Price Volume Trend Strategy

This strategy uses momentum indicators to track short-term price movements and determine market trend directions for buy and sell operations. The strategy name "Price Volume Trend Strategy" reflects the idea of using…

Pine ScriptTrend following Breakout Momentum EMA Volume

Author: ChaoZhang
Complexity: Intermediate · 144 lines
Parameters: 8
Backtest bar: 1d · swing
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

Principles

The strategy first calculates the momentum of prices. By calculating the difference between the current period price and the previous period price, it can reflect the absolute change in prices over the latest period. A positive value indicates a price increase, and a negative value indicates a price decrease. Then the moving average of this difference value is calculated for filtering to obtain the average momentum indicator.

When the latest price is greater than the average momentum, it indicates that the price is rising. When the latest price is less than the average momentum, it indicates that the price is falling. Determine the price trend direction based on this indicator. Combined with volume amplification filtering, only signals with relatively large trading volumes are selected in actual trading.

According to the identified upward and downward price trends, corresponding buy and sell operations are carried out.

Advantage Analysis

The strategy judges trends quickly and can quickly capture short-term price movements, which is suitable for short-term operations
Avoid being misled by false breakouts through volume filtering
Implemented the operating logic of chasing rises and killing falls
High trading frequency, suitable for aggressive investors

Risk Analysis

Vulnerable to the impact of abnormal market fluctuations, with certain false signal risks
Slippage risks caused by frequent trading
May miss medium and long term trends, and long-term profitability needs to be verified

Optimization Directions

Adjust the parameters of momentum indicators to optimize judgment effects
Optimize volume filtering parameters to improve signal quality
Increase stop-loss mechanisms to control single loss
Incorporate more factors to ensure multi-factor driven

Conclusion

The strategy overall tracks short-term price change trends through momentum indicators, and quickly determines entry and exit timing. The advantages are fast operation, chasing rises and killing falls. The disadvantages are signal quality and long-term profitability need to be examined. Through parameter adjustments and enhanced risk control mechanisms, the strategy can become an important component of high-frequency strategies, combined with other low-frequency strategies.

Parameters

Name	Default	What it controls
v_input_1	true	From Month
v_input_2	true	From Day
v_input_3	2019	From Year
v_input_4	12	To Month
v_input_5	31	To Day
v_input_6	2020	To Year
v_input_7	true	strength toggle
v_input_8	600	strength elimination

Source Pine Script

/*backtest
start: 2022-12-18 00:00:00
end: 2023-12-24 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/
// © russtic

//@version=2

strategy("HA smoothed eliminator v2  ",pyramiding=1, slippage=10, default_qty_type=strategy.percent_of_equity, 
     commission_type=strategy.commission.percent, commission_value=0.075, overlay=true, 
     default_qty_value=100, initial_capital=1000)

FromMonth1 = input(defval=1, title="From Month", minval=1, maxval=12)
FromDay1 = input(defval=1, title="From Day", minval=1, maxval=31)
FromYear1 = input(defval=2019, title="From Year", minval=2010)
ToMonth1 = input(defval=12, title="To Month", minval=1, maxval=12)
ToDay1 = input(defval=31, title="To Day", minval=1, maxval=31)
ToYear1 = input(defval=2020, title="To Year", minval=2010)
start1 = timestamp(FromYear1, FromMonth1, FromDay1, 00, 00)
finish1 = timestamp(ToYear1, ToMonth1, ToDay1, 23, 59)
window1() => true
    
t1 = time(timeframe.period, "0300-1200")
t2 = time(timeframe.period, "0930-1700")
London = na(t1) ? na : green
NY = na(t2) ? na : red

bgcolor(London, title="London")
bgcolor(NY, title="New York")
///////////////////////////
// HA smoothed

len=(1 )
o=ema(open,len)
c=ema(close,len)
h=ema(high,len)
l=ema(low,len)

haclose = (o+h+l+c)/4
haopen = na(haopen[1]) ? (o + c)/2 : (haopen[1] + haclose[1]) / 2
hahigh = max (h, max(haopen,haclose))
halow = min (l, min(haopen,haclose))

len2=(len)
o2=ema(haopen, len2)
c2=ema(haclose, len2)
h2=ema(hahigh, len2)
l2=ema(halow, len2)

buy= (o2<c2) 

closebuy= (o2>c2)

sell= (o2>c2)

closesell= (o2<c2)

