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.

Table of Contents

Introduction

In the book “Short Term Trading Strategies That Work” by Larry Connors and Cesar Alvarez, a widely used entry strategy for Index markets is discussed, which takes advantage of the index mean reversion edge through the 2-period RSI. This RSI trading strategy involves going long only when the 2-period RSI falls below an oversold limit. In this article, we will explore whether the RSI works in different timeframes and index markets, and we will use the Volatility Index (VIX) as a filter to enhance the strategy.

To establish a reference point, let’s consider a random pick from the ES market:

Market: @ES 
Timeframe: 120 mins.
Entry RSI2 crosses below 20. (Long only).
Stop loss: $1500.
Profit target: $1500.
Slippage: $12.5 per contract.
Commission: $2.5 per trade.

Based on the above parameters, the results are as follows:

We could say there is an edge, but it can be further improved to increase its effectiveness by reducing the number of trades. In order to achieve this, I want to use the VIX as a filter.

Why VIX?

Let’s explore its definition:

 “The Cboe Volatility Index (VIX) is a real-time index that represents the market’s expectations for the relative strength of near-term price changes of the S&P 500 Index (SPX). Because it is derived from the prices of SPX index options with near-term expiration dates, it generates a 30-day forward projection of volatility. Volatility, or how fast prices change, is often seen as a way to gauge market sentiment, and in particular the degree of fear among market participants.

The index is more commonly known by its ticker symbol and is often referred to simply as “the VIX.” It was created by the Cboe Options Exchange (Cboe) and is maintained by Cboe Global Markets. It is an important index in the world of trading and investment because it provides a quantifiable measure of market risk and investors’ sentiments.”

Source: https://www.investopedia.com/terms/v/vix.asp

As we can see from the definition, incorporating the VIX in our strategy allows us to leverage market sentiment as a filtering mechanism. To achieve this, we will consider the following cases:

• Case 1: High of the VIX is higher than X bars ago.
• Case 2: High of the VIX is lower than X bars ago.
• Case 3: VIX RSI of X number bars ago is greater than 50.
• Case 4: VIX RSI of X number bars ago is lower than 50.
• Case 5: VIX RSI of X number bars ago is greater than 80.
• Case 6: VIX RSI of X number bars ago is lower than 20.

Considerations

Charting Software: TradeStation 10.0.

Code development language: Easylanguage 10.0.

Code Considerations:

• Entries type: 
  • Long only.
• Entries Direction: 
  • Mean Reversion.
• Exits:
  • Stop Loss
  • Profit Target: Stop Loss 
• Trigger:
  • Long when Close crosses below Oversold.
  • VIX Filter on data 2

First approach

OK, now that the code is ready, let’s add Data2 to our reference strategy:

And look for the following cases and parameters:

Results

The more effective filter case is number 4: where the RSI of the VIX with 7 bars back is above 50, even if the net profit is reduced, the strategy is more effective in reducing more than half of the trades, duplicating the average per trade and reducing the drawdown by $20.000. 

After seeing the improvement of the strategy, some questions arise:

• Will it work in another timeframe?
• Will it work on other Index futures markets?
• What will happen if the VIX has a different timeframe from the primary Market?

This research will take me a considerable big amount of time to do it manually, so what better tool than Tradesq to help me out.

Applying Tradesq

Tradesq has been chosen as a Futures trading system research tool.

On Tradesq, I will enter the code, and on Data1, select the four main Index futures markets: 

@ES: E-MINI S&P 500.

@NQ: E-MINI NASDAQ 100.

@RTY: E-MINI RUSSELL 2000.

@YM: E-MINI DOW 30.

11 timeframes, from 15 minutes to daily.

For Data 2, the market will be @VX, and timeframes from 15 minutes to daily. 

Results

After a few hours, we obtained results based on the following criteria:

Nasdaq 60min

Data1: Nasdaq (@NQ) 60min

Data2: VIX (@VX) 180min

ES 120min

Data1: S&P (@ES) 120min

Data2: VIX (@VX) Daily

YM 60min

Data1: DOW (@YM) 60min

Data2: VIX (@VX) 60min

YM 120min

Data1: RTY (@YM) 120min

Data2: VIX (@VX) 1440min

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

Conclusion

Incorporating the VIX as a filter has significantly improved the RSI trading strategy’s performance across different timeframes and Index futures markets. The results show promising potential and further research and testing can be conducted to refine and optimize the strategy.

Author:

Juan Fernando Gómez

Email: jgomezv2@gmail.com

The post Enhancing the 2-Period RSI2 on Indexes Using the VIX Futures Market – an RSI trading strategy appeared first on Tradesq Blog.

This article will focus on one of the edges listed in Tradesq’s Seasonality Library. Seasonal Edge based free trading system giveaway.

The July 1st ES (S&P 500) long edge

Browsing the Tradesq seasonality database, one of the upcoming edges is an ES (S5P 500) long edge on July 1st:

What does it mean? Analyzing historical time series data for ES, our Seasonality Engine identified a new seasonal edge formed around 2014. This edge signifies a strong statistical bias for S&P500 (and related markets) for a successful long trade.

How to trade this edge? Buy ES (S&P 500) on July 1st at market close. Hold the trade for 17 trading days and exit the position on the close. Doing this will give you a 90% statistical chance of winning the trade. In the past 10 years, trading this edge, traders could average a $3,750 average trade size. This means a total of $37,500 profit during the last decade.

For algo traders, Tradesq offers a generated EasyLanguage code to utilize this edge quantitatively:

//TO be placed to @ES symbol - free trading system follows
if date >= 1130701 and date <= 1130701 + 2 and marketposition = 0 then Buy this bar at close;
if date >= 1140701 and date <= 1140701 + 2 and marketposition = 0 then Buy this bar at close;
if date >= 1150701 and date <= 1150701 + 2 and marketposition = 0 then Buy this bar at close;
if date >= 1160701 and date <= 1160701 + 2 and marketposition = 0 then Buy this bar at close;
if date >= 1170701 and date <= 1170701 + 2 and marketposition = 0 then Buy this bar at close;
if date >= 1180701 and date <= 1180701 + 2 and marketposition = 0 then Buy this bar at close;
if date >= 1190701 and date <= 1190701 + 2 and marketposition = 0 then Buy this bar at close;
if date >= 1200701 and date <= 1200701 + 2 and marketposition = 0 then Buy this bar at close;
if date >= 1210701 and date <= 1210701 + 2 and marketposition = 0 then Buy this bar at close;
if date >= 1220701 and date <= 1220701 + 2 and marketposition = 0 then Buy this bar at close;

if marketposition <> 0 and Barssinceentry = 17 then Sell this bar at close;

Multiple ways to trade a seasonality edge

Depending on your trading approach, you can gain an advantage on this edge independently if you are a discretional or quantitative trader.

• You can trade the raw edge: you can enter and exit the trade based on the edge rules without any specific risk management. This reflects a Buy and Hold mentality.
• You might define a breakout (or momentum) type of entry trigger to potentially increase the average win size.
• You can consider introducing risk management by adding Stop Loss or Profit Target rules.
• If you have a well-established trading system for ES, you might increase the position size on July 1st and the coming 17 trading days.

What is next?

Looking beyond the potential of a great trade, the question of what comes next often arises. Luckily, Tradesq has the answer with its remarkable seasonality edges—a secret weapon designed to elevate the Profit Factor of your trades, irrespective of your preferred trading style. With over 700 seasonality edges on various futures symbols housed within Tradesq’s extensive database, similar to the one highlighted in this very article, you gain access to a wealth of valuable insights.

To unlock this vast edge database, all you need to do is subscribe to our monthly or yearly Futures Seasonality package, enabling you to commence trading immediately.

As a special introductory offer, we are thrilled to provide you with the exclusive SEASONAL10 voucher code, granting you a 10% discount on your subscription. Seize this opportunity and harness the power of seasonality to optimize your trading endeavors like never before.

The post Introducing Tradesq Seasonal Edges – free trading system givaway appeared first on Tradesq Blog.

