is Cody buys 100 shares of Apple stock at a purchase price of $104.00 to open a long position (aiming to profit from a rise in AAPL stock). Cody does not want to risk more than $200 on his trade.
If AAPL’s price falls to $102.00 then Cody’s 100 shares would result in a $200 loss. Cody can set a stop loss order at $102.00 so that if and when AAPL falls to $102.00 his shares will be automatically sold.
When the market price reaches the stop price, the stop-loss order is converted to a market order and is generally executed immediately thereafter assuming during market hours. It is important to note that the market’s liquidity will determine the actual fill price of the stop loss order after conversion to a market order.
If there is a buyer bidding to buy at least 100 shares at $102.00 then Cody’s stop loss order will sell to that buyer at that share price thus closing his position for a $200 loss.
However, if the best bid for 100 shares is at a lower price of $101.98 then Cody’s stop market order to sell will be executed at a stock price $0.02 below his stop level resulting in a loss of $202. This is because the stop loss order converters to a market order and takes the available current market price.
Market Price or Limit Price? What is the difference between a Stop Loss and a Stop Limit Order?
In the example above, the best bid could have been $101.08 instead of $101.98 and when price falls below the stop and the next available market price is significantly below the trader’s desired exit level then significant losses may ensue.
What if in this scenario Cody would prefer to hold the position and wait for the share price rises or current price to recover before selling?
The stop limit order allows the trader to enter a stop price and a limit price which converts the stop order into a limit order (instead of a market order) after the stop price has been hit. Then the trade will be executed at the limit price (or better). This could alleviate some of the concerns around market orders.
Let’s revisit Cody’s position from earlier and assume he placed a stop limit order with a stop price of $102.0 and limit price $101.90. In the first example, Cody’s transaction price would be $101.98. In the second example, Cody’s stop would trigger but the limit order would never execute because price was already below his limit price ($101.08 vs $101.90). In this scenario, Cody would hold the shares and until the security price rebounds (hopefully).
The big difference is that a stop loss converts into a market order and a stop limit order converts into a limit order. The risk with stop limit orders are thinly traded markets or gaps beyond the stop limit. There are no stop limit orders guarantee to get filled at the specified limit price.
How to Calculate Stop Prices? Where to place them?
Method 1: beyond a recent high or low-price level
Those using technical analysis and chart patterns to trade will advise you to put a stop below (above) a recent low (high) that would invalidate the chart or pattern that prompted the entry.
However, one should not choose the minimum price below the most recent low but a decent margin below the recent price level so all the stock drops and natural volatile markets can happen without stopping you out of your position prematurely.
However, I have now spent years working with a high frequency trading, market making firm and I can assure you this is not a great strategy for existing positions especially in fast moving markets. We joke and call this the “draw a line stop loss”. This rings true for long and short positions and all trading hours.
Method 2: based on your risk tolerance
Another exit strategy to determining when to sell stocks on adverse price moves is to determine your entry, determine how much you are willing to lose and place your stop sell order at this amount. This may apply to traders that have pre-determined their position sizing based on the entry or their account size.
This strategy lacks forethought and traders must realize the market does not care where your pain threshold is. You may choose a price level that can be hit easily.
Method 3: something testable
Calculating stop loss orders is no different any other part of your trading system; that is, it should be fully testable. Often times using method one above does not result in something that can be backtested. Often times backtesting can result in isolating situations that produce more money or more favorable market conditions. This can also apply to placing sell stop or limit orders. Some easy ideas for something testable are:
fixed dollar amount
fixed Average True Range amount
dynamic dollar amount
a rolling N-day low (or high for a short position)
What is a Trailing Stop Loss?
A trailing stop loss aims to protect profits moving the stop price higher as the market price moves higher. Trailing stops are often an excellent tool to protect gains and combat market volatility that wishes to shake you out of profitable positions. Of course, one can use either a trailing stop market order or a trailing stop limit order.
As the trading day progress and the trade becomes more profitable, the trailing stop can be updated instantaneously or at the end of each bar.
How to Calculate a Trailing Stop Loss?
Trailing stops are typically calculated from the maximum trade price or high watermark while in a long trade. Short positions just have reversed logic calculating from the minimum trade price during the trade.
Cody’s original transaction price was to enter a long trade at $104.00. Let’s assume Cody placed a $1 trailing stop which would initially start at $103.00 or $1.00 below his entry price. After the market opens, AAPL’s price shot up to $104.77 raising Cody’s trailing stop price to $103.77.
If trading fails to make a new high above $104.77 then Cody’s trailing stop would remain unchanged at $103.77. However, if the security price rises to a maximum price of $105.66 then Cody’s trailing stop would move to $104.66 which is above his entry price of $104.00. At this point, Cody has “locked” in some gains even if price drops. However, investing involves risk and anything can happen (look above to thinly traded or overnight gap risk comment).
What is the best Stop Loss strategy? Static or Dynamic?
Stop losses, stop limit orders, trailing stops, oh my. What are the best stop loss strategies to add to your trading systems? First, let’s discuss a few variations of what we have discussed so far.
Static vs Dynamic Stop Losses
A static stop loss is a stop that is calculated the same way regardless of the current market price and market volatility. For example, a trader may employ a $200 stop for every trade he places regardless if the stock price significantly moves from the time of his first trade to the time of his most recent trade.
Static Stop Loss Pros
Simplify what to expect
Always understand your risk
Easy to think in fixed (static) terms
Static Stop Loss Cons
Fails to account for changes to underlying
Fails to account for different volatility regimes
A dynamic stop loss is calculated based of the underlying price or market volatility. This allows the sell stop order to maintain an appropriate distance from the entry regardless of the market conditions. In periods of high volatility, the stop price will be farther from the stock price and in periods of low volatility the stop price will be closer to the stock price dynamically adjusting how “risky” the trade is.
Dynamic Stop Loss Pros
Adjusts to current market environment
Accounts for drastic changes in the underlying (stock going from $50 to $500)
Dynamic Stop Loss Cons
Difficult to calculate on the fly
Difficult to think and understand your risk quickly
Is a Stop Loss a good idea?
In some cases, a backtest may show a strategy perform better without a stop loss. This is much more common in mean reverting strategies as price tends to move farther away from the mean, the edge or advantage of entering (or holding) the trade actually may increase! A stop loss in this scenario could limit upside and increase what you pay in exchange commission and broker fees.
Here is a popular 2-period RSI strategy tested across common market ETFs SPY, DIA, QQQ, and IWM. The strategy buys when 2-RSI crosses above 20 and sells when 2-RSI crosses below 80.
The equity curve on the left shows using a fixed stop loss of 1% or $100 per trade assuming $10,000 per position. The equity curve on the right shows the same strategy with no stop logic! Neither strategy is great as a standalone but the version without a stop produced 2x the net profit in backtests.
On the other hand, trend following, or convex strategies almost always benefit from having a short leash for when a stock falls.
Position Sizing based on Stop Price
The late Dr. Van Tharp wrote extensively about calculating position size based on the distance between your entry point and your stop loss. Here is his complete guide: Van Tharp Position Sizing
In short, the trader determines what percentage of his account is acceptable to risk. Typically, this is set to 2%. Let’s assume Cody has $100,000 account so he is willing to risk 2% of $100,000 or $2,000 per trade. Cody has determined his stop loss order to sell be placed at $102.00 or $2.00 below his $104.00 price entry or the prevailing market price.
We can then calculate how many shares Cody should purchase by dividing the $2.00 distance by the $2,000 max risk allowance. This results in Cody buying 1,000 shares of AAPL.
This sizing method can really up the position sizing and traders should understand the risks that you may not get executed at a price level equal to the stop price level or $102.00 which can result in substantial or significant losses. Reminder about thinly traded markets and overnight gaps. Trading liquid futures contracts can help alleviate some of this concern.
Build Alpha algo trading software supports this method with its “Volatility” based position sizing method. This is method is both dynamic and sizes based on the stop loss distance.
What is the Best Stop Loss Strategy? Optimizing various Stop Losses on different trading strategies
Enough with the discussion, let’s look at some data. We can test some specific stop loss strategies and see which one performs the best using Build Alpha. First a quick overview of stop types.
Fixed Dollar Stop – risking the same dollar amount each trade regardless of the underlying price.
Fixed Percentage Stop – risking the same percentage each trade or risking the same dollar amount and using the same fixed dollar amount to size your position. Both achieve the same.
ATR based Stop – risking a multiple of ATR units away from the entry price. This is dynamic as the stop will widen or narrow as ATR fluctuates. For example, two ATR units below the long entry. If ATR is 0.44 then the stop would be 0.88 below the entry.
Fixed Dollar Trailing Stop – risking the same dollar amount each trade but using a trailing stop instead of a fixed dollar stop.
ATR based Trailing Stop – risking a multiple of ATR units but as a dynamic trailing stop.
For this test, we will choose three popular strategies, trade $10,000 per position, and backtest each strategy from 2006 to mid 2022. I will use the following stops in the test
$100 Stop Loss (1% of position)
$200 Stop Loss (2% of position)
1 ATR Stop Loss
2 ATR Stop Loss
$100 Trailing Stop
$200 Trailing Stop
1 ATR Trailing Stop
2 ATR Trailing Stop
The three automated trading strategies we will use are the following:
The RSI20-80 strategy – Buy when the 2-period RSI crosses above 20 and sell when it falls below 80. Same strategy mentioned above.
Moving Average Crossover – we will buy when the 10-period SMA crosses the 50-period SMA. I will leave any moving average optimization up to the reader.
Here are the individual results sorted by strategy type
The $100 trailing stop was the best performer for both the RSI and Turnaround Tuesday strategy; however, it was one of the worst performers for the moving average crossover strategy!
This drives the point home that you must TEST EVERYTHING! There is no best and everything is relative to the strategy and symbols traded.
There are tons of other variations we could test such as combinations of stops, larger stops, smaller stops, etc. but that is the beauty of Build Alpha which allows you to quickly build and test strategies with no code. It is the first professional no code algo trading software available to all traders.
Need to Know about Stop Loss Orders
Stop losses aim to protect traders from significant losses
A Stop Loss is a sell order executed at a specified price to limit risk
A Stop loss converts to a market order after the stop price is reached
Stop limit order converts to a limit order after the stop price is reached
Trailing stop adjusts every trade or every bar and trails the stop price in the favorable direction as the trade moves in the favorable direction and remains unchanged with unfavorable price movement
Dynamic stops adjust to market volatility. Static stops are simpler to calculate.
There is no best stop loss strategy. Each strategy requires individualized testing
Stop Loss Summary
Stop losses are a risk management tool in every trader’s toolbox. Stop losses and stop limit orders aim to limit risk and protect against significant losses. Trailing stops protect against initial losses and then improve to protect gains as the trade proceeds. Mean reversion strategies are often harmed by stop losses where trend following strategies are often improved. Finding the right stop loss vs stop limit and trailing stop strategy depends on the market, timeframe, and strategy type they are applied to. Having useful algo trading software like Build Alpha to run countless backtests and optimize price levels can help identify optimal stop loss strategies for any automated trading strategy.
Author
David Bergstrom – the guy behind Build Alpha. I have spent a decade-plus in the professional trading world working as a market maker and quantitative strategy developer at a high frequency trading firm with a Chicago Mercantile Exchange (CME) seat, consulting for Hedge Funds, Commodity Trading Advisors (CTAs), Family Offices and Registered Investment Advisors (RIAs). I am a self-taught programmer utilizing C++, C# and python with a statistics background specializing in data science, machine learning and trading strategy development. I have been featured on Chatwithtraders.com, Bettersystemtrader.com, Desiretotrade.com, Quantocracy, Traderlife.com, Seeitmarket.com, Benzinga, TradeStation, NinjaTrader and more. Most of my experience has led me to a series of repeatable processes to find, create, test and implement algorithmic trading ideas in a robust manner. Build Alpha is the culmination of this process from start to finish. Please reach out to me directly at any time.
Why Do Automated Trading Strategies Fail?
In my decade-plus of professional trading experience, research effort, and working with thousands of individual traders, consulting for hedge funds, registered investment advisors, Commodity Trading Advisors, and family offices, I have discovered that algorithmic trading strategies fail for two primary reasons or errors and out of sample testing can help.
Algo strategies are often built for one market environment
Algo strategies are overfit to the historical data (overoptimized)
In this post, we will take a deep dive into how out of sample testing (OOS) can be an excellent approach to combating both of these algo trading pitfalls, remove some uncertainty, increase knowledge, and help estimate better forecasts.
What is Overfitting? What is Curve Fitting?
Curve-fitting, or more commonly referred to as overfitting, is creating a model that too “perfectly” fits your sample data and will not generalize well on new unseen data. In trading, this is a trading strategy that trades the historical data too well and will fail to adapt to new live data (large forecast error). Not the answer we need.
That is, the strategy has memorized the historical data values finding random, spurious patterns that will fail to repeat in new data. Developing your own trading idea, price forecasting, data mining or using machine learning models can all lead to overfitting.
Here are two visuals I found to help illustrate this idea of curve-fitting.
A curve-fit trading system is a trader’s worst nightmare; that is, a trading system that looks great while testing but fails miserably once live. The trading system simply memorized the historical data’s noise and will struggle to predict the noise of new market data.
It is very unlikely the next set of (live) data points will make a milk saucer for the cat in the above photo. Drawing the cat is overfitting the data and poor analysis. It is seeing something that is not really there. A trading strategy that sees price anomalies that are not there will struggle in live trading. OOS testing can help detect overfitting and discover robust trading strategies.
What is a Robust Trading Strategy?
A robust trading strategy is one that is adaptable to changing markets and data conditions. A fragile or overfit trading strategy is one that needs the exact data from the historical period to continue performing as expected which we know the market will not give us.
It is very unlikely that the fragile trading system will help us reach our trading goals. All algorithmic traders strive for robust trading strategies; that is, automated trading systems that have a high probability of performing as expected no matter what the market does.
Finding robust trading strategies is my passion. I took the time to write an in-depth guide on professional stress tests and checks (including OOS) that can help aid in this pursuit here: Robustness Testing Strategy Guide. If you are interested in learning how to better test trading algorithms, then I highly recommend giving it a read next. I am sharing organizational knowledge to be frank.
What is Out of Sample Testing?
Out of sample testing method is splitting the historical time series data into two or more data partitions with the idea of withholding some of the historical data to act as a second, unseen test set. The OOS period acts as an estimation period for live trading.
In trading, the strategy developer would split the historical price data into two sections: the in-sample data and the out-of-sample data. He would then build his trading strategy model on the first section of the data, the in-sample data. This is called model fitting or the learning phase. Once pleased with the strategy after making tweaks, optimizations, adding or deleting rules and filters, doing analysis, he is ready to test the strategy on the second section of data, the out-of-sample data.
