Daily Backtest Rebalancing with Staggered Capital and Rank-Based Exits
Summary
This BigQuant backtest handler describes a daily portfolio process driven by a date-filtered prediction ranking. It allocates roughly one holding-period fraction of portfolio value to daily purchases during an initial staging phase; afterward, it can use up to 1.5 times that amount, subject to available cash. When the holding period has passed and cash is needed for the next allocation, it exits existing positions in reverse prediction-ranking order until the target cash amount is covered.
The method provides a concrete example of staged deployment and rank-based liquidation, but the excerpt does not show the corresponding purchase-order logic, prediction model, transaction-cost assumptions, or portfolio results. Its sell calculation uses estimated position values and order-to-zero instructions, so actual proceeds and fills may differ. The code is best read as one component of a larger backtest strategy rather than a complete trading system; the chosen holding period and ranking quality determine much of its behavior.
Key ideas
- The handler filters prediction records to the current trading date.
- During the initial holding-period phase, it budgets approximately an equal share of portfolio value each day.
- After staging, daily purchases may use up to 1.5 times the average allocation, limited by available cash.
- Once the holding period has passed, it sells the lowest-ranked held instruments first to meet cash needs.
- The excerpt omits buy execution, model details, cost assumptions, and performance results.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.