Welcome back to the Seasonal Trading Strategies – Part 2 series! In our previous blog post, we embarked on a journey to explore the fascinating concept of seasonality in trading. We laid the foundation by introducing and defining what seasonal trading is and delved into how it can provide valuable insights for traders. If you missed it, we highly recommend giving it a read to grasp the fundamentals.

As we progress further in this series, we will shift our focus to the practical aspect of utilizing seasonality in trading. This article will explore how to uncover those elusive seasonality edges that can potentially enhance our trading strategies. By understanding how to find and exploit seasonality patterns, we aim to equip traders with a powerful tool that can inform their decision-making processes.

As we navigate this exploration, it’s important to remember that seasonality edges are not foolproof predictors of future market behavior. They are merely historical tendencies that may or may not persist in the future. Nevertheless, by combining them with other analytical tools and prudent risk management strategies, traders can gain valuable insights and potentially improve their trading outcomes.

So, without further ado, let’s embark on the quest to uncover seasonality edges and harness their potential in trading. By the end of this article, you will have a comprehensive understanding of the techniques and approaches that can assist you in identifying and leveraging these patterns effectively.

Finding seasonal trading strategies using data science tools

Identifying seasonality edges in time series data is a challenging task. It is more like a data science project than a trading strategy development assignment.

One effective method is to use time series analysis to identify seasonal patterns in the data. This involves breaking down the data into its component parts, including trend, seasonality, and noise. By isolating the seasonal component, we can identify recurring patterns that are specific to certain times of the year.

Another approach is to use machine learning algorithms to predict future market trends based on historical data. These algorithms can be trained to recognize seasonal patterns and use them to make predictions about future market movements. This can be a powerful tool for traders looking to capitalize on seasonality trends.

Finding seasonality edges using Tradesq

Tradesq offers a comprehensive software solution for trading strategy development, monitoring, and evaluation. A recent enhancement to this platform is the introduction of the Market Seasonality Edges module.

With the Market Seasonality Edges module, traders gain access to a continuous monitoring system that analyzes numerous future markets across various sectors. This module identifies and tracks seasonality edges (seasonal trading strategies) that emerge over time. As of the time of writing, over 450 distinct edges have been identified for futures symbols.

Traders using Tradesq can conveniently view and explore these identified edges. They have the flexibility to list the edges by specific markets or by periods. This feature enables traders to effectively leverage the insights from seasonality analysis to inform their trading strategies.

Example seasonality edge

Now, let’s take a look at an illustrative seasonality edge sourced from the database. We have randomly selected a seasonality edge from the Soybean Meal (SM) market to showcase.

As seen in Tradesq, this specific edge has been in existence for the past 15 years and is associated with a trading system that boasts an impressive 86% win rate. Considering its longevity, a trading system aligned with this edge would have executed a total of 15 trades, with an average trade size amounting to $499.

Implementing this trading edge manually is straightforward. Here’s a simple approach: On January 2nd of each year, initiate a long position at market close. Hold the trade for a duration of 5 days and then exit the position at the closing price. By following this strategy, traders can take advantage of the identified seasonality edge effectively.

For the convenience of the quantitative strategy trader, this edge is built as an Easylanguage strategy. This code can easily be pasted to Tradestation or Multicharts over Soybean Meal (@SM) daily chart:

