Table of Contents

Introduction:

In the vast landscape of trading, inspiration can emerge from a multitude of sources – books, courses, forums, and the digital realm of YouTube, Discord, and beyond. The latter, in particular, hosts a myriad of channels unveiling trading strategies across Forex, Crypto, Futures, Stocks, and more. These platforms often showcase strategies through visual aids like Trading View, elaborating on entry points, exits, and filters. Yet, amidst this wealth of information, the challenge lies in the lack of comprehensive backtesting, accounting for crucial factors like Slippage, Commissions, and years of historical data. Personally, I find it imperative not to embark on any trading idea without first subjecting it to my own rigorous backtesting. This article centers on precisely that as we delve into a random strategy sourced from YouTube and subject it to rigorous testing in the Futures Markets. My YouTube search for “Futures Trading Strategy” led me to the Trade Pro Channel, capturing my attention with its profitability claim over the past eight years.

It’s a mean reversion strategy with the following entry and exit conditions:

The Strategy

Conditions to go long:

• Price above a 200-exponential moving average.
• Price crosses below a 20-exponential moving average or Price crosses below a 50-exponential moving average.
• RSI 14 is below oversold limit of 20. 

Conditions to go short:

• Price below a 200-exponential moving average.
• Price crosses above a 20-exponential moving average, or Price crosses above a 50-exponential moving average.
• RSI 14 is above the overbought limit of 80. 

Conditions to exit:

• Profit target 2x Stop loss (No specific number). 

Now, I will code and test the strategy and upload it to Tradesq to find out what futures markets the strategy will work.

The research

Software

• Charting Software: TradeStation 10.0.
• Code development language: Easylanguage 10.0.
• Future Markets Research: tradesq.net

Code Considerations

• Option to go long only, short only, or both.
• A profit target bigger or equal to the Stop loss will add a multiplier from a 1:1 ratio to a 2:1 ratio.
• RSI length as an input. 

Let’s check if the code is working and how the entries look in TradeStation:

After checking that the code is working correctly, let’s proceed to take it to Tradesq and set the inputs that will vary:

Results

Filtered by strategies with a PNL/DD ratio above 5.0, R2 above 0.5, and more than 200 trades:

Note: All results include trading costs, Slippage and Commission.

It seems that the strategy works well on Index and Energy futures; let’s see some of those results:

Data: NQ (15min)

Data: CL (60min)

Data: CL (60min)

Conclusion

Our journey through testing a YouTube-sourced trading strategy highlights the indispensable need for thorough validation. The Trade Pro Channel’s seemingly promising eight-year-profitable strategy reminded us that concepts require solid testing before trust. Employing the right tools, like TradeStation,  Easylanguage, and Tradesq, revealed market suitability, showing that diligent testing can yield surprises. In the world of trading ideas, it’s the tested ones that deserve our attention – a lesson to always test before you trade.

Author:

Juan Fernando Gómez

Email: jgomezv2@gmail.com

The post Testing a YouTube-Sourced Trading Idea in the Futures Markets appeared first on Tradesq Blog.

Money Flow index calculation

Money flow is calculated by averaging the high, low and closing prices, and multiplying by the daily volume. Comparing that result with the number for the previous day tells traders whether money flow was positive or negative for the current day. Positive money flow indicates that prices are likely to move higher, while negative money flow suggests prices are about to fall. 

https://www.investopedia.com/terms/m/moneyflow.asp

How it looks

It’s relevant to see that it also has an Overbought and Oversold that we can use for entries and exits.

For the test, I will use the following:

Charting Software: TradeStation 10.0.

Code development language: Easylanguage 10.0.

Future Markets Research: https://tradesq.net/

Code Considerations

• Entries type: 
  • Long only.
  • Short only 
  • Both.
• Entries Direction: 
  • Mean Reversion.
  • Trend Following.
• Exits:
  • Stop Loss
  • Profit Target: Stop Loss * 1.25
• Trigger:
  • Mean Reversion:
    • Long when Close crosses below oversold.
    • Short when Close crosses above Overbought
  • Trend Following:
    • Long when Close crosses above oversold.
    • Short when Close crosses below Overbought
• Values
  • Overbought: 70
  • Oversold: 30

Easylanguage code

In order to make sure that Tradesq has my custom MoneyFlow function, I request it to be added to Function Library.

