Hurst Future Lines of Demarcation Strategy

Pine ScriptTrend following SMA Pivots / S&R

Author: ChaoZhang
Complexity: Intermediate · 107 lines
Parameters: 12
Backtest bar: 10m · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

The Hurst Future Lines of Demarcation Strategy is a trading strategy based on the concept of Future Line of Demarcation (FLD) introduced by J.M. Hurst in the 1970s. The strategy predicts future price movements by drawing a simple yet profound line on a financial chart, which is constructed by offsetting the price data half a cycle ahead on the time axis. Specifically, the strategy focuses on the interplay between three Hurst Cycles: the Signal Cycle, the Trade Cycle, and the Trend Cycle. By observing the crossover and divergence patterns between the price and the FLD lines, traders can gauge market trends or consolidations and determine entry and exit points.

Strategy Principle

The core of the Hurst Future Lines of Demarcation Strategy is to offset the price data half a cycle ahead on the time axis to construct the Future Line of Demarcation (FLD). For example, in the context of a 40-day cycle, the FLD would be represented by shifting the current price data 20 days forward on the chart. The strategy primarily focuses on three Hurst Cycles: the Signal Cycle (default: 20 days), the Trade Cycle (default: 20 days), and the Trend Cycle (default: 80 days). By observing the crossover and divergence patterns between the price and these three FLD lines, traders can determine market trends or consolidations. When the price is above the Signal FLD, the Signal FLD is above the Trade FLD, and the Trade FLD is above the Trend FLD, the market is in an uptrend (Phase A); when the price breaks below the Signal FLD, the market enters a pullback (Phase B). Similarly, other phases (C through H) have corresponding price and FLD line interaction patterns. The strategy also includes adjustable "Close the Trade" triggers, such as Price, Signal FLD, Trade FLD, or Trend FLD, to determine trade exits.

Strategy Advantages

The main advantages of the Hurst Future Lines of Demarcation Strategy include:

Simplicity: The strategy is based on the simple concept of FLD and is easy to understand and apply.
Forward-looking: By offsetting the price data forward, the FLD provides a forecast of future price movements.
Multi-cycle analysis: The strategy combines three different Hurst Cycles, offering a more comprehensive market analysis.
Trend and consolidation identification: By observing the interaction patterns between the price and FLD lines, traders can determine market trends or consolidations.
Customizability: The strategy provides adjustable "Close the Trade" triggers, allowing traders to set exit points based on their preferences.

Strategy Risks

Despite its advantages, the Hurst Future Lines of Demarcation Strategy also has some potential risks:

Parameter sensitivity: The strategy's performance may be sensitive to parameters such as cycle lengths, and different parameter settings may lead to different results.
Market adaptability: The strategy may underperform in certain market conditions, such as unclear trends or high volatility.
Lag: Since the FLD is calculated based on historical data, there may be a certain degree of lag.
Overtrading: If the "Close the Trade" triggers are not set properly, it may lead to overtrading and high transaction costs.

To mitigate these risks, traders can consider parameter optimization, adjusting the strategy for different market conditions, and setting appropriate stop-loss and risk management measures.

Strategy Optimization Directions

The Hurst Future Lines of Demarcation Strategy can be optimized in the following aspects:

Parameter optimization: Optimize parameters such as cycle lengths and "Close the Trade" triggers to improve the strategy's performance.
Multi-timeframe analysis: Apply the strategy to different timeframes to gain a more comprehensive market perspective.
Combination with other indicators: Combine the FLD with other technical indicators (e.g., moving averages, oscillators) to enhance signal reliability.
Risk management: Introduce stop-loss and position sizing mechanisms to control risks and optimize returns.
Market adaptability: Develop targeted optimization approaches for different market conditions (e.g., trending, oscillating).

Through these optimization measures, the Hurst Future Lines of Demarcation Strategy can better adapt to various market environments, improving its stability and profitability.

Conclusion

The Hurst Future Lines of Demarcation Strategy is an innovative trading strategy based on J.M. Hurst's concept of Future Line of Demarcation. By offsetting the price data half a cycle ahead on the time axis to construct the Future Line of Demarcation and combining three different Hurst Cycles (Signal Cycle, Trade Cycle, and Trend Cycle), the strategy provides a forecast of future price movements. Traders can determine market trends or consolidations and identify entry and exit points by observing the crossover and divergence patterns between the price and FLD lines. Although the strategy has advantages such as simplicity, forward-looking nature, and multi-cycle analysis, it also has some potential risks, including parameter sensitivity, market adaptability, and lag. To optimize the strategy, traders can consider parameter optimization, multi-timeframe analysis, combination with other indicators, risk management, and market adaptability. Overall, the Hurst Future Lines of Demarcation Strategy offers traders a unique perspective and tool to anticipate and seize market opportunities.