if date >= 1080102 and date <= 1080102 + 2 and marketposition = 0 then Buy this bar at close;
if date >= 1090102 and date <= 1090102 + 2 and marketposition = 0 then Buy this bar at close;
if date >= 1100102 and date <= 1100102 + 2 and marketposition = 0 then Buy this bar at close;
if date >= 1110102 and date <= 1110102 + 2 and marketposition = 0 then Buy this bar at close;
if date >= 1120102 and date <= 1120102 + 2 and marketposition = 0 then Buy this bar at close;
if date >= 1130102 and date <= 1130102 + 2 and marketposition = 0 then Buy this bar at close;
if date >= 1140102 and date <= 1140102 + 2 and marketposition = 0 then Buy this bar at close;
if date >= 1150102 and date <= 1150102 + 2 and marketposition = 0 then Buy this bar at close;
if date >= 1160102 and date <= 1160102 + 2 and marketposition = 0 then Buy this bar at close;
if date >= 1170102 and date <= 1170102 + 2 and marketposition = 0 then Buy this bar at close;
if date >= 1180102 and date <= 1180102 + 2 and marketposition = 0 then Buy this bar at close;
if date >= 1190102 and date <= 1190102 + 2 and marketposition = 0 then Buy this bar at close;
if date >= 1200102 and date <= 1200102 + 2 and marketposition = 0 then Buy this bar at close;
if date >= 1210102 and date <= 1210102 + 2 and marketposition = 0 then Buy this bar at close;
if date >= 1220102 and date <= 1220102 + 2 and marketposition = 0 then Buy this bar at close;

if marketposition <> 0 and Barssinceentry = 5 then Sell this bar at close;

If you wonder, seasonality edges are spread relatively evenly during the year. For example, Tradesq’s database contains 38 seasonality-based, statistically proven trading strategies for July.

What is next or seasonal trading?

Thanks to Tradesq, you can access hundreds of seasonality edges in the futures markets. But what is next?

• How can you utilize them if you are a discretional or algo trader?
• What if you trade stocks? Is a stock seasonality screener available to list seasonal patterns in the stock market?
• What is seasonal trading for forex traders? Are there any seasonal trading strategies available for forex pairs?

Stay tuned and follow our blog series about seasonal trading strategies.

The post Best Seasonal Trading Strategies – Part 2 appeared first on Tradesq Blog.

Introduction:

In the trading realm, where timing is everything, understanding and leveraging seasonality can give traders a distinct advantage. Seasonality refers to the recurring patterns and trends that occur within specific time periods, often influenced by natural or human factors. This blog post explores the significance of seasonality in trading and discusses how it can be harnessed for both discretionary and algorithmic trading strategies.

Understanding Seasonality in Trading:

Seasonality manifests itself in various ways, influenced by factors such as weather conditions, agricultural cycles, holidays, and human behavior. By analyzing historical data, traders can identify patterns and trends that repeat at particular times each year. These patterns can provide valuable insights into the future performance of commodities.

The Importance of Seasonality in Commodity Trading:
Commodity markets are intrinsically linked to the forces of nature and human consumption patterns. Understanding the impact of seasonality is particularly crucial in commodity trading due to the following reasons:

Supply and Demand Imbalance: Seasonality often leads to imbalances between supply and demand. For instance, agricultural commodities like wheat or soybeans are subject to seasonal planting and harvesting cycles. The demand for heating oil surges during winter months, while gasoline consumption increases during summer due to travel and vacation season. By recognizing these patterns, traders can make more informed decisions regarding when to enter or exit positions.

Price Volatility: Seasonal factors can significantly impact commodity prices, leading to increased volatility. Natural disasters, like hurricanes or droughts, can disrupt crop production and affect prices. Similarly, geopolitical events or regulatory changes can influence supply chains, resulting in price fluctuations. Traders who comprehend seasonal trends can navigate these volatile periods more effectively, identifying opportunities for profit or managing risks.

Utilizing Seasonality in Discretionary Trading:

Discretionary traders can benefit from seasonality by integrating it into their decision-making process. Here are a few key strategies:

News and Fundamental Analysis: As well as fundamental analysis and relevant news, seasonal factors should be considered. By monitoring weather reports, crop reports, or economic calendars, traders can gain insights into potential seasonal impacts on commodity prices.

Seasonal patterns can serve as valuable tools for discretionary traders, allowing them to align their trades with the prevailing seasonal bias. By analyzing historical data and identifying recurring patterns during specific time periods, discretionary traders can make informed decisions about entering trades that are aligned with seasonal trends. This approach enables traders to capitalize on the potential advantages presented by seasonal biases and enhance their overall trading strategies. By selectively trading in the direction of the seasonal bias, traders can increase the probability of successful trades and potentially achieve improved trading performance.

