How to Create an Algo Trading Strategy
The success rate of algo trading is probably not much different than any other style of trading, although there are algo traders who absolutely crush it. While mishaps while trading can happen, you can reduce that chance by creating an algo trading strategy that works within the model you want to follow.
Computer-driven algorithmic trading, or simply algorithmic trading, has become one of the most prominent forms of trading. Driven by artificial intelligence (AI), it's now been possible to write trading strategies that are set to execute trades without the intervention of a human trader at all.
Even more traders may find that many brokerage houses employ an algorithmic trading strategy for their own trades. These not only reduce operating costs but also market risks.
If you're an active investor or trader, you know that having a successful strategy is key to making good money in the markets. If you would like to create a strategy on your own, keep reading as we discuss what algorithmic trading is and how you can draft your own strategic methods of trading.
Computer-driven algorithmic trading, or simply algorithmic trading, has become one of the most prominent forms of trading. Driven by artificial intelligence (AI), it's now been possible to write trading strategies that are set to execute trades without the intervention of a human trader at all.
Even more traders may find that many brokerage houses employ an algorithmic trading strategy for their own trades. These not only reduce operating costs but also market risks.
If you're an active investor or trader, you know that having a successful strategy is key to making good money in the markets. If you would like to create a strategy on your own, keep reading as we discuss what algorithmic trading is and how you can draft your own strategic methods of trading.
What Is Algo Trading and What Is an Algo Trading Strategy?
Algorithmic trading is a system that uses computers to make trades based on pre-determined criteria that you create. These trades are executed by algorithms. The strategy itself is how the trading plan is executed.
These strategies operate off of a set of rules that you would use to determine when to buy or sell an asset. You'll always base these rules on conditions of the market. These different parameters that would be set while strategizing would lay out different points for entries and exits.
These strategies operate off of a set of rules that you would use to determine when to buy or sell an asset. You'll always base these rules on conditions of the market. These different parameters that would be set while strategizing would lay out different points for entries and exits.
Types of Algorithmic Trading Strategies
While creating your own strategy can seem like a daunting task, it can be broken down when you know what you want to do. Before we get into how to design your own, let's take a closer look at the different types of algo trading strategies. Making yourself familiar with some of them might give you an idea of how to model yours.
When it comes to a trading algorithm, there are a number of different strategies that can be used. The most common algorithmic trading strategies are:
Mean Reversion
Mean reversion is a type of algorithmic trading strategy that focuses on taking advantage of price discrepancies in the market. It does this by buying assets when they're undervalued and selling them when they're overvalued.
The reversion often suggests that with the prices of assets or how volatile returns are, they will go back to their starting levels.
Momentum
Momentum-based strategies are as they sound. This strategy seeks to take advantage of upward or downward trends in the market. It does this by buying assets that are trending up and selling them when they start to trend down.
Arbitrage
Arbitrage strategies take advantage of price differences across the market. The strategy is based on the premise that prices for identical assets are not always the same across different markets.
There is also another form of this. The other form is called statistical arbitrage. This is a type of strategy that uses more statistical techniques to identify and take advantage of pricing discrepancies in the market.
Market Making
Market making is a type of algorithmic trading strategy that seeks to provide liquidity to the market. Overall, you're actively dealing with certain assets or shares when you make a market. This is based on the thought that there is always a buyer and seller for every asset, and that by providing liquidity, the market maker can earn a small profit on each trade.
Scalping
Scalping is a trading approach that tries to stick with small profits and small winners. A tick profit here or there is what the typical scalper is looking for. The drawback to scalping is that many times scalpers let little losses turn into big losses. So, they end up with a bunch of small profits, and a few large losers, and overall lose money.
Index Rebalancing
Index rebalancing is a type of algorithmic trading strategy that seeks to keep an index's weightings in line with its underlying constituents.
Overall, algorithmic trading strategies can be broadly classified into two categories: discretionary and systematic.
Discretionary algorithmic trading strategies are those that require the trader to make decisions on when to buy or sell. Systematic algorithmic trading strategies are those that are rules-based and do not require the trader to make any decisions.
- Mean reversion
- Momentum
- Arbitrage
- Statistical arbitrage
- Market making
- Scalping
- Index rebalancing
When it comes to a trading algorithm, there are a number of different strategies that can be used. The most common algorithmic trading strategies are:
Mean Reversion
Mean reversion is a type of algorithmic trading strategy that focuses on taking advantage of price discrepancies in the market. It does this by buying assets when they're undervalued and selling them when they're overvalued.
The reversion often suggests that with the prices of assets or how volatile returns are, they will go back to their starting levels.
Momentum
Momentum-based strategies are as they sound. This strategy seeks to take advantage of upward or downward trends in the market. It does this by buying assets that are trending up and selling them when they start to trend down.