The OOS data acts as unseen, untouched data that provides an unbiased view of how the strategy may perform on unseen data. This is a close simulation to new live data as an algo trader can hope.
If the strategy was overfit to the in-sample data, then we could detect such overfitting or curve-fitting on the out-of-sample data by noticing a degradation of performance on the out-of-sample test. That is, if we expect an average profit of $200 per trade from the in-sample data but the out-of-sample test shows a negative -$100 loss per trade then we have overfit to the historical data and the strategy is not robust. A large difference between in-sample and out-of-sample performance may indicate an error or bad assumptions.
This blog will cover the details and methods of OOS testing and how it can help combat against overfitting in algorithmic trading strategy development. On the other hand, if you prefer, please take a look at this video version: Out of Sample Testing for Algorithmic Trading.
In Sample Testing vs. Out of Sample Testing
The in-sample data is the portion of data used to develop the initial strategy, run backtests, optimize parameters, make tweaks, add filters, delete rules, etc. The strategy should arrive in its final form using only the in-sample data.
The out-of-sample data is withheld and unused during the strategy development process. After the strategy model is completed, the trader can test the strategy on the OOS data points. It is often said that if the trading model performs similarly in both the in and out of sample period then we can have increased confidence the trading model generalizes well enough to new data points. That is, we have a good estimation or forecast of what to expect live.
Proper Automated trading software should split the test results for you to reference the In-sample, the out-of-sample, and the combined results of a trading model like Build Alpha does.
How Much Out of Sample Data to use?
The default setting most algorithmic traders will use is 70% in-sample period and the last 30% of the historical data reserved for out of sample testing. That is, if the trader has 10 years of historical data, then the first seven years will be used to develop the trading strategy and the remaining three years will be used for OOS.
Other common approaches to in sample vs out of sample splits are to divide the data directly in half with a 50/50 split. The first 50% of the data used for in-sample and the second 50% used for OOS.
However, the test period, OOS location, and the percentage of OOS chosen can be very critical to the trading strategy’s success. I have always heard that good science is often mostly attributable to good experimental design. In the trader’s case, good science would be setting up a proper test by choosing an appropriate test period, OOS location, and OOS percent.
Out of Sample Test Period Selection – How the Human Adds Value
Below is the S&P 500 from 2004 to 2017 with the last 40% of data designated to be OOS (highlighted in red).
We create a trading strategy on the data from 2004 to 2011 – the blue in sample period. However, 2012 to 2017 (red OOS) was largely straight up! If we build a long strategy that avoids most of 2008 via some filter then the strategy may do well on our OOS data, simply because of the bull market.
Did the strategy pass OOS testing or would any strategy have passed this period? The question shows research effort. You can see the importance of intelligently selecting your OOS test period’s location and size.
Let’s use the first 40% of data as OOS. In this case, it allows us to build our strategy on the most recent data (the last 60%) from 2009 to 2017.
Many prefer to build their models on the recent data as it is the most like the live data they will soon experience. They then test out of sample using older data and in our case 2004 to 2008 (the first 40% highlighted red above).
Why 40%? I selected a percentage that would capture the financial crisis. If we build a trading strategy from 2009 to 2017 and then test from 2004 to 2008 and it performs similarly in both periods, then we likely have uncovered persistent edge that generalizes over two unique sets of data.
Selecting out of sample location and percentage is mission critical to better forecasting. Design your test to be as difficult as possible to pass – try to break your system in the testing process. If you do not, then the market will surely break it once you go live!
Out of Sample Selection Improves Trading Strategy Robustness
Testing design and set up is undoubtedly where the human still adds value to the automated trading process. Build Alpha allows users to leverage computational power in system design, validation, and testing; however, the test set-up in BA is still an area where a smarter, more thoughtful trader can capture an edge over his competitors while adding robustness to the output.
Below I have some photos of some terrible experiment design to help drive the point home. Both present fairly simple OOS tests to “pass” and potentially increase the trader’s risk.
The main takeaway is the human can still add value to the automated trading process by proper backtest and experiment design. That is why BuildAlphasoftware allows the trader/money manager to adjust everything (or nothing) from OOS percent, OOS location, test periods, the minimum number of trades in-sample, and the minimum number of trades OOS.
Randomized Out of Sample Testing – Avoid Luck
Traders are lazy and may not put in the work to find the exact percentage of out of sample data to make their test as difficult as possible to pass. An alternative method is to avoid using a single location for OOS and rather use a randomized selection. Build Alpha software can automatically and randomly select the out of sample period to avoid this common pitfall.
What are the benefits and pitfalls of Out of Sample Testing?
To explain simply, the main benefits of out of sample testing for algorithmic trading strategy development is a first line of defense against curve fitting and better forecasting performance. That is, out of sample testing helps discard obvious curve fit systems that fail to perform well on unseen historical data. This can save a trader who takes backtest research and immediately goes live with hard earned money only to see the strategy fail immediately. We have all been there.
The pitfall, and an often-missed error, is using an overly optimistic period of data that is too easy to pass. For example, any long strategy should do well in an out of sample period that goes straight up. Being aware of this pitfall, intelligently designing your in and out periods, or using the Randomized Out of Sample test are all ways to combat this and improve your trading research.
Key Takeaways
Overfitting first line of defense
out of sample testing can help avoid the avoidable by estimating strategy performance on unseen data. OOS testing simulates live trading.
Human can add value
merit to automated trading process by selecting the proper location and size of out of sample data can help create more robust trading models and reliable statistics. Attempt to break your strategies before the market does by designing hard to pass backtests.
OOS is rarely enough alone
out of sample testing is a great first step but is not a be all end all. Check out the full Robustness Tests Trading Guide to see the next tests for creating robust trading systems and more accurate expectations.
Need to Know
Out of sample (OOS) testing splits the historical data prior to a backtest.
Develop trading systems on the in-sample data first then test the signal OOS
OOS can be any percentage of the historical data
OOS can be at the beginning, middle or end of the historical data
Randomized out of sample selection is a great test to combat strong trending out of sample data and compare model performance across various market conditions
Algos that pass OOS are a great sign but by no means a complete green light to start live-trading
Out of Sample Testing Summary
Out of sample testing is a research effort and first line of defense to discover overfit and curve fit trading strategies that are destined to fail in live trading. Out of sample testing does not guarantee trading success but can surely help avoid the avoidable. Splitting historical data into a training and testing period allows the trader to properly design, estimate, tweak and optimize a trading system on the in-sample data before doing a final test on the withheld and unseen OOS data. Out of sample testing is the most popular and common stress test for any algorithmic trading strategy. Put this test in your trading toolbox immediately.
Thank you for the professional user contributions “licensed” and to those that share knowledge. That is what makes the Build Alpha community great. I hope you enjoyed this Out of Sample Testing Guide.
Author
David Bergstrom – the guy behind Build Alpha. I have spent a decade-plus in the professional trading world working as a market maker and quantitative strategy developer at a high frequency trading firm with a Chicago Mercantile Exchange (CME) seat, consulting for Hedge Funds, Commodity Trading Advisors (CTAs), Family Offices and Registered Investment Advisors (RIAs). I am a self-taught programmer utilizing C++, C# and python with a statistics background specializing in data science, machine learning and trading strategy development. I have been featured on Chatwithtraders.com, Bettersystemtrader.com, Desiretotrade.com, Quantocracy, Traderlife.com, Seeitmarket.com, Benzinga, TradeStation, NinjaTrader and more. Most of my experience has led me to a series of repeatable processes to find, create, test and implement algorithmic trading ideas in a robust manner. Build Alpha is the culmination of this process from start to finish. Please reach out to me directly at any time.
What is Algorithmic Trading?
Algorithmic trading is the act of placing buy and sell orders through a computer. The trading strategy rules can be defined and given to a computer to execute whenever the entry and exit conditions are true. Algorithmic trading can also be referred to as algo trading, automated trading, systematic trading, or mechanical trading.
Algorithmic trading, or algo trading, is the fastest growing trading style as reports already show
60-73% of all U.S. equity trading was done via algorithmic trading in 2018
The algorithmic trading market is growing at a CAGR of 11.23% between 2021-2026.
Algo trading can be applied to any market, timeframe or holding period. Many traders incorrectly assume algorithms only apply to day traders or high frequency traders. All high frequency traders utilize algorithms, but swing traders and longer-term investors may also take advantage of automated trading.
In this complete guide, I will use my decade plus of professional algorithmic trading experience and years of developing Build Alpha to walk you through the
basic principles
simple trading frameworks
sample strategy types
portfolio construction
This guide is for traders looking to get into algorithmic trading as well as those well versed.
Can you make money with algorithmic trading systems?
The most common question I receive is, “can you make money with algorithmic trading”? First, the leading 12 investment banks earned about $2 billion from the portfolio and algorithmic trading in 2020, according to Coalition Greenwich. Check out the key algo trading stats link above.
Furthermore, here is a Registered Investment Advisor client with over $100 million in AUM who shared returns of a few of his clients.
Also, plenty of individual traders can do quite well. There are no limits or restrictions for account sizes. Here are some statements from Build Alpha traders using algorithmic trading strategies with all different account sizes.
Here is a simple strategy that was given away many years ago that continues to do well. This strategy may be too simple as a standalone, but a great idea of what building blocks are possible with algo trading.
It is important to note that all trading, automated or not, involves risk. Additionally, trading returns are often a function of the risk taken. Finally, markets change, and strategies should be monitored on-going. Please respect risk and compete against yourself, not against another trader with a different bankroll. There will be more on risk, monitoring, and position sizing below.
What are the benefits of algorithmic trading?
More markets A trader can only watch so many charts at a time. Algorithmic traders can cover more markets. More opportunity means more edge. More uncorrelated edge should mean more profit; more on this later.
Smarter risk-taking having rules can help avoid taking unnecessary risk when the market data does not support risk taking. If the data supported it, then you would probably have an algo trading system already.
The computer never sleeps fatigue, burnout, hangovers, etc. all play into a manual trader’s profit and loss and buy and sell orders. We all have times where we could have used a couple Z’s. Computers don’t.
Fewer mistakes a wrong hot key, buy instead of sell, fat finger an extra zero, a wrong symbol. It is rare, but trading is hard enough as it is. Avoid these mistakes by using automated trading.
Quantified edge trading rules can be backtested and quantified. How much can I expect to make in the next N trades? How much can I expect to lose in the next N trades? Quantifying leads to smarter trades.
Less emotions angry at the reality of losing hard-earned money? The next trade after a difficult trade can make or break your month if you are not careful. Automated trading avoids trading on tilt and revenge moments. Human traders can benefit.
Rule based [left brain] Having clear trading rules, expectations for profit and loss, and treating trading as a quantifiable endeavor are intensely satisfying for left-brains as this is their modus operandi elsewhere.
Creative [right brain] traders can get lost (in a creative way) in the process of developing an algo portfolio. Developing a new algo can be as, or more, gratifying than making a profitable trade due to the creativeness the development process evokes.
Can you automate stock trading? Can you automate ETF trading?
Stocks and ETFs are the most popular markets and can certainly be automated. The sheer number of symbols and unique companies presents seemingly endless opportunities for algorithmic traders to capitalize on. The 6.5-hour trading day from 9:30 ET to 16:00 ET are perfect for most traders but even better for algorithm traders. Automated stock traders can review pre and post market results and contain their workday within normal business hours.
Stocks and ETFs do present larger gap risks as unscheduled or surprising news can be released during hours where markets are closed. Since computers never sleep some automated traders feel no need to subject themselves to these risks and explore other markets.
Futures markets are arguably the favorite market for automated traders. Futures such as equity indexes, gold, oil, and other commodities like corn, wheat, and lean hogs all trade much more frequently than stocks and ETFs; most futures contracts trade nearly 23 hours a day, 5 days a week. This extended trading schedule certainly lends itself to more algorithmic traders and the need to automate.
Additionally, Futures follow the 60/40 tax rule which taxes the first 60% of gains as long-term capital gains and the remaining 40% at ordinary income rates. Remember the lowest two ordinary brackets for long-term capital gains are 0% so futures provide excellent entry points for small account traders looking to compound their gains while minimizing the tax hit. To read more check out the Section 1256 contract rules.
Foreign Exchange (Forex) is trading one country’s currency for another and trades 24 hours per day, 5 days per week. This around the clock schedule is perfect for automated trading. Forex is often quoted or displayed as six letter symbols such as EURUSD where if the trader were to buy EURUSD pair he would be buying euros (EUR) and selling dollars (USD). Forex does not have a central exchange the way that stocks and futures do. That means, the forex execution price is often left up to the forex trader’s broker dealer (more on brokers later). It is important to note that futures contract equivalents of most of the major forex pairs do exist.
Cryptos trade 24 hours per day, seven days per week, and 365 days per year. Talk about the need for algorithmic trading! For this reason, algorithm trading is very popular among crypto traders.
For those that are unfamiliar, crypto Currencies are digital or virtual currency that uses cryptography to secure transactions. At the time of writing this, the largest market capitalization coins are Bitcoin and Ethereum. Most of crypto currencies are built upon blockchains or online digital ledgers that track all the transactions of a cryptocurrency in public purview. To search for the largest cryptocurrencies, I would recommend using Coin Market Cap.
What kind of Data is needed?
Market data is the lifeblood of algo traders and comes in many forms. Regardless of the market, the most common form of data are time-based bars. Each bar contains a time interval’s Open, High, Low, and Closing price. These bars are often referred to as OHLC bars.
Bars come in all timeframes such as 5-minute, 15-minute or 60-minute bars. 15-minute bars simply means that one bar occurs every 15 minutes and at the end of 15 minutes another bar begins. Here is what a sample data file and OHLC chart look like:
There are many great free sources of daily financial data for all financial instruments. Here are a few free S&P500 daily data sources:
Side note: Build Alpha does come with a full database of all markets, 1 minute up to daily, weekly, monthly for all symbols.
What Type of Programming Language is best for algorithmic trading?
Excel
is expanding by creating functionality to retrieve live stock prices in Excel. Excel was my starting point into quantitative trading and allowed me to view price data, calculate indicators, and build some rudimentary backtests. For longer-term trading, excel is a suitable solution. However, you will quickly outgrow excel when you need faster testing.
Python
is the fastest growing and most popular programming language. First, python code simpler to read and learn than any other language as python is known for its simple syntax and easy-to-read code. Second, many public code libraries are posted all over the internet. Odds are someone already built a python library for your idea. Don’t reinvent the wheel – just use the existing python library. Third, it is easy for data science and machine learning which many professional quantitative traders utilize daily.