Leveraging Seasonality in Algorithmic Trading:

Seasonality can also be harnessed effectively in algorithmic trading strategies. Automated systems can process vast amounts of historical data and execute trades based on predefined rules. Here are a couple of ways to utilize seasonality in algorithmic trading:

Algorithmic trading models can be designed to identify and capitalize on seasonal trends. These algorithms can automatically execute trades when specific conditions related to seasonality are met, optimizing the timing of entry and exit points.

Enhancing Trading Algorithms with Seasonality: Seasonal trends can significantly impact various trading strategies, not limited to just trend-following algorithms. By incorporating seasonality as an entry filter, trading algorithms across different types can be optimized to identify opportune moments for executing trades, improving the timing of entry and exit points.

Conclusion:

Seasonality plays a pivotal role in commodity trading, offering traders valuable insights into future price movements and supply-demand dynamics. By comprehending and leveraging seasonal patterns, both discretionary and algorithmic traders can gain a competitive edge. Whether it’s through historical analysis, fundamental research, or automated systems, traders who integrate seasonality into their strategies are better equipped to navigate market fluctuations and seize profitable opportunities. Remember, harnessing the power of seasonality is not a crystal ball, but it can undoubtedly increase the odds of success in commodity trading.

I hope this post has provided some valuable insights into the concept of seasonality in trading and how it can be used to gain an edge in the markets. In the next blog post in this series, we will delve deeper into the topic and explore different ways to find seasonality edges on various markets. Stay tuned for more informative content!

The post Seasonality in Trading – Unveiling the Super Power of Seasonality appeared first on Tradesq Blog.

Table of Contents

A Tradesq Case Study

I want to see that the simple pattern called Hammer or Hanging man works in future markets, but before beginning the test let’s see what the pattern is all about:

Definition: Source https://www.thinkmarkets.com/

How to use it

Short trades: Hammer Pattern and Close are higher than the open. 

Long trades: Hammer Pattern and Close is lower than the open.

Now that we understand what a hammer pattern is, lets code in TradeStation Easylenguage the signals, the easiest way to do it, is to look at the resources that TradeStation already has: 

Within the Prebuilt Studies, ShowMe Section, we can find: C_Hammer_HangingMan

And it will show us every hammer (Green and Red dots) on the chart:

Let’s have a look at the code that TradeStation has already developed:

As we can see, there is already a function called C_Hammer_HangingMan, so let’s take advantage and proceed to create our code and implement the entry signals:

Consideration for creating the code:

• Ability to choose Long only, short only, or both.
• Random Stop Loss and Profit Target exits.

Let’s check that our code is working correctly: (Red dot should be a short entry and the green dot a long entry)

We are ready to upload the code into Tradesq and select all 43 futures markets, the 11 timeframes, and the inputs range we want to test.

Let’s see the results:

We have many edges in the currency, energy, grains, index, and metals sectors.

Some examples

Nasdaq(@NQ) 180min:

VIX(@VX) 15min

Wheat(@W) 480min

High Grade Copper(@HG) 240m

Note: All results include trading costs, slippage and commission.

Conclusion

This test used a really simple pattern with some random Profit Target and Stop losses, and was only intended to find good edges on the futures markets. Using Tradesq saved us a lot of time and effort in selecting the best results, which found edges on five (5) different sectors of the futures markets. It does not mean these are strategies ready to trade. Additional filters and robustness tests must be applied, but it is a terrific starting point. 

Author:
Juan Fernando Gómez (jgomezv2@gmail.com)

The post Does the Hammer-Hangman Pattern work on Futures markets? appeared first on Tradesq Blog.

One of the key principles of Williams’ trading system is the idea of “buying low and selling high.” This means that he looks for opportunities to purchase assets when they are undervalued and then sells them when they reach a higher price. Williams believes that this approach allows him to capitalize on market trends and make consistent profits over the long term.

The 3-bar system

Larry Williams’ 3-bar system is a popular technical analysis tool traders use to identify potential trade opportunities. This system uses three consecutive bars on a price chart to signal potential buying or selling opportunities.

