R&D Blog
Relative Strength Index (RSI) Model | Trading Strategy (Filter)
I. Trading Strategy
Developer: Larry Connors (The 2-Period RSI Trading Strategy), Welles Wilder (The RSI Momentum Oscillator). Source: (i) Connors, L., Alvarez, C. (2009). Short Term Trading Strategies That Work. Jersey City, NJ: Trading Markets; (ii) Wilder, J. W. (1978). New Concepts in Technical Trading Systems. Greensboro: Trend Research. Concept: The long equity trading system based on the 2-Period RSI (Relative Strength Index). Research Goal: Performance verification of the simple trading strategy that buys pullbacks in a bull market. Specification: Table 1. Results: Figure 1-2. Trade Filter: The 2-Period RSI closes below RSI_Threshold (Default Value: RSI_Threshold = 5). Portfolio: Five equity futures markets (DJ, MD, NK, NQ, SP). Data: 36 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: RSI_Threshold & Exit_Look_Back (Definitions: Table 1):
Figure 1 | Portfolio Performance (Inputs: Table 1; Commission & Slippage: $0).
| STRATEGY | SPECIFICATION | PARAMETERS |
| Auxiliary Variables: | The 2-Period Relative Strength Index (RSI): The Relative Strength Index (RSI) is a momentum oscillator that compares the magnitude of recent gains to recent losses to determine overbought and oversold conditions. RSI(Close, RSI_Look_Back) is the Relative Strength Index of the close price over a period of RSI_Look_Back; Default Value: RSI_Look_Back = 2. Formula: We use an exponential smoothing. Up[i] = max(Close[i] − Close[i − 1], 0); Down[i] = max(Close[i − 1] − Close[i], 0); AvgUp[i] = (AvgUp[i − 1] * (RSI_Look_Back − 1) + Up[i]) / RSI_Look_Back; AvgDown[i] = (AvgDown[i − 1] * (RSI_Look_Back − 1) + Down[i]) / RSI_Look_Back; RS[i] = AvgUp[i] / AvgDown[i]; RSI[i] = 100 − 100/(1 + RS[i]); Index: i ~ Current Bar. Note: The first “AvgUp” (i.e. AvgUp[1] ) is calculated as a simple average of “Up” values over a period of RSI_Look_Back. The first “AvgDown” (i.e. AvgDown[1]) is calculated as a simple average of “Down” values over a period of RSI_Look_Back. | RSI_Look_Back = 2; |
| Setup: | Long Setup: MA(Close, Setup_Look_Back) is a simple moving average of the close price over a period of Setup_Look_Back; Default Value: Setup_Look_Back = 200; Setup Rule: Close[i] > MA[i]; Index: i ~ Current Bar. | Setup_Look_Back = 200; |
| Filter: | Long Filter: The RSI closes below RSI_Threshold; Default Value: RSI_Threshold = 5; Filter Rule: RSI[i] < RSI_Threshold; Index: i ~ Current Bar. | RSI_Threshold = [2, 30], Step = 1; |
| Entry: | Long Entry: A buy at the open is placed after a bullish Setup/Filter. Note: In the original model, a buy at the close is placed on the same bar as a bullish Setup/Filter. | |
| Exit: | Trend Exit: MA(Close, Exit_Look_Back) is a simple moving average of the close price over a period of Exit_Look_Back; Default Value: Exit_Look_Back = 5. Long Exit: A sell at the open is placed if Close[i − 1] > MA[i − 1]; Index: i ~ Current Bar. 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 Stop: A sell stop is placed at [Entry − ATR(ATR_Length) * ATR_Stop]. | Exit_Look_Back = [5, 30], Step = 1;; ATR_Length = 20; ATR_Stop = 6; |
| Sensitivity Test: | RSI_Threshold = [2, 30], Step = 1 Exit_Look_Back = [5, 30], Step = 1 | |
| Position Sizing: | Initial_Capital = $1,000,000 Fixed_Fractional = 1% Portfolio = 5 Equity Futures (DJ, MD, NK, NQ, SP) ATR_Stop = 6 (ATR ~ Average True Range) ATR_Length = 20 | |
| Data: | Five equity futures markets (DJ, MD, NK, NQ, SP); 36 years (1980/01/01−2016/04/30) |
Table 1 | Specification: Trading Strategy.