Arbitrage
Arbitrage strategies take advantage of price differences across the market. The strategy is based on the premise that prices for identical assets are not always the same across different markets.
There is also another form of this. The other form is called statistical arbitrage. This is a type of strategy that uses more statistical techniques to identify and take advantage of pricing discrepancies in the market.
Market Making
Market making is a type of algorithmic trading strategy that seeks to provide liquidity to the market. Overall, you're actively dealing with certain assets or shares when you make a market. This is based on the thought that there is always a buyer and seller for every asset, and that by providing liquidity, the market maker can earn a small profit on each trade.
Scalping
Scalping is a trading approach that tries to stick with small profits and small winners. A tick profit here or there is what the typical scalper is looking for. The drawback to scalping is that many times scalpers let little losses turn into big losses. So, they end up with a bunch of small profits, and a few large losers, and overall lose money.
Index Rebalancing
Index rebalancing is a type of algorithmic trading strategy that seeks to keep an index's weightings in line with its underlying constituents.
Overall, algorithmic trading strategies can be broadly classified into two categories: discretionary and systematic.
Discretionary algorithmic trading strategies are those that require the trader to make decisions on when to buy or sell. Systematic algorithmic trading strategies are those that are rules-based and do not require the trader to make any decisions.
Best Algo Trading Approach For Retail Traders
Most of the above methods of trading are very difficult for the retail trader. For example, market making is usually the domain of high frequency firms, and small retail traders just do not have the resources (speed, computing power, co-location to exchange) that can help them win.
If you are looking to get into algo trading, I'd stick with mean reversion and momentum (trend following) approach, preferably on a longer timeframe (think hours to days to weeks, rather the seconds).
If you are looking to get into algo trading, I'd stick with mean reversion and momentum (trend following) approach, preferably on a longer timeframe (think hours to days to weeks, rather the seconds).
How to Develop an Algorithmic Trading Strategy
Now here is where things get interesting. Developing your own strategy doesn't have to be difficult and you can do it in only a few steps. If you want to develop your own algorithmic trading strategy, here's how:
Select a Market and Time Frame
The first step is to select a market and time frame. You can trade any asset class using algorithmic trading, but some markets are more suitable than others.
For example, algorithmic trading works well in the foreign exchange (Forex) market because it is highly liquid, which only means that there is a lot of data available. The Forex market is the largest and easiest market to start trading within. You can also start trading within this market with a lot less capital.
Stock markets are also good candidates for algo trading, but you will need to consider factors such as overall market liquidity and volatility when choosing a market. There are also ETF markets, which are funds that represent things like currencies, different sectors, and specific industries.
You also need to decide on the time frame you want to trade within. Some algorithmic traders only make trades that last for a few seconds, while others hold their positions for hours or even days.
Develop Your Ideas
To start development with your trading ideas, you can use technical analysis, fundamental analysis, or a combination of both to come up with more approaches.
If you're using more technical analysis, you will need to identify support and resistance levels, trend lines, and other chart patterns. You can also use technical indicators, such as moving averages, to generate buy and sell signals.
If you're using fundamental analysis, you will need to consider factors such as economic data releases, company earnings reports, and political events. You will also need to keep an eye on market sentiment in this case.
Start Backtesting
Once you have developed your trading ideas, you need to backtest your strategy to see if it works. It's another form of simulation that we often talk about.
You'll be comparing it against historical data to see how it would have performed in the past. There are a number of ways to backtest a trading strategy, but the most common method is to use software that allows you to simulate trades in live markets.
Some of the best software to use for testing out your trading strategies are platforms like Metastock, Tradingview, and Trade Station. If you're trading on a global scale, you should look into doing your backtesting with a platform like Quantshare.
NOTE: Backtesting is definitely the toughest part of this process. I estimate probably 90% of traders backtest incorrectly. I teach a proven successful testing approach in my workshop.
Paper Trade Your Strategy
Once you have back-tested your strategy, the next step is to paper trade it. Paper trading is the process of simulating your returns on trades in the market. The thing is though, you'll be doing this without selling or buying any of those assets in real time.
Using demo accounts is a good way you could give this a try. Overall, this is a great way to test your strategy in live market conditions without risking any capital.
Live Trade Your Strategy
Once you have paper traded your strategy and you've determined that it's performing well, you can start live trading it. Live trading is when you make your trades in live markets using real money.
This is the end of the process and by this point, you're in the implementation phase. How your strategy works in real-time should determine if you continue to work with it or develop another approach.
Select a Market and Time Frame
The first step is to select a market and time frame. You can trade any asset class using algorithmic trading, but some markets are more suitable than others.
For example, algorithmic trading works well in the foreign exchange (Forex) market because it is highly liquid, which only means that there is a lot of data available. The Forex market is the largest and easiest market to start trading within. You can also start trading within this market with a lot less capital.
