AlphaTrend Cross Period Strategy

This strategy is based on the AlphaTrend indicator, which combines the advantages of RSI and MFI indicators and can achieve good results in both bullish and bearish trending markets. The strategy mainly judges the…

Pine ScriptTrend following Breakout Volatility RSI SMA ATR

Author: ChaoZhang
Complexity: Intermediate · 62 lines
Parameters: 11
Backtest bar: 30m · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

Strategy Logic

Calculate the ATR indicator to measure market volatility
Use RSI to determine market direction if no volume data; use MFI if volume data exists
Calculate upper and lower bands based on ATR and market direction
Compute AlphaTrend curve, which incorporates dynamic upper and lower bands
Generate buy and sell signals when price crosses above or below the AlphaTrend curve

The strategy relies mainly on the AlphaTrend curve to determine the price trend direction. It takes into account ATR, RSI/MFI, and can track the trend effectively. When price penetrates the curve, it signals a change in the trend and forms the entry point.

Advantages

AlphaTrend combines the strengths of RSI and MFI, adaptable to both bull and bear markets
Dynamic upper and lower bands automatically adjust based on market volatility
Incorporates both price and volume information, avoiding false signals
Breakout approach clearly identifies trend direction
Simple and easy to understand logic

In summary, this strategy works for both bullish and bearish markets, filters out market noise effectively, identifies trends accurately, and is an efficient trend following strategy.

Risks

AlphaTrend curve may have false breakouts, requiring confirmation from other indicators
Many false signals may occur during market consolidation
Ineffective results from poor parameter tuning
Stop loss may be taken out during spikes, incurring large losses

To address the risks, stop loss can control single trade loss; combine with other indicators to avoid false signals; adjust parameters based on different markets.

Enhancement Opportunities

Test different parameter combinations for optimized settings
Incorporate other indicators to form confirmation conditions
Employ dynamic or trailing stop loss to control risks
Trade on different timeframes (5m, 15m, etc) based on market conditions
Refine entry timing system for more precise entry

Further optimizations can be done by testing on different markets and parameters so that the strategy is adaptable to more market conditions.

Conclusion

Overall this AlphaTrend strategy is a simple and efficient trend following system. It incorporates both price and volume information to adapt to bullish and bearish markets. The breakout mechanism provides clear entry signals. With proper risk control, it can achieve good results. Further testing and enhancement can help stabilize its profitability over more market conditions.

Parameters

Name	Default	What it controls
v_input_float_1	1.5	Multiplier
v_input_1	15	Common Period
v_input_2	false	Change calculation (no volume data)?
v_input_3	timestamp(01 Jan 2014 00:00 +0000)	(?Backtesting)Backtesting Start Time
v_input_4	timestamp(01 Jan 2100 23:59 +0000)	Backtesting End Time
v_input_string_1		(?PV Settings)Exchange
v_input_string_2		Symbol
v_input_string_3		Account
v_input_string_4		PV Alert Name Longs
v_input_string_5		PV Alert Name Shorts
v_input_bool_1	false	Test Alerts

Source Pine Script

/*backtest
start: 2023-09-20 00:00:00
end: 2023-09-26 00:00:00
period: 30m
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// author © KivancOzbilgic
// developer © KivancOzbilgic
// pv additions, simplification and strategy conversion @ treigen
//@version=5
strategy('AlphaTrend For ProfitView', overlay=true, calc_on_every_tick=true, process_orders_on_close=true, default_qty_type=strategy.percent_of_equity, default_qty_value=100, commission_type=strategy.commission.percent, commission_value=0.1, initial_capital=1000)
coeff = input.float(1.5, 'Multiplier', step=0.1)
AP = input(15, 'Common Period')
ATR = ta.sma(ta.tr, AP)
novolumedata = input(title='Change calculation (no volume data)?', defval=false)

i_startTime = input(defval = timestamp("01 Jan 2014 00:00 +0000"), title = "Backtesting Start Time", inline="timestart", group='Backtesting')
i_endTime = input(defval = timestamp("01 Jan 2100 23:59 +0000"), title = "Backtesting End Time", inline="timeend", group='Backtesting')
timeCond = true

pv_ex = input.string('', title='Exchange', tooltip='Leave empty to use the chart ticker instead (Warning: May differ from actual market name in some instances)', group='PV Settings')
pv_sym = input.string('', title='Symbol', tooltip='Leave empty to use the chart ticker instead (Warning: May differ from actual market name in some instances)', group='PV Settings')
pv_acc = input.string("", title="Account", group='PV Settings')
pv_alert_long = input.string("", title="PV Alert Name Longs", group='PV Settings')
pv_alert_short = input.string("", title="PV Alert Name Shorts", group='PV Settings')
pv_alert_test = input.bool(false, title="Test Alerts", tooltip="Will immediately execute the alerts, so you may see what it sends. The first line on these test alerts will be excluded from any real alert triggers" ,group='PV Settings')

upT = low - ATR * coeff
downT = high + ATR * coeff
AlphaTrend = 0.0
AlphaTrend := (novolumedata ? ta.rsi(close, AP) >= 50 : ta.mfi(hlc3, AP) >= 50) ? upT < nz(AlphaTrend[1]) ? nz(AlphaTrend[1]) : upT : downT > nz(AlphaTrend[1]) ? nz(AlphaTrend[1]) : downT


k1 = plot(AlphaTrend, color=color.new(#0022FC, 0), linewidth=3)
k2 = plot(AlphaTrend[2], color=color.new(#FC0400, 0), linewidth=3)

buySignalk = ta.crossover(AlphaTrend, AlphaTrend[2])
sellSignalk = ta.crossunder(AlphaTrend, AlphaTrend[2])

var exsym = ""
if barstate.isfirst
    exsym := pv_ex == "" ? "" : "ex=" + pv_ex + ","
    exsym := pv_sym == "" ? exsym : exsym + "sym=" + pv_sym + ","


if barstate.isconfirmed and timeCond 
    if strategy.position_size <= 0 and buySignalk
        strategy.entry("Buy", strategy.long)
        alert(pv_alert_long + "(" + exsym + "acc=" + pv_acc + ")", alert.freq_once_per_bar_close)
    if strategy.position_size >= 0 and sellSignalk
        strategy.entry("Sell", strategy.short)
        alert(pv_alert_short + "(" + exsym + "acc=" + pv_acc + ")", alert.freq_once_per_bar_close)


//  Only used for testing/debugging alert messages
if pv_alert_test
    alert("<![Alert Test]!>\n" + pv_alert_long + "(" + exsym + "acc=" + pv_acc + ")", alert.freq_once_per_bar)
    alert("<![Alert Test]!>\n" + pv_alert_short + "(" + exsym + "acc=" + pv_acc + ")", alert.freq_once_per_bar)

Published by ChaoZhang on the FMZ strategy square · last modified 2023-09-28. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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