III. Sensitivity Test with Commission & Slippage
Tested Variables: RSI_Threshold & Exit_Look_Back (Definitions: Table 1):
Figure 2 | Portfolio Performance (Inputs: Table 1; Commission & Slippage: $50 Round Turn).
IV. Benchmarking
We benchmark the base case strategy (default parameters) against alternatives:
Case #1: RSI_Threshold = 5; Exit_Look_Back = 5 (Base Case).
Case #2: RSI_Threshold = 5; Exit_Look_Back = 10.
Case #3: RSI_Threshold = 10; Exit_Look_Back = 10.
Case #4: RSI_Threshold = 15; Exit_Look_Back = 10.
| Fixed Fractional Sizing | Case #1 | Case #2 | Case #3 | Case #4 |
| Net Profit ($) | 119,305 | 215,290 | 472,423 | 410,503 |
| Sharpe Ratio | 0.28 | 0.38 | 0.56 | 0.44 |
| Ulcer Performance Index (UPI) | 0.30 | 0.50 | 0.93 | 0.67 |
| Profit Factor | 1.40 | 1.59 | 1.71 | 1.47 |
| CAGR (%) | 0.34 | 0.59 | 1.17 | 1.04 |
| Max. Drawdown (%) | (4.64) | (4.86) | (3.96) | (4.24) |
| Percent Profitable Trades (%) | 69.82 | 73.17 | 75.50 | 74.89 |
| Avg. Win / Avg. Loss Ratio | 0.61 | 0.58 | 0.55 | 0.49 |
Table 2 | Inputs: Table 1; Fixed Fractional Sizing: 1%; Commission & Slippage: $50 Round Turn.
V. Research
Connors, L., Alvarez, C. (2009). Short Term Trading Strategies That Work. Jersey City, NJ: Trading Markets:
Most traders use the 14-period RSI. But our studies have shown that statistically, there is no edge using the 14-period RSI. However, when you shorten the time frame of the RSI (meaning you go much lower than the 14-period) you start seeing some very impressive results. Our research shows that more robust and consistent results are obtained by using a 2-period RSI and we have built many trading methods that incorporate the 2-period RSI […] The lower the RSI, the greater the performance. The average returns of stocks with a 2-period RSI reading below 2 were greater than those stocks with a 2-period RSI reading below 5, etc.
VI. Rating: Relative Strength Index (RSI) Model | Trading Strategy
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VII. Summary
(i) The trading strategy based on the 2-Bar Relative Strength Index underperforms alternative momentum models; (ii) The preferred parameters are: 5 ≤ RSI_Threshold ≤ 13; 8 ≤ Exit_Look_Back ≤ 13 (Figure 1-2).
Related Entries: Relative Strength Index (RSI) Model (New Exits) | Long Equity Trading System (Filter & Exit) | 3-Bar Momentum Pattern (Filter & Exit) | Hikkake Pattern (Filter & Exit)
Related Topics: (Public) Trading Strategies
CFTC RULE 4.41: HYPOTHETICAL OR SIMULATED PERFORMANCE RESULTS HAVE CERTAIN LIMITATIONS. UNLIKE AN ACTUAL PERFORMANCE RECORD, SIMULATED RESULTS DO NOT REPRESENT ACTUAL TRADING. ALSO, SINCE THE TRADES HAVE NOT BEEN EXECUTED, THE RESULTS MAY HAVE UNDER-OR-OVER COMPENSATED FOR THE IMPACT, IF ANY, OF CERTAIN MARKET FACTORS, SUCH AS LACK OF LIQUIDITY. SIMULATED TRADING PROGRAMS IN GENERAL ARE ALSO SUBJECT TO THE FACT THAT THEY ARE DESIGNED WITH THE BENEFIT OF HINDSIGHT. NO REPRESENTATION IS BEING MADE THAT ANY ACCOUNT WILL OR IS LIKELY TO ACHIEVE PROFIT OR LOSSES SIMILAR TO THOSE SHOWN.
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Codes: matlab/connors/rsi-1