I also thought this looked promising, but I would have stress-tested it more just to see how it affects the results:
- Test with at least 3 times more stocks in the simulation than you will be trading in reality.
- See how the strategies perform against other strategies.
- I think it is rarely desirable to have a turnover over 400%, but it is important that the turnover is not too low because you do not get enough test situations in your simulation.
- Try removing the biggest winners in your strategy to see the results.
- A test on 12 stocks is extremely vulnerable to small adjustments, so test on a slightly larger number when you optimize.
Otherwise, I have included some tips that have come from others over the years. This is based on the agent inn Perplexity Comet's: .
General Setup & Philosophy
- Explore the Portfolio123 Community for shared user lessons and mistakes: Forum thread: Lessons for new users
- Use public "designer models" as working templates: Designer Models overview
- Begin with pre-built "Live Portfolios": Live Portfolio strategies tips
- Study the Help Center articles for each topic: Help Center
- Participate regularly in forum discussions: Forum Categories
- Familiarize with core concepts by reading "Core Combination" explorations: Forum thread: Core Combination Ranking
Ranking System Optimization
- Use at least 30–50 factor nodes, testing broad coverage: Core Combination factors
- Diversify factor groups (growth, value, etc.): Ranking system design
- Focus on "point-in-time" universe data for honest backtests: Point-in-time advice
- Create a custom universe with liquid stocks: Universe setup discussion
- Always check the weight distribution between factors: Ranking optimization thread
- Try “zeroing out” nodes to see impact: Practical node testing
- Compare system performance across multiple universes: Performance in different universes
- Visualize ranking outputs with quantiles: Quantile analysis
- Avoid factors with unreliable/hard-to-interpret jumps: Quality of ranking nodes
Buy & Sell Rules
- Keep buy/sell logic simple (Rank threshold): Simple rule example
- Place as much logic as possible in the ranking system: Ranking system focus
- Experiment with varying buy/sell threshold sensitivity: Rule tuning
- Test holding periods beyond pure rank trigger: Holding period effects
- Adjust “rebalancing window” for volatility: Rebalancing window tips
- Add real-world costs/slippage when simulating: Slippage advice
- Ban penny/illiquid stocks via minimum price/volume rules: Liquidity rules
- Backtest with and without slippage to compare effects: Forum thread: Lessons for new users
Simulation Settings
- Employ out-of-sample testing rigorously: OOS testing best practices
- Use rolling windows in your backtests: Rolling window example
- Always check max drawdown figures: Drawdown study
- Keep annual turnover below 350% for practical use: Turnover management
- Stress test different portfolio sizes (5, 10, 25, 50 stocks): Portfolio size stress-testing
- Use custom universes to test niche or thematic ideas: Universe setup discussion
- Benchmark performance versus broad indices: Benchmarking advice
Strategy Search & Stress Testing
- Don’t chase “perfect” results; stress test for robustness: Optimization mindset
- Split your universe by sector, cap-size, or geography and compare: Universe breakout testing
- Run market-neutral simulations to isolate alpha: Market-neutral testing
- Combine (“book”) several strategies for smoother returns: Portfolio books
- Repeat backtesting with slightly randomized buy/sell dates (“staggered start”): Staggered rebalancing
- Test every rule with both small and large universes: Universal rule testing
- Always check the worst 12-month span within your simulation: Drawdown analysis
Advanced Practical Tips
- Review the investment commentary on major market drawdowns: Historical crisis analysis
- Factor in real-world tax impacts if relevant: Tax tips discussion
- Try ranking and simulation within sector universes (Tech, Energy, etc.): Sector strategies forum
- Adjust universe screening as indexes change over time: Index screening advice
- Use combination of sector risk ban and factor-based ranking: Risk management post
- Study “factor decay” and manage derived risk accordingly: Factor decay forum
- Double check survivorship bias for all simulated runs: Survivorship bias advice
- Join topical discussions on optimal factor combinations: Optimal combo thread
Mindset and Community
- Ask “what’s missing?” – regularly review forum threads for gaps in your own setups: Community advice
- Post your ranking system in forums for feedback: Feedback request
- Accept volatility, understand drawdowns, and plan accordingly: Volatility management
- Keep a digital log of each strategy version and backtest result: Backtest record keeping
- Contribute your own experience as you learn – that’s how the Portfolio123 ecosystem evolves: Forum contribution