Stock markets are also good candidates for algo trading, but you will need to consider factors such as overall market liquidity and volatility when choosing a market. There are also ETF markets, which are funds that represent things like currencies, different sectors, and specific industries.
You also need to decide on the time frame you want to trade within. Some algorithmic traders only make trades that last for a few seconds, while others hold their positions for hours or even days.
Develop Your Ideas
To start development with your trading ideas, you can use technical analysis, fundamental analysis, or a combination of both to come up with more approaches.
If you're using more technical analysis, you will need to identify support and resistance levels, trend lines, and other chart patterns. You can also use technical indicators, such as moving averages, to generate buy and sell signals.
If you're using fundamental analysis, you will need to consider factors such as economic data releases, company earnings reports, and political events. You will also need to keep an eye on market sentiment in this case.
Start Backtesting
Once you have developed your trading ideas, you need to backtest your strategy to see if it works. It's another form of simulation that we often talk about.
You'll be comparing it against historical data to see how it would have performed in the past. There are a number of ways to backtest a trading strategy, but the most common method is to use software that allows you to simulate trades in live markets.
Some of the best software to use for testing out your trading strategies are platforms like Metastock, Tradingview, and Trade Station. If you're trading on a global scale, you should look into doing your backtesting with a platform like Quantshare.
NOTE: Backtesting is definitely the toughest part of this process. I estimate probably 90% of traders backtest incorrectly. I teach a proven successful testing approach in my workshop.
Paper Trade Your Strategy
Once you have back-tested your strategy, the next step is to paper trade it. Paper trading is the process of simulating your returns on trades in the market. The thing is though, you'll be doing this without selling or buying any of those assets in real time.
Using demo accounts is a good way you could give this a try. Overall, this is a great way to test your strategy in live market conditions without risking any capital.
Live Trade Your Strategy
Once you have paper traded your strategy and you've determined that it's performing well, you can start live trading it. Live trading is when you make your trades in live markets using real money.
This is the end of the process and by this point, you're in the implementation phase. How your strategy works in real-time should determine if you continue to work with it or develop another approach.
You Could Use Some Programming Basics
If you want to algorithmic trade and put that strategy to work, you will need to have a basic understanding of programming. Most algorithmic trading strategies are programmed in languages such as Basic, C++ or Python or derivatives thereof. C++ or C will give you a harder time due to the complexity of the language unless you are already familiar with it.
Tradestation, as an example, uses a proprietary language called Easy Language. It is almost like reading and writing English! I recommend it for most newer programmers.
When seeking the best alternative, Python is more of the preferred language for quantitative research and monitoring though. You could use R, which is the default option for more statistical analysis. In the end, the programming language you decide to use should be based on your comfort level with the product., and any associated trading platform.
Tradestation, as an example, uses a proprietary language called Easy Language. It is almost like reading and writing English! I recommend it for most newer programmers.
When seeking the best alternative, Python is more of the preferred language for quantitative research and monitoring though. You could use R, which is the default option for more statistical analysis. In the end, the programming language you decide to use should be based on your comfort level with the product., and any associated trading platform.
What to Avoid When Making an Algo Trading Strategy
Knowing what to do when creating a trading strategy is helpful, but knowing what to avoid doing can be almost as important. When making a trading strategy, it is important to avoid common mistakes like these:
Overfitting
Overfitting is the process of creating a model that is too specific to the data set it was trained on. This can lead to poor performance on out-of-sample data. When you overfit, you take away the opportunity for the algorithm to generalize when it needs to.
It can be difficult to identify when you are doing this because the algorithm will give you a false sense of accuracy and confidence. To combat this issue, make sure that you are ensuring accuracy and losses throughout your metrics to determine if you are overfitting.
Overfitting essentially increases the chances of an ineffective system. That is usually the result that you will get if you design your trading system too focused on one data set.
Not Diversifying
It is important to diversify your algorithmic trading strategies. This means having a variety of different strategies that you can use in different market conditions. If you've ever heard of the phrase, "don't put all of your eggs in one basket," that phrase resembles the reason why you should diversify.
Another way to think about this is to hold anything that's too connected too close to each other. If one asset or certain stock decreases in value, another classification such as another asset class or sector could increase in value. The main thing here is to remain versatile.
Not Managing Risk
Managing risk is one of the most important components when it comes to algorithmic trading. This means setting stop losses and taking profits when your strategy is performing well. If you aren't making risk management efforts, you run a higher risk of allowing your algorithm to fail before you can see a turn in profit for your portfolio.
Risk can come from more than just algorithm failure or issues with your chosen strategy. Some failures have come from actual IT systems failures or incorrect coding. With that, you want to make sure that you assess risk from multiple perspectives so that you can limit issues you might face.
