In this article, I want to highlight some cool enhancements for the Tradesq platform.
Several months ago, we introduced you to Tradesq with this article, Here is My Secret Weapon to Trade Like a Hedge Fund. If you're unfamiliar with Tradesq, you'll definitely want to read that article.
For a quick summary, Tradesq is a web-based tool to help you streamline your strategy development process.
- Compatible with TradeStation and MultiCharts.
- Strengthen your portfolio with robust strategies.
- Automated backtesting across multiple markets and timeframes, without having to spend hours doing research or managing spreadsheets.
- Automatically track your trading strategies performance on out-of-sample data.
It has been six months since the original article, and today I will give you an update on what we've been working on to improve Tradesq.
We have been working on three key areas that will help you find trading strategies and test trading strategies so you can build out our trading portfolio.
2. "Smart Backtesting" new features
3. "Forward testing" new features
To build trust companies must be transparent about the services and products they offer. As the quote from Freda Lewis-Hall says, “Transparency is the currency of trust”.
That is why our main focus has been designing new features to increase Tradesq’s transparency above all. As a result, we have added two new features:
- Tradesq Strategies Explorer. Anyone can look at our up-to-date performance statistics and equity curves for all public strategies stored at Tradesq.
- Market Configuration. Clear specifications used for market data feeds. This feature dramatically helps Tradesq users when replicating strategy backtests at their platform’s choice.
1.1. Tradesq Strategies Explorer
Imagine how nice it would be to track how some public EasyLanguage strategies perform in out-of-sample data for as long as you want without needing a subscription. We have done this by opening up our Tradesq platform to the public to display every public strategy’s performance and statistics. We call it, “Tradesq Strategies Explorer”.
Anyone can now access our Forward Testing Strategy to understand how specific strategies perform in different markets. Directly access https://tradesq.net and press the “Tradesq Strategies Explorer” button on our main website.
This action will bring you to our Forward Testing Strategies database.
This database has more than 2,000 strategies currently. Anyone can easily filter and access strategies with out-of-sample data according to their needs.
For example, I filtered “hot” strategies with at least 6 months out-of-sample data, 20 out-of-sample trades, $1,000 profit in out-of-sample data, and $1,000 profit this year to obtain some results as shown below:
And if I click on strategy “OOS_13542”, I can drill down on some detailed performance information for this specific strategy:
We have made available the strategy statistics and reporting data for every public EasyLanguage strategy stored at the Forward Testing, remaining the EasyLanguage strategy codes and some private features exclusively to our Tradesq subscribers.
1.2. Market Configuration
As you may know, a quantitative trader must have a strategy code and market data to obtain strategy backtest performance results.
For our case, a quantitative trader who uses TradeStation or MultiCharts platforms to trade automatically will follow this formula:
Tradesq currently runs EasyLanguage code on different futures markets such as Gold, Lean Hogs, or Crude Oil. The data for a specific futures market can look quite different depending on the market session or rollover rule chosen to build up the chart. So, the same EasyLanguage code could have a nice backtesting equity curve and an ugly one depending on the market data configuration.
To help our Tradesq subscribers, we included for each symbol (or market) specs about Slippage, Commissions, Timezone, Session, and Rollover rules. This information will help quickly build the market data chart in TradeStation or MultiCharts where the trader confirms the EasyLanguage code.
The example below shows the specs for AD (Australian dollar), such as the trading session from 17:00 to 16:00 UTC -6, Sunday to Friday, and a rule to rollover the AD contract two trading days before the expiration date.
To sum up, we have added two new features to improve transparency in Tradesq:
• Possibility to track the forward-tested strategies before signing up to Tradesq. No black box, no emails with strategies with promises of out-of-sample performance. Thanks to Tradesq you can note some strategies that you are interested in, and come months later and confirm how they performed with out-of-sample data.
• More clarity about the market data used (such as market session and rollover rule) to be able to replicate the strategy in your preferred strategy automation platform.
2. "Smart Backtesting" new features
We have added two new features to enhance Smart Backtesting.
