Spiral Cross Strategy with Moving Average Confirmation

Pine ScriptTrend following Machine learning EMA SMA ATR

Author: ChaoZhang
Complexity: Starter · 59 lines
Parameters: 6
Backtest bar: 1d · swing
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

This strategy combines the Vortex Indicator and Moving Average lines to identify the direction and strength of price trends in order to generate potential long and short signals. When the Vortex Positive line (VI+) crosses above the Vortex Negative line (VI-), each crossover is highlighted on the chart. If the closing price is above the Moving Average line, a long signal is generated. When VI- crosses above VI+, if the closing price is below the Moving Average line, a short signal is generated.

Strategy Logic

Vortex Indicator: Consists of two lines - Vortex Positive (VI+) and Vortex Negative (VI-). It is used to identify the direction and strength of price trends.
Moving Average (MA): Uses a chosen Moving Average method (SMA, EMA, SMMA, WMA or VWMA) to smooth the price data. The smoothed line is referred to as the "Smoothing Line".
Determine Long and Short Signals: When VI+ crosses above VI-, each crossover is highlighted. If the close is above the Smoothing Line, a long signal is generated. When VI- crosses above VI+, if the close is below the Smoothing Line, a short signal is generated.

Advantages

Combines trend identification and smoothing to capture trends in trending markets, avoiding false signals in choppy markets.
Vortex Indicator effectively identifies trend direction and strength. Moving Averages filter out some noise.
Simple and clear strategy logic, easy to understand and implement.
Customizable parameters, adapts to different market environments.

Risks

May generate false signals and whipsaws in range-bound or trendless markets.
Inappropriate parameter settings can impact strategy performance. For example, a Moving Average that is too short has poor smoothing capability and a longer one lags in recognizing trend changes.
Unable to safeguard against extreme price swings from major unanticipated events.

Enhancements

Incorporate other indicators like volume to determine trend reliability.
Optimize parameters to balance trend-following and noise filtering of Moving Averages.
Add stop loss to control losses.
Utilize machine learning for automated parameter optimization.
Incorporate risk management modules to adjust position sizing.

Conclusion

This strategy effectively combines the Vortex Indicator and Moving Averages to capture trends. It identifies trend direction while having some noise filtering capability to reduce false signals. The logic is simple and flexible to use, performing well in trending markets. Further improvements in risk control can be achieved by incorporating more filters, optimizing parameters, and adding stop losses.

Parameters

Name	Default	What it controls
v_input_int_1	14	Vortex Length
v_input_int_2	9	MA Length
v_input_1_close	0	Source: close \| high \| low \| open \| hl2 \| hlc3 \| hlcc4 \| ohlc4
v_input_int_3	false	Offset
v_input_string_1	0	(?Smoothing)Method: SMA \| EMA \| SMMA (RMA) \| WMA \| VWMA
v_input_int_4	5	Length

Source Pine Script

/*backtest
start: 2023-02-01 00:00:00
end: 2024-02-01 00:00:00
period: 1d
basePeriod: 1h
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/
// © DraftVenture

//@version=5
strategy("Vortex + Moving Average Strategy", overlay=true)

//Vortex settings
period_ = input.int(14, title="Vortex Length", minval=2)
VMP = math.sum( math.abs( high - low[1]), period_ )
VMM = math.sum( math.abs( low - high[1]), period_ )
STR = math.sum( ta.atr(1), period_ )
VIP = VMP / STR
VIM = VMM / STR
plot(VIP, title="VI +", color=color.white)
plot(VIM, title="VI -", color=color.white)

len = input.int(9, minval=1, title="MA Length")
src = input(close, title="Source")
offset = input.int(title="Offset", defval=0, minval=-500, maxval=500)
out = ta.sma(src, len)
plot(out, color=color.blue, title="MA", offset=offset)

ma(source, length, type) =>
    switch type
        "SMA" => ta.sma(source, length)
        "EMA" => ta.ema(source, length)
        "SMMA (RMA)" => ta.rma(source, length)
        "WMA" => ta.wma(source, length)
        "VWMA" => ta.vwma(source, length)

typeMA = input.string(title = "Method", defval = "SMA", options=["SMA", "EMA", "SMMA (RMA)", "WMA", "VWMA"], group="Smoothing")
smoothingLength = input.int(title = "Length", defval = 5, minval = 1, maxval = 100, group="Smoothing")

smoothingLine = ma(out, smoothingLength, typeMA)
plot(smoothingLine, title="Smoothing Line", color=#f37f20, offset=offset, display=display.none)

// Determine long and short conditions
longCondition = ta.crossover(VIP, VIM) and close > smoothingLine
shortCondition = ta.crossunder(VIP, VIM) and close < smoothingLine
crossCondition = ta.crossunder(VIP, VIM) or ta.crossunder(VIM, VIP)

// Strategy entry and exit logic
if longCondition
    strategy.entry("Long", strategy.long)

if shortCondition
    strategy.entry("Short", strategy.short)

bgcolor(crossCondition ? color.new(color.white, 80) : na)

// Strategy by KP

Published by ChaoZhang on the FMZ strategy square · last modified 2024-02-02. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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