The 3-bar system is based on the idea that the market tends to move in trends. When a stock or other asset is in an uptrend, it will often make higher highs and higher lows, which can be identified by looking at a price chart. Similarly, when a stock is in a downtrend, it will often make lower highs and lower lows.

The 3-bar system uses these trends to identify potential trade signals. For example, if a market is in an uptrend and makes three consecutive higher highs, it could be a signal to buy. Similarly, if it is in a downtrend and makes three consecutive lower lows, it could be a signal to sell.

Overall, the 3-bar system is a popular tool used by traders to identify potential trade opportunities based on trends in the market. While it is not a foolproof method and should not be used in isolation, it can be a valuable addition to a trader’s toolkit.

Testing the 3-bar system

In this article, we will see how this simple concept can be implemented in Tradestation’s EasyLanguage and how it performs when backtesting it across multiple asset sectors.

To start this research project, we used a publicly available EasyLanguage implementation for the 3-bars system:

Var: mal(0), mah(0);
mal = Average(Low,3);
mah = Average(High,3);

//Trend analyzer:
Var: trnd(0), TotalPrice(0);
TotalPrice = (O+H+L+C)/4;
Trnd = LinearRegSlope(TotalPrice, 3);

//Uptrend VS Downtrend
Var: c1(0),c2(0);
c1=(Trnd[1]>0);
c2=(Ttrnd[1]<0);

// 3-Bar System long orders
If Marketposition<=0 and c1 then BUY 1 contract at mal[1] limit;
If Marketposition=1 and c2 then SELL next bar at mah[1] limit;

// 3-Bar System short orders
If Marketposition>=0 and c2 then SELLSHORT 1 contract at mah[1] limit;
If Marketposition=-1 and c1 then BuyToCover next bar at mal[1] limit;

The concept is rather simple:

• We calculate the average bar price (Open + High + Low + Close) / 4
• We calculate a 3 days linear regression value based on the average price of the bar
• When the linear regression line is on the positive side, we consider an uptrend. When the linear regression line is on the negative side, we consider a downtrend.
• We enter the direction of the trend with limit orders. The limit price is the 3-day average of the low for the long and the 3-day average of the high for the short.

Before moving on to the backtesting phase, there is an important thing to mention. When backtesting with EasyLanguage, the danger of using limit orders is that they may not have been available at the time and on the exchange where the historical data is from, which could affect the accuracy of the backtest results.

To see how this code performs on different asset sectors and timeframes, let’s execute a backtest with Tradesq.

First, we open the Tradesq scheduler, and we copy and paste the EasyLanguage code to the code editor box. The next step is to define which markets and timeframes to backtest. As I like to run my backtests with at least a year of out-of-sample data, I set the desired ‘in-sample backtest to’ date.

This EasyLanguage code contains 2 optimizable input variables: AvgPeriod and LRSlopePeriod. The default variable values are 17 and 12. As we are testing this system on various markets and timeframes, a more comprehensive range of variables was defined (between 5 and 35).

Backtesting results across all market sectors

The results are pretty convincing. According to the default screening parameters, good backtesting results were found in the Currency, Energy, Grain, Index and Metal sectors. The distribution of timeframes is also extraordinary. This EasyLanguage system shows promising results at 120, 180, 240, 360, 480, 600 minutes, and 1-day bar size:

As an example, let’s inspect the backtesting results for Heating Oil (HO) on 600 minutes time resolution:

As a final warning, I must emphasize that the above-tested code cannot be considered a complete trading system. As a potential point of improvement, an entry filter and a stop-loss logic could be added.

The post Testing a Larry Williams Tradestation system appeared first on Tradesq Blog.

Well, this is fundamentally true, but we should not forget about other exciting strategy types as well:

• pattern-based strategies
• seasonality strategies
• daily, weekly or monthly bias-based strategies

Now, let’s stick a little bit with bias-based strategies. This strategy type utilizes an important underlying characteristic of the traded market: the average statistical change during a specific time interval over and over again. This time interval can be set during a trading day, a trading week or an entire month.

