Podcast
Questions and Answers
Match the terms related to trading strategies with their correct descriptions:
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TOOS = Out-of-sample testing DD = Drawdown IS = In-sample SR = Success rate
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Workflows = Processes for executing multiple strategies Ins = In-sample data storage OOS = Out-of-sample data storage Ranking = Method to evaluate and order strategies
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Run 1 = 100 passes in OOS Run 2 = 11 passes Trades = 1697 trades used as filter Success Rate = SR=93% when NP >= 0
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Study Notes
Strategy Selection and Optimization
- IS Strategies are selected for each run and Time Out of Sample (TOOS)
- Strategies are evaluated using the number of passes for each TOOS
- For the first TOOS, strategy 61 was selected and achieved 57 passes with a negative or zero net profit, resulting in a success rate of 93%.
- 1080 IS strategies were used in one example.
In-Sample (IS) and Out-of-Sample (OOS) Testing
- 100 strategies passed in OOS testing for run 1, and 89 for run 2.
- 1697 trades were used as a filter in IS testing.
Drawdown Filtering Impact in IS Testing
- A drawdown filter was applied in one run, which reduced the number of strategies passing.
- The success rate wasn't as high as without filtering, but this may be useful in strategies where drawdown is a concern.
Workflow and Result Analysis
- A workflow is created to run IS and OOS tests.
- The goal is to select strategies which pass through the Time Out of Sample (TOOS)
- Results from insample and out-of-sample are saved.
Workflow Bug and Date Specification Issues
- Multiple workflows used different specific dates to correct potential bugs during OOS testing.
Data Analysis and Optimization Methods
- Multiple testing workflows were designed for out-of-sample tests with dedicated date ranges.
- Data is saved to insample and oos folders.
- Another workflow type selects the top scoring results out of a large set by genetic and other optimized methods.
- Workflow runs using 10 x 4 workflows ST1 to ST4 for all workflows
- Saved results are analysed to review best strategies overall.
- Genetic algorithm (10,000 different workflows with varied parameters) used.
- Data is saved in folders and duplicated python source code when re-running
- Running jobs in parallel on multiple computers to improve speed.
Success Criteria
- Success is defined by a net profit (NP) greater than or equal to zero. (e.g., NP >= 500).
- Additional performance metrics may be included, but not stated.
- Success criteria are important for selecting the most optimal strategies.
Optimization Methods and Metrics
- Sequential Optimization (Seq Opt) used, after simple optimization.
- Strategies are sorted by average trade value in the tests.
- The use of seq optimisations potentially improves OOS results, but impacts must be validated.
- This method helps to validate that the model isn't overfitting to the in-sample data.
Problems
- Overly complex Machine Learning (ML) models can be ineffective and aren't necessarily better or more useful than basic ones
- Results for 2 runs stated as not being quite clear.
Other Comments
- Discussions in the comments focus on issues such as referral programs, account size tradeoffs, OOS degradation, and potentially overoptimistic expectations.
- Complexity of strategy selection methodology is a discussed topic.
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Description
This quiz covers the methods of selecting and optimizing trading strategies through In-Sample (IS) and Out-of-Sample (OOS) testing. It examines metrics such as passes, success rates, and drawdown filtering effects on strategy performance. Test your understanding of these critical concepts in strategy evaluation!