Multi-Stock Basket Trading

Typically 3-10 stocks. Fewer than 3 doesn't provide much diversification. More than 10 becomes complex to manage and may not add much additional diversification benefit. 5 stocks is a good starting point.

Equal weight is simpler and avoids concentration in the largest stocks. Market cap weight matches index behavior. For most traders, equal weight is a good default. Use market cap weight if replicating an index.

Sum the P&L of all positions. Calculate percentage return as total P&L divided by total investment. Use a spreadsheet or portfolio tracking tool. Compare to a sector ETF as benchmark.

Baskets have no MER fee, let you select only the best stocks (not all sector stocks), and give full control over weighting and timing. ETFs are simpler but include all stocks including weaker ones.

Rebalance when weights drift significantly (5%+ from target) or on a fixed schedule (weekly or monthly). Calendar rebalancing is simpler; threshold rebalancing responds to actual drift.

Calculate relative strength (sector price / TSX price) for each sector. Rank sectors by RS momentum. Buy the top 1-2 sector baskets. Rotate when rankings change. Alternatively, use economic cycle to guide sector selection.

Short the index ETF (XIU) or futures (SXF) proportional to your basket's beta × value. This neutralizes market risk. You can also buy puts on the sector ETF or on individual stocks for downside protection.

Weight stocks inversely to their volatility (risk parity). Higher volatility stocks get lower weight. This equalizes each stock's risk contribution to the basket, resulting in lower overall basket volatility.

Basket beta = weighted average of individual stock betas. If RY beta is 0.9, TD is 0.8, and they're equal weight (50% each), basket beta = 0.5×0.9 + 0.5×0.8 = 0.85.

Attribution breaks down basket performance into components: which stocks contributed most, impact of weighting decisions, sector effect vs stock selection. It helps you understand what worked and what didn't.

Mean-variance optimization finds weights that maximize the Sharpe ratio (return per unit of risk). It uses expected returns, volatilities, and correlations as inputs. Can also minimize variance or target specific characteristics.

Use statistical methods (like Johansen test) to find weights where the basket is cointegrated with a benchmark. The spread is mean-reverting, creating opportunities when it diverges. Requires quantitative tools and regular recalibration.

Implementation shortfall is the difference between the price when you decided to trade and your actual execution price. Execution algorithms balance speed (alpha decay) vs market impact. Optimal execution minimizes this shortfall.

Screen stocks for desired factor exposure (value, momentum, quality, low vol). Weight stocks by their factor scores. This tilts the basket toward factors expected to outperform while maintaining diversification.

Value at Risk (VaR) estimates the maximum loss at a confidence level over a period. For baskets, use historical simulation (past returns) or parametric VaR (using volatility and correlations). Account for correlation effects - diversification reduces VaR.