R&D Blog
Reversal Patterns: Part 1 | Trading Strategy (Exits)
I. Trading Strategy
Developer: Richard Wyckoff; Toby Crabel. Source: Crabel, T. (1990). Day Trading with Short Term Price Patterns and Opening Range Breakout. Greenville: Traders Press, Inc. Concept: Trading strategy based on reversal patterns. Research Goal: Performance verification of reversal patterns. Specification: Table 1. Results: Figure 1-2. Trade Setup: Long Setup: A price move below a Demand Pivot (Definition in the Table 1) is followed by a reversal to the upside. Short Setup: A price move above a Supply Pivot (Definition in the Table 1) is followed by a reversal to the downside. Trade Entry: Long Trade Entry: A buy at the open is placed after a long setup. Short Trade Entry: A sell at the open is placed after a short setup. Trade Exit: Table 1. Portfolio: 42 futures markets from four major market sectors (commodities, currencies, interest rates, and equity indexes). Data: 38 years since 1980. Testing Platform: MATLAB®.
II. Sensitivity Test
All 3-D charts are followed by 2-D contour charts for Profit Factor, Sharpe Ratio, Ulcer Performance Index, CAGR, Maximum Drawdown, Percent Profitable Trades, and Avg. Win / Avg. Loss Ratio. The final picture shows sensitivity of Equity Curve.
Tested Variables: Trade_Duration & Reward_Ratio (Definitions: Table 1):
Figure 1 | Portfolio Performance (Inputs: Table 1; Commission & Slippage: $0).
| STRATEGY | SPECIFICATION | PARAMETERS |
| Auxiliary Variables: | Pivots: Supply Pivots are surrounded on either side by lower highs. Demand Pivots are surrounded on either side by higher lows. The significance of pivots is determined by the number of surrounded highs and lows. Example of pivots with Pivot_Size = 2:  | Pivot_Size = 5; |
| Setup: | Top Reversal: It is defined as a price move above a Supply Pivot followed by a reversal to the downside that meets the following criteria: (A) It closes below the two previous days’ closings; (B) The close is below the Supply Pivot; (C) The close is below the opening and the mid-range of the day; (D) The daily range is greater than the previous day’s range; (E) Points A-D materialize within 5 bars from the first breakout of the Supply Pivot (Trap_Duration = 5). Bottom Reversal: It is defined as a mirror image of the “Top Reversal”.  | Trap_Duration = 5; |
| Filter: | N/A | |
| Entry: | Long Trades: A buy at the open is placed after the long setup (i.e. after the Bottom Reversal defined above). Short Trades: A sell at the open is placed after the short setup (i.e. after the Top Reversal defined above). | |
| Exit: | Time Exit: (n+1)th day at the open, n = Trade_Duration. Risk-Reward Exit: Long Trades: Target = Entry + (Initial Risk * Reward_Ratio). Short Trades: Target = Entry − (Initial Risk * Reward_Ratio). An exit at the open is placed once the target was reached on the previous day. Quick Exit: Long Trades: A sell stop is placed one tick below the true low of the setup bar (Defined above in the “Setup”). Short Trades: A buy stop is placed one tick above the true high of the setup bar (Defined above in the “Setup”). Stop Loss Exit: ATR(ATR_Length) is the Average True Range over a period of ATR_Length. ATR_Stop is a multiple of ATR(ATR_Length). Long Trades: A sell stop is placed at [Entry − ATR(ATR_Length) * ATR_Stop]. Short Trades: A buy stop is placed at [Entry + ATR(ATR_Length) * ATR_Stop]. Stop Loss Exit is used to normalize risk via position sizing. | Trade_Duration = [1, 40], Step = 1; Reward_Ratio = [1, 10], Step = 0.25; ATR_Length = 20; ATR_Stop = 6; |
| Sensitivity Test: | Trade_Duration = [1, 40], Step = 1 Reward_Ratio = [1, 10], Step = 0.25 | |
| Position Sizing: | Initial_Capital = $1,000,000 Fixed_Fractional = 1% Portfolio = 42 US Futures ATR_Stop = 6 (ATR ~ Average True Range) ATR_Length = 20 | |
| Data: | 42 futures markets; 38 years (1980/01/01−2018/3/31) |
Table 1 | Specification of Trading Strategy.
III. Sensitivity Test with Commission & Slippage
Tested Variables: Trade_Duration & Reward_Ratio (Definitions: Table 1):
Figure 2 | Portfolio Performance (Inputs: Table 1; Commission & Slippage: $50 Round Turn).
IV. Benchmarking
We benchmark the base case strategy against alternatives:
Case #1: Reward_Ratio = 2.0; Trade_Duration = No limit (Base Case).
Case #2: Reward_Ratio = 3.0; Trade_Duration = No limit.
Case #3: Reward_Ratio = 4.0; Trade_Duration = No limit.
Case #4: Reward_Ratio = 8.0; Trade_Duration = No limit.
| Fixed Fractional Sizing | Case #1 | Case #2 | Case #3 | Case #4 |
| Net Profit ($) | (397,595) | (88,035) | 124,012 | 1,441,055 |
| Sharpe Ratio | (0.32) | (0.03) | 0.09 | 0.39 |
| Ulcer Performance Index (UPI) | (0.05) | (0.01) | 0.02 | 0.37 |
| Profit Factor | 0.94 | 0.99 | 1.01 | 1.09 |
| CAGR (%) | (1.32) | (0.24) | 0.31 | 2.36 |
| Max. Drawdown (%) | (42.11) | (31.81) | (27.83) | (18.10) |
| Percent Profitable Trades (%) | 34.43 | 30.45 | 28.24 | 25.44 |
| Avg. Win / Avg. Loss Ratio | 1.78 | 2.26 | 2.57 | 3.19 |
Table 2 | Inputs: Table 1; Fixed Fractional Sizing: 1%; Commission & Slippage: $50 Round Turn.
V. Rating: Reversal Patterns (Part 1) | Trading Strategy
A/B/C/D
VI. Summary
(a) Reversal patterns are very sensitive to trading costs; (b) Reversal patterns with larger target exits are preferred.
Related Entries: False Breakout (Setup & Exit 1) | False Breakout (Setup & Exit 2)
Related Topics: (Public) Trading Strategies
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