Bias-based strategIES in EasyLanguage

Let’s do a small experiment. My goal now is to find a couple of days each month when buying or selling Crude Oil during an average trading month has an edge. To start our research project, I prepared a very simplistic, Easy Language code that enters one long and one short trade each month. Both the entry day and the length of the trade are optimizable parameters (on the long and short side as well):

Our next step is scheduling optimization for long entry dates, long trade lengths, short entry dates and short trade lengths.

Before analyzing the collected results, there is an important point I’d like to raise. When performing algo research, I ensure that reasonable slippage and commission are configured for the appropriate market. In this example, we are working with Crude Oil (CL). I set a $30 roundtrip slippage and a $2.5 broker commission per trade. Why is this important? We often find great looking edges with small average trade sizes when working with strategy optimizers, especially with bias-based strategies. When trading these edges in real life, the actual broker commission and the trade execution slippage can easily melt these edges away. A properly configured slippage and commission values can ensure realistic expectations. With all that in mind, let’s have a look at the optimization results:

As you can see, the optimizer listed many long and short entries using variable trade lengths with positive expectations. Before choosing the optimal parameters, consider the Net-Profit, the Average-Trade, Max-Intraday-Drawdown and Profit factor values. When developing bias-based strategies, I prefer to select one of the largest average trade sizes from the optimizer output list.

In this case, these values represent the following trading system for Crude Oil (CL):

• go long on the 21st day of each month and let this trade run for 5 days
• go short on the 29th day of each month and let this trade run for 5 days

This trading system utilizes an interesting up-down oscillation of Crude Oil close to the end of each month.

Let’s have a look at the backtesting results of this edge:

We found a very simplistic but steady bias-based edge for Crude. Can we consider it as a trading system or a market-ready algo? Probably not, but we are pretty close to it. If you’re planning to build your own algo based on this edge, I recommend you add a stop-loss, profit target and perhaps an entry filter rule. In a future article, I will write more about the trading system development steps I would like to follow.

Algo-research on the assembly line

Now let’s bring our research project to the next level. Why did I pick Crude for my initial research? I’m not sure. It was a somewhat random idea. Actually, what I really try to find is the top 3 Futures market symbol with the most substantial monthly bias edge. How can we have this information without spending significant time in front of the optimizer? Let’s use Tradesq to scan multiple Futures markets for similar monthly bias edges.

Logging into Tradesq, let’s navigate to the Schedule Backtest function in the Smart backtesting menu. In the scheduler screen, we paste our EasyLanguage strategy code to the code editor box, and we choose daily or 1440 minutes timeframe for this experiment. The next step is to specify which symbols to include in the research. Tradesq currently supports 45 future market symbols across multiple sectors (index, currency, metal, interest rates, etc.). For the sake of versatile results, let’s choose them all. At the bottom of the screen, we can specify the optimization ranges and values of our EasyLanguage variables as we did during our initial research on the desktop.

When Tradesq finishes testing, we see that we received good results from 23 symbols. Based on these results, we can conclude that the currency, energy, grain, index, interest rate, metal and meat sectors show solid monthly bias properties. This represents the majority of the futures market.

Our last goal is to determine the best 3 symbols for the monthly bias system. Let’s ask Tradesq’s help again. Using the Strategy Library / Strategy Screener function, all search results can be listed ordered by R2 value (smoothness of the equity curve). The top 3 results are:

• Canadian Dollar (CD) using parameters longentryday=27, longbarsintrade=4, shortentryday=16, shortbarsintrade=4
• 5 Years US Treasury Note (TY) using parameters longentryday=21, longbarsintrade=3, shortentryday=4, shortbarsintrade=1
• 10 Years US Treasury Note (TS) using parameters longentryday=20, longbarsintrade=4, shortentryday=3, shortbarsintrade=1

All backtest results produced by tradesq include the market-specific average slippage rates and broker commission fees.

Finally, let’s look at the results sheet of the best system executed on the Canadian Dollar. It’s a pretty neat equity curve.

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