//
/// END NEW SCRIPT 

//
//
//                  MERGE SCRIPTS
a1= o2<c2
b1=o2>c2
is_uptrend = (a1)// and (p> 0)
is_downtrend =  (b1)// and (p <0)
barcolor(b1 ? red: a1 ? lime : blue)

//end


// =========================start     PVT -GIVES EACH BAR A VALUE
facton = (true)//, title="arrow elimination (factor) on ")
Length1 = 2//input(2, title="PVT Length", minval=1)

xPrice = close//input(title="Source", type=source, defval=close)
xsma = wma(xPrice, Length1) 
nRes = xPrice - xsma  
pos = iff(nRes > 0, 1,
	     iff(nRes < 0, -1, nz(pos[1], 0))) 
forex= input(true, title = 'strength toggle ')
forexyes = (forex == true)? 10000 : (forex == false)? 1: na

plot(nRes*forexyes , color=aqua, title="strength", transp=100)
// =========================         end pvt
//
//=============================     start factor // ELIMINATES  weak signals
//                  start trend
//
factor = input(600.00, title = "strength elimination") 
factor1 = factor - (factor*2)//input(-100.00, title = "sell strength elimination ") 
facton1 = (facton == true) and is_uptrend == 1 and nRes*forexyes>factor ? 1 : (facton == true) and is_downtrend == 1 and nRes*forexyes<factor1 ? -1 : (facton == false)
// ==================== =====
// 
//===========================    end factor
nRestrend = (nRes*forexyes)
//=========================== plot arrows 
plot1 = iff(is_uptrend[1] == 1, 0 , 1)  
plot2 = iff(is_downtrend[1]  == 1, 0 , 1)
uparrowcond =  is_downtrend ? false : nz(uparrowcond[1], false) == true ? uparrowcond[1] : (facton1 and is_uptrend and nRes*forexyes>factor)
downarrowcond =  is_uptrend ? false : nz(downarrowcond[1], false) == true ? downarrowcond[1] : (facton1 and is_downtrend and nRes*forexyes<factor1)
//prevarrowstate = uparrowcond  ? 1 : downarrowcond ? -1 : nz(prevarrowstate[1], 0)


candledir = (open < close)? 1: (open>close)? -1 : na // ONLY OPENS ON SAME BAR DIRECTION AS SIGNAL



up=nz(uparrowcond[1], false) == false and ( is_uptrend and nRes*forexyes>factor) and candledir ? 1:na
dn=nz(downarrowcond[1], false) == false and ( is_downtrend and nRes*forexyes<factor1) and candledir? -1:na



sig=0
if up==1 
    sig:=1
else
    if dn==-1
        sig:=-1
    else
        sig:=sig[1]
plotarrow(sig[1]!=1 and sig==1?1:na, title="BUY ARROW", colorup=lime, maxheight=80, minheight=50, transp=0)// up arrow 
plotarrow(sig[1]!=-1 and sig==-1?-1:na, title="SELL ARROW", colordown=red, maxheight=80, minheight=50, transp=0)// down arrow

//========================= alert condition
alertcondition(sig[1]!=1 and sig==1?1:na, title="BUY eliminator", message="BUY " ) 
alertcondition(sig[1]!=-1 and sig==-1?-1:na, title="SELL eliminator",  message="SELL ") 


strategy.entry("B", true, when=(sig[1]!=1 and sig==1?1:na) and window1())
strategy.entry("S", false,when=(sig[1]!=-1 and sig==-1?-1:na) and window1())

Published by ChaoZhang on the FMZ strategy square · last modified 2023-12-25. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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