Python’s drawback is speed. If you want to search large intraday datasets, then python is rarely the best choice. If you are interested in seeing a concrete example of building a trading signal in Excel and Python then please check out: How to Build an Algo in Excel, Python, Build Alpha
C++
is the fastest, most robust coding language. It is widely considered the best choice for competitive programming by 75% of programmers, according to Geeks For Geeks: Why Cpp Is Best. However, the added benefits of C++ come from the steep learning curve and complex syntax used. My market maker, high frequency trading mentor started me with C++ but there are definitely easier starting points in hindsight.
Most will not need the speed and reliability of C++ and it is often best reserved for professional quants, high frequency trading, and those looking to quickly test trading ideas across tons of data.
No Code
If you do not want to program, then keep reading to learn about Build Alpha – no code algo trading software.
What is a trading broker?
A broker is an intermediary between those who want to trade and the exchange. You need a broker because exchanges require those who execute trades on the exchange to be licensed. Brokers typically charge a small fee, referred to as a commission, for their service. However, many brokers have begun offering $0 commissions.
What is the best trading broker for algorithmic trading?
The brokers I recommend for algorithmic trading are TradeStation and NinjaTrader. Let me breakdown the pros and cons as well as some feedback from the Build Alpha community.
TradeStation
is an award-winning broker well known for their trading platform and proprietary coding language Easy Language. Easy Language is a simplified language created to help traders code. Traders can create strategies with much less effort than a traditional coding language. TradeStation also has beautiful charts and a relatively fast platform.
If you are interested in opening a TradeStation account, please contact me and I can put you in touch with the Build Alpha rep. TradeStation is assisting new traders get started with Build Alpha. Ask me how.
NinjaTrader
is a fast-growing broker that also has their own coding language called NinjaScript. NinjaScript is a simplified version of C# but is a bit more complex than TradeStation’s Easy Language. The added complexity does come with additional benefits and flexibility albeit with a steeper learning curve. NinjaTrader also has a great ecosystem of third-party vendors who release indicators and other trading tools.
Both TradeStation and NinjaTrader allow you code strategies in their proprietary coding languages and automate them directly inside their trading platforms. I know, what if you can’t code? Keep reading!
How to automate my trading system? Coding to Broker or no coding to broker.
You can code your strategy in Python or C++ (or any other language) and connect to a broker’s API or application programming interface. An API is essentially a public library that explains how your code can communicate with someone else’s code.
Both options above are for programmers and those traders with the appetite to learn programming. Coding strategies yourself can lead to errors and has a steep learning curve. Certainly, a measure twice to cut once endeavor.
But what if you do not want to write code? You need strategy testing software that builds algorithmic strategies and generates code for you. Does that exist? Yes. Automate your trading with no coding.
What is the Best Algorithmic Trading Software?
Build Alpha Strategy Builder
Build Alpha is built after my own hardships learning to code and the need to test an endless stream of trading ideas in financial markets.
Instead of coding every single trading idea, I thought I would build software that could test all my ideas at once with no coding.
Build Alpha allows the trader a point-and-click interface to select
symbols
date range
entries and exits
stops and targets
filters and regimes
add your own custom signals
and then passes all inputs to a genetic algorithm which will find the best algo strategies for you.
For traders that do not have time to learn to code or want to test their trading ideas faster than coding can enable, then Build Alpha is the answer. Check out the Build Alpha homepage or request a demo.
What are the types of Algorithmic Trading Strategies?
Different trading rules will create different trading system styles and characteristics. Let’s breakdown the two most popular trading strategy frameworks and their associated characteristics.
Trend Trading
is a trading strategy that attempts to capture large directional moves or continuations in price trends. Trend trading will hold the trade until momentum shifts and is most popular among the swing trading and longer-term trading accounts. The simplest form of trend trading is buying when price crosses above a simple moving average and selling when price falls below a simple moving average. Trend Trading is often characterized by frequent small losses and infrequent large wins. The large infrequent wins often make up for the small infrequent losses.
A quick algorithmic trading history lesson: The Turtle Traders were 23 novice traders who became literal millionaires overnight in the 1970s due to a trend following strategy. Two professional traders and industry experts shared their strategy with these novices to test the novice traders’ discipline. It is an amazing story, and the strategy is well known now after being detailed in books. The strategy is not quite as successful over the most recent decades, however.
Mean Reversion
often called countertrend, trading looks to capitalize on extreme moves in price action assuming price will revert to its average price. Mean reversion can be thought of as the opposite of trend following. Mean reversion can be simply described as a deviation away from a simple moving average or previous average price with the assumption price will return to its average price. As price moves away from the average, traders would look to buy (or sell) shares until price returns to the average. Countertrend trading is often characterized by small, frequent wins and large, infrequent losses.
Are there different types of Algorithm trading?
There are many different types of trading algorithms and many will be left out of this guide to keep this comprehensible. There are a handful of other common algo trading styles worth noting.
Seasonality
According to Investopedia.com, seasonality is a characteristic of a time series in which the data experiences regular and predictable changes that recur every calendar year. A simple example is retail stocks catching a bid before holiday sales numbers or energy stocks selling off after a mild winter. Seasonal trading is the most basic kind of trading rules we can define because it is as simple as buy on this day of the year and sell on this day of the year.
Breakout Trading
is a subset of trend trading. A breakout occurs when price breaks above a significant past price level. Many technical analysts and chart pattern traders enter on breakouts. Traders must be sure to quantify the set up and breakout to truly understand if the breakout is a repeatable edge and works across symbols. Most breakout traders look to exit quickly instead of riding the potential ensuing trend.
“This category of systematic approaches whose signals do not seek to profit from either continuations or reversals of trends. These types of systems are designed to identify patterns that suggest a greater probability for either higher or lower prices over the near term.”
Simple examples would be if today’s close is below yesterday’s low or if today’s high is above the previous two day’s highs. These price patterns can often be combined to gain a sense of where the market is likely to go next.
There are various other types of trading styles. Here are a few more (not an exhaustive list). However, these are not great starting places.
Pairs trading
Index fund rebalancing
Statistical Arbitrage
Market Making
News or Event Driven
How to get started with algorithmic trading? What signals are best for automated trading systems?
Price Action
is comparisons of open, high, low, and close data. The close is higher than the close two bars ago or today opened above yesterday’s high. Candlestick patterns are variations of price action; however, they often have misleading names that do not match the data as I pointed out here: Trading Truths.
Fundamentals
Is the company growing revenue? Are margins expanding? Is the company reinvesting? Quantifying fundamentals is great for institutional investors but rarely used for short-term trading. Public companies are only required to give updates once per quarter.
Technical Analysis Indicators
like volume weighted average price (VWAP) and Stochastics provide price summarizations. I have done many tests with technical analysis, and there is no holy grail. One technical indicator may provide value on a certain symbol or timeframe but may show completely different results on another. Here is a quick video I did on technical indicators: Build Alpha Auto Trading Software testing RSI and MACD Strategies.
Chart patterns
are geometric shapes connecting price levels. Traders draw these lines under the assumption price consolidates before an explosive move. It is important to quantify and test chart patterns. I worked with a PhD in geometry to quantify chart patterns as signals in Build Alpha. Here are the most common chart patterns:
Intermarket
Perhaps you only want to trade stocks if gold is doing XYZ and avoid stocks if bonds are doing ABC. These inter-market relationships are extremely powerful and popularized by the late Murray Ruggiero. His book Cybernetic Trading Strategies is still one of my favorites.
Multi-timeframe
Analyzing what a symbol is doing across timeframes may also lend key insights or confirmation to take a shorter-term trade. For example, enter a long position on the 30-minute if the daily and weekly charts are trending up.
Additional and Alternative Data to Give Context to Trading Algorithms
Economic Data
How healthy is the economy? Is Gross Domestic Product expanding or contracting? Is employment strong or is a recession looming? Deflation or inflation? Any of these factors may influence if we are long or short or what markets we should trade.
Market Breadth
can give insights into what is happening under the market’s hood. How healthy is the market? How many S&P500 stocks are trading above their moving average? Do we notice lots of buying activity on exchange or is most in dark pools? What percent of stocks are near all-time highs?
Risk Factors
watching the volatility index, VIX, can help gauge traders’ and market makers’ appetite for risk. If VIX is spiking, then perhaps trading styles should change. Maybe VIX above a certain level should activate hedging algos. Ignore the market’s risk readings at your own peril.
Options Flow
Understanding options can provide an edge to your strategy development. Options metrics such as risk reversal pricing, gamma exposure, and the relationship between implied and realized volatility can add needed context to trading the underlying securities. How are market makers and dealers positioned? Do you want to fight the tide or ride the wave?
Alternative Data
many professional traders and firms utilize alternative data with price data. This extra data can give their algorithms insights the price data cannot. Alternative data can include scrapping job postings to see who is expanding, tracking rail car weights and shipping cargo weights, monitoring satellite images over parking lots to gauge production levels, sentiment data scraped from twitter, etc. Creativity can often win the day. Build Alpha was designed to accept alternative data as I know how flexible traders need to be.
Backtesting Algo Trading Strategies and Automated Trading Systems
To create a strategy, we need to have historical price data and pre-determined trading rules or signals. We can apply any trading rules to the data to generate the historical trade results – this process is known as Backtesting. I wrote a full guide on backtesting here: Backtesting Trading Strategies.
Build Alpha can create any strategy type we have discussed and generate our list of trades with automatic backtesting. We can also view:
Equity curve – a profit and loss graph to show you how your account would have grown over time.
Performance metrics – such as winning percentage, total profit, drawdown, profit factor, and more. These metrics will be covered in detail later.
Ability to view trades on your chart – is an important aspect to make sure you understand what the strategy is doing.
What is the best strategy tester and backtesting software?
Build Alpha’s backtesting engine is extremely fast and unique as the code is written in C++ making it extremely fast and accurate. Accuracy is incredibly important when reviewing backtested results. I am extremely proud of this software as it helps me (and many other traders) quantify and view strategies in a matter of seconds.
There is no need to write any code to test your strategies as it is a completely code-free software. Simply select your entries, exits, risk management and hit Simulate. Build Alpha’s algorithm does the rest.
Algorithmic Trading Strategy Examples and Building Blocks
Losing money? Let’s backtest three example algorithmic trading strategies. All three focus on SPY, the S&P 500 ETF. Each strategy only contains one entry rule and is not optimized. I will leave optimization, adding filters, and testing on you the reader.
Price Action strategy example 1
Today’s low is below yesterday’s low then buy the next open
Sell after one day.
This strategy beats buy and hold from 2006 through 2022.
Price Action strategy example 2
2-period Relative Strength Index (RSI) is below 20 then buy the next open
Sell after the 2-period RSI closes above 80.
This strategy has outpaced buy and hold while registering significantly less drawdown than the overall market.
Price Action strategy example 3
Current bar closes in the bottom 20% of its daily range, then buy the next open.
Sell after any day closes in the top 15% of its daily range.
This strategy has also beat buy and hold over the testing period.
These three strategies are not meant to be standalone strategies, but act as an example to demonstrate how price action signals can provide a tremendous starting point to trading system development. All these strategies were tested using Build Alpha’s built-in signal library. The next steps could be to add filters, indicators, alternative context, etc. but first let’s explore performance metrics.
Best Algorithmic Trading Performance Metrics
After running a backtest to see the hypothetical trades, we can view performance metrics to help us determine how good our algo is. Below are the most common metrics every quantitative and algorithm trader should know.
Profit and loss or P&L
This is simply the total profit or loss generated by the strategy
Drawdown
the amount the profit and loss fell from its highest amount. If the strategy was up $10,000 and then later was up only $8,000 then this is a $2,000 drawdown. Often simplified to the maximum drawdown as many drawdowns occur.
P&L / Drawdown
is a metric comparing the total profit earned by the drawdown. The higher the ratio the better, generally speaking.
Win Percentage
is the percentage of trades that returned more than $0 divided by the total count of all trades.
Ratio Win to Loss
is a risk:reward metric comparing the average winning trade to the average losing trades. The higher the number the better.
Profit Factor
compares the total gross dollars made on winning trades divided by the absolute value of total gross dollars lost on losing trades. This value is a ratio of two positive numbers so we cannot experience a profit factor less than 0. Additionally, any profit factor from 0 to 1 is not a profitable strategy with losses exceeding gains.
Sharpe Ratio
developed by Nobel laureate William F. Sharpe is a metric showing the ratio of average return compared to the drawdown. More simply, the average annual return divided by the standard deviation of the return. There are arguments for Sortino Ratio which does not penalize “upside volatility” as Sharpe does, but this guide is not the place for a deeper dive.
I need to share the common pitfalls of strategy testing. Later in this guide I will link to a more exhaustive list of tests we can use to improve our probabilities of success, sidestepping these below landmines.
Poor data
missing data can materially impact your results. Assume you had missing data from 2007-2008. Not good.
Look ahead bias
mixing data sets such as intermarket data or multiple timeframes brings a possibility you incorporate unknowable future information. For example, we cannot use the day’s closing price to make a trade at 12 noon as the day’s close is not yet known.
Survivorship bias
many data providers only provide actively listed stocks. We need to include stocks that delisted, went bankrupt, merged, went private, etc. Without these symbols then our backtest may be more favorable than it should be.
Favorable conditions
maybe you tested in a bull market, and any strategy would have done well. Test on all market conditions and environments! I wrote about ways to combat this here: Randomized Out of Sample Testing
Real market environment
can be impossible to know the historical liquidity and some software will allow you to buy more shares than the total volume. This could not happen in real market environment without drastically affecting the share price.
Underfitting
an algo that is too simple will “underfit” the market’s past data. This means it will not capture any unique price anomalies in the future.
Overfitting
an algo that is too complex will “overfit” the data capturing noise and mistake it for unique price anomalies in the future. Over optimizing parameters or having too many rules are the most common culprits of overfitting. Overfitting is often referred to as curve fitting in finance. Curve-fit strategies fail as the market changes and trust me it changes.
Robustness Tests for Algorithmic Trading Strategies
A robust trading strategy can stand the test of time and changing market conditions. Quantitative trading firms employ a litany of statistical tests, stress tests, and robustness checks to attempt to find good fit and robust systems. In this section, I will introduce useful concepts from my professional trading experience and a decade-plus of primary research into the topic of trading strategy robustness.
Robustness testing is a vast area, but if done well, can be a trading edge over other market participants because many fall short here. This is by no means an exhaustive list but can surely increase the probabilities of algorithmic success.
In sample and Out of Sample Testing for Algorithmic Trading Strategies
The first line of defense against overfitting is to split the historical data into two segments: the in-sample data and the out-of-sample data. The split can be placed anywhere but traditionally most traders use 70% of the data as in-sample and the last 30% of the data as out-of-sample.
To learn about intelligently splitting the data, please check out:
We build our algorithmic trading strategy on the in-sample data leaving the out-of-sample data untouched. Once happy with our strategy’s performance, we then test the strategy on the unseen, untouched out-of-sample data.
