The TrendSurfing strategy is a trend tracking strategy based primarily on double moving average crossover signals. It also incorporates triangle visual indicators, 200-day EMA, ROC indicator and RSI indicator to filter out noise and accurately capture trend reversals. This strategy is suitable for medium-to-long-term holding and can achieve steady growth in a bull market.
The TrendSurfing strategy mainly relies on golden cross and death cross formed by fast moving average and slow moving average to generate buy and sell signals. When the fast MA crosses above the slow MA, a buy signal is generated. When the fast MA crosses below the slow MA, a sell signal is generated.
In addition, the strategy incorporates several auxiliary indicators to filter out false signals or determine trend quality, including:
By comprehensively judging various indicators, the TrendSurfing strategy can accurately locate trend turning points and track definite medium-to-long term trends without being misguided by market noise or short-term corrections.
1. Catch Medium-to-Long Term Trend
The strategy basically judges trend reversal based on MA crosses, and uses indicators like 200-day EMA to filter out short-term noise, with focus on medium-to-long term trend capture.
2. Multiple Indicators Ensure High Quality Entry
On top of MA crossover itself, the incorporation of ROC, RSI and other indicators enables avoidance of consolidation zones on reversal points and ensures quality entry.
3. Intuitive Triangle Visual Indicators
Green downward triangles indicate long entries, red upward triangles indicate short entries. Clean and straightforward.
4. Customizable Parameters for Different Needs
Users can freely adjust parameters like MA periods, ROC length, RSI length etc according to their own trading style.
5. Stop Loss and Take Profit Control
The strategy sets stop loss and take profit based on ATR value multiplied by risk percentage, enabling per trade risk control.
1. Risk of Missing Trades
Any MA crossover based strategy has inherent risk of missing trades or being stopped out when MA is oscillating.
2. Over-optimization from Improper Parameter Settings Users should avoid chasing hypothetically ideal parameter values. Parameters should be tested and adapted based on different market conditions and products.
3. Inability to Fully Filter Black Swan Events
Under extreme market conditions, strategies could still face large losses from market systemic risks.
1. Test and Optimize Parameter Values
Periods of MAs, length of ROC, values of RSI etc should go through rigorous backtesting and optimization to fit characteristics of different trading products.
2. Test and Incorporate Other Auxiliary Indicators
Continue testing combinations of other indicators like BOLL, KDJ etc with MA crosses for better performance.
3. Coordinate with Algorithmic Trading for Better Risk Control Introduce machine learning algorithms to enable more intelligent stop loss and take profit, adapting to dynamic market environments.
4. Explore Combinations with Other Strategies or Models
Combining with fundamentals-based stock picking strategies, statistical arbitrage strategies, portfolio optimization models etc could further enhance risk control and return.
The TrendSurfing strategy is a simple, straightforward trend tracking strategy with controllable risk. Trading signals are generated from MA crosses and filtered by multiple auxiliary indicators. It is suitable for medium-to-long term holding to steadily track bull market trends. We will continue optimizing this strategy through parameter testing, indicator expansion, risk control etc to achieve more reliable performance across diverse markets.
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/*backtest
start: 2023-12-27 00:00:00
end: 2024-01-03 00:00:00
period: 1m
basePeriod: 1m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/
//@version=4
strategy("Moving Average Crossover with Triangles, 200 EMA, ROC, and RSI", overlay=true)
// Define input parameters
fast_length = input(9, title="Fast MA Length")
slow_length = input(21, title="Slow MA Length")
roc_length = input(14, title="ROC Length")
rsi_length = input(14, title="RSI Length")
// Calculate moving averages
fast_ma = sma(close, fast_length)
slow_ma = sma(close, slow_length)
// Plot moving averages
plot(fast_ma, color=color.green, title="Fast MA")
plot(slow_ma, color=color.red, title="Slow MA")
// Plot 200 EMA
ema_200 = ema(close, 200)
plot(ema_200, color=color.white, title="200 EMA", linewidth=2)
// Calculate Rate of Change (ROC)
roc = roc(close, roc_length)
// Calculate RSI
rsi = rsi(close, rsi_length)
// Define strategy entry and exit conditions
long_condition = crossover(fast_ma, slow_ma) and roc > 0 and close > ema_200 and rsi > 55
short_condition = crossunder(fast_ma, slow_ma) and roc < 0 and close < ema_200 and rsi < 45
// Execute strategy
strategy.entry("Long", strategy.long, when=long_condition)
strategy.entry("Short", strategy.short, when=short_condition)
// Define stop loss and take profit levels
risk_percent = input(1, title="Risk Percentage", minval=0.1, maxval=5, step=0.1) / 100
atr_value = atr(14)
stop_loss = close - atr_value * risk_percent
take_profit = close + atr_value * risk_percent
strategy.exit("Take Profit/Stop Loss", from_entry="Long", loss=stop_loss, profit=take_profit)
strategy.exit("Take Profit/Stop Loss", from_entry="Short", loss=stop_loss, profit=take_profit)
// Plot larger triangles on crossover and crossunder
plotshape(series=long_condition, title="Long Entry", color=color.green, style=shape.triangleup, location=location.belowbar, size=size.small)
plotshape(series=short_condition, title="Short Entry", color=color.red, style=shape.triangledown, location=location.abovebar, size=size.small)