Overtrading
Overtrading is the act of making too many trades. This can lead to large losses if the market goes against you. Too much of something isn't always a good thing.
With overtrading, you risk mismanagement. This can often come about if you don't have the best handle on your target areas with an increase in trading activity. The main thing here is that you could potentially expand your trading operation a bit too fast.
Before falling into an overtrading pit, ensure that you start off small and that you have the financial resources that will support how you plan to run your trading efforts.
Not Following the Rules You’ve Set
It's important to follow your rules when algorithmic trading. This is because this would be one of the best ways to monitor your methods. The best way to look at this is that you won't know if something will work consistently if you don't try it initially.
After setting the parameters that you want your strategy to operate within, test it out on the live market after back-testing it and forming your own simulations. Following the rules you've set will allow you to better analyze trends that were identified earlier with other strategies that might be working in other ways.
Overfitting
Overfitting is the process of creating a model that is too specific to the data set it was trained on. This can lead to poor performance on out-of-sample data. When you overfit, you take away the opportunity for the algorithm to generalize when it needs to.
It can be difficult to identify when you are doing this because the algorithm will give you a false sense of accuracy and confidence. To combat this issue, make sure that you are ensuring accuracy and losses throughout your metrics to determine if you are overfitting.
Overfitting essentially increases the chances of an ineffective system. That is usually the result that you will get if you design your trading system too focused on one data set.
Not Diversifying
It is important to diversify your algorithmic trading strategies. This means having a variety of different strategies that you can use in different market conditions. If you've ever heard of the phrase, "don't put all of your eggs in one basket," that phrase resembles the reason why you should diversify.
Another way to think about this is to hold anything that's too connected too close to each other. If one asset or certain stock decreases in value, another classification such as another asset class or sector could increase in value. The main thing here is to remain versatile.
Not Managing Risk
Managing risk is one of the most important components when it comes to algorithmic trading. This means setting stop losses and taking profits when your strategy is performing well. If you aren't making risk management efforts, you run a higher risk of allowing your algorithm to fail before you can see a turn in profit for your portfolio.
Risk can come from more than just algorithm failure or issues with your chosen strategy. Some failures have come from actual IT systems failures or incorrect coding. With that, you want to make sure that you assess risk from multiple perspectives so that you can limit issues you might face.
Overtrading
Overtrading is the act of making too many trades. This can lead to large losses if the market goes against you. Too much of something isn't always a good thing.
With overtrading, you risk mismanagement. This can often come about if you don't have the best handle on your target areas with an increase in trading activity. The main thing here is that you could potentially expand your trading operation a bit too fast.
Before falling into an overtrading pit, ensure that you start off small and that you have the financial resources that will support how you plan to run your trading efforts.
Not Following the Rules You’ve Set
It's important to follow your rules when algorithmic trading. This is because this would be one of the best ways to monitor your methods. The best way to look at this is that you won't know if something will work consistently if you don't try it initially.
After setting the parameters that you want your strategy to operate within, test it out on the live market after back-testing it and forming your own simulations. Following the rules you've set will allow you to better analyze trends that were identified earlier with other strategies that might be working in other ways.
Develop Useful Algo Strategies Without the Hassle
When you partner with KJ Trading Systems, you'll be working with an industry expert to develop winning algo trading systems that will help you once implemented. While strategizing the best way to navigate the market, it can be hard to not focus your system too directly.
The key is to keep your strategy versatile yet tailored to the end results you want. To learn more, reach out for a one-on-one to get the best algo trading education experience there is.
The key is to keep your strategy versatile yet tailored to the end results you want. To learn more, reach out for a one-on-one to get the best algo trading education experience there is.
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About The Author: Kevin Davey is an award winning private futures, forex and commodities trader. He has been trading for over 25 years.Three consecutive years, Kevin achieved over 100% annual returns in a real time, real money, year long trading contest, finishing in first or second place each of those years.
Kevin is the author of 5 highly acclaimed books, including "Building Algorithmic Trading Systems: A Trader's Journey From Data Mining to Monte Carlo Simulation to Live Trading" (Wiley 2014). Kevin provides a wealth of trading information at his website: https://www.kjtradingsystems.com
Copyright, Kevin Davey and KJ Trading Systems. All Rights Reserved. Reprint of above article is permitted, as long as the About The Author information is included.
Kevin is the author of 5 highly acclaimed books, including "Building Algorithmic Trading Systems: A Trader's Journey From Data Mining to Monte Carlo Simulation to Live Trading" (Wiley 2014). Kevin provides a wealth of trading information at his website: https://www.kjtradingsystems.com
Copyright, Kevin Davey and KJ Trading Systems. All Rights Reserved. Reprint of above article is permitted, as long as the About The Author information is included.