If the strategy performs well on the out-of-sample data, then we should have heightened confidence our strategy should do well on other unseen data.
Additional Robustness Checks for Automated Trading System
Vs Random
Is this a good strategy or was it pure luck? This test was discussed by Jaffray Woodriff of Quantitative Investment Management in the aforementioned Hedge Fund Market Wizards (Schwager, 2012).
The test creates the best possible random strategy to use as the benchmark to beat. The best possible random strategy is the best possible by chance (random). If we beat the best random strategy’s performance, then it is more likely real edge and not something lucky.
Build Alpha runs this every simulation. Below shows a real strategy vs. the best random strategies possible.
Vs Others
Does the strategy work on other markets? A SPY strategy that fails on other market ETFs such as QQQ, DIA, or IWM is likely overfit to the noise specific to the historical S&P500 data and will fail when noise changes. Strong performance across related markets is a strong sign of robustness.
Vs Shifted and Vs Noise
Markets like oil and corn have natural consumers whereas this is untrue in stocks. Testing across other markets may not make sense in these markets. The fix? Shift or add noise to the original data then re-trade the strategy on the shift or noise-adjusted data.
If the algo remains profitable on the adjusted data, then we get a confidence boost we did not overfit to the historical noise and the strategy can survive future noise variations.
If the noise test shows losing strategies, then we have overfit to the historical data. To read more about the noise test check these out:
These quick explanations skip nuances and certain tests were left out. Please read the comprehensive Robust Trading Strategy Guide to learn about other tests such as: variance testing, delayed testing, liquidity testing, and more.
Trading Algorithm Position Sizing. Can a computer size my trades?
There are a handful of popular position sizing methods used by algo traders. All these methods could be coded or generated from Build Alpha to apply to any strategy. In this section, let’s break down a few of the most popular sizing methods:
Fixed Size
taking the same size position every trade, i.e., 100 shares or maybe 1 futures contract or 1 forex lot. The algorithm would continue to buy and sell 100 shares regardless of price movement.
Fixed Dollar
the trader sets a dollar amount per trade and then the algorithm would buy that dollar amount of shares each trade. As the share price rises, the computer would purchase less shares. As the share price falls, the computer would purchase more shares. It is important to note that the share amount is calculated prior to each entry and unchanged while in a position.
Volatility Based
the computer will size the position based on the symbol’s volatility (typically Average True Range). As volatility rises, the computer will trade smaller positions. As volatility falls, the computer will trade larger positions. It is natural to reduce position sizes as risk (volatility) increases.
Risk Percentage
this method can only be used when the strategy uses a stop loss. The trader determines an amount he is willing to lose per trade. The computer will determine the distance between the entry and stop loss and then size the position so that the max loss is the amount the trader is willing to lose. This aggressive style does not account for when you cannot exit at your stop loss (gaps, illiquidity, missed orders, etc.). Some losses can exceed the amount a trader is willing to lose.
Do I have enough capital for this automated trading system?
One of the largest and most common pitfalls traders make is not setting aside enough capital for their strategy algo. Many traders will look at the historical backtest’s maximum loss or the maximum drawdown and determine this as the amount of capital needed to trade the strategy.
Build Alpha offers a variety of Monte Carlo tests that can quickly aid traders in understanding how flawed the above sizing logic is. Let’s work from an example that had a worst trade maximum loss of $816.88 and a maximum drawdown of $981.38.
Running the Monte Carlo Analysis, we easily see that the average drawdown from the Monte Carlo variations was about $1,500 or 1.5x the original backtest’s drawdown. Not using the Monte Carlo Analysis tool could lead to undercapitalizing the strategy causing a premature plug pull and abandoning ship too soon.
However, there is a better approach to find the correct allocation to any trading strategy or portfolio, the Monte Carlo Drawdown method which was popularized in Howard Bandy’s book Quantitative Technical Analysis.
Funding an algorithmic trading system with Monte Carlo Drawdown Analysis
To run the Monte Carlo Drawdown analysis, the trader selects a percentage drawdown he could stomach and a starting trading account size. Most traders cannot withstand a 20% drawdown.
Next, run 1,000+ Monte Carlo tests resampling from the historical trades to create 1,000+ new equity curves (can reshuffle the trade order as well). Then calculate the drawdown of each new equity curve and plot the 1,000+ drawdowns as a histogram. Finally, plot a cumulative distribution line on top of the histogram (in blue below). This line plots the total percentage of drawdowns as we move from left to right across the horizontal x-axis.
Tracking up the Y-axis to see where 95% intersects with the blue cumulative distribution line. This spot indicates 95% of all Monte Carlo drawdowns were less than this. Connecting this red X to the x-axis shows the drawdown of 30%.
We can say, “we are 95% confident drawdowns from this trading system will not exceed 30%”.
This value is unacceptable to most. We should strive for a drawdown less than 20%. Meaning, the starting trading account value is too small to trade this strategy and we could easily see a drawdown greater than our pre-determined 20%.
If we increase the account size and run the test again, we can see the 95% confidence spot (red X) is now at slightly less than a 20% drawdown on the x-axis.
Sizing strategies – and portfolios – properly is one of the easiest ways to ease the emotions of drawdowns. It also makes sticking to a system long enough to escape randomness more doable in turbulent markets.
To define failing, we must first define passing behavior. Monte Carlo testing once again gives us a guideline. Important to note that other more advanced tests such as noise testing, Vs shifted, Monte Carlo permutation, and Variance testing could also apply. Monte Carlo results give us a cone of what is possible at trade number X.
In the graph above, we can see that after 100 trades the strategy’s P&L should be anywhere from $0 to $15,000. This is a large range but gives an idea of what our expectations should be at trade 100.
If at trade 100 the P&L is between $0 and $3,000 then we certainly had some bad luck, but the strategy is not broken! The strategy is just in the lower range of what was possible (bad luck). On the other hand, if the strategy’s P&L at trade 100 is between $12,000 and $15,000 then we had tremendous luck. This success was possible despite not being a conservative estimate at trade 0.
If we are at trade 100 and the P&L is less than $0 or greater than $15,000 then we are outside expectations. This is the first sign things are broken (not going as expected).
Build Alpha has a tool that generates Monte Carlo bands from the last trade and plots forward as many trades as desired. It is best practice to only trade strategies that have a flat or upward sloping lower band.
If performance is falling outside the red Monte Carlo bands, then it is best to do at least one of the following:
Algorithmic trading success needs the portfolio approach
The perfect strategy or “holy grail” hunt is often the worst adventure one can embark upon. I lived this for many difficult years chasing chat rooms, magic indicators, and more to hopefully learn that the one missing piece. Turns out there is no one missing piece.
The holy grail is combining uncorrelated strategies to smooth returns. Ray Dalio of Bridgewater, the largest hedge fund in the world, shared a graphic in his book Principles showing the math behind the true holy grail: adding uncorrelated strategies together.
On the right-hand side, the more uncorrelated strategies you combine the lower your probability of losing money. Lightbulb moment. I don’t need a perfect strategy; I need a bunch of good strategies working in concert.
Michael Jordan needed someone to do the dirty work: play aggressive defense, check defenders down low, grab rebounds, make hustle plays. He did not need someone else to score points. Your portfolio needs a team mentality. Some players are not great on their own (cough Rodman), but on a team, they fill a role making everyone exponentially better.
That’s right, some strategies may not be profitable but still additive to your portfolio! Certain strategies do well in certain markets while other strategies do well in other market conditions (aka doing the dirty work the core strategies cannot).
In the graphic above, strategies A and B are the best standalone strategies as strategy C has a negative return! However, when combined (and scaled to the same volatility), A+C do much better than A+B. When A yins, C yangs. Conversely, A and B ebb and flow at the same time (two scorers). A+C provide an awesome example to the team, or portfolio, approach Dalio mentioned.
There is more to be gained understanding this than hunting the perfect strategy.
Key Takeaways
Algorithmic trading volume is rapidly growing
Algo trading is used by the largest banks and best hedge funds
Algorithmic trading is possible for all asset classes: stocks, futures, mutual funds, forex and crypto
TradeStation and NinjaTrader are the best data providers and brokers
Python and C++ are popular programming languages for algorithmic trading
Build Alpha software enables traders to build algo strategies without writing any code
A portfolio of algos is better than hunting a single perfect “holy grail” strategy
Need to Know
Algo trading is when a computer executes pre-determined buy and sell rules for you
Algo trading does not require programming. Build Alpha is a no-code strategy builder
Hedge fund and professional software and testing is now available to all algo traders
Finding robust strategies requires additional testing after backtesting
Monte Carlo Drawdown technique can help size automated trading strategies correctly
Portfolio approach to algo trading is the “holy grail”
Summary of the Complete Algorithmic Trading Guide
Automated trading systems are the fastest growing portion of the market comprising nearly 75% of the daily volume according to the previously mentioned 2018 study. The 12 largest banks and top hedge funds, including the best hedge fund of all time Renaissance Technologies, all utilize algorithms and automated trading.
The data and tools to build your own algorithmic trading strategy have never been more accessible or even available to individual traders as they are today. Build Alpha is an all-in-one, start to finish algorithmic trading strategy development tool that enables traders to follow the steps laid out in this guide without having to write any code.
I am on a mission to develop Build Alpha as the best automated trading software. Please take the ten minutes to check out the demo and see how it can help any level of trader save time, reduce uncertainty, and hopefully gain an edge in algorithmic trading. If you have any questions, please email me anytime. For more on Algorithmic Trading please check out FINRA the Financial Industry Regulatory Authority.
Author
David Bergstrom – the guy behind Build Alpha. I have spent a decade-plus in the professional trading world working as a market maker and quantitative strategy developer at a high frequency trading firm with a Chicago Mercantile Exchange (CME) seat, consulting for Hedge Funds, Commodity Trading Advisors (CTAs), Family Offices and Registered Investment Advisors (RIAs). I am a self-taught programmer utilizing C++, C# and python with a statistics background specializing in data science, machine learning and trading strategy development. I have been featured on Chatwithtraders.com, Bettersystemtrader.com, Desiretotrade.com, Quantocracy, Traderlife.com, Seeitmarket.com, Benzinga, TradeStation, NinjaTrader and more. Most of my experience has led me to a series of repeatable processes to find, create, test and implement algorithmic trading ideas in a robust manner. Build Alpha is the culmination of this process from start to finish. Please reach out to me directly at any time.
What does EDGE in trading mean?
In financial markets, trading edge is a temporary advantage over other market participants. Having an edge in trading can exist in many different forms but the two most common are information edge and price edge.
Information edge would be insider trading or material non-public information. For example, knowing a company signed a large deal that will greatly increase revenues before other market participants know would be an information edge.
On the other hand, price edge exists from research and data analysis and is a far more common way to build a trading career. Quantified technical analysis or knowing that prices that reach this level have historically been cheap over the next N days can present a price edge. Renaissance Technologies, the greatest hedge fund of all time, is quoted as saying
“Renaissance essentially attempts to predict the future movement of financial instruments, within a specific time frame, using statistical models. The firm searches for something that might be producing anomalies in price movements that can be exploited. At Renaissance they’re called “signals.” The firm builds trading models that fit the data.” Source: The Secret World of Jim Simons | Institutional Investor
Regardless of the trading style, swing trader or day trader, a successful trader must possess some edge – it is of vital importance. Trading edges are necessary in the stock market, forex trading, futures markets, crypto, etc.
Trading results will evade you and success will always flow to other traders who possess the ability to identify an edge and manage risk.
What is an example of a trading edge?
An information trading edge can be knowing a company’s quarterly numbers before the public does or knowing merger and acquisition news ahead of the public. This is not legal activity but unfortunately has existed in the stock market since day one.
An example of price edge can simply be putting on a position at a price that has been historically cheap compared to other markets or to its own historical patterns. Trading edge based on price can often be quantified and is 100% legal. This is the most popular form of trading edge and what will be referred to in this post.
Trading Edge Formula
In short, edge is positive expectancy. What is and how do we calculate expectancy?
Expected value is the amount a trader should expect to gain if he were able to place infinite similar bets. EV, for short, is technically defined as the sum of all possible outcomes multiplied by the probability of each outcome’s occurrence. EV can be used as the most rudimentary form of trading edge.
EV = (Probability of Win * how much you win) – (probability of loss * how much you lose)
If EV is negative, we should not take the trade. We do not need a high win rate if the winning amount is large in comparison to what we could lose (think trend following).
There are two levers to move: win rate AND risk:reward. Too many traders get focused on one or the other. This graph is great to visualize how risk:reward and win rate relate.
All values based on $1 risk. 2.5 Reward indicates a profit of $2.5 and a loss of $1
Notice experienced traders can make more trading a 50% win rate strategy with 2:1 risk:reward compared to a trader who is accurate 70% of the time with a 1:1.
Now let’s dive into an example to make sure we fully understand expected value. Let’s discuss a coin-toss game where we have two scenarios:
Heads happens 90% of the time and you win $1 each time.
Tails happens 10% of the time and you lose $10 each time.
Would you play this game? Most beginner day traders salivate at 90%-win rates. However, the EV for this game is negative! The formula calculates 90% x $1 + 10% x -$10 = -$0.10. Edge needs positive expectancy.
This is a very important lesson that even with a 90%-win rate you can lose money! And trust me, you won’t win 90% of the time.
Do you need edge in trading?
Yes. If you do not have an edge, you are trading randomly, and no one can succeed randomly as trading has costs and randomness includes negative performance streaks. Most traders and trading strategies fail because they do not know or have an edge.
Most traders do not know or have an edge because they have never quantified their edge to see if it is truly an advantage over time, across symbols, and timeframes. New traders must understand this is necessary to becoming a profitable trader.
The first thing we need is an edge and a way to quantify that edge. Enter e-ratio or edge ratio.
What is Edge Ratio?
Edge Ratio is a quantified metric demonstrating how much favorable price movement occurs in comparison to how much adverse price movement occurs. Edge Ratio measures how much a trade goes in your favor compared to how much a trade goes against you. Edge Ratio or eratio quantifies trading edge.
I wrote extensively about e-ratio here: Edge Ratio in Automated Trading but will give a quick synopsis here.
We can plot eratio where the X-axis is the bars from the entry and the y-axis is how much more the trade is in your favor versus against you.
Blue edge ratio for trading signal. Red edge ratio for random trading signal for comparison
In the image above, we can clearly see that we have the most edge (blue line) six bars into this trade. The edge ratio is about 1.6 meaning we get about 0.6 more units of volatility in our favor than against us.
Trading any signal or entry that has an edge ratio below 1.0 is no better than a random entry determined by a monkey throwing darts.
