Trend Following Strategy Based on Volume Ratio

This article explains in detail a quantitative trend following strategy based on volume ratio analysis. It generates buy and sell signals by calculating the moving averages of bullish and bearish volume. I. Strategy…

Pine ScriptTrend following Breakout Mean reversion EMA SMA

Author: ChaoZhang
Complexity: Intermediate · 110 lines
Parameters: 13
Backtest bar: 3h · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

I. Strategy Logic

The core indicator of this strategy is bullish and bearish volume. The specific calculation steps are:

Calculate daily total volume.
Label volume as bull volume when the daily bar closes up, and bear volume when closes down.
Calculate moving averages separately for bull and bear volumes.
A buy signal is generated when the bull volume MA crosses above bear volume MA, and vice versa.
Price rate of change indicator is also used as a filter, only taking trades when a clear trend exists.
Set stop loss and take profit based on signals to lock in profits.

By judging the trend direction through volume ratio, and filtering with price rate of change, the signal quality can be improved. The stop loss and take profit also ensures controllable profit and loss per trade.

II. Advantages of the Strategy

The biggest advantage of this strategy is using volume to determine trend direction, which is one of the most basic trend following methods. Volume reflects market participant behavior.

Also, volume indicators can early reflect breakout signals, being relatively sensitive. Compared to only using price indicators, it can capture trend reversals earlier.

Lastly, filtering with price rate of change also enhances signal quality.

III. Potential Risks

While the strategy has merits, the following risks should be considered for live trading:

Firstly, parameters for the volume indicators need to be set prudently to avoid false signals.

Secondly, relying solely on one indicator makes it susceptible to price invalidations. Other indicators should be combined for verification.

Lastly, stop loss set too close risks being stopped out prematurely.

IV. Summary

In summary, this article has explained a quantitative strategy using volume ratio to determine trends. It generates trading signals by calculating moving averages of bullish and bearish volume. The strategy has a certain degree of lead and sensitivity, but needs to be combined with other indicators for verification. In addition, proper parameter tuning and prudent money management are also key to its viability. Overall, it provides a unique approach of using volume for trend analysis, but requires further enhancements.

Parameters

Name	Default	What it controls
v_input_1	2017	Backtest Start Year
v_input_2	true	Backtest Start Month
v_input_3	true	Backtest Start Day
v_input_4	2019	Backtest Stop Year
v_input_5	12	Backtest Stop Month
v_input_6	31	Backtest Stop Day
v_input_7	0	Moving Average Type: Simple \| Exponential \| Double Exponential
v_input_8	6	MA Length
v_input_9	3.1	Factor For Breakout Candle
v_input_10	12	roclength
v_input_11	2	pcntChange
v_input_12	2	Stop Loss %
v_input_13	900	Take Profit %

Source Pine Script

/*backtest
start: 2023-08-14 00:00:00
end: 2023-09-13 00:00:00
period: 3h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=4
// Based on Volume Flow v3 indicator by oh92
strategy("Volume Flow BF", overlay=false, initial_capital=10000, default_qty_type=strategy.percent_of_equity, default_qty_value=100, commission_type=strategy.commission.percent, commission_value=0.0)

/////////////// Time Frame ///////////////
testStartYear = input(2017, "Backtest Start Year") 
testStartMonth = input(1, "Backtest Start Month")
testStartDay = input(1, "Backtest Start Day")
testPeriodStart = timestamp(testStartYear,testStartMonth,testStartDay, 0, 0)

testStopYear = input(2019, "Backtest Stop Year")
testStopMonth = input(12, "Backtest Stop Month")
testStopDay = input(31, "Backtest Stop Day")
testPeriodStop = timestamp(testStopYear,testStopMonth,testStopDay, 0, 0)

testPeriod() => true
    
maType =    input(title="Moving Average Type", options=["Simple", "Exponential", "Double Exponential"], defval="Simple")
length =    input(6, title="MA Length")
x      =    input(3.1, title="Factor For Breakout Candle")