Parameters

Name	Default	What it controls
v_input_1_ohlc4	0	Source: ohlc4 \| high \| low \| open \| hl2 \| hlc3 \| hlcc4 \| close
v_input_bool_1	false	Smooth FLD
v_input_2	33	FLD transparency
v_input_int_1	5	FLD Smoothing
v_input_string_1	0	Input Close Trigger 1: Price \| Signal \| Trade \| Trend \| None
v_input_string_2	0	Input Close Trigger 2: Trade \| Signal \| Price \| Trend \| None
v_input_color_1	#da00ff	Quarter Cycle Color
v_input_int_2	5	Signal Cycle Length
v_input_color_2	#ff9800	Trade Cycle Color
v_input_int_3	20	Trade Cycle Length
v_input_color_3	aqua	Double Cycle Color
v_input_int_4	80	Trend Cycle Length

Source Pine Script

/*backtest
start: 2024-04-27 00:00:00
end: 2024-04-28 00:00:00
period: 10m
basePeriod: 1m
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/
// © BarefootJoey

//@version=5
strategy("Hurst Future Lines of Demarcation Strategy", overlay=true)

// FLD Settings
source      = input(ohlc4, 'Source')
smoothFLD   = input.bool(false, 'Smooth FLD')
FLDtransp   = input(33, 'FLD transparency')
FLDsmooth   = input.int(5, "FLD Smoothing", minval=1, tooltip="Number of trading days to smooth the FLD")   
FLD_out = ta.sma(source , smoothFLD ? FLDsmooth : 1)

close_buy_in_1 = input.string('Price', 'Input Close Trigger 1', options=['Price', 'Signal', 'Trade', 'Trend', 'None'])
close_buy_in_2 = input.string('Trade', 'Input Close Trigger 2', options=['Price', 'Signal', 'Trade', 'Trend', 'None'])

// Quarter Cycle (Default: 20 day) Length Pivot Cycle
col_q = input.color(#da00ff, "Quarter Cycle Color")
cyc_q = input.int(5, "Signal Cycle Length")
plot(FLD_out, color=color.new(col_q, FLDtransp), title='Signal FLD', offset = math.round(cyc_q/2) )

// Trade Cycle (Default: 20 day) Length Pivot Cycle
col = input.color(#ff9800, "Trade Cycle Color")
cyc = input.int(20, "Trade Cycle Length")
plot(FLD_out, color=color.new(col, FLDtransp), title='Trade FLD', offset = math.round(cyc/2) )

// Double Cycle (Default: 80 day) Length Pivot Cycle
col_d = input.color(color.aqua, "Double Cycle Color")
cyc_d = input.int(80, "Trend Cycle Length")
plot(FLD_out, color=color.new(col_d, FLDtransp), title='Trend FLD', offset = math.round(cyc_d/2) )

// Strategy Plots
price = source
signal = FLD_out[math.round(cyc_q/2)]
trade = FLD_out[math.round(cyc/2)]
trend = FLD_out[math.round(cyc_d/2)]

// Trend State
var state = 0
if signal > trade and trade > trend 
    state := 1 // (A)
    state
if state == 1 and price < signal
    state := 2 // (B)
    state
if signal < trade and trade > trend 
    state := 3 // (C)
    state
if state == 3 and price < signal 
    state := 4 // (D)
    state
if signal < trade and trade < trend 
    state := 5 // (E)
    state
if state == 5 and price < signal
    state := 6 // (F)
    state
if signal > trade and trade < trend
    state := 7 // (G)
    state
if state == 7 and price < signal
    state := 8 // (H)
    state
state := state

// Strategy Definitions
close_buy_out_1 = close_buy_in_1 == 'Price' ? price : close_buy_in_1 == 'Signal' ? signal : close_buy_in_1 == 'Trade' ? trade : close_buy_in_1 == 'Trend' ? trend : na
close_buy_out_2 = close_buy_in_2 == 'Price' ? price : close_buy_in_2 == 'Signal' ? signal : close_buy_in_2 == 'Trade' ? trade : close_buy_in_2 == 'Trend' ? trend : na
buy = ta.crossover(price, signal) and state == 1
close_buy = strategy.position_size>0 and ta.crossunder(close_buy_out_1, close_buy_out_2)
sell = ta.crossunder(price, signal) and state == 6
close_sell = strategy.position_size<0 and ta.crossover(close_buy_out_1, close_buy_out_2)

// FLD Interaction State Background
interaction_color = state == 1 ? color.green : // A
  state == 2 ? color.aqua : // B
  state == 3 ? color.blue : // C
  state == 4 ? color.purple : // D
  state == 5 ? color.white : // E
  state == 6 ? color.red :// F
  state == 7 ? color.orange : // G
  state == 8 ? color.yellow : na // H

bgcolor(color.new(interaction_color, 90), title= "A-H Background")

bar_color = strategy.position_size>0 ? #00ff0a : strategy.position_size<0 ? #FF0000 : na
barcolor(bar_color)

if buy
    strategy.entry("Buy", strategy.long)
if close_buy
    strategy.close("Buy", qty_percent=100)

if sell
    strategy.entry("Sell", strategy.short)
if close_sell
    strategy.close("Sell", qty_percent=100)

// EoS made w/ ❤ by @BarefootJoey ✌??

Published by ChaoZhang on the FMZ strategy square · last modified 2024-04-29. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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