I also spoke about edge ratio on the Better System Trader podcast.
Can we know where to exit positions? Using edge ratio, or eratio, we can see when a trading edge begins to deteriorate. Knowing the edge ratio of an algorithmic trading strategy or any entry can help you determine the best exits and when your trade has overstayed its welcome.
In the above eratio image, you can see that after a hold time of six bars, the trading edge begins to deteriorate. In these cases, the best exit is around the six-bar mark as that is when the trading edge has shown most powerful.
More Winning Trades than Losing Trades does not mean Edge
Retail traders develop a trading strategy and wind-up optimizing parameters until the past performance shows more money than God. This trading approach often leads to overoptimized strategies that look great on historical data but have miserable live trading future results.
An individual trader should look for robust strategies that possess true edge and can survive various market conditions. A backtest that shows more winning trades than losing trades but has a negative or flat e-ratio is often an obvious sign of overfitting. Before you start trading new strategies live, always check the e-ratio to see there is actual edge to be captured and the backtest wasn’t pure luck.
Ways to Visualize Trading Edges
Edge Ratio is the best quantified measure of edge for algo traders, but there are a handful of other approaches to visualize your trading edge that are noteworthy.
Heatmap
we can view the forward returns from the entry where the x-axis shows bars since entry and each y-axis row is an individual trade. The color represents how profitable (green) the return was at each bar from entry. Analyzing two different signals on two different heat maps can clearly signal what was more profitable.
Equity Curve
the equity curve of an algorithmic trading system sums all trades to show the historical account value over time. We can think of the equity curve as the money earned (or lost) over time had a trader followed a specific rule or set of rules. Often strategy choices are obvious.
Distributions
viewing all possible outcomes from a potential strategy can yield greater insights than just the heatmap or equity curve. We can run statistical tests like Monte Carlo Simulation and Analysis – more on this later – or other tests to generate a better sense of what is possible. We can think of this as how does my algo trading strategy do in alternate universes where the price data was similar but not exactly what my universe experienced.
Which P&L range would you prefer to be in after 50 trades? Some strategy paths are negative on the left where all are positive on the right. If markets change, trading systems like the one on the right can provide a buffer.
To read more about visualizing trading edges please check out my original post on See It Market here: What Does Trading Edge Look Like
Best Edge in Trading
Not every trade is a winner and there is no single best edge in trading. The best edge in trading is simply having one and executing it with discipline. We can quantify edge with edge ratio so that presents one way to rank our trading strategies. More capital should go toward stronger edges than weaker ones.
However, weaker edges can be combined via ensemble methods to potentially create stronger ones. I wrote about ensemble methods and strategies here: Ensemble Strategies – Build Alpha
Mental Edge in Trading for additional winning trades
Having a mental edge can also constitute a trading edge. The trading industry has convinced many traders that day trading and using a trading journal creates some mental edge. However, a mental edge should be thought of as additive and not a stand-alone edge!
For example, having a mental edge but no price edge cannot make for profitability. A mental edge in trading is the ability to remain disciplined and execute your price edge as planned (and as many times as possible).
With no price edge, having mental fortitude or mental discipline still means executing randomly but just in a calm fashion. Trading starts when we combine both mental edge and price edge.
Additionally, a mental edge can be aided with automation and algorithms. Utilizing automation is the simplest way to ensure your price edge is executed in a systematic fashion each and every time the edge reveals itself. To learn more about algorithmic trading please check out this full guide: Algorithmic Trading Guide.
Trading Edge, Winning Trades, and the Law of Large Numbers
In the short-term, everything is random. In the long run, things tend to converge toward their expected value (which is our trading edge). This mathematical concept of convergence is known as the law of large numbers.
Many traders fail due to strategy hopping. That is, chasing the new shiny object, the next best strategy.
A terrible trader’s journey that most are on: find a strategy that does well for a while; however, when it starts to struggle either a) tweak it or b) learn some new strategy.
Either option changes the initial strategy aka strategy hopping. This tweaked or new strategy does well live trading for a while but ultimately arrives at another decision point between a) tweaking it again or b) finding a new shiny strategy.
Traders fail to give any strategy time to play itself out. This keeps them in short-term randomness!
The best illustration of short-term randomness and long-term expected value is to view a coin toss over time. Fair coins should land on heads 50% of the time. However, in small amounts of coin tosses we see large variations in the percentages. In only 30 tosses, we saw heads come up 73% of the time and in two other 30 coin toss trials we saw heads come up 43%.
However, after 100 coin tosses the percentages of heads converge toward the expected value of 50%. The law of large numbers is starting to work its magic!
After 10,000 coin tosses the percentages are almost always 50%. Assuming all things remain the same, the law of large numbers is inevitable.
Trading Example of the Law of Large Numbers
This algo strategy below has a remarkably smooth equity curve that most of us would love to see on our brokerage statements. This algo system averaged about $170 in profit per trade.
However, if we track this algorithmic trading strategy’s average trade over time, we can see that in the beginning, when our trust in the trading system is the lowest, it is a rather bumpy ride and far from the $170 per trade edge we wind up with. It takes well over 100 trades for the average trade to converge to the actual average!
Most traders cannot stomach this short-term “randomness” and wind-up abandoning ship to chase a new shiny object, indicator, or “holy grail” strategy.
Traders that fall prey to strategy hopping never let the edge play itself out and sadly never make it. If this is you – stay on the demo account! Trading decisions like this are often missed by other participants and take time to learn.
Some never escape this short-term randomness. My mentor explained this short-term randomness, long-term obviousness concept to me and a lightbulb clicked. Edge plus time is the name of the game.
Escaping Randomness was the perfect title of my Chat with Traders interview where I discuss how to overcome randomness and why algorithmic trading can help traders think about edge and risk-taking in a more productive way. If you haven’t already, please check out Aaron’s wonderful work here: Escaping Randomness with David Bergstrom.
Trading Edge Ideas and Trading Edge Examples
This blog post would be incomplete without mentioning some simple trading edges and where to begin your search to generate ideas for your own trading edges and limit your own risk.
Time of day trading
many stocks and commodities exhibit strong time of day patterns. For example, the S&P500 has been very strong for day traders from 2 AM ET to 3 AM ET for the last 15-plus years.
Buying one contract in e-mini S&P 500 at 2:00 AM ET and selling one hour later
Seasonality
many stocks show strong seasonal tendencies where repeatable patterns or price movement happens during the same calendar dates year after year.
Price Action
bar patterns and price comparisons can be a strong starting point to identify trading edges. However, always investigate and test thoroughly. Do not be misled by industry given names such as Bearish Engulfing. Read about trading “truths” here.
Relative Value
comparing one asset to another can provide an edge relative to another asset. This can assist non-directional traders, pair traders, and relative value traders with many trades in many markets.
Alternative Data
Using alternative data such as market breadth, economic data, interest rate spreads, Vix term structure, options flows, and more can provide additional areas to amplify or add trading edge to your portfolio.
some available alternative data signals in Build Alpha
Using Build Alpha to enhance Trading Edge
Build Alpha allows you to require entry signals forcing the software to use these required signals on every strategy the genetic algorithm creates. The trader can also select thousands of other non-required signals to combine with any required signals. This process will then find the best complimentary signals for the required trading signals. This can take a known edge and enhance it without much effort or time.
Additionally, Build Alpha provides various ways to visualize trading edge for any strategy including e-ratio, monte carlo distributions, equity curves, and heat maps.
Finally, no edge is complete until it passes stress testing. Build Alpha offers the largest suite of robustness tests and checks for algorithmic trading strategies. Robustness testing is an edge of its own as many strategies fail in live markets.
Trading edge is necessary for trading success in any market
Information edge is often illegal. Price edge is used by the best
Expected Value and positive expectancy is often a great first step to quantify edge
Edge Ratio is the best way to quantify edge and risk
Let your market edge play out in time. Law of Large Numbers takes time to be profitable.
Various types of trading edge exist including time of day, price action, alternative data
Build Alpha algorithmic trading software can speed up the edge hunting process
Summary of Edge in Trading
Finding an edge in trading is often the difference between account growth and trading failure. There are two primary sources of edge in trading: information edge and price edge. Information edge consists of insider information or material non-public information. Price edge consists of data analysis and finding opportunities that others are unwilling to take.
The best traders quantify and rank their edges before deciding allocations. Edge Ratio is the best way to quantify and compare trading edges. In the end, finding one setup or trading edge is the key to long-term success in the markets. Creating a process or using software to quickly identify edges can be the difference between growth or trading randomly. Day traders be aware!
Author
David Bergstrom – the guy behind Build Alpha. I have spent a decade-plus in the professional trading world working as a market maker and quantitative strategy developer at a high frequency trading firm with a Chicago Mercantile Exchange (CME) seat, consulting for Hedge Funds, Commodity Trading Advisors (CTAs), Family Offices and Registered Investment Advisors (RIAs). I am a self-taught programmer utilizing C++, C# and python with a statistics background specializing in data science, machine learning and trading strategy development. I have been featured on Chatwithtraders.com, Bettersystemtrader.com, Desiretotrade.com, Quantocracy, Traderlife.com, Seeitmarket.com, Benzinga, TradeStation, NinjaTrader and more. Most of my experience has led me to a series of repeatable processes to find, create, test and implement algorithmic trading ideas in a robust manner. Build Alpha is the culmination of this process from start to finish. Please reach out to me directly at any time.
Complete Guide + Free Simulator
Monte Carlo Simulation for Algorithmic Trading
A statistical technique that injects randomness into a dataset to create probability distributions for better risk analysis and decision-making.
Monte Carlo Simulation is a statistical technique that injects randomness into a dataset to create probability distributions for better risk analysis and quantitative decision-making.
In trading, Monte Carlo simulation is most commonly used to randomize or resample the order of historical trades to estimate a strategy’s potential drawdowns, streaks, and equity curve variability in alternate “possible histories.”
The most common Monte Carlo Simulation randomizes the order of a data set to demonstrate an alternative path a data set could have experienced — this is particularly useful for algorithmic traders and trading systems to see different outcomes of past trades.
In financial markets, quantitative traders use the most common Monte Carlo Simulation method to reshuffle the order of their historical trades to help them better understand how a trading system could have happened. Algo traders aim to answer the question: if the order of trades was not identical to the backtest’s order, would I still be comfortable trading this strategy?
The equity curve — the cumulative profit of trade results — can provide insights into how smooth a trader’s account may grow following a particular algorithmic trading strategy. Monte Carlo provides a method of re-simulating the trading strategy to see how “bumpy” the ride could have been in an alternate reality and even acts as a peek into what the future may hold.
The purpose of Monte Carlo Simulation is to detect lucky backtests and misleading performance metrics before risking real capital.
Why are Monte Carlo Simulations Used?
Monte Carlo Simulations help better simulate the unknown and are typically applied to problems that have uncertainty such as: trading, insurance, options pricing, games of chance, etc. The goal is to gain a better understanding of all the possible outcomes and potential minimum and maximum values.
Why re-simulate an equity curve? Algo traders use Monte Carlo simulations to determine how much luck was involved in a strategy’s backtest and if future performance is likely to look like past performance. If the trading system was overly lucky, then it would be nice to know before risking real capital.
A trader may backtest a trading strategy and notice an acceptable maximum drawdown; however, after running a Monte Carlo test the drawdown may be much less tolerable. This method could save the trader risking capital on a strategy he could not stomach in live trading.
Benefits of Monte Carlo Simulation
Better Understanding of Drawdown
Reshuffling the order of your trades can lead to different profit and loss sequences which can result in a greater drawdown. A trader may believe the backtest’s drawdown is the worst it can get, but Monte Carlo analysis may show a much larger drawdown for most trading systems.
Properly Fund Your Trading Strategy
Noticing a larger maximum drawdown from a Monte Carlo simulation can help a trader better capitalize a trading strategy. This can make a live drawdown more bearable and allow the trader to stick to the original plan. A trader that experiences a live drawdown greater than the backtest’s drawdown may prematurely turn off a winning strategy if not familiar with Monte Carlo simulations.
Understanding Possible Win and Loss Streaks
The backtest may show a maximum of 5 or 6 losing trades in a row but a Monte Carlo test may show that 8 or 9 losing trades in a row is possible. The trader can better prepare for this adverse situation equipped with insights from a Monte Carlo simulation.
Set Better Expectations with Quantitative Analysis
Many traders view the backtest as a guide for what to expect. However, a Monte Carlo test may show a wide range of possible profit and loss scenarios. This data analysis can help a trader remain calm enough to stick to the strategy when luck becomes favorable or unfavorable.
How to Use Monte Carlo Results
Compare the backtest drawdown to the 90th or 95th percentile Monte Carlo Drawdown
Size the strategy so you can survive a statistically likely drawdown (not just the backtest’s max drawdown)
Use streak distributions (max loss streak) to set expectations and avoid turning a good strategy off at the worst time
If Monte Carlo shows extreme sensitivity, revisit constraints (trade frequency, exits, filters) before going live
You can also import your own strategies into Build Alpha to run Monte Carlo Simulations on them!
Monte Carlo Testing for Drawdowns
Beginner traders find a successful backtest and think they have struck gold. However, many beginning traders are misled by overly optimistic backtests. Often the most important performance metrics such as net profit, standard deviation of historical trades, and consecutive winning trades will be inflated. The maximum drawdown from a backtest is often the most misleading metric!
This example strategy below shows a backtest drawdown of $1,663.90.
However, after running a simple Monte Carlo Simulation on the same trading system we can see the worst resample drawdown from all simulations is $5,195.17 or 3.1 times as large as the backtest’s drawdown!
A trader sized based on the backtest would prematurely cease trading and turn a potential winning strategy off or not have enough capital allocated. A simple Monte Carlo analysis could prevent this.
Monte Carlo Methods: Different Types and Uses
Reshuffle
Reshuffles historical trade order 1,000 times creating 1,000 new equity curves. All curves end at the same total P&L — but the paths change dramatically, revealing alternate drawdown scenarios.
Resample
Randomly selects historical trades with replacement until reaching trade count. The same trade can appear multiple times. Creates broader distribution — curves don’t end at the same spot.
Randomized
Re-trades original entries while randomizing each trade’s exit. If results remain profitable, your entry likely contains true edge. Catches overfit exits — a key lying backtest detector.
Permutation
Reshuffles log inter/intrabar price changes and exponentiates to create synthetic data with same statistical properties but destroyed patterns. Re-trade on 1,000 new price series.
Reshuffle
This method reshuffles the original trades, so all 1,000 equity curves end at the same total profit and loss amount but with wildly different paths.
Resample
Resampling with replacement means not all simulations end at the same amount. This test provides more variation as the worst (or best) trade can be selected multiple times.
Randomized
If the Randomized Monte Carlo results remain profitable, then it is likely our entry contains true edge. The above example saved us from a lying backtest.
