Volume based Trend Following Trading Strategy

This is a trend following trading strategy based on a modified volume oscillator indicator. It utilizes volume moving averages to identify increasing volume Signals and determines entries or exits. Meanwhile, it…

Pine ScriptTrend following Machine learning Volatility

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

Calculate volume moving average vol_sum with length of vol_length and smooth it by vol_smooth period moving average.
Generate long Signals when vol_sum rises above threshold and short Signals when vol_sum falls below threshold.
To filter false Signals, only take long when price trend checked in past direction bars is up and vice versa.
Set two threshold values threshold and threshold2. threshold generates trade Signals while threshold2 acts as a stop loss.
Manage open/close orders through a state machine logic.

Advantage Analysis

Volume indicator captures changes in market buying/selling power for more accurate Signals.
Combining with price trend avoids wrong Signals during price swings.
Two threshold values better control risks.

Risk Analysis

Volume indicator has lag and may miss price turning points.
Wrong parameter settings lead to over-trading or signal delays.
Stop loss may be hit during spikes in trading volumes.

Risks can be mitigated by tuning parameters, optimizing indicator calculation, and combining other confirmations.

Optimization Directions

Adaptive optimization of parameters based on market conditions.
Incorporate other indicators like volatility index to further verify Signals.
Research applying machine learning models to improve signal accuracy.

Conclusion

This strategy utilizes an improved volume oscillator with price trend to determine entries and exits with two stop loss threshold values. It is a stable trend following system with optimization space in parameter tuning, signal filtering and stop loss strategies. Overall it has practical value worth further research and optimization.

Parameters

Name	Default	What it controls
v_input_1	2017	Start Year
v_input_2	12	Start Month
v_input_3	17	Start Day
v_input_4	9999	End Year
v_input_5	true	End Month
v_input_6	true	End Day
v_input_7	34	Volume - Length
v_input_8	200	Volume - Smoothing
v_input_9	21	Volume - Risinglength
v_input_10	13	Volume - Fallinglength
v_input_11	true	threshold
v_input_12	1.2	Threshold 2
v_input_13	13	amount of bars

Source Pine Script

/*backtest
start: 2023-12-01 00:00:00
end: 2023-12-31 23:59:59
period: 1h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=4
strategy('Volume Advanced', default_qty_type=strategy.percent_of_equity, default_qty_value=100, commission_type=strategy.commission.percent, commission_value=0.075, currency='USD')
startP = timestamp(input(2017, "Start Year"), input(12, "Start Month"), input(17, "Start Day"), 0, 0)
end    = timestamp(input(9999, "End Year"),   input(1, "End Month"),   input(1, "End Day"),   0, 0)
_testPeriod() =>
    iff(time >= startP and time <= end, true, false)

source = close 
vol_length  = input(34, title = "Volume - Length")
vol_smooth  = input(200,title = "Volume - Smoothing")
volriselen  = input(21,  title = "Volume - Risinglength")
volfalllen  = input(13, title = "Volume - Fallinglength")
threshold   = input(1,"threshold")
threshold2  = input(1.2,step=0.1, title="Threshold 2")
direction = input(13,"amount of bars")


volsum  = sum(volume, vol_length) / (sum(volume, vol_smooth) / (vol_smooth / vol_length))


LongEntry  = (rising(volsum, volriselen) or crossover (volsum, threshold)) and close > close[direction]
ShortEntry = (rising(volsum, volriselen) or crossover (volsum, threshold)) and close < close[direction]
LongExit1  = falling (volsum,volfalllen)
ShortExit1 = falling (volsum,volfalllen)
LongExit2= (crossover(volsum, threshold2) and close < close[direction])


_state = 0
_prev = nz(_state[1])
_state := _prev

if _prev == 0
    if LongEntry
        _state := 1
        _state
    if ShortEntry
        _state := 2
        _state
if _prev == 1
    if ShortEntry or LongExit1
        _state := 0
        _state
if _prev == 2
    if LongEntry or ShortExit1
        _state := 0
        _state

_bLongEntry = _state == 1 
_bLongClose = _state == 0 

long_condition = _bLongEntry and close > close[direction]
strategy.entry('BUY', strategy.long, when=long_condition)  
 
short_condition =  _bLongClose or LongExit2
strategy.close('BUY', when=short_condition)

plot(volsum,      color = color.green,    title="Vol_Sum")
plot(threshold, color = color.fuchsia, transp=50, title="Threshold")
plot(threshold2, color=color.white, transp = 50, title="Threshold 2")

Published by ChaoZhang on the FMZ strategy square · last modified 2024-01-29. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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