// Basic Volume Calcs //
vol  =  volume
bull =  close>open?vol:0 
bear =  open>close?vol:0

// Double EMA Function //
dema(src, len) => (2 * ema(src, len) - ema(ema(src, len), len))

// BULL Moving Average Calculation
bullma = maType == "Exponential" ?        ema(bull, length) :
         maType == "Double Exponential" ? dema(bull, length) :
         sma(bull, length)

// BEAR Moving Average Calculation //
bearma = maType == "Exponential" ?        ema(bear, length) :
         maType == "Double Exponential" ? dema(bear, length) :
         sma(bear, length)

///////////// Rate Of Change ///////////// 
source = close
roclength = input(12, minval=1)
pcntChange = input(2, minval=1)
roc = 100 * (source - source[roclength]) / source[roclength]
emaroc = ema(roc, roclength / 2)
isMoving() => emaroc > (pcntChange / 2) or emaroc < (0 - (pcntChange / 2))

/////////////// Strategy ///////////////
long = bullma > bearma and isMoving()
short = bullma < bearma and isMoving()

last_long = 0.0
last_short = 0.0
last_long := long ? time : nz(last_long[1])
last_short := short ? time : nz(last_short[1])

long_signal = crossover(last_long, last_short)
short_signal = crossover(last_short, last_long)

last_open_long_signal = 0.0
last_open_short_signal = 0.0
last_open_long_signal := long_signal ? open : nz(last_open_long_signal[1])
last_open_short_signal := short_signal ? open : nz(last_open_short_signal[1])

last_long_signal = 0.0
last_short_signal = 0.0
last_long_signal := long_signal ? time : nz(last_long_signal[1])
last_short_signal := short_signal ? time : nz(last_short_signal[1])

in_long_signal = last_long_signal > last_short_signal
in_short_signal = last_short_signal > last_long_signal

last_high = 0.0
last_low = 0.0
last_high := not in_long_signal ? na : in_long_signal and (na(last_high[1]) or high > nz(last_high[1])) ? high : nz(last_high[1])
last_low := not in_short_signal ? na : in_short_signal and (na(last_low[1]) or low < nz(last_low[1])) ? low : nz(last_low[1])
sl_inp = input(2.0, title='Stop Loss %') / 100
tp_inp = input(900.0, title='Take Profit %') / 100 
 
take_level_l = strategy.position_avg_price * (1 + tp_inp)
take_level_s = strategy.position_avg_price * (1 - tp_inp) 

since_longEntry = barssince(last_open_long_signal != last_open_long_signal[1]) 
since_shortEntry = barssince(last_open_short_signal != last_open_short_signal[1]) 

slLong = in_long_signal ? strategy.position_avg_price * (1 - sl_inp) : na
slShort = strategy.position_avg_price * (1 + sl_inp)
long_sl = in_long_signal ? slLong : na
short_sl = in_short_signal ? slShort : na

/////////////// Execution /////////////// 
if testPeriod()
    strategy.entry("Long",  strategy.long, when=long)
    strategy.entry("Short", strategy.short, when=short)
    strategy.exit("Long Ex", "Long", stop=long_sl, limit=take_level_l, when=since_longEntry > 0)
    strategy.exit("Short Ex", "Short", stop=short_sl, limit=take_level_s, when=since_shortEntry > 0)
    
///////////// Plotting /////////////
bgcolor(isMoving() ? long ? color.green : short ? color.red : na : color.white, transp=80)
bgcolor(long_signal ? color.lime : short_signal ? color.red : na, transp=30) 
plot(bullma, color=color.lime, linewidth=1, transp=0, title="Bull MA", transp=10)
plot(bearma, color=color.red, linewidth=1, transp=0, title="Bear MA", transp=10)

Published by ChaoZhang on the FMZ strategy square · last modified 2023-09-14. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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