The new permutated data contains the statistical properties of the original data but destroyed most of the patterns. Profitable results are a good sign the strategy is robust. Popularized by Timothy Masters in Permutation and Randomization Test for Trading System Development.
Advanced Uses of Monte Carlo for Trading
Monte Carlo Equity Curve Bands
Is my trading strategy broken? Randomly select 100 historical trades and add them to the end of the backtest’s equity curve. Repeat 1,000 times and keep the 5th and 95th percentile equity curves. If future trading falls outside these bands, it’s an early warning sign the strategy is broken.
Monte Carlo Drawdown Technique
Answers: “How confident am I in this strategy’s drawdown?” Uses the resample method to create 1,000 new equity curves and 1,000 new drawdown values. The blue cumulative distribution line shows what percentage of drawdowns fall below each value.
The red X indicates where 95% of all Monte Carlo drawdowns were less than the corresponding value. “We are 95% confident that drawdowns from this system will not exceed 30%.”
Understanding the drawdown probability distributions and sizing with statistical confidence can make drawdowns easier to manage.
The ideal number is 1,000 or more. To truly leverage the law of large numbers and get reliable results, one should strive for 1,000+ simulations; however, results are generally acceptable with 100 or more.
Running a single simulation could create a lucky result. Imagine a Resample test that luckily resamples only winning trades. Is this likely? No. Is this possible? Yes. Should we base trading decisions off of this? Absolutely not.
There may still be lucky results in 1,000 runs, but we can get a better sense of what is reasonable to expect from a larger probability distribution.
Equity Curve Simulator and Probability Distribution
An equity curve simulator is a tool that accepts winning percentage, average win, and average loss amounts to simulate how an equity curve’s sequence may happen. It is very important to note that a trader can be profitable with a lower winning percentage and larger winning trades or with smaller winning trades and a higher winning percentage.
To learn more about expected value please check out the complete guide to Algorithmic Trading.
Look at this expected value display below to see how winning percentage and average win to loss ratio affect profitability:
All values based on $1 risk. 2.5 Reward indicates a profit of $2.5 and a loss of $1.
I have built a free equity curve simulator where you can input your expected winning percentage, average winning trade and average losing trade to simulate your expected equity curves. Knowing how these values cooperate can help with algorithmic trading strategy design, risk management, avoid a poor strategy, and analyze trading results.
Monte Carlo Simulator
Probabilistic trade outcome modeling across randomized trial sequences
Free Tool
Mean Final P&L
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Median Final P&L
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Profit Probability
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5th Percentile
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95th Percentile
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Avg Max Drawdown
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Equity Curves (sample paths)
Median
Profitable
Unprofitable
Run simulation to view equity curves
Final P&L Distribution
Run simulation to view distribution
For educational purposes only. Monte Carlo simulations assume random trade ordering and normally distributed outcomes. Past performance is not indicative of future results. Risk capital only.
Monte Carlo Simulation in Excel
Microsoft Excel is often good enough for simple quantitative trading endeavors. Simply copy and paste your trades into column A and it will generate your equity curve in column B. Then drag columns D:N down to end at the same row number as total trade count. Press ‘F9’ to simulate a new Monte Carlo test.
Build Alpha is powerful automated trading software that enables traders to create hundreds of algorithmic trading strategies with no programming needed. Any strategy created can be put through all the Monte Carlo methods listed in this guide. Simply highlight the desired strategy in the results window, then select Monte Carlo Analysis on the right-hand side.
Build Alpha also returns statistics on winning and losing streaks from the simulations. You’ll see additional buttons for the advanced methods — Monte Carlo Equity Bands and Drawdowns.
What is the Best Monte Carlo Simulation?
There is no best method as each has different intended uses. Reshuffle and resample are primarily for drawdown and risk estimation. Randomized and permutation tests are for strategy robustness and future viability — true stress tests. MC Drawdown creates confidence intervals around expected drawdowns. Equity bands monitor live trading and identify broken strategies.
A professional quant trader should incorporate all into the strategy development process.
Key Takeaways
Monte Carlo simulation is a statistical technique to help uncover luck in backtests
Most popular method is to reshuffle historical trades to view alternative account paths
Can help estimate realistic drawdowns and possible outcomes
Can help size trading strategy properly
Need to Knows
There are many different Monte Carlo methods
Inserting randomness into historical trade results to better estimate uncertainty
Drawdown estimation is the most common use
Large number of simulations needed — 100 minimum, 1,000+ ideal
Can be used to monitor strategy performance and identify broken strategies
Summary
Monte Carlo Simulation works in various ways. The most popular methods help traders identify luck and more appropriate drawdown measures than a simple backtest can provide. Reshuffle and resample help simulate various equity curves with alternative trade sequences. Randomized and Permutation tests aim to test strategy robustness. Equity bands aid in identifying early signs of a broken strategy. MC Drawdown assists traders in finding proper confidence intervals.
Monte Carlo Simulations are arguably the most popular quantitative trading tool to add to your algorithmic trading toolbox.
Questions
Monte Carlo FAQ
Does Monte Carlo prove a strategy will work?
No. It helps traders estimate risk ranges and detect fragile, luck-driven backtests.
Reshuffle or resample — which should I use?
Reshuffle preserves the exact set of trades but changes order; resample can repeat trades and creates a broader distribution. Many traders review both and take the worst case scenario.
What’s a good number of simulations?
1,000 is a good rule of thumb. Fewer can be informative but less stable and potentially misleading.
What if my live equity curve breaks outside the Monte Carlo bands?
This can be an early warning sign that conditions changed or assumptions are off. Re-check data, slippage, regime alignment, and whether the strategy’s edge is still present.
The guy behind Build Alpha. A decade-plus in the professional trading world as a market maker and quantitative strategy developer at a high frequency trading firm with a CME seat, consulting for hedge funds, CTAs, family offices, and RIAs. Self-taught C++, C#, and Python programmer with a statistics background specializing in data science, machine learning, and trading strategy development. Featured on Chat With Traders, Better System Trader, Quantocracy, Benzinga, TradeStation, NinjaTrader, and more.
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Build Alpha includes Reshuffle, Resample, Randomized, Permutation, Equity Bands, and Drawdown analysis. No coding required.
Backtesting is applying a trading strategy on historical market data to view how a particular strategy would have performed in the past. A backtest will return the hypothetical total profit, a list of historical trades, and many other performance metrics to analyze a trading system’s potential viability in financial markets.
For example, we can take the past ten years of data and apply our strategy – in hindsight – to view how the strategy would have performed over these past ten years. The backtest’s results can help drive trading decisions such as:
discard the trading strategy
tweak the strategy
investigate the strategy further
move the strategy to a demo account for monitoring
What is Backtesting Used For?
Backtesting gives traders and investors a sense of a strategy’s profitability on historical data which may give insights into how the strategy can be expected to perform in the future.
However, please note, all disclaimers in financial markets will warn that historical performance is not indicative of future performance. However, seeing positive past performance can grant a trader confidence.
In short, backtesting is testing trading opportunities on historical data to assess the historical profitability of a trading strategy or market environment.
Backtesting can be used to check the viability of a single trading system, gain a better understanding of how a strategy would perform during certain market environments, or to compare competing trading signals when capital may be limited.
Why is Backtesting Important?
Backtesting provides a way to analyze risk prior to risking real capital. The immediate insights a backtest provides can potentially save the trader money by helping him avoid a potential losing strategy.
If a trading strategy performs poorly, losing money during a backtest, then there should be less incentive to live trade the strategy with real capital as it may result in losing money rapidly.
On the other hand, a good backtest should be a requirement for risking capital. This does not mean that a good backtest is a greenlight to bet the farm, but a trader should desire to see a profitable backtest before committing real capital.
Backtesting provides important performance statistics of a potential trading strategy such as:
potential profit and loss
potential drawdowns
total trades
winning percentage
risk/reward
largest winning trade
largest losing trade
consecutive winning streaks
consecutive losing streaks
All these insights can better help prepare the trader for live trading the strategy, the risks involved, gain confidence, or choosing between two competing strategies which all help contribute to long-term success.
What do you need for Backtesting a Trading Strategy?
There are only two things you need to conduct a proper backtest:
Historical Price Data for desired time period for the test
Trading Idea (or Idea Generator)
These two points are enough to conduct manual backtesting where you can replay the charts, view where you would have entered, and track your strategy on paper or with virtual money. This is often enough for testing purposes before you start trading. A bit more on these two points below.
Historical Data Needed for Backtesting
What kind of Data is needed for backtesting?
The most important element for running a backtest is historical financial data. Financial data normally comes as a time series in OHLC or open, high, low, close format. Below is an example of historical SPY data.
The goal is to get enough trades from the backtest to feel confident the trades were not lucky and perhaps find an effective trading strategy.
For example, if you flip a coin 10 times and get seven heads then we cannot determine if the coin is rigged. However, if we flip the coin 10,000 times and get 7,000 heads then we can be sure the coin is rigged.
This is because as the sample size increases, we should converge toward the expected value. This mathematical concept is known as the Law of Large Numbers. I give a full breakdown in my Robust Trading Strategies Guide.
We really want to see at least 100 trades or more to avoid “luck” playing a role. Note this is a rule of thumb and not a steadfast number. If our strategy trades once per month then we would need 100 months of data or roughly eight years. However, if our strategy trades more frequently then you can backtest with smaller data histories.
Bull and Bear Data
There are many other considerations such as all data coming from a bull market or should data be from both a bull and bear market, etc.? However, these advanced questions are answered in the above-mentioned guide.
For now, we need enough data to generate enough trades. This should be your only concern.
Backtesting different time periods and market conditions
It is wise to include different time periods or market environments in your backtest. For example, a long breakout strategy may do well in a bull market and poorly in a bear market. If your backtest only contains historical data during a bull market, we may be deceived into believing we have found a holy grail. Does it work on another time period?
Backtesting different markets
Do you need stock market data if you are testing a forex strategy? A forex trading strategy may only require currency pair data and fx traders may have no need for stock or future data. Trading cfds and futures or stocks may require different data sets or time periods but a strategy that backtests well across different markets can be a reliable indicator to future results. Quantitative strategies that perform well across financial instruments, showing similar performance on various symbols, can be a key component to success.
Finding Trading Ideas for Backtesting
If you have been around the markets long enough, you will begin to create your own trading ideas but there are various other ways to quickly stumble upon trading ideas to backtest. Below are some basic steps to sourcing new trading ideas.
Screen Time
The more time you spend watching live market charts and order books the more patterns you will identify. These patterns are great ideas to test for viable trading strategies.
Trading Books
Most naïve traders make false claims about trading books, “if this was profitable why would the author write about it”? In reality, there are many great trading books and ideas out there.
Trading Papers
Brilliant researchers publish white papers and research papers that leave hints, ideas, trading methods, and further areas to research. Arxiv.org is a great resource and even has a quantitative finance filter.
Technical Indicators
Viewing technical analysis indicators can give insights into how other traders are looking to summarize trading concepts. These can be incorporated into your own strategies and backtests.
Alternative Data
Using alternative data can help add context to price action signals or provide new trading ideas. For example, companies that have many job openings and listings may be expanding quickly!
Genetic Algorithm or Machine Learning
A genetic algorithm takes massive amounts of inputs and attempts to mix and match to find the best possible combinations (crude simplification). In theory, it is generating new ideas for you. This is the core behind Build Alpha no code algo trading software which I will describe later.
Machine learning works in much the same way and can also be thought of as a way to create new trading ideas or a predictive model.
Backtesting Programming Language or Software
To apply our trading idea on the historical data to complete our backtest we need some type of programming language or software. Below are the best options for both new and advanced backtesters.
Backtesting in Excel
Microsoft excel is excellent for quick and easy backtesting. I wrote a full example here: Backtesting in Excel. In short, here is a quick example on SPY that buys when price closes in the bottom 20% of the day’s range and sells the next day.
Backtesting with Python
Python is the fastest growing programming language due to its easy to learn syntax and easy to read coding style. Python has many various libraries (publicly available code) you can incorporate into your own code to create technical indicators, run scenario analysis, backtest and more. Here are a few favorites:
C++ is much faster than python and primarily used by high frequency traders to backtest terabytes of tick data and more. However, C++ is much more complex to learn and harder to read. If you are new to programming this language has a steep learning curve but is worth it once mastered.
No Code Backtesting Software
For those that do not want to be limited by excel or learn a programming language then I would highly recommend Build Alpha as backtesting software. Manual backtesting can be a time-consuming process. With a little computing power, Build Alpha’s automated backtesting engine can test hundreds of thousands of strategies per second.
Build Alpha also has 6,000+ built-in entry and exit signals, the ability to create your own signals, and advaned robustness tests to identify lying backtests.
The best part is Build Alpha is completely code free and requires no programming! I will share more on Build Alpha below.
Backtesting Trading Strategies Example
Let’s backtest a trading strategy. Actually, let’s backtest three simple trading ideas so we can see the results, what a sample trade looks like, and more.
First, let’s backtest a moving average crossover. Let’s buy when the 5-period simple moving average (SMA) crosses above the 10-period SMA. We will sell when the 5-period SMA crosses below the 10-period SMA. Here is a simple image of the entry and exit signal.
Below is a sample equity curve applying this strategy to SPY historical data over the time period 2008 to 2022. Obviously not confidence inspiring.
The second strategy will buy SP500 futures e-mini contract after a negative Monday and hold for one day. This strategy is known as “Turnaround Tuesday” which I originally wrote about in 2017. Let’s check performance since then using Build Alpha automated software to backtest a trading strategy.
Finally, let’s buy SPY after a bearish engulfing candle and hold for one day. This strategy was originally published in 2017 where I exposed some market “truths” that could be debunked with backtesting. Let’s check performance to see if it has maintained its stellar win percentage. Spoiler: it has with a 62%-win rate since 2017 following a “bearish” engulfing candle.
Analyzing Backtest Performance
In the above examples we viewed different metrics. In the first two, we viewed total profit via the equity curve, and in the third example we viewed winning percentage.
What are the best backtest performance metrics and how can we compare trading strategies after viewing backtesting results?
Main backtest performance metrics
Total Profit – total money gained or lost during the backtest
Equity Curve – cumulative profit graph of all historical trades, trade-by-trade
Maximum Drawdown – the worst peak to trough drop in total profit during the backtest
Average Trade – typical gain or loss per trade
Winning Percentage – total number of winning trades divided by total trades
Total Trades – total number of trades taken during the backtest
Profit Factor – total dollars earned divided by absolute value of total dollars lost
Sharpe Ratio – a risk-adjusted return metric created by Nobel laureate, William F. Sharpe
Calculating Sharpe Ratio
According to Investopedia, Sharpe ratio is a measure of risk-adjusted return and describes how much excess return you receive for the volatility of holding the riskier asset. It is calculated as the return minus the risk-free rate divided by the standard deviation to achieve that return. The higher the Sharpe ratio the better. Risk-adjust returns are a great way to compare strategies.