2.1. Out-of-sample Backtest data
Tradesq divides the market data into three blocks: In-Sample (IS) Backtest data, Out-of-Sample (OOS) Backtest data, and Out-of-Sample (OOS) Forward test data.
- In-Sample Backtest data: This is the market data used to create the strategy. In other words, the data used by the backtest. For the equity curve example below, the In-Sample Backtest data used is from January 2010 until April 2021 (in blue).
- Out-of-Sample Backtest data (NEW): This is the market data that was not used on the day of the backtest because we decided to leave it for an extra test. This "untouched" OOS data helps us quickly test how the strategy performs right after the backtest, without the need to wait some additional months to get a confirmation. For the equity curve example below, we left one year of OOS Backtest data from April 2021 until April 2022 (in yellow).
- Out-of-Sample Forward Test data: This data starts counting after we run the backtest and is the ultimate test to check how the strategy performs. For the equity curve example below, the OSS FT data (or the day we ran the backtest) started on April 2022 and continues to be forward tested until today (in red).
Tradesq users now have a new feature to leave OOS or "untouched" data when performing backtests. As shown within the "Schedule Backtest" screen, a Tradesq user can now select the date where the In-Sample (IS) data backtest ends (in blue in the equity curve chart above).
The data between “In-Sample Backtest To” field and the date where the backtest was executed is considered OOS Backtest data (in yellow in the equity curve chart above). This means that Tradesq users can have an idea of how well a specific strategy performs using OOS without the need to wait to be forward tested.
2.2. Strategy backtests with two market data streams
Typically, a trading strategy is coded with one market data stream to decide when to buy or sell a specific position size in this market.
Tradesq is now offering the possibility to backtest strategy codes with two market data streams by checking "Use Data2" field as shown below:
Data2 feature allows Tradesq subscribers to take advantage of the whole "Smart Backtesting" existing functionality for strategies with two market data streams.
3. "Forward testing" new features
We also included the Smart Backtesting new features into Forward Testing when a Tradesq user adds private strategies, as shown in the “Schedule Forward Test” screen:
Diversification often called the only free lunch in trading, is a risk management technique that consists of mixing various investments within a portfolio. So, a well-diversified portfolio allocates uncorrelated trading strategies among different market/timeframe combinations.
Imagine you had access to a bunch of EasyLanguage strategies, and you would like to group them together in an efficient way as a well-diversified portfolio. This exercise is not as simple as it seems because, for example, different strategies could create very similar results, and you would have higher risks than planned.
Tradesq offers now the possibility to create your own custom portfolios. In other words, a Tradesq user can take advantage of diversification by assigning strategies to their portfolio. The strategies within this portfolio will be automatically tracked as they belong to the Forward Testing Strategies Library.
In the example below, I created a portfolio called, “My diversified breakouts” that includes 8 strategies with decent equity curves that have been performing well in out-of-sample data on different markets:
If a Tradesq user presses the “Portfolio Report” button, they can see some charts showing how these 8 strategies work together as a portfolio.
Portfolio PnL: This is a graphical representation of the change in $ of a trading account over time if the strategies that form the portfolio are traded simultaneously with a position size of one contract.
Portfolio Drawdown. This is a graphical representation of the losses from any high point in $ of a trading account over time if the strategies that form the portfolio are traded simultaneously with a position size of one contract. This chart helps discover the Max Drawdown and how long it takes for the portfolio to recover from it and perform new highs.
Strategy Correlation Matrix. This is a matrix to analyze the degree of correlation among the strategies that form the portfolio.
Risk Per Sector. This is a pie chart to illustrate the sector’s risk proportion of the strategies that form the portfolio.
The portfolio example created earlier (My diversified breakouts) shows that the combination of these 8 strategies had:
- A Net profit of almost $700,000 since 2010
- A max Drawdown of $25,000 in early 2012
- High levels of correlation, the lowest correlation was 0.87, perhaps because the portfolio was only created with breakout strategies.
- Each strategy belonging to a different sector, with the highest risk in the interest rates futures sector (19.1%).
Those are the latest updates to Tradesq. Please leave your questions or comments below this video, and I'll get back to you.
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