1-hour trend tracking strategy based on RSI and dual moving averages (2024)

Overview

The strategy uses the Relative Strength Index (RSI) and two Simple Moving Averages (SMAs) as the main indicators to generate long and short signals within a 1-hour timeframe. By liberalizing the conditions for RSI and SMAs, the frequency of signal triggers is increased. Additionally, the strategy employs the Average True Range (ATR) indicator for risk management, dynamically setting take-profit and stop-loss levels.

The main ideas of the strategy are as follows:

1. Use the RSI indicator to identify potential overbought and oversold conditions as signals for going long and short, respectively.
2. Use the crossover of fast SMA and slow SMA to determine potential uptrends (golden cross) and downtrends (death cross).
3. Open positions in the corresponding direction when both RSI and SMA conditions are met for going long or short.
4. Utilize the ATR indicator to calculate dynamic take-profit and stop-loss levels, controlling the risk of each trade.
5. Visually display the triggering of strategy signals through changes in the chart background color, facilitating debugging and understanding of the strategy logic.

Strategy Principles

1. RSI Indicator: When RSI is below 50, it indicates that the market may be oversold, and prices have the potential to rise, thus triggering a long signal. When RSI is above 50, it indicates that the market may be overbought, and prices have the potential to fall, thus triggering a short signal.
2. Dual Moving Average Crossover: When the fast SMA crosses above the slow SMA (golden cross), it indicates a potential uptrend and triggers a long signal. When the fast SMA crosses below the slow SMA (death cross), it indicates a potential downtrend and triggers a short signal.
3. Entry Conditions: Positions are only opened in the corresponding direction when both RSI and dual moving average conditions are met for going long or short, improving the reliability of signals.
4. Risk Management: The ATR indicator is used to calculate dynamic take-profit and stop-loss levels. The take-profit level is set at 1.5 times the ATR above/below the entry price, and the stop-loss level is set at 1 times the ATR above/below the entry price. This allows for adjusting the take-profit and stop-loss levels based on market volatility, controlling the risk of each trade.

Strategy Advantages

1. Adaptability: By liberalizing the conditions for RSI and dual moving averages, the strategy can adapt to different market conditions within a 1-hour timeframe and capture more trading opportunities.
2. Risk Management: Utilizing the ATR indicator to dynamically set take-profit and stop-loss levels allows for flexible adjustments based on market volatility, effectively controlling the risk exposure of each trade.
3. Simplicity and Ease of Use: The strategy logic is clear, and the indicators used are simple and easy to understand, facilitating comprehension and implementation.
4. Visual Aid: The triggering of strategy signals is visually displayed through changes in the chart background color, aiding in debugging and optimization.

Strategy Risks

1. Frequent Trading: Due to the liberalized conditions for RSI and dual moving averages, the strategy may generate relatively frequent trading signals, leading to increased transaction costs and affecting overall profitability.
2. Sideways Market: In low-volatility sideways markets, RSI and dual moving averages may produce frequent false signals, resulting in poor strategy performance.
3. Lack of Trends: The strategy primarily relies on RSI and dual moving averages to determine trends, but in some cases, the market may lack clear trend characteristics, causing strategy signals to be ineffective.
4. Parameter Sensitivity: The performance of the strategy may be sensitive to the parameter settings of RSI, SMAs, and ATR. Different parameter combinations may lead to significant differences in strategy performance.

Strategy Optimization Directions

1. Parameter Optimization: Optimize the parameters of RSI, SMAs, and ATR to find the best-performing parameter combinations on historical data, improving the stability and reliability of the strategy.
2. Signal Filtering: Introduce other technical indicators or market sentiment indicators to provide secondary confirmation of signals generated by RSI and dual moving averages, reducing the occurrence of false signals.
3. Dynamic Weight Adjustment: Dynamically adjust the weights of RSI and dual moving average signals based on the strength of market trends, assigning higher weights when trends are evident and lower weights in sideways markets, enhancing the adaptability of the strategy.
4. Take-Profit and Stop-Loss Optimization: Optimize the ATR multiplier to find the optimal take-profit and stop-loss ratios, improving the risk-adjusted returns of the strategy. Additionally, consider introducing other take-profit and stop-loss methods, such as support/resistance-based or time-based methods.
5. Multi-Timeframe Analysis: Combine signals from other timeframes (e.g., 4-hour, daily) to filter and confirm signals in the 1-hour timeframe, improving signal reliability.

Summary

The strategy combines two simple and easy-to-use technical indicators, RSI and dual moving averages, to generate trend-following signals within a 1-hour timeframe while utilizing the ATR indicator for dynamic risk management. The strategy logic is clear and easy to understand and implement, making it suitable for beginners to learn and use. However, the strategy also has some potential risks, such as frequent trading, poor performance in sideways markets, and lack of trends. Therefore, in practical applications, the strategy needs to be further optimized and improved, such as parameter optimization, signal filtering, dynamic weight adjustment, take-profit and stop-loss optimization, and multi-timeframe analysis, to enhance the robustness and profitability of the strategy. Overall, the strategy can serve as a basic template, providing traders with a feasible idea and direction, but it still requires personalized adjustments and optimizations based on individual experience and market characteristics.

Strategy source code

/*backtest
start: 2024-02-01 00:00:00
end: 2024-02-29 23:59:59
period: 1h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/
//@version=5
strategy("Debugged 1H Strategy with Liberal Conditions", shorttitle="1H Debug", overlay=true, pyramiding=0)
// Parameters
rsiLength = input.int(14, title="RSI Length")
rsiLevel = input.int(50, title="RSI Entry Level") // More likely to be met than the previous 70
fastLength = input.int(10, title="Fast MA Length")
slowLength = input.int(21, title="Slow MA Length")
atrLength = input.int(14, title="ATR Length")
atrMultiplier = input.float(1.5, title="ATR Multiplier for SL")
riskRewardMultiplier = input.float(2, title="Risk/Reward Multiplier")
// Indicators
rsi = ta.rsi(close, rsiLength)
fastMA = ta.sma(close, fastLength)
slowMA = ta.sma(close, slowLength)
atr = ta.atr(atrLength)
// Trades
longCondition = ta.crossover(fastMA, slowMA) and rsi < rsiLevel
shortCondition = ta.crossunder(fastMA, slowMA) and rsi > rsiLevel
// Entry and Exit Logic
if (longCondition)
 strategy.entry("Long", strategy.long)
 strategy.exit("Exit Long", "Long", profit=atrMultiplier * atr, loss=atr)
if (shortCondition)
 strategy.entry("Short", strategy.short)
 strategy.exit("Exit Short", "Short", profit=atrMultiplier * atr, loss=atr)
// Debugging: Visualize when conditions are met
bgcolor(longCondition ? color.new(color.green, 90) : na)
bgcolor(shortCondition ? color.new(color.red, 90) : na)

Strategy parameters

The original address: RSI and Dual Moving Average Based 1-Hour Trend Following Strategy (fmz.com)