Sharpe Ratio Calculator
Below is a free Sharpe Ratio calculator where you can enter your backtest’s return percentage and standard deviation to calculate your Sharpe Ratio. Play around and see how return and volatility coexist.
Free Web Based Backtester! Backtesting with no code.
This simple web backtester will let you create and test your own ideas. It lacks a lot of the features and functionality that Build Alpha Algo Trading Software (mentioned later) has but will help you get started backtesting with no coding or data retrieval. For those of you serious about backtesting, strategy development, strategy validation and more please check out Build Alpha and speed up your learning curve.
But for now… test for free. Please share and be kind.
Backtesting Benefits
There are five major benefits to backtesting a given strategy. Below I cover each one.
1. Helps avoid bad strategies
Backtesting a strategy before risking capital can help avoid losing strategies that stand zero chance to make a profit and will perform poorly in live trading.
2. Paper trading
Backtesting can help a trader visualize what following a trading strategy would be like. Finding a strategy that matches your personality can be a benefit to long-term success. Utilizing a risk-free demo account to conduct paper trading to gain confidence is an excellent idea before risking money.
3. Evaluate different market conditions
Backtesting enables traders to analyze a strategy on different market conditions from different historical data periods. How does this strategy do during the 2008 Great Financial Crisis? How does it do during a strong bull run from 2013 to 2015? How did it do during the Covid drop of 2020 or some future market conditions we do not know yet?
Additionally, intraday strategies can be evaluated on different days of the week or during different time windows.
4. Set proper expectations
Backtest results can help traders understand how profit and loss expectations should be set and engage in smarter money management. If a strategy earned $10,000 per 100 shares per year, then expecting a strategy to make $1,000,000 on 100 shares next year would be unreasonable investment research.
Moreover, noticing a steep drawdown during a backtest can help a trader properly fund a strategy prior to going live.
Not all traders have unlimited capital and must choose between strategies. Backtests can help traders understand where and when their capital is best put to use. Spread bets, algo trading, futures, stock gappers, trading cfds, etc. can all be compared objectively.
Backtesting Pitfalls and Common Mistakes
Poor backtest results often indicate a strategy unworthy of live trading. However, good backtest results are not a greenlight to live trade. There are a handful of backtesting pitfalls we must watch. Some backtests may be too good to be true!
Overfitting or Curve Fitting Backtesting Risk
Over optimization or curve fitting is finding a trading strategy that too closely fits the historical data and will fail to adapt to new data. The strategy essentially learns the nuances of the historical data during the backtest but fails on new data when the exact nuances do not play out exactly as they did in the previous time period.
Overfitting is the largest risk system traders face. Overoptimizing parameters to improve backtest results can lead to great test results and good emotions, but horrible live trading performance.
There are many advanced techniques to overcome this pitfall which I wrote about here: Curve Fitting – Build Alpha
Look Ahead Bias
Another backtesting pitfall, look ahead bias, is using data that was not known at the time of the historical trade. A tragic example of look ahead bias in a multi-timeframe strategy would be using the daily close to make a trading decision midday. At the time of the trade, midday, it is impossible to know the close of the same day.
These look ahead bias risks are often negated and removed with proper data cleaning or professional grade automated backtesting software.
Survivorship Bias
Running a backtest on only currently traded symbols introduces too much optimism in the backtest results as it can avoid buying stocks that eventually went bankrupt or delisted. This phenomenon is known as survivorship bias.
Much like look ahead bias, survivorship bias deals with underlying historical data issues. Many data providers only have symbols currently listed on exchanges. A proper backtest should consider all symbols that were available at all historical dates and times. This includes stocks that went bankrupt, merged, went private or delisted.
Additionally, choosing to run a backtest on only the best stocks also introduces survivorship bias. The best stocks cannot be known ahead of time and undoubtedly leads to overly optimistic results. If we could identify AAPL or GOOGL ten years ago then your personal finance situation would be significantly different.
We must be unbiased in our symbol universe selection to have truly reliable backtest results. Ask your data provider if they provide listed and delisted securities!
Ignoring Transaction Costs
Backtesting a trading strategy without transaction costs such as slippage or commissions can mislead traders into believing unrealistic backtesting results thus depleting their accounts in live trading.
Backtest results can show an average trade of $10 or $15 but in real markets where slippage and commissions are present the average trade may be negative.
Ignoring trading costs during backtesting can find unrealistic and unobtainable results. Always include transaction costs in your backtests for reliable results!
Backtesting vs. Forward Testing
Forward testing is creating a trading strategy and allowing it to trade on live data on a paper or simulated account. Some forward test in a real account with small size. Many traders view forward testing as a necessary component of moving a strategy from the research stage to the live trading phase.
Backtesting is great for quickly identifying the viability of a trading strategy. Forward testing creates the same hypothetical results but on new data. Forward testing takes more time but is done on the most recent data.
There is no need for a trader to choose one or the other as both can have their place in a proper strategy pipeline; however, forward testing is often not necessary if statistical significance can be obtained during the backtestingphase and rigorous robustness testing is applied.
Backtesting Portfolio
In financial markets, backtesting single trading strategies is a tremendous achievement and necessary for trading system developers. However, backtesting a portfolio of strategies goes one step farther.
Ray Dalio, founder of Bridgewater – the world’s largest Hedge Fund, released a compelling graphic demonstrating the math behind combining multiple strategies together.
The graphic shows the more uncorrelated strategies one can combine, the lower the probability of losing money in a given year. Wow! This epiphany is a true key to algo trading success. I wrote about it here: Algorithmic Trading Complete Guide.
That being said, it is important to have the ability or software to backtest portfolios of strategies not just single strategies. This was a key driver behind Build Alpha’s Portfolio Mode upgrade.
Advanced Backtesting Techniques
Due to the various pitfalls and brilliant minds in quantitative finance, various advanced techniques have been created to identify more reliable backtest results which hopefully lead to more sustainable live trading performance.
Out of Sample Testing (OOS)
Out of Sample testing is withholding some historical data for future testing – typically some percentage. First, develop and tweak your trading strategy on the In-Sample data. Once you have achieved desirable backtest results you can test your strategy on the withheld out of sample data.
Ideally, performance results should be similar in both the in-sample and out-of-sample data periods. If trading performance fails on the out of sample data, then we likely avoided an overfit or poor strategy.
Selecting the proper out of sample period is a challenge to creating reliable backtest results. What if you select an out of sample period that contains only a bull market (like the image above)? The out of sample test may be easy to pass. Randomized out of sample testing can help avoid this.
Randomized Out of Sample testing randomly selects non-congruent dates from your historical data set to be part of the out of sample period.
The trader can create 1,000 or more randomized out of sample periods then compare results on all of them. This helps overcome randomly selecting data points from a bull market only. Learn more about Randomized Out of Sample.
Lying Backtests. Are backtests enough?
Let’s discuss a simple case study that uses intermarket signals from the Japanese Yen to trade US 30-year bond futures. The strategy has only 2 rules for entry and produces fairly stable in-sample returns for being so simple (2003-2012 backtesting period).
Graph shown shows results based trading 1 contract per signal for demonstration purposes
The intermarket strategy performed quite well on the unseen, out of sample data (2012-2016 OOS). The ability for a strategy to continue to perform on unseen data is paramount and mission critical for many system traders and strategy architects.
Graph shown shows results based on 1 contract per signal for demonstration purposes
However, can we trust this backtest? In Build Alpha, there are many validation and robustness tests that can add confidence to your system development and backtesting process.
There is a special test called the “Noise Test” which creates 1,000 new price series by adding and subtracting varying amounts of noise (read volatility) from the historical data. The test then “re-trades” the strategy on the new 1,000 price series.
The idea is that if a strategy is overfit then it is overfit to the noise of the original data and is not trading the underlying signal we hope it is. Changing the noise will reduce performance if the strategy is overfit to the noise of the original data. Ideally, we want to see a strategy continue to perform well on the new, noise-adjusted price series.
This “Noise Test” can help prevent a backtest from lying to us and hopefully prevent us from wasting further research time or taking a weak strategy live! You can see that our original backtest in blue is significantly better than the 1,000 “Noise Test” results. This is worrisome and most likely a sign of an overfit strategy aka a lying backtest that will surely fail on live data.
Viewing this simple strategy’s performance since 2016 confirms the value of the Noise Test. The strategy has been in a nosedive since going “live”, but a smart trader would have never taken it live – no matter how pretty the backtest – due to the failed Noise Test.
Any kind of trading – manual or discretionary – should be subjected to these rigorous tests before getting real money allocations. If not, you may be in for a surprise from a lying backtest or unreproducible hypothetical returns!
Build Alpha is the best backtesting software available. To manually backtest trading strategies is a time consuming and error prone process. Build Alpha enables the trader to test any and all ideas in a few clicks and requires no coding or programming. Build Alpha also offers the most advanced validation and robustness tests to help identify lying backtests as this has been the focal point of my professional trading career.
Build Alpha’s genetic algorithm can take thousands of input signals, exit ideas, risk management rules, and more to find the best trading strategies for you. The robustness checks and tests give the ability to analyze any backtest result in depth before risking real capital. Portfolio mode allows for traders to combine strategies and backtest portfolios. To learn more head over to the Build Alpha homepage or sign up to see the demo.
Backtesting Need to Knows
Backtesting applies a trading strategy on historical data to view hypothetical results
Need data, a trading idea, software or coding to implement a backtest
Can backtest all financial instruments, any time period, any market condition
Use to identify poor strategies ahead of risking capital. A fast way of paper trading
Backtesting performance metrics can compare strategies or run scenario analysis
Positive backtest results are not a greenlight to trade a strategy; more testing required
Build Alpha makes automated backtesting and validation possible with no coding
Summary
Many traders implement trading strategies without testing them which often leads to depleted retail investor accounts. Backtesting enables traders to visualize historical performance of any trading strategy prior to risking real capital. Traders only need market data and a trading idea to conduct a proper backtest. A backtest can be used to weed out poor strategies, identify those worthy of future research, compare competing strategies, and more. Backtesting is a vital tool in any serious trader’s repertoire but is not a be all, end all to print money. Backtesting is a great first step, but other validation methods are needed before one should risk real capital.
Author
David Bergstrom – the guy behind Build Alpha. I have spent a decade-plus in the professional trading world working as a market maker and quantitative strategy developer at a high frequency trading firm with a Chicago Mercantile Exchange (CME) seat, consulting for Hedge Funds, Commodity Trading Advisors (CTAs), Family Offices and Registered Investment Advisors (RIAs). I am a self-taught programmer utilizing C++, C# and python with a statistics background specializing in data science, machine learning and trading strategy development. I have been featured on Chatwithtraders.com, Bettersystemtrader.com, Desiretotrade.com, Quantocracy, Traderlife.com, Seeitmarket.com, Benzinga, TradeStation, NinjaTrader and more. Most of my experience has led me to a series of repeatable processes to find, create, test and implement algorithmic trading ideas in a robust manner. Build Alpha is the culmination of this process from start to finish. Please reach out to me directly at any time.
How to Build an Automated Trading System In Excel, Python, and Build Alpha
It is no secret that many traders fail to achieve success and a level of consistency. The persistent traders will eventually understand they need to quantify what their edge is. This inevitably leads them down the road of systematic or quantitative trading but with no direction on how to begin.
I am often asked how to build an automated trading system or how to create a trading algorithm or become a software trader. In this post, I will walk through testing a simple two rule system for the SP500 using Excel, Python and Build Alpha. The goal is to show how simple investigating quantifiable edges can be.
I will use the same simple trading algo idea in all three platforms to hopefully show some algo trading 101 level steps in each platform. The strategy will buy when IBR (defined later) is less than 20 and the closing price is below the 10 period simple moving average. It will hold for 1 day and will not have any position sizing or risk management. Very simple to create.
We will also create visuals for the equity curve (P&L graph) and drawdown.
How to Create a Trading Algo in Excel
Starting with excel makes the most sense as the majority will be most familiar with it. I will share many screenshots and explain the steps one by one on how to do backtesting in excel. This is a simple example – no risk management stock trading excel example yet. So if you are an excel expert this may feel like a slow walk in the park – that is by design!
Step 1: Open the data file in Excel. Finance data is almost always going to be displayed in this format of Date, Time, Open, High, Low, Close, Volume (and Open Interest if futures or options). It is often referred to as OHLCV data.
Step 2: Calculate IBR or Interbar Rank. This is sometimes referred to as IBS or Interbar strength. It is a 1 period stochastics where we view where the close is in relation to the bar’s trading range. An IBR of 20% or lower would mean the close was in the lower 20% of the bar’s range.
The formula is simple and can be seen below. It is the difference between the close and the low of the bar divided by the range of the bar. Please note I added 0.0001 to the denominator to avoid any divide by 0 errors when/if the high and low are equal to each other. I have also multiplied IBR by 100 in order to express it as a percent.
After typing in the formula, drag this value down for all rows of data in order to calculate the IBR for each bar.
Step 3: Calculate the 10 period Simple Moving Average. Go down to the 10th bar and add the formula below. Drag this down for all rows of data to calculate the 10SMA for each bar.
Step 4: Let’s add a new column and calculate our 10 period SMA signal. I have named this column Signal1. We will check if the current close is below the current SMA.
In order to check this, we can use an excel if statement. If the close is below the SMA return a 1 else return a 0.
Step 5: Adding another column named Signal2 in order to calculate our IBR signal. Again using an if statement to check if the current IBR is less than or equal to 20%. That is, if the current bar’s IBR is less than 20% return a 1 else return a 0.
Step 6: We need to combine our two signals into one signal. That is, whenever we close below the 10 SMA and have a weak close in the bottom 20% of the day’s range we want to return a 1 otherwise return a 0. A 1 would mean a green light to trade and a 0 would sit us on the sidelines as we have no edge.
All we need to do is check the sum of the previous two columns we created. If their sum is 2 then we return a 1 else we return a 0.
Step 7: Next let’s calculate the raw one day returns on the SP500. We have been using the closing price in all of our signals so we cannot truly know if our signal(s) are true until the close of the bar. Thus we cannot enter until the next bar’s open.
Let’s assume we get a true signal on Monday’s close. We would then enter on Tuesday’s open and hold for one bar exiting on Wednesday’s open. We could exit on Tuesday’s close but for simplicity’s sake we won’t in this example.
Our excel function checks if we have data the next two days by checking if the opening price is non-zero and exists. If we have data, we can then subtract tomorrow’s open from from the open of two days from now. That is, take the difference of Wednesday and Tuesday’s opens per our example and store it in Monday’s row.
