The Wave Trend Trading Strategy Based on LazyBear

This is a trading strategy based on LazyBear's Wave Trend indicator. The strategy identifies market sentiment through computing the wave trend of price fluctuations, and makes long and short decisions accordingly.

Pine ScriptTrend following Grid Machine learning EMA SMA

Author: ChaoZhang
Complexity: Intermediate · 67 lines
Parameters: 13
Backtest bar: 1h · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

Strategy Logic

The core of this strategy is LazyBear's Wave Trend indicator. It first calculates the average price (AP), then the exponential moving average of AP (ESA) and the absolute price movement (D). Based on ESA and D the strategy calculates the Volatility Index (CI), which then feeds into an exponential moving average to generate the Wave Trend line (WT). WT is further processed into WT1 and WT2 using simple moving averages. When WT1 crosses over WT2, it triggers the golden cross and goes long. When WT1 crosses below WT2, it triggers the death cross and goes short.

Advantage Analysis

This is a very simple but practical trend following strategy. The main advantages are:

It identifies price trend and market sentiment clearly based on the Wave Trend indicator
Simple trading logic of going long/short based on golden/death crosses of WT lines
Customizable parameters to adjust sensitivity of WT for different cycles
Flexibility to add further filters such as trading time window

Risk Analysis

There are some risks to this strategy:

As a trend following strategy, it can generate many false signals during range-bound markets
The lagging nature of WT may cause missed turns
Default parameters may not suit all products and cycles
No stop loss mechanism, holding period can be very long

The main solutions are:

Optimize parameters to tune sensitivity of WT
Add other indicators for confirmation to avoid false signals
Employ stop loss and take profit
Limit daily trades or positions

Optimization Directions

There is room for further optimization:

Optimize WT parameters for better sensitivity or stability
Use different parameter sets based on cycles
Add indicators like volume, volatility for confirmation
Add stop loss and take profit
Enrich trading logic like pyramiding, grid trading
Explore better features and rules using machine learning

Summary

In summary, this is a very simple and practical wave trend following strategy. By modeling the wave trend of price fluctuations, it identifies overbought and oversold market conditions to generate trade signals using WT's golden crosses and death crosses. The strategy is easy to implement but may require further optimization for sensitivity and stability. As a trend following strategy, it also needs additional filters and logic to avoid false signals. Overall this serves as a useful strategy template with lots of room for improvements.

Parameters

Name	Default	What it controls
v_input_1	true	From Month
v_input_2	true	From Day
v_input_3	2021	From Year
v_input_4	true	Thru Month
v_input_5	true	Thru Day
v_input_6	2112	Thru Year
v_input_7	true	Show Date Range
v_input_8	10	Channel Length
v_input_9	21	Average Length
v_input_10	60	Over Bought Level 1
v_input_11	53	Over Bought Level 2
v_input_12	-60	Over Sold Level 1
v_input_13	-53	Over Sold Level 2

Source Pine Script

/*backtest
start: 2023-11-18 00:00:00
end: 2023-12-18 00:00:00
period: 1h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//
// @author LazyBear
//
// If you use this code in its original/modified form, do drop me a note. 
//
//@version=4
     
// === INPUT BACKTEST RANGE ===
fromMonth = input(defval = 1,    title = "From Month",      type = input.integer, minval = 1, maxval = 12)
fromDay   = input(defval = 1,    title = "From Day",        type = input.integer, minval = 1, maxval = 31)
fromYear  = input(defval = 2021, title = "From Year",       type = input.integer, minval = 1970)
thruMonth = input(defval = 1,    title = "Thru Month",      type = input.integer, minval = 1, maxval = 12)
thruDay   = input(defval = 1,    title = "Thru Day",        type = input.integer, minval = 1, maxval = 31)
thruYear  = input(defval = 2112, title = "Thru Year",       type = input.integer, minval = 1970)

// === INPUT SHOW PLOT ===
showDate  = input(defval = true, title = "Show Date Range", type = input.bool)

// === FUNCTION EXAMPLE ===
start     = timestamp(fromYear, fromMonth, fromDay, 00, 00)        // backtest start window
finish    = timestamp(thruYear, thruMonth, thruDay, 23, 59)        // backtest finish window
window()  => true       // create function "within window of time"

n1 = input(10, "Channel Length")
n2 = input(21, "Average Length")
obLevel1 = input(60, "Over Bought Level 1")
obLevel2 = input(53, "Over Bought Level 2")
osLevel1 = input(-60, "Over Sold Level 1")
osLevel2 = input(-53, "Over Sold Level 2")
 
ap = hlc3 
esa = ema(ap, n1)
d = ema(abs(ap - esa), n1)
ci = (ap - esa) / (0.015 * d)
tci = ema(ci, n2)
 
wt1 = tci
wt2 = sma(wt1,4)

plot(0, color=color.gray)
plot(obLevel1, color=color.red)
plot(osLevel1, color=color.green)
plot(obLevel2, color=color.red, style=3)
plot(osLevel2, color=color.green, style=3)

plot(wt1, color=color.white)
plot(wt2, color=color.fuchsia)
plot(wt1-wt2, color=color.new(color.blue, 80), style=plot.style_area)

//Strategy
strategy(title="T!M - Wave Trend Strategy", overlay = false, precision = 8, max_bars_back = 200, pyramiding = 0, initial_capital = 1000, currency = currency.NONE, default_qty_type = strategy.cash, default_qty_value = 1000, commission_type = "percent", commission_value = 0.1, calc_on_every_tick=false, process_orders_on_close=true)
    
longCondition  = crossover(wt1, wt2)
shortCondition = crossunder(wt1, wt2)

strategy.entry(id="Long Entry", comment="buy", long=true, when=longCondition and window())
strategy.close("Long Entry", comment="sell", when=shortCondition and window())      

//strategy.entry(id="Short Entry", long=false, when=shortCondition)

Published by ChaoZhang on the FMZ strategy square · last modified 2023-12-19. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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