Chapter 2: Maximizing Positive Expectancy Lesson 5: Selective Trading: How Filtering Setups Raises Expectancy
Selective trading improves expectancy by increasing win rate or average win size, or by decreasing average loss size. Filtering setups means only taking trades that meet specific, quantifiable criteria. This reduces the number of trades, but increases the probability of profit on each trade taken.
Expectancy Review
Expectancy is the average profit or loss per trade over a large sample size. The formula for expectancy (E) is:
E = (Win Rate * Average Win Size) - (Loss Rate * Average Loss Size)
A positive expectancy indicates a profitable system over time. Maximizing expectancy involves increasing the first term (Win Rate * Average Win Size) or decreasing the second term (Loss Rate * Average Loss Size). Selective trading directly addresses both.
Filtering Criteria
Filtering criteria are objective rules that define a high-probability setup. These rules can include:
- Volume thresholds: Minimum average daily volume (ADV) or intraday volume.
- Volatility metrics: Average True Range (ATR) or implied volatility (for options).
- Price action patterns: Specific candlestick formations, support/resistance levels, or trend confirmations.
- Time of day: Restricting trades to specific hours when liquidity or volatility is optimal.
- Market correlation: Only trading when a specific index (e.g., SPY, QQQ) confirms the trade direction.
Each filter reduces the number of available setups. The goal is to eliminate low-probability trades without removing high-probability ones.
Impact on Win Rate
Filtering out marginal setups directly increases the win rate. Consider a trader with a baseline system.
Baseline System Metrics:
- Total Trades: 100
- Winning Trades: 45
- Losing Trades: 55
- Win Rate: 45% (45/100)
- Average Win Size: $150
- Average Loss Size: $100
Expectancy (E) = (0.45 * $150) - (0.55 * $100) = $67.50 - $55.00 = $12.50 per trade.
Now, the trader implements a filter: only take long trades on stocks with ADV > 5,000,000 shares and a 5-minute chart showing a clear higher low after a pullback. This filter eliminates 30 of the original 100 trades. Of these 30 eliminated trades, 25 were losers and 5 were winners.
Filtered System Metrics:
- Total Trades: 100 - 30 = 70
- Winning Trades: 45 - 5 = 40
- Losing Trades: 55 - 25 = 30
- Win Rate: 40/70 = 57.14%
- Average Win Size: $150 (remains constant for this example)
- Average Loss Size: $100 (remains constant for this example)
Expectancy (E) = (0.5714 * $150) - (0.4286 * $100) = $85.71 - $42.86 = $42.85 per trade.
The win rate increased from 45% to 57.14%. Expectancy increased from $12.50 to $42.85 per trade. This represents a 242.8% increase in per-trade profitability.
Impact on Average Win Size and Average Loss Size
Filtering can also affect average win and loss sizes. A filter might identify setups with higher profit potential or tighter risk parameters.
Example: Futures Trading with Volatility Filter
A futures trader trades E-mini S&P 500 (ES) contracts. Baseline System:
- Win Rate: 50%
- Average Win Size: 8 ticks ($100 per contract)
- Average Loss Size: 6 ticks ($75 per contract)
- Expectancy = (0.50 * $100) - (0.50 * $75) = $50 - $37.50 = $12.50 per contract.
The trader adds a filter: only trade when the 15-minute ATR is above 4 points (16 ticks). This filter identifies periods of higher volatility. In these periods, targets are often hit faster, and larger moves are possible. It also allows for slightly wider stops while maintaining a favorable risk/reward.
Filtered System (Hypothetical Impact):
- Win Rate: Remains 50% (for this example, the filter primarily affects size)
- Average Win Size: Increases to 10 ticks ($125 per contract) due to larger price swings.
- Average Loss Size: Increases to 7 ticks ($87.50 per contract) due to wider stops in higher volatility.
Expectancy = (0.50 * $125) - (0.50 * $87.50) = $62.50 - $43.75 = $18.75 per contract.
Even with a slightly larger average loss, the increased average win size due to the volatility filter improved expectancy from $12.50 to $18.75 per contract, a 50% increase.
Step-by-Step Numerical Example: Options Trading
Consider an options day trader trading SPY calls and puts. Baseline Strategy:
- Trades: 200 per month
- Win Rate: 40%
- Average Win Size: $200 per contract (e.g., buying 1 contract for $1.50 and selling for $3.50)
- Average Loss Size: $120 per contract (e.g., buying 1 contract for $1.50 and selling for $0.30)
Calculate baseline expectancy: E = (0.40 * $200) - (0.60 * $120) E = $80 - $72 E = $8 per contract.
Total monthly profit (200 trades): 200 * $8 = $1,600.*
The trader implements a new filter: only take trades where SPY has moved at least 0.5% from its open price within the first hour of trading, and the 5-minute chart shows a clear retest of a major support/resistance level. This filter aims to identify higher conviction moves.
Backtesting the Filter: The trader reviews the past 200 trades.
- The filter would have eliminated 120 trades.
- Of the eliminated 120 trades, 100 were losers, and 20 were winners.
- The remaining 80 trades (200 - 120) are considered "filtered" trades.
New Metrics for Filtered Trades:
- Total Filtered Trades: 80
- Original Winning Trades: 200 * 0.40 = 80
- Original Losing Trades: 200 * 0.60 = 120
- Filtered Winning Trades: 80 - 20 (eliminated winners) = 60
- Filtered Losing Trades: 120 - 100 (eliminated losers) = 20
Calculate new win rate: New Win Rate = Filtered Winning Trades / Total Filtered Trades New Win Rate = 60 / 80 = 75%
Assume average win and loss sizes remain constant for the filtered trades, as the filter primarily targets win probability.
- Average Win Size: $200
- Average Loss Size: $120
Calculate new expectancy: E_filtered = (0.75 * $200) - (0.25 * $120) E_filtered = $150 - $30 E_filtered = $120 per contract.
Comparison:
- Baseline Expectancy: $8 per contract
- Filtered Expectancy: $120 per contract
The expectancy increased by $112 per contract, a 1400% improvement.
Monthly Profit Comparison:
- Baseline Monthly Profit: $1,600 (200 trades * $8)
- Filtered Monthly Profit: $9,600 (80 trades * $120)
Even though the number of trades decreased significantly (from 200 to 80), the total monthly profit increased by 500% due to the higher expectancy per trade. This demonstrates the power of selective trading.
Implementation and Backtesting
Implementing selective trading requires rigorous backtesting.
- Define a baseline strategy: Document all entry, exit, and position sizing rules.
- Collect historical data: Obtain sufficient data (e.g., 6-12 months) for the instruments traded.
- Backtest the baseline: Calculate expectancy and all relevant metrics.