This gives us a data column that says, if we have a signal on this bar and buy the next open, hold for one day, what would our return be?
Step 8: Now let’s get our trade returns. If we have an actual signal in our ‘SignalFinal’ column from our two indicators what would our return be.
I also multiplied by 50 to represent the point value of the SP500 emini futures contract. That is, if you buy at 3346 and sell at 3347 then you’ve earned $50 per contract not $1 per contract. Each futures contract has a point value and this one’s is 50.
Step 9: Let’s copy our Date column and Trades column into a new tab. I clicked on the ‘A’ above ‘Date’ to highlight the entire column. I then hold ctrl and click on the ‘O’ above the ‘Trades’ column. This should highlight both columns.
Press ctrl + c to copy. Open a new tab and press ctrl + v to paste.
Step 10: Let’s do some house-keeping to remove the days we did not trade. We first need to sort by our Trade column and delete all days with “—” in the cell.
After deleting non-trade days, I then re-sort by date from oldest to newest.
To sort please highlight the columns, go to the data menu and select sort.
Step 11: Time to create our P&L Graph. We need to add each trade value to a rolling sum of all trade values. The simple formula is below.
Drag this formula down to calculate the equity curve’s value after each trade.
Step 12: In order to calculate the drawdown of our simple trading program we need to first calculate the high watermark or the rolling maximum amount our account would have achieved following this simple trading system.
Once we have the maximum or high watermark, we can then subtract our current P&L from the maximum to determine the current drawdown. I have added new columns for the maximum P&L (MAX) and the Drawdown (DD).
Step 13: Now we can plot our P&L Graph and Drawdown. Simply highlight the column, go to the Insert menu and select a line chart.
How to Build a Trading Algo in Python
I have done a few other blogs on how to read a text file in python and using pandas but this example will show not only how to read in data, but how to complete all the steps above in one simple python script. The goal is building trading algorithms with python – or at least the first steps.
That way you have a very rudimentary framework for testing automated trading systems and creating trading algos in python. Hopefully this serves as an intro example of how to backtest a trading program or strategy.
Step 1: Create a new file, import our plotting library and pandas. Matplotlib is arguably the most popular python visualization library.
In the pandas read_csv call I have specified the file I’d like to read in, how to separate my columns, and what column should be the index of my pandas dataframe.
Step 2: Let’s calculate our indicators. To calculate IBR we will use list comprehension which is a very cool python trick. It allows us to do a for loop over our entire dataframe all in one line of code. The calculation is the same as excel and will create a dataframe column named IBR as we had in excel.
We will then use the pandas rolling function to create our 10 period simple moving average (mean). I have saved our moving average values in a new dataframe column named ‘SMA’ similar to excel.
Step 3: Following similar steps as we did in excel, we can convert our indicators into actionable signals using ‘if’ logic. We will use python’s list comprehension feature which allows us to loop through our entire dataframe in a single line of code.
For instance, we will create a new dataframe column named Signal1 which will store either a 1 if IBR is less than 20 or a 0 if it is not less than 20.
Step 4: Similar to excel’s footsteps let’s continue creating our python trading algo by calculating the returns and then mapping our returns and signals into actual trade results.
First, we can use pandas built-in shift function to access tomorrow’s opening price and the opening price from two day’s from now. This is similar to how we calculated returns in excel.
To create the trade results, we need to know if there was a signal or not. We can multiply our SignalF column and our return column. If we have a signal we will have a trading return and if we do not have a signal (0) then the return will get zeroed out. I’ve also multiplied by 50 to account for the SP500 emini future contract’s point value to convert our Trades into actual dollar values for 1 contract.
Step 5: In excel we had to copy and paste our trade returns into a new tab, sort them, remove days we did not trade, etc. In Python we can simply create a new pandas dataframe, named Trades, and filter our original dataframe. We will simply return only the rows of our original dataframe where our signal was equal to 1. That is, only returning the values when we took a trade.
Step 6: To calculate our P&L and drawdown we can use the following two simple lines of code. Since our P&L is simply the cumulative sum of our trades we can easily just call the cumsum function on our Trades column.
In order to calculate the drawdown we can just subtract our current P&L from the rolling cumulative maximum P&L. Python and pandas has a built-in cummax function we can use which will save us from creating an additional column to store our maximum values (high watermarks). This means one less column than excel.
Step 7: Plotting in python is simple. We already imported matplotlib – our plotting library – in the first step of this python walk through. Now we can call the pandas plot function, specify that we want to use subplots, and then display our plot with the show function.
How to Build a Trading Algo in Build Alpha
Step 1: Configure Build Alpha’s main screen. Set the symbol to ES which is the symbol for the SP500 emini futures contract.
Set the date range to start in 1997 and end near Sep 2020 to match the same data used in excel and python.
In the lower left set the max holding time to 1 bar in order to again match excel and python.
Step 2: Let’s select our signals. Type IBR into the ‘Filter’ search bar near the top. Scroll down and select the signal IBR <= 20 as an ‘Entry’.
Then type in SMA in the ‘Filter’ search bar near the top. Scroll down until you find the signal Close <= SMA(10). Select this signal as an ‘Entry’.
Then hit Simulate!
Step 3: In the Results window you will view our two rule strategy at the top (highlighted in blue). Double click on the strategy to view the P&L graph. Toggle drawdown off and on by hitting the drawdown button.
What's Next?
Some logical next questions might be how can I add a stop? How can I add more signals? How can I trade automated strategies after I’ve tested in excel or python? I encourage you to try adding on to this simple trading program idea in both excel and python. Then there can be a greater appreciation for how simple Build Alpha can make things!
Want to add a stop? Click a button. Want to add a signal? Click a button.
I am often asked how to build automated trading systems or how to create automated trading systems in excel or what is the best automated trading software and what software do professional traders use, etc. This blog post and the rest of the Build Alpha blog can answer those questions. The information is out there, the tools are out there.
Trading is not easy, but it is simple. Hunt for edges, collect them, execute them.
If you want access to the python script or the excel sheet please send me an email at david@buildalpha.com.
Other Build Alpha Python Resources
You can now add custom python signals to Build Alpha’s genetic algorithm. Learn how here: Use Python in Build Alpha
David Bergstrom – the guy behind Build Alpha. I have spent a decade-plus in the professional trading world working as a market maker and quantitative strategy developer at a high frequency trading firm with a Chicago Mercantile Exchange (CME) seat, consulting for Hedge Funds, Commodity Trading Advisors (CTAs), Family Offices and Registered Investment Advisors (RIAs). I am a self-taught programmer utilizing C++, C# and python with a statistics background specializing in data science, machine learning and trading strategy development. I have been featured on Chatwithtraders.com, Bettersystemtrader.com, Desiretotrade.com, Quantocracy, Traderlife.com, Seeitmarket.com, Benzinga, TradeStation, NinjaTrader and more. Most of my experience has led me to a series of repeatable processes to find, create, test and implement algorithmic trading ideas in a robust manner. Build Alpha is the culmination of this process from start to finish. Please reach out to me directly at any time.
Another Success Story [trader turnaround story]
I am back! I want to share another Build Alpha success story with you guys for a few reasons. First, I have been very quiet on social media, etc. lately and that is because I do not need to promote Build Alpha every day (although I probably should). I haven’t tweeted in month(s), but I have maintained my typical support, however. Anyways, think twice about traders that push their service, product, software, etc. every single day – it means they rely on it. Secondly, this Build Alpha user is the EXACT reason why I started on this journey of making Build Alpha publically available. His story also echoes my own – which is why I am so fascinated by it and could not resist sharing it with everyone.
First, let me reiterate my main goal with Build Alpha, the one written on the homepage of the site since day one: “Bridge the gap between the programming world, the quantitative trading world, and the money manager/trader who seeks to evolve with the times.”
In other words, can I take someone with no programming/trading experience and help turn them into a successful systematic trader? That is what I set out to do. I know the depths of the trader struggle and I want to pull as many traders as I can from that pain.. because man, do I still remember it like it was yesterday!
I do not wish to teach programming or wish to give you some PhD in statistics/finance, etc. My main goal was to create a tool to bring a trader who is completely unexposed (or partially unexposed) to algorithmic trading the ability to create their own portfolio of strategies, understand how pros think about risk, AND automate these strategies all without ANY programming. That is, take someone from no programming experience to algo trading profits without any programming.
The trader who I am talking about wishes to remain nameless (for now) and I will refer to him as TraderX. His email to me and account statement are below.
Testimonial Disclosure: Testimonials appearing on this website may not be representative of other clients or customers and is not a guarantee of future performance or success.
*click to zoom*
His story is eloquently explained in his email as only a trader experiencing the full journey could. I’ve only summarized his highlights for convenience, but his words are better.
Highlights
+$34,000 in one month, trading small size across a diverse basket of markets, timeframes.
Started learning from online sources and not formally educated by any big bank, business school, etc. with regard to trading. Takeaway: Self-taught is possible!
Payed for educators and services, alerts, chat rooms with mixed to negative results. Takeaway: there are no shortcuts in this game.
Went ALL-IN on learning and understanding BuildAlpha and its training videos. Takeaway: Build Alpha is a tool, but ultimately the end user is responsible for his/her own success with it.
No desire to program and still does not care to learn… because he does not need to. Takeaway: tools needed to succeed exist in BA
Diversified his portfolio across assets/symbols, timeframes, strategy types, etc. Takeaway: no holy grail hunting, but only professional portfolio building.
Understands how to set expectations and let systems play out. Takeaway: zero to hero. Simplicity usually wins.
TraderX is a great case study because he was literally the struggling/learning trader I can relate to from my own experiences and watching/talking to him now with his evolved understanding of trading is truly incredible and cannot wish this success to a more deserving trader. The answer is simple: admit where you are, agree to put the work in, watch the videos, do the work, be patient. This is a general recipe for success in life – TraderX only got into trading after selling his own business. Some people just get it… others need to hear it first; that is why I do these posts. The answers are certainly there and exist!
I normally hate publishing these testimonials but am glad to have explained why I do. The results are simply the byproduct of great workflow/process learned through hardwork, Build Alpha training videos and experimenting. TraderX is not alone in the BA community in this regard. Here is another user’s statements over the past few months where you can see successful trading. He is taking his time, abiding by the numbers/expectations he’s created and ultimately growing himself and his account. Remarkable.
*click to zoom – had to cut his original image into two images so some overlap between*
To learn more, please contact me at david@buildalpha.com. Reminder: Build Alpha comes with 50+ training videos where I explain system trading principles, system trading basic, and of course the details of how to maximize the Build Alpha software.
Thanks for reading,
David
Testimonial Disclosure: Testimonials appearing on this website may not be representative of other clients or customers and is not a guarantee of future performance or success. To view full risk disclosures please visit Disclaimers – Build Alpha
Happy Friday!
For this Free Friday edition, I am going to do something new. I am going to make this slightly educational and give away some code.
I get tons of questions every week, but they mainly fall into two categories. The first question is in regards to adding custom data to Build Alpha. You can add intraday data, weekly data, custom bar type data, sentiment data, or even simple single stock data. The second question is in regards to using or learning Python.
In this post, I will attempt to “kill two birds with one stone” and show a simple Python code to download stock data from the Yahoo Finance API.
In fact, we will use Python to pull and save data ready formatted for Build Alpha for all 30 Dow stocks in less than 30 seconds.
You can view the entire script later in this code or in the video below.
The first few lines are simple to import statements pulling public code that we can reuse including the popular pandas library.
import pandas as pd from datetime import datetime from pandas_datareader import data
I then define a function that downloads the data using the built-in DataReader function of the pandas_datareader library. I also adjust the open, high, low and close prices by the split ratio at every bar. This ensures we have a consistent time series if a stock has undergone a split, for example. **Please note other checks could be recommended like verifying high > open and high > close and high > low, but I have left these up to Yahoo in this post**. I then end the function returning a pandas data frame that contains our downloaded data. This get_data function will be valuable later in the code.
I then go ahead and put all 30 dow tickers in a Python list named DJIA. I also go ahead and create our start and end dates in which we desire to download data. DJIA=["AAPL","AXP","BA","CAT","CSCO","CVX","KO","DD","XOM","GE","GS","HD","IBM","INTC","JNJ","JPM","MCD","MMM","MRK","MSFT", "NKE","PFE","PG","TRV","UNH","UTX","V","VZ","WMT","DIS"] start = datetime(2007,1,1) end = datetime.today()
Finally, and the guts of this code, I loop through all 30 of our tickers calling the get_data function on each one of them. After downloading the first one, AAPL in our case, I open a file named AAPL.csv and then loop through the downloaded price series retrieved from our get_data function. I then write each bar to the file appropriately named AAPL.csv. I then close the AAPL.csv file before downloading the second symbol, AXP in our case. This process is repeated for each and every symbol. The result is 30 seconds to download 30 stocks worth of data! Each symbol’s data is saved in a file named Symbol.csv.
for ticker in DJIA:
DF = get_data(ticker,start,end) fh = open("%s.csv" % ticker,'w+') for i,date in enumerate(DF.index): fh.write("%s,%.2f,%.2f,%.2f,%.2f,%dn" % (date.strftime('%Y%m%d'),DF['Open'][i],DF['High'][i],DF['Low'][i],DF['Close'][i],DF['Volume'][i])) fh.close()
Now to the second part. Using this data in BuildAlpha is as simple as clicking on settings and searching for your desired file. I’ve attached a photo below that shows how the trader/money manager can now run tests on the newly downloaded AAPL data using the symbol “User Defined 1”. Pictures below for clarity.
I’m showing a strategy created for $AAPL stock, but it is only to prove this Python code and Build Alpha feature work. There is major selection bias creating a strategy on a stock that has basically been in a major uptrend for 90%+ of its existence. That being said, and in a later post, I will show a new Build Alpha feature that allows you to test strategies across different symbols to make sure the strategy holds up on both correlated and uncorrelated securities. Either way here is the AAPL strategy.
Buy Rules:
1.Today’s Low > Open of 3 Day’s Ago 2.Today’s 14 Period Stochastics > Yesterday’s 14 Period Stochastic 3. Today’s Upper Keltner Channel > Yesterday’s Upper Keltner Channel
Exit Rules: 1. Two Day Maximum Hold 2. 1.00 * 20 Period ATR Stop Loss
I like this strategy because it is convex. We limit the downside, but let the market give us as much as possible in 2 days. Below is the equity graph with the highlighted part being out of sample and based on 1 share as this is just for demonstration purposes!
I hope this makes life easier for some of you and as always… Happy Friday,
Dave
PS: This is all extra. You do not need to know Python (or any programming) to use BuildAlpha. This is just for advanced users that want additional help.