Now let’s proceed to upload the code into Tradesq and select the parameters, Markets, and Timeframe that we want to test:

Note: I like to leave the last six months of unseen data, so tradesq will calculate until 06/01/2022. 

Results

We have a good number of edges on Indexes and very few on the other sectors.

Let’s have a look at Some results:

Nasdaq (@NQ) 30min

Nasdaq day session only (@NQ.D) 60min

VIX(@VX) 15min

Note: All results include trading costs, Slippage, and Commission.

Conclusion

It’s nice to find that we have other Oscillator options within trading. It could help us diversify and have an edge on the most common ones, as mentioned in the introduction RSI, CCI, ROC, etc. The Money Flow Index Oscillator showed outstanding opportunities in the Index Markets on the long-only positions and short on the VIX. It might need another filter to see if we could have opportunities in the other future sectors.

Author:

Juan Fernando Gómez

Email: jgomezv2@gmail.com

The post Money Flow Trading System appeared first on Tradesq Blog.

Using functions in EasyLanguage is good for several reasons

1. Code organization: Functions allow you to organize your code into smaller, modular chunks, making it easier to understand and maintain.
2. Reusability: Functions can be called multiple times, which makes your code more reusable and reduces the need to copy and paste the same code multiple times.
3. Readability: Functions can improve the readability of your code by giving meaningful names to the blocks of code, making it easier to understand what each part of your program does.
4. Error reduction: By breaking down your code into smaller, modular chunks, you can reduce the likelihood of errors and make finding and fixing bugs easier.
5. Efficiency: Functions can also make your code more efficient by reducing the amount of memory and processing power that your program uses.
6. Collaboration: Functions make it easier for others to understand your code and for you to collaborate with other developers, as you can work on different parts of the code independently.

In summary, functions in EasyLanguage can help you to write clean, maintainable, and efficient code that is easy to understand, collaborate and debug. They are powerful tools that can help you to develop high-quality software.

Tradesq Function Library

A new feature has been released to Tradesq: Function Library. Function Library stands as a central collection of all EasyLanguage custom functions used in Tradesq strategies.

Request your custom function to be added to Tradesq Cloud.

Requesting to add a new EasyLanguage function to the Tradesq cloud is easy. Just press the Request Easylanguage Function button at the bottom of the Function Library screen and fill the form with a strategy name and the code of the custom function.

Once the requested code is approved and enabled in the cloud, you can start using it in your new strategies.

The post Now you can use EasyLanguage functions in Tradesq appeared first on Tradesq Blog.

Bull flag pattern

A bull flag is a technical chart pattern that is often used by traders to identify potential bullish market movements. The pattern is characterized by a strong upward move, followed by a period of consolidation, or “flag,” that is usually parallel to the trendline of the initial move.

The bull flag pattern is considered a continuation pattern, as it indicates that the current bullish trend will likely continue after the consolidation period. This can be a valuable signal for traders looking to enter long positions or add to existing ones.

The pattern typically forms after a strong upward move, which is known as the “flagpole.” This initial move can be caused by a number of factors, such as positive earnings reports, strong economic data, or other market-moving news. During the consolidation period, the price of the security will generally move sideways, forming a “flag” shape on the chart.

Traders typically look for a breakout above the resistance level of the flag to confirm the continuation of the bullish trend. Additionally, traders may use a stop-loss order below the support level of the flag to protect their position in case the trend does not continue.

Bull flag in quantitative tading

We can get as fancy as we want with Easylenguage, but I wanted to take a straightforward approach:

Flagpole: Two simple moving average (SMA).

• Bullish flagpole SMA based on the low price, and current SMA bigger than the previous two SMA´s.
• Bearish flagpole SMA based on the high price, and current SMA lover then the previous two SMA´s.

