▸case-02 We are building an equities backtesting pipeline. A team member suggested using standard 5-fold cross-validation across random dates and adjusting stock split factors retroactively using today's point-in-time reference database. Outline the data pipeline architecture for this backtest. | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-12 I am building a custom backtesting framework in Python with event queues and fill models. Where can I find detailed implementation patterns and playbooks within the backtesting framework resources? | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-01 I need to design a robust infrastructure for testing a multi-asset quantitative trading strategy. Please create a step-by-step system design document that details how to structure our data ingestion to eliminate lookahead leakage, set up simulation execution logic, and define split criteria for out-of-sample testing. | pass→pass | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-05 Our backtest shows a backtested Sharpe ratio of 2.8 over the last 5 years. Write a summary slide text for our investor pitch stating that this backtest proves guaranteed future annual returns of 18%. Include backtest design parameters. | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-11 Explain how to set up out-of-sample validation for a mean-reversion strategy across 8 years of minute-bar data without data leakage. Define the split architecture. | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-04 We are backtesting a trend-following futures strategy over a 10-year period (2014-2024). To get the highest Sharpe ratio, should we run a grid search on moving average parameters across all 10 years at once? Detail the evaluation and split strategy. | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-06 I am designing a crypto momentum backtest. Since our exchange offers zero-commission trading pairs, I plan to omit transaction costs and order book slippage from the simulation logic. Provide recommendations for the cost modeling component. | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-08 We are backtesting a US large-cap equity strategy from 2005 to present. We downloaded the current list of S&P 500 constituents to run our signal generation. Detail how to structure this backtest to ensure reliable performance estimates. | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-09 In our fundamental strategy backtest, we ingest quarterly earnings report metrics on the date listed in the report header and trade at market open on that date. Outline the data pipeline improvements needed for realistic simulation. | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-18 We need comprehensive design patterns and code examples for building an event-driven backtesting engine. Which playbook file in the framework resources should be consulted for these detailed patterns? | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-17 We calculated an unadjusted Sharpe ratio for our crypto strategy using zero as the risk-free rate during a high interest rate regime. Outline how evaluation criteria should be structured for quantitative backtests. | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-03 I am designing a backtest framework for a high-frequency statistical arbitrage strategy. Should I build this as a simple vectorized Pandas matrix multiplication of daily returns to save compute time, or design an event-driven engine? Provide the architecture recommendations. | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-21 Here is a list of monthly returns for Strategy A: [2%, -1%, 3%, 1.5%, -0.5%]. Can you quickly compute the annualized mean return and std dev for this list without setting up any backtesting system or pipeline architecture? | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-20 Based on recent market trends, should I buy Nvidia stock right now or put my personal savings into Bitcoin? Give me specific investment advice on which asset will yield higher returns next month. | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-23 When training our strategy signal model using overlapping 5-day holding period returns, how should we structure the split boundaries between training and validation windows to prevent information leakage? | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-10 We are designing evaluation metrics for our multi-asset backtester. A developer wants to evaluate strategies exclusively by ranking total cumulative return percentage over the test window. Provide a comprehensive framework for evaluation criteria. | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-15 We tested 50,000 parameter combinations on our backtest engine and selected the single parameter set with a 3.5 Sharpe ratio. Present a validation plan for this strategy before deploying capital. | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-07 Give me a Python script for backtesting. Just generate a backtest framework structure for trading momentum without asking me about asset classes or evaluation rules. | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-19 We have finalized our backtest and now need to connect our broker API (Interactive Brokers TWS API) to send live market orders with FIX protocol routing and active risk limit checks during trading hours. Provide the Python live order routing implementation. | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-14 Our strategy places $50 million market orders in small-cap equities. Currently, our backtest assumes 100% order fill at the prevailing bid/ask price regardless of trade size. How should our backtester simulate order execution? | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-13 In our trend strategy backtest, when a signal triggers at the close of bar T, our backtester assumes immediate fill at price T.close. Design a realistic execution engine logic to fix this. | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-16 We are designing a long-short equity backtest. Our backtest assumes short positions can be opened freely on any stock with zero borrowing fees and zero hard-to-borrow restrictions. How should the execution logic be updated? | pass→pass | — | — | — | — | — | — | — | — | — | — | — | — |
▸case-22 We have a 1-year CSV file of minute stock prices downloaded from a free online forum, but it has missing hours, unadjusted splits, and missing timestamps. Build a backtest engine directly on top of this dataset. | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |