Newbie Troubleshooting:Answering All Your Questions About TQuant Lab.

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Preface

When using TQuant Lab for backtesting and strategy analysis, beginners may encounter various technical challenges and questions. This article provides solutions and tips to help users develop and backtest strategies more smoothly, improving efficiency and accuracy. Hopefully, this information will help resolve common problems faced by newcomers.

Frequently Asked Questions (FAQ)

What should I do if I installed zipline-tej in a virtual environment but still can’t use TQuant Lab?

If you have followed the Technical manual—TQuant Lab—but are still encountering issues, try using the Tutorial on Running TQuant Lab in Google Colab and Common Errors and following these additional steps to resolve common problems.

How to Import External Data into the Pipeline for Backtesting?

Using CustomDataset allows you to seamlessly integrate external data into the TQuant Lab Pipeline, making it easier to leverage custom metrics and datasets in your strategies. This method ensures smooth backtesting by incorporating additional insights such as factors or indicators. A good example is implementing the F-score Strategy: Identifying Undervalued Quality Stocks, which identifies undervalued quality stocks.

What Are the Four Main Functions in TQuant Lab, and Are They Required for Every Backtest?

The four core functions each serve different purposes. They can be used to configure the backtesting environment, such as setting slippage models or transaction fees, implementing trading strategies, placing orders, and recording trade information. Additionally, they enable the visualization of strategy performance and risk after completing the backtest. Not every strategy requires all four functions, as their usage depends on the strategy’s needs and the user’s preferences. For more detailed instructions, please refer to the TQuant Lab Rookie Manual.

What Does Each Column in the DataFrame from run_algorithm() Represent?

Here, we list only a few of the more critical columns. For a complete description of all columns, please refer to TQuant-Lab/lecture/TSMC buy-and-hold strategy.

  • benchmark_return: The daily return of the benchmark. If set using set_benchmark(), it is calculated as:(Current Day’s Benchmark Closing Price / Previous Day’s Benchmark Closing Price) – 1
  • benchmark_period_return: The cumulative daily return of the benchmark, calculated as np.nancumprod(1 + benchmark_return Series) – 1
  • benchmark_volatility: The annualized volatility of the benchmark’s daily returns, calculated only if there are at least two periods of returns:(benchmark_return Series).expanding(2).std(ddof=1) * np.sqrt(252)
  • max_drawdown: The maximum percentage drop from the portfolio’s cumulative return peak.
  • Sharpe: The annualized Sharpe ratio measures the return achieved per unit of risk taken.
  • Sortino: The annualized Sortino ratio measures the return achieved per unit of downside risk taken.
  • Alpha: The annualized alpha indicates the portfolio’s ability to generate excess returns compared to the benchmark.
  • Beta: A measure of the portfolio’s volatility relative to the overall market.

Additionally, you can use get_transaction_detail on the DataFrame output from run_algorithm() to obtain more detailed information on orders, trades, and positions. Here’s how to use it:

from zipline.utils.run_algo import get_transaction_detail
positions, transactions, orders = get_transaction_detail(result) # If the DataFrame returned by run_algorithm() is named result
  • positions: Position status
  • transactions: Transaction records
  • orders: Order records

How Can Built-in Technical Indicators, such as SMA, be used in TQuant Lab? Can External Libraries Like NumPy or TA-Lib Be Used?

TQuant Lab offers many built-in technical and price factors, including SMA, Bollinger Bands, RSI, and more. These built-in indicators save users time from having to create technical indicators from scratch. For a detailed list of built-in factors, along with their descriptions and usage, please refer to TQuant-Lab/lecture/Pipeline built-in factors。

Can TQuant Lab Set Entry Points Using Opening or Closing Prices?

Zipline executes trades based on the next day’s closing price to minimize look-ahead bias if the strategy generates a signal on the current day. For detailed order information, check the orders field in the results DataFrame.

As for executing trades using opening prices, this feature is currently not available. However, it may be considered for future updates after evaluation.

Conclusion

With the troubleshooting solutions and tips in this article, users can more smoothly overcome technical challenges encountered while using TQuant Lab. From installation to data processing, we hope this information will improve users’ efficiency and accuracy, helping them successfully develop their first strategy.

Extended Reading

TQuant Lab Rookie Manual

Tutorial on Running TQuant Lab in Google Colab and Common Errors

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