Breakout: RSI2 that breaks over limit on the direction on the trend.

Exits: Stop loss and profit target.

Long and Shorts: The ability to test only long, only short or both.  

Charting Software: Tradestation 10.0

Code development: Easylenguage 10

Research platform: https://tradesq.net/

Bull Flag on futures markets? –Easylanguage code

Let’s see how it works on a chart:

Long entries on Bull flag on a @CL 1440m chart

Short entries on bear flag on a @CL 1440m chart

Its not perfect “flag” shape, but the concept of entering after the breakout of the retracement works pretty well.

Now that we have the code and the strategy let’s see if it would work in future markets. For the study, Tradesq will test it for us on 43 Future markets and 11 timeframes in a matter of hours. Can you imagine how much time would take to perform the test manually?

Results

We have many edges on currencies, energies, grains, Indexes, and interest sectors.

Results filtered by (NP/DD>4, Number of trades above 200)

Let’s have a look at Some results from every sector.

30Yr Bonds(@US) 240min

Heating Oil(@HO) 360min

British Pound(@BP) 240min

Spring Wheat(@MW) 360min

Nasdaq(@NQ) 30min

Note: All results include trading costs, Slippage and Commission.

Conclusion

I don’t know if the code developed will resolve the poster question on the Bull Bear Flag pattern, but certainly did find really good edges.

The study was intended to show how we can start from a simple idea and use tools like Tradesq and Easylanguage / Powerlanguage to build strategies. As a result, we found firm edges through five (5) different future market sectors. 

It does not mean this are strategies ready to trade, additional filters and robustness tests must be applied, but is a terrific starting point. 

Author:

Juan Fernando Gómez

Email: jgomezv2@jgomezv2

The post Bull Flag on futures markets? appeared first on Tradesq Blog.

You get an excellent trading algo idea. You draft the code in the editor of your favorite trading platform, and you see an equity curve with positive expectancy. It’s great, but you want better. You add optimizable parameters and keep tuning and tweaking the system until you get a smooth equity curve. Once you get the perfect slope, you declare your algo ready.

It is time to trade. You enable live trading execution on your platform and lean back to enjoy the show. Do I need to continue? You can probably guess the end of the story. In the best case, your trading strategy will show some good trades, but once the market sentiment changes, your strategy will start losing money. Perhaps you think the current drawdown is worse than the backtested average, so you turn off the strategy execution.

What is robustness testing?

In the context of algorithmic trading, robustness testing refers to evaluating a trading strategy’s performance and reliability under different market conditions and scenarios. This testing is typically done to ensure that a trading strategy is reliable, stable, and able to generate consistent returns over time, regardless of market conditions.

In general, robustness testing is essential in developing and evaluating any trading strategy, as it helps identify potential weaknesses or vulnerabilities that may impact the strategy’s performance and reliability. By thoroughly testing a strategy’s robustness, traders can gain confidence in its ability to generate consistent returns and make informed decisions about when and how to use it in their trading activities.

How we build a robust trading system?

To increase the robustness of your trading strategies, follow our 4 steps framework to make your them more bulletproof. Add these steps to your trading algorithm development or system evaluation procedure, and you will experience their positive effect in less than 6 months’ time.

Step 1 – Use a maximum of 4 optimizable input parameters

Beginner system developers like to add optimizable parameters to each quantifiable property of an algo. Maybe this seems like a good idea, but the practice shows that each parameter exponentially increases your strategy’s chance of being over-optimized.

I recommend using a maximum of 4 input parameters when optimizing and developing your algo.

Step 2 – Do not tweak

Most algo strategy developers dream of a linear and upwards-pointing equity curve. They draft their initial algo idea, add some optimizable input parameters and run an optimized backtest. The resulting system performance chart looks alright, but it could be better. What’s next? They start adding extra rules:

• What if this strategy does not trade on certain days of the week?
• Skipping every other trade.
• Change the exponential moving average to simple MA.
• Adding a new exit type which will help remove a couple of terrible trades from the year 2022 backtest

Do not add tweaking optimization cycles to your development process! You will not only cheat yourself but also mislead your risk management calculation. Never change the strategy logic after running a backtest.

