A StockRanker Strategy with Staggered Rebalancing and Rank-Based Exits

Summary

This Chinese-language tutorial describes implementing a stock-ranking strategy in BigTrader by passing StockRanker predictions directly to the trading engine and coding the trade logic there. It allocates purchases among the top-ranked stocks using weights that decline with rank, while limiting each instrument’s portfolio allocation. The target holding period is five trading days; the strategy plans to deploy roughly one fifth of portfolio value per day, subject to available cash and a larger purchase allowance after the initial build-up period.

After the initial holding period, it sells existing holdings from the bottom of that day’s ranking until the planned cash need is met, then buys the top-ranked names. The tutorial includes commission settings and code, but presents no backtest results or performance evidence. It says the more detailed implementation is complex and generally offers no significant gain over a simpler template, which it recommends when the comparison shows little difference. The approach depends on ranking quality, execution assumptions, and the stated portfolio constraints.

Key ideas

• The strategy sends StockRanker predictions directly to BigTrader for coded trade handling.
• Purchase weights decline with rank, and each stock has a maximum portfolio allocation.
• The portfolio is built and rebalanced in daily portions based on a five-day holding period.
• After the build-up phase, lower-ranked held stocks are sold to fund new purchases.
• The document reports no performance evidence and recommends a simpler template when results are similar.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.