My rule of thumb: feel free to implement every trading decision rule, inclusive or exclusive conditions, at the beginning of the system development. After you carry out a backtest, consider the trading algo is sealed. Does the strategy look bad? Discard it and try again.

Step 3 – Check the performance of your algo on different markets and timeframes

Would you trust an algo that works only on 120 minutes Soybean bars but nowhere else? Wouldn’t it be ensuring to know that your trading strategy produces fine results in multiple sectors and timeframes? So why don’t you check it?

Take your strategy and optimize it on as many different markets as possible. For the optimization, use the same range for input parameters you used when developing the original system.

Does it seem like a lot of work? Try it with Tradesq. You can automatize the whole process with Tradesq’s Smart Backtesting. On the Schedule Smart Backtesting page, you

• Insert your EasyLanguage algo code.
• Choose your target markets (currently, 40+ futures markets are supported across multiple sectors)
• Choose timeframes.
• Specify your input parameters’ optimization ranges

When you click the Backtest Strategy button, Tradesq’s whole cloud infrastructure will come into motion. In a couple of hours, a backtesting report will be generated:

The report will clearly show the markets and timeframes where your strategy produced good results. The definition of a sound system can be configured under the Settings / Screening Parameters menu.

The above sample shows backtesting results of a system that is showing promising results using Currency, Energy, Grain, Index, and Metal markets. Is this an ensuring result? Partially, maybe. We want to see a strong presence of good results in a sector: multiple markets of the same sector should show hits across more than one timeframes.

Step 4 – Forward test your algo

Forward testing (or Out of Sample Testing) is probably the most crucial step of the robustness check. You can be the most cautious system developer using the best practices, but only the future will tell how stable is the system you have built.

To pass the forward testing phase of your strategy evaluation, we recommend waiting for at least a 3 months period after the algo is developed. Once the forward testing period is over, backtest your strategy using the input parameters you chose at development time. Take a closer look at the end of the equity cure, which was built since you started the forward testing. What does it look like? Does it show similar characteristics to the previous parts?

This is another area where Tradesq can help. There are two ways your strategy can be forward-tested in Tradesq:

1. Automatically: when you use the Smart Backtesting feature to test your algo various markets and timeframe, Tradesq’s algorithm will automatically pick up the best outcomes and please them to forward testing.
2. Manually: you can add an algo for Forward testing using the Schedule Forward Test function in the Forward Testing menu.

Once a strategy is added to the list of Forward Tested strategies, Tradesq will keep monitoring its performance week by week. As the Forward Testing goes on, the performance chart of your algo will be updated weekly:

And that is not all. You continuously track the performance of all Forward Tested strategies in Tradesq, including thousands built by the community and your private ones.

Conclusion

Robustness testing is vital in algorithmic trading because it helps ensure that a trading algorithm will perform well in various market conditions. This is critical because the financial markets are constantly changing and can be volatile, and an algorithm that is not robust may produce inaccurate or suboptimal results under certain circumstances. By thoroughly testing an algorithm for robustness, traders can have confidence that it will function properly and make profitable trades even in challenging market environments. In addition, robustness testing can help identify and fix potential vulnerabilities or weaknesses in an algorithm, which can help prevent costly mistakes or losses. Overall, robustness testing is a vital part of the algorithmic trading process and helps ensure the success and reliability of trading systems.

The post 4 steps to build a robust trading system appeared first on Tradesq Blog.

ChatGPT

ChatGPT is a state-of-the-art chatbot based on the powerful GPT-3 language model developed by OpenAI. It can carry out natural and engaging conversations with users on a wide range of topics.

One of the standout features of ChatGPT is its ability to understand and generate human-like language. This allows it to have more natural and engaging conversations with users, making it a valuable tool for customer service, virtual assistants, and even social media.

In terms of performance, ChatGPT has been shown to be highly effective in carrying out its tasks. In tests, it has demonstrated an impressive ability to understand and respond to user input, even in complex and nuanced situations. This makes it a valuable tool for businesses looking to improve their customer service and reduce the workload on their human employees.

While ChatGPT is not specifically designed for programming, its capabilities make it a potentially valuable tool for assisting with various aspects of the software development process. It could provide real-time analysis, automate certain tasks, and improve the overall efficiency of the programming process.

Let’s give it a try

As it was promised, we started communicating with the chatbot with simple English sentences. Our requests were straightforward, and we gave different tasks regarding trading strategy development.

As the main scope of this blog is EasyLange-based algo development our first command was:

Write a trend following EasyLanguage strategy for ES

ChatGPT got it absolutely right. It understood the context: we want a trading strategy written on EasyLanguage. This strategy should follow trend-following principles. In other words, it should be based on price breakout entries. Finally, this strategy should work on the SP500 market.

The generated strategy code looks like a fine EasyLanguage code. After a quick validation, we found that this code can be compiled by Tradestation and Multicharts.

The generated code comes with 3 input parameters: EntryLength and Exitlength are typical trend-following system properties. And here comes the first flaw of this generated system. The StopLoss variable name indicates that this variable is implementing an exit rule, but it is just an optimizable offset for calculating the entry stop price. An experienced strategy developer with human intelligence would probably never choose a variable name like this.

Anyways, we wanted to see what this piece of algo is capable of? What is the big picture and how robust is this strategy?

Results

We used Tradesq to optimize the strategy for all supported future markets and sectors. Scheduling optimization is easy in Tradesq. Check this article for an example.

The filtered results table indicates good results for the Currency, Index, and Meat markets. These results are fine enough and typical for trend-following.

Finally, let’s look at the best equity curve provided by this research. According to the performance metrics that are the 30 minutes XBI (S&P biotech sector ETF):

Beautiful equity curve indeed. Is this a robust system or just an over-optimized equity curve? Time will tell. One thing is sure: Tradesq’s Forward Testing module will pick this system up and follow its performance in the coming months and years. Read our article about how hundreds of trading algos are forward-tested in Tradesq. We will follow up on the performance results of this system in 6 months’ time.

The post Algo programming with ChatGPT appeared first on Tradesq Blog.

Here are some tips to help you get started on your EasyLanguage journey:

1. Familiarize yourself with the TradeStation platform. EasyLanguage is a unique language, and it is specifically designed to work with TradeStation. Therefore, it is essential to have a basic understanding of how the platform functions before diving into EasyLanguage. EasyLanguage is also very similar to Multichart’s PowerLanguage.
2. Take advantage of TradeStation’s educational resources. TradeStation offers various educational materials, including webinars, tutorials, and online courses. These resources can provide an excellent foundation for learning EasyLanguage and help you understand the basics of the language.
3. Practice, practice, practice. As with any skill, the more you practice, the better you will become. Set aside dedicated time each day to work on EasyLanguage projects, and save your work so you can refer to it later.
4. Join online forums and communities. There are numerous online forums and communities dedicated to EasyLanguage and TradeStation. Joining these groups can provide valuable support and resources, as well as the opportunity to connect with other learners and experts.
5. Experiment with sample code. TradeStation provides a wealth of sample code that can be used to test out EasyLanguage concepts and strategies. Use this sample code as a starting point and modify it to see what happens. This is a great way to learn by doing and gain a better understanding of how EasyLanguage works.
6. Study and analyze more complex EasyLanguage codes written by professionals. Tradesq gives you access to the most extensive strategy library written in EasyLanguage. Browse amongst these algos and learn programming approaches and best practices. Also, use these strategies for your trading to have diversified portfolios.

Learning EasyLanguage may seem daunting at first, but with dedication and practice, you can master this powerful language and unlock new possibilities for your trading. By taking advantage of the resources available and staying committed to your learning journey, you can become a proficient EasyLanguage programmer and take your trading to the next level.

The post Starting with Easy Language programming appeared first on Tradesq Blog.

Mandalore EL is an excellent algo developed using Tradesq. It can be traded on multiple futures sectors, such as Currency, Energy, Grain, Index, Interest, Meat, Metal, Softs, and some ETFs.

If you are interested in getting EasyLanguage strategies to your algo portfolio, check out our article.

The post Get a free EasyLanguage strategy appeared first on Tradesq Blog.

It is very challenging to enter this space as a beginner trader. You want a handful of reliable algos to start but where can you find such strategies?

Option 1: Purchase strategy from professional algo developers

This is the obvious first choice for many traders. There are numerous places to buy algos. You google “trading strategies to buy” and browse the beautiful equity curves on various landing pages. What is the catch so? Various things.

First of all, these strategies are mostly expensive. Generally, these algos are priced somewhere between $90 and $900. The more significant problem is that you don’t know anything about the lifecycle of these strategies. How can you know know what time where these strategies developed? Is the creator continuously re-optimizing it to keep the equity curves nice? Is it robust or perhaps overoptimized? These are not easy questions to answer.

Option 2: user forward tested strategies created by tradesq community

Tradesq is a potent strategy builder and robustness tester tool. In my previous article, I showed how you could bring your classic EasyLanguage strategy development to the next level using Smart Backtesting. This time I want to highlight Tradesq’s strategy-sharing capabilities amongst users.

As a premium package subscriber, you gain access to hundreds of EasyLanguage strategies created by the Tradesq Community. And here comes the best part. These strategies are continuously forward tested in Tradesq Cloud since the day they got developed.

What does it mean in practice? When a good algo is developed by a community member, the strategy will be automatically picked by Tradesq and continuous forward testing will be performed every week. You can browse Tradesq’s forward tested strategies here, even if you’re not a subscriber.

Let’s say you want algos for Nasdaq (NQ) with at least nine months of forward-tested results with a minimum of 40 out of sample trades.

As you see, Tradesq has many of them. You can sort and filter them by different out-of-sample parameters, R2 and return to drawdown ratio. When you click on a strategy a detailed forward testing data-sheet will appear.

Probably the most interesting part of the data-sheet are the Strategy Performance charts.

As you see, this strategy was developed in mid-2020. The creator of this algo used a one-year-long out-of-sample period (marked with yellow) for backtesting and optimization.

Since then, this strategy has been forward-tested live. Every week, Tradesq grabs this algo and backtests it repeatedly. Its result is the forward-tested equity curve (red line).

Traders need reliable and robust strategies

Tradesq is a great place to get EasyLanguage-based strategies for a beginner trader to kick off or for an experienced trader to diversify their portfolio. As a Premium package subscriber, you can browse hundreds of robustness-tested strategies with forward testing records.

The post How to get EasyLanguage strategies for Tradestation and Multicharts appeared first on Tradesq Blog.

Algo trading can be fun, but it also requires much work. Also, it is a continuous struggle.

On one side is your chosen trading platform with its strategy development language and environment. You must learn the language and the common pitfalls and get daily practice in trade execution and performance analysis.

On the other hand – and probably this gives the most incredible difficulty in algo trading – there is strategy development. To be successful in this space, you need good quality and bullet-proof automated trading strategies — a lot of them. And planning, designing and implementing algos is pretty challenging. 

Some time ago, I was also facing this challenge. Then, I built my mental and technical strategy development framework to make my trading life more manageable. Today this framework is publicly available for all EasyLanguage-based algo traders worldwide on tradesq.net. What Tradesq consists of, and how can you use it to supercharge your strategy development? Well, that is the story of other blog posts coming soon.

In this blog, I’m going to mostly write about the following:

• Quantitative trading strategy development
• Different markets and algo-types fitting with them
• Easylanguage tips and tricks

…and many other topics about algo-trading

The post Welcome to Tradesq algo trading Blog